directed the model-makers towards the weighted models. ..... of fuzzy number (M) to the domain of real numbers (R) is called a triangular fuzzy number, if its ...
A MODEL FOR SELECTION AND OPTIMAL ALLOCATION TO SUPPLIERS UNDER FUZZY CONDITIONS
MAHSHID SHERAFATI
MASTER OF BUSINESS ADMINISTRATION GRADUATE SCHOOL OF MANAGEMENT MULTIMEDIA UNIVERSITY
FEBRUARY 2015
COPYRIGHT
The copyrights of this thesis belongs to the author under the terms of the Copyright Act 1987 as qualified by Regulation 4(1) of the Multimedia University Intellectual Property Regulations. Due acknowledgment shall always be made of the use of any material contained in, or derived from, this thesis.
© MahshidSherafati, 2014 All rights reserved
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DECLARATION
I hereby declare that this BMP 7164 Research Project is my original work except for quotations, statements, explanations and summaries, which I have already mentioned their sources. No portion of this Research Project has been submitted in support of any application for any other degree or qualification of this or any other university or institute of learning.
Signature
:
Date
Name
: Mahshid Sherafati
Student’s ID : 1111200056
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:
DEDICATION
Special Thanks: I would like to firstly thank my parents and my lovely husband for their kind and caring support during various stages of my life. I have to state that I owe them all I am. I also would like to thank every single one of my friends and family, who backed me along the completion of my Master’s program. I like to express my deepest gratitude to the teachers and university professors, those from the program and those involved in my previous education, for their enthusiasm and support. In addition, my special thanks is to Prof.Dr.GovindanMarthandan, who guided and assisted me ominously with the thesis as my supervisor, and he has been always a sincere and priceless advisor, which I have not seen before in my life. Finally, I owe a special thanks to Prof.Dr. Hassan Tajik, the academic consultant in the Iranian embassy in Malaysia, who facilitated the approval of my Master’s degree in Iran.
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TABLE OF CONTENTS
COPYRIGHT
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DECLARATION
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DEDICATION
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TABLE OF CONTENTS
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LIST OF TABLES
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LIST OF FIGURES
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ABSTRACT
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CHAPTER ONE: INTRODUCTION
1
1.1
Introduction
1
1.2
The Research Statement
2
1.3
The Necessity and Importance of the Research
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1.4
Research Questions
4
1.5
Research Objectives
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1.6
Research Methodology
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1.7
Data Collection Method
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1.8
Data Analysis Method
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1.9
Contribution of the Research
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1.9.1 Contribution to Body of Knowledge
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1.9.2 Contribution to Practice (Practitioners)
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1.10
Limitations of the Study (Briefly)
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1.11
Identification of Research Key Words
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1.12
Organisation of the Report
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1.13
Chapter Summary
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CHAPTER TWO: LITERATURE REVIEW 2.1
Part One: Supply Chain Management
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2.1.1 Introduction
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2.1.2 Definitions of Supply Chain
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2.2
2.1.3 Definitions of Supply Chain Management
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2.1.4 Why the Supply Chain Management Became a Challenge in the 1990s?
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2.1.5 The Bases of the Supply Chain Framework
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2.1.6 Components of the Supply Chain
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2.1.7 Solutions to the Supply Chain Problems
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Part Two: Significance of the Supplier Evaluation and Selection
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2.2.1 Significance of the Supplier Evaluation and Selection
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2.2.2 The causes of Complexities in the Supplier Selection Decisions
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2.2.3 A Theoretical Framework for the Supplier Selection Criteria
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2.2.4 The Prevalent Models of Supplier Selection
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2.2.5 Reasons for Selecting the AHP Model and its Practical Features
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2.3
Part Three: Research Background
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2.4
Conclusions
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CHAPTER THREE: METHODOLOGY OF INVESTIGATION
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3.1
Introduction
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3.2
Methodology
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3.3
3.4
3.5
3.2.1 Type of Study
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3.2.2 Statistical Population
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3.2.3 Statistical Sample
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3.2.4 Data Collection Method and Tools
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Measuring the Reliability and Validity of Data Collection Tools 3.3.1 Validity of the Assessment Tool
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3.3.2 Reliability of the Assessment Tool
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Data Analysis Method
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3.4.1 Descriptive Statistical Methods
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3.4.2 Inferential Statistical Methods
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Introduction of a Model for Order Quantity Allocation to Suppliers in Saipa Company 76
CHAPTER FOUR: DATA ANALYSIS 4.1
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Introduction
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4.2
Descriptive Statistics for the General Characteristics of Respondents of the Second Questionnaire 79
4.3
Estimating the Suppliers’ Weights According to the Identified Criteria through the Delphi Method 82
4.4
Optimal Order Allocation
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4.4.1 Using Lingo Software for Solving the Problem
CHAPTER FIVE: DISCUSSION AND CONCLUSION
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5.1
Summary of Chapters
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5.2
Research Findings
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5.3
Recommendations
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REFERENCES
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LIST OF TABLES
Table 2.1: Lehmann and O’Shaughnessy’s Supplier Selection Criteria
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Table 2.2: Evaluation Indicators in 4 Groups of Environmental Factors
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Table 2.3: Choi and Hartley’s Supplier Selection Criteria
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Table 2.4: Garvin’s Supplier Selection Criteria
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Table 2.5: Dickson’s Supplier Selection Criteria
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Table 2.6: Various Types of Supplier Selection Models
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Table 2.7: The Evaluation Criteria for the Categorical Models
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Table 2.8: Key Indicators of Operation Evaluation in SCOR Model
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Table 4.1: Characteristics of the Statistical Sample in Terms of Gender
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Table 4.2: Characteristics of the Statistical Sample in Terms of Education Level
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Table 4.3: The Respondents’ Work Experience
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Table 4.4: Initial Pairwise Comparison Matrix of Criteria after Data Combination 83 Table 4.5: The Final Matrix for Prioritization Of The Criteria By Using The FAHP Method
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Table 4.6: The Initial Pairwise Comparison Matrix of Suppliers According to the Cost Criterion after Data Combination
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Table 4.7: The Final Matrix for Supplier Prioritization According to the Cost Criterion by Using the FAHP Method
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Table 4.8: The Initial Pairwise Comparison Matrix of Suppliers According to the Transportation Criterion after Data Combination
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90
Table 4.9: The Final Matrix for Supplier Prioritization According to The Transportation Criterion by Using the FAHP Method
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Table 4.10: The Initial Pairwise Comparison Matrix of Suppliers According to the Quality Criterion after Data Combination
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Table 4.11: The Final Matrix for Supplier Prioritization According to the Quality Criterion by Using The FAHP Method
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Table 4.12: The Initial Pairwise Comparison Matrix of Suppliers According to the Technology Criterion after Data Combination
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Table 4.13: The Final Matrix for Supplier Prioritization According to the Technology Criterion by Using the FAHP Method
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Table 4.14: The average purchase cost, delay rate in the delivery and the defectiveness rate of suppliers
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Table 4.15: Result of Model
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LIST OF FIGURES
Figure 2.1: An Example of Supply Chain
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Figure 2.2: The Supply Chain of an Automotive Manufacturer
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Figure 2.3: The Reduction Trend of Direct Suppliers of Automotive Manufacturers27 Figure 3.1: The Intersection between Two Fuzzy Numbers (M1 and M2) and the Possibility Degree of M1 ≥ M2
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Figure 4.1: The Statistical Sample in Terms of Gender Percentage
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Figure 4. 2: The Statistical Sample in Terms of Education Level
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Figure 4.3: The Respondents’ Work Experience
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Figure 4.4: Decision Tree
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ABSTRACT
In this research, a model for the selection of suppliers to achieve optimal order allocation under fuzzy conditions is studied. For this purpose, Delphi method, Fuzzy AHP and linear programming technique were used. In the first part, the library method was utilized to identify the criteria affecting supplier selection. In this regard, previous studies were scrutinized and the relevant criteria were extracted. In the second part, by using the Delphi method, the criteria recognized in the previous section were distributed among experts and were either approved or rejected. In this section, new criteria might be added to the library criteria. In the third part, the information necessary for the ranking of suppliers by directors will be gathered via a questionnaire, which will be designed based on fuzzy AHP method. Finally, in the fourth part, the data needed for the estimation of the linear model and for the optimized allocation of orders through the company’s database will be collected and presented. According to results obtained from the Delphi method, four criteria of cost, delay in delivery, quality, and technology have been selected in that order as the most important criteria to rank suppliers of Saipa Company. Based on the results gained from the fuzzy analytical hierarchy process method, the weights for supplier selection criteria were chosen as follows: Technology criterion 0.28, cost criterion 0.25, delay in delivery criterion 0.24, and quality criterion 0.23.
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CHAPTER ONE INTRODUCTION
1.1
Introduction Nowadays, in competitive markets, firms that are capable of predicting the
future and acquiring opportunities at higher pace can sustain as the leaders in the business environment. Supply chain management, due to its impact on the competitiveness of companies in today’s global economy, has been determined as an important and fundamental subject in the world of academic investigations. Supply chain management has been acknowledged as a present-day concept, which can offer both strategic and operational benefits. Among the effective components in supply chain management is the evaluation and selection of suppliers. Naturally, a great portion of a company’s success relies on its ability to achieve goals such as improved quality, reduced prices, and on-time delivery. In other words, it should be expressed that in order for the firm to move towards the customer satisfaction components to provide grounds for presence in the global markets, the outstanding and desirable performance of the firm’s suppliers play a critical and effective role. Thus, the supplier evaluation process, previously accounted as a merely operational task, is now considered as a strategic activity, which requires more attention. It seems that the most important reason behind the unsuccessfulness of the performance evaluation programs refers to the existing measurement methods and mental practices of the evaluators. These programs have been affected negatively by the unilateral attitudes involved in the design of the performance evaluation systems. Now, if it can be proved that mathematical tactics are able to moderate the subjective effects, comply with the objective routines, and integrate different subjective attitudes in the measurement and evaluation process, a model can be designed for comprehensive assessment of the suppliers’ performance based on the proficient mathematical techniques.
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The current literature exhibits that buyer firms have extended the beneficial collaborative and mutual relationships with suppliers and have considered such connections as an integrated element of their organizations. Superior suppliers are often capable of increasing the quality of the buyer firms’ products and quickly apply technology advancements in their products. Therefore, in order for producers to present beneficial programs, develop the alternate conceptual designs, select the finest equipment and technologies, and help in evaluation of plans, they need to engage suppliers in the design process of their products. Such synergies can be accomplished only when the firm has established its procurement relationships with only a few numbers of suppliers. This trend can be apparently observed in today’s business environment that many companies strive to decrease the number of their suppliers. 1.2
The Research Statement One of the most important determinants for survival in today's competitive
environment is to reduce the production costs. The selection of appropriate suppliers can considerably lower the procurement costs and increase the competitiveness of organizations, because in the majority of industries, the expense of raw materials and other related constituents of the products comprise a great part of the costs (De Boer et al., 2001). Recently, the two concepts of supplier selection process and also supply chain management have been significantly concentrated on, in management studies. In the 1990s, many companies were looking for ways to partner with suppliers in order to enhance their competitiveness and managerial performance (Jafarnejad andShahaie, 2008). In general, the problem of supplier selection can be expressed in the following categories: -
Supplier selection, where there is no limitation, i.e. when each of the suppliers alone is capable of satisfying all the customer’s needs, including the demands, quality level, delivery time, etc.
-
Supplier selection, in case there is a number of limitations in the capacity and ability of the supplier, the product quality, etc. (Chen et al., 2006). In other words, the supplier is unable to satisfy all the customer’s needs, and thus, the
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customer has to satisfy some parts of his/her needs by turning to the other supplier in order to counterbalancethe low capacity or poor quality of one supplier with another(Kumar et al., 2004). In the first case, where one supplier is viable to satisfy all the demands of the customer (single sourcing), the related manager is only required to decide on which supplier can be the most appropriate one. However, in the other case, none of the suppliers are able to fulfil all the customer’s needs and therefore, more than one supplier should be selected (Wang and Elhag, 2006). The main goal of the supplier selection process is to recognize the suppliers who have the upmost capacity to satisfy the ongoing needs of companies (Araz et al., 2007). In general, supplier selection is known as a multi-criteria issue (including the tangible and intangible measures) that a number of them, such as low prices and high quality, are in some conflict with each other (Ustun and Demirtas, 2008). A number of methods such as mathematical programming, artificial intelligence, hierarchical process, statistical models, and data envelopment analysis have been used individually for supplier selection, but each has its own limitations. For example, the AHP method has been widely applied for solving the multi-criteria decision making problem (Chan et al., 2008). However, this approach is subjected to a paramountconstraint and refers to this postulation that different criteria are independent. However, in real conditions, different criteria are interdependent, and this interdependence among the issues may affect the outcome of the decision making process. Therefore, this study is an attempt, while solving this problem in the domain of supplier selection by employing novel methods and combining them with a linear model, to present an integrated process for supplier evaluation under fuzzy conditions (interdependence of the supplier selection criteria). 1.3
The Necessity and Importance of the Research Nowadays, choosing the most appropriate supplier amongst the numerous
available providers is amongst the top customers’ programs. Such a choice, especially in situations where customers are the senior managers and decision makers in large projects, requires applying high precision in decision making by using
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specific methods and tools for the analysis of various factors. In other words, they will face a multi-criteria decision making problem. On the other hand, with regard to a variety of criteria that must be involved in the decision making process, using multi-criteria decision making methods for the supplier selection problem can provide an acceptable measurement. By increasing the sensitivity around the subject of purchasing, in addition to the developments of technology and consequently limitations in the number of suppliers, and by increasing the prices of required goods and services and complications in diagnosing and determining the criteria, it seems natural that the decision making process has become even more complex, and as a result, will demand further deliberation and application of the scientific codified methods (Zhang et al., 2009). While the number of different purchasing methods is increasing, the decision making and supplier selection for purchasing has also become more important and difficult. The more dependent the organizations on the suppliers, the more devastating the direct and indirect results of wrong decisions. The increasing globalization of commerce and the development of the internet have complicated different methods of selecting a supplier. Maintaining customer satisfaction, and satisfying customer needs and priorities, both require a rapid and suitable supplier selection. The new organizational structures have led to a situation where in the supplier selection process, more individuals are included and engaged in decision making, and therefore, the importance and position of decision making has been enhanced (Weber et al., 2000). 1.4
Research Questions -
How can the supplier selection process and optimal allocation be carried out via a model under fuzzy conditions?
-
What are the criteria to be used for selected supplier and how can they be prioritized?
-
How can the orders be allocated optimally to suppliers by using the linear programming techniques in supply chain management?
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1.5
Research Objectives
The main objective: -
To developa model for selection and optimal allocation to suppliers under fuzzy conditions
The secondary objectives: -
To identifythe criteria forsuppliers selection
-
To rankthe suppliers based on the criteria by using fuzzy AHP
-
To findtheoptimal allocation of orders to suppliers using linear programming techniques
1.6
Research Methodology In a general categorization, various types of scientific research can be
classified according to five bases, which are the result, objective, data type, researcher’s control, and methodology bases (Rezvani, 2011). According to the above five bases, the type of this research can be expressed as follows:
In terms of the results, this study is among the applied researches.
This study, considering the type of objective, is a descriptive research.
In terms of the data type, this study is considered as a quantitative-qualitative research.
On the basis of the researcher’s control, this survey is amongst the nonexperimental studies.
In terms of the methodology, this study is a survey research.
The main steps of this investigation are as below:
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Library study to identify the effective factors for supplier selection (carried out in the second chapter)
Identification of the influential local factors for supplier selection by employing the Delphi technique
Weighting suppliers under fuzzy conditions according to the identified criteria and by using fuzzy AHP technique
The order quantity allocation to suppliers by proposing and solving a linear model
1.7
Data Collection Method In order to gather information in this study, a combination of library and field
methods will be employed. In the library method, references, books, libraries, and the internet are used as sources from which the data will be collected. In the field method, the data will be collected through administering two sets of questionnaires prepared by the researcher. The first questionnaire is designed to confirm and complete the criteria based on the Delphi method and is then delivered to the experts. The second questionnaire is designed to evaluate suppliers by using fuzzy AHP method. After gathering the information based on the questionnaires, a model for resource allocation to suppliers will be formulated. 1.8
Data Analysis Method -
In order to finalize the supplier evaluation criteria, the Delphi method will be used.
-
For selecting and rating suppliers with the aid of the identified criteria, fuzzy AHP method will be applied.
-
For optimal allocation to suppliers, the linear programming techniques will be used. In this section, Lingo software will be utilized.
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1.9
Contribution of the Research
1.9.1
Contribution to Body of Knowledge A variety of methods, including statistical models, data envelope analysis,
hierarchical process, artificial intelligence, mathematical programming, etc. have been applied to solve the supplier selection issue; however, each has its specific constraints. Specifically, AHP method has been widely applied to resolve multicriteria decision making issues. However, this method is subjected to a critical constraint, which is the presumption of the independence of the different measures from each other, whereas in real conditions, different criteria are interdependent. This dependency among various issues may affect the decision-making results. Thus, in this study, by making use of a collection of the fuzzy analytical hierarchy process method and the linear allocation model, attempt was made to take the dependencies of different criteria into account. In this study, the supplier selection criteria were not determined by only reviewing previous researches or merely from the interviews, but a combination of the above two mentioned methods with the aid of the Delphi technique was employed. 1.9.2
Contribution to Practice (Practitioners) The weighting of suppliers and allocating of resources to Saipa Company
suppliers were performed for the first time through this research. A combination of quantitative and qualitative techniques and methods was used for solving the research problem. 1.10
Limitations of the Study (Briefly)
a) Research limitations which were out of the researcher’s control
Lack of cooperation: Due to the attitudes of some of the respondents, they avoided to respond straightforwardly. This factor was amongst the most
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significant uncontrollable factors for correct and complete collection of questionnaires.
Insufficient familiarity of respondents with the Delphi technique and the pairwise comparison questionnaire: In order to eliminate this limitation, the researcher held a training course for respondents.
b) Research limitations within the researcher’s control
The research scope was constrained and as a result, the questionnaires were only completed by the main suppliers of Saipa Company.
1.11 -
Identification of Research Key Words Supply chain Supply chain includes all the life cycle processes, incorporating the physical,
financial information, and knowledge streams. Its objective is to meet the final user’s needs with the related goods and services of several interdependent providers. -
Supply chain management Supply chain management is an integrated and coherent attitude for
controlling the streamof a distribution network from the supplier to the final consumer. -
Analytic hierarchy process Analytic hierarchy process (AHP) is a model to present the prioritization
according to the defined qualitative and quantitative criteria. 1.12
Organisation of the Report
This research will be presented in five main chapters: In chapter I, the introduction and research overview will be discussed. The second chapter tables the relevant literature. This chapter also provide reviews of the literature in relation to the supply chain along with the concerned criteria, and then,
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the analyses of the methods and tools for supplier selection. After extracting the initial evaluation criteria in chapter II, in order to finalize the criteria in chapter III, the supply chain experts will be referred. It should be noted that in this part, the Delphi method will be used. After finalizing the criteria for supplier selection by using the identified criteria, the Fuzzy AHP method, and for optimal allocation to suppliers, the linear programming technique will be used. In chapter IV, the presented model in chapter III will be executed in a real case. In this section, Lingo software will be operated. Ultimately, the fifth chapter will present the conclusions and recommendations. 1.13
Chapter Summary In this chapter, the significance of the supplier evaluation and selection
process in an industry was initially described. Afterwards, the research problem and necessity of this survey were explained in order to propose a model, which can be employed for the selection of and optimal allocation to suppliers under fuzzy conditions. Then, the research objectives and the corresponding questions were depicted. Finally, the scope of research was studied. In chapter 2, the research literature will be explored.
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CHAPTER TWO LITERATURE REVIEW
2.1
Part One: Supply Chain Management
2.1.1
Introduction In order to compete in today’s international marketplaces, different
corporations have endeavoured to provide their goods and services through effective and competent methods. One of the most influential elements in such efforts refers to the design of and coordination between the supply and distribution networks, which is known as supply chain management (SCM) (Sengupta et al., 2006) SCM is the centre stage for aligning resources and optimizing activities throughout the supply chain to secure a beneficial advantage over competitors (Gunasekaran et al., 2008). Due to the salient growths in the SCM theories and practices, it has extensively evolved during recent decades (Theeranuphattana and Tang, 2008). Alongside the evolution of supply chain practices, some large corporations have the tendency to develop the cooperation level between all supply chain members (both at high and low levels). It is worth mentioning that this collaboration is of remarkable importance for these organizations and has become the centre standpoint for informational and managerial coordination and control of the entire supply chain. Further, along with this continuously improving coordination between these organizations, the supply chain has become highly integrated (Wu and Song, 2005). Supply chain is known as an incorporated process, in which raw materials are converted into end products, and afterwards, supplied to the consumers (through distribution, retailing, or a combination of them). Moreover, supply chain can be considered as a set of facilities, logistics, design agencies, implementation teams, customers, products, inventory control techniques, purchase, and distribution, which
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are interconnected to each other through the forward stream of materials and goods, and also, the backward stream of data (Evans et al., 1999; Sabri and Beaman, 2000; Wu and Song, 2005). In general, integration in the field of supply chain management is defined as “the interaction and coordination between divisions and organizations for achieving common supply chain objectives”. The chief purpose in integrating the supply chain is to facilitate the process for offering products and services to customers (Tan and Tracey, 2007). The major areas in this subject include sales and marketing, and production and logistics (Wouters and Sportel, 2005). It can be stated that SCM typically reflects the logistics management (Kannan and Tan, 2004). In fact, in the field of supply chain, logistics usually indicates the management of upper and lower relationships with suppliers and customers for delivering value to the customers at the lowest achievable expense in the supply chain (Zhu et al., 2008). The logistics mission can be explained as “the coordination in supply chain via an effective, efficient, and creative method to offer optimal services to customers” (Wouters and Sportel, 2005). Evolutions of the supply chain exhibit the constant integration process among the involved organizations. At first, suppliers, producers, distributers, and customers are separated between different companies and also from each other. In the first step, the supply chain integration is performed only in the internal divisions of a single organization. This internal integration pattern in a company may be called the internal integration (Chang et al., 2006). Adding to that, logistics cooperation relations require information sharing, which can be enabled by integrating the separated information systems of the partners (Klein et al., 2007). In addition, any increase in integration or coordination would not occur without sharing a sufficient amount of information between the supply chain members. According to a number of forecasts with the highest occurrence probability, it has been expressed that the high level of information sharing is the key to secure future relations in a supply chain (Ogden et al., 2005). Moreover, uncertainty is known as a complex and significant issue in the supply chain management. Actually, the main problem here relates to the operational
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evaluationof the supply chain’s operation. For so many years, the procurementmanufacturing, manufacturing-distribution, and inventory-distribution systems have been under explored. The majority of such investigations have focused on a particularelement of the general system of supply, manufacturing, and distribution. However, it needs to be mentioned that a limited advancement towards the incorporation of these elements in a single supply chain has been accomplished (Chang et al., 2006). It should be noted that even at preliminary stage, such integration of suppliers in this process, reflects a major adjustment of attitudes and internal procedures that need to be accepted all over the organization (Tan and Tracey, 2007). When supply management is engaged in the strategic decisions, the comprehension of various strategies, which could be employed, can be very critical. Additionally, Ogden and his colleagues (2005) implemented an investigation, in which the multi-round Delphi method was utilized amongst the supply and procurement executives in order to gain a more viable comprehension of the supply and procurement strategies, which can assist in accomplishing unique improvement of the supply chain in the next 5 to 10 years. The results of that research showed that strategies, such as increasing integration and information sharing between supply chain participants, are of higher likelihood to be executed and to provide useful impacts for the organizations (Ogden et al., 2005). 2.1.2
Definitions of Supply Chain At first, the notion of supply chain was applied to explain the stream of
materials from the initial sources (producers) to the organization and after that, the flow of materials inside the organization to the needed locations. Adding to that, the demand chain was determined to describe the processes of receiving the orders. Soon, it was realized that these two notions are interrelated to each other. Consequently, both were integrated under a unique notionof the “supply chain” or the “extended supply chain”. Supply chain includes the life cycle procedures involving the knowledge, information, physical, and financial streams, with the aim to meet the final
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consumer’s needs through the products or services, which are associated with several interrelated supply chains. As a more detailed definition, supply chain is composed of the practices that engage a wide arrayof actions such as sourcing, producing, transportation, and sales of physical goods. This definition involves the compatibility existed between different activities. Life cycle indicates the market and also the consumption life span. Physical, information, and financial flows have been expressed as various facets of the supply chain. The initial attitude towards supply chain as a physical distribution process is very limited. In most supply chains, the data and financial streams are as effective as the tangible flows. In addition, the role of knowledge as an input has not been considered in the supply chain processes. Today, the added value in the form of intellectual capital is very critical for the sake of marketing. Supply chain must satisfy the end user’s needs. These needs are the fundamental reasons for the existence of a supply chain. Clearly, supply chain is not restricted with regard to the stream’s orientation. Numerous people judgethe supply chain as a one-way streamfrom suppliers to the final consumer. Regarding the physical procedures, this opinion is quiet correct; however, the design of supply chain could not disregardthe reverse streams for item returns, discounts, motivational payments, etc. Most flows in the supply chain are required to be two-sided, which flow the physical products, knowledge, and information. Contrariwise, it is worth to add that services have supply chains as well as products. Production planning for the R&D unit, where plans (and not products) are produced, can generate useful outcomes by using the techniques that are similarly employed by producers of products. Federal Express and UPS are service companies, but have complicated supply chains (Kasarda, 2001). While the concept of supply chain has been generalized during recent years, different definitions have been presented for the supply chain:
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APICS defines the supply chain as follows: 1.
The procedures, which are launchedfrom the raw state of the materials and finishedin the final consumption of the completed product and relate the supplier-consumer companies to each other horizontally.
2.
The input and output functions of a firm that allow the value chain to produce its goods and deliver services to the end user (Cox et al., 1998).
The supply chain association (1997) uses the following definition: Supply chain involves every attempt related to the manufacturing and distribution of the end product from the provider to the final user. Four basic processes of planning, sourcing, producing, and delivering, broadly describe these attempts, which involve the supply and demand management, raw materials and pieces sourcing, production and assembly, storing and stock control, order reception and administration, distribution to various routes, and final transport to the consumer (Lummus et al., 2001). Finally, supply chain explains the stream of materials, payments, data, and services from the raw material providers, to the plants and repositories, and then, to the final consumers. Supply chain engages all the companies and procedures, which produce the information, products, and services and then, deliver them to the final consumers. It involves a number of duties such as buying, payments, material management, manufacturing scheduling and controlling, procurement and supply management, storage, distribution, and delivery, in addition to the information systems required for controlling all these activities (Bernard et al., 2002). 2.1.3
Definitions of Supply Chain Management Along with the definitions of supply chain, a number of researchers have
explicated the notion of supply chain management. According to Ellram and Cooper, supply chain management can be defined as follows: “A coherent and integrated attitudeto administerall the flows of a distribution route from the supplier to the final user”.
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Monczka and Morgan expressed that an integrated supply chain management starts from the final user and continues by handling the whole needed procedures to serve consumer by adding value in a horizontal line. They believed that these are the supply chains (and not organizations) that compete with each other, and the chains of the competitors that are stronger can engage the management and leadership in a fully coherent and integrated supply chain, which includes the final customer, primary suppliers, and providers of their suppliers (Monczka and Morgan, 1999). Scott and Westbrook (1991) and Payne and New (1995) portrayed the supply chain as a connection to link every component of the production and supply processes from the raw materials to the final consumer which includes different organizational limits, and describes the entire organization as an independent potential entity in the supply chain. This new philosophy of management focuses on this issue of how companies use the processes, technologies, and capabilities of their providers to promote a viable advantage over competitors and create compatibility among the elements of production, supply, materials, distribution, and transportation in the organization. The supply chain management emphasizes on the overall coherence between all the business elements in a supply chain. However, since most supply chains become very complicated in gaining the full coherence, a practical approach would only be to take the strategic suppliers and customers into account. Many producers and traders have acknowledged that the notion of supply chain management is to improve the product development process, characteristics, and production objectives. This has allowed companies to take advantage of their strong powers as well as technologies of suppliers to strengthentheir new product development campaigns. A great majority of the supply chain management literature concentrated more on the purchase factor and emphasized that supply chain management is a strategic base business process compared with a special support factor. The supply chain management is a managerial viewpoint, which develops conventional internal activities by using an intra-organization authority and gathers business partners with the general goal of optimization and efficiency improvement.
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Superior companies involve their strategic suppliers in their efforts for new product development. Therefore, supplier selection for product design can be managed in a way to lower the associated costs, which often leads to innovation in the process, materials, and ability and provides an effective competition in the global market. By engaging suppliers in the early design stages, producers will have the capability to expand alternate engineering outcomes (unconventional), choose the most suitable parts and technologies, and evaluate the proposed plan prior to taking any action. Supply chain management typically looks to enhance the functionality by removing the wastages and using the technology and internal/external capabilities of the supplier more efficiently. Transportation and procurement elements emphasize on a different dimension in the process of the supply chain management. Supply chain management involves the procurement procedures in making business strategic decisions. This would allow the channel’s members to compete as the independent elements rather than moving in the supply chain. Supply chain management benefits from the advantages of vertical integration through lining up the procurement tasks of self-governingentities in the supply chain. While the timely procurement means to saturate the warehouses with sufficient inventories, the new philosophy has stressed on the incorporation of internal and external actions, such as stock management, communications with sellers, transportation, distribution, and delivery services. The objective is to interchangematerials with data in order to achieve transparency. Consequently, the raw materials and made products can be completed more rapidly and consumed in smaller dimensions, particularly in just-in-time systems. Thus, the undersizedand consistentordering cycles, in addition to the capability of managing the input orders, are considered as the key elements in delivering the service to the customer (Tan et al., 2002). Overall, it can be summarized that the tasks of supply chain management includes planning, organization, and matching of all the supply chain conducts. Today, the concept of supply chain management as a complete systematic method refers to the total organizationof the supply chain.
16
Although the theoretical importance of supply chain management has been highlighted recently, there are still a limited number of practical studies on the issues of how the management factor evaluates the suppliers’ chain, how supply chain management methods are defined and executed, and how such methods affect the company’s performance (Tan et al., 2002). 2.1.4
Why the Supply Chain Management Became a Challenge in the 1990s? The primary reason was focused on this fact that few organizations continued
to perform their vertical integration in the 1990s. In fact, organizations became more specialized and instead of being their own suppliers, sought to find providers who could supply goods or services at lesser cost and higher quality. Thus, supply chain management is very critical for organizations to optimize their total performance. The second reason was related to the severe national and international competition. Customers have access to several resources and could select among them in order to satisfy their needs. Determining the product position in the distribution channel became very critical in order to maximize the accessibility of customers at a minimum cost. Previously, organizations satisfied their concerns about the problem of distribution through storing the inventories in different locations in the chain. In spite of that, the dynamic dispositionof the market led to a situation in which storing inventories became a very uncertain and possibly unbeneficial business activity. The customers’ purchasing behaviours changed and rivals started to constantly add and remove their products due to specific circumstances. The demand variations confirmed that a company would probably be subjected to insufficient inventories. In addition, the maintenance costs of each inventory caused the organizations not to be able to suggest a product with the lowest possible price. The third reason was due to this fact that most organizations recognized that maximizing the performance of one sector or one person might consequently lead to the reduction of the optimal performance of the entire organization. The purchase unit may enter the negotiation process to minimize the price of a part and enjoy a proper price reduction, but the total expense of the completed product might rise
17
because of the ineptitudes in the rest of the plan. Organizations have to take the whole supply chain into consideration to be able to assess the effect of decisions in each section (Lummus and Vokurka, 1999). 2.1.5
The Bases of the Supply Chain Framework Generally, a supply chain includes many partners like customers, distributers,
producers, and suppliers. Supply chain comprises some other parts including the materials, resources, and the processes inside all the units. Hence, the supply chain management needs a formula, in which the units are incorporated at the structural level and are positioned in the systems that are able to support this integration. In the first step, for creating an outlinefor supply chain, the bases are considered. The related units, the relationships between the units, the integration level of unit networks among the business processes are shown alongside the links between the units. Moreover, the requirements are identified based on which the supply chain has been constructed, which include the components and relationships, planning in unit networks, integration of unit networks, and the integration level. Hence, the structurecan be the basis for developing the supply chain in the industry and can be extended for the electronic commerce environments such as electronic commerce (Samaranayake, 2005). 2.1.6
Components of the Supply Chain The notion of supply chain has been obtained from the picture that shows
how the participating organizations are linked together in a specific supply chain. Figure 2.1 exhibits a fairly modestsupply chain that connects the firm to its suppliers (on the left-hand side) and distributors and customers (on the right-hand side). It should be considered that suppliers could be supported by their own suppliers. Besides the materials stream, there are other flows of information and money as well. The money flow moves in the opposite direction of the material flow (Bernard et al., 2002).
18
Supply chain includes three sections: 1. The upstream supply chain This section shows the first tier suppliers of the organization, who can produce or assemble and have their own suppliers. Such relationships can be extended from the left side, to different extents, towards all the routes linked to the material sources. 2. The internal supply chain This section presents all the procedures that are applied by the company to convert the inputs (that are transferred to the organization by suppliers) to outputs (from the point materials enter the organization until the point the product is transferred to outside for distribution).
Intern al
Upstrea m Second tier suppliers
Downstream
Distributers Informatio n flow
First tier suppliers Second tier suppliers
Production, assembly, and packaging First tier suppliers
Material flow
Second tier suppliers
Figure 2.1:An Example of Supply Chain
19
Retailers
Customers
3. The downstream supply chain This section includes all the processes related to the product delivery to final customers. Supply chain can exist in all forms and sizes and can be relatively complicated, which have been exhibited in figure 2.2. This figure demonstrates the supply chain of an automotive manufacturer, which as shown includes hundreds of suppliers, tens of spare-parts manufacturers and assemblers, sellers, direct business customers, wholesalers, customers, and support factors such as the product and purchase engineering. It should be noticed that in this case, the chain is not merely a line. In fact, some loops and nodes may be observed in the process. Moreover, it is worth noticing that sometimes the information and product flows are two-sided. For instance, in this figure, it presents the process of returning of cars to sellers due to defects, called the reverse logistics. In addition, the supply chain is more than a single physical flow as it also involves both the data and financial streams. In fact, the supply chain of a digital product or service may have no physical element. The flows of products, services, information, and financial resources are normally devised by considering the effective transfer and conversion of raw materials to the final products, and also due to this fact that this is an effective method. Furthermore, the stream has to be accompanied with a rise in value that can be investigated through a value chain.
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Wholesaler Organizational sellers Automobiles
Customers
Automobiles
Mediators
Material assembly planning (19 plans) Automobile final Transportation
Collecting constructible orders
assembly
Selling operation
Assembly line
Pre-assembly Engineering
New orders and engineering changes
Sequence arrangement
Releasing
Visiting Engineering changes
Spare parts Production (57 plans) Engineers
Transportation
Warehousing
Spare parts planning
Pressing
Demand
Founding Demand
Wholesalers
Spare parts group
Electronics
Window
Plastic
Conditioner and decorations
Parts
Purchase
Materials
Hundreds of Suppliers
Placing orders Engineering changes and liberation
Figure 2.2:The Supply Chain of an Automotive Manufacturer
2.1.7
Solutions to the Supply Chain Problems During the years, different corporations have proposed numerous answers on
the supply chain issues. Among the first solutions is the vertical integration. Undoubtedly, the most conventional method used by companies was to accumulate
21
Spare
the inventory as the assurance against the uncertainties of the supply chain. In this method, products and spare parts are flowing steadily in the production process. The major difficultyof this techniqueis that the precise determination of the inventory level for each product is very difficult. If the inventory level is very high, the maintenance costs will be very high, accordingly. Conversely, if the inventory level is very low, there will be no guarantee that the company can respond to high demands or short delivery times. Every time that there is lack of inventory, the costs of consequences including missed sales opportunities and unfavourable reputation would be very significant. Therefore, firms try hard to manage their stock. The proper management of the supply chain and inventory needs to be compatible with all the actions and relationships in the supply chain. In fact, successful and welldefined compatibility helps in a way that goods would flow smoothly and timely from suppliers to producers and from producers to customers. This would enable the organization to maintain the inventory down and lower the costs. Such compatibility is needed when companies are dependent on each other but do not work toward a single goal. To achieve such collaboration, business associates have to become experienced to rely on each other. Suppliers and buyers should cooperate in the design or re-design of the supply chain in order to obtain their common and also nonshared targets. In order to manage the uncertainties stated previously, identifying the reasons of uncertainties, determining how these uncertainties influence the up and down activities in the supply chain, and regulating proper methods to reduce or eliminate the uncertainties, all need an effective communication between the business associates. A fast information stream among the supply chains can make the firms effective. For instance, the computer data of a sales point can be transferred to distribution centres, suppliers, and transportation unit in one day or even in real time, which could enable companies to determine the optimal level of inventory. In the following, some other solutions for the supply chain management problems are given:
Outsourcing, rather than performing all the jobs by the company itself during the peak demand times
22
Buying whenever possible, rather than making input products by the company itself
Adjusting all the optimal transportation programs
Using lesser number of suppliers
Improving the supplier-buyer relationships
Obtaining precise demands by carrying out close cooperation with suppliers
Making strategic collaborations with suppliers
Using the just-in-time production strategy for purchases, in a way that suppliers deliver small amounts of raw materials and spare parts, every time needed
Reducing the period before the beginning and finishing of production for selling and buying. Some of the above-mentioned solutions can be promoted with information
technology support. For special solutions presented by the information technology, you can simply refer to page 249 of this reference (Bernard et al., 2002). 2.2
Part Two: Significance of the Supplier Evaluation and Selection
2.2.1
Significance of the Supplier Evaluation and Selection The supplier evaluation and selection decisions are considered as a critical
section in the supply chain management. This issue holds true for both the production and service corporations and also for obtaining different goods and services such as the materials and equipment. In the current severe competition, production at low expense and with high quality outcomes is impossible without having satisfactory providers. In an article referred to purchasing, which was published in 1943, Lewis stated that for the buying company, there is no task more important than selecting the proper resource. The costs of raw materials, spare parts, and services received from the product and service suppliers, constituteabout 55% of the income. For firms with high technology, the bought products and services compose up to 80% of the total
23
expenses of production. In a study in 1982, the total value of all the goods and services purchased in all American industries was estimated as 4.11 trillion dollars. This number is 37% more than the value of GDP in 1982, which was equal to 3.074 trillion dollars. Although it seems that most corporate purchases are simple and frequent and they only need to be consistent according to production indicators such as quality, price, and delivery, all of a firm’s purchases are one-go when they are bought for the first time, and assessment and choosing of suppliers is performed. American commercial companies spend about 250 million dollars annually for the elements known as MRO (namely the maintenance, repair, and operating supplies). To elaborate, these costs for example add 530 dollars to the price of a special Ford car (Vokurka et al., 1996). 2.2.2
The causes of Complexities in the Supplier Selection Decisions The decisions engaged in the supplier selection process have become
complicated due to several reasons:
The initial selection is usually done according to some criteria. In a review by Weber in 1991 on the criteria and methods of choosing suppliers, 47 articles out of 76 cases had considered more than one influential criterion.
The second complexity in the decisions of supplier selection refers to this reality that a supplier might have different functioning features for various measures. For instance, a supplier that is able to provide one part at the minimum cost might be unable to secure the best performance in delivery or quality the same as its competitors.
The third complexity originates from the fact that the applied criteria may be different among various groups of products and in different locations. In fact, there might be some commonalitiesamong different criteria, which are not easily identifiable.
24
The fourth complexity relates to the limitations of internal policies, and restrictions applied to buying processes by the external system. The limitations of internal policies are applied either implicitly or explicitly to the purchase processes for the items such as the number and category of suppliers who are to be employed, the minimum and maximum amount of orders, etc. Similarly, suppliers apply some limitations on the purchase processes such as the minimum and maximum amount of orders according to their production capacity or their tendency towards dealing with a special company.
Other complexities may be because of the inaccessibility to data or availability of unverified data.
Such limitations will ultimately influence the number of required suppliers for employment and the size of their orders in every purchase (Weber et al., 2000). In recent years, the relationships between buyers and suppliers have changed. Currently, many companies consider helping their suppliers to gain a better competitive situation. In the past, there was often a competitive or semi-competitive relationship between the buyer and seller. However, the latest business trends such as the decreased life cycle of products, increased speed of technology changes, and outsourcing have increased the development trend of communications and partnership between the two sides. Recent developments reflect a new situation in which the decision of supplier selection has become more pivotalthan ever. If buyers have lower tendency to change, the selection of a proper supplier will cause more damages to them. In addition, when a supplier is acceptable and appropriate, the buyer will have the chance to build long-term relationships with the supplier, which might create a strategic benefit. When companies have a strategic partnership with the suppliers, a new collection of selection measures will be added to the current requirements, which are as imperativeas the conventional measures (Vokurka et al., 1996). Companies such as General Motors, Xerox, General Electric, Black & Decker, and others help their suppliers to gain a stronger competitive situation. In this evolution, buyers have considered closer and more synergetic relationships, but
25
with fewer number of suppliers. Companies look for supplier input in the earlier stages of the design process and have information sharing with their suppliers in longer periods. Such close relationships can be kept only when companies work with a lesser number of suppliers. For example, Xerox has reduced its suppliers down by 50% and the target of Renault Corporation has been to have only 350 to 400 suppliers until 2000. Figure 2.3 shows this as a general tendency among all automotive manufacturers. Despite the present general strategy, different companies follow it at different degrees. Companies such as Renault and Volkswagen have a conservative political strategy to reduce the number of suppliers, while the Ford Company has been more active in this area (Veloso, 2000). According to a research based on the responses of more than 500 purchase experts, it was proved that the higher level of the supplier-buyer participation will increase the quality of a product and decrease the total costs, and the higher quality of products will generally reduce the total associated costs (Larson, 1994). Despite the observed successes in this field, some buyers are not willing to leave their old habits. Traditional purchase approaches cannot secure the desired results. Figure 2.3 shows the decrease in the number of direct suppliers for the automobile producers. A company must be shaped according to its suppliers’ experiences and commitments in order to maintain its position as a global competitor. When buyers look for a strategic partnership, the supplier selection becomes a critical issue, but when traditional approaches are unsuitable for longer periods, most buyers have no idea on how to select their suppliers (Spikman, 1998).
26
Figure 2.3: The Reduction Trend of Direct Suppliers of Automotive Manufacturers
2.2.3
A Theoretical Framework for the Supplier Selection Criteria The relationships between suppliers in a company are according to the
various measures of supplier selection. The studies in this area have provided a variedrange of measures for supplier selection to expeditethe process of supplier selection decision making (Swenson, 2004). Kahraman and his colleagues in 2003 employed the Fuzzy AHP method to select the suppliers with the highestcapacity. In this model, the selection criteria were placed in one of the 4 classes of supplier criterion, product performance criterion, service performance criterion, and cost criterion. Park and Krishna (2001) explored the exercises of selecting suppliers between small-sized business executives and utilized three models, including rational-normative, external control, and strategic choice. Tracy and Tan in 2001 employed the factor and path analyses for assessingthe relationships among the supplier selection measures, supplier involvement in design parties and in significant development plans, aspects of customer satisfaction, and the company’s total functioning. This study relied on the opinions of 180 managers of production companies in the USA and proved that the upper degrees of customer satisfaction (including competitive price, product quality, production variety, and delivery services) and the company’s operation can be
27
secured as a result of the selection and evaluation of suppliers based on their capability to assurequality pieces, reliable delivery, and production’s high efficiency. It was proved that the participation of suppliers in constant improvement and product development teams will have a positive impact on the company’s functioning (Tracy and Tan, 2001). Weber (2000) showed a method to assess the number of suppliers for deployment with the aid of the multi-objective programming and data envelopment analysis. This approach supports the development of quantitative seller–order solutions via multi-objective programming, and the efficiency evaluation of these sellers based on multiple criteria via data envelopment analysis. Jayaraman in 1991 presented a multi-source policy method to ensure the flowability of supply chain. Because of the economic significance and inherent complexity of the supplier selection issue, a combined integer programming method has been offered to resolve the issue. Motwani in 1999 presented an approach to source and select in a global environment. In practice, this model emphasizes on the fallof the qualitative uncertainties, related to the developing countries. Min in 1994 discussed that the effective selection of suppliers must be related to a large number of qualitative and quantitative factors that conflict each other. Such multiple measures and indefinite decision-making settings should be handled by the multi-attribute utility theory. Supplier selection in a multinational environment is a complex and risky task, which is due to the cultural variations and ethical values. Mandal and Deshmakh in 1994 used the explanatorystructural modelling and applied about 12 supplier selection criteria in India. This study was dependent on an approach that studies the qualitative and quantitative variables with the aid of the explanatorystructural modelling. Weber in 1991 explored the literature of supplier selection criteria as well as the concerned methods. This study found several techniques such as linear programming, mixed integer programming, and other quantitative methods.
28
Thompson in 1990 developed an analysis framework of the current or historical status of suppliers in order to rank them in an unstable environment based on some weighted criteria through a simulation technique. Soukup in 1987 employed a basic evaluation method by considering uncertainty in the supplier selection problem, which estimated the functioning of suppliers in unpredicted programs by appraising the likely divergences from the initial buying program. Ellram in 1987 identified some features that are effective on the supplier selection process such as the financial status, organizational culture, strategic approaches, technology status, and etc. Gregory in 1986 used a basic approach that assessed providers based on the weighted scores, which were attained from a collection of pre-specified yardsticks. Timmerman in 1986 introduced a total weighting method to determine the elements with their relative weights and grade the possible suppliers according to these weighted factors. Abratt in 1986 examined 9 supplier selection criteria and their relative importance in South Africa. Similarly, Lehmann and O'Shaughnessy in 1974 studied 17 supplier selection criteria in the USA and England. Win in 1968 examined the relative importance of 10 supplier selection criteria in the USA. Dickson in 1966 introduced tens of qualitative and quantitative measures of supplier selection such as the unit price, repair services, geographical location, financial status, manufacturing facilities, technology capabilities, and the performance historical records.
29
In the following, some of these criteria have been investigated in detail: A. Kahraman’s supplier selection criteria According to Kahraman (2003), the selection criteria can be mainly classified into the following groups:
Supplier criterion
Product performance criterion
Service performance criterion
Cost criterion
Supplier criterion The company uses the supplier criteria to assess whether the supply strategy and the user’s technology is appropriate or not. Such contemplations are generally detached from a specific product or service under study. Supplier criteria have been expanded to estimate the key commercial facets of the supplier including the monetary power, managerial approaches, technical abilities, support sources, and quality systems. Product performance criterion The company can make use of the product performance criterion to evaluate important operational features and assess the usability of the product, which is bought. The applied measures are contingent on the product’s category, including its final usage, administration and packaging, its application in production, in addition to other business considerations such as the ergonomic features, technology life cycle, and etc. Service performance criterion The company can use the service performance criterion to assess the gains, which can be obtained through the supplier’s services. While services are appraised, the company has to pinpointits expectations. Since in any purchase, there are some services such as the order process, delivery and support mechanism, a company must take the service factor into account in its evaluations. When a high-quality product is
30
purchased, the service facetscould be easily ignored among the product features. Some concepts that are used for the evaluation of products can be applied for the services as well. However, the terms and expressions are often different and services require some other considerations. Some of these criteria include customer support, value added, control of customer satisfaction, and professional knowledge and skill. Cost criterion Cost criterion identifies the cost elements of the purchase. The most common expenses related to a product are the cash expenditures including the buying price, and transportation and tax expenses, which are often considered in the process of selection. Operational costs such as the negotiation process costs may also be considered, even though their satisfaction requires more efforts (Kahraman et al., 2003). 1.
The routine order products: As obvious by the label, this category includes the products that are ordered routinely.
2.
The products with procedural problems: These products may face some problems in utilization due to the lack of staff’s awareness and ability.
3.
The performance problem products: These products may be inefficient for the functions other than their special applications.
4.
The political problem products: In these products, there exist some disagreements between the stakeholders. For each of these groups, the evaluation indicators have been suggested as
depicted in table 2.1.
31
B. Lehmann and O’Shaughnessy’s supplier selection criteria In the studies by Lehmann and O’Shaughnessy, at first, the industrial products have been classified into 4 categories (Vokurka et al., 1996): Table 2.1:Lehmann and O’Shaughnessy’s Supplier Selection Criteria 1st category
Delivery’s reliability Price Flexibility Reputation Technical features
2nd category
Flexibility Technical services Ease of use Training Delivery’s reliability
3rd category
Delivery’s reliability The information related to credit Flexibility Reputation Experience
4th category
Price Delivery’s reliability Reputation The information related to credit Flexibility
C. Ellram’s supplier selection criteria Ellram emphasized on the collaborative relationships and as a result, the evaluation indicators presented by him have been applied more frequently in the decision-making operation for selecting the supplier. Table 2.2 introduces the desired indicators by Ellram. These indicators have been classified into 4 categories of financial, corporate culture, strategy, and other groups.
32
Table 2.2:Evaluation Indicators in 4 Groups of Environmental Factors Evaluation indicators Financial
Descriptions
Organizational culture and strategy
Technical and technological
Others
Economic efficiency Financial stability Sense of trust and job security Management ethics/ future vision Strategic fitness Senior management compatibility Compatibility among different levels and performance of the supplier and buyer firms Organizational structure and suppliers’ staff Estimating the status of facilities/current production capabilities Estimating the future production capabilities Supplier design capabilities Supplier development speed Re-examining the pieces Re-processing the pieces Supplier safety records Commercial references Supplier’s customer opinions
D. Choi and Hartley’s supplier selection criteria In a study by Choi and Hartley in the automotive industry of the USA in 1996, 8 main factors of supplier selection criteria were identified. These factors and the related components are provided in table 2.3 (Choi and Hartley, 1996).
33
Table 2.3:Choi and Hartley’s Supplier Selection Criteria Criteria 1. Financial resources Financial conditions Supplier profitability Tendency to reveal financial records Performance bonuses 2. Stability/Compatibility/Constancy Sustainable quality Continuous timely delivery Philosophy of quality Quick response 3. Relationships Long term relationships Closeness of relationships Transparencyof relationships Reputation and credit 4. Flexibility The ability to change the production volume Short time of implementation Short time of delivery Tendency to solve conflict
1. Technological capability Design capability Technical capability
2. Customer services After sale services Supplier qualification
3. Credit Increased improvement Product credit
4. Price Lowest price
E. Garvin’s supplier selection criteria Garvin in his studies in 1993 presented the performance criteria in detail (Garvin, 1993). In these studies, 5 indicators were proposed:
Quality
Cost
Timely delivery
Services
Flexibility
These indicators are given in table 2.4.
34
Table 2.4: Garvin’s Supplier Selection Criteria Quality
Cost
Performance Features Credit Compatibility Durability Service capabilities Appearance Delivery quality
Initial cost Cost Exploitation Repair costs
Timely delivery
Sufficiency Reliability Availability Access speed to information Quality Ordering easiness Flexibility of ordering Flexibility in transportation Easiness in returns
Services
Customer support Sales support Solving the information problems
Flexibility
Flexibility in the product Flexibility in production volume Flexibility in the process
F. Dickson’s supplier selection criteria Dickson in 1966 proposed 23 indicators to conclude decisions in the supplier selection process. The indicators with their importance degrees are given in table 2.5 (Lewis et al., 2000). Table 2.5:Dickson’s Supplier Selection Criteria Rank Factor 1 2 3 4 5 6 7 8 9 10 11 12 13 14
15
Quality Delivery Performance records Warranty policies for pieces Capacity and production facilities Price Technical capability Financial position Procedure enhancement Communications system Credit and position in the industry Tendency for trading (working motivation) Management and organization Operational controls Repair and maintenance
35
Average score 3.508 3.417 2.998 2.849 2.775 2.758 2.545 2.514 2.488 2.426 2.412 2.256 2.216 2.211
2.187
Evaluation
High significance
Considerable significance
16 17 18 19 20 21 22
23
services Consideration Effectiveness Packaging capability Records of work relationships Geographical location Sales and commercial records Providing the training requirements in relation to the product Mutual transactions
2.120 2.054 2.009 2.003 1.872 1.597 1.535
Average significance
0.610
Low significance
Comparing the criteria presented in various studies: Based on the results of those studies, the criteria that were presented in the different studies can be summarised as follows: Criteria Delivery’s reliability Price Flexibility Reputation Technical features Financial , Organizational culture and strategy and Technical and technological Financial resources Technological capability Financial resources Stability/Compatibility/Constancy Relationships Flexibility Flexibility Technological capability Price Customer services Credit Quality Cost Timely delivery Services Flexibility Quality Delivery
36
Resource - Lehmann and O’Shaughnessy
Ellram’s
Choi Hartley
Garvin
Dickson
and
Performance records Warranty policies for pieces Capacity and production facilities Price Technical capability Financial position Procedure enhancement Communications system Credit and position in the industry Tendency for trading (working motivation) Management and organization Operational controls Repair and maintenance services Consideration Effectiveness Packaging capability Records of work relationships Geographical location Sales and commercial records Providing the training requirements in relation to the product Mutual transactions
Below, the literature on the practical researches, carried out in the field of supplier selection and strategic supply, is briefly provided. 2.2.4
The Prevalent Models of Supplier Selection The main methods and models of supplier selection are shown in table 2.6
with an emphasis on the industry. A. Traditional models of supplier selection
A. 1. Judgment models In these models, the supplier selection criterion is the judgments of individuals (mainly experts and managers) about the candidates. In other words, the organization’s manager can evaluate and select suppliers based on his/her own judgment or willingness. In this method, in most cases, special criteria are considered
37
for selection, and in others, it is merely based on the decision-maker’s viewpoint. This method is often used in non-competitive and monopolistic conditions where the buyer’s power is much bigger than the supplier’s, except for cases in which the supplier is selected due to its uniqueness. The experiences in Iran’s industry show that in previous years, this model has been the main and most frequently applied model for supplier selection in Iranian companies (Goudarzi, 2003). Table 2.6:Various Types of Supplier Selection Models Supplier selection models Traditional models
Qualitative models
Mathematical models * Judgment * Idealistic models programming * Linear programming * DEA * Mixed integer programming
Quantitative models Statistical Weighting models models * Cluster * Analytical analysis hierarchy model process model * Probabilistic * Analytic model network process model * Taxonomy model SCOR model
Models based on artificial intelligence Other models
* Total cost theory * Total cost of ownership * Multiattribute utility theory
* Neural networks * Performance evaluation expert system
A. 1.1. Categorical models Categorical methods are fundamentally qualitative. These models are the first ones between the conventional methods of supplier selection. Based on these models, suppliers are assessed according to their historical records, the buyer experience, and a set of criteria. The method used in this model is simple, understandable, and of restricted functions. This model can be explained with the following matrix.
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Table 2.7:The Evaluation Criteria for the Categorical Models Supplier
Delivery
Quality
Cost
Overall
1
-
-
+
-
2
0
+
-
0
3
0
-
0
-
4
+
+
+
+++
5
-
0
-
--
6
0
0
0
0
Key: + = Good, - = Unsatisfactory, 0 = Neutral
The performance of each supplier for each criterion is assessed as positive, neutral, or negative. In fact, by using this method, all the suppliers are divided into three main groups. In this method, supplier (i) will be preferred more than supplier (j), if supplier (i) has more positive signs (+S). In addition, it has been assumed that all attributes are of the same significance. Moreover, it should be mentioned that the weights (Wj) allocated to attributes of +, 0, and -, are then coded as (-1), 0, and (1). The condition on these weights is that (Wj) has to comply with two major principles of probability. If Sij is the score of seller (i) for attribute (j) and TS(Vi) is the total score of seller (i), therefore:
Suppliers are rated in accordance with the total score and the suppliers with the highest scores will be chosen (Yousef et al., 1996).
39
A. 2. Quantitative models Quantitative models have been employed and studied more than the rest of the models, which include the weighting, mathematical, and statistical models. A. 2.1. Mathematical models If a proper set of decisions is introduced into a mathematical programming model, the decision maker will have this opportunity to formulate the decision problem according to the mathematical objective function and maximize or minimize the objective function (e.g. number of orders to supplier x) through the variables’ values. It may seem as though the mathematical programming models are mostly objective models rather than ranking models, since they compel the decision maker to overtly express the objective function. Likewise, most of these techniques just ponder the quantitative measures. The major mathematical models used in the supplier selection are idealistic programming model, linear programming model, and mixed integer programming model. Mathematical models mainly by considering the issues, such as the supplier performance score or the opportunity cost of selection as the objective function, and by considering the constraints like communications, quality, etc., model the supplier selection problem and optimize the decision-making task. A great majority of such modelling are costly and expensive and hence, are less applicable (Vokurka, 1996). A. 2.1.1. Data Envelopment Analysis (DEA) This method has been created based on the concept of the “efficiency of a decision alternative”. Alternatives are assessed according to profit criterion (output) and cost criterion (input). The efficacy of an alternative (supplier) is equal to the ratio of its output’s weighted sum (the supplier performance) to input’s weighted sum (the costs used by supplier). This method helps the buyer to segment the suppliers into two groups of efficient and inefficient suppliers (Beralika and Petroni, 2000). A. 2.2. Weighted point model The complexity and costliness of mathematical models have directed the model makers to the weighted point models. In these models, criteria are given different weights, where the largest weight shows the highest significance. The score of each criterion is multiplied to its weight and the obtained results are added
40
together to give a value for every supplier. After that, the supplier with the best rank will be chosen. In fact, this approach tries to develop the theoretical characterof categorical methods by giving numerical weights to the evaluation criteria. Consequently, a combined performance indicator is computed and the sellers can be compared accordingly. Generally, the weighted point models can be shown based on the following formula:
Where, Aj= The final value, which presents the total predicted performance of seller j ai= The significance related to an evaluation criterion bij= The performance rank of seller j based on evaluation criterion i n = The number of evaluation criteria Thompson believed that the weighted point models are perfect means because of different reasons. For example, these models are much uncomplicatedand can be adjusted easily to any buying decision and are very economical. The main weighted point models are Analytical hierarchy process (AHP), Analytic network process, and Taxonomy model (Weber, 1996). A. 2.2.1. Analytical Hierarchy Process (AHP) This model is an adaptable modelling means that can compare a large collection of quantitative and qualitative measures. The best advantage of this method is its hierarchical structure. This model considers the mental information and uses them with a rational method. The easiness-to-use is known as its other advantage and it is useful when there is a decision-making event between some suppliers with compatible goals.
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By using this model, the buyer should only express one quality phraseaccording to the relative importance of a criterion compared with another one and should indicate the relative preference of a supplier compared with another one based on each criterion (Kahraman et al., 2003). A 2.2.2.Supply Chain Operations Reference (SCOR) model: Supply Chain Council (SCC) developed the Supply Chain Operations Reference (SCOR) in 1996 (Persson, 2011) (Sherafati et al., 2014). Performance attributes is a single classification dimension that SCC (2010) defined four metrics (Garcia et al., 2012), (Sherafati et al., 2014). The model extended by the voluntary participation of the council members to portray business measures in connection with all the phases of meeting customer’s demand (Hugos, 2011) (Sherafati et al., 2014). SCOR (version 10.0) comprises four key elements (Georgise et al., 2012) (Sherafati et al., 2014) of: Performance: Standard metrics to describe process performance and define strategic goals. Processes: Standard descriptions of management processes and process relationships. Best Practices: Management practices that produce significant better process performance. People: Standard definitions for skills required to perform supply chain process. SCOR is a reference model, in which unlike other optimization methods, has no mathematical formula or heuristic solution to solve supply chain problems. Instead, there are some standardized terms and processes to help to understand the supply chain process. Different entities of supply chain can be demonstrated by configuring the processes and then compared them with each other. This allows the companies to (Hugos, 2011), (Sherafati et al., 2014): Evaluate the processes efficiently. Compare their performance with companies in and out of the industry. Follow specific competitive advantages.
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Set the priority for the activities according to these evaluations. Quantify the profits yielded by the changes. Identify the best software that meets the requirements of a specific process SCOR is based on 3 major pillars (Hugos, 2011), (Sherafati et al., 2014): A 2.2.2.1 Process Modelling Pillar: by describing supply chains, the model can be applied to display simple or complex supply chains with the aid of a common group of definitions to understand the existing and future conditions based on five distinctive management processes of: plan, source, make, deliver, and return. At this stage, the current conditions of the company are identified and the competitive advantages and changing business conditions are defined. A 2.2.2.2 Performance Measurements Pillar: SCOR contains critical indicators that determine the functioningof supply chain operations. The metrics are used in conjunction with performance attributes. The performance metrics stemmed from the proficiencyand influenceof the council members. The performance metrics permit us to analyse and evaluate a supply chain against the other in their competing strategies. The comparisons will be a base for future changes to improve the supply chain of a company. A 2.2.2.3 The Best-Practices Pillar: SCOR outlines the most effective approach as a current, structured, verified, and repetitive system to provokea constructive effect on the desirable functional results. When the implementation of the supply chain processes has been appraised and the performance breaches are recognized, it becomes inevitable to determine which actions need to be executed in order to eliminate the gaps to improve company operation indicators, inspired by the successful company’s operations. SCOR is according to five distinctive management processes (Francis, 2007), (Sherafati et al., 2014): Plan: Organizations require a strategy to handle all the resources in order to move toward satisfying customer demands for their own goods and services.
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Providing equilibriumbetween the aggregate demand and supply to implement a course of action that suitably fulfils sourcing, production, and delivery necessities. Managing commercial regulations, supply chain operation, collecting data, inventory, stocks, transportation and planning qualification. Adjusting the supply chain and financial plans together. Source: Processes that source products and services to satisfy the intended or actual demands. Company chooses suppliers to deliver the goods and services which it needs for production. Supply chain manager develops a collection of pricing, delivery, and payment procedures with suppliers and establishes some metrics to monitor and improve the related relationships. It includes the procedures for handling the products and services, inventory, receiving and confirming cargoes, transporting to manufactories and approving supplier payments as below: Scheduling deliveries, receipt, and verification and transferring product. Identifying and selecting supply sources. Managing commercial regulations, evaluation of suppliers operation and data protection. Controlling
inventories,
stocks,
suppliers’
communication
network,
regulations for incoming and outgoing products and suppliers’ obligations and commitments. Make: Processes which convert the product into its final state to respond to the strategic or real demand. At this stage, supply chain managers plan the actions that are essential for manufacturing, examining, packaging, and preparing to deliver the products. The mentioned process is the most metric-intensive part of the supply chain, in which corporations can appraisethe quality levels, production output, and worker productivity. Scheduling production activities, examining, packaging, and preparing for delivery Engineering the product based on the order
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Managing trade laws, operations, data, under working process stocks, facilities, transportation, and production network. Deliver: Logistics; the company manages the receiving of order from customer, builds a complex of storages, selects carriers to deliver product to the customer and function the invoicing system to collect payments: Coordinating the receipt of orders from customers, pricing and picking carrier product to customers. Developing warehouses from receiving time and transferring product for loading and sending. Receiving and verification of the product in customer’s website. Managing commercial regulations regarding receiving and sending product, operation, data, inventories, stocks, transportation, product life cycle and regulations regarding incoming and outgoing products. Return: Supply chain planners must design a responsive and adaptable network, which could obtain the flawedand surplusgoods from the users and also support them: All steps of returning defective product from the source of identified deficiency, transportation, and issuing license for returning the product, receiving and transporting defective product. All steps of returning repairable product from the source of identified deficiency, transportation, and issuing license for returning the product, receiving and transporting repairable product. All steps of returning excess product from the source of identified deficiency, transportation, and issuing license for returning the product, receiving and transporting excess product. Managing commercial regulations regarding returning defective, repairable or excess products, collecting data, returning inventory, stocks, transportation, and configuring requirements of network. Key indicators of operation evaluation for supply chain management in SCOR model are listed below:
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Table 2.8:Key Indicators of Operation Evaluation in SCOR Model Scope Row
Operation key indicators
Inside scope
Scope of customer
Capital Cost Flexibility Responsibility Reliability 1 2
Delivery operation Orders delivery operation 3 Orders fulfilment 4 Supply chain * responsiveness 5 Production * flexibility 6 Aggregate costs of logistic * management 7 Workers added * value productivity 8 Guarantee cost * 9 Cash to cash cycle * time 10 Days of supplying * inventory 11 Capital turnover * Reference: (Bolstorff and Rosenbaum, 2003), (Sherafati et al., 2014).
* * *
A. 2.3. Statistical models These models are applied in random uncertain conditions in the supplier selection process. The statistical-probabilistic approaches include the cluster analysis (CA), random probability model, etc. (Iravani, 1993). A. 2.3.1. Cluster Analysis This is one of the basic methods of statistics that applies a classification algorithm to clusteritems. These items are determined by a group of numerical characteristics and are allocated to each of the classes in a way to have the minimum difference between the items of each class and the highest difference between the items of different classes. It is clear that this method can be used to classify suppliers,
46
where each supplier receives a different score for each criterion. The output would be the classification of suppliers into comparable groups of suppliers. A. 2.3.2. Probabilistic model This model is employed as an element of a system for purchasing different needed parts for large-scale projects. The stochastic process is related to random variables that explains the changes of physical processes according to time. In fact, for every t ϵ T, x(t) is a random variable. Index t changes mostly as the process or time parameter; hence, x(t) can be considered as the stochastic process status at time t. For instance, x(t) may be all the customers who have entered the store until time t, or the customers who have entered during time t, or the total recorded sales in the store until time t. A. 3. Other traditional models of supplier selection A. 3.1. Cost-based models These models provide a reasonable and acceptable solution for the evaluation of important and critical suppliers. Based on Monczka and Trechal, an evaluation method of supplier performance based on the total costs reflects the real costs of operating the transaction with suppliers (Youssef et al., 1996). The advantages that a purchaser can acquire through utilizing this method have been shown as below:
The capacity to supply needs according to the total cost considerations
A method to improve the control and increase the supplier accountability
A reasonable and coherent evaluation tool
Determine the expectations of the supplier performance
The relationship of the company’s purchase priorities with suppliers
The ability to assess the supply risk
Promoting internal communications to report the critical suppliers information
The capacity to secure the desirable capabilities of the supplier
A basis to plan the supplier’s bonuses
Monczka and Morgan presented two indices for their model, which are the supplier performance index and service factor ratio. Prior to computing the two
47
mentioned indices, a decision maker must specify the critical elements that need to be assessed and the performance indices. Service performance index is calculated as follows:
Where SPIi= Supplier performance index NPCi= Supplier non-performance cost The service factor ratio measures the performance factors, which are difficult to be measured and quantified. These factors may include the following issues: -
The ability of supplier to solve problems
-
The tendency of supplier to provide the needed technical data
-
The effort of supplier to develop or maintain the relationships
In order to calculate the service factor ratio, each internal customer with a direct previous experience with the supplier will rank the supplier based on a group of qualitative factors on a precise scale. For instance, the internal customers is requested to rank a supplier based on “the ability to solve problems” or “the tendency to provide the needed technical data” from 1 to 7. The service factor ratio is computed as below:
Where Fi= The determined level for factor i
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A. 3.2. Total cost of ownership (TCO) model Economists have underscored the significance of considering all the aspects of costs in addition to the main price when purchasing from an external resource. They have also carried out the cost analysis of a purchase from its manufacturing or purchasing perspective, in comparison with the internal production of the product or service against its purchase from the market, which in fact, is the basis for the TCO analysis. From the time that the TCO analysis is applied to discern between internal manufacturing and external purchase of a commodity or service, and even when the company has already decided on supplying the commodity through its internal manufacturing processes or from the external resources, the TCO analysis should be concurrently applied during the operation. The cost of purchasing a commodity or service can differ considerably among different suppliers. This is an important index for decision-making. Therefore, the purchase cost analysis approach from an economic perspective can be taken into consideration as the theoretical basis for the TCO analysis of purchases. Considering the application of the purchase cost analysis for marketing purposes, Heed pointed out that sometimes, performing a purchase might involve the human capital in the problem, which ended in a situation where its compensation becomes very problematic and costly. For instance, the simultaneous function of the engineering department and the customer service unit of a supplier may have a significant effect in reducing the costs of a purchase itself and its arrangement process from a specific supplier. Having considered the typical mistrust existing in the organization’s external environment, which makes the organization prone to opportunism, it is generally indicated in marketing subjects that if the organization’s boundaries are free, the rate of opportunism in the environment will be decreased. This issue pertains to the total cost of ownership in the buyer and supplier relationships, where the costs for the establishment of good relationships between the buyer and supplier need to become known. These costs include devoting assets such as assigning key personnel. In addition, by building close relationships between the buyer and supplier, the purchase costs are expected to decrease. An example of this issue would be the
49
reduction in the costs for attracting suppliers, evaluating numerous offers of suppliers, and searching for the new suppliers. According to the mentioned points about the TCO analysis, it has been clarified that the purchase costs include those related to sales, which encompasses the commodity price, and those related to after-sales issues, which incorporates the associated costs with the product delivery. The above-mentioned costs have been confirmed in the marketing section. Therefore, the TCO analysis is a valuable tool to support the theory of purchase cost analysis for the relationships between the buyer and the supplier (Ellram, 1993). In the TCO method, all criteria are divided into four main groups (Ellram, 1993):
Production costs (including raw materials, wages, etc.)
Quality-related costs (rework costs, inspection costs, etc.)
Technology costs (including design, engineering, etc.)
After-sales service costs TCO may include some elements such as the research and development costs,
transportation costs, maintenance costs, etc. However, since these criteria are less important than the aforementioned criteria, they are often omitted from the model, unless in cases where one of these criteria plays an essential role in measuring the suppliers’ performance (Degraeve and Roodhooft, 1999). A.3.2.1. Advantages and limitations of the TCO method
TCO enhances the buyer’s perceptionabout the supplier’s problems and its cost configuration. It offers the buyer some important information for the sake of having constructive negotiations with the supplier.
TCO justifies the high initial prices for managers on the basis of higher quality and lower total expenses in the long run.
By using the TCO analysis, the performance of different suppliers can be compared during a given period.
The TCO output provides some criteria to assess the present and future suppliers.
50
The complexity involved in the TCO elements is a major obstacle in the utilization of this approach.
There is no standard and universal approach for the TCO analysis.
The literature review has demonstrated that the TCO models have been widely used, but with diverse criteria for different companies. Hence, the type of utilization of this model is highly reliant on the buyer’s criteria.
The implementation of the TCO requires a change in the buyers’ culture; i.e. a cultural change is needed from price orientation towards total-cost orientation.
A.3.3. Multi-criteria selection model Lubben proposed a multi-criteria selection model, which is applied to analytically relate the capability of a supplier to a company’s needs. In addition, this model compares the competences of different providers and evaluates the probability of their performance progression. The input requirements of the multi-criteria selection model can be categorized into three groups: 1.
A system to present the supplier’s characteristics or necessities.
2.
A questionnaire which assesses how perfect a supplier can satisfy such requisites.
3.
A structurethrough which the supplier’s characteristics can be assessed (Youssef et al., 1996).
A.3.4. The vendor profile analysis (VPA) model In order to cover the constraints of weighted models, Thompson has proposed a vendor profile analysis (VPA) model. According to Thompson’s theories, the VPA model could be displayed as below:
51
Where: Ajk= The total score of vendor (j) on iteration (k) in the simulation process ai = Significance of the weight of the evaluation criterion (i)
Where: Xi = The score of the variable attribute (X) Yi = The score of the variable attribute (Y) Wi = The relative significance of attribute (i) Ranking of Yi, Xi based on the DA ratio. Based on the above model, the evaluation processes include a set of one-onone assessments. The DA ratio, which was attained from the above equation, can have one of the following values of DA=1, DA1. The first value of DA assumes that suppliers X and Y have the same rank. The second value presumes that the rank of supplier Y is higher than that of supplier X. Finally, the third DA value supposes that the rank of supplier X is higher than that of supplier Y. Although the DA model is more structured than the majority of quantitative models, which were argued here, the one-on-one evaluation method is subject to a number of limitations. Firstly, DA=1 causes the decision-maker to be unconcernedtoward the supplier to be selected. Secondly, in a large group of suppliers, the selection process will become very exhausting and time-consuming (Youssef et al., 1996). A.3.5. Multi-attribute utility theory (MAUT) The application of this theory can direct the purchasing experts to align the strategies of practical resourcing, since the theory is capable of handling several contradictory attributes that are effective on the supplier selection process. The theory enables the purchasing managers to assess the programs, which are linked to the amendments in the organization’s policy. In a period of global resourcing, the success of a multi-national company is often dependent on the selection of the most
52
appropriate external suppliers. The selection of international suppliers is a highly intricateand uncertain task due to many unmanageable and unstable factors, which have influence on decisions (Kharam et al., 2002). B. Artificial-intelligence-based models Artificial-intelligence-based models are founded based on computer systems, which can be taught with the aid of the purchasing experts or the historical data. Any non-expert individuals facing similar cases such as these models, but in new decision-making situations, can apply this system. Some of these models, which have been employed for supplier selection, are neural networks, expert systems, and reasoning-based systems (Vokurka et al., 1996). One of the strengths of these models refers to this fact that they do not need the decision-making process formulation. B.1. Neural networks Compared to traditional methods, neural networks can better overcome the complexities and uncertainties, because these approaches are designed in such a way that their performance is similar to the humans’ judgments. Users of these systems should only provide the specifications of the current position to the neural network. After that, the neural network performs the required exchanges for the user. These exchanges are accomplished based on what the network has “learnt” from the experts or from similar past occasions. On the other hand, this strength is sometimes deemed as a weakness because the user of the neural network cannot explain the performed exchanges to others such as the suppliers, who have not been selected. This makes the neural networks a suitable option in cases where the external judgment is of a low importance or when these networks are used as a “shadow” pattern in conjunction with conventional methods.
53
B.2. The performance evaluation expert system Since the supplier evaluation and selection process is a very complicated procedure due to some reasons such as numerous limitations, inaccessibility to information, inexistence of the suppliers’ up-to-dated information, incompatibility of indices, etc., it has been recommended that the expert systems be exploit as a structureto strengthenthe decision-making procedure. An expert system is actually a computer program that imitates the characteristics and knowledge of an expert, who in the reality is able to solve problems. Expert systems have many advantages among which the following ones can be indicated:
The awareness (knowledge) basis is usually stable, persistent, and easy-touse.
This system is virtually low-cost.
It is capable of being used simultaneously in different situations.
Its reporting process is easy-going.
It yields compatible results.
It enables the analysis and interpretation of results while working, and thereby it always train the experts and make them more knowledgeable.
Nowadays, with the aid of expert systems, capacity planning, network management,
purchasing,
storage
control,
production
management,
production planning, workshop planning, quality control, and prediction have become achievable. 2.2.5
Reasons for Selecting the AHP Model and its Practical Features The complexity, expensiveness, and costliness of mathematical models have
directed the model-makers towards the weighted models. In the weighted models, the criteria are weighted in a way that the largest weight reflects the highest importance. Such models are suitable evaluation tools, because their application is easy and almost well-matched with any purchasing decision. Moreover, they are low-cost and are capable to rank different decisions (Weber, 1996).
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The most important weighted model is the analytical hierarchy process model, which has been deployed in the present research since it is practical, accurate, easy-to-use, flexible, cheap, and secures the possibility of using qualitative and quantitative criteria all together. The analytical hierarchy process is amongst the methods proposed for complex, fuzzy, and unstructured issues based on the analysis of human brain. This method assists in taking appropriate decisions for complex issues by simplifying and managing the decision-making stages. The process has been designed in a way to be compatible and harmonious with the human mind and nature. It includes a set of judgments, decisions, and personal valuations in a logical manner, so that it can be claimed that this technique is dependent in one hand, on personal thoughts and experiences for the sake of forming and planning the problem, and on the other hand, on the logic, perception, and experience for the sake of having the final decision and judgment. This technique is one of the most popular multi-attribute decision-making methods in which both quantitative and qualitative criteria are considered. The model was firstly proposed for individual decision-making. Then, in the 1980s, its application in collective decision-making was scrutinized. The use of the AHP in collective decision-making provides a situation, in which not only the benefits of decision-making techniques can be preserved, but also their defects, such as cost, speed, and single-thoughtfulness can be resolved. Furthermore, the deployment of AHP model has reduced the difficulties of point estimations in the mathematical and cost-based models and in turn, by using a qualitative term, it evaluates the supplier by considering the relative importance of one criterion compared to another (Kahraman et al., 2003). 2.3 1.
Part Three: Research Background An investigation was accomplished by AdelehAsemi and AsefehAsemi (2014), titled “Intelligent MCDM method for supplier selection under fuzzy environment”. In this paper, MCDM methods utilized for supplier selection in a steel firm. Current MCDM procedures do not notice the effectiveness the
55
natural environment on the assessment process and ranking. The purpose of the paper is to suggest an active fuzzy hybrid MCDM procedure for assessing, ranking, and choosing. The suggested procedure utilizes FAHP (Fuzzy Analytical Hierarchy Process) to weigh the criteria, and also FIS (Fuzzy Inference System). 2.
A research was carried out by Kannan et al. (2013), titled “the integrated fuzzy multiple criteria decision-making (MCDM) method and multi-objective programming approach for supplier selection and order allocation in a green supply chain”. In this study, it was made known that the environmental performance of an organization is influenced by the environmental performance of suppliers and hence, choosing green suppliers is a strategic choiceto compete more constructively in today’s international market. The problem of supplier selection engages many qualitative and quantitative measures. In the process of supplier selection, if suppliers are subject to restricted volume or other constraints, it is required that the most suitable resources and the amount of orders from every resource is identified. The aim of this paper has been to show an integrated method according to fuzzy multiattribute theory and multi-objective programming, in order to rank and choose the finest green suppliers by considering the economic and environmental factors, and allocate the optimal amounts of orders between them. Firstly, fuzzy analytical hierarchy process and the fuzzy method were applied for preference order by similarity to the standard solution to examinethe significance of multiple criteria with the aid of experts’ opinions to recognize the best green suppliers. In the next stage, the multi-objective linear programming is applied to codify and devise different limitations including quality control, capacity, and other targets. The goal of this mathematical model is to maximize the total amount of purchases and at the same time, minimize the total cost of purchases. In order to study the subjectivity of decision makers’ inclinations, the fuzzy logic is employed. The productivity and utilization of the presented method have been shown in the case study of
56
an automotive firm. The achieved findings from this investigation could assist in offering a systematic method to cope with the green supplier selection issue. 3.
Another study was carried out by Wu and Barnes (2011), titled “the dynamic feedback model for partner selection in the agile supply chain”. The goal of this study is to demonstrate a four-stage dynamic feedback model to select partners in the agile supply chains (ASCs). Generally, ASCs have been increasingly applied in dynamic markets as the response. This model enables decision makers to, while taking advantage of the expanded volumeof available information more efficiently and effectively, benefit from a comprehensive, well-ordered, and accurate approach for a complex problem in today’s information-centred society. The aim of this article has been to review the details of each of the four stages of the presented comprehensive model, which were carried out for the first time.
4.
An investigation was accomplished by Wu and Barnes (2010), titled “presentation of a model to constantly improve the supplier selection process in the agile supply chain”. In this article, a model has been designed to present the feedback and continuously improve the process of supplier selection in the agile supply chains (ASCs). This model seeks to invest on software to evolve the trend of supplier selection and make use of the constant improvement principles and organizational learning. The objective of this model is to organizationally assist decision makers in the related plans to optimize the efficiency of the supply chain by guaranteeing that the most suitable providers are always being chosen.
5.
Another study was done by Bilgen (2010), titled “supply chain network modelling in a golf club industry via fuzzy linear programming approach”. This article deals with the programming problem of supply chain network that the supply chain manager of a firm’s golf club has confronted with. The supply chain network for producing several products through the assembly of three presented modules by different suppliers has been taken into consideration. In the mentioned article, a linear programming model has been offered to
57
elucidate the problem, in which different components can be assembled in the planning horizon from one to twelve months. This model includes the provision of different parts from a group of suppliers and allocation of the assembled products to customers. Ultimately, the presented model has been examined by using a case study of the supply chain of a golf club industry. In addition, the sensitivity analysis has been performed on different methods to obtain some insights with regard to the proposed model. The numerical outcomes elaborated that the fuzzy model could provide a better and more flexible method for the concerned industry. 6.
Another research was carried out by Nilay and his colleagues (2011), titled “presenting a solution to supplier selection problem in the global supply chain via the AHP and ANP by using the methods under fuzzy environment”. In this paper, a model for global supplier selection has been demonstrated by using the analytical hierarchy process and analytical network process according to the linguistic variable weight. The fuzzy ANP and AHP methods have been proposed as good tools to solve the presented multiple criteria decision-making problem. The fuzzy AHP-based method assessed various decision-making measures, including service quality, costs, risk factors, and product features, in the global supply chain for the selection of the best supplier. In addition, the ANP is showed to be an effective tool in order to present a suitable solution for managers.
7.
An investigation was performed by Buyukuzkan (2012), titled “an integrated fuzzy multi-criteria collective decision-making method for assessment of green suppliers”. This research offered a decision-making model to assess the suppliers’ functioning by considering various criteria related to the environmental efficiency. An integrated fuzzy collective decision-making method was ratified to assess the alternatives of green supplier. In detail, a fuzzy analytical hierarchy process was employed to identify the relative weight based on the evaluation criteria and axiomatic design (AD) according to fuzzy collective decision-making method, which has been used to rank the green
58
suppliers. Ultimately, in order to show the model’s potential, a case study has been used. 8.
In a research by Kubat and Yuce (2012), titled “a hybrid intelligent method for supply chain management systems”, the supplier selection process has been realized as a very important practice in the industry, in which suppliers play a critical characterto secure a beneficial advantage over competitors. Thus, it is critical to determine and choose the desired suppliers. The supplier selection can be accounted as a decision-making issue with numerous measures including both qualitative and quantitative elements, like the purchase cost, quality level, supplier risk, and etc. Designing the most beneficial process for supplier selection would need a concession based on the above-mentioned components. In this study, a general framework has been presented, which combines the analytical hierarchy process (AHP), fuzzy AHP, and genetic algorithm (GA), to recognize the most advantageous group of providers.
9.
Another investigation was performed by Muralidhar et al. (2012), titled “evaluation of green supply chain management strategies by using fuzzy AHP and TOPSIS”. In this article, a novel decision-making process has been presented for group multi-criteria assessment of the green supply chain management strategies. This method includes the green supply and logistics, green production, and green services to customers. It also enables a dynamic supply chain to deal with the market variations in order to consider the environmental management process for order allocation. The fuzzy AHP was originally used for the assessment of the GSCM strategies and the weight was defined as a meaningful qualitative concept. Afterwards, by using fuzzy TOPSIS method, application of the criteria was qualitatively measured for order allocation amongst the selected strategies. Thus, this approach, which produces the decision-making knowledge, was approved. Then, a developed combination out of the regulations can be interpreted for easy allocation, while at the same time in case necessary, can be revised by the decision makers.
10.
A research was carried out by Balaji and his co-workers (2012), titled “the hierarchical analysis according to the agile supply chain”. It was understood
59
that the selection of base suppliers and distributers is vital for the supply chain operation. This key point is amongst the most important facets in selecting the best suppliers and distributers for the supply chain. The target of the mentioned study has been to propose a hierarchical decision-making method to overcome the problems of supplier and distributor selection in the supply chain systems. In this article, the theoretical value has been used to appraisethe weight of supplier and distributer selection. This paper employs the analytical hierarchy process to identify the weight of subjective judgments. 11.
Another study was done by Wu (2012), titled “The TOPSIS-AHP model and its application in the supply chain management (SCM)”. The SCM emphasizes on strategic collaborative relationships between the principal firm and the affiliated one. From this perspective, choosing strategic partners is an influential decision-making task in the SCM and is accounted as a success key to the SCM. For the purpose of this paper and in order to solve the supplier selection problem, the researcher has scrutinized a category of the analytical hierarchy process and simulation method, where the uncertainty in the hierarchical analysis was studied and used to fairly reduce the uncertainty in the hierarchical analysis. Afterwards, the method has been demonstrated for solving the problem of the selection of a simple supplier in the SCM.
12.
Another research was performed by Chauhan (2012), titled “selection of supply chain partner for Coke Energy Ltd. India by using combined analytical hierarchy process (AHP) and the weighted sum model (WSM)”. In this study, a case study with the aid of the AHP and WSM has been presented. In the first stage, the weights of criteria were computed through the analytical hierarchy process and then, by using the WSM, suppliers were assessed. Furthermore, the results disclosed that by applying an extension of the multiple criteria decisionmaking model, the assessment and selection of suppliers can be done using the WSM model, and consequently, according to suppliers’ performance, the consumed time for distributer selection can be lowered.
13.
A research was carried out by Chung (2009), titled “an integrated FANPMOLP for supplier evaluation and order allocation”. The purpose of this paper
60
was to study an integrated FANP-MOLP to assess the suppliers and distribute the orders. The results demonstrated that it is an all-inclusive decision-making method
to
specify
suppliers
by
considering
the
impacts
of
the
interdependencies that exist between different selection criteria. It was also revealed that it is a technique to obtain the optimum allocation of orders between different suppliers. 14.
Another study was accomplished by Wang and his co-workers (2009), titled “a fuzzy model for supplier selection in quantity discount environments”. Traditionally, the supplier selection was assumed to consider numerous heterogeneous criteria, which was a mind-numbing job for the purchasing decision-makers. The problem became even more obscured when the quantity discounts were taken into account all at once. However, it should be mentioned that this method most of the time ignores to consider the scaling and subjective weighting problems. Therefore, for moderating the complexities of the aforementioned problem, and to acquire a more beneficial compromise solution to allocate the order amounts between different suppliers with their proposed quantity discount rate, the analytical hierarchy process (AHP) and fuzzy compromise programming have been introduced in this study.
15.
Percin et al. (2008) employed a combined method including the AHP and multi-objective linear programming for selecting suppliers. The main purpose of this study was to identify the important criteria for supplier selection and express the significance of each criterion based on the managers’ opinions in choosing vendors with the aid of the fuzzy analytical hierarchy process method. After that, via the fuzzy multi-objective linear programming method, the supplier selection will be optimized according to the identified criteria. It was demonstrated that the result of the hybrid model, in comparison with the situation where the supplier selection problem is solved only through the AHP model, is more compatible with the reality and thus, the application of the hybrid model has been proposed for the parts in class A.
16.
An investigation was performed by Zaim and his colleagues (2006), in which for solving the multi-criteria decision-making problem for supplier selection,
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the fuzzy analytical hierarchy process method has been suggested. Their case study was aimed at selecting among the television producers in Turkey with a non-FAHP method. In this survey, the results were compared with the fuzzy method and it was disclosed that this method is a more useful method for supplier selection and evaluation. 17.
Franklin and Hai (2006) proposed a novel approach entitled the “voting analytical hierarchy process” for choosing suppliers. This approach is a novel weighting approach in preference to the pairwise comparisons, in which the AHP method is utilized for supplier selection. It is worth mentioning that even though this method is known as a simpler method compared to AHP, it does not demonstrate an orderly approach that could extract the applied weights and ranking of the suppliers’ performance.
18.
In a research by Chuang (2002), a new model was developed based on the “quality function deployment” method to design the distribution network and select suppliers. In this model, customers’ requirements are initially identified from the network. Afterwards, these factors are categorized and ranked and by using a tool called quality house, these needs help recognize the structure of the network and distributers. This paper only deals with the distribution centre location problem and does not appraise the flow between the distribution network’s elements.
19.
A study was performed by Talebi and Molla-tayefeh (2011), titled “the supplier evaluation and selection approach along the supply chain by using a hybrid technique incorporating the fuzzy analytical hierarchy process and fuzzy multiobjective linear programming”. In this survey, they tried to primarily recognize the important factors affecting supplier selection and determine the significance of each criterion according to the experts’ opinions and finally, select the best supplier. This research has tried to determine the rank and weight of every supplier based on each criterion with the aid of the fuzzy analytical hierarchy process method according to the development analysis method. Afterwards, a fuzzy objective function is created for each criterion aiming at maximizing the supplier’s performance in relation to each criterion.
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Lastly, by taking the model out of the fuzzy state and by using the weight addition method, the supplier is selected. 20.
An investigation was carried out by Bagherzadeh and Behrouzdari (2010), titled “application of the ANP for best supplier selection in the supply chain”. The major goal of this research was to demonstrate a beneficial approach with the aid of the analytic network process (which is a multi-criteria decisionmaking methodology) to assess the issues pertaining to supplier selection problem. This survey has proposed an analytic network process model as a framework to assist managers determine the evaluation objective, specify the most important evaluation factor, and choose the most suitable strategic supplier in the supply chain. Analytic network process has been applied as a decision analytic means to solve the multi-criteria supplier selection issues, which involve internal dependencies. It should be mentioned that the analytic network analysis process comprises a complex methodology, seeks excessive efforts, and needs a larger number of comparisons more than the traditional analytical hierarchy process method.
21.
A research was performed by Darabi and Saeidi (2008), titled “designing an integrated method for supplier performance evaluation and order allocation by using data envelopment analysis models and multi-objective mathematical programming”. The presented model of this study, according to the empirical process of supplier selection in real environments and by using the data envelopment analysis (DEA) method, firstly ranks the suppliers based on the model’s inputs and outputs. Afterwards, more detailed information about the selected suppliers’ selling conditions are obtained and assessed with the aid of a multi-objective mathematical programming model. The ultimate result would specify the final list of selected suppliers added to the purchase amount from each one. In this research, the way to create an integrated model according to the data envelopment analysis (DEA) and mathematical programming for identifying and evaluating suppliers and allocating orders were explained and its capability in real environments, elucidated.
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22.
Another investigation was undertaken by Chaharsoughi and Sahraeian (2008), titled “proposing a systematic method for supplier evaluation and selection”. The aim of this research is to offera systematic and step-by-step method for the mentioned task. The presented method is highly general and comprehensive and can be employed for any case. However, the details of the supplier selection process may be dissimilar for different suppliers. In this paper, a systematic method has been suggested in 10 stages for the step-by-step evaluation and selection of suppliers. They endeavoured to make this method general and comprehensive in the first place, and integrated and coherent in the second place. The process can be triggered by the creation of a need (e.g. a new product) or by the improvement of the current state. Furthermore, the process will be completed by selecting suppliers, forming the supply chain, and grouping suppliers. The existing integrity in this method facilitates the possibility of its application in various cases. Despite this fact, it should be stated that the details in every stage of each case, are not necessarily the same. For example, the type of new product or service or the type of financial criteria in different cases differs.
23.
A study was carried out by Jafarnejad et al. (2007), titled “supplier evaluation and selection in the supply chain in the case of single souring with fuzzy approach”. The aim of this article is to suggest a fuzzy decision-making approach to deal with the supplier selection issues in the supply chain. In this paper, the verbal expressions, stated by the elites, have been used to evaluate and determine the performance of each supplier in accordance with each criterion and to specify the weights of the criteria. Verbal rankings were indicated by triangular and trapezoidal fuzzy numbers and finally, the multicriteria decision-making approach was applied in fuzzy environment for supplier selection and a method for calculating the weight and ranking the alternatives in fuzzy TOPSIS technique was presented. In the end, an example has been included to exhibit the solution process.
24.
A research was done by Faez and his colleagues (2006), titled “designing an integrated model for supplier selection and order allocation by using the case-
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based reasoning method and multi-objective mathematical programming”. The suggested model in this article has been created in accordance with the empirical procedure for supplier selection in the real environments. This method with the aid of the case-based reasoning method, which has been built based on similar past experiences used for solving new alike problems, has identified and prepared a concise list of qualified suppliers and has recognized the general criteria important to the buyer, which are employed for selecting among potential suppliers. Thereafter, more detailed information about the selected suppliers’ selling conditions are obtained and assessed by using a multi-objective mathematical programming model. The ultimate result would specify the final list of selected suppliers added to the purchase amount from each one. In this paper, the way to create an integrated model according to the CBR and mathematical programming for supplier identification and evaluation, and order allocation, has been specifically explained and its capability is real environments, expounded accordingly. 2.4
Conclusions It should be stated that suppliers always take a significant part in the
managerial policies of a company, even though the organization and its suppliers have not had close relationships from the past. The objective of supplier selection for an organization is to identify the most suitable supplier, which can fulfil all the needs of the organization at an acceptable cost. In order to choose a suitable supplier, the organization considers and judges the abilities of different suppliers in meeting the organization’s needs and evaluates their economical offers by selecting the suitable conditions and indices. The intended conditions and indices should be chosen in a way to encompass all the organization’s requirements and assess suppliers from all aspects and in equal conditions. According to the above elucidations, it is clear that selecting a supplier for an organization can be very important and an organization can obtain significant successes through correctly defining the indices to prioritize suppliers and also by using a proper method to study these indices in their prioritizations.
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In this chapter, having presented some explanations about the supply chain management, the significance of the supplier evaluation and selection process was explored, and the conducted studies in this context were deliberated. Moreover, all the extracted indices for supplier selection were identified from these investigations. Finally, different available models for supplier evaluation and selection were expressed and the background of research considered in the third part of this chapter.
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CHAPTER THREE METHODOLOGY OF INVESTIGATION
3.1
Introduction Methodology can be defined as a set of rules, tools, and valid procedures to
examine facts, explore unknowns, and find solutions to problems. The choice of research methodology is one of the most important and technical step, which needs to be taken through designing the right tool for data collection. The investigator gathers and examines the data through statistical sampling. In addition, the selection of the appropriate research method is an eminentand difficult task. Thus, identifying the suitable research method and being fully aware of its accuracy is very profound (Khaki, 2005). This chapter deals with the research methodology, and the statistical population and sample, the realm of research, the research data, and their measurement methods, which will be explained accordingly. 3.2
Methodology
3.2.1
Type of Study As a general classification, all types of scientific research can be categorized
based on five major bases (i.e. the results, objectives, type of data, researcher control, and methods) (Rezvani, 2011). According to the five mentioned bases, the current study can be depicted as below: According to results, this research is an applied survey. The present study is descriptive based on the research objective. This study is quantitative considering the type of data. The current research is a non-experimental study in terms of the researcher control.
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The current study is a survey with regard to its methodology. The main steps of this research include the following items: -
Using the library study to identify the factors influencing the selection of suppliers (this stage was carried out in the second chapter).
-
Determining the local factors affecting the supplier selection process by using the Delphi method.
-
Selecting suppliers under fuzzy conditions with respect to the identified criteria and the fuzzy AHP technique.
3.2.2
Allocating orders to suppliers via the proposed linear model and the solution. Statistical Population The statistical population is defined as all the elements having one or more
common characteristics. In other words, this population includes all elements of the research subject and is used for inference (Homan, 1994). It should be expressed that in this study, all experts in the field of the automotive supply chain will be selected as the statistical population. 3.2.3
Statistical Sample
The statistical sample of this study consists of two parts: The first part consists of 10 experts, in the field of supply chain in the automotive industry, who were interviewed. The interviews will be continued until the theoretical saturation is obtained. After collecting the supplier evaluation indicators, the validity of indicators is examined by using the Delphi method and consequently, the final indicators will be specified. Afterwards, a questionnaire will be designed based on fuzzy analytic hierarchy process to rank the suppliers. The built questionnaires will be delivered to the supply chain managers of Saipa automotive company.
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3.2.4
Data Collection Method and Tools
Data collection tools for this study were library and field studies: In the first part, the library method was utilized to identify the criteria affecting the supplier selection. In this regard, previous studies were scrutinized and the relevant criteria were extracted. In the second part, by using the Delphi method, the criteria recognized in the previous section were distributed among the experts and were either approved or rejected. In this section, new criteria might be added to the library criteria. In the third part, the information necessary for the ranking of suppliers by directors will be gathered via a questionnaire, which will be designed based on fuzzy AHP method. Finally, in the fourth part, the data needed to estimate a linear model and for the optimized allocation of orders through the company’s database will be collected. 3.3
Measuring the Reliability and Validity of Data Collection Tools The assessment tools must have the necessary reliability and validity to help
the researcher confidently collect the research data, test the hypotheses through the analysis of the data, and answer the research questions fully and accurately. The standardized assessment tools are of the adequate level of reliability and validity, and therefore, researcher can employ them with confidence. However, the researchermade tools lack the sureness and prior to any application, their validity and reliability need to be confirmed (Hafez Nia, 2003). 3.3.1
Validityof the Assessment Tool The validated instruments are those tools that their scale and content can
precisely measure the variables and subject of research. In other words, it can be mentioned that the data collected through the validated tools are not excessiveand no part of the required data related to assessment of the variables, will be removed.
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Validation methods include three categories, which are content validity, criterion validity (index), and construct validity (Sarmad et al., 2000), that are described below: Content validity: This is related to the suitability of the tool’s questions in measuring the concept of the study. In order to inspectthe suitability of the questions’ contents, the opinionsof the referees and experts on the subject are used. Criterion validity: This indicates the efficiency of a measuring tool in predicting a person’s behaviour in specific situations. For this purpose, the responses of each individual are compared with the standard paradigm (criterion). Construct validity: This is a measuring instrument, which can obtain the answer that is consistent with the accepted theory. Inadequate measurements can make any scientific research illegitimateand invalid. The reality might be questioned if the validity is suspected. Validity can be studied without any doubt about the scale of variables. Without examining the nature and meaning of the research variables, studying the validity cannot be completed. Additionally, obtaining validity is a technical matter to a great extent. However, validity is far from being a single technique and it should be expressed that it is within the nature and essence of science. Validity relates to the appropriate information provided by the test for the decision-making process. As a result, the judgments about validity are always studied in relation to a specific decision or particularapplication. 3.3.2
Reliability of the Assessment Tool Reliability can be considered as equal to accuracy. If a reliable assessment
tool, which has been made for measuring a variable, is employed at different times and places but with the same conditions, similar results should be derived. In other words, a reliable tool has the repeatability feature for measurement and yields the same results. The reliability and validity of assessment tool are usually influenced by several factors such as the complexity of the subject, specifications of the data provider, and the data collection methods and conditions.
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To calculate reliability, there are different ways such as retest, parallel counterpart method, bisection method, and Cronbach’s alpha coefficient. Since the instrument used for data collection in this study (including Delphi and AHP) is considered as one of the standard methods, the validity of data has been ascertained automatically. 3.4
Data Analysis Method
3.4.1
Descriptive Statistical Methods The descriptive analysis of data refers to the characteristics of the population
and sample. According to the questionnaire presented in this study, these characteristics include gender, age, education, and work experience of the subjects that are mentioned in the first section of the questionnaire. 3.4.2
Inferential Statistical Methods In this study, in order to finalize the supplier selection criteria, the Delphi
method along with Excel software will be exploited. For this purpose, the most important criteria, identified through literature review and conducting several interviews with experts, will be recognized by using the Delphi method. These criteria will be subsequently applied to rank and weigh suppliers. It is worth mentioning that the employed technique in this section is fuzzy analytical hierarchy process method. Moreover, the order quantity allocation to suppliers is carried out by using a linear model. The output of the AHP method will be used in this model. Afterwards, in order to solve this model, the Lingo software will be employed. -
Delphi method The Delphi questionnaire was designed to communicate with the experts in
order to identify the most critical metrics affecting suppliers. According to Helmer (1997), Delphi is a useful communication tool between a group of experts, which is basically employed to formulate and organize ideas.
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Wissema (1982) emphasized on the importance of the Delphi technique as a method for a univariate exploration to predict the future of technology. He added that the purpose of this method is to facilitatethe debate among experts. As a result, it may prevent the effect of social interaction behaviours that usually occur in group discussions and impede the formation of ideas. Baldwin (1975) argued that the Delphi method could be applied under the condition of inadequacy of the current scientific knowledge. The Delphi technique is based on this principle that the viewpoints of all the scientific experts about the future prediction is of the highest importance. Therefore, unlike the survey research methods, the validity of the Delphi method relates to the number of research participants, which is dependent on the scientific validity of the experts. The Delphi method is designed to establish a collection of views without any restriction on the personal or group reflection. The steps are as follows: 1. Select the experts 2. Prepare the questionnaire according to the pre-defined criteria 3. Obtain the experts’ opinions 4. Collect the responses 5. Evaluate and summarize the views and complete the questionnaires 6. Send the feedbacks of integrated results to experts 7. Gather the opinions once again and complete the questionnaires 8. Repeat the process 9. Analyse and conclude
-
Fuzzy numbers and fuzzy AHP
Fuzzy numbers Fuzzy numbers are the expression and generalization of ordinal numbers. An ordinal number like can be presented by the membership function as shown below:
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Thus, any real number can be expressed as a fuzzy number and triangular fuzzy numbers are the simplest ones (JafariSamimi et al., 2010). The transformation of fuzzy number (M) to the domain of real numbers (R) is called a triangular fuzzy number, if its membership function
is equal to:
Triangular fuzzy numbers are expressed with (l, m, u). The parameters l, m, and u respectively represent the lowest possible value, the greatest certain value, and the greatest possible value, which describe a fuzzy event (Ertugrul and Karakasoglu, 2009). The two important operations on fuzzy numbers, which have been used in this article, are as follows, where if there are two positive triangular fuzzy numbers (l1, m1, u1) and (l2, m2, u2), then:
Fuzzy AHP AHP is one of the most renownedmulti-criteria decision-making procedures invented by Saaty in the 1970s. The indicators can be either quantitative or qualitative. AHP is typically based on the latent paired comparisons. In this approach, the decision maker identifies his/her decision making options by creating a hierarchy decision tree. Afterwards, s/he specifies the weight of each factor by
73
performing a series of paired comparisons between the competing alternatives (Saaty, 1980). The traditional AHP method is a difficult technique, since it considers precise values for various options in different decision-making states (Wang and Chen, 2007). Therefore, it cannot be held accountable for the judgments in uneven scales and for the paired comparisons for correcting the inaccuracies and uncertainties (Deng, 1999). In order to overcome this problem, the FAHP has been developed for hierarchical problems. It would be more reliable for the decision maker to use distance judgment than point judgment (Kahraman et al., 2003). In this paper, FAHP has been employed, which was initially introduced by Chang (1996). Assume X = {x1,...,xn} is a set of items (criteria) and G = {g1,..., gn} is the goal set. In Chang’s extent analysis for each criterion, the extent analysis is done for each goal, individually. Therefore, m extent analysis values for each criterion can be obtained as follow:
are all the triangular fuzzy numbers. The procedures are as follow: Step 1. The artificial extent value for the ith criterion is defined as below:
has been developed for multiplication. To obtain
, the fuzzy add operation
m for the particular matrix is:
To obtain
, the fuzzy add operation of values
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is:
Step 2. Since
and
are two triangular fuzzy
numbers, the possibility degree
is defined as
the following:
In order to compare M1 and M2, both values of V(M1 ≥ M2)and V(M2 ≥ M1) are needed. Figure 1 portrays that d, the highest intersection width at point D, is between μMl and μM2.
Figure 3.1:The Intersection between Two Fuzzy Numbers (M1 and M2) and the Possibility Degree of M1 ≥ M2
Step 3. The possibility degree for a convex fuzzy number, which is greater than k convex fuzzy numbers Mi (i = 1,..., k), is defined as follows: V(M ≥ M1,…, MK) = V[(M ≥ M1),…., (M ≥ MK)] = Min V(M ≥ Mi), I = 1,…, k (10)
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Assume d(Ai) = min V(Si ≥ Sk ) for k =1,..., n; k
i. The weight vector will be as
follows:
Where Ai (i =1,..., n) and n is the member. Step 4. With normalization, the normalized weight vectors can be derived:
w is an un-fuzzy number (Mohammadi et al, 2014). 3.5
Introduction of a Model for Order Quantity Allocation to Suppliers in Saipa Company According to the structure of suppliers in the Saipa Company, the proposed
model in this section has four objective functions to facilitate the optimal order allocation among suppliers. The four objective functions deal with minimizing the product purchasing costs, delivery times, number of defective products, and maximizing the total value of orders. This model includes three sets of constraints in terms of demand, supplier capacity, and buyer quality, which are defined by the following indices:
is the order quantity from supplieri
is the purchasing costs from supplieri
is the delivery delay rate of supplieri
is the defectiveness rate of supplieri
is the weight of supplieri, which will be determined based on the identified criteria and via the fuzzy AHP method
D is the total demand is the maximum acceptable defectiveness rate
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is the production capacity of supplieri Therefore, the concerned model with four objective functions, n variables, and three constraints can be defined as shown below: o
o
o
o
Subject To:
Conclusion: In this chapter, the research methodology was studied. For this purpose, the type of research, statistical population, statistical samples, and the data collection method and tools were investigated and the data analysis methods were considered. As previously indicated, three techniques were employed in order to analyse the obtained information: a) The Delphi technique to obtain the final criteria of supplier evaluation. The data collection tool for this part was the questionnaire. b) Fuzzy analytical hierarchy process technique to weigh suppliers and rank the criteria. The utilized tool for this part was the pairwise comparison questionnaire. c) Resource allocation to suppliers. A linear programming model was employed for allocation.
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After designing the research tools in chapter three and distributing sets of questionnaires between statistical samples, it will be attempted in the next chapter to analyse the gathered data by using these criteria.
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CHAPTER FOUR DATA ANALYSIS
4.1
Introduction After tool designing and data collection in the third chapter, in this chapter,
the gathered information will be analysed. The questionnaire obtained from the Delphi technique in the third chapter was analysed and the final criteria for supplier selection were chosen. Now, in this chapter, by using the fuzzy analytical hierarchy process method, the weights of criteria and suppliers will be specified and the method of allocating resources to suppliers by using the linear programming method will be analysed. 4.2
Descriptive Statistics for the General Characteristics of Respondents of the Second Questionnaire The descriptive analysis of data indicates the characteristics of statistical
population and sample. In this research, according to the presented questionnaire, some characteristics have been investigated including the gender, education level, and work experience of the statistical samples, which are mentioned in the first part of the questionnaire.
The respondents’ gender
The number and percentage of respondents in terms of gender is presented below: Table 4.1:Characteristics of the Statistical Sample in Terms of Gender Row 1 2
Gender Number Percentage Female 9 %25.7 Male 26 %74.3 35 %100 Total Source: Research findings
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Figure 4.1:The Statistical Sample in Terms of Gender Percentage
The respondents’ education level The number and percentage of respondents in terms of education level is
displayed in the following table: Table 4.2:Characteristics of the Statistical Sample in Terms of Education Level Row 1 2 3 4
Education level Number Doctorate 3 Master degree 10 Bachelor degree 18 Associate degree 4 35 Total Source: Research findings
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Percentage %8.6 %28.6 %51.4 %11.4 %100
Figure 4.2:The Statistical Sample in Terms of Education Level
The respondents’ work experience The number and percentage of respondents in terms of work experience is
exhibited in the following section: Table 4.3:The Respondents’ Work Experience Row 1 2 3 4 5
Word Experience Number Less than 5 years 5 5 to 10 years 7 10 to 15 years 10 15 to 20 years 10 More than 20 years 3 35 Total Source: Research findings
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Percentage %14.2 %20.0 %28.6 %28.6 %8.6 %100
Figure 4.3:The Respondents’ Work Experience
4.3
Estimating the Suppliers’ Weights According to the Identified Criteria through the Delphi Method Having finalized the four criteria (cost, delay in delivery, quality, and
technology), in order to rank suppliers, the following process will be operated to calculate the weights: 1.
At first, by using the fuzzy AHP method, the 4 identified criteria will be compared and the weights determined.
2.
Then, the suppliers will be ranked according to every single one of the 4 identified criteria. By multiplying the matrix of the second stage to the first stage, the weights of
the suppliers will be obtained.
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Objective: To weight and rank suppliers
Technology
Delay in delivery
Quality
S7
S6
S5
S4
S3
Cost
S2
S1
Figure 4.4:Decision Tree
Weighting the four identified criteria: Table 4.4:Initial Pairwise Comparison Matrix of Criteria after Data Combination Criteria I1 I2 I3 I4
I1 (1,1,1) (0.33,0.85,1) (0.25,0.41,0.33) (0.25,0.81,3)
I2 (1,1.18,3) (1,1,1) (0.33,0.69,1) (0.5,1,2)
I3 (2,2.44,4) (1,1.44,3) (1,1,1) (2,3.57,7.14)
I4 (0.33,1.23,4) (0.5,1,2) (0.14,0.28,0.5) (1,1,1)
Calculation of vector Si: This vector is calculated by multiplying two vectors, which is displayed below: For obtaining the first vector, the components of fuzzy numbers in each row are summed up. The second vector has been inversed, which is the sum of all triangular numbers in the above matrix. This vector remains the same for all Si. Inversing a triangular number can be performed as shown below:
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If aij = (l, m, u) is a triangular number, its inverse will be as below:
aij1 (1/ u,1/ m,1/ l )
Considering the above explanations, Si vectors is calculated as presented below.
1 1 1 S (2.66,4.23,6) ( , , ) (0.09,0.35,0.54) 1 31.06 12.16 11.03 1 1 1 S 2 (2.70,4.00,6) ( , , ) (0.09,0.33,0.54) 31.06 12.16 11.03 1 1 1 S3 (2.33,3.39,8.03) ( , , ) (0.08,0.28,0.73) 31.06 12.16 11.03 1 1 1 S 4 (3.33,4.77,11.03) ( , , ) (0.011,0.39,0.1) 31.06 12.16 11.03
Si vectors will be compared in the fuzzy analytical hierarchy process algorithm via the following formula: V ( S1 S 2 ) 1..........ifS 1 S 2 V ( S1 S 2 )
l2 u1 ............ifS 2 S1 (m1 u1 ) (m2 u2 )
Where: S (l1 , m1 , u1 ) 1 S 2 (l2 , m2 , u2 )
Hence, according to the above formula, vectors S1 to S7 are compared as shown here:
V ( S1 S 2 ) 1 V ( S1 S3 ) 1 V ( S1 S 4 ) 091
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V ( S 3 S1) 0.90 V ( S 3 S 2) 0.93 V ( S 3 S 4) 0.85
V ( S 2 S1) 0.96 V ( S 2 S3 ) 1 V ( S 2 S 4 ) 0.87
V ( S 4 S1) 1 V ( S 4 S 2) 1 V ( S 4 S 3) 1
In the subsequent step, the values of d(I) can be shaped as follows:
d ( I1) MIN ( S1 S 2 , S3 , S 4 ) 0.91 d ( I 2) MIN ( S 2 S1 , S3 , S 4 ) 0.87 d ( I 3) MIN ( S3 S1 , S 2 , S 4 ) 0.85 d ( I 4) MIN ( S 4 S1 , S 2 , S3 ) 1
The values of d(I) will create the final matrix:
W (0.91,0.87,0.85,1)T W (0.25,0.24,0.23,0.28)
Thus, on the basis of the FAHP method, suppliers will be prioritized as described in the following:
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Table 4.5:The Final Matrix for Prioritization Of The Criteria By Using The FAHP Method Criteria I1 I2 I3 I4
Criteria weights (the row average) 0.25 0.24 0.23 0.28
A. Weighting suppliers according to the cost criterion by using fuzzy AHP
Criteria
Table 4.6:The Initial Pairwise Comparison Matrix of Suppliers According to the Cost Criterion after Data Combination
S1 S2 S3 S4 S5 S6 S7
S1
S2
S3
S4
S5
S6
S7
(1,1,1)
(1,1.18,3)
(2,2.44,4)
(0.33,1.23,4)
(0.33,0.89,1)
(0.5,2.78,5)
(3,3.38,4)
(0.33,0.85,1)
(1,1,1)
(1,1.44,3)
(0.5,1,2)
(0.2,0.78,2)
(1,2.22,4)
1,2.23,3))
(0.25,0.41,0.33)
(0.33,0.69,1)
(1,1,1)
(0.14,0.28,0.5)
(0.14,0.19,0.5)
(0.14,0.36,3)
0.5,1.29,3))
(0.25,0.81,3)
(0.5,1,2)
(2,3.57,7.14)
(1,1,1)
(0.5,0.82,1)
(0.33,1.22,3)
(1,2.29,3)
(1,1.12,3)
(0.5,1.28,5)
(2,5.26,7.14)
(1,1.22,2)
(1,1,1)
(1,2.13,3)
(2,4.48,5)
(0.2,0.36,2)
(0.25,0.45,1)
(0.33,2.78,7.14)
(0.33,0.82,3)
(0.33,0.47,1)
(1,1,1)
(0.33,2.02,4)
(0.25,0.30,33)
(0.33,0.45,1)
(0.33,0.78,2)
(0.33,0.44,1)
(2,0.22,0.5)
(0.25,0.5,3.03)
(1,1,1)
Calculation of vector Si: This vector is calculated by multiplying two vectors as shown below: For achieving the first vector, the components of fuzzy numbers in each row are adjoined together.
86
The second vector has been inversed, which is the sum of all triangular numbers in the above matrix. This vector remains the same for all Si. Inversing a triangular number will be done as shown below: If aij = (l, m, u) is a triangular number, the inverse will become the following: aij1 (1 / u,1 / m,1 / l )
According to the aforementioned explanations, Si vectors are calculated as follows: 1 1 1 S (8.16,12.90,22) ( , , ) (0.07,0.25,0.62) 1 121.88 52.53 35.26 1 1 1 S 2 (5.03,9.52,16) ( , , ) (0.04,0.18,0.45) 121.88 52.53 35.26 1 1 1 S3 (2.5,4.22,9.5) ( , , ) (0.02,0.08,0.27) 121.88 52.53 35.26 1 1 1 S 4 (5.58,10.81,20.17) ( , , ) (0.05,0.20,0.57) 121.88 52.53 35.26 1 1 1 S5 (8.50,16.5,26.17) ( , , ) (0.07,0.31,0.74) 121.88 52.53 35.26 1 1 1 S 6 (2.78,7.90,19.17) ( , , ) (0.02,0.15,0.54) 121.88 52.53 35.26 1 1 1 S 7 (2.7,3.67,8.86) ( , , ) (0.02,0.07,0.25) 121.88 52.53 35.26
Si vectors are compared in the fuzzy analytical hierarchy process algorithm by using the following formula: V ( S1 S 2 ) 1..........ifS 1 S 2 V ( S1 S 2 )
l2 u1 ............ifS 2 S1 (m1 u1 ) (m2 u2 )
Where: S (l1 , m1 , u1 ) 1 S 2 (l2 , m2 , u2 )
87
Thus, according to the above formula, vectors S1 to S7 are compared as shown here:
V ( S1 S 2 ) 1 V ( S1 S 3 ) 1 V ( S1 S 4 ) 1 V ( S1 S 5 ) 0.89 V ( S1 S 6) 1 V ( S1 S 7 ) 1
V ( S 2 S1) 0.86 V ( S 2 S3 ) 1 V ( S 2 S 4 ) 0.95 V ( S 2 S5 ) 0.74 V ( S 2 S 6) 1 V ( S 2 S 7) 1 V ( S 3 S1) 0.55 V ( S 3 S 2) 69 V ( S 3 S 4) 0.64 V ( S 3 S 5) 0.46 V ( S 3 S 6) 0.78 V ( S 3 S 7) 1 V ( S 4 S1) 0.92 V ( S 4 S 2) 1 V ( S 4 S 3) 1 V ( S 4 S 5) 0.82 V ( S 4 S 6) 1 V ( S 4 S 7) 1 V ( S 5 S1) 1 V ( S 5 S 2) 1 V ( S 5 S 3) 1 V ( S 5 S 4) 1 V ( S 5 S 6) 1 V ( S 5 S 7) 1
88
V ( S 6 S1) 0.83 V ( S 6 S 2) 0.94 V ( S 6 S 3) 1 V ( S 6 S 4) 0.90 V ( S 6 S 5) 0.74 V ( S 6 S 7) 1 V ( S 7 S1) 0.51 V ( S 7 S 2) 0.65 V ( S 7 S 3) 0.96 V ( S 7 S 4) 0.61 V ( S 7 S 5) 0.43 V ( S 7 S 6) 0.74
In the subsequent step, the values of d(I) are produced as shown below: d ( I1) MIN ( S1 S 2 , S3 , S 4 , S5 , S 6 , S 7 ) 0.89 d ( I 2) MIN ( S 2 S1 , S3 , S 4 , S5 , S 6 , S 7 ) 0.74 d ( I 3) MIN ( S3 S1 , S 2 , S 4 , S5 , S 6 , S 7 ) 0.46 d ( I 4) MIN ( S 4 S1 , S 2 , S3 , S5 , S 6 , S 7 ) 0.82 d ( I 5) MIN ( S5 S1 , S 2 , S3 , S 4 , S 6 , S 7 ) 1 d ( I 6) MIN ( S 6 S1 , S 2 , S3 , S 4 , S5 , S 7 ) 0.74 d ( I 7) MIN ( S 7 S1 , S 2 , S3 , S 4 , S5 , S 6 ) 0.43
The values of d(I) will make the final matrix:
W (0.89,0.74,0.46,0.82,1,0.74,0.43)T W (0.18,0.15,0.09,0.16,0.20,0.15,0.08)
Therefore, with the aid of the FAHP method, suppliers are prioritized as described in the following section:
89
Table 4.7:The Final Matrix for Supplier Prioritization According to the Cost Criterion by Using the FAHP Method Criteria weights Criteria
(the row average)
S1 S2 S3 S4 S5 S6 S7
0.18 0.15 0.09 0.16 0.20 0.15 0.8
B. Weighting suppliers according to the criterion of delay in delivery by using fuzzy AHP
Table 4.8:The Initial Pairwise Comparison Matrix of Suppliers According to the Transportation Criterion after Data Combination Criteria S1 S2 S3 S4 S5 S6 S7
S1
S2
S3
S4
S5
S6
S7
(1,1,1)
(1,1.18,3)
(3,2.44,4)
(0.33,1.23,4)
(0.33,0.89,1)
(0.2,1.80,5)
(1,2.54,5)
(0.33,0.85,1)
(1,1,1)
(1,1.44,3)
(0.5,1,2)
(0.2,0.78,2)
(0.33,1.12,3)
1,1.97,3))
(0.25,0.41,0.33)
(0.33,0.69,1)
(1,1,1)
(0.25,0.38,0.5)
(0.50,0.58,1)
(1,1,2)
0.11,0.98,2))
(0.25,0.81,3)
(0.5,1,2)
(2,2.63,4)
(1,1,1)
(1,0.82,1)
(5,6.78,7)
(3,4.23,5)
(1,1.12,3)
(0.5,1.28,5)
(1,1.72,2)
(1,1.22,2)
(1,1,1)
(1,1.19,2)
(1,1.14,2)
(0.2,0.56,5)
(0.33,0.89,3.03)
(0.5,1,1)
(0.14,0.15,3)
(0.50,0.84,1)
(1,1,1)
(0.14,0.28,1)
(0.2,0.39,1)
(0.33,0.51,1)
(0.5,1.02,9)
(0.20,0.24,0.33)
(0.5,0.88,1)
(1,3.57,7.14)
(1,1,1)
Calculation of vector Si: This vector is calculated by multiplying two vectors, as exhibited below: For obtaining the first vector, the components of fuzzy numbers in each row are added together.
90
The second vector, which is the total of all triangular numbers in the above matrix, has been inversed. This vector remains the same for all Si. Inversing a triangular number can be done as follows: If aij = (l, m, u) is a triangular number, the inverse will be as displayed below: aij1 (1 / u,1 / m,1 / l )
Considering the above-mentioned explanations, Si vectors will be calculated as follows: 1 1 1 S (6.86,11.08,23) ( , , ) (0.06,0.22,0.58) 1 118.69 51.48 39.97 1 1 1 S 2 (4.36,8.16,15) ( , , ) (0.04,0.16,0.38) 118.69 51.48 39.97 1 1 1 S3 (3.44,5.04,7.83) ( , , ) (0.03,0.10,0.20) 118.69 51.48 39.97 1 1 1 S 4 (12.25,17.27,23.03) ( , , ) (0.10,0.34,0.58) 118.69 51.48 39.97 1 1 1 S5 (6.50,8.68,17.03) ( , , ) (0.05,0.17,0.43) 118.69 51.48 39.97 1 1 1 S 6 (2.82,4.72,12.23) ( , , ) (0.02,0.09,0.31) 118.69 51.48 39.97 1 1 1 S 7 (3.73,7.61,20.57) ( , , ) (0.03,0.15,0.51) 118.69 51.48 39.97
Si vectors will be compared in the fuzzy analytical hierarchy process algorithm via the following formula: V ( S1 S 2 ) 1..........ifS 1 S 2 V ( S1 S 2 )
l2 u1 ............ifS 2 S1 (m1 u1 ) (m2 u2 )
Where: S (l1 , m1 , u1 ) 1 S 2 (l2 , m2 , u2 )
91
Therefore, based on the above formula, vectors S1 to S7 will be compared as below:
V ( S1 S 2 ) 1 V ( S1 S 3 ) 1 V ( S1 S 4 ) 0.8 V ( S1 S 5 ) 1 V ( S1 S 6) 1 V ( S1 S 7 ) 1
V ( S 2 S1) 0.85 V ( S 2 S3 ) 1 V ( S 2 S 4 ) 0.61 V ( S 2 S5 ) 0.97 V ( S 2 S 6) 1 V ( S 2 S 7) 1 V ( S 3 S1) 0.54 V ( S 3 S 2) 0.72 V ( S 3 S 4) 0.28 V ( S 3 S 5) 0.67 V ( S 3 S 6) 1 V ( S 3 S 7) 0.77 V ( S 4 S1) 1 V ( S 4 S 2) 1 V ( S 4 S 3) 1 V ( S 4 S 5) 1 V ( S 4 S 6) 1 V ( S 4 S 7) 1 V ( S 5 S1) 0.89 V ( S 5 S 2) 1 V ( S 5 S 3) 1 V ( S 5 S 4) 0.66 V ( S 5 S 6) 1 V ( S 5 S 7) 1
92
V ( S 6 S1) 0.67 V ( S 6 S 2) 0.80 V ( S 6 S 3) 0.98 V ( S 6 S 4) 0.45 V ( S 6 S 5) 0.77 V ( S 6 S 7) 0.83 V ( S 7 S1) 0.87 V ( S 7 S 2) 0.98 V ( S 7 S 3) 1 V ( S 7 S 4) 0.69 V ( S 7 S 5) 0.96 V ( S 7 S 6) 1
In the later stage, the values of d(I) are shaped as follows: d ( I1) MIN ( S1 S 2 , S3 , S 4 , S5 , S 6 , S 7 ) 0.80 d ( I 2) MIN ( S 2 S1 , S3 , S 4 , S5 , S 6 , S 7 ) 0.61 d ( I 3) MIN ( S3 S1 , S 2 , S 4 , S5 , S 6 , S 7 ) 0.28 d ( I 4) MIN ( S 4 S1 , S 2 , S3 , S5 , S 6 , S 7 ) 1 d ( I 5) MIN ( S5 S1 , S 2 , S3 , S 4 , S 6 , S 7 ) 0.66 d ( I 6) MIN ( S 6 S1 , S 2 , S3 , S 4 , S5 , S 7 ) 0.45 d ( I 7) MIN ( S 7 S1 , S 2 , S3 , S 4 , S5 , S 6 ) 0.69
The values of d(I) will produce the final matrix:
W (0.80,0.61,0.28,1,0.66,0.45,0.69)T W (0.18,0.14,0.06,0.22,0.15,0.10,0.15)
Thus, according to the FAHP method, suppliers will be prioritized as described in the following section:
93
Table 4.9:The Final Matrix for Supplier Prioritization According to The Transportation Criterion by Using the FAHP Method Criteria S1 S2 S3 S4 S5 S6 S7
Criteria weights (the row average) 18.0 14.0 06.0 22.0 15.0 10.0 15.0
C. Weighting suppliers according to the quality criterion by using fuzzy AHP
Criteria
Table 4.10: The Initial Pairwise Comparison Matrix of Suppliers According to the Quality Criterion after Data Combination
S1 S2 S3 S4 S5 S6 S7
S1
S2
S3
S4
S5
S6
S7
(1,1,1)
(1,1.08,2)
(0.33,1.23,2)
(0.33,1.23,4)
(0.33,0.89,1)
(0.2,1.80,5)
(1,2.54,5)
(0.5,0.93,1)
(1,1,1)
(1,1.44,3)
(0.5,1,2)
(0.2,0.78,2)
(0.33,1.12,3)
1,1.97,3))
(0.5,0.81,3)
(0.33,0.69,1)
(1,1,1)
(0. 5,0.88,1)
(1,1.34,4)
(2,3.78,5)
2,3.43,4))
(0.25,0.81,3)
(0.5,1,2)
(1,1.14,2)
(1,1,1)
(0.5,1.12,2)
(2,4.23,5)
(3,4.19,5)
(1,1.12,3.03)
(0.5,1.28,5)
(0.25,0.75,1)
(0.5,0.89,2)
(1,1,1)
(0.5,1.29,3)
(1,1.16,2)
(0.2,0.56,5)
(0.33,0.89,3)
(0.2,0.26,0.5)
(0.2,0.24,0.5)
(0.33,0.78,2)
(1,1,1)
(0.14,0.29,1)
(0.2,0.39,1)
(0.33,0.51,1)
(0.25,0.29,0.5)
(0.2,0.24,0.33)
(0.5,0.88,1)
(1,3.57,7.14)
(1,1,1)
94
Calculation of vector Si: This vector is calculated by multiplying two vectors, as expressed below: For obtaining the first vector, the components of fuzzy numbers in each row are summed up. The second vector has been inversed, which is the sum of all triangular numbers in the above matrix. This vector remains the same for all Si. Moreover, inversing a triangular number is performed as below: If aij = (l, m, u) is a triangular number, the inverse will become as follows: aij1 (1 / u,1 / m,1 / l )
Considering the aforementioned explanations, Si vectors will be calculated as below: 1 1 1 S (4.19,9.77,20) ( , , ) (0.04,0.19,0.57) 1 116.1 52.06 34.94 1 1 1 S 2 (4.53,8.24,15) ( , , ) (0.04,0.16,0.43) 116.1 52.06 34.94 1 1 1 S3 (7.33,11.94,19.03) ( , , ) (0.06,0.23,0.54) 116.1 52.06 34.94 1 1 1 S 4 (8.25,13.53,20.03) ( , , ) (0.07,0.26,0.57) 116.1 52.06 34.94 1 1 1 S5 (4.75,7.47,17.03) ( , , ) (0.04,0.14,0.49) 116.1 52.06 34.94 1 1 1 S 6 (2.41,4,13.03) ( , , ) (0.02,0.08,0.37) 116.1 52.06 34.94 1 1 1 S 7 (3.48,6.88,11.98) ( , , ) (0.03,0.13,0.34) 116.1 52.06 34.94
Si vectors will be compared in the fuzzy analytical hierarchy process algorithm by using the following formula: V ( S1 S 2 ) 1..........ifS 1 S 2 V ( S1 S 2 )
l2 u1 ............ifS 2 S1 (m1 u1 ) (m2 u2 )
95
Where: S (l1 , m1 , u1 ) 1 S 2 (l2 , m2 , u2 )
Hence, according to the above formula, vectors S1 to S7 will be compared as reflected in the following section:
V ( S1 S 2 ) 1 V ( S1 S3 ) 0.92 V ( S1 S 4 ) 0.87 V ( S1 S5 ) 1 V ( S1 S 6) 1 V ( S1 S 7 ) 1 V ( S 2 S1) 0.93 V ( S 2 S3 ) 84 V ( S 2 S 4 ) 0.78 V ( S 2 S5 ) 1 V ( S 2 S 6) 1 V ( S 2 S 7) 1 V ( S 3 S1) 1 V ( S 3 S 2) 1 V ( S 3 S 4) 0.94 V ( S 3 S 5) 1 V ( S 3 S 6) 1 V ( S 3 S 7) 1
V ( S 4 S1) 1 V ( S 4 S 2) 1 V ( S 4 S 3) 1 V ( S 4 S 5) 1 V ( S 4 S 6) 1 V ( S 4 S 7) 1
96
V ( S 5 S1) 0.91 V ( S 5 S 2) 0.97 V ( S 5 S 3) 0.83 V ( S 5 S 4) 0.78 V ( S 5 S 6) 1 V ( S 5 S 7) 1 V ( S 6 S1) 0.75 V ( S 6 S 2) 0.80 V ( S 6 S 3) 0.67 V ( S 6 S 4) 0.62 V ( S 6 S 5) 0.83 V ( S 6 S 7) 0.86 V ( S 7 S1) 0.85 V ( S 7 S 2) 0.92 V ( S 7 S 3) 0.74 V ( S 7 S 4) 0.68 V ( S 7 S 5) 0.96 V ( S 7 S 6) 1
In the subsequent step, the values of d(I) are shaped as follows: d ( I1) MIN ( S1 S 2 , S3 , S 4 , S5 , S 6 , S 7 ) 0.87 d ( I 2) MIN ( S 2 S1 , S3 , S 4 , S5 , S 6 , S 7 ) 0.78 d ( I 3) MIN ( S3 S1 , S 2 , S 4 , S5 , S 6 , S 7 ) 0.94 d ( I 4) MIN ( S 4 S1 , S 2 , S3 , S5 , S 6 , S 7 ) 1 d ( I 5) MIN ( S5 S1 , S 2 , S3 , S 4 , S 6 , S 7 ) 0.78 d ( I 6) MIN ( S 6 S1 , S 2 , S3 , S 4 , S5 , S 7 ) 0.62 d ( I 7) MIN ( S 7 S1 , S 2 , S3 , S 4 , S5 , S 6 ) 0.68
The values of d(I) form the final matrix:
W (0.87,0.78,0.94,1,0.78,0.62,0.68)T W (0.15,0.14,0.17,0.18,0.14,0.11,0.12)
97
Thus, according to the FAHP method, suppliers are prioritized as described in the following section: Table 4.11:The Final Matrix for Supplier Prioritization According to the Quality Criterion by Using The FAHP Method Criteria weights Criteria
(the row average)
S1 S2 S3 S4 S5 S6 S7
15.0 14.0 17.0 18.0 14.0 11.0 12.0
D. Weighting suppliers according to the technology criterion by using fuzzy AHP
Criteria
Table 4.12: The Initial Pairwise Comparison Matrix of Suppliers According to the Technology Criterion after Data Combination
S1 S2 S3 S4 S5 S6 S7
S1
S2
S3
S4
S5
S6
S7
(1,1,1)
(2,3.12,5)
(1,1.23,2)
(1,1.19,2)
(0.33,1.08,3)
(0.5,1.19,3)
(0.5,1.67,3)
(0.2,0.32,0.5)
(1,1,1)
(1,1.44,3)
(0.5,1,2)
(0.2,0.78,2)
(0.14,0.68,1)
0.2,0.95,2))
(0.5,0.81,1)
(0.33,0.69,1)
(1,1,1)
(0.5,0.91,1)
(0.50,1.34,3)
(0.5,1.23,3)
0.2,2.19,3))
(0.5,0.84,1)
(0.5,1,2)
(1,1.1,2)
(1,1,1)
(1,1.98,3)
(0.5,1.87,3)
(1,2.03,4)
(0.33,0.93,3)
(0.5,1.28,5)
(0.3,0.75,2)
(0.33,0.51,1)
(1,1,1)
(1,1.07,2)
(1,1.14,2)
(0.33,0.84,2)
(1,1.47,7.14)
(0.3,0.81,2)
(0.33,0.53,2)
(0.33,0.53,2)
(1,1,1)
(1,1.21,2)
(0.33,0.6,2)
(0.5,1.05,5)
(0.3,0.46,5)
(0.25,0.49,1)
(0.25,0.49,1)
(0.5,0.83,1)
(1,1,1)
Calculation of vector Si: This vector is calculated by multiplying two vectors, as expressed below:
98
For obtaining the first vector, the components of fuzzy numbers in each row are summed up. The second vector has been inversed, which is the sum of all triangular numbers in the above matrix. This vector remains the same for all Si. Moreover, inversing a triangular number is performed as below: If aij = (l, m, u) is a triangular number, the inverse will become as below: aij1 (1 / u,1 / m,1 / l )
Considering the aforementioned explanations, Si vectors will be calculated as below: 1 1 1 S (6.33,10.48,19) ( , , ) (0.06,0.24,0.61) 1 108.67 42.94 31.02 1 1 1 S 2 (3.24,6.17,11.5) ( , , ) (0.03,0.14,0.37) 108.67 42.94 31.02 1 1 1 S3 (3.53,8.18,13) ( , , ) (0.03,0.19,0.42) 108.67 42.94 31.02 1 1 1 S 4 (5.5,9.82,16) ( , , ) (0.05,0.23,0.52) 108.67 42.94 31.02 1 1 1 S5 (4.5,6.67,16.03) ( , , ) (0.04,0.16,0.52) 108.67 42.94 31.02 1 1 1 S 6 (4.5,6.8,17.14) ( , , ) (0.04,0.16,0.55) 108.67 42.94 31.02 1 1 1 S 7 (3.42,5.3,16) ( , , ) (0.03,0.12,0.52) 108.67 42.94 31.02
Si vectors will be compared in the fuzzy analytical hierarchy process algorithm by using the following formula: V ( S1 S 2 ) 1..........ifS 1 S 2 V ( S1 S 2 )
l2 u1 ............ifS 2 S1 (m1 u1 ) (m2 u2 )
Where: S (l1 , m1 , u1 ) 1 S 2 (l2 , m2 , u2 )
99
Hence, according to the above formula, vectors S1 to S7 will be compared as below:
V ( S1 S 2 ) 1 V ( S1 S 3 ) 1 V ( S1 S 4 ) 1 V ( S1 S 5 ) 1 V ( S1 S 6) 1 V ( S1 S 7 ) 1
V ( S 2 S1) 0.76 V ( S 2 S 3 ) 0.88 V ( S 2 S 4 ) 0.79 V ( S 2 S 5 ) 0.97 V ( S 2 S 6) 0.96 V ( S 2 S 7) 1 V ( S 3 S1) 0.87 V ( S 3 S 2) 1 V ( S 3 S 4) 0.91 V ( S 3 S 5) 1 V ( S 3 S 6) 1 V ( S 3 S 7) 1 V ( S 4 S1) 0.97 V ( S 4 S 2) 1 V ( S 4 S 3) 1 V ( S 4 S 5) 1 V ( S 4 S 6) 1 V ( S 4 S 7) 1 V ( S 5 S1) 0.84 V ( S 5 S 2) 1 V ( S 5 S 3) 0.93 V ( S 5 S 4) 0.86 V ( S 5 S 6) 0.99 V ( S 5 S 7) 1
100
V ( S 6 S1) 0.85 V ( S 6 S 2) 1 V ( S 6 S 3) 0.94 V ( S 6 S 4) 0.88 V ( S 6 S 5) 1 V ( S 6 S 7) 1 V ( S 7 S1) 0.79 V ( S 7 S 2) 0.96 V ( S 7 S 3) 0.88 V ( S 7 S 4) 0.82 V ( S 7 S 5) 0.94 V ( S 7 S 6) 0.93
In the subsequent step, the values of d(I) are shaped as follows: d ( I1) MIN ( S1 S 2 , S3 , S 4 , S5 , S 6 , S 7 ) 1 d ( I 2) MIN ( S 2 S1 , S3 , S 4 , S5 , S 6 , S 7 ) 0.76 d ( I 3) MIN ( S3 S1 , S 2 , S 4 , S5 , S 6 , S 7 ) 0.87 d ( I 4) MIN ( S 4 S1 , S 2 , S3 , S5 , S 6 , S 7 ) 0.97 d ( I 5) MIN ( S5 S1 , S 2 , S3 , S 4 , S 6 , S 7 ) 0.84 d ( I 6) MIN ( S 6 S1 , S 2 , S3 , S 4 , S5 , S 7 ) 0.85 d ( I 7) MIN ( S 7 S1 , S 2 , S3 , S 4 , S5 , S 6 ) 0.79
The values of d(I) will form the final matrix:
W (1,0.76,0.87,0.97,0.84,0.85,0.79)T W (0.16,0.12,0.14,0.16,0.14,0.14,0.13)
Therefore, according to the FAHP method, suppliers will be prioritized as described in the following section:
101
Table 4.13:The Final Matrix for Supplier Prioritization According to the Technology Criterion by Using the FAHP Method Criteria S1 S2 S3 S4 S5 S6 S7
Criteria weights (the row average) 0.16 0.12 0.14 0.16 0.14 0.14 0.13
Having specified the priorities of criteria and weights of suppliers based on each of the identified criteria, the final step is to determine the weight of suppliers according to the overall identified criteria. Therefore, the matrix of “supplier prioritization according to each of the criteria” is multiplied with the matrix of “criteria priorities”. Thus, we have:
4.4
Optimal Order Allocation One of the most important subjects in the supply chain is about the issue of
managing material flow in the supply chain. The importance of this issue is intensified when the share of logistics costs become considerable in the selling price of products. For example, in the USA, on average, 30 percent of the selling price of a product refers to its logistics costs. A supply chain is a string of processes and
102
procedures that are positioned within and between different stages and combinations, to satisfy the needs of a customer. As yet, for managing the material flow in the supply chain, various models have been employed, for solving which different methods have been applied. In practice, it has been realized that some algorithms are required for solving various problems of large sizes. As indicated in the third chapter, in this study, for optimal order allocation in Saipa Company, the model used by Lin (2012) has been employed. In this company, 7 suppliers are responsible for supplying different parts. In addition, the total demand of Saipa Company is approximately 24,500 units per annum. The average purchase cost from different suppliers, the delay rate in the delivery of different suppliers, and the defectiveness rate of suppliers were obtained according to the below table. Table 4.14: The average purchase cost, the delay rate in the delivery and the defectiveness rate of suppliers i 1 2 3 4 5 6 7
ci (the purchasing ti (the late delivery qi (the defect rate cost of supplier i) rate of supplier i) of supplier i) 149 0.024 0.007 147 0.033 0.011 157 0.028 0.006 169 0.024 0.007 137 0.018 0.009 129 0.019 0.013 143 0.031 0.010
However, considering the lack of precise data about the weight of suppliers, the fuzzy AHP has been utilized in the previous section to collect the necessary information. As determined in the previous section, the above coefficients for 7 suppliers were attained as follows: W1 W2 W3 W4 W5 W6 W7 0.1675 0.1369 0.1152 0.1790 0.1574 0.1260 0.1200
103
Based on the collected information, modelling of the above problem will be performed as below: Objective Function Min=149*x1+147*x2+157*x3+169*x4+137*x5+129*x6+143*x7; Min=0.024*x1+0.033*x2+0.028*x3+0.024*x4+0.018*x5+0.019*x6+0.031*x7; Min=0.007*x1+0.011*x2+0.006*x3+0.007*x4+0.009*x5+0.013*x6+0.010*x7; Max=0.1675*x1+0.1369*x2+0.1152*x3+0.1790*x4+0.1574*x5+0.1260*x6+0.12*x 7; Subject to x1+x2+x3+x4+x5+x6+x7=24500; 0.007*x1+0.011*x3+0.006*x3+0.007*x4+0.009*x5+0.013*x6+0.010*x7
