Taguchi Methodology: Quality Optimization and Customer Satisfaction with Design of Experiments Vrasidas-John N. Leopoulos Assistant Professor Mechanical Engineering Dept., Industrial Management & Operational Research Section National Technical University of Athens 15780 Zografos, GREECE Tel: +3210 7723585, Fax: +3210 7723571, E-mail:
[email protected]
Catherine A. Poustourli Dip. Production & Management Engineer, Assistant Instructor Faculty of Administration & Economics, Business Administration Dept., Technological Institution of Serres 62124 Serres - GREECE Tel: +323210 49228, Fax: +323210 59174, E-mail:
[email protected]
Abstract This paper presents a theoretical analysis of Dr. Taguchi’s model for quality optimization in operational systems. The methodology improves the quality of existing processes and simultaneously reduces their costs very rapidly, with minimum business resources and development man-hours. The method achieves this by making the process performance “insensitive” to variations in factors such as materials, manufacturing equipment, workmanship and operating conditions. Quality related cost and money losses are not considered just for the manufacturer at the time of production, but for the consumer and to society as a whole. Case study is included from a company activating in software production and customization. The experiment was conducted to evaluate the ability of programmers in software customization. The case study focuses on applying quality engineering to a customization process including human beings with many uncertain factors. This research is the initial point of a further research in the evaluation of capability and error in programming and in the evaluation of programmer ability in software production and/or customization with the usage of experimental methods. Keywords: Taguchi’s Total Loss Function, Signal/Noise Ratios, Orthogonal Arrays, Evaluation of Programmers Capability.
1. Introduction 1.1 General The proposed model for implementing robust design is based on Dr. Taguchi’s innovative approach to quality. This approach integrated with traditional methods for the design of experiments, resulted in a series of interrelated techniques that help minimize unwanted variability, reduce production waste, and provide greater customer satisfaction [ASI (2001)]. The model gives an efficient way of designing experiments for industrial and operational problems and provides a tool for optimizing processes. Listening to the voice of the customer helps organizations create good systems designs. The model improves the quality of existing products or processes and simultaneously reduces their costs very rapidly, with minimum business resources and development man-hour, similar to the network flow problems which
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are solved by techniques namely the shortest path problems, the maximum flow problems and the minimum cost flow problems. The Taguchi’s model determines the response characteristics (measurable and quality characteristics), separates factors that affect the product/process response and classify these factors. The loss function is measured by the deviation from the ideal value. Techniques like Orthogonal Arrays have been developed to reduce the elements of (product/process) variation around the (product/process) mean in Total Loss Function [CNDE (2001)]. The model is applicable over a wide range of manufacturing and operating fields such as health care (patient monitoring), manufacturing, electrical industry (fire alarm system optimization, employee’s appearance inspection), geophysics (earthquake or weather forecasting), automotive collision prevention system, computer-aided-design, software industry (valuation of programmers capability), banking (credit evaluation) and service systems. Taguchi methodology can be applied off-line in design or on-line in production. The methodology breaks down off-line quality control into three stages: System Design (design of the first degree), Parameter Design (design of the second rank) and Tolerance Design (tertiary design) [WTEC (1994)]. 1.2 Objectives 1.2.1
Objectives of the paper
In this paper is presented a case study of evaluation programmers’ performance with the usage of Taguchi’s methodology. The main object is to present how the above model will be a useful tool for quality optimization and customer satisfaction in a business system and especially in an Enterprise Resource Planning Provider. Simultaneously are presented the changes that will be appeared in workflows from the experimental results. 1.2.2
Objectives of the model
The Taguchi’s methodology aims to improve the quality of products and processes. Quality results are improved when a higher level of performance is consistently obtained. The highest possible performance is obtained by determining the optimum combination of design factors. The cost of quality is measured as a function of product’s or process’s performance variation and the losses measured system wide (Total Loss Function) [Yeow Nam (2001)]. The “loss” includes the cost of customer dissatisfaction that leads to the loss of company reputation. This loss function takes the following basic quadratic form: L(x) = k(x-m)2 Where L is the loss in money, m is the point at which characteristic should be set, x is where the characteristic actually is set, and k is a constant that depends on the magnitude of the characteristic and the monetary unit involved. The uncontrolled sources of variation are called noise factors. The model based on Taguchi’s methodology is essentially an eight-step procedure, which can best be illustrated as follows [Apte (2000)]: 1. Identify the main function, side effects, and failure mode 2. Identify the noise factors, testing conditions and quality characteristics 3. Identify the objective function to be optimized 4. Identify the control factors and their levels 5. Select the orthogonal array matrix experiment 6. Conduct the matrix experiment 7. Analyze the data; predict the optimum levels and performance (finding better quality characteristics or signal-to-ratios and different control factors and levels, considering interactions among the control factors). 8. Perform the verification experiment and plan the future action.
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Interactions can be incorporated into Taguchi methodology and the model presents a simple graphical codification of these (the linear graphs) to enable the analyst to introduce them systematically and easily. This is a similarity with the network flow problems, which can be formulated and solved as linear programs. However, only limited numbers can be conveniently introduced without leading to great increase in prototype or experimental sizes. 1.3 Comparison between conventional design techniques and Taguchi’s model [Colorado University (2001)] Conventional techniques transformations location and dispersion effects fractional factorials aliasing procedures nested designs sequential designs response surface methods effect analysis residual plots
Taguchi’s model signal-to- ratios signal-to- ratios orthogonal arrays linear graphs inner & outer arrays one-shot designs pick the winner complex anova outliers not considered
2. The case study 2.1 Company’s general profile In this paper is presented a case study of evaluation programmers’ performance with the usage of Taguchi’s methodology. The main object is to present how the above model will be a The company under consideration is a Greek consulting company, activating in customised development, consulting and installation of industrial and administrative systems. Experienced engineering professionals and academic researchers founded the company in 1992. The IT department employed 10 junior software engineers, two production & management engineers and one ERP general manager. The mainstream work concerns customised development and installation of management information systems and especially in enterprise resource planning systems (ERP). 2.2 Design of survey data The Case study concerns the development of a dedicated software system for the Production Planning and Control (PPC) of the productive unit of an industrial firm. The system should support the PPC functions within the existing infrastructure. During the project the management attempted to distinguish people’s programming capabilities from survey data, using the Taguchi’s model. The ERP Manager collected the data in order to assess the relationship between the answers in the questions and how programmers will respond in the ERP system customization according to the special needs of the industry. The benefit of the Taguchi’s model is that once the factors of the model are established, people’s capabilities can be evaluated only based on survey data. Further, the experimental data will allow the management of the company determine: - How to substitute less expensive projects to get the higher programmers performance - How much money they can save from the design improvement that the model propose - How to reengineer their processes to make them insensitive to the uncontrollable factors - Which and how factors have more influence on the programmers’ performance
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The survey results affect the workflows and they lead the company to reengineer those that affect mostly the quality characteristics. With the results of the model the company expects to achieve the quality optimization and customer satisfaction.
3. Methodology 3.1 Goals of the Taguchi’s model The central idea in the model is that variations in a product’s or process’s performance can inevitably result in poor quality and monetary losses during the product’s life span or the process’s evaluation. The sources of these variations can be classified in two categories of factors namely control and noise. The proposed model of robust design is based on the identification of optimal settings for product or process parameters which: - maximize performance - minimize variation of the factors. Although variability reduction – or the Taguchi approach – is primarily on the production line of factories, the philosophy can also be applied in the workplace or office. The example of “meet on time” is representative. If there is a meeting or a training class at work that is supposed to start at say 10 am, some people will come early; some people will get right ontime, while others will come in a little late. There is usually at least one person who gets to the meeting or class, real late. This variation is wasteful for the people who come too early and disruptive from people who come late. Ideally, everyone should come to the meeting exactly on time. In additional the Taguchi’s model for business systems can be applied to two major objectives: diagnosis and forecasting. Taguchi’s Signal-to-Ratio for smaller-the-better quality characteristics is usually an undesired output e.g. Failures in computer-aided-design Time minimization for making a telephone connection Time minimization for a patient to recover Taguchi’s Signal-to-Ratio for larger-the-better quality characteristics is usually a desired output e.g. Valuation of programmers capability Predictions for loan or credit-card approval Taguchi’s Signal-to-Ratio for nominal-the-best quality characteristics is usually a nominal output e.g. Nominal dimensions of mechanical components Ratios of chemicals or mixtures Variability reduction in the workplace or office (meet on time). 3.2 Development of the model 3.2.1
Signal to ratios
Goal of the model is to minimize one of 3 typical signal-to-ratios (SNRs) [Apte (2000)]. For (non-negative) smaller-the-better = – 10 Log10 ( 1/n Yi2 )
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The Y variable denotes the raw performance of a system of n repeated measurements per experiment. Maximization of the smaller-the-better Signal-to-Ratio is equivalent to minimization of the loss function. For larger-the-better = – 10 Log10 ( 1/n 1/Yi2 ) Maximization of a larger-the-better type of problem can easily be converted to maximization of a smaller-the-better type problem by considering the reciprocal of the quality characteristics. Many cases in business-process forecasting, use a “larger-the-better’ type of SN ratio for two reasons. One reason is convenience, because “larger-the-better’ of SN ratio is easier to understand and easier to calculate. Another reason is because the true values that are needed to calculate the dynamic SN ratio are unknown in many cases. For target-the better (nominal the best) = 10 Log10 ( 2 / 2 ) Where μ is the mean and σ2 is the variance. 3.2.2
Orthogonal Arrays
Constructing matrix experiments using special matrices, called Orthogonal Arrays (OA) allows the effects of several parameters to be determined efficiently and is an important technique in Taguchi’s model. To actually construct an OA, control parameters or design variables must be assigned to the columns of an array, and the integers in the array columns are translated into actual settings of the assigned parameters. The unassigned columns are deleted from the array [CNCE (2000)]. The purposes of conducting matrix experiments (using Orthogonal Arrays) [Apte (2000)] are: to achieve insensitivity to noise by determining the best settings of control factors (minimize variations in its output Yi) to identify the ‘adjustment factor’ (to adjust the level of Yi) This is achieved by Taguchi’s 2-Steps First step minimize variance Second step adjust the mean-on-target 3.2.3
Interactions between Factors
The model considers interaction between noise factors and control factors. Interaction between control factors is not taken into consideration. Use of Orthogonal Array implies that we cannot or ought not consider interactions between control factors. We include only control factors that do not interact with each other, say A, B, C and D.. This is shown in the following table 1. The method can include interaction between control factors, let it be ‘R’ and (A, B, C or D) with repeated use of Orthogonal Arrays (OA). If there is a single control factor ‘R’’ that may have strong interactions with several control factors, then we can simply repeat the entire OA experiments at two different levels of ‘R’’.
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Expt.No. 1 2 3 4 5 6 7 8 9
Control Factor A 1 1 1 2 2 2 3 3 3
Table 1 Control Factor B 1 2 3 1 2 3 1 2 3
Control Factor C 1 2 3 2 3 1 3 1 2
Control Factor D 1 2 3 3 1 2 2 3 1
All interactions, between ‘R’ and any one of A, B, C and D, can be studied taken into consideration. • The OA based experiments, say L9 as in this example, require that two samples are made for each row (each row indicates one combination of control factors A, B, C and D) • The measured values of quality characteristics are noted for that row • Analysis then gives ‘best’ settings for control factors A, B, C and D that would give least sensitivity to the noise factor, say hardness as in the above example. 3.2.4
Causes of variations-noise factors
Selecting a product design or a process that is insensitive to uncontrolled sources of variation improves quality. Dr. Taguchi calls these uncontrolled sources of variation noise factors. Three main types of noises presented in the following table 2, with a referring to programmer’s evaluation case study.
TYPE OF NOISE External Noise
Internal Noise
Table 2 CAUSES OF NOISE Education and intelligence qualifications
PROGRAMMER’S EXAMPLE Inappropriate knowledge or qualifications
Environmental conditions
Illness
Working environment and conditions
Stress, inconvenient
Relationships Demotion factors (management policy)
nervousness Lack of motives
Personality characteristics
emotional qualifications
Aging
Fatigue, lack of productivity Noise which act upon Reasons that causes variations Un-useful placing to people in different between different people even ways when they had similar studies or Changes in personal and professional development. professional life
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4. Performance maximization of people with programming capability In the following case study we describe the attempt to distinguish people’s programming capabilities from the survey data by using the Taguchi’s model. The main target of that project was to finding the reasons that causes the ERP projects delays. The management demands diminution of the ERP’s projects cost (which means diminution in man-hours, in moving expenses etc). In three brainstorming meetings with the projects employees, the ERP Manager tried to achieve three tasks. First he selected the appropriate data for the Taguchi’s model application, secondly he constituted a questionnaire for the programmers’ evaluation and third he constituted a set of bugs in a part of the software system for the Production Planning and Control (PPC) based on SQL code, as a programming realization test. In the end he assessed the relationship between the pattern of the answer in the questions and the relationship between the pattern of the debugging software system and how people achieved these tasks. 4.1 Preparation for the collection of the appropriate data for the Taguchi’s model The collected data of the first task regarded to answers in the following questions: Objectives of survey and evaluation criteria - What are the criteria of evaluation? - How are each of these criteria measured? - How are these criteria combined into a single number? - What is the common characteristic of these criteria? - What is the relative influence these criteria exhibit? Factors - What are the factors that influence the performance criteria? - Which factors are more important that others? Noise factors - Which factors can’t be controlled in real life? - Is the performance depended on the application environment? Factor levels - What are the ranges of values the factors can assume within practical limits? - How many levels of each factor should be used for the survey? - What is the tradeoff for a higher level? Interaction between factors - Which factors are most likely to interact? - How many interactions can be studied? Scope of survey - How many experiments can we run? - When do we need the results? - How much does each experiment cost? Additional items - What do we do with factors that are not included in the survey? - In what order do we run these experiments? - Who will do these experiments? 4.1 Questionnaire for the programmers’ evaluation The Projects Managers asked each member (the 10 software engineers) of their team to complete the questionnaire and to debug a part of the software system for the Production Planning and Control (PPC) based on SQL code that included specific errors. The debugging attempt ion for every programmer could be lasted one hour. The ERP Manager collected the data in order to assess the relationship between the answer in the questions and how programmers will respond in the debugging ERP system according to
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the corrective actions of similar international errors and special needs of the industry. He classified the type of mistakes in two main categories, in “no error” (successful debugging result) and in “error” (not successful debugging result). The successful debugging result it was equivalent with a pattern (already debugging part) that ERP Manager had in his own. At the beginning the ERP Manager determined 30 factors (survey questions) which influence the quality characteristics of programming performance. The questionnaire could be answered by programmers with a YES or a NO. The survey questions are presented in the following table 3:
No of Question Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 Q19 Q20 Q21 Q22 Q23 Q24 Q25 Q26 Q27 Q28 Q29 Q30
Table 3 Survey Questions Age? Gender? If you are expecting an important announcement a week from now, does it affect your work? Do you care about your appearance? # of years using personal computer, (familiar with up-today technology) Do you clean up around you, or clean up PC files? # of years in ERP programming experience (ERP expertise) When you are doing something you love, do you lose track of time? If your job function/content changes drastically from day to day, do you enjoy the change? Do you like puzzles? Do you enjoy working on a computer? (PC, work station) Do you like to talk in front of people? Do you like programming no matter what the project or content is? Do you play TV games often? When someone asks a question, do you like to take the time to explain? Do you like arts and crafts? When something bothers you, do you get affected by it for a long time? Do you enjoy sports programs on TV? Do you like to read technical books? Can you stand simple tasks? Do you like to draw or paint? Do noisy surroundings bother you when you are in a train of thought? Do you forget to button your shirt? Do you like to know how a machine operates? Are you a perfectionist? Do you act on the spare of the moment? Do you fell you are different from others? When you are on a job, does the immediate issue catch your attention? Do you tend to get lost in a unfamiliar place? Do you misspell or mistype words often?
A wide examination of the above factors leads in the reduction of the 23 factors. In the end he concludes that 7 factors influence mostly the quality characteristics of programmers’ performance. For each of them he chooses two values (levels). As result, he need to work with the L8(27) Orthogonal Array, that is an array with 8 rows of experiments, 7 columns of factors and 2 levels. The seven (7) factors that influence mostly the quality characteristics of programmers’ performance are presented in the following table 4:
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No. A (Q5)
B (Q7)
C (Q15)
D (Q30)
E (Q28)
F (Q13)
G (Q17)
Table 4 Factors # of years using personal computer (familiar with up-today technology) # of years in ERP programming experience (ERP expertise) When someone asks a question do you like to take the time to explain (cooperative spirit) Do you misspell or mistype words often (designate of hidden problems) When you are in job, does the immediate issues catch your attention (reflecting) Do you like programming no matter what the project or content is (special interesting for programming) When something bothers you, do you get affected by it for a long time (emotional qualifications)
Level 1 t>10 years
Level 2 t5 years
t
