INTERNATIONAL JOURNAL OF INFORMATION AND SYSTEMS SCIENCES Volume 6, Number 3, Pages 293−317
©2010 Institute for Scientific Computing and Information
AN ANALYTICAL FRAMEWORK FOR CRITICAL LITERATURE REVIEW OF SUPPLY CHAIN DESIGN DARSHAN KUMAR, OM PAL SINGH, JAGDEV SINGH
Abstract There can be little dispute that supply chain design is an area of importance in the field of engineering, yet there have been few literature reviews on this topic. Over the past decade, the traditional purchasing and logistics functions have evolved into a broader strategic approach to materials and distribution management in engineering, known as supply chain management. This paper sets out not to review the mathematical models developed in supply chain literature per se, but rather to contribute to a critical theory debate through the presentation and use of a framework for the categorisation of literature linked to supply chain design through mathematical modelling. In addition, this article attempts to clearly describe supply chain management since the literature is replete with buzzwords that address elements or stages of this new management philosophy. The present study is based on the analysis of a large number of publications on supply design.
Key Words, Supply chain design, analytic hierarchy process, time-compression, JIT.
1. Introduction A supply chain may be defined as an integrated process, wherein a number of various business entities, i.e., suppliers, manufacturers, distributors, retailers and end users work together in an effort to acquire raw material, convert them into specified final products, deliver these final products to retailers and then to the end users. Thus, supply chain is comprised of two basic, integrated processes: (a) The Production Planning and Inventory control Process, and (b) The Distribution and Logistics Process. This chain is traditionally characterized by a forward flow of material and a backward flow of information. From a practical standpoint, the supply chain concept arose from a number of changes in the manufacturing environment, the shrinking resources of manufacturing bases, shortened product life cycles, the levelling of the playing field within manufacturing and the globalization of market economies. The current interest has sought to extend the traditional supply chain to include “reverse logistics”, to include the product recovery for the purpose of recycling, remanufacturing and reuse [1].
Received by the editors September 15, 2008 and, in revised form, November 15, 2008. 293
The
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supply chain process can be described by figure 1. During the 1990s, many manufacturers and service providers sought to collaborate with their suppliers to upgrade their purchasing and supply management functions from a
clerical role to an integral part of a new phenomenon known as supply chain design [2]. Wholesalers and retailers have also integrated their physical distribution and logistics functions into the transportation and logistics perspective of supply chain design to enhance competitive advantage [3]. This article reviews the literature base and development of supply chain design on the bases of mathematical models developed in the different fields of engineering, using analytic hierarchy process (AHP), time-compression and just-in-time (JIT) approach. This paper has been organized as follows. In, section 2, suply chain has been defined. In section 3, evolution of supply chain has been discussed. In section 4, literature review of papers dealing with modelling developed in the different fields of engineering using analytic hierarchy process (AHP), linear and non-linear programming, time-compression and just-in-time (JIT) approach have been discussed. Conclusion of the paper has been summed up in section 5. 2. The supply chain defined The new view of supply chain management and one that should take hold of in the new millennium is the inter-organizational approach. The goals of entire supply chain become the common objectives of each firm. Cost and service improvements that were not achievable by individual firms will now be attained by the companies acting together. Thus, managing the coalition of firms becomes very important to ensure that the supply chain runs smoothly [4]. Supply chain management has been defined by various experts.
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A few of these definitions are given below in table 1[5]: Table 1. Definitions of supply chain management
Authors
Tan et al. (1998)
Definition
Supply chain management encompasses materials/supply management from the supply of basic raw materials to final product (and possible recycling and re-use). Supply chain management focuses on how firms utilise their suppliers' processes, technology and capability to enhance competitive advantage. It is a management philosophy that extends traditional intra-enterprise activities by bringing trading partners together with the common goal of optimisation and efficiency.
Berry et al. (1994)
Supply chain management aims at building trust, exchanging information on market needs, developing new products, and reducing the supplier base to a particular original equipment manufacturer(OEM) so as to release management resources for developing meaningful, long term relationship.
Jones and Riley (1985)
An integrative approach to dealing with the planning and control of the material flow from supplier to end-user.
Saunders (1995)
External Chain is the total chain of exchange from original source of raw material, through the various firms involved in extracting and processing raw materials, manufacturing, assembling, distributing and retailing to ultimate end customers.
Ellram (1991)
A network of firms interacting to deliver product or service to the end customer, linking flows from raw material supply to final delivery.
Christopher (1992)
Network of organisations that are involved, through upstream and downstream linkages, in the different processes and activities that produce value in the form of products and services in the hands of the ultimate consumer.
Lee and Billington (1992) Networks of manufacturing and distribution sites that procure raw materials, transform them into intermediate and finished products, and distribute the finished products to customers.
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Kopczak (1997)
The set of entities, including suppliers, logistics services providers, manufacturers, distributors and resellers, through which materials, products and information flow.
Lee and Ng (1997)
A network of entities that starts with the suppliers' supplier and ends with the customers’ custom the production and delivery of goods.
According to Singh and Chand [6], supply chain is comprised of following three basic integrated processes: (1) procurement process (2) production planning and inventory control processes and (3) distribution and logistics processes. Procurement process encompasses the purchasing of raw materials from the identified suppliers and their storage and control at the manufacturing facilities. Production planning processes encompasses the entire manufacturing processes including production scheduling and material flow system design and control. Inventory control describes the design and management of storage policy and procedures for raw materials, work-in-process inventories and finished products. Distribution and logistics process determines how products are replenished and transported from plants to distribution centres. These products may be transported directly to the customers. These processes interact with one another in an integrated supply chain. They defined supply chain with the help of a block
diagram, shown as figure 2.
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3. Evolution of supply chain In the 1950s and 1960s, most manufacturers emphasized on mass production to minimize unit production cost as the primary operations strategy, with little product or process flexibility. New product development was slow and relied exclusively on in-house technology and capacity. 'Bottleneck' operations were cushioned with inventory to maintain a balanced line flow, resulting in huge investment in work in process (WIP) inventory. Sharing technology and expertise with customers or suppliers was considered too risky and unacceptable and little emphasis appears to have been placed on cooperative and strategic buyer-supplier partnership. The purchasing function was generally regarded as being a service to production, and managers paid limited attention to issues concerned with purchasing. In the 1970s, Manufacturing Resource Planning was introduced and managers realized the impact of huge WIP on manufacturing cost, quality, new product development and delivery lead-time [2]. The term supply chain management was originally introduced by consultants in the early 1980s. Since the early 1990s, academics have attempted to give structure to supply chain management [7-8]. More recently, just-in-time (JIT), time compression and other management initiatives are being utilized to improve manufacturing efficiency and cycle time. In the fast-paced JIT manufacturing environment with little inventory to cushion production or scheduling problems, buyer-supplier relationship has become an important factor to be taken care of by the strategic management. The concept of supply chain management helped the top management relationship with their immediate suppliers. Once this relationship established, experts in transportation and logistics carried the concept of materials management a step further to incorporate the physical distribution and transportation functions. This resulted in the integrated logistics concept, also known as supply chain management. More and more work on the evolution of supply chain management continued into the 1990s with drastic improvements in IT sectors. This broadened the supplier efficiency to include more sophisticated reconciliation of cost and quality considerations. More recently, many manufacturers and retailers have adopted this concept of supply chain management to improve their profits. Manufacturers now commonly exploit supplier strengths and technology in support of new product design and development. Retailers seamlessly integrate their physical distribution function with transportation partners to successfully achieve results in the field of JIT. A key facilitating mechanism in the evolution of supply chain management is a customer-focus corporate vision, which drives change throughout a firm's internal and external linkages, as shown in figure 3.
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4. A framework of literature review on different models developed for supply chain design It has been observed from the review of open literature that researchers have developed various models in the field of supply chain using different techniques. Chan and Lee [9] have very nicely summed up these techniques in their book. They have given detailed models for independent policies for buyers and vendors, considering various options. Various models in the field of supply chain have been classified and discussed below. 4.1 Analytic Hierarchy Process (AHP) models The Analytic Hierarchy Process (AHP) was originally developed by Satty in 1980. It is a revolutionary breakthrough which empowers people to relate intangibles to tangibles, the subjective to the objective and to link both to their purposes. It offers a way to integrate complexity, set the right objectives, establishes their priorities and determines the overall value of each alternative solution. The AHP uses hierarchical decision models and it has a sound mathematical basis. AHP is based on the following
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three principles: decomposition, comparative judgments and the synthesis of priorities. AHP starts by decomposing a complex, multi criteria problem into a hierarchy where each level consists of a few manageable elements which are then decomposed into another set of elements. The second step is to use a measurement methodology to establish priorities among the elements within each level of hierarchy. The third step in using AHP is to synthesise the priorities of the elements to establish overall priorities for the decision alternatives. Many researchers used this methodology in different fields to select one alternative from the many available depending upon different selection criteria, as studied from the open literature. Korpela et al. [10] utilized AHP for supporting the process re-engineering approach called “PROPER” and demonstrated with the help of illustrative examples. They used AHP to compare the performance level of a company to that of the best-in-class companies and also for analysing customer requirements, taking reliability, flexibility and relationship as main service elements and for benchmarking the logistics operations taking reliability, flexibility, lead time, customer effectiveness and value-addition as logistic critical success factors. It was concluded that owing to its flexibility to support many types of problems, the AHP shows good potential in supporting PROPER-based supply chain development process. Elsewhere, Korpela et al. [11] presented an approach based on the analytic hierarchy process (AHP) and mixed integer linear programming for similar study i.e., for customer requirement. An integrated framework was presented for an approach which enables a customer-oriented evaluation of each alternative link and node in the logistic network and optimized the overall customer service capability of the network. To evaluate the importance of the customers, the criteria considered were the long term profitability potential, the possibility of establishing a partnership-type relationship with the customer, the volume purchased by certain customer and the long term financial viability of a customer. From the study, conclusion drawn was that, that integrating the analytic hierarchy process (AHP) and mixed integer linear programming expands the scope of the traditional approaches to a more customer oriented direction by implementing customers’ preferences on the decision process. Similar combination of AHP and linear programming has also been applied by Ghodsypour and O’Brien [12] to study the problems related to supplier selection, including both qualitative and quantitative factors. Here supplier selection problem has been divided between two categories, one, when there is no constraint and two, when there are some limitations in suppliers’ capacity, quality etc. Real quantitative data has been used to improve the system’s consistency. Five steps have been used in application of AHP: define the criteria; calculate the weights of the criteria; rate the alternatives; compute the overall score for each alternative and built the linear model based on the results achieved. They took cost,
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quality and service as the main criteria for the supplier selection to maximize the total value of purchasing and concluded that AHP enables management to make a trade-off between several tangible and intangible priorities, but in this method, errors can creep in due to perception or biased behaviour of the decision making managers and independent nature of attributes used. This problem was solved by Murlidharan et al. [13], who used AHP for selection and rating of vendors, by getting the rating done by a group of decision makers for continuous evaluation of vendors, following the principle of anonymity and integrated the method with a managerial tool-Delphi method. For further better results, emphasis has been laid on establishment of confidence limits in group decision making. The persons, whose opinions fall outside the group’s confidence limit, were further studied to understand the source of variation. It was concluded that AHP applied this way, resulted in better communication leading to clearer understanding among the members of decision making groups and hence greater commitment to the chosen alternative.
Supplier-customer relationship was further studied by Korpela et al.
[14] from different angle using the same methodology of combination of AHP and mixed integer programming. Here, using already published data, an AHP model has been developed to prepare a sales plan for a company where the risk related to the Supplier-customer relationship in the decision process has also been included. In this work, mixed integer programming (MIP) has been used to divide the production capacity between the customers. The factors considered were profitability, relationship and volume of sales from a particular customer. The conclusion drawn from this work was that, that traditionally, the focus had been on company’s own quantitative view-points, but approach used here enabled focusing on the customers’ viewpoint. Still, another criterion in supplier selection was used by Handfield et al. [15], i.e., environmental performance indicators (EPI). They illustrated the use of the analytic hierarchy process (AHP) as a decision support model that included relevant environmental criteria and that could be readily applied to a variety of industry applications. They identified a number of EPIs from the literature. To assess the overall ranking of criteria individually and for group consensus, a Delphi group study was conducted and a model was then created that refined and consolidated the set of measures to include those that could be easily accessed and were important from environmental standpoint. Pilot tests were conducted on an automotive manufacturer, a paper manufacturer and an apparel manufacturer and examined how AHP could be incorporated into a comprehensive information system supporting Environmentally Conscious Purchasing (ECP). It was concluded that the model developed could be further improved as some purchasing managers did not value all the environmental measures given to them. Second limitation stated was data availability. They proposed that for viable solution to these drawbacks, system of
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equations could be to develop and this way, environmental costs should be integrated with the “Total Cost of Ownership (TCO)”. This concept was applied by Bhutta and Huq [16], who applied AHP in the supplier selection process and compared it with the total cost ownership (TCO) method. It was concluded that TCO tends to focus more on the pricing issues and ignores qualitative issues, its strength being the ability to use the same model to evaluate suppliers across the board, based on lowest transaction costs and AHP helps in comparing seemingly incomparable issues to select optimal supplier. But, in this model, capacity constraint of the supplier has not been considered, as AHP does not consider such constraints. This problem was addressed by Wang et al. [17]. They developed an integrated analytic hierarchy process (AHP) and pre-emptive goal programming (PGP) based multi-criteria decision-making methodology to take into account both qualitative and quantitative factors in supplier selection. AHP was used to match product characteristics to qualitatively determine supply chain strategy and PGP to mathematically determine the optimal order quantity from chosen suppliers. PGP model considered capacity constraint of the supplier also. Till early 2000, no feedback from the customers was incorporated in the process and the attributes considered were largely independent.
These problems can be
overcome by using more general form of AHP, called Analytic Network Process (ANP). Agarwal and Shankar [18] used Analytic Network Process (ANP), which incorporates feedback and interdependent relationships among decision attributes and alternatives. They made a model to aid the decision makers in prioritizing the options related to improvement in the supply chain management by considering three factors: market sensitiveness, information driven and process integration. Considering the additional benefits of Analytic Network Process (ANP) given above, it has also been used to study the effect of environmental factors on strategic planning considering life cycle, operational life cycle, performance measures and environmentally influential organizational policy elements [19].
In this paper, it has been concluded that the major
disadvantage of this methodology is the large amount of decision-maker input required, for the analysis of which some software should be developed.
Also, the computational
and data requirements still make “what-if” analysis geometrically more cumbersome. These problems have been addressed by Min and Melachrinoudis [20] who applied AHP to present a real-world case study involving the re-location of a combined manufacturing and distribution (warehousing) facility for a firm which primarily manufactures and distributes home improvement hardware. The authors designed the configuration of supply chain networks and assessed the viability of the proposed sites from supply chain perspective using the analytic hierarchy process (AHP). The various location factors of proposed sites, such as site characteristics, cost, traffic access, market opportunity,
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quality of living at the proposed sites, local incentives were studied and these factors were used in designing an AHP model. Based on the study of designed model, they were able to reach the conclusion that traffic access was the most important criteria for the company under study. The relative advantages of other candidate sites have also been mentioned. The model enabled the location planner to evaluate ‘what-if’ scenarios associated with shifts in the company’s management philosophy and competitive positions and also enabled the location planner to determine the extent of conflicts among the competing objectives. The problem of large data analysis cited in [19] was taken up by Yang and Kuo [21], who proposed an AHP and data envelopment analysis (DEA) methodology to solve a plant layout design problem. To avoid large input-data collection problem, they adopted a computer aided layout planning tool to facilitate the layout alternatives generation process as well as quantitative performance data and then applied AHP to collect qualitative performance data. Finally, DEA has been used to solve the layout design problem by simultaneous considering both quantitative and qualitative performance data. This methodology was also used for an anonymous leading packaging company to illustrate its efficiency and effectiveness. But, a large data had to be collected for the development of such models, making these processes cumbersome. To avoid this problem of large data collection, Poyhonen and Hamalainen [22], collected data through internet. They collected data for different persons who selected the particular attributes in the job evaluation task and applied AHP for the evaluation of the same. The subjects were allowed to create the alternatives and attribute themselves in their experiment. Each subject used five methods to assess attribute weights- one version of AHP, direct point allocation, simple multi-attribute rating technique, swing weighting and trade-off weighting. The results computed with these different methods were compared and it was concluded that weights differ because decision makers choose their responses from a limited set of numbers and the spread of weights and the inconsistency between the preference statements depend on the number of attributes that a decision maker considers simultaneously. AHP has also been utilized to select the most appropriate technology for seawater desalination [23]. AHP is mainly used in crisp decision applications with a very unbalanced scale of judgement and to overcome this problem, AHP has been used by various researchers along with fuzzy logic application for the purpose of comparison. A new and general decision making method for evaluating weapon systems using fuzzy AHP based on entropy weight has been used [24]. But, the method used is very subjective and calculations are very complicated. Simpler way to solve the same problem of evaluating weapon systems has been used by Chen [25]. Here, fuzzy AHP has been used by a new, but, simpler way and it has been concluded that the method used is simpler and faster.
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But the shortcoming in the method used is that the criterion’s score has not been normalized. The same problem was solved, taking this shortcoming into consideration by Cheng, using a new method- ranking fuzzy numbers [26].Yet another method, fuzzy logarithmic least square method has been used in AHP to solve a theoretical problem [27]. Constraints have not been considered in the problem solved. Salo [28] used fuzzy ratio comparison in hierarchy decision models through linear programming and considered constraints also. Fuzzy synthetic method has also been applied for evaluation of customer loyalty [29]. AHP based on fuzzy simulation has also been proposed, characterizing fuzzy linguistic variables as triangular fuzzy variables [30-31]. Carrera and Mayorga [32] proposed a fuzzy inference system for supplier selection problem for new product development to handle the impreciseness and uncertainty, considering technological level, economical situation, production capacity and market share as strategic options. However, the researchers stated that they were not able to give an answer whether decision makers do indeed take into account the attribute ranges in assessing the weights, which might give variation in the results achieved and this may be taken for the future study. 4.2 Models using Linear and Non Linear Programming The development of an integrated supply chain requires the management of material and information flows to be viewed from the three perspectives: strategic, tactical and operational. At each level the use of facilities, people, finance and systems must be coordinated and harmonized as a whole. Researchers have developed mathematical models using different aspects of these perspectives. Arntzen et al. [33] considered strategic decisions, such as location of customers and suppliers, location and availability of inexpensive skilled labour, cost of various transportation modes, export regulations etc. for a computer company- Digital Equipment Corporation to develop a mathematical model and Pyke & Cohen [34] considered strategic decisions in a firm to avoid conflicts of the production staff with those of marketing staff. For this purpose, a model of an integrated production-distribution system was prepared, that comprised of a single station model of a factory, a stockpile of finished goods and a single retailer. For similar purposes, Talluri et al. [35] utilized another approach for mathematical model development, i.e., data envelopment analysis for improvements in strategic decisions to find out efficient candidates for designing, manufacturing and distribution etc. and proposed a two-phased quantitative framework to aid the decision making process in effectively selecting an efficient and a compatible set of partners. Same approach of data envelopment analysis, mixed with another operations research technique, multi-objective programming was used by Weber et al. [36] also to prepare a mathematical model for
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studying another part of strategic decisions, i.e., JIT supply and an approach for evaluating a number of vendors was presented to employ in a procurement situation. Similar work of supplier selection was considered by Talluri and Sarkis [37] to develop a mathematical model for the supplier performance evaluation and monitoring processes, which assisted in maintaining effective customer-supplier linkages, considering price, quality, delivery and flexibility as variables apart from JIT supply. The model and its applications have been demonstrated through a previously published illustrative case example. Further, Talluri and Baker [38] presented a multi-phase mathematical programming approach for effective supply chain design considering potential suppliers, manufacturers and distributors. The model developed was based on game theory concepts and linear & integer programming methods, taking cost, product variety, quality and lead time as main attributes. Same area of manufacturers and distributors has also been analysed for competitive behaviour using Nash equilibrium theory by Bing and Dao-li [39]. Chan et al. [40] developed a model to find the vendor's optimal production policy, considering two-level supply chain of retailer and manufacturer. In the model developed, it has been assumed that the manufacturer has full knowledge of his demand throughout the time interval which is equal to the retailer's optimal production rate in the time interval considered. Yet another part of strategic decisions, i.e., to react on time and on the design of systematic decision-making processes for the supply chain by using control laws to manage the dynamic system was studied by Perea et al. [41]. A mathematical model was developed for a polymer manufacturer for the analysis of the ability of enterprise systems, taking customer satisfaction and inventory oscillation as variables. Another important aspect of strategic decisions for vendor–buyer synchronization was studied by Chan and Kingsman considering single-vendor multi-buyer supply chain [42]. A mathematical model was developed to achieve synchronization by scheduling the actual delivery days of the buyers and coordinating them with the vendor’s production cycle whilst allowing the buyers to choose their own lot sizes and order cycles. Three examples have been considered to show that the synchronization policy works by using the mathematical model developed.
Another
important aspect of strategic decisions was taken up by Vidal et al. [43] to present a mathematical model for optimization of a global supply that maximizes the after tax profits of a multinational corporation, using linear programming technique. Decision variables considered were transfer prices and transportation costs. Location of distribution centres was identified as an area for future study. Hammel et al. [44] took up this area as research field and developed a mathematical model for the re-engineering of Hewlett-Packard’s CD-RW supply chain. They considered technological innovation and delivering innovative products at competitive prices as variables for locating distribution
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centres and the business was able to save $50m annually along with quality improvement and introduction of new products at some new distribution centres. Same area of locating distribution centres, along with bill of materials, being another important aspect of strategic decisions, was studied by Yan et al. [45]. A strategic production-distribution mathematical model was developed for supply chain design, with consideration of bills of materials, considering logical constraints. The operations research tool used was mixed integer programming; using purchasing cost, production cost, transportation and distribution cast as variables. The developed model was used on an international computer company in Southeast Asia for illustration. Instead of locating distribution centres, Chauhan et al. [46] developed a mathematical model for strategic level decision when production-distribution of a new market opportunity has to be launched in an existing supply chain. The tool used was mixed integer linear programming, considering cost associated with production enhancement to the existing capacity for new market and additional transportation cost. Mathematical modelling on operational level considerations, to determine the operating parameters required to be monitored for day to day planning of various echelons of the supply chain has been another field of research. Wong et al. [47] remodelled the dynamics of an already developed model so that optimal production rate obtained becomes a continuous function of time instead of having jump discontinuities. Cohen and Lee [48] presented a comprehensive mathematical model to answer how production and distribution control policies could be coordinated to achieve synergies in performance and how material input, work-in-process and finished goods availability affect costs, lead-times and flexibility. For illustrative purposes, a problem has been examined that consisted of two finished products, three raw materials, one plant, two production lines within the plant and three distribution centres. Conflict between production and marketing departments due to varying interests has not been considered and efficiency improvement in process has been mentioned as future scope of work. Pyke and Cohen [49] considered this conflict. Batch size is wanted to be bigger by production section to reduce set-up costs and work force change cost, whereas distribution section wants it to be small so that response to the changing market demands is quick and later on mathematical models have also been developed to streamline the operations to improve the efficiency and responsiveness of a supply chain in a fine chemical industry and for a consumer goods industry [50-51]. Emphasis was on integrated information framework that would address issues of resource availability, lot sizing and plant responsiveness. Chan et al. [52] and Sabri & Beamon [53] also proposed mathematical model for efficiency improvement of the system. A model of a typical, single channel logistics network has been developed after assessing each of the order
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release mechanism and proposed a new order release approach, which was found to be superior and led to improved supply chain performance and multi-objective supply chain to allow use of a performance measurement system that included cost, customer service levels and flexibility in volume or delivery of products. Persson and Olhager [54] also considered quality, lead-times and costs as the key performance parameters to develop mathematical model for a firm manufacturing mobile communication systems. Ways have been proposed to increase the understanding of the interrelationships among above mentioned parameters and other parameters, relevant for the design of supply chain structure. For further improvements in efficiency, Vergera et al. [55] developed an algorithm using the economic delivery and scheduling model and analyzed supply chain dealing with multi-components. Another approach, called algebraic targeting approach to locate the optimum production rate for aggregate planning in a supply chain has also been reported. The proposed algorithm was tested and it was shown that the model provided near optimal solutions for a wide range of problems [56]. 4.3 Models using Time Compression and JIT Approaches A great deal of progress has been achieved in improving the existing business setup by focusing on cost reduction and quality improvement. As revealed by the literature surveyed, time reduction of production cycle is another fundamental element through which competitive advantage can be achieved. In ‘time-compression’, material is made to move faster rather than moving the people faster throughout the process. This approach looks at the flow of product and reduces the time period during which no work is being carried upon it, a period found to be significantly large part of the total production time. The commonly used time related performance measures are lead time, delivery time, cycle time, processing time, waiting time, transportation time etc. The essence of time-compression is to reduce or eliminate the maximum possible idle time out of these parts of material flow. The main groups of non value-adding time are queuing time, rework time, decision making time, waiting time etc. and emphasis has been laid to reduce these time-periods. Researchers have tried to reduce total production time by working on various strategies: refocus on sequence of activities, synchronization of lead times and capacities, reduction in number of process steps, combination of activities, minimizing material delay and waiting time, reducing the sub lot size, keeping low level of work in progress inventory, overlapping more than one activities and reducing variety in the inbound flow of material etc.
Beesley [57] stated that the key to
achieving time compression is to remove waste and refocus the sequence of the activities so that the time consumption is reduced for the total supply chain system.
A tool,
time-based-process-mapping has been developed that helped to answer the questions
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such as who the end user was, identified the core processes that took place to serve the end user and related the core processes and the resources utilized to the consumption of time. Further time compression has been achieved by optimizing information & material flows and by workflow management in the supply chain and the results achieved have been verified using a simulation model [58-59]. It has been concluded that to maximize production cycle time compression, a supply chain must re-design order information usage strategy. This way of time compression through fast information flow to the suppliers and customers has further been utilized by Salvador et al. [60]. It has been illustrated through both model development and empirical analysis of 164 plants. It has been suggested that when an organization interacts with suppliers and with customers on quality management issues, the organization would improve its time performances indirectly as a result of complete mediation by internal practices for quality improvement, low management, inter-unit coordination and vertical coordination. Interactions with suppliers and customers should improve time-related performances in terms of delivery punctuality and throughput time. Time compression while working with suppliers can also be achieved with better and timelier information about orders, new products and special needs and through focusing on minimum reasonable inventory (MRI) [61]. It has been stressed that production should look to manufacture in economic batch quantities so as to achieve economies of scale. Further emphasis has been on reduction and elimination of delays and proper design of feedback loops. On the basis of industrial studies, it has been confirmed that collapsing cycle times drive the business into more competitive scenario. Along with reduction in inventory, time compression is possible through standardization of the products; working on not value added inspection and internal transport also [62]. The ratio of lead time to value added time has been minimized by reducing the queuing effect to its minimum.
Jones and Towill [63]
further stressed upon total cycle time (TCT) compression in an agile supply chain through reduction in information lead time. Time compression has been applied on a fashion supply chain. Emphasis has been on the use of IT for information flow from one end to another. It has been concluded that in the information enriched supply chain, each player receives the marketplace data directly. While working with suppliers, another aspect studied for total cycle compression has been through concentrating on purchasing and transportation processes [64]. A set of total cycle time factors related to purchasing and transportation has been extracted to produce a conceptual model. Five objects of managerial attention identified for time compression have been the nature of relationship with important suppliers including transportation carriers, the characteristics of
the
physical process of transportation, the characteristics of the information system that processes purchasing and transportation transactions and information, the composition
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and functioning of management decision-making teams and the characteristics of strategy and strategy formulation for purchasing and transportation. Ramasesh [65] et al. reduced total cycle time of production through lot streaming, overlapping of process steps and by reducing work-in-process inventories. Lot streaming provides an opportunity to lower the cost of work-in-process inventories. Economic production lot size model has been presented that minimized the total relevant cost by using lot streaming in the illustrative numerical examples. Through computational analysis, the potential for substantial cost saving by the application of lot-streaming model has been demonstrated. Nieuwenhuyse and Vandaele [66] also worked on time compression by determining the optimal number of sub lots in a single-product, deterministic flow shop with overlapping operations. Formal expressions have been derived for the calculation of gaps and transfer batch lead times and a cost model has been formulated that considered the inventory holding cost, the transportation cost and the gap cost per production cycle. Conclusion drawn was that, that idling of machines is not technologically prohibited but does entail a penalty. Hoque ang Goyal [67] also developed a model for time compression through decreasing sub lot size. In this work, it has been proposed that for minimum in-process inventory the production flow should be synchronized by shifting the lot from a stage to next in equal shipment sizes. The model developed has been compared with already developed model based on the assumption of transferring the lot in equal sized batches through all stages. Two numerical problems have also been solved following the algorithm developed in the model and cost reduction was concluded. Overlapping of various activities was again proposed a way of time compression, along with reduction in variety of the inbound flow of material during the process for an automobile industry [68]. It was proposed that on the basis of a specific product configuration decided by consumer, it was possible to deduct the materials required for the production planning by applying a simple and fast algorithm on each line in the complex bill-of-material and certain combinations of options could actually be produced in connection with order placement. Reduction in lead time and elimination in variety of the inbound flow of materials was concluded. Jayaram et al. [69] also studied time compression through the time based performance of first tier suppliers GM, Ford and Chrysler in North America. Different engineering tools for time compression at different stages of the product were suggested, e.g., CAD/CAM, Concurrent Engineering (CE), Design for manufacturing (DFM), Standardization of product and process design, Preventive Maintenance, Just-in-time (JIT) purchasing, supplier partnering etc. Similar approach of reducing number of steps for time compression has been used by Gehani [70]. Stress has been on the reduction in the series of inspections and approvals required, either at shop floor or in field service. Use of Kanban style clip boards where each
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shop-floor worker and supervisor is given information, on an ongoing basis, about how fast they are doing their work, and how much more work they need to do during the rest of that day has been suggested by citing some examples. Further stress to compress cycle time has been on reduction in serial departmentalization of organizations’ structure. Bhattacharya [71] et al. also worked on similar techniques of time compression. Time based process analysis, designed to eliminate non-vale added time and hence render the resultant system more responsive to the needs of customer has been suggested. Total production time has been divided into two parts-technical time and managerial time and ways have been suggested to compress time in both cases for a garment manufacturing company. They suggested eliminating non-value-added time like waiting and breakdown from each activity in the process; launch smaller batches so that the total time taken for each activity was reduced and individual part throughput time is reduced; Run activities in parallel. Time consumed as a result of managerial factors, referred to as managerial time could be compressed in two ways- Integrating the elements that made up the process so that there were fewer hand-offs and deleting those decision processes that contributed to delays, off-line from the core process. Hum and Sim [72] also studied the effect of synchronization of lead times for time compression. It has been suggested that every manager should measure the elapsed time in every step from product conceptualization to product consumption and then work continuously to reduce all critical time intervals. The study has been done in view of introducing new product in a competitive market. Total cycle time compression has also been achieved by Singh and Chand [73-74] for the electronic industry. A model was also developed to reduce inventory cost considering ordering cost, inventory holding cost and backorder penalty cost. Appreciable reduction in total cycle time was concluded after study of layouts of various production shops and process flow charts for different subassemblies and instrument assembly. Usage of optimal sub-lot size instead of fixed sub-lot size was suggested and based on the suggestions made, revised process flow charts were prepared for showing the time compression in the process. But, it should be kept in mind that analysis of a project, proposed for lead time reduction is important. Consequences should be analysed by representatives of variety of business functions [75]. JIT has been another area of interest of the researchers in the field of supply chain management. Miltenburg [76] studied theoretically how JIT reduces cost, inventory, cycle time and improves quality, with a mathematical framework. The mechanism developed was able to identify and reduce waste also. Apart from these benefits, JIT supply of bought out component brings with it the possibility and necessity of improving the business cycle, when applied in an automotive industry [77-78]. It has been pointed out that electronic data interchange between supplier and manufacturer is required to
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make the cycle work. Its application enables the industry to have improved cash management and facilitates in generating exact delivery schedules-an important parameter in the field of automotive industry. The impact of supplier-manufacturer linkages in JIT environment has been further explored by Wafa et al. [79] along with the roles of information, communication and relationship with vendors. It has been shown that successful implementation of JIT resulted in production flexibility, reduction in inventory of finished goods, work-in-process and raw material significantly. Researchers [80-82] have analysed the inventory costs of JIT and economic order quantity (EOQ) purchasing also through comparison of the two methods by developing mathematical models. Existing data has been used for illustration of the developed models. It has been pointed out that under what conditions one system was superior to the other from cost perspective. JIT has been found less costly alternative when the level of annual demand for the inventory item was lower than a certain limit and when the demand increased beyond this limit, EOQ became less costly. It has been cautioned that stock outs caused by JIT ordering policy could add to the costs along with loss of price discounts. This problem was taken up by Min and Pheng [83] to develop a mathematical model for a concrete industry, taking into account the price discount also. The developed model has been compared with the other models where price discount has not been considered and it has been concluded that EOQ with price discount could prove to be better option when demand is more than a certain value, whereas Singh and Chand [84] showed that JIT purchasing is better and economical when compared with EOQ purchasing for an electronics industry. A mathematical model has been developed considering ordering cost, inventory holding cost at central stores and backorder penalty cost. Using this model it has been concluded that unit cost of controlling raw material reduced by 53-59% along with reduction in production cost. For the future scope of study, purchasing through internet and information flow during JIT purchasing has been suggested. But, when supply lead times are uncertain, use of dual-sourcing technique offers savings in inventory holding, the magnitude of which depends on the level of uncertainty in lead times [85]. 5. Conclusion As discussed earlier, the development and evolution of supply chain management owes much to the purchasing and supply management, and transportation and logistics literature. But, for the optimum use of resources, integration of supply chain at purchase, manufacturing, storage and delivery to customer levels is the need of the hour. Genuinely integrated supply chain management requires a massive commitment by all members of the value chain for its successful implementation. Integrating the purchasing with
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manufacturing and delivery functions can create a closely linked set of processes. It allows organizations to deliver products and services to both internal and external customers in a more timely and effective manner and thus help in achieving the corporate goals. The results can still be better if time compression and JIT approaches are also added to integrated supply chain implementation, as discussed above. Although supply chain management has been discussed along separate paths of mathematical models development, it has eventually merged into a unified body of literature with a common goal of increased efficiency, better customer satisfaction and better financial results. One of the most significant findings from our literature analysis has been the relative lack of integrated supply chain design for the whole process when compared to design of supply chain for individual sections and can be taken as future scope of study. Other related areas where work still needs to done are simulation-based optimization methodologies; optimization under uncertainty; incorporation of negotiation abilities etc.
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Darshan Kumar, Assistant Professor in Mechanical Engineering Department of Beanr College of Engineering & Technology, Gurdaspur (Punjab), India,
received the B.E.
degrees in Mechanical Engineering from Thapar Institute of Engineering & Technology, Patiala, Punjab (India) in 1990 and M.Tech. degree in Mechanical Engineering from Punjab Technical University, Jalandhar Punjab (India) in 2003. Presently, he is pursuing his PhD degree from Punjab Technical University, Jalandhar Punjab (India) in the field of Supply chain design. He is also a member of professional bodies like ISTE and IE India. E-mail:
[email protected]
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Dr. Om Pal Singh, Professor in Mechanical Engineering Department of Beanr College of Engineering & Technology, Gurdaspur (Punjab), India, did his graduation and post graduation from University of Roorkee, Roorkee (India). He did is Doctoral inSupply Chain Design field from MNNIT Allahabad,U.P., India and presently working as Professor and Head of Mechanical Engineering at Beant College of Engineering & Technology, Gurdaspur (Pb.), India. He has held many administrative responsibilities like member of Board of Studies, Coordinator Mechanical Engg. Department etc. He has vast experience of 18 years in his field. Research interest includes Supply Chain Management, Operations Research and Welding Technology. He has contributed in ISIJ International, Int. J. Services and Operations Management, Trans. Indian Inst. Met., Icfai Journal of Management Research, Icfai Journal of Supply Chain Management, OPSEARCH and PARADIGM etc. E-mail:
[email protected]
Dr. Jagdev Singh, Assistant Professor in Mechanical Engineering Department of Beant College of Engineering & Technology, Gurdaspur (Punjab), India, received the B.E. and M.E. degrees in Mechanical Engineering from Punjab University, Chandigarh (India) in 1988 and 1998, respectively and completed his Ph.D. from Punjab Technical University, Jalandhar Punjab (India). His current research interests centre on the fuzzy logic and its applications in mechanical engineering systems; Supply chain design & management. He is also life member of professional bodies like
[email protected]
ISTE
and
IE
India. E-mail:
