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Understanding the impact of manufacturing and supply chain ...

  1. 1. Enterprise Modeling of Supply Chains Involving Multiple Entity Flows: Role of Flexibility in Enhancing Lead Time Performance Prof. S. Wadhwa Mr K.S. Rao Indian Institute of Technology New Delhi INDIA E-mail: swadhwa@mech.iitd.ernet.in Abstract: There is a growing need for developing effective enterprise models for supply chain systems to help improve the time- based performance measures. Developing of these models becomes challenging when multiple entity flows need to be simultaneously modeled over a time horizon. For instance most research efforts on supply chains do not explicitly model the multiple entity flows of a manufacturing system that may be an explicit and important part of the supply chain. Often some important facets of modern enterprises such as its flexibility are ignored. In our experience there is a crucial need to develop and study enterprise models of supply chains with a key focus on judicious deployment and exploitation of flexibility. This implies modeling of both manufacturing flexibility and supply chain flexibility. The effective use of flexibility requires a good decision- information synchronization to dynamically control the multiple entity flows through the enterprise subsystems. However, such studies involving the combined effect of flexibility at more than one level have received less attention of the researchers. One possible reason could be the complexity involved in such studies. But, it is important to understand the interactions between flexibility at various levels of the supply chain to understand their trade-offs and to ensure that they reinforce each other in a useful manner. It is also possible that some flexibility types may substitute each other in a more cost effective manner. Keeping this in view, this paper presents the results of a conceptual study complemented by simulation models aimed at understanding the individual as well as combined impact of manufacturing flexibility and supply chain flexibility on the lead-time performance of a supply chain enterprise. The studies indicate that both types of flexibility are equally important and to some extent they can substitute for each other. However, the combined effect of these two flexibility types appears to be more beneficial than their individual effects. Further, this benefit is observed to be much higher at lower levels of flexibility. These observations are important for the designers and managers of the flexible supply chain systems to arrive at appropriate types and judicious levels of flexibility to attain significant improvements in lead-time performance. Enterprise modeling of supply chains with a focus on flexibility offers an enormous potential. This paper addresses this interesting and challenging domain. Keywords: Flexibility, Enterprise Modeling, Manufacturing Flexibility, Supply Chain Flexibility, Control, Lead Time Performance Professor Swbhash Wadhawa (Eur. Ing., C. Emg.) received his PhD from NUI, Ireland, while working on an ESPRIT project at CIM Research Unit., Galwai. He extensively contributed to the development of generalized simulators and expert systems for flexible systems. He is currently Professor and Group In-charge (P&I) at Indian Institute of Technology, New Delhi (IITD). He has originated novel research themes: decision and information delays involving Decision-Information Synchronization (DIS) applications in CIM, Supply Chain, e-Business; DRIS architecture for Agile Manufacturing; SAMIN architecture in IMS context, etc. He is an active contributor to EC projects in the IT&C domain and has coordinated several EU workshops. He has been a Consultant/Contributor/National-Expert to many International bodies EC, UNIDO, CW, APO (Tokyo), etc. He is dedicated to the goal of bringing synergy between Academics, Industry and Research. 1. Introduction and Motivation Flexibility refers to the ability to respond to changes in business environment. To be competitive in a dynamic business environment, supply chains must be flexible and responsive. From a historical perspective, the notion of flexibility originated in the economics literature, in 1930s, in the context a firm’s ability to accommodate greater variations in the demand for its outputs (Carlsson, 1989). Later the idea was widened to encompass all forms of turbulence in the firm’s environment. Since then, the concept of flexibility has been attracting the attention of many researchers, but the advent of information technology in 1980s gave a big fillip to the research in this area. The research in 80s and mid 90s mainly focused on the flexibility of the manufacturing systems and led to the development of flexible manufacturing systems and a considerable body of knowledge on the manufacturing flexibility. In 1984, the seminal work by Browne et al (1984) provided a classification of flexibility types and their inter-relationships. This is followed by numerous studies and attempts to develop conceptual frameworks, models and measures for the manufacturing flexibility (for example, Gerwin, 1987; Gupta and Goyal, 1989; Wadhwa and Browne (1990), Sethi and Sethi, 1990; Gerwin, 1993; Upton, 1994; De Groote, 1994; Tincknell and Radcliffe, 1994; Benjaafar and Ramakrishnan, 1996; Beach et al, 1998; Parker and Wirth, 1999). Recently the focus of research is shifting to the flexibility of organizations and supply chains (for example, Golden and Powell, 1999a; Koste and Malhotra, 1999a; Wadhwa and Rao, 2000; Ozer, 2002). The potential of certain types of flexibility to enhance the lead-time performance of the manufacturing systems attracted the attention of many researchers and as a result number of studies could be found in the literature (for example, Jensen et al, 1996; Studies in Informatics and Control, Vol.12, no.1, march 2003 1
  2. 2. Albino and Garavelli, 1998; Wadhwa and Bhagwat, 1998; Albino and Garavelli, 1999; Newman and Maffei, 1999; Tsubone and Horikawa, 1999; Borenstein, 2000; Wadhwa and Agarwal (2000), Garavelli, 2001; Seifert and Morito, 2001, Jack and Raturi, 2001). Similarly, the potential of flexibility to enhance the lead-time performance of the supply chains has been identified in the literature and is being currently focused as an important area. With the growing turbulence in the business environment and competition shifting to the supply chain level, the supply chain flexibility is emerging as one of the key competitive priorities for the future. Developing a core competence in this area is expected to have a long-term impact on the supply chain competitiveness and business performance. Realizing this fact, recently a number of authors have started discussing flexibility from a supply chain perspective. For example, Koste and Malhotra (1999b), while presenting a perspective on research opportunities in manufacturing flexibility, emphasized that the presence or absence of flexibility in supply chains and its relationship with performance should be explored and the effect of supply chain integration on the development of flexibility in supply chains should be examined. They observed that, the Flow Shop Lean Supply Chain Volume Volume Flexible Flexible Manufacturing Supply Chain System System Job Shop Agile Supply Chain Variety Variety Manufacturing Paradigm Supply Chain Paradigm competitive priorities of the supply chain might impact flexibility, as efficient supply chains may emphasize certain flexibility dimensions, while responsive supply chains focus on other. An understanding of these differences, if any, would enhance the management of supply chains. Adrian Mello (2001) views supply-chain flexibility as the ability to restructure the system quickly and inexpensively. He observes that after the Sept. 11 terrorist attacks on USA, many businesses were forced to wonder just how vulnerable their supply chains are to unforeseen disruptions. These unforeseen events can all have devastating effects on manufacturing and distribution operations and result in millions of lost dollars. He argues that business must bulletproof their operations by building in supply-chain flexibility. Figure 1: The Concept of a Flexible Supply Chain System Duclos et al (2001) propose a conceptual model of supply chain flexibility and identified six components of the supply chain flexibility e.g. production flexibility, market flexibility, logistics flexibility, supply flexibility, organizational flexibility, and information systems flexibility. They observes that as the basis of competition extending to supply chains and time becoming increasingly important, supply chain flexibility will be a critical issue for competitiveness. They argue that if manufacturing flexibility improves performance, supply chain flexibility, which would include the manufacturing flexibility of firms within the supply chain, should further improve performance when measured across the entire supply chain. Another closely related work to supply chain flexibility is in the domains of lean and agile supply chains (Naylor et al, 1999; Mason-Jones et al, 2000; Christopher and Towill, 2000). Wadhwa et al (2002) highlight the role of flexibility in the supply chains using a demo model of a supply chain. However the manufacturing system within the supply chain is not explicitly modelled. This motivated our current research to understand the impact of manufacturing flexibility as well as supply chain flexibility on the supply chains performance. To study this it is important to develop an effective enterprise model of a supply chain where a manufacturing subsystem with multiple entity flows is explicitly modeled. This paper presents this endeavor and highlights the key insights. In our opinion, it is expedient to extend some of the concepts of manufacturing enterprise paradigm into the supply chain paradigm as shown in the Figure-1. Lean supply chain is based on the idea of a flow shop and JIT philosophy of elimination of all waste. Agile supply chain is based on an extension of job shop ideas into the supply chain domain and aimed at mass customization. However, from an operational point of view, it would be more appropriate to concentrate on the idea of flexibility. Keeping this in view, our research intends to extend the ideas of FMS into the supply chain domain and proposes the concept of a flexible supply chain system as shown in Figure-1.The objective is to stimulate the development of flexibility-based strategies for supply chains that would be more effective 2 Studies in Informatics and Control, Vol.12, no.1, march 2003
  3. 3. to compete in the dynamic business environment. As a further step in this direction, we developed a model of the flexible supply chain system and carried out simulation studies to understand the impact of flexibility on the performance of this supply chain system. Some of the results of these studies are presented in this paper. This paper presents the above results in four parts; first part gives an overview of the model and details of simulation experimentation, the second part presents the effect of manufacturing flexibility on the supply chain lead-time performance, the third part presents the effect of supply chain flexibility on the supply chain lead-time performance and the fourth part presents the combined effect of manufacturing flexibility and the supply chain flexibility on the supply chain lead-time performance. Part –1: Overview of the Supply Chain Enterprise Model and Simulations For the purpose of the above studies, we have developed a hierarchical simulation model of a flexible supply chain system as shown in Figure-2. At the highest level, the model comprises of a flexible supply chain system connected to the customers and the suppliers. The flexible supply chain system accepts orders from the customers, source the required materials from the suppliers and fulfill the customer orders in the best possible way. The flexible supply chain system comprises of a number of supply chains interconnected in parallel through an order management system. The order management system accepts the orders for the entire supply chain system and depending upon the level of flexibility and the supplier selection criteria, allocates the orders to different supply chains in a dynamic manner. It is important to study the role of flexibility in enterprises covering the supply chain systems. This requires Flexible Supplier(s) Supply Chain Customer(s) System Supply Chain 1 Supply Chain 2 Supply O rder M gt. M gt. Supply Chain... Supply Chain n Supply Chain Supply Chain Supply Chain N ode N ode N ode (Supplier) (M fg Sys) (D istributor) Source Plan D eliver Return M ake Return M achine 1 M achine 2 U nloading Loading Station Station M achine... M achine n development of effective enterprise modeling framework with a capability to model multiple entity flows over a time horizon. We suggest that flexibility may be seen as a means towards greater enterprise synchronization. Flexibility Studies in Informatics and Control, Vol.12, no.1, march 2003 3
  4. 4. offers alternatives that can lead to changes in the flow of entities in a direction that may result in greater entity flow synchronization. The entities may consist of products, customer orders, information, decision and resources etc. It is important that we are able to model the flow of these entities and their synchronization over a time horizon. Simulation modeling offers useful tool if these can be easily developed over the enterprise-modeling framework employed. Such framework should also allow the modeling of the Decision-Information Synchronization (DIS) delays. As demonstrated by Wadhwa and Bhagwat (1998) the DIS delays can cause counter-productive impacts of flexibility. It is important to study the same in the context of supply chains modeled as enterprises. Figure 2: Hierarchical Enterprise Model of A Supply Chain When the supply chain system operates under no-supply chain-flexibility conditions, each supply chain will be able to handle only one type of product and accordingly the order management system does not have any role to play. However, as flexibility increases, each supply chain will handle more than one product type and the order management system will have to take a decision on the allocation of orders to the supply chains. For this purpose we have incorporated a protocol as follows; (a) upon receiving a customer order, the order management system sends a request for bid to all the supply chains indicating the product type and quantity required. (b) all supply chains respond to this call and submit their bids for supplying the above quantity. In case any supply chain is not in a position to supply a particular product type, it will send a regret message. In their bids, each supply chain will indicate how many orders it has pending with it, (c) based on the bids received from different supply chains, the order management system selects the most appropriate supplier and issues a supply request on that supply chain. In this model, the criteria for selection of a supplier is set to be the minimum number of orders waiting i.e. a supply chain with lowest number of orders waiting to be fulfilled will be selected for issuing the supply request, (d) upon receiving the supply request, the supply chain will backlog the order and initiate necessary action to source the materials from its supplier and this process will continue up to the end of the chain, (e) the supply chain node at the end of the chain is called the end supplier and is modeled to deliver any kind of materials immediately. The materials thus originated travel through the chain back to the customer who placed the order and the time elapsed between the placements of an order the receipt of products is measured as supply chain lead-time performance. In the next level of the model each supply chain comprises of a number of supply chain nodes. A supply chain node is a generic entity developed based on the Supply Chain Operations Reference (SCOR) model of the supply- chain council (www.supply-chain.org). Supply chain node is comprised of a number of functional entities such as plan, source, make, deliver and return in accordance with the Supply Chain Operations Reference (SCOR) model. Among these entities, the entity called “make” refers to the manufacturing system. Accordingly, we have embedded a model of the flexible manufacturing system into the supply chain node to function as “make”. In the last level of the model, the manufacturing system (make) comprises of a number of machines, a loading station and an unloading station. The loading station receives all the orders and depending upon the level of flexibility and the machine selection logic, allocates the orders to the machines. In this model the machine selection logic is implemented as minimum waiting time. Under the conditions of no- manufacturing system-flexibility, each machine will be capable of manufacturing only one product type and hence no decision-making is required. However, as the flexibility level increases, each machine will manufacture more than one type of products and the loading station controller will make the decision on the allocation of products to machines in a dynamic manner. Defining Flexibility Levels For the purpose of this investigation, a particular type of flexibility is considered that concerns with the R1 P1 R1 P1 R1 P1 R2 P2 R2 P2 R2 P2 R3 P3 R3 P3 R3 P3 R4 P4 R4 P4 R4 P4 R5 P5 R5 P5 R5 P5 R6 P6 R6 P6 R6 P6 L e v e l-1 L e v e l-2 L e v e l-3 R1 P1 R1 P1 R1 P1 R2 P2 R2 P2 R2 2 P2 R3 P3 R3 P3 R3 P3 P3 R4 P4 R4 P4 R4 P4 P4 4 R5 P5 R5 Studies in Informatics and Control, Vol.12, no.1, march 2003 P5 R5 P5 P5 R6 P6 R6 P6 R6 P6 P6 L e v e l-4 L e v e l-5 L e v e l-6
  5. 5. relationship between product types and resources as shown in the Figure-3. Figure 3: The Flexibility Type Being Studied Under the conditions of no-flexibility, each resource will be able to handle only one type of products, whereas as the flexibility increases, the types of products that could be handled by each resource increases. Based on these logics, six levels of flexibility has been defined as shown in the Figure-3. Flexibility level-1 corresponds to the condition of no flexibility. Flexibility level-2 corresponds to a condition where each product type can be handled by two resources, and so on. Levels of Abstraction The above flexibility type has been studied at two levels of abstraction. The first level of abstraction is to consider the resource as a machine. This give rise to manufacturing flexibility at six levels i.e. MSFL-1 to MSFL-6. The second level of abstraction is to consider the resource as a supply chain. This gives rise to supply chain flexibility at six levels i.e. SCFL-1 to SCFL-6. The levels MSFL-1 and SCFL-1 corresponds to the conditions of no flexibility and the levels MSFL-6 and SCFL-6 correspond to condition of full flexibility. All the 12 flexibility types considered in the present study are shown in Table-1. Table 1: The Flexibility Types Considered for the Present study Level of Flexibility Flexibility Types 1 2 3 4 5 6 Manufacturing System Abstraction R = Machine MSFL-1 MSFL-2 MSFL-3 MSFL-4 MSFL-5 MSFL-6 Level of P = Product Type Supply Chain System R = Supply Chain SCFL-1 SCFL-2 SCFL-3 SCFL-4 SCFL-5 SCFL-6 P = Product Type Details of Simulation Experimentation With the help of the above model, simulation studies have been carried out to understand the effect of various flexibility types identified above on the supply chain lead-time performance. The factors incorporated into the simulation experimentation are given in Table-2. The independent variables include; order variability represented through volume variations and mix variations, manufacturing flexibility levels and supply chain flexibility levels. The three levels of volume variations and six levels of mixed variations used in this simulation experimentation are given in Table-3. The dependent variable is the supply chain order fulfillment lead-time performance. Table 2: Details of Simulation Experimentation Parameters Levels Order Variability: Volume variations Volume-1, Volume-2, and Volume-3 Mix variations Mix-1, Mix-2, Mix-3, Mix-4, Mix-5, and Mix-6 Supply Chain Characteristics: Operating Environment Make-to-Order Supplier Selection Minimum pending orders Criteria Queue Discipline First Come First Served Flexibility Levels SCFL-1, SCFL-2, SCFL-3, SCFL-4, SCFL-5, and SCFL-6 Manufacturing System Characteristics: Operating Environment Make-to-Order Machine Selection Minimum waiting time Criteria Queue Discipline First Come First Served Flexibility Levels MSFL-1, MSFL-2, MSFL-3, MSFL-4, MSFL-5, and MSFL-6 Dependent Variable: Performance Measure Supply chain order fulfillment lead-time, defined as the time elapsed Studies in Informatics and Control, Vol.12, no.1, march 2003 5
  6. 6. between the placement of an order and receipt of the products. Table 3: The Volume and Mix Variations Mix Variation Mix-1 Mix-2 Mix-3 Mix-4 Mix-5 Mix-6 P1=10 P1=20 P1=30 P1=40 P1=50 P1=60 P2=20 P2=30 P2=40 P2=50 P2=60 P2=60 Volume-1 P3=30 P3=40 P3=50 P3=60 P3=60 P3=60 V = 360 P4=90 P4=80 P4=70 P4=60 P4=60 P4=60 Product Types = 6 P5=100 P5=90 P5=80 P5=70 P5=60 P5=60 P6=110 P6=100 P6=90 P6=80 P6=70 P6=60 P1=40 P1=50 P1=60 P1=70 P1=80 P1=90 P2=50 P2=60 P2=70 P2=80 P2=90 P2=90 Volume-2 P3=60 P3=70 P3=80 P3=90 P3=90 P3=90 V = 540 P4=120 P4=110 P4=100 P4=90 P4=90 P4=90 Product Types = 6 P5=130 P5=120 P5=110 P5=100 P5=90 P5=90 P6=140 P6=130 P6=120 P6=110 P6=100 P6=90 Volume Variation P1=70 P1=80 P1=90 P1=100 P1=110 P1=120 P2=80 P2=90 P2=100 P2=110 P2=120 P2=120 Volume-3 P3=90 P3=100 P3=110 P3=120 P3=120 P3=120 V = 720 P4=150 P4=140 P4=130 P4=120 P4=120 P4=120 Product Types = 6 P5=160 P5=150 P5=140 P5=130 P5=120 P5=120 P6=170 P6=160 P6=150 P6=140 P6=130 P6=120 Based on the above details, a full factorial simulation experimentation has been carried out and the results are analyzed using analysis of variance (ANOVA). The results are discussed in the following three parts. Part-2: Effect of Manufacturing Flexibility on Supply Chain Performance: This part of the studies focused on three important questions concerning the effect of manufacturing flexibility on the supply chain performance. The first question is – how does the flexibility of a manufacturing system influence the lead-time performance of a supply chain system that encompasses it? The second question is - how does the flexibility of a manufacturing system influence the ability of a supply chain system to absorb the volume variations? And the third question is - how does the flexibility of a manufacturing system influence the ability of a supply chain system to absorb the mix variations? The result of the simulation experiments on these three questions are discussed below: Influence of manufacturing flexibility on the lead-time performance of the supply chain system: Lead-time performance of a supply chain system depends on the characteristics of the supply chain elements Plot of Means (unweighted) MSFL Main Effect F(5,1080)=453.68; p<0.000 24000 22000 20000 18000 Variable: LTIME 16000 14000 12000 10000 8000 6000 4000 1 2 3 4 5 6 MSFL and many other operating conditions of the supply chain. It is observed that, under certain conditions, the supply chain lead-time reduces with the increase in manufacturing flexibility. This motivated us to study 6 Studies in Informatics and Control, Vol.12, no.1, march 2003
  7. 7. how the flexibility of a manufacturing system may influence the lead-time of a supply chain that encompasses it. The results of the simulation study on this question are shown in Figure-4. Figure 4: Influence of manufacturing flexibility on the supply chain lead-time From the above results the following observations may be noted: (a) The supply chain lead-time monotonously decreases with increasing levels of manufacturing flexibility. This indicates that manufacturing flexibility negatively influences the supply chain lead-time. The influence of manufacturing flexibility on the supply chain lead-time appears to be strong (F=453.68) and highly significant (p<0.000). The influence is not uniform at all the flexibility levels. The results indicate a lead-time reduction of 66.27%, for a change in the flexibility levels from a condition of no-flexibility (MSFL=1) to a condition of full-flexibility (MSFL=6), in a diminishing manner. The distribution of this lead-time reduction among various flexibility levels is shown in the Table-4. Table 4: Pattern of Lead-Time Variation with the Increasing Levels of Manufacturing Flexibility To (Flexibility Level) Lead-Time Variation 2 3 4 5 6 1 -60.97% -82.11% -92.95% -97.94% -100% From 2 -21.14% (Flexibility 3 -10.84% Level) 4 -4.99% 5 -2.05% The above observations imply that supply chain systems that use manufacturing flexibility are likely to achieve shorter lead-times as compared to those that do not use it. Supply chain systems that use greater levels of manufacturing flexibility are likely to achieve shorter lead-times, but the benefit diminishes with increasing levels of flexibility. The results also indicate that the first level of manufacturing flexibility (MSFL=2) provides the greatest benefit, followed by lesser and lesser benefits at subsequent levels. It is also observed that, the lead-time accomplished with the first level of flexibility (MSFL=2) is closer to the lead-time accomplished with full-flexibility (MSFL=6) rather than to the lead-time under the conditions of no-flexibility (MSFL=1). Similarly, the lead-time accomplished with the second level of flexibility (MSFL=3) is closer to the lead-time accomplished with the full-flexibility (MSFL=6) rather than to the lead-time accomplished with the first level of flexibility (MSFL=2). This pattern continued throughout. Since it is generally expected that the levels of investment, magnitude of transition penalties and the performance penalty of having and using flexibility increase with increasing levels of flexibility, the above pattern of lead-time reduction has two implications. Firstly, at lower levels of flexibility the benefits due to flexibility may always outweigh the penalty of using flexibility. Secondly, at higher levels of flexibility the penalty of using flexibility may outweigh the benefits due to flexibility. Hence, there is a need to arrive at judicious levels of flexibility to balance the penalties and benefits. Influence of manufacturing flexibility on the ability of a supply chain to absorb volume variations: Increase in the order volumes increases the order fulfillment lead-time. However, it is observed that under certain conditions, this lead-time variation due to volume change decreases with the increase in manufacturing flexibility. This would give rise to a certain ability to absorb volume changes. As a part of our studies, we investigated to what extent the manufacturing flexibility may contribute to this ability in supply chains. The results of the simulation study are shown in Figure-5. Studies in Informatics and Control, Vol.12, no.1, march 2003 7
  8. 8. Plot of Means (unweighted) 2-way interaction F(10,1188)=73.20; p<0.000 35000 VOLUME 30000 1 VOLUME 25000 2 Variable: LTIME 20000 VOLUME 3 15000 10000 5000 0 1 2 3 4 5 6 MSFL Figure 5: Influence of Manufacturing Flexibility on the Ability of Supply Chain to Absorb Volume Variations An examination of the lead-time variation between volume-1 and volume-2 indicate that, with the increase in manufacturing system flexibility from a condition of no-flexibility (MSFL=1) to a condition of MSFL=2, the lead-time variation due to volume change decreases to 57.66% of the lead-time variation under no-flexibility condition. That means there is reduction of lead-time variation by 42.34%. With further increase in flexibility to MSFL=3, MSFL=4, MSFL=5, MSFL=6, the lead-time variation due to volume change decreases to 44.93%, 38.65%, 35.46% and 34.20% of the lead-time variation under no- flexibility condition, respectively. The corresponding reductions in lead-time variations are 12.73%, 6.28%, 3.19% and 1.25%, respectively. Similarly, an examination of the lead-time variation between volume-2 and volume-3 indicate that, with the increase in manufacturing system flexibility from a condition of no-flexibility (MSFL=1) to a condition of MSFL=2, the lead-time variation due to volume change decreases to 58.58% of the lead-time variation under no-flexibility condition. That gives a reduction of lead-time variation by 41.44%. With further increase in flexibility to MSFL=3, MSFL=4, MSFL=5, MSFL=6, the lead-time variation due to volume change decreases to 45.01%, 38.04%, 34.74% and 33.12% of the lead-time variation under no- flexibility condition, respectively. The corresponding reductions in lead-time variations are 13.56%, 6.98%, 3.29% and 1.62%, respectively. The above results indicate that, under certain conditions, lead-time variation due to volume change decreases with increasing levels of manufacturing flexibility. This indicates that manufacturing flexibility positively influences the ability of a supply chain to absorb volume variations. This influence appears to be weak (F=73.20) but highly significant (p<0.000). These observations imply that supply chain systems that use manufacturing flexibility are likely to have greater ability to absorb volume variations as compared to those that do not use it. Supply chain systems that use greater levels of manufacturing flexibility are likely to have greater ability to absorb volume variations, but the benefit diminishes with increasing levels of flexibility. The first level of manufacturing flexibility (MSFL=2) provides the greatest benefit, followed by lesser and lesser benefits at subsequent levels. Influence of manufacturing flexibility on the ability of a supply chain to absorb mix variations: Changes in the product mix changes the order fulfillment lead-time. Some times the lead-time may increase and some times it may decrease, depending upon the nature of change in the product mix. However, it is observed that under certain conditions, this lead-time variation due to mix change decreases with the increase in manufacturing flexibility. This would give rise to a certain ability to absorb mix changes. As a part of our studies, we investigated to what extent the manufacturing flexibility may contribute to this ability in supply chains. The results of the simulation study are shown in Figure-6. 8 Studies in Informatics and Control, Vol.12, no.1, march 2003
  9. 9. Plot of Means (unweighted) 2-way interaction F(25,1080)=1.40; p<.0913 30000 25000 MIX MIX MIX MIX MIX MIX 1 2 3 4 5 6 20000 Variable: LTIME 15000 10000 5000 0 1 2 3 4 5 6 MSFL Figure 6: Influence of Manufacturing Flexibility on the Ability of Supply Chain to Absorb Mix Variations An examination of the lead-time variation between different mixes with the increase in manufacturing system flexibility indicates that there is a certain reduction in the lead-time variation as flexibility increases. The lead-time variations quantified in the form of standard deviations of the lead-times obtained at a given level of flexibility, are 6617.53, 6018.55, 5439.85, 4850.98, 4959.35, 4799.99, for flexibility levels MSFL-1 to MSFL=6, respectively. The above results indicate that, under certain conditions, lead-time variation due to mix change decreases with increasing levels of manufacturing flexibility. This indicates that manufacturing flexibility positively influences the ability of a supply chain to absorb mix variations. This influence appears to be very weak (F=1.40) and moderately significant (p<0.0913). These observations imply that supply chain systems that use manufacturing flexibility are likely to have greater ability to absorb mix variations as compared to those that do not use it. Supply chain systems that use greater levels of manufacturing flexibility are likely to have greater ability to absorb mix variations, but the benefit appear to diminish with increasing levels of flexibility. The first level of manufacturing flexibility (MSFL=2) appears to be providing the greatest benefit, followed by lesser and lesser benefits at subsequent levels. Part-3: Effect of Supply Chain Flexibility on Supply Chain Performance: This part of the studies is focused on three questions concerning the effect of supply chain flexibility on the supply chain performance. The first question is – how does the flexibility of a supply chain system influence its lead-time performance? The second question is - how does the flexibility of a supply chain system influence its ability to absorb the volume variations? And the third question is - how does the flexibility of a supply chain system influence its ability to absorb the mix variations? The result of the simulation experiments on these three questions are discussed below: Influence of supply chain flexibility on the lead-time performance of the supply chain system: As discussed above, supply chain lead-time depends on the characteristics of the supply chain elements and many operating conditions of the supply chain. It is observed that, under certain conditions, the supply chain lead-time reduces with the increase in supply chain flexibility. This motivated us to study how the flexibility of a supply chain system may influence its lead-time. The results of the simulation study on this question are shown in Figure-7. Studies in Informatics and Control, Vol.12, no.1, march 2003 9
  10. 10. Plot of Means (unweighted) SCFL Main Effect F(5,1080)=472.47; p<0.000 24000 22000 20000 18000 Variable: LTIME 16000 14000 12000 10000 8000 6000 4000 1 2 3 4 5 6 SCFL Figure 7: Influence of Supply Chain Flexibility on Supply Chain Lead-Time From the above results the following observations may be noted: (a) The supply chain lead-time monotonously decreases with increasing levels of supply chain flexibility. This indicates that supply chain flexibility negatively influences its lead-time. The influence of supply chain flexibility on the supply chain lead-time appears to be strong (F=472.47) and highly significant (p<0.000). The influence is not uniform at all the levels of flexibility. The results indicate a lead-time reduction of 69.74%, for a change in the flexibility levels from a condition of no-flexibility (SCFL=1) to a condition of full- flexibility (SCFL=6), in a diminishing manner. The distribution of this lead-time reduction among the various flexibility levels is shown in Table-5. Table 5: Pattern of Lead-Time Variation with the Increasing Levels of Supply Chain Flexibility To (Flexibility Level) Lead-Time Variation 2 3 4 5 6 1 -56.69% -73.37% -86.48% -94.33% -100% From 2 -16.68% (Flexibility 3 -13.11% Level) 4 -7.85% 5 -5.66% The above observations imply that supply chain systems that use supply chain flexibility are likely to achieve shorter lead-times as compared to those that do not use it. Supply chain systems that use greater levels of supply chain flexibility are likely to achieve shorter lead-times, but the benefit diminishes with increasing levels of flexibility. The results also indicate that the first level of supply chain flexibility (SCFL=2) provides the greatest benefit, followed by lesser and lesser benefits at subsequent levels. In all the above properties, the influence of supply chain flexibility closely resembles that of the manufacturing flexibility. Influence of supply chain flexibility on ability of chain to absorb volume variations As discussed above, increase in the order volumes increases the order fulfillment lead-time. However, it is observed that under certain conditions, this lead-time variation due to volume change decreases with the increase in supply chain flexibility. This would give rise to a certain ability to absorb volume changes. As a part of our studies, we investigated to what extent the supply chain flexibility may contribute to this ability in supply chains. The results of the simulation study are shown in Figure-8. 10 Studies in Informatics and Control, Vol.12, no.1, march 2003
  11. 11. Plot of Means (unweighted) 2-way interaction F(10,1188)=75.40; p<0.000 35000 30000 VOLUME 1 25000 VOLUME 2 Variable: LTIME 20000 VOLUME 3 15000 10000 5000 0 1 2 3 4 5 6 SCFL Figure 8: Influence of Supply Chain Flexibility on the Ability of Supply Chain to Absorb Volume Variations An examination of the lead-time variation between volume-1 and volume-2 indicate that, with the increase in the supply chain flexibility from a condition of no-flexibility (SCFL=1) to a condition of SCFL=2, the lead-time variation due to volume change decreases to 58.66% of the lead-time variation under no-flexibility condition. That means there is reduction of lead-time variation by 41.34%. With further increase in flexibility to SCFL=3, SCFL=4, SCFL=5, SCFL=6, the lead-time variation due to volume change decreases to 47.28%, 39.44%, 35.27% and 30.57% of the lead-time variation under no- flexibility condition, respectively. The corresponding reductions in lead-time variations are 11.38%, 7.84%, 4.16% and 4.70%, respectively. Similarly, an examination of the lead-time variation between volume-2 and volume-3 indicate that, with the increase in supply chain flexibility from a condition of no-flexibility (SCFL=1) to a condition of SCFL=2, the lead-time variation due to volume change decreases to 59.37% of the lead-time variation under no-flexibility condition. That gives a reduction of lead-time variation by 40.63%. With further increase in flexibility to SCFL=3, SCFL=4, SCFL=5, SCFL=6, the lead-time variation due to volume change decreases to 46.75%, 39.47%, 34.07% and 30.20% of the lead-time variation under no-flexibility condition, respectively. The corresponding reductions in lead-time variations are 12.62%, 7.28%, 5.39% and 3.88%, respectively. The above results indicate that, under certain conditions, lead-time variation due to volume change decreases with increasing levels of supply chain flexibility. This indicates that supply chain flexibility positively influences the ability of a supply chain to absorb volume variations. This influence appears to be weak (F=75.40) but highly significant (p<0.000). These observations imply that supply chain systems that use supply chain flexibility are likely to have greater ability to absorb volume variations as compared to those that do not use it. Supply chain systems that use greater levels of supply chain flexibility are likely to have greater ability to absorb volume variations, but the benefit diminishes with increasing levels of flexibility. The first level of supply chain flexibility (SCFL=2) provides the greatest benefit, followed by lesser and lesser benefits at subsequent levels. Here again, on all the above properties, supply chain flexibility closely resemble the manufacturing flexibility. Influence of supply chain flexibility on the ability of a supply chain to absorb mix variations: As discussed above, changes in the product mix changes the order fulfillment lead-time. Some times the lead-time may increase and some times it may decrease, depending upon the nature of change in the product mix. However, it is observed that under certain conditions, this lead-time variation due to mix change decreases with the increase in supply chain flexibility. This would give rise to a certain ability to absorb mix changes. As a part of our studies, we investigated to what extent the supply chain flexibility may contribute to this ability in supply chains. The results of the simulation study are shown in Figure-9. Studies in Informatics and Control, Vol.12, no.1, march 2003 11
  12. 12. Plot of Means (unweighted) 2-way interaction F(25,1080)=1.61; p<.0289 30000 25000 MIX MIX MIX MIX MIX MIX 1 2 3 4 5 6 20000 Variable: LTIME 15000 10000 5000 0 1 2 3 4 5 6 SCFL Figure 9: Influence of Supply Chain Flexibility on the Ability of Supply Chain to Absorb Mix Variations An examination of the lead-time variation between different mixes with the increase in supply chain flexibility indicates that there is a certain reduction in the lead-time variation as flexibility increases. The lead-time variations quantified in the form of standard deviations of the lead-times obtained at a given level of flexibility, are 6809.17, 6157.64, 5542.59, 4926.51, 5025.47, 4902.63, for flexibility levels SCFL-1 to SCFL=6, respectively. The above results indicate that, under certain conditions, lead-time variation due to mix change decreases with increasing levels of supply chain flexibility. This indicates that supply chain flexibility positively influences the ability of a supply chain to absorb mix variations. This influence appears to be very weak (F=1.61) and moderately significant (p<0.0289). These observations imply that supply chain systems that use supply chain flexibility are likely to have greater ability to absorb mix variations as compared to those that do not use it. Supply chain systems that use greater levels of supply chain flexibility are likely to have greater ability to absorb mix variations, but the benefit appear to diminish with increasing levels of flexibility. The first level of supply chain flexibility (SCFL=2) appears to be providing the greatest benefit, followed by lesser and lesser benefits at subsequent levels. In respect of these results also, the influence of supply chain flexibility appear to be very close to that of the manufacturing flexibility. Part-4: Combined influence of manufacturing flexibility and the supply chain flexibility on the lead-time performance of the supply chain: As discussed above, it is important to understand the interactions between flexibility types at different levels. This motivated us to study the combined effect of the manufacturing flexibility and the supply chain flexibility on the lead-time performance of the supply chains. The results of the simulation study are shown in Figure-10 and the corresponding lead-time reductions are summarized in Table-6. 12 Studies in Informatics and Control, Vol.12, no.1, march 2003
  13. 13. Plot of Means (unweighted) 2-way interaction F(25,1080)=84.99; p<0.000 60000 50000 MSFL MSFL MSFL MSFL MSFL MSFL 1 2 3 4 5 6 40000 Variable: LTIME 30000 20000 10000 0 1 2 3 4 5 6 SCFL Figure 10: Combined Influence of Manufacturing Flexibility and Supply Chain Flexibility on the Supply Chain Lead-Time Table 6: Supply Chain Lead-Time Reduction due to Combined Influence of Manufactuing Flexibility and Supply Chain Flexibility Supply Chain Flexibility % of Lead-Time Reduction SCFL-1 SCFL-2 SCFL-3 SCFL-4 SCFL-5 SCFL-1 to SCFL- to SCFL- to SCFL- to SCFL- to 2 3 4 5 SCFL-6 MSFL-1 49.96% 66.42% 74.71% 79.90% 83.02% Manufacturing Flexibility MSFL-1 to MSFL-2 49.94% 70.67% 77.19% 82.41% 85.73% 87.45% MSFL-2 to MSFL-3 66.55% 78.04% 81.88% 85.69% 87.19% 88.51% MSFL-3 to MSFL-4 74.84% 82.39% 84.87% 86.84% 87.79% 88.99% MSFL-4 to MSFL-5 79.80% 85.28% 84.97% 86.70% 87.90% 89.06% MSFL-5 to MSFL-6 83.07% 85.31% 84.99% 86.71% 87.88% 89.06% The above results indicate that, while both the manufacturing flexibility as well as the supply chain flexibility are equally effective in improving the lead-time performance of the supply chains, their combined effect gives much more benefit. The above results indicate that with one level of increase in the supply chain flexibility, the lead-time is reduced by 49.96% whereas with one level of increase in the manufacturing flexibility the lead-time is reduced by 49.94%. With one level of increase in both flexibility types, the lead-time is reduced by 70.67%. A similar trend could be observed in other flexibility levels also, but with a diminishing benefit. These results indicate that the manufacturing flexibility and supply chain flexibility to some extent reinforce each other and at the same time can be substituted for one another. This observation is important from the fact that building and exploiting these flexibility types may require completely different approaches. Under certain conditions one may be more cost effective than the other. Thus, this would give an option to select a more appropriate type of flexibility. Similarly the diminishing benefits of combined flexibility at higher flexibility levels indicate the need to arrive at judicious levels of flexibility to balance the penalties and benefits. Combined influence of manufacturing flexibility and supply chain flexibility on the ability of a supply chain to absorb volume and mix changes. As a part of these studies, we have carried out analysis of three way interactions between flexibility, volume change and the corresponding lead-time variation, and also between flexibility, mix change and the corresponding lead-time variations. These studies indicated that, similar to the case of lead-time reduction, the combined effect of manufacturing flexibility and supply chain flexibility is more beneficial Studies in Informatics and Control, Vol.12, no.1, march 2003 13
  14. 14. in absorbing the volume and mix changes as compared to their individual influences. This benefit also is observed to diminishing with increasing levels of flexibility. At lower levels of flexibility the combined influence is much greater than the individual influences. Conclusions There is a crucial need towards enterprise modeling of supply chains with explicit modeling of flexibility and its impact on multiple entity flows. This is especially important for supply chains that involve modern manufacturing systems as a key component. These systems possess flexibility and suitable control systems to effectively exploit it. This paper discussed the development and study of one such enterprise model. Simulation of this model was aimed at understanding the impact of flexibility on the lead time performance of the supply chain. The study focused on flexibility at two levels; the flexibility at manufacturing system level and the flexibility at supply chain level. The flexibility at manufacturing level involves the ability of a given product type of being able to be manufactured by more than one machines, whereas the flexibility at supply chain level involves the ability of a given product type of being able to be handled by more than one supply chain. The impact of these two flexibility types on the lead-time performance of supply chain has been studied with the help of simulation experimentation. For this purpose a hierarchical simulation model has been developed based on the supply chain operations reference model. The studies indicated that both types of flexibility are equally important and to some extent they can substitute for each other. However, the combined effect of these two flexibility types appears to be more beneficial than their individual effects. This benefit is observed to be much higher at lower levels of flexibility. These observations are important for the designers and managers of flexible supply chain systems to arrive at judicious types and levels of flexibility to attain a given lead-time performance. This paper demonstrates that enterprise modeling of supply chains with a focus on flexibility offers an enormous potential. REFERENCES 1. ADRAIN MELLO, (2001), Bulletproof your supply chain, ZDNet Tech Update, 31 October 2001, http://techupdate.zdnet.com/techupdate/stories/main/0,14179,2821506,00.html. 2. ALBINO, V., GARAVELLI, A.C., (1998), Some effects of flexibility and dependability on cellular manufacturing system performance, Computers in Industrial Engineering, 35, 3-4, 491-494. 3. ALBINO, V., GARAVELLI, A.C., (1999), Limited flexibility in cellular manufacturing systems: A simulation study, International Journal of Production Economics, 60-61, 447-455. 4. BEACH, R., MUHLEMANN, A.P., PRICE, D.H.R., PATERSON, A., SHARP, J.A., 1998. A review of manufacturing flexibility. European Journal of Operational Research 122, 41-57. 5. BENJAAFAR, S., RAMAKRISHNAN, R., 1996. Modelling, measurement and evaluating of sequencing flexibility in manufacturing systems. International Journal of Production Research, 1195-1219. 6. BORENSTEIN, D., 2000. A directed acyclic graph representation of routing manufacturing flexibility. European Journal of Operational Research, 127, 78-93. 7. BOYER, K. K., KEONG, G. L, 1996. Manufacturing flexibility at the Plant Level. International Journal of Management Science, 24, 5, 495-510. 8. BROWNE, J., DUBOIS, D., RATHMILL, K., SETHI, S.P., STECKE, K.E., (1984), Classification of flexible manufacturing systems, The FMS Magazine, 114-117. 9. BUCKI, J., PESQUEUX, Y., 2000. Flexible workshop: about the concept of flexibility. International Journal of Agile Management Systems 2/1(2000), 62-70. 14 Studies in Informatics and Control, Vol.12, no.1, march 2003
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