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Performance improvement of supply chain processes by coordinated inventory and capacity management
ARTICLE IN PRESS
Int. J. Production Economics 108 (2007) 183–190 www.elsevier.com/locate/ijpe
Performance improvement of supply chain processes by coordinated inventory and capacity management Werner Jammernegga,b, Gerald Reinera,b,,1 a
Department of Information Systems and Operations, Vienna University of Economics and Business Administration, NordbergstraX e 15, 1090 Vienna, Austria b Enterprise Institute, Faculty of Economics, University of Neuchaˆtel, Rue de la Maladie`re 23, 2000 Neuchaˆtel, Switzerland Available online 31 December 2006
Abstract This study discusses the opportunities and challenges for improving the performance of supply chain processes by coordinated application of inventory management and capacity management. We illustrate our approach by a supplier company in the telecommunication and automotive industry (tier 2), where a manufacturer (production facility) is located in a country with low labor costs and high worker deployment flexibility. Using process simulation, we demonstrate how the coordinated application of methods from inventory management and capacity management result in improved performance measures of both intraorganizational (costs) and interorganizational (service level) objectives. r 2007 Elsevier B.V. All rights reserved. Keywords: Supply chain processes; Inventory management; Capacity management; Performance measurement; Simulation model
1. Introduction This study deals with performance measurement and improvement of supply chain processes. An important lever in this respect is inventory management. On the one hand, different types of inventory are necessary to buffer against market and operational uncertainties but, on the other hand, inventory is sometimes the result of inefficient management of the supply chain processes. Therefore, inventory management has been a focal point of managing supply chain processes. In this study, we will conduct a quantitative model-oriented research, based on empirical data. Corresponding author. 1
E-mail address: [email protected] (G. Reiner). http://www2.unine.ch/iene
Bertrand and Fransoo (2002) pointed out that the methodology of quantitative model-driven empirical research offers a great opportunity to further advance the theory. In general, quantitative modelbased empirical researches generate models of causal relationships between control variables and performance variables. These models are analyzed or tested. The primary concern of our research is to ensure that there is a model fit between observations and actions in reality and the model made of that reality, which is more or less not ideal. The research type can be descriptive or normative. In our case, we use the normative empirical quantitative research which is interesting for developing policies, strategies and actions to improve the current situation. First, we discuss in Section 2 the opportunities and challenges for improving the performance of supply chain processes by coordinated application
0925-5273/$ - see front matter r 2007 Elsevier B.V. All rights reserved. doi:10.1016/j.ijpe.2006.12.047
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of inventory management and capacity management. Second, we illustrate our approach in Section 3 by an internal supply chain process of a supplier company in the telecommunication and automotive industry. Finally, in Section 4 we make some concluding remarks and discuss further research opportunities.
2. Theoretical background 2.1. Trade-offs Traditionally, in the course of the management of supply chain processes, inventory management is challenging because it directly impacts both cost and service. Uncertain demand and uncertain supply and/or production cycle times make it necessary to hold inventory at certain positions in the supply chain to provide adequate service to the customers. As a consequence, increasing supply chain process inventories will increase customer service and revenue, but it comes at higher cost. Therefore, the management of supply chain processes has to resolve this trade-off by identifying possibilities of decreasing inventories by simultaneously improving customer service. A well-known management lever in this respect is risk pooling by different types of centralization or standardization, e.g. central warehouses, product commonalities, postponement strategies (cf., e.g., Tallon, 1993). In this way, it is usually possible to reduce inventory costs to a large extent. However, this reduction of inventory costs often is related with an increase in other costs, like transportation costs or production costs. In multistage inventory literature capacity aspects are usually disregarded, or capacity (transportation and production) is assumed to be constant (Biller et al., 2002; Dellaert and de Kok, 2004). If activities in the supply chain are postponed downstream in the process by shifting the customer order decoupling point upstream in the process, the order cycle time is affected. For example, if no additional resources are allocated to the postponed activities, the order cycle time and thus the delivery time for a customer will be increased. Therefore, additional resources (labor and/or equipment) have to be taken into account for the evaluation of such process changes and the additional production costs have to be traded off with the reduction in inventory costs.
Basically, the trade-off between inventory cost reduction and increased cost for resources depends on the positioning of the customer order decoupling point (CODP) in the supply chain process (push/ pull boundary). In the case of make-to-stock (MTS) production, the decoupling point is at the finished goods inventory, whereas in the case of make-toorder (MTO) production, it is located at the raw material inventory. If only a part of the production is carried out after the arrival of a customer order we speak about assemble-to-order (ATO) production. In ATO production, the production steps upstream the decoupling point are performed in MTS mode (forecast driven), and the downstream steps are made to order (demand driven) (van der Vorst et al., 1998). Olhager (2003) identified two major factors that affect the strategic positioning of the order decoupling point, the production to delivery lead time ratio and the relative demand volatility (standard deviation of demand relative to the average demand). Clearly, if the production lead time is larger than the delivery time of a customer order, then MTO production is not possible because of poor customer service. On the other hand, MTS production, in case of many finished goods, is not efficient because of high inventory cost. The high inventories are necessary to achieve the promised level of customer service, for MTS production mainly expressed by the fill rate. For the purpose of our research work, it is necessary to be precise in the usage of the terms cycle time and lead time. Cycle time is defined as the time span, an individual flow unit takes to traverse a process from entering to leaving. One example is production cycle time, the actual cycle time between commencement and completion of a manufacturing process, as it applies to MTS products, order fulfillment cycle time, actual cycle time from customer order origination to customer order receipt, i.e. all activities from the decoupling point downstream to the customer. In contrast, lead time is specified by the management and it is used to indicate the maximum allowable cycle time for an entity. One example is production lead time, the time allowed on the manufacturing process (Hopp and Spearman, 2000). Delivery time is the negotiated or promised time to fill a customer order from start to finish. The delivery time is composed of the average order fulfillment cycle time and the safety time, which mainly depends on the promised
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delivery performance for MTO and ATO production (see Eq. (1)). Supply chain performance improvement is measured by reduced total costs (transport, inventory carrying and resources), as well as improved customer service (delivery performance). Now consider the following situation that is very often observed in praxis: The supply chain manager negotiates with customers the delivery time and the level of customer service. For MTO and ATO production, delivery performance (percentage of orders fulfilled within the promised delivery time (or due date)) is used as measure of delivery reliability. Therefore, the manager has to decide about the most efficient type of production, i.e. MTS, MTO or ATO. In the case of MTO or ATO production, the trade-off between inventory cost and capacity cost has to be considered. The reduction of inventory cost can be increased by exploiting part commonalities by positioning the push/pull boundary such that several finished goods can be produced from standardized parts, the so-called product platforms in automobile industry (Desai et al., 2001). On the contrary, for the downstream-located production activities, additional resources (people, equipment) will be necessary to be able to deliver the customer order within the specified time and with the specified service. Thus, the manager must determine the safety capacity such that the cycle time of the order fulfillment process is not larger than the delivery time. Usually, the order fulfillment cycle time will vary owing to market risks like varying demand and owing to operational risks like equipment breakdowns, employee absenteeism or defective products. As a consequence, the quoted delivery time for a customer Td depends on the mean order cycle time X¯ , its variability represented by the standard deviation sp and the promised delivery performance d and is specified by T d XX¯ þ k sp ,
(1)
ProbðX pkÞ ¼ d,
(2)
where k is the d-quantile of the order fulfillment cycle time X and T d X¯ represents its minimal safety time. In general, the probability distribution of the order fulfillment cycle time X cannot be determined in an analytical way. Even for a one-stage production process only approximate values of the parameters of X can be computed, like the mean-order
185
cycle time that is composed of a variability component, a utilization component and a time (capacity) component (Hopp and Spearman, 2000). 2.2. Values of performance measures Thus, for general multi-stage production and supply chain processes, stochastic simulation can be employed to determine the empirical distribution of the order cycle time X. In this way, it is possible to investigate whether the resource capacities are in accordance with the delivery contract. By means of formulae (1) and (2) the supply chain manager is able to check whether the contract parameters delivery time and delivery performance are fulfilled. The evaluation of existing process designs and the comparison of alternative configurations require concrete values of different performance measures. In the case of existing processes, these values could be obtained from the supply chain partners’ performance measurement systems. However, in many instances the desired performance measures are not provided by these systems. In the case of alternative process configurations, the values of performance measures are never available a priori as existing data. If not available, these values can be calculated, estimated or obtained by simulation. The possibility of exact calculations is limited by the complexity of the problem, and estimation usually is too imprecise. Therefore, dynamic, stochastic computer simulation can be utilized to deliver the required input for the evaluation of supply chains. As already mentioned, risk is an essential factor for supply chain process evaluation. Stochastic simulation can deal with random variables and it can generate not only mean values of performance measures, but it also gives useful information about their probabilistic distributions. For an overview of the use of simulation in supply chain management, refer to Wyland et al. (2000). Using an international supply chain from the electronics industry, we demonstrate in the next section how to apply this procedure of coordinated inventory and capacity management to improve the performance of a supply chain process. 3. Illustration First, we will give a short introduction to the company considered. The example refers to the current situation of a three-stage supplier network of a company in the telecommunication and
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automotive industry (tier 2). The telecommunication industry is an example of a heterogeneous industry where the clockspeed has increased considerably. This industry is also characterized by outsourcing of manufacturing and R&D to suppliers, short product life cycles, compressed time-tomarket and increased demand for on-time deliveries and generally short lead times (see Agrell et al., 2004). The automotive industry shows a similar tendency, but R&D and final assembly are mostly conducted by the original equipment manufacturers (OEM). But in the not-so-distinct future also, in the automotive industry a power shift from OEMs to suppliers will be expected (see Fine, 2000; Biller et al., 2002). The company under consideration is faced with keen competition and, consequently, it is forced to keep its costs low (company strategy). In addition, this supplier has to operate in a supply chain that can be characterized as agile (supply chain strategy), i.e. supply has to react quickly and flexibly (service level is the market winner) to the changing demand (see Hill, 1994; Christopher and Towill, 2000).
raw material storage
Manufacturer I
3.1. Process description The internal supply chain process includes three stages (manufacturer I in a Western European country, manufacturer II in an Eastern European country and the distribution center in a Western European country). Originally, the whole supply chain process (see Fig. 1) is forecast driven because the average cycle time from the raw material storage and component storage manufacturer II to departure of the finished products to the customer is longer than the requested delivery time (Td ¼ 5 days). The customer provides rolling forecasts every week for the next 6 weeks on a weekly basis. The frozen horizon is one week. The problem is that the forecasts differ from the quantity that is ordered for the frozen horizon. This causes a high variability of the production cycle times and has to be compensated by high safety stocks in the finished goods storage at the distribution center. A possibility to reduce this variability could be to change a part of the manufacturing process from push to pull.
component storage automated production
shipping
raw material and component storage
Manufacturer II
finished good storage
manual production
packaging
shipping
finished good storage
Distribution center
customer places order
fill order
shipping
Fig. 1. Original supply chain process flow.
customer
ARTICLE IN PRESS W. Jammernegg, G. Reiner / Int. J. Production Economics 108 (2007) 183–190 raw material storage
Manufacturer I
187
component storage automated production
shipping
customer places order
raw material and component storage
Manufacturer II
finished good storage manual production
packaging
shipping
finished good storage
Distribution center
shipping
customer
work in process Fig. 2. Improved supply chain process flow.
Fig. 2 illustrates this alternative that will be analyzed in more detail. The production mode will be changed to ATO production. The automated production process and the shipping to manufacturer II are still forecast driven. The new CODP is upstream the manual production (manufacturer II). Downstream the CODP, the process is demand (customer) driven. The goal is to reduce inventories and simultaneously fulfill the customer requirements. In the case of ATO production, the order fulfillment cycle times is the sum of the production cycle time (manufacturer II), and the transport cycle time to the distribution center up to the departure of the finished goods to the customers and has to be executed within 5 days. Actually, the production cycle time at manufacturer II and the shipping cycle time from manufacturer II to the distribution center are too long. To reach the above-described goal, it is necessary to deploy safety capacity (labor and transportation) that should reduce the mean production cycle time. To evaluate this alternative, a simulation study is developed.
3.2. A simulation study In detail, we built a discrete event simulation model of the order fulfillment process (production manufacturer II to the distribution center) for a selected product (high runner) to explore the real situation (see Fig. 2). The purpose of this model is to conduct experiments regarding process alternatives. The model can be used to measure relevant performance indicators. We conducted 25 simulation runs with a run length of 9 months to evaluate the effects of the supply chain process alternative (ATO) scenarios. We show the results of the following most relevant scenarios (see Table 1): Scenario SC 1: Current situation (see Fig. 1), the information about the current situation is based on real data and estimates from the plant management. Scenario SC 2– 7: We moved the decoupling point upstream the supply chain. The production process downstream the CODP is now customer driven (see Fig. 2).
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188 Table 1 Simulation results
Resources Number of employees— manufacturer II Average number of scheduled truck shipping Number of trucks per shipping (scheduling) Performance measures b-service level (fill rate) of finished good storage—distribution center Delivery performance (percentage of orders fulfilled within 5 days)—mean and (SD) Work in process pallets (manual production manufacturer II to distribution center)—mean and (SD)
SC 1
SC 2
SC 3
SC 4
SC 5
SC 6
SC 7
15
15
30
38
42
30
30
2.3
2.3
2.3
2.3
2.3
3.5
7
2
2
2
2
2
2
1
99.3%
—
—
—
—
—
—
—
25.2% (0.8%)
60.6% (1.2%)
66.4% (1.3%)
66.6% (1.1%)
81.5% (0.9%)
99.5% (0.3%)
25.7
8.15 (5.45)
6.59 (4.99)
6.32 (4.94)
6.31 (4.93)
6.04 (4.66)
5.5 (4.58)
Scenario SC 2: The number of employees and the number (2 trucks per shipping) and schedule (every third day ) approximately 2.3 times per week) of the shipping resources (trucks) are not changed. Scenario SC 3– 5: The number of employees is increased. These employees are allocated to production and packaging activities at manufacturer II. The number and schedule of the shipping resources (trucks) are not changed (see scenario SC 1 and SC 2). Scenario SC 6: The number of employees is increased to 30. These employees are allocated to production and packaging activities at manufacturer II. The schedule (every second day ) approximately 3.5 times per week) of the shipping resources (trucks) is changed. Scenario SC 7: The number of employees is increased to 30. These employees are allocated to production and packaging activities at manufacturer II. The number (1 truck per shipping) and schedule (every day ) 7 times per week) of the shipping resources (trucks) are changed. As one can see from Table 2, the ATO production process can be used to save inventory carrying costs (the sum of opportunity cost, shrinkage, insurance and taxes, total obsolescence cost) by an average work in process (WIP) reduction of at least 68.6% (scenario SC 2) and up to 78.6% (scenario SC 7). As shown in Section 2, the relocation of the CODP is possible only if additional resources are used (e.g., employees and
trucks). It is necessary to fulfill the customer requirement that is represented by delivery performance quoting the percentage of orders fulfilled within 5 days. The b-service level (fill rate) can be used as lower bound for the delivery performance. The labor costs of manufacturer II are relatively low and employees are, to a large extent, flexible. This is the characteristic of economies in several East European countries, India and China. In particular, for manufacturer II, the proportion of the labor costs is not relevant. Qualified labor is easily accessible more or less without fixed cost, which is why labor cost is not included in our simulation model. In advanced market economies, the simulation model would show different results because labor cost has to be taken into consideration owing to the low worker deployment flexibility. Our simulation results show that using only additional labor capacity (more than 30) is not sufficient (see scenarios SC 2, SC 3, SC 4 and SC 5) because there is a saturation point for the delivery performance of approximately 66%. Therefore, it is necessary to use additional transportation resources (scenarios SC 6 and SC 7) that are much more expensive. There is no difference between scenarios SC 6 and SC 7 in the total number of trucks used (7 trucks); only the schedule (frequency) differs. In our case, scenario SC 7 is the only alternative that fulfills the delivery performance restriction, which mainly results from a more continuous shipping schedule (one truck shipping every day).
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189
Table 2 Financial results Costs
SC 1 (%)
SC 2 (%)
SC 3 (%)
SC 4 (%)
SC 5 (%)
SC 6 (%)
SC 7 (%)
Shipping costs Inventory carrying costs— mean and (SD) Total costs (transport+inventory carrying)—mean and (SD)
100 100
100 32 (21)
100 26 (19)
100 25 (19)
100 25 (19)
150 24 (18)
150 21 (18)
100
68 (10)
65 (9)
64 (9)
64 (9)
90 (9)
89 (8)
We want to show primarily how the relevant performance measures (delivery performance, WIP) can be used to identify appropriate process design alternatives and input parameters (average number of scheduled trucks, number of trucks per shipping, number of employees) for further decision-making. Of course, the traditional financial evaluation should be the next activity. As explained above only the shipping costs and inventory carrying costs are used for the final financial evaluation. The results of the financial evaluation presented in Table 2 display that the ATO production leads to a reduction of the total cost. Scenario SC 7 fulfills the customer requirement and also leads to a reduction of the total costs (shipping and inventory carrying) of 11% on average.
under consideration of capacity aspects and more or less identical customer service level. Future research activities should take further potentials of ATO production processes into account. An additional reduction of inventory cost can be realized by exploiting part commonalties (e.g. raw material and component storage–manufacturer II) such that different finished goods can be produced from standardized parts. Furthermore, the geographical layout of the supplier network might be thought about. The next research activity will be an extension of the supply chain process analysis under consideration to an additional network partner in Asia.
4. Conclusion
Agrell, P., Lindroth, R., Norrman, A., 2004. Risk, information and incentives in telecom supply chains. International Journal of Production Economics 90 (1), 1–16. Bertrand, J.W.M., Fransoo, J.C., 2002. Modelling and simulation: Operations management research methodologies using quantitative modeling. International Journal of Operations and Production Management 22 (2), 241–264. Biller, S., Bish, E.K., Muriel, A., 2002. Impact of manufacturing flexibility on supply chain performance in the automotive industry. In: Song, J.-S., Yao, D.D. (Eds.), Supply Chain Structures—Coordination, Information and Optimization. Kluwer Academic Publishers, Boston. Christopher, M., Towill, D.R., 2000. Supply chain migration from lean and functional to agile and customised. International Journal of Supply Chain Management 5 (4), 206–213. Dellaert, N., de Kok, T., 2004. Integrating resources and production decisions in a simple multi-stage assembly system. International Journal of Production Economics 90 (3), 281–294. Desai, P., Kekre, S., Radhakrishnan, S., Srinivasan, K., 2001. Product differentiation and commonality in design: Balancing revenue and cost drivers. Management Science 47 (1), 37–51. Fine, C.H., 2000. Clockspeed-based strategies for supply chain design. Production and Operations Management 9 (3), 213–221. Hill, T., 1994. Manufacturing Strategy: Text and Cases, second ed. Irwin Inc., Chicago. Hopp, W.J., Spearman, M.L., 2000. Factory Physics—Foundations of Manufacturing Management, second ed. Irwin— McGraw-Hill, Boston.
We discussed, in this paper, the opportunities and challenges for improving the performance of supply chain processes by coordinated application of inventory management and capacity management. Our approach was illustrated by the internal supply chain process of a tier 2 supplier company in the telecommunication and automotive industry. Using process simulation we demonstrate how the coordinated application of methods from inventory management and capacity management results in reduced inventory carrying costs and improved delivery performance. We performed a simulation study of a three-stage supplier network of a company in the telecommunication and automotive industry (tier 2), where one manufacturer (production facility) is situated in a country with low labor costs and high worker deployment flexibility. The company has to operate in a supply chain that can be characterized as agile. The results shows that a change from MTS to ATO production leads to reduction of total costs (shipping and inventory carrying) of 11% on average
References
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Olhager, J., 2003. Strategic positioning of the order penetration point. International Journal of Production Economics 85 (3), 319–329. Tallon, W.J., 1993. The impact of inventory centralization on aggregate safety stock: The variable supply lead time case. Journal of Business Logistics 14 (1), 185–203.
Van der Vorst, J.G.A.J., Beulens, A.J.M., De Wit, W., Van Beek, P., 1998. Supply chain management in food chains: Improving performance by reducing uncertainty. International Transactions in Operational Research 5 (6), 487–499. Wyland, B., Buxton, K., Fuqua, B., 2000. Simulating the Supply Chain. IIE Solutions 32 (1), 37–42.
Int. J. Production Economics 108 (2007) 183–190 www.elsevier.com/locate/ijpe
Performance improvement of supply chain processes by coordinated inventory and capacity management Werner Jammernegga,b, Gerald Reinera,b,,1 a
Department of Information Systems and Operations, Vienna University of Economics and Business Administration, NordbergstraX e 15, 1090 Vienna, Austria b Enterprise Institute, Faculty of Economics, University of Neuchaˆtel, Rue de la Maladie`re 23, 2000 Neuchaˆtel, Switzerland Available online 31 December 2006
Abstract This study discusses the opportunities and challenges for improving the performance of supply chain processes by coordinated application of inventory management and capacity management. We illustrate our approach by a supplier company in the telecommunication and automotive industry (tier 2), where a manufacturer (production facility) is located in a country with low labor costs and high worker deployment flexibility. Using process simulation, we demonstrate how the coordinated application of methods from inventory management and capacity management result in improved performance measures of both intraorganizational (costs) and interorganizational (service level) objectives. r 2007 Elsevier B.V. All rights reserved. Keywords: Supply chain processes; Inventory management; Capacity management; Performance measurement; Simulation model
1. Introduction This study deals with performance measurement and improvement of supply chain processes. An important lever in this respect is inventory management. On the one hand, different types of inventory are necessary to buffer against market and operational uncertainties but, on the other hand, inventory is sometimes the result of inefficient management of the supply chain processes. Therefore, inventory management has been a focal point of managing supply chain processes. In this study, we will conduct a quantitative model-oriented research, based on empirical data. Corresponding author. 1
E-mail address: [email protected] (G. Reiner). http://www2.unine.ch/iene
Bertrand and Fransoo (2002) pointed out that the methodology of quantitative model-driven empirical research offers a great opportunity to further advance the theory. In general, quantitative modelbased empirical researches generate models of causal relationships between control variables and performance variables. These models are analyzed or tested. The primary concern of our research is to ensure that there is a model fit between observations and actions in reality and the model made of that reality, which is more or less not ideal. The research type can be descriptive or normative. In our case, we use the normative empirical quantitative research which is interesting for developing policies, strategies and actions to improve the current situation. First, we discuss in Section 2 the opportunities and challenges for improving the performance of supply chain processes by coordinated application
0925-5273/$ - see front matter r 2007 Elsevier B.V. All rights reserved. doi:10.1016/j.ijpe.2006.12.047
ARTICLE IN PRESS 184
W. Jammernegg, G. Reiner / Int. J. Production Economics 108 (2007) 183–190
of inventory management and capacity management. Second, we illustrate our approach in Section 3 by an internal supply chain process of a supplier company in the telecommunication and automotive industry. Finally, in Section 4 we make some concluding remarks and discuss further research opportunities.
2. Theoretical background 2.1. Trade-offs Traditionally, in the course of the management of supply chain processes, inventory management is challenging because it directly impacts both cost and service. Uncertain demand and uncertain supply and/or production cycle times make it necessary to hold inventory at certain positions in the supply chain to provide adequate service to the customers. As a consequence, increasing supply chain process inventories will increase customer service and revenue, but it comes at higher cost. Therefore, the management of supply chain processes has to resolve this trade-off by identifying possibilities of decreasing inventories by simultaneously improving customer service. A well-known management lever in this respect is risk pooling by different types of centralization or standardization, e.g. central warehouses, product commonalities, postponement strategies (cf., e.g., Tallon, 1993). In this way, it is usually possible to reduce inventory costs to a large extent. However, this reduction of inventory costs often is related with an increase in other costs, like transportation costs or production costs. In multistage inventory literature capacity aspects are usually disregarded, or capacity (transportation and production) is assumed to be constant (Biller et al., 2002; Dellaert and de Kok, 2004). If activities in the supply chain are postponed downstream in the process by shifting the customer order decoupling point upstream in the process, the order cycle time is affected. For example, if no additional resources are allocated to the postponed activities, the order cycle time and thus the delivery time for a customer will be increased. Therefore, additional resources (labor and/or equipment) have to be taken into account for the evaluation of such process changes and the additional production costs have to be traded off with the reduction in inventory costs.
Basically, the trade-off between inventory cost reduction and increased cost for resources depends on the positioning of the customer order decoupling point (CODP) in the supply chain process (push/ pull boundary). In the case of make-to-stock (MTS) production, the decoupling point is at the finished goods inventory, whereas in the case of make-toorder (MTO) production, it is located at the raw material inventory. If only a part of the production is carried out after the arrival of a customer order we speak about assemble-to-order (ATO) production. In ATO production, the production steps upstream the decoupling point are performed in MTS mode (forecast driven), and the downstream steps are made to order (demand driven) (van der Vorst et al., 1998). Olhager (2003) identified two major factors that affect the strategic positioning of the order decoupling point, the production to delivery lead time ratio and the relative demand volatility (standard deviation of demand relative to the average demand). Clearly, if the production lead time is larger than the delivery time of a customer order, then MTO production is not possible because of poor customer service. On the other hand, MTS production, in case of many finished goods, is not efficient because of high inventory cost. The high inventories are necessary to achieve the promised level of customer service, for MTS production mainly expressed by the fill rate. For the purpose of our research work, it is necessary to be precise in the usage of the terms cycle time and lead time. Cycle time is defined as the time span, an individual flow unit takes to traverse a process from entering to leaving. One example is production cycle time, the actual cycle time between commencement and completion of a manufacturing process, as it applies to MTS products, order fulfillment cycle time, actual cycle time from customer order origination to customer order receipt, i.e. all activities from the decoupling point downstream to the customer. In contrast, lead time is specified by the management and it is used to indicate the maximum allowable cycle time for an entity. One example is production lead time, the time allowed on the manufacturing process (Hopp and Spearman, 2000). Delivery time is the negotiated or promised time to fill a customer order from start to finish. The delivery time is composed of the average order fulfillment cycle time and the safety time, which mainly depends on the promised
ARTICLE IN PRESS W. Jammernegg, G. Reiner / Int. J. Production Economics 108 (2007) 183–190
delivery performance for MTO and ATO production (see Eq. (1)). Supply chain performance improvement is measured by reduced total costs (transport, inventory carrying and resources), as well as improved customer service (delivery performance). Now consider the following situation that is very often observed in praxis: The supply chain manager negotiates with customers the delivery time and the level of customer service. For MTO and ATO production, delivery performance (percentage of orders fulfilled within the promised delivery time (or due date)) is used as measure of delivery reliability. Therefore, the manager has to decide about the most efficient type of production, i.e. MTS, MTO or ATO. In the case of MTO or ATO production, the trade-off between inventory cost and capacity cost has to be considered. The reduction of inventory cost can be increased by exploiting part commonalities by positioning the push/pull boundary such that several finished goods can be produced from standardized parts, the so-called product platforms in automobile industry (Desai et al., 2001). On the contrary, for the downstream-located production activities, additional resources (people, equipment) will be necessary to be able to deliver the customer order within the specified time and with the specified service. Thus, the manager must determine the safety capacity such that the cycle time of the order fulfillment process is not larger than the delivery time. Usually, the order fulfillment cycle time will vary owing to market risks like varying demand and owing to operational risks like equipment breakdowns, employee absenteeism or defective products. As a consequence, the quoted delivery time for a customer Td depends on the mean order cycle time X¯ , its variability represented by the standard deviation sp and the promised delivery performance d and is specified by T d XX¯ þ k sp ,
(1)
ProbðX pkÞ ¼ d,
(2)
where k is the d-quantile of the order fulfillment cycle time X and T d X¯ represents its minimal safety time. In general, the probability distribution of the order fulfillment cycle time X cannot be determined in an analytical way. Even for a one-stage production process only approximate values of the parameters of X can be computed, like the mean-order
185
cycle time that is composed of a variability component, a utilization component and a time (capacity) component (Hopp and Spearman, 2000). 2.2. Values of performance measures Thus, for general multi-stage production and supply chain processes, stochastic simulation can be employed to determine the empirical distribution of the order cycle time X. In this way, it is possible to investigate whether the resource capacities are in accordance with the delivery contract. By means of formulae (1) and (2) the supply chain manager is able to check whether the contract parameters delivery time and delivery performance are fulfilled. The evaluation of existing process designs and the comparison of alternative configurations require concrete values of different performance measures. In the case of existing processes, these values could be obtained from the supply chain partners’ performance measurement systems. However, in many instances the desired performance measures are not provided by these systems. In the case of alternative process configurations, the values of performance measures are never available a priori as existing data. If not available, these values can be calculated, estimated or obtained by simulation. The possibility of exact calculations is limited by the complexity of the problem, and estimation usually is too imprecise. Therefore, dynamic, stochastic computer simulation can be utilized to deliver the required input for the evaluation of supply chains. As already mentioned, risk is an essential factor for supply chain process evaluation. Stochastic simulation can deal with random variables and it can generate not only mean values of performance measures, but it also gives useful information about their probabilistic distributions. For an overview of the use of simulation in supply chain management, refer to Wyland et al. (2000). Using an international supply chain from the electronics industry, we demonstrate in the next section how to apply this procedure of coordinated inventory and capacity management to improve the performance of a supply chain process. 3. Illustration First, we will give a short introduction to the company considered. The example refers to the current situation of a three-stage supplier network of a company in the telecommunication and
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automotive industry (tier 2). The telecommunication industry is an example of a heterogeneous industry where the clockspeed has increased considerably. This industry is also characterized by outsourcing of manufacturing and R&D to suppliers, short product life cycles, compressed time-tomarket and increased demand for on-time deliveries and generally short lead times (see Agrell et al., 2004). The automotive industry shows a similar tendency, but R&D and final assembly are mostly conducted by the original equipment manufacturers (OEM). But in the not-so-distinct future also, in the automotive industry a power shift from OEMs to suppliers will be expected (see Fine, 2000; Biller et al., 2002). The company under consideration is faced with keen competition and, consequently, it is forced to keep its costs low (company strategy). In addition, this supplier has to operate in a supply chain that can be characterized as agile (supply chain strategy), i.e. supply has to react quickly and flexibly (service level is the market winner) to the changing demand (see Hill, 1994; Christopher and Towill, 2000).
raw material storage
Manufacturer I
3.1. Process description The internal supply chain process includes three stages (manufacturer I in a Western European country, manufacturer II in an Eastern European country and the distribution center in a Western European country). Originally, the whole supply chain process (see Fig. 1) is forecast driven because the average cycle time from the raw material storage and component storage manufacturer II to departure of the finished products to the customer is longer than the requested delivery time (Td ¼ 5 days). The customer provides rolling forecasts every week for the next 6 weeks on a weekly basis. The frozen horizon is one week. The problem is that the forecasts differ from the quantity that is ordered for the frozen horizon. This causes a high variability of the production cycle times and has to be compensated by high safety stocks in the finished goods storage at the distribution center. A possibility to reduce this variability could be to change a part of the manufacturing process from push to pull.
component storage automated production
shipping
raw material and component storage
Manufacturer II
finished good storage
manual production
packaging
shipping
finished good storage
Distribution center
customer places order
fill order
shipping
Fig. 1. Original supply chain process flow.
customer
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Manufacturer I
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component storage automated production
shipping
customer places order
raw material and component storage
Manufacturer II
finished good storage manual production
packaging
shipping
finished good storage
Distribution center
shipping
customer
work in process Fig. 2. Improved supply chain process flow.
Fig. 2 illustrates this alternative that will be analyzed in more detail. The production mode will be changed to ATO production. The automated production process and the shipping to manufacturer II are still forecast driven. The new CODP is upstream the manual production (manufacturer II). Downstream the CODP, the process is demand (customer) driven. The goal is to reduce inventories and simultaneously fulfill the customer requirements. In the case of ATO production, the order fulfillment cycle times is the sum of the production cycle time (manufacturer II), and the transport cycle time to the distribution center up to the departure of the finished goods to the customers and has to be executed within 5 days. Actually, the production cycle time at manufacturer II and the shipping cycle time from manufacturer II to the distribution center are too long. To reach the above-described goal, it is necessary to deploy safety capacity (labor and transportation) that should reduce the mean production cycle time. To evaluate this alternative, a simulation study is developed.
3.2. A simulation study In detail, we built a discrete event simulation model of the order fulfillment process (production manufacturer II to the distribution center) for a selected product (high runner) to explore the real situation (see Fig. 2). The purpose of this model is to conduct experiments regarding process alternatives. The model can be used to measure relevant performance indicators. We conducted 25 simulation runs with a run length of 9 months to evaluate the effects of the supply chain process alternative (ATO) scenarios. We show the results of the following most relevant scenarios (see Table 1): Scenario SC 1: Current situation (see Fig. 1), the information about the current situation is based on real data and estimates from the plant management. Scenario SC 2– 7: We moved the decoupling point upstream the supply chain. The production process downstream the CODP is now customer driven (see Fig. 2).
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188 Table 1 Simulation results
Resources Number of employees— manufacturer II Average number of scheduled truck shipping Number of trucks per shipping (scheduling) Performance measures b-service level (fill rate) of finished good storage—distribution center Delivery performance (percentage of orders fulfilled within 5 days)—mean and (SD) Work in process pallets (manual production manufacturer II to distribution center)—mean and (SD)
SC 1
SC 2
SC 3
SC 4
SC 5
SC 6
SC 7
15
15
30
38
42
30
30
2.3
2.3
2.3
2.3
2.3
3.5
7
2
2
2
2
2
2
1
99.3%
—
—
—
—
—
—
—
25.2% (0.8%)
60.6% (1.2%)
66.4% (1.3%)
66.6% (1.1%)
81.5% (0.9%)
99.5% (0.3%)
25.7
8.15 (5.45)
6.59 (4.99)
6.32 (4.94)
6.31 (4.93)
6.04 (4.66)
5.5 (4.58)
Scenario SC 2: The number of employees and the number (2 trucks per shipping) and schedule (every third day ) approximately 2.3 times per week) of the shipping resources (trucks) are not changed. Scenario SC 3– 5: The number of employees is increased. These employees are allocated to production and packaging activities at manufacturer II. The number and schedule of the shipping resources (trucks) are not changed (see scenario SC 1 and SC 2). Scenario SC 6: The number of employees is increased to 30. These employees are allocated to production and packaging activities at manufacturer II. The schedule (every second day ) approximately 3.5 times per week) of the shipping resources (trucks) is changed. Scenario SC 7: The number of employees is increased to 30. These employees are allocated to production and packaging activities at manufacturer II. The number (1 truck per shipping) and schedule (every day ) 7 times per week) of the shipping resources (trucks) are changed. As one can see from Table 2, the ATO production process can be used to save inventory carrying costs (the sum of opportunity cost, shrinkage, insurance and taxes, total obsolescence cost) by an average work in process (WIP) reduction of at least 68.6% (scenario SC 2) and up to 78.6% (scenario SC 7). As shown in Section 2, the relocation of the CODP is possible only if additional resources are used (e.g., employees and
trucks). It is necessary to fulfill the customer requirement that is represented by delivery performance quoting the percentage of orders fulfilled within 5 days. The b-service level (fill rate) can be used as lower bound for the delivery performance. The labor costs of manufacturer II are relatively low and employees are, to a large extent, flexible. This is the characteristic of economies in several East European countries, India and China. In particular, for manufacturer II, the proportion of the labor costs is not relevant. Qualified labor is easily accessible more or less without fixed cost, which is why labor cost is not included in our simulation model. In advanced market economies, the simulation model would show different results because labor cost has to be taken into consideration owing to the low worker deployment flexibility. Our simulation results show that using only additional labor capacity (more than 30) is not sufficient (see scenarios SC 2, SC 3, SC 4 and SC 5) because there is a saturation point for the delivery performance of approximately 66%. Therefore, it is necessary to use additional transportation resources (scenarios SC 6 and SC 7) that are much more expensive. There is no difference between scenarios SC 6 and SC 7 in the total number of trucks used (7 trucks); only the schedule (frequency) differs. In our case, scenario SC 7 is the only alternative that fulfills the delivery performance restriction, which mainly results from a more continuous shipping schedule (one truck shipping every day).
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Table 2 Financial results Costs
SC 1 (%)
SC 2 (%)
SC 3 (%)
SC 4 (%)
SC 5 (%)
SC 6 (%)
SC 7 (%)
Shipping costs Inventory carrying costs— mean and (SD) Total costs (transport+inventory carrying)—mean and (SD)
100 100
100 32 (21)
100 26 (19)
100 25 (19)
100 25 (19)
150 24 (18)
150 21 (18)
100
68 (10)
65 (9)
64 (9)
64 (9)
90 (9)
89 (8)
We want to show primarily how the relevant performance measures (delivery performance, WIP) can be used to identify appropriate process design alternatives and input parameters (average number of scheduled trucks, number of trucks per shipping, number of employees) for further decision-making. Of course, the traditional financial evaluation should be the next activity. As explained above only the shipping costs and inventory carrying costs are used for the final financial evaluation. The results of the financial evaluation presented in Table 2 display that the ATO production leads to a reduction of the total cost. Scenario SC 7 fulfills the customer requirement and also leads to a reduction of the total costs (shipping and inventory carrying) of 11% on average.
under consideration of capacity aspects and more or less identical customer service level. Future research activities should take further potentials of ATO production processes into account. An additional reduction of inventory cost can be realized by exploiting part commonalties (e.g. raw material and component storage–manufacturer II) such that different finished goods can be produced from standardized parts. Furthermore, the geographical layout of the supplier network might be thought about. The next research activity will be an extension of the supply chain process analysis under consideration to an additional network partner in Asia.
4. Conclusion
Agrell, P., Lindroth, R., Norrman, A., 2004. Risk, information and incentives in telecom supply chains. International Journal of Production Economics 90 (1), 1–16. Bertrand, J.W.M., Fransoo, J.C., 2002. Modelling and simulation: Operations management research methodologies using quantitative modeling. International Journal of Operations and Production Management 22 (2), 241–264. Biller, S., Bish, E.K., Muriel, A., 2002. Impact of manufacturing flexibility on supply chain performance in the automotive industry. In: Song, J.-S., Yao, D.D. (Eds.), Supply Chain Structures—Coordination, Information and Optimization. Kluwer Academic Publishers, Boston. Christopher, M., Towill, D.R., 2000. Supply chain migration from lean and functional to agile and customised. International Journal of Supply Chain Management 5 (4), 206–213. Dellaert, N., de Kok, T., 2004. Integrating resources and production decisions in a simple multi-stage assembly system. International Journal of Production Economics 90 (3), 281–294. Desai, P., Kekre, S., Radhakrishnan, S., Srinivasan, K., 2001. Product differentiation and commonality in design: Balancing revenue and cost drivers. Management Science 47 (1), 37–51. Fine, C.H., 2000. Clockspeed-based strategies for supply chain design. Production and Operations Management 9 (3), 213–221. Hill, T., 1994. Manufacturing Strategy: Text and Cases, second ed. Irwin Inc., Chicago. Hopp, W.J., Spearman, M.L., 2000. Factory Physics—Foundations of Manufacturing Management, second ed. Irwin— McGraw-Hill, Boston.
We discussed, in this paper, the opportunities and challenges for improving the performance of supply chain processes by coordinated application of inventory management and capacity management. Our approach was illustrated by the internal supply chain process of a tier 2 supplier company in the telecommunication and automotive industry. Using process simulation we demonstrate how the coordinated application of methods from inventory management and capacity management results in reduced inventory carrying costs and improved delivery performance. We performed a simulation study of a three-stage supplier network of a company in the telecommunication and automotive industry (tier 2), where one manufacturer (production facility) is situated in a country with low labor costs and high worker deployment flexibility. The company has to operate in a supply chain that can be characterized as agile. The results shows that a change from MTS to ATO production leads to reduction of total costs (shipping and inventory carrying) of 11% on average
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