Journal of Food Engineering 70 (2005) 351 364 www.elsevier.com/locate/jfoodeng A system dynamics modeling framework for the strategic supply chain management of food chains Patroklos Georgiadis *, Dimitrios Vlachos, Eleftherios Iakovou Department of Mechanical Engineering, Aristotle University of Thessaloniki, Division of Industrial Management, P.O. Box 461, Thessaloniki 541 24, Greece Received 3 October 2003; received in revised form 22 December 2003; accepted 23 June 2004 Available online 25 November 2004 Abstract The need for holistic modeling efforts that capture the extended supply chain enterprise at a strategic level has been clearly recognized first by industry and recently by academia. Strategic decision-makers need comprehensive models to guide them in efficient decision-making that increases the profitability of the entire chain. The determination of optimal network configuration, inventory management policies, supply contracts, distribution strategies, supply chain integration, outsourcing and procurement strategies, product design, and information technology are prime examples of strategic decision-making that affect the long-term profitability of the entire supply chain. In this work, we adopt the system dynamics methodology as a modeling and analysis tool to tackle strategic issues for food supply chains. We present guidelines for the methodology and present its development for the strategic modeling of single and multi-echelon supply chains. Consequently, we analyze in depth a key issue of strategic supply chain management, that of long-term capacity planning. Specifically, we examine capacity planning policies for a food supply chain with transient flows due to market parameters/constraints. Finally, we demonstrate the applicability of the developed methodology on a multi-echelon network of a major Greek fast food chain. Ó 2004 Elsevier Ltd. All rights reserved. Keywords: System dynamics; Supply chain management; Food logistics; Capacity planning 1. Introduction * Corresponding author. Tel.: +30 2310 996046; fax: +30 2310 996018. E-mail address: geopat@eng.auth.gr (P. Georgiadis). Supply chain management (SCM) has been met with increased recognition during the last decade both by academicians as well as practitioners. However, despite its significant advances and dramatic improvements in information technology (IT), the discipline of SCM remains incapable of addressing satisfactorily many practical real-world challenges. One key reason for this inadequacy is the interdependencies among various operations and the autonomous partners across the chain, which renders all traditional myopic models invalid (Iakovou, 2001; Tayur, Ganeshan, & Magazine, 1999). Rather, strategic decision-makers need comprehensive models to guide them in the decision-making process so as to increase the total profitability of the chain. A critical shortcoming of most of the existing strategic models is their inability to take into account the impact of regulatory legislation within todayõs already volatile environment. This is particularly important for food supply chains because of their unique characteristics, stemming among others from product storage and transportation specifications (Hobbs & Young, 2000; Van der Vorst, Beulens, De Wit, & Van Beek, 1998). For example, product perishability creates uncertainty for the buyer with respect to product quality, safety and reliability (i.e. quantity) of supply. It creates 0260-8774/$ - see front matter Ó 2004 Elsevier Ltd. All rights reserved. doi:10.1016/j.jfoodeng.2004.06.030
352 P. Georgiadis et al. / Journal of Food Engineering 70 (2005) 351 364 uncertainty for the seller in locating a buyer, as perishable products must be moved promptly to the marketplace to avoid deterioration, leaving sellers unable to store the products awaiting favorable market conditions. This further leads to the need for frequent deliveries, through dedicated modes of transportation (e.g. refrigerators). Moreover, food products usually exhibit high seasonality in raw materials availability and in end-products demand, and therefore they need efficiently designed storage facilities to further ensure their quality. In addition, food safety issues have profound ramifications on the design of the supply chain. For instance, proper monitoring and response to food safety problems requires the ability to trace back small lots, from retailer to processor or even back to the supplying farm. Another feature of food chains is that few products are transformed from commodity to differentiated branded foods, while others undergo packaging but remain essentially intact in character. All these characteristics along with the dynamically evolving legislative framework further hinder the task of managing efficiently food supply chains. The motivation behind this research is (i) to facilitate the decision-making process for capacity planning of multi-echelon supply chains in such uncertain environments by studying the long-term behavior of supply chains and (ii) to further offer a generic methodological framework that could address a wider spectrum of strategic SCM related problems. Most of the standard methodologies for the analysis of supply chains study the steady state of the system, i.e. they assume that all transient phenomena have been diminished. This assumption may be valid in several supply chains, where product demand exhibits a smooth pattern, i.e. demand has a low coefficient of variation (functional items, in(fisher, 1997)). However, there is an increasingly important family of products with shorter life cycles and larger demand variability, for which the utilization of the traditional methodologies may lead to considerable errors (innovative items, in(fisher, 1997)). While focusing on the latter, we employ the system dynamics (SD) methodology, well known and proven in strategic decision-making, as the major modeling and analysis tool in this research. Forrester (1961) introduced SD in the early 60s as a modeling and simulation methodology for the analysis and long-term decision-making of dynamic industrial management problems. Since then, SD has been applied to various business policy and strategy problems (Sterman, 2000). The version of the well-known Beer Distribution Game, an experiential educational game presented in (Sterman, 1989), is a role playing SD model of a supply chain originally developed by Forrester. Towill (1995) uses SD in supply chain redesign to gain added insights into SD behavior and particularly into its underlying casual relationships. The outputs of the proposed model are industrial dynamics models of supply chains. Minegishi and Thiel (2000) use SD to improve the understanding of the complex logistic behavior of an integrated food industry. They present a generic model and then provide practical simulation results applied to the field of poultry production and processing. Sterman (2000) presents two case studies where the SD methodology is used to model reverse logistics problems. Georgiadis and Vlachos (2004) use the SD methodology to estimate stocks and flows in a reverse supply chain providing specific mechanisms with a fixed remanufacturing capacity change per year. Sterman (2000) introduced a generic SD model of the stock management structure which is used to explain the sources of oscillation, amplification and phase lag observed in supply chains. Haffez, Griffiths, Griffiths, and Nairn (1996) describe the analysis and modeling of a two-echelon industry supply chain encountered in the construction industry, using an integrated system dynamics framework. Simulation results are further used to compare various re-engineering strategies. In this work we develop an SD-based holistic model of the entire supply chain, which may be used as decisionmaking aid tool, mainly for strategic decision-making. More specifically, we design generic single-echelon inventory systems that incorporate all state variables (stocks on-hand and on order) and policies for both inventory control and capacity planning. Using this single-echelon model as a basic module we demonstrate how generic multi-echelon supply chain models can be constructed. Although such an analysis may differ from one product (or stock keeping unit, SKU) to another, we keep the proposed model as generic as possible to facilitate its implementation on a wide spectrum of real-world cases. The next section presents the problem under study and the modeling approach along with the major underlying assumptions. In Section 3we demonstrate the applicability of the developed model on a multi-echelon network of a major Greek fast food chain. Finally, we wrap-up with summary and conclusions in Section 4. 2. Problem and model description Strategic supply chain management deals with a wide spectrum of issues and includes several types of decisionmaking problems that affect the long-term development and operations of a firm, namely the determination of number, location and capacity of warehouses and manufacturing plants and the flow of material through the logistics network, inventory management policies, supply contracts, distribution strategies, supply chain integration, outsourcing and procurement strategies, product design, decision support systems and information technology.
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P. Georgiadis et al. / Journal of Food Engineering 70 (2005) 351 364 363 Total_trans_demand_SR = ARRSUM(Trans_demand_SR)/Hours_per_Shift_SR[driver-shifts] Trans_Cap_Expansion_NR = INT(K_NR * (PULSE(Smoothed_Tran_Cap_Shortage_NR,4000,Pr_NR))) [trucks/hour] Trans_Cap_Expansion_SR = INT(K_SR * (PULSE(Smoothed_Tran_Cap_Shortage_SR,4000,Pr_SR))) [trucks/hour] Transportation_Capacity_Leasing_NR = Transportation_Capacity_Shortage_NR * Desired_Fill_Rate_NR [trucks] Transportation_Capacity_Leasing_SR = Transportation_Capacity_Shortage_SR * Desired_Fill_Rate_SR [trucks] Transportation_Capacity_Needed_NR = Total_trans_demand_NR/Driver_Shifts_per_Truck_NR[trucks] Transportation_Capacity_Needed_SR = Total_trans_demand_SR/Driver_Shifts_per_Truck_SR[trucks] Transportation_Capacity_Shortage_NR = MAX(Transportation_Capacity_Needed_NR- Transportation_Capacity_NR,0)[trucks] Transportation_Capacity_Shortage_SR = MAX(Transportation_Capacity_Needed_SR- Transportation_Capacity_SR,0)[trucks] Constants: const a_ao_sr = 12 [hour] dim a_d = (D = 1.. 60) const a_d = 12 [hour] dim a_d_sr = (D = 1.. 69) const a_d_sr = 12 [hour] const a_tc_nr = 12 [hour] const a_tc_sr = 12 [hour] const Acquisition_Time_TC_SR = 720 [hour] const Acquisition_Time_TC_NR = 720 [hour] const Capacity_Life_Cycle_NR = 40,000 [hour] const Capacity_Life_Cycle_SR = 40,000 [hour] const Desired_Fill_Rate_NR = 1 [ ] const Desired_Fill_Rate_SR = 1 [ ] const Driver_Shifts_per_Truck_NR = 3[driver-shifts/truck] const Driver_Shifts_per_Truck_SR = 3[driver-shifts/truck] const Hours_per_Shift_NR = 8 [hour/driver-shifts] const Hours_per_Shift_SR = 8 [hour/driver-shifts] const Inventory_Position_Adjustment_Time_DC = 1 [hour] dim Inventory_Position_Adjustment_Time_NR = (D = 1.. 60) const Inventory_Position_Adjustment_Time_NR = 1 [hour] dim Inventory_Position_Adjustment_Time_SR = (D = 1.. 69) const Inventory_Position_Adjustment_Time_SR = 1 [hour] const K_NR = 1 [1/hour] const K_SR = 1 [1/hour] const Lead_Time_DC = 9 [hour] dim Lead_time_NR = (D = l.. 60) const Lead_time_NR = [...] [hour] dim Lead_time_SR = (D = 1.. 69) const Lead_time_SR = [...] [hour] dim m_nr = (D = 1.. 60) const m_nr = [...] [items/hour] dim m_sr = (D = 1.. 69) const m_sr = [...] [items/hour] const Order_Handling_Time_CW = 1 [hour] const Order_Handling_Time_NR = 1 [hour] const Order_Handling_Time_SR = 1 [hour] const Pr_NR = 4320 [hour]: NR transportation capacity review period const Pr_SR = 4320 [hour] : SR transportation capacity review period
364 P. Georgiadis et al. / Journal of Food Engineering 70 (2005) 351 364 dim Response_Time_NR = (D = l.. 60) const Response_Time_NR = 0.1 [hour] dim Response_Time_SR = (D = 1.. 69) const Response_Time_SR = 0.1 [hour] const S_DC =...[items] dim S_NR = (D = 1.. 60) const S_NR = [...] [items] dim S_SR = (D = 1.. 69) const S_SR = [...] [items] const s_dc =...[items] dim s_nr = (D = 1.. 60) const s_nr = [...] [items] dim s_sr = (D = 1.. 69) const s_sr = [...] [items] dim sd_nr = (D = 1.. 60) const sd_nr = [...] [items/hour] dim sd_sr = (D = 1.. 69) const sd_sr = [...] [items/hour] References Fisher, M. L. (1997). What is the right supply chain for your product? Harvard Business Review(March April), 105 116. Forrester, J. W. (1961). Industrial dynamics. Cambridge, MA: MIT Press. Georgiadis, P., & Vlachos, D. (2004). The effect of environmental awareness on product recovery networks design. European Journal of Operational Research, 157, 449 464. Haffez, K., Griffiths, M., Griffiths, J., & Nairn, J. (1996). System design of a two-echelon steel industry supply chain. International Journal of Production Economics, 45, 121 130. Hobbs, J. E., & Young, L. (2000). Closer vertical co-ordination in agrifood supply chains: A conceptual framework and some preliminary evidence. Supply Chain Management: An International Journal, 5(3), 131 142. Iakovou, E. (2001). A new framework for supply chain management: Review concepts and examples. In Proceedings of the Third Aegean International Conference on Design and Analysis of Manufacturing Systems, Tinos, Greece, 27 36. Martinich, J. S. (1997). Production and operations management. Wiley. Minegishi, S., & Thiel, D. (2000). System dynamics modeling and simulation of a particular food supply chain. Simulation Practice and Theory, 8, 321 339. Nahmias, S. (2001). Production and operation analysis (4th ed.). McGraw-Hill. Sterman, J. D. (2000). Business dynamics: Systems thinking and modeling for a complex world. New York: McGraw-Hill. Sterman, J. D. (1989). Modeling managerial behavior: Misperceptions of feedback in a dynamic decision making experiment. Management Science, 35(3), 321 339. Tayur, S., Ganeshan, R., & Magazine, M. (1999). Quantitative models for supply chain management. KluwerÕs International Series. Towill, D. (1995). Industrial dynamics modeling of supply chains. International Journal of Physical Distribution & Logistics Management, 26(2), 23 42. 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.