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 rec-ognized 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 stra-tegic issues for food supply chains. We present guidelines for the methodology and present its development for the strastra-tegic mod-eling 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.
2004 Elsevier Ltd. All rights reserved.
Keywords:System dynamics; Supply chain management; Food logistics; Capacity planning
1. Introduction
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 re-mains incapable of addressing satisfactorily many prac-tical 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
inva-lid (Iakovou, 2001; Tayur, Ganeshan, & Magazine, 1999). Rather, strategic decision-makers need compre-hensive 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 strate-gic models is their inability to take into account the im-pact of regulatory legislation within todayÕs already volatile environment. This is particularly important for food supply chains because of their unique characteris-tics, 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 2004 Elsevier Ltd. All rights reserved.
doi:10.1016/j.jfoodeng.2004.06.030
* Corresponding author. Tel.: +30 2310 996046; fax: +30 2310 996018.
E-mail address:[email protected](P. Georgiadis).
www.elsevier.com/locate/jfoodeng Journal of Food Engineering 70 (2005) 351–364
uncertainty for the seller in locating a buyer, as perish-able products must be moved promptly to the market-place to avoid deterioration, leaving sellers unable to store the products awaiting favorable market condi-tions. This further leads to the need forfrequent deliver-ies, 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 effi-ciently designed storage facilities to further ensure their quality. In addition, food safety issues have profound ramifications on the design of the supply chain. For in-stance, 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 prod-ucts are transformed from commodity to differentiated branded foods, while others undergo packaging but re-main essentially intact in character. All these character-istics along with the dynamically evolving legislative framework further hinder the task of managing effi-ciently 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 environ-ments 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 stra-tegic 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 short-er life cycles and largshort-er 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 ( Ster-man, 2000). The version of the well-known Beer Distri-bution Game, an experiential educational game presented in (Sterman, 1989), is a role playing SD model of a supply chain originally developed by Forrester. To-will (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 sup-ply chains. Minegishi and Thiel (2000) use SD to im-prove 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 ob-served 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 decision-making aid tool, mainly for strategic decision-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 sin-gle-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 under-lying 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 decision-making problems that affect the long-term development and operations of a firm, namely the determination of number, location and capacity of warehouses and man-ufacturing plants and the flow of material through the logistics network, inventory management policies, sup-ply contracts, distribution strategies, supsup-ply chain inte-gration, outsourcing and procurement strategies, product design, decision support systems and informa-tion technology.
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aux Total_trans_demand_SR = ARRSUM(Trans_demand_SR)/Hours_per_Shift_SR[driver-shifts]
aux Trans_Cap_Expansion_NR = INT(K_NR * (PULSE(Smoothed_Tran_Cap_Shortage_NR,4000,Pr_NR))) [trucks/hour]
aux Trans_Cap_Expansion_SR = INT(K_SR * (PULSE(Smoothed_Tran_Cap_Shortage_SR,4000,Pr_SR))) [trucks/hour]
aux Transportation_Capacity_Leasing_NR = Transportation_Capacity_Shortage_NR * Desired_Fill_Rate_NR [trucks]
aux Transportation_Capacity_Leasing_SR = Transportation_Capacity_Shortage_SR * Desired_Fill_Rate_SR [trucks]
aux Transportation_Capacity_Needed_NR = Total_trans_demand_NR/Driver_Shifts_per_Truck_NR[trucks] aux Transportation_Capacity_Needed_SR = Total_trans_demand_SR/Driver_Shifts_per_Truck_SR[trucks] aux Transportation_Capacity_Shortage_NR =
MAX(Transportation_Capacity_Needed_NR-Transportation_Capacity_NR,0)[trucks]
aux 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
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
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