اثر شلاقی در صلاحیت دار کردن زنجیره تامین با در نظر گرفتن جنبه های چرخه عمر محصول
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|1570||2012||14 صفحه PDF||سفارش دهید||11700 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : International Journal of Production Economics, Volume 136, Issue 2, April 2012, Pages 318–331
This paper presents an analysis of the bullwhip effect and net-stock amplification in a three-echelon supply chain considering step-changes in the production rates during a product's life-cycle demand. The analysis is focused around highly complex and engineered products (e.g., automobiles), that have relatively long production life-cycles and require significant capital investment in manufacturing. Using a simulation approach, we analyze three stages of the product life-cycle including low volumes during product introduction, peak demand, and eventual decline toward the end of the life-cycle. Parts of the simulation model have been adopted by a major North-American automotive OEM as part of a scenario analysis tool for strategic supply network design and analysis. The simulation results show that performance of a system as a whole deteriorates when there is a step-change in the life-cycle demand. While restriction in production capacity does not significantly impact the bullwhip effect, it increases the net stock amplification significantly for the supply chain setting under consideration. Furthermore, a number of important managerial insights are presented based on sensitivity analysis of interaction effect of capacity constraints with other supply chain parameters.
The bullwhip effect is one of the most widely investigated phenomena in the modern day supply chain management research. It is the tendency to see an increase in variability in the replenishment orders with respect to true demand due to distortion in the demand information as we move upstream in the supply chain. While Lee et al. (1997) first introduced the term “bullwhip effect” to explain this phenomenon, it was first described by Forrester (1961) to demonstrate the demand and variance amplification in an industrial system. His idea has been studied further and illustrated through the “Beer Distribution Game”—a simulation based teaching tool to explain the economic dynamics of stock management problem (Sterman, 1989). Lee et al. (1997) identified the following four reasons for the bullwhip problem: demand signal processing, the rationing game, order batching, and price variations. Since then, there have been a significant number of studies on this problem with respect to all the major causes of the bullwhip effect ( Chen et al., 2000a and Dejonckheere et al., 2003; Disney and Towill, 2003; Moyaux et al., 2007 and Boute and Lambrecht, 2007). Recently, third-party warehousing has also been cited as one of the causes of the bullwhip effect ( Duc et al., 2010). Classical inventory management models for multi-echelon supply chains require that the product demand process be fairly smooth in order to make it mathematically tractable (Williams, 1982). In comparison, simulation models are well suited to study complex supply chains with non-smooth demand process (e.g., lumpy demand during life-cycle) and allows for transient analysis. It is therefore widely used in the bullwhip effect analysis as well (Disney et al., 2004a, Wanphanich et al., 2010 and Coppini et al., 2010). While simulation based games like “Beer Game” have helped researchers and practitioners understand dynamics of order and inventory fluctuations in a supply chain (SC) system, very few examples can be found that incorporate the life-cycle demand aspects into those dynamics (Disney et al., 2004b and Reddy and Rajendran, 2005). It is a well-known fact that every product has its own life-cycle demand curve—slow market growth at introduction, rapid growth during peak, and sluggish demand during saturation (maturity) or decline phase (Mahajan and Muller, 1979). Kaipa et al. (2006) discuss the nervousness of demand planning and its impact on bullwhip in an electronic SC in the face of changing demand at various life-cycle phases. Hoberg et al. (2007) also argue that the conventional approach of using a lower smoothing constant in forecasting will take a significantly long time to detect step-changes in demand. While the notion of a product life-cycle is not new and somewhat witnessed in all industries (e.g., see Kaipa et al., 2006 and Berry and Towill, 1982), the “signature” of the production life-cycle is unique to industries that produce complex engineered products (e.g., automobiles). During the first few months of introduction, the vehicle is typically produced at low volume to address any production quality and supply issues. Given the complexity of the assembly and other production facilities, it is not practical to change the production volume continuously due to the need to “balance” the assembly line and the supply chain. Instead, the production volume typically undergoes step changes at distinct epochs as the demand picks up for the product in the market place, e.g., in the form of additional production shifts and step changes in volume per shift (Kisiel, 2008). For example, most original equipment manufacturer (OEM) automotive products in the North-American market undergo a four to five year life-cycle (with some product “refreshing” every year), with demand typically waning after couple of years due to introduction of more competitive products in the market place with better functions, features, and option content. These life-cycles however tend to be different from industry to industry, with complex engineered product typically experiencing longer life-cycles due to complexity, costs, and risks associated with product development and launch. Another characteristic, at least typical of the North-American automotive OEMs, is that they predominantly operate in a “build to stock” production mode rather than a “build to order” mode. This is also attributable to complexity of the product, production facilities, challenges associated with coordination of the supply chains to support the “take rates” for the different options/features, and the long order-to-delivery lead-times. Accordingly, the companies rely heavily on dynamic pricing strategies (in the form of dealer or customer incentives) and marketing to influence demand while adjusting supply in the long-run to match demand. Hence, this study focuses more on modeling and control of the bullwhip effect in the supply networks as a function of fluctuations in the OEM final-assembly (FA) line production volume rather than end customer “demand”. Fig. 1 shows a typical production pattern life-cycle for an automobile. In such a case, adopting a “one size fits all” production and inventory management policy results in a chaotic situation especially in multi-echelon SC settings. It is necessary to investigate how inventory and order variances propagate as a product passes through different phases of the life-cycle. Capturing such transient trajectories of different SC performance measures will be very helpful especially in designing or configuring a SC network for future products, such as the automotive industry example addressed in this paper. Full-size image (20 K) Fig. 1. Typical production pattern life-cycle for an automobile with introduction, peak, and end-of-life stages. Figure options The unique contributions of this paper are several-fold. First, we extend the work of Chen et al. (2000b) and present analytical expressions of the bullwhip effect and net-stock amplification for OEM using a different sequence of events in a replenishment period. These results confirm the bullwhip effect results developed in the literature for slightly different ordering policy. The analysis is then extended via simulation to a three-echelon SC consisting of OEM, Tier 1, and Tier 2 suppliers. Through simulation based models, we investigate the transient and steady-state impact of step-changes in OEM production volume on the bullwhip effect and net stock amplification. These analyses are performed for both with and without capacity constraint scenarios. For ease of presentation, we approximate different phases of the product life-cycle with three stationary phases (labeled, “introduction”, “peak”, and “end-of-life” stages) corresponding to the three stages of Fig. 1. Various sensitivity analyses are performed to explore the impact of interaction between capacity constraint and other SC parameters on the bullwhip effect and inventory variance (or net-stock amplification). Several insights of managerial importance are drawn based on the sensitivity analyses. The remainder of the paper is organized as follows. A brief review of related literature on the bullwhip effect analysis is presented in Section 2. Section 3 describes the model and the undertaken SC policies. In Section 4, we present the analytical expression for inventory variance and bullwhip effect (from the literature) for OEM. Section 5 describes the simulation model and sensitivity analysis results for a three-echelon SC with and without capacity constraints. Finally, conclusions and directions for future research are outlined in Section 6.
نتیجه گیری انگلیسی
The bullwhip effect has generated tremendous interest in the SC research community. Numerous simulation-based and analytical models have been developed in the literature to quantify as well as to reduce the bullwhip effect. Apart from the information sharing issue, the prior works mostly dealt with traditional problem settings such as the correlated demand process, the common forecasting techniques, and an OUT inventory policy. Most of the prior models have been developed around a single stage supply chain. Furthermore, these models have rarely considered some of the real-world issues such as capacity restriction and life-cycle demand. This paper has presented an analysis of a three echelon SC considering capacity restriction and step-changes in supply consumption rate due to life-cycle demand phases. An analytical expression has been presented to quantify the net-stock amplifications for a single stage SC using exponential forecasting. The analysis was then extended to a three stage SC network via simulation and empirical testing. The three-echelon SC setting consisted of an OEM and Tier 1, Tier 2 suppliers. While each node had capacity restriction, there were no short shipments meaning that back orders were allowed. We studied the impact of step-changes in the life-cycle demand (which caused sudden change in the supply consumption rates at each echelon) on the bullwhip effect and NS amplification, at three phases (introduction, peak, and decline or end-of-life) of the product life-cycle. Four scenarios were analyzed through simulation. The first scenario involved a simple three-echelon SC network with unlimited capacity, AR(1) demand process, and exponential smoothing forecasting. As anticipated, it showed that key performance metrics such as the bullwhip effect and NS amplification increased exponentially as we moved up the chain. The second scenario considered a capacitated SC network with all the others parameters remaining unchanged from the previous scenario. It was discovered that the NS amplification increased at much higher rate in the capacitated network than that in the uncapacitated network at all echelons. Obviously, the rate of the NS increase was the worst for T2, followed by T1 and OEM, respectively. Interestingly enough, although the bullwhip effect was present at all echelons, a direct correlation between the bullwhip effect and capacity level could not be established. In the third and fourth scenarios, we studied relationship between the step-changes in production rate due to life-cycle demand and the change in the performance level of the SC system with and without capacity constraints, respectively. The simulation results showed that performance of a system as a whole deteriorated when there was a step-change in the life-cycle demand. Particularly, key performance metrics such as the bullwhip effect and the NS amplification rose sharply during the peak phase. Lastly, the NS amplification was less adversely affected by the limited capacity when there were step changes in the supply consumption rate. In addition, a number of sensitivity analyses were performed to examine the impact on the supply chain performance due to interaction of capacity constraint with the other SC characteristics and operating parameters like forecasting, autocorrelation coefficient, and duration of life cycle phases. The sensitivity analysis results also showed that there was no evidence to establish the correlation between the capacity constraint and the bullwhip effect regardless of its interaction with the other parameters. However, there was significant difference in the magnitudes of the NS amplification between the capacitated and the uncapacitated supply chains in all the cases. Sensitivity analysis of duration of life cycle phases on the supply chain performance was also performed to ascertain the impact of a step-change on the bullwhip effect and the NS amplification. However, the results have confirmed our original hypothesis that a step-change in demand during different phases of life cycle would directly contribute to the bullwhip effect and the NS amplification regardless of duration of lifecycle phases. While this study has revealed many important insights with respect to capacity constraint and step-changes in demand, it has made some critical assumptions. For example, the paper assumed complete shipment of orders (although with penalty costs as presented in Lee et al., 2000) and common SC policies throughout the life-cycle regardless of the demand situation. Future work will involve the analysis with both incomplete shipments and target finished goods inventory. Furthermore, we used AR(1) to model demand process. However, it would be interesting to analyze if the similar conclusions on the bullwhip effect hold true in a situation where unexpected events such as product recalls can affect the demand.