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    Assessing the supply chain performance: a causal analysis
    (Springer, 2020) Bayraktar, Erkan; Sari, Kazim; Tatoglu, Ekrem; Zaim, Selim; Delen, Dursun
    Measuring the performance-related factors of a unit within a supply-chain is a challenging problem, mainly because of the complex interactions among the members governed by the supply chain strategy employed. Synergistic use of discrete-event simulation and structural equation modeling allows researchers and practitioners to analyze causal relationships between order-fulfillment characteristics of a supply-chain and retailers' performance metrics. In this study, we model, simulate, and analyze a two-level supply-chain with seasonal linear demand, and using the information therein, develop a causal model to measure the links/relationships among the order-fulfillment factors and the retailer's performance. According to the findings, of all the order-fulfillment characteristics of a supply-chain, the forecast inaccuracy was found to be the most important in mitigating the bullwhip effect. Concerning the total inventory cost and fill-rate as performance indicators of retailers, the desired service level had the highest priority, followed by the lead-time and forecast inaccuracy, respectively. To reduce the total inventory cost, the bullwhip effect seems to have the lowest priority for the retailers, as it does not appear to have a significant impact on the fill rate. Although seasonality (to some extent) influences the retailer's performance, it does not seem to have a significant impact on the ranking of the factors affecting retailers' supply-chain performance; except for the case where the backorder cost is overestimated.
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    The role of forecasting on bullwhip effect for E-SCM applications
    (Elsevier Science Bv, 2008) Bayraktar, Erkan; Koh, S. C. Lenny; Gunasekaran, A.; Sari, Kazim; Tatoglu, Ekrem
    The bullwhip effect represents the information distortion in customer demand between orders to supplier and sales to the buyer. Demand forecasting is one of the main causes of the bullwhip effect. The purpose of this study is to analyze the impact of exponential smoothing forecasts on the bullwhip effect for electronic supply chain management (E-SCM) applications. A simulation model is developed to experiment the different scenarios of selecting right parameters for the exponential smoothing forecasting technique. It is found that longer lead times and poor selection of forecasting model parameters lead to strong bullwhip effect in E-SCM. In contrast, increased seasonality helps to reduce the bullwhip effect. The most significant managerial implication of this study lies in the need to reduce lead times along the E-supply chain to mitigate the bullwhip effect. While high seasonality would reduce the forecast accuracy, it has a positive influence on the reduction of bullwhip effect. E-SCM managers are therefore strongly suggested to utilize exponential smoothing by selecting lower values for alpha and beta and a mid-value for gamma to keep the bullwhip ratio low, while at the same time to increase forecast accuracy. (c) 2007 Elsevier B.V. All rights reserved.