Assessing the supply chain performance: A causal analysis
Yükleniyor...
Tarih
2019
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Springer US
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
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.
Açıklama
Anahtar Kelimeler
SCM, Retailers’ Performance, Bullwhip Effect, Service Level, Causal Analysis
Kaynak
Annals of Operations Research
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
282
Sayı
372
Künye
Bayraktar, E., Sarı, K., Tatoğlı, E., Zaim, S., ve Delen, D. (2019). Assessing the supply chain performance: A causal analysis. Annals of Operations Research, 282 (372).
