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The real world supply chains are inherently subject to uncertainties that manifest from several causes. Two of the more common causes arise on account of the large supply lead times and their standard deviations. Manufacturing organizations the world over have resorted to many innovative approaches in order to counter the presence of these large lead times and standard deviations particularly from a logistics view point (Simchi-Levi et al., 2007). Recent advancements in information technology have often been seen as the panacea for effecting substantial reductions in supply lead times with commensurate improvements in customer responsiveness and operating efficiencies. One potent approach, that has also received considerable attention from the research community, has been the advent of demand information sharing amongst partnering SC echelon members. One of the firsts to highlight the potential benefits that accrue from information sharing were Lee et al. (1997), notable among these being the reduction of demand distortion upwards across the supply chain (commonly known as “Bullwhip Effect”), and the reduction in information lead times (Simchi-Levi et al., 2007).
The impact of long lead times and their standard deviations has been well investigated in the literature with the general observation that longer lead time and standard deviations progressively mitigate the benefit of information sharing (e.g., Parunak et al., 1999; Beamon & Chen, 2001). However, in the context of the average inventory level performance measure, the impact of lead time and its standard deviation on information sharing in the presence of other pertinent factors that significantly affect the SC performance is largely ignored in the literature. Further, most of the research on information sharing has been analyzed within single stylized supply chain structures in isolation. In contrast, we investigate the impact of varying lead times and their standard deviations on SC performance within alternate hypothetical supply chain structures.
It is now widely recognized that sharing of demand information across the echelon of an SC network is an effective means of countering the adverse impact accruing from the uncertainties that manifests within real-world SC networks (Lee et al., 1997; Lau et al., 2004; Reddy & Rajendran, 2005). Information sharing, however, is not costless. Researchers have observed that the high adoption costs of joining inter-organizational information systems and information sharing under different operational conditions of organizations may hurt some SC members (Zhao & Wang, 2002). Clearly then, information sharing involves both benefits and costs, and this warrants a more focused trade-off analysis between the relative benefits versus costs involved for a given SC network configuration.
Motivated from the above discussion, we analyzed the relative performance within two alternative supply chain network structures, keeping in view the fact that the decisions contingent upon the premise of information sharing could pivot on variations in SC network configurations. Also, with due consideration to the fact that extant research on information sharing has focused on SC networks with either complete information sharing or none at all, we were motivated to investigate the incremental impact that partial information sharing has in the continuum between the two extremes mentioned above. Towards this end, we included “Information Sharing Level” as a distinct design factor in our experimental framework, along with two other design factors: “Review Period Magnitude”, and “Customer Demand”. Importantly, it is noted here that several researchers have observed that a prime objective in pursuing an information sharing policy is to downsize safety stock levels (and hence average inventory levels) by controlling the uncertainties arising from lead times and their standard deviations (Beamon & Chen, 2001; Aigbedo, 2004; Hwarng et al., 2005; Zanoni et al., 2006). Hence, we explicitly included their factors as control factors in our experimental setup.