Handbook of Information Exchange in Supply Chain Management by Albert Y. Ha, Christopher S. Tang (eds.)

By Albert Y. Ha, Christopher S. Tang (eds.)

Sharing actual and well timed offer and insist info all through a provide chain can yield major functionality advancements to all participants of the provision chain. regardless of the advantages, many companies are reluctant to proportion info with their provide chain companions as a result of an unequal distribution of dangers, bills, and merits one of the companions. hence, incentive mechanisms needs to be in position to urge conversation, cooperation, and collaboration between all individuals of a provide chain.
The factor of data exchange/sharing has been tested through numerous researchers over the past 15-20 years. in spite of the fact that, there isn't any examine e-book that compiles a number of methods, analyses, key implications, in addition to destiny improvement of this sector. This e-book will function a instruction manual for researchers who're drawn to studying the state-of-the-art of the road of study during this quarter and discover open study issues during this sector.
Contributors, all best researchers, have devoted to offering 18 chapters, damaged into 4 targeted sections masking the worth of knowledge Sharing, Contracting and data, info Signaling, and Incentives for info Sharing.

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In this chapter, we provide a literature review related to sharing different types of information in a supply chain. We focus on empirical studies, however our definition of “empirical” is rather broad—any relevant work that involves collecting data is permissible. Hence it includes experimental work, surveys, and even case studies. Moreover, it is often necessary to discuss theoretical models as they serve as the motivation or guide for some empirical work. When that happens, we will do so briefly.

In this case, var(Ot ) may be greater or less than var(Dt−1 ), depending on the covariance between St − St−1 and Dt−1 . Below we consider several different demand processes, to quantify the variance ratio between order and demand. 2 AR(1) Demand Process LPW considered an autoregressive AR(1) demand process for modeling the bullwhip effect. d. normal random variable with N(0, σ02 ). Let d = (1 − ρ )μ . We can rewrite the above equation as follows: Dt = d + ρ Dt−1 + εt . 2) To ensure the chance of a negative demand is negligible, we assume σ0 d.

In a supply chain context, research seems to favor sharing product development information with their supply chain partners. Kulp et al. (2004) collect data from the food and consumer packaged goods industry, and find that when manufacturers work closely with their retailers by sharing product development-related information, they are more likely to have higher profit margins. 9 Sharing Inaccurate Information Most of the supply chain modeling work assume that information shared in supply chains is accurate.

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