Collaborations in supply chain - value of information sharing

Abstract

Collaboration between members of the supply chain is advocated for improving efficiency and reducing the cost of delivering the products. Mechanisms to coordinate decisions and share information between members of the supply chain have become feasible options due to recent advances made in the area of Information Technology. Considering a single-supplier, multiple-distributor setting, we show how the use of relevant information shared through collaboration aids the decision making process of the supplier. We incorporate information flow related to demand distribution parameters and the realized demand at the distributors' end along with the orders placed by the distributors in estimating the demand in each period of the horizon for the supplier. Based on the extent of information shared by the distributors with the supplier through collaboration, following levels of collaboration are defined for the study. a) Collaboration Level 0: Supplier takes replenishment decisions based on the knowledge derived from the distributors' order history. b) Collaboration Level 1: Supplier takes replenishment decisions based on the knowledge derived from the distributors' demand distribution parameters for each period of the horizon. c) Collaboration Level 2: Supplier takes replenishment decisions based on the knowledge derived from the realized demand at the distributors' end along with the distributors' demand distribution parameters for each period of the horizon We show that depending upon the level of collaboration; the additional information can be used to readjust the inventory policy parameters at the supplier through our inventory policy model. The decision problem at the supplier resembles a finite horizon, non-stationary stochastic multi-echelon inventory policy problem. We develop a stochastic dynamic programming solution procedure to solve the decision problem of the supplier and compute the order up-to levels for each period of the horizon minimizing the supplier's average inventory holding and stock-out cost over the entire horizon. Based on ow experimentation, the demand range approximation considered captures real-world complexities with less than 0.3% deviation from best case analysis and less than 0.05% increase in average cost. The computation effort reduces by linear level with increase in the magnitude of demand range. We rigorously measure the value of information sharing through numerical analysis based on an experiment design developed to represent various operating environments. This can serve as a decision support tool for firms wishing to collaborate with their supply chain partners. The main results are: The cost reduction can be as high as 70% for the situation when the supplier identifies that the mean demand at the distributors' end varies across different periods in a range which is 100% of the mean value.

If the distributors face high variability in demand in a period, then the extent of benefit of information sharing will reduce. For a case when the mein demand varies by 100% over the mean value across different periods, the cost saving will only be up-to 40% (coefficient of variation is 30%) instead of being up-to 70% (coefficient of variation is 10%).

If the distributors share information related to the realized end consumer demand with the supplier in each period within their replenishment interval along with the demand parameters, the supplier could additionally reduce costs up-to 47%.

The total cost saving at the supplier could rise up-to 82%, if information related to the uncertainty of distributors' demand is shared along with the information related to end consumer demand. As more information related to distributors' demand and operating environment is made available to the supplier, it would be able to serve the distributors in a better way. The number of units short (stocked-out) at the supplier will reduce marginally (by up-to 0.2%).

We also have taken an organization's supply chain, as an example to demonstrate the need for collaboration between members of the supply chain. Using simulation model, we compare the supplier's performance under the present practices within the supply chain with that under a proposed inventory policy for the supplier based on the knowledge derived from distributors' order history. This serves as a benchmark for supplier's performance for future savings possible due to collaboration with the distributors. We show that under the proposed policy the supplier can benefit between 40 - 75% in terms of Percentage Cost Benefit while maintaining almost the same levels of customer service (Order Fill Rate and Item Fill Rate).