A Novel Continuous KNN Prediction Algorithm to Improve Manufacturing Policies in a VMI Supply Chain

Abstract

This paper examines and compares various manufacturing policies which manufacturer may adopt so as to improve the performance of a vendor managed inventory (VMI) partnership. The goal is to maximize the combined cumulative profit of supply chain while minimizing relevant inventory management costs. The supply chain is a two-level system with a single manufacturer and single retailer at each level, in which the manufacturer takes the responsibility of overall inventories of supply chain. A base system dynamics (SD) simulation model is first employed to describe the dynamic interactions between the variables and parameters of manufacturer and retailer under VMI. Then, the mentioned policies are constructed using the base SD model that lead us to differentiate the behavior supply chain for each policies within a same duration of time. In this paper, we use continuous K-nearest neighbor (CKNN) as one of the instance-based learning methodologies to predict the best manufacturing rates. This algorithm effectively increases the combined profit of supply chain in comparison with other two policies. Accordingly, a numerical example along with a number of sensitivity analyses is conducted to evaluate the performance of mentioned policies.

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