Title: Optimal decision policy for a retailer in a stochastic manufacturing process involving a rework process for defective items and two-level trade credit finance

Authors: Om Prakash; Shubham Priyadarshi; Nipa Biswas

Addresses: Department of Mathematics, National Institute of Technology Sikkim, Barfung Block, Ravangla Sub-Division, South Sikkim 737139, India ' Department of Mathematics, National Institute of Technology Sikkim, Barfung Block, Ravangla Sub-Division, South Sikkim 737139, India ' Department of Mathematics, National Institute of Technology Sikkim, Barfung Block, Ravangla Sub-Division, South Sikkim 737139, India

Abstract: Though production facilities are advancing day by day, some imperfection in manufacturing process still remains. This paper develops a manufacturing system with rework process of defective items. We have assumed that the defective items are repairable and a portion of the reworked items is considered to be scrap. In model, it is proposed that demand of items, proportion of defective products and scrap rate are randomly distributed with known probability density function. In the model it is also assumed that the supplier of raw materials offers a delay period to the manufacturer and manufacturer extends a similar delay policy to his customers. The main objective of the system is to minimise the manufacturer's overall cost and to determine manufacturer's optimal replenishment policy. Even though the selling price and scrap cost are rising, the production cost reduces, which is beneficial to the manufacturer to provide longer credit period to the retailer. The optimal replenishment policies are also discussed with the help of some theorems. Numerical examples are illustrating the theoretical results and sensitivity of important cost parameters is discussed.

Keywords: probabilistic inventory; production process; rework process; trade credit; stochastic demand.

DOI: 10.1504/IJSCIM.2024.140217

International Journal of Supply Chain and Inventory Management, 2024 Vol.5 No.1, pp.68 - 92

Received: 06 Jun 2023
Accepted: 29 Feb 2024
Published online: 30 Jul 2024 *

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