Abstract
Manufacturing firms operating in today’s competitive global markets must continuously find the appropriate manufacturing scheme and strategies to effectively meet customer needs for various types of quality of merchandise under the constraints of short order lead-time and limited in-house capacity. Inspired by the offering of a decision-making model to aid smooth manufacturers’ operations, this study builds an analytical model to expose the influence of the outsourcing of common parts, postponement policies, overtime options, and random scrapped items on the optimal replenishment decision and various crucial system performance indices of the multiproduct problem. A two-stage fabrication scheme is presented to handle the products’ commonality and the uptime-reduced strategies to satisfy the short amount of time before the due dates of customers’ orders. A screening process helps identify and remove faulty items to ensure the finished lot’s anticipated quality. Mathematical derivation assists us in finding the manufacturing relevant total cost function. The differential calculus helps optimize the cost function and determine the optimal stock-replenishing rotation cycle policy. Lastly, a simulated numerical illustration helps validate our research result’s applicability and demonstrate the model’s capability to disclose the crucial managerial insights and facilitate manufacturing-relevant decision making.
Highlights
This study builds an analytical model to explore a multiproduct fabrication problem featuring postponement, external suppliers, overtime, and scrap
The admission controls decisions related to acceptance or rejection of orders based on current stock-level and whether they are profitable
The researchers further extended their work by considering the overtime options in the improving stage to increase capacity and save the total system costs
Summary
This study builds an analytical model to explore a multiproduct fabrication problem featuring postponement, external suppliers, overtime, and scrap. Boctor and Poulin (2007) proposed composite heuristics to explore a dynamic-demand economic lot-sizing and scheduling problem featuring multi-product, multi-stage, and limited capacity. The researchers further extended their work by considering the overtime options in the improving stage to increase capacity and save the total system costs. They offered numerical illustrations to validate their heuristics and solution procedures and show their performance. Abdel-Aal (2019) utilized a mixed-integer linear programming approach to study a multi-period multi-product lot-sizing problem featuring uncertain demands/budgets, setup times, limited capacity, overtime and permitted backlogging. Fewer past works focused on investigating the impact of external suppliers, postponement, overtime, and scrap on the multiproduct fabrication problem; we aim to bridge this gap
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