Facing current client expectations for high quality, timely order response, and multiple shipments of various needed merchandise, today’s producers must simultaneously satisfy external requirements and operate internally with minimum overall expenses and capacity constrained. Aiming to help present-day producers achieve the operational goals mentioned above, this work develops a decisional scheme to determine the best manufacturing-delivery policy for a multi-item economic production quantity (EPQ) system with multi-shipment, quality assurance, overtime, postponement, and external source. Combining a production postponement strategy in our multi-item batch fabricating procedures intends to first make all required standard/common parts for various client-needed merchandise and make finished goods in the 2nd phase. Two fabricating-uptime-shortening strategies are adopted: contracting out a proportion of the standard part’s batch and overtime-making of finished goods. We include screening and rework tasks in fabricating procedures to help us remove the identified scraps and correct the repairable faulty items. The quality-assured finished batches are divided into multiple equal-amount shipments transported to meet client requests. The overall manufacturing-transportation relevant expenses, including quality and uptime-expedited costs, are mathematically modeled and minimized using optimization methodology to help derive the best manufacturing-delivery operating policy. Moreover, we offer an illustration to validate the results and our research scheme’s capability numerically. This work mainly contributes to the literature by presenting a practical decision-making model. It enables the producers to expose numerous crucial problem-related managerial insights to facilitate producers in deciding the most appropriate manufacturing-delivery policy to meet clients’ multi-criteria demands.

When making a batch production decision for a buyer-vendor coordination system, the management must simultaneously consider the operating expenses incurred in in-house manufacturing and inventory, finished goods’ shipping, and stock holding at the retailer end. Achieving the operational goals of desirable quality, minimal production disruption, and shortening fabrication time help minimize overall in-house operating costs and maximize customer satisfaction. This work builds an operating cost minimization model for buyer-vendor coordination batch system with scrap, breakdowns, overtime, multi-shipment, and an external source to assist the management in optimizing their production-delivery plan. Removing inevitable scrap items ensures product quality, and correction action on stochastic equipment breakdown prevents unacceptable production delays. Implementing partial overtime and adopting an external source expedites in-house manufacturing time. Model construction and cost analysis enable us to decide the operating expense function. Then, we verify the function’s convexity and decide our model’s best manufacturing runtime with the differential calculus and a proposed algorithm. Furthermore, the numerical demonstrations are used to exhibit our work’s applicability and show what kinds of crucial in-depth information can be disclosed and made accessible to the production planners for their decision-making.

Operating in today’s highly competitive global markets, transnational enterprises always seek to optimize internal vendor-buyer coordinated systems to ensure timeliness and quality deliveries, given the reality of unreliable machines and limited capacity. To facilitate accurate decision making to help organizations gain competitive advantages in such situations, this study explores an intra-supply-chain problem featuring a partial outsourcing batch fabrication plan, random scrap, Poisson-distributed breakdown rate, and multiple shipments of end-product. First, we build a model to characterize the problem clearly. Then, we carry out formulations, analyses, and derivations of the model to attain the problem’s cost function. We then use differential calculus and propose a specific algorithm to confirm the convexity of the obtained cost function and derive the optimal runtime. Finally, we offer a numerical illustration to demonstrate the result’s applicability for other business circumstances. Additional elements of the problem are then discussed, including the individual and combined influence of variations in scrap, outsourcing, breakdown, and shipping frequency. The features of an optimal operating policy and cost relevant parameters are now revealed to assist management with strategic planning and decision making in real-world intra-supply-chain environments.

Operating in today’s turbulent and competitive world marketplaces, manufacturers must find the best production scheme and delivery policy to meet timely client’s multiproduct requirements and minimize the total manufacturing-shipment expenses. This study proposes a two-stage delayed differentiation model for a multiproduct manufacturer-retailer coordinated supply chain featuring the adjustable-rate for making common parts and a multi-shipment policy for transporting finished goods. The aim is to help present-day manufacturers achieve their operational goals mentioned above. The mathematical techniques help us build a specific model to explicitly represent the problem and derive its overall operating expense. Then, the convexity of the total expense is verified by Hessian matrix equations. The differential calculus helps derive the cost-minimized fabrication-shipment decision. This study offers an example to demonstrate the applicability and capabilities of our proposed model numerically. The following crucial information has been made available to the managers to facilitate their operating decision makings: (1) the problem’s best fabrication-shipment policy; (2) the collective influence of various common part’s completion rates and values on the problem’s total expenses and optimal fabrication-shipment policy; (3) the impact of various adjustable-rates in stage one on utilization and stage one’s uptime; (4) the details of cost contributors to the problem; and (5) the collective impacts of critical features on the problem’s performance.