Optimal production and predictive maintenance decisions for deteriorated products under advance-cash-credit payments

In the 4th industrial revolution era, manufacturers tend to integrate predictive maintenance into production systems to maximize the lifespan of equipment and avoid costly disruptions. Using internet of things sensors and data analyses, predictive maintenance can significantly reduce the production downtime by detecting and predicting system problems before they become uncontrollable, resulting in a lower defective rate. Additionally, an advance-cash-credit (ACC) payment scheme is commonly applied in real-world business transactions to enhance cash flow flexibility among supply chain members. In this study, we developed a supplier–manufacturer chain, wherein a predictive-maintenance-adopting manufacturer received an ACC payment from a supplier, to demonstrate a supply chain management situation within the imperfect economic production quantity framework. The inventory system of the manufacturer was modeled as a cost minimization problem to determine the optimal replenishment cycle time and predictive maintenance effort for deteriorated products. The proposed model also adopted a discounted cash-flow analysis to consider the time value of the cost function, which was proven to be strictly convex in the replenishment time and predictive maintenance effort. Furthermore, a numerical and sensitivity analysis was conducted to illustrate the effectiveness of the proposed model and gain management insights.