在工業4.0的時代，製造商傾向於將預測性維修整合到生產系統中，以最大化機器設備的使用壽命來避免設備中斷所造成的龐大成本。透過使用物聯網的感測器以及數據分析，預測性維修可以在機器設備失控前進行偵測與預測，大幅降低生產停機時間進而降低故障率。此外，預先-現金-信用付款(ACC)的模式已普遍應用於現實世界的商業交易中，以提升在供應鏈成員之間的金流靈活性。此篇研究考慮了一個採用預測性維修的製造商，供應商提供預先現金支付(ACC)給製造商，研究建立在這些狀況下的不完美經濟生產批量模型(EPQ)。此篇研究將製造商的庫存系統建構收益最大化的數學模型，以決定最佳補貨時間與預測性維修投入程度。此模型亦採用了貼現現金流量分析來考量最佳利潤的時間價值，而此最佳利潤對補貨時間與預測性維修量投入程度具有凸性。此模型之效用會透過數值範例與敏感度分析來驗證。最後，根據模型之結果我們能獲取一些管理意涵。

In the era of industry 4.0, manufacturers tend to integrate predictive maintenance into production systems to maximize lifespan of equipment and avoid costly disruptions. Through the use of IoT sensors and data analysis, predictive maintenance can substantially reduce production downtime by detecting and predicting systems before out of control resulting in a lower defective rate. Additionally, advance-cash-credit (ACC) payment scheme is commonly applied in real world business transaction to enhance cash flow flexibility among supply chain members. This paper develops a supplier-manufacturer chain which a predictive maintenance adopting manufacturer receives an ACC payment from the supplier to display supply chain management situation within the imperfect economic production quantity (EPQ) framework. Then, we model the manufacturer’s inventory system as a profit maximization problem to determine the optimal replenishment cycle time and predictive maintenance effort for deteriorated product. This model also adopts the discounted cash-flow analysis to consider time value of optimal profit which is proved to strictly concave in both replenishment time and preventive maintenance effort. Numerical and sensitivity analysis then are conducted to illustrate the effectiveness of proposed model. Finally, management insights are provided based on the result of model.

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