Λεπτομέρειες βιβλιογραφικής εγγραφής
| Τίτλος: |
Operationalizing advance-postponement for replenishment-assembly synchronization in VMI-ATO systems under demand-forecast evolution. |
| Συγγραφείς: |
Li, Mingxing1,2,3 (AUTHOR), Hong, Yan1,2,3 (AUTHOR), Li, Wanshan1,2,3 (AUTHOR) liws23@jnu.edu.cn, Yang, Qiang1,2,3 (AUTHOR), Qu, Ting1,2,3 (AUTHOR), He, Zhen4 (AUTHOR), Huang, George Q.5 (AUTHOR) |
| Πηγή: |
Computers & Industrial Engineering. Sep2026, Vol. 219, pN.PAG-N.PAG. 1p. |
| Θεματικοί όροι: |
*Vendor-managed inventory, *Flexible manufacturing systems, *Inventory control, Synchronization, Demand forecasting, Stochastic programming |
| Περίληψη: |
• The first attempt to study replenishment-assembly synchronization under uncertain demand and its evolution. • The strategy realizes a finer-grained balance between postponement and advancement strategies at the operational level. • A "1 + N" replenishment priority scoring mechanism is designed to balance current demand and future forecasts. • A stochastic programming-based synchronization model is formulated to implement differentiated strategies. This paper focuses on a novel dynamic synchronization problem of replenishment and assembly in integrated Vendor Managed Inventory − Assemble-to-Order (VMI-ATO) systems under uncertain demand and its evolution. The problem is characterized by the complexity of managing diverse components and making multi-period replenishment decisions; the dilemma of balancing replenishment frequency and various costs; and the challenge of the mismatch between replenishment and assembly in decisions and execution under uncertainty. This paper proposes a novel "advance-postponement hybrid strategy (AP strategy)" to achieve a more flexible and adaptable synchronization by operationalizing advanced or postponed replenishment and assembly. First, the Martingale Model of Forecast Evolution is applied to depict the demand updating and uncertainty evolution. Second, a " 1 + N " replenishment priority scoring mechanism that combines current demand and future forecasts with temporally differentiated weights is designed to dynamically quantify replenishment urgency. Third, a synchronization decision model is then formulated based on stochastic programming to implement differentiated strategies. Experiments show that the AP strategy, by integrating dynamic advanced and postponed operations, effectively improves the temporal allocation of picking resources, controls the risk of component shortages, and reduces the total cost across different scenarios, and thus balancing economies of scale and rapid response to diversified demands. [ABSTRACT FROM AUTHOR] |
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