An order postponement strategy for multi-stage production-logistics synchronization towards zero-warehousing smart manufacturing

Nowadays, driven by market dynamics and evolving consumer demands, customized production has emerged as a prevalent trend. This paradigm shift from mass production to customization introduces challenges in production-logistics management, compelling manufacturers to pursue efficient strategies. Zero-warehousing smart manufacturing (ZWSM), an advanced form of Lean manufacturing (LM) and Just-In-Time production (JIT), presents a potential solution to these challenges. ZWSM leverages Industry 4.0 technologies to facilitate seamless production-logistics for eliminating warehouses and minimizing inventory in workshop. Despite the encouraging visions, field study reveals that ZWSM requires highly coordinated material supply, production, and delivery operations, misalignment among these stages frequently results in operational inefficiencies and resource waste, especially when confronted with volatile and diversified customer demand. It is defined as Multi-Stage Production-Logistics Synchronization (MS-PLSync) problem. This study proposes a novel order postponement strategy for MS-PLSync towards ZWSM, a generalizable MS-PLSync model under assemble-to-order (ATO) is formulated using mixed-integer linear programming for production-logistics operations under intricate spatiotemporal constraints. Considering dynamic order arrivals, a postponement strategy is designed and integrated into MS-PLSync model to enhance overall operational efficiency through postponed real-time decision-making, achieving a balance between rapid response to demand fluctuations and economy of scale in customized production-logistics. Numerical analysis validates the effectiveness of the proposed postponement strategy in addressing MS-PLSync problem. Notably, a little postponement can yield substantial operational benefits, while excessive postponement only generates minimal marginal benefits. Furthermore, sensitivity analysis reveals that the proposed postponement strategy performs particularly well in mass customization scenarios characterized by large-scale orders, diverse product portfolios, and extensive distribution networks.