What question did this study set out to answer?

To develop a modular framework that integrates inventory management and distribution planning in precast supply chains.

March 10, 2026Open Access

Inventory and Distribution Planning in Precast Supply Chains: A Modular Decision-Support Framework with Feasibility-Envelope Mapping

Key Points

To develop a modular framework that integrates inventory management and distribution planning in precast supply chains.
Developed a simulation-driven decision-support framework.
Quantified inventory shortfalls as cumulative backlog.
Employed particle swarm optimization for candidate plan exploration.
Utilized response-surface models and feasibility-envelope mapping for decision synthesis.
Analyzed impacts of storage, project starts, and service distance on backlog and fleet-inventory regions.
Doubling storage reduces cumulative backlog by approximately 25%.
Staggering project start times decreases backlog by about 75-80% without additional storage.
Expanding service distance from 50 to 200 km decreases the feasible fleet-inventory region.
Increased delivery shortages are more likely with longer distances, shifting penalties to shortage dominance.

Abstract

• Modular, simulation-driven framework links inventory buffering with fleet-based distribution in precast supply chains. • Inventory shortfalls are quantified as cumulative backlog (piece-days) from time-accumulated unmet demand. • PSO-based exploration with response surfaces and feasibility-envelope mapping yields interpretable decision maps. • In the studied scenarios, doubling storage reduces backlog by ∼ 25%; staggering project starts reduces backlog by ∼ 75–80% without extra storage. • Expanding service distance ( ≈ 50 → 200 km) shrinks the feasible fleet-inventory region; delivery shortages become more likely and shortage penalties dominate the objective. Multi-project precast supply chains face coupled constraints from time-phased demand, limited storage, production/availability lags, and travel time. This study presents a modular, simulation-driven decision-support framework that links a production–inventory buffering module with storage limits and availability delay and a factory–truck–project distribution module constrained by fleet size, service distance, and per-truck limits on trips and working hours. Candidate plans are explored using particle swarm optimization, and results are synthesized via response-surface models and feasibility-envelope mapping to reveal sensitivities and regime shifts. Inventory shortfalls are quantified as cumulative backlog (piece-days), i.e., time-accumulated unmet demand. In the studied scenarios, doubling storage reduces backlog by about 25%, while staggering project start times reduces it by roughly 75–80% without additional storage. On the distribution side, increasing service distance from about 50 to 200 km substantially shrinks the feasible region and shifts outcomes from transport-driven trade-offs toward shortage-penalty dominance. Overall, the framework delivers interpretable decision maps to support storage sizing, demand shaping, and fleet/service-radius design.

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