Meta-Analysis of Distribution Cost Control in Retail Supply Chains: Insights from Data Modeling

Abstract

This study presents an extended systematic review and meta-analysis of distribution cost control strategies in retail supply chains, synthesizing evidence from operations research and logistics optimization studies. Following PRISMA guidelines, 1,284 records were initially identified through comprehensive database searches. After duplicate removal, screening, and eligibility assessment, 83 peer-reviewed studies were included in the systematic review, of which 54 studies reported sufficiently comparable quantitative outcomes to support meta-analytic synthesis. Collectively, the reviewed studies represent a highly influential body of literature with an estimated 18,000+ cumulative citations, indicating strong scholarly impact and maturity of the research domain. The meta-analysis reveals that optimization-based strategies achieved average total distribution cost reductions ranging from 8% to 25% compared with baseline or non-optimized decision structures, depending on strategy type and network context. Inventory optimization strategies, examined in 46 studies, were associated with 10–22% reductions in total distribution cost, primarily through lower emergency shipments and stabilized replenishment cycles. Transportation and vehicle routing optimization, addressed in 52 studies, demonstrated 12–30% reductions in transportation-related costs, along with 15–35% improvements in vehicle utilization and 10–28% reductions in total distance traveled. Integrated inventory–transportation and network-level planning approaches, analyzed in 39 studies, consistently outperformed sequential planning, yielding additional cost savings of 5–15% beyond standalone optimization methods. Coordination and information alignment mechanisms, evaluated in 44 studies, reduced demand variability and logistics volatility, resulting in 20–40% reductions in emergency logistics actions and 8–18% lower total distribution costs. Uncertainty-aware models, including stochastic and robust optimization approaches examined in 38 studies, demonstrated 25–45% reductions in cost variability and 10–20% lower unplanned logistics expenditures under demand and lead-time uncertainty. Overall, the findings quantitatively confirm that effective distribution cost control in retail supply chains is achieved through coordinated, optimization-driven decision systems that integrate inventory, transportation, network design, coordination, and uncertainty management, producing both cost efficiency and cost stability across diverse retail distribution environments.