Systematic literature review on developing a decision support framework to minimize loss mileage and its impact in Sri Lanka’s FMCG transportation sector: a case-based Delphi-ism approach

Abstract

The Fast-Moving Consumer Goods (FMCG) sector in Sri Lanka depends on high frequency, time sensitive deliveries, where “loss mileage” (unproductive/empty travel) erodes cost efficiency, asset utilization, and service reliability. This systematic literature review (SLR) consolidates fragmented evidence on drivers of loss mileage and their cost implications to ground a context specific decision support solution. Searches were conducted across Google Scholar (primary), IEEE Xplore, and ScienceDirect using a structured Boolean string targeting loss mileage/empty mile, FMCG logistics, Transportation Management Systems (TMS), decision support, and Delphi/ISM methods. Following PRISMA procedures and predefined inclusion-exclusion and quality criteria, 48 studies were retained (16 from the direct query; 32 added via snowballing and targeted subdomain searches). The synthesis yields a validated set of 15 high leverage factors prioritized by frequency in literature, cost impact, consolidation of overlaps, and impact intensity including transportation utilization, load efficiency, route and infrastructure design, vehicle performance and reliability, idle fuel consumption, driver scheduling, cold chain logistics, and enforcement against overloading. Mapped cost areas span fuel, maintenance, labor/time, and service levels. analytics: exposes four persistent gaps: (i) absence of an integrated, context-aware framework to minimize loss mileage; (ii) underutilization of TMS data for predictive/optimization analytics; (iii) limited combined application of Delphi with Interpretive Structural Modelling (ISM) anchored in operational datasets; and (iv) scant Sri Lanka specific empirical work. Addressing these gaps, the paper motivates a case-based Delphi-ISM decision support framework that fuses expert consensus with TMS evidence to structure causal linkages among factors and guide managerial interventions for measurable cost reduction.