Demand Forecasting in Retail Supply Chains Using the Delphi Consensus Building Method

Abstract

Reliable demand forecasts keep retail supply chains in harmony, curtailing expensive stockouts while preventing overabundant inventory. Established statistical methods and popular machine-learning techniques work well most of the time yet falter during sudden market shifts, because both depend heavily on past data. This paper introduces the Delphi Consensus Forecasting Model to overcome that gap by weaving expert judgement directly into the analytic pipeline. A guided series of structured exchanges builds a shared view of the forces driving demand and pairs those insights with forecasting tools suited to the retailors problem. The procedure stresses careful selection of panellists, several iterative rounds, and thorough validation checks, so the final projection reflects real consensus rather than fleeting agreement. Case studies show expert groups steadily fine-tune hybrid-model weights and flag the context-sensitive variables that genuinely move sales. When measured against standard benchmarks, the Delphi system proves nimbler, powering live dashboards that forecasters can tweak as conditions change. The article closes with clear steps for linking Delphi projections to operational planning, underscoring that insight-driven forecasts boost precision and speed up organisational response.