Abstract
Forecasting accuracy in context of fresh meat products with short shelf life is studied. Main findings are that forecasting accuracy measures (i.e. errors) should penalize deviations differently according to product characteristics, mainly dependent on whether the deviation is large or small, negative or positive. This study proposes a decision-based mean hybrid evaluation which penalize deviations according to type of animal, demand type, product life cycle and product criticality, i.e. shelf life, inventory level and future demand.
Highlights
Today’s competitive fresh food grocery market is growing with high requirements to price, availability and quality [1, 2]
As planning relies on forecasting future demand, quality and cost-effectiveness of planning depends on the forecasting accuracy [4, 5]
It is paramount to ensure that any chosen forecasting model performs best out of its alternatives
Summary
Today’s competitive fresh food grocery market is growing with high requirements to price, availability and quality (i.e. freshness) [1, 2]. Fresh meat products (FMPs) have down to only few days total shelf-life (production to expiration) [3]. This puts high requirements on planning for the wholesaler who merely balances converging and diverging flows between industry and stores [2]. Over-forecasting creates risk of reduced sales price (at best) or obsolescence [6] and under-forecasting lost sales; both reducing the profit base. It is paramount to ensure that any chosen forecasting model performs best out of its alternatives. While a model’ relevance relates to e.g. data characteristics, time periods to forecast and data availability, its performance relates to the forecasting accuracy i.e. error [7]
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