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Full bibliographic details must be given when referring to, or quoting from full items including the author's name, the title of the work, publication details where relevant (place, publisher, date), pagination, and for theses or dissertations the awarding institution, the degree type awarded, and the date of the award.

Purpose The purpose of this paper is to show that intentional demand forecast bias can improve warehouse capacity planning and labour efficiency. It presents an empirical methodology to detect and implement forecast bias. Design/methodology/approach A forecast model integrates historical demand information and expert forecasts to support active bias management. A non-linear relationship between labour productivity and forecast bias is employed to optimise efficiency. The business analytic methods are illustrated by a case study in a consumer electronics warehouse, supplemented by a survey among 30 warehouses. Findings Results indicate that warehouse management systematically over-forecasts order sizes. The case study shows that optimal bias for picking and loading is 30-70 per cent with efficiency gains of 5-10 per cent, whereas the labour-intensive packing stage does not benefit from bias. The survey results confirm productivity effects of forecast bias. Research limitations/implications Warehouse managers can apply the methodology in their own situation if they systematically register demand forecasts, actual order sizes and labour productivity per warehouse stage. Application is illustrated for a single warehouse, and studies for alternative product categories and labour processes are of interest. Practical implications Intentional forecast bias can lead to smoother workflows in warehouses and thus result in higher labour efficiency. Required data include historical data on demand forecasts, order sizes and labour productivity. Implementation depends on labour hiring strategies and cost structures. Originality/value Operational data support evidence-based warehouse labour management. The case study validates earlier conceptual studies based on artificial data.

No abstract

Full bibliographic details must be given when referring to, or quoting from full items including the author's name, the title of the work, publication details where relevant (place, publisher, date), pagination, and for theses or dissertations the awarding institution, the degree type awarded, and the date of the award.