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.

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doi.org/10.1108/IJPDLM-10-2017-0313, hdl.handle.net/1765/111990
International Journal of Physical Distribution & Logistics Management
Department of Econometrics

Kim, T. Y., Dekker, R., & Heij, C. (2018). Improving warehouse labour efficiency by intentional forecast bias. International Journal of Physical Distribution & Logistics Management, 48, 93–110. doi:10.1108/IJPDLM-10-2017-0313