Improving warehouse labour efficiency by intentional forecast bias

Purpose – This paper shows 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 thirty 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 percent with efficiency gains of 5-10 percent, whereas the labour-intensive packing stage does not benefit from bias. The survey results confirm productivity effects of forecast bias.
Research 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 includes 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.

This is the accepted version of the manuscript "Improving warehouse labour efficiency by intentional forecast bias", forthcoming/published in the journal "International Journal of Physical Distribution & Logistics Management" available via the journal site at: https://doi.org/10.1108/IJPDLM-10-2017-0313.
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