The impact of forecasting errors on warehouse labor efficiency: A case study in consumer electronics
AbstractEfficiency of outbound warehouse operations depends on the management of demand forecasts and associated labor planning. A case study in consumer electronics shows that warehouse management systematically over-forecasts actual orders, by 3% on average and by 6-12% in busy periods (at the end of each month and also in the months September, October, and November). A time series model that corrects order forecasts for the biases in preceding weeks reduces the bias to less than 2%, both on average and also in busy periods. The arrangements with the labor provider imply potential benefits of intentional over-forecasting and the associated ample labor supply for the warehouse. As compared to under-forecasted days, labor productivity on over-forecasted days is higher by 12% for loading activities and by 4% for picking and total outbound activities. Similar productivity gains are found if unbiased forecasts are compared with the optimal bias obtained from non-linear models estimated from daily data on bias and labor efficiency. The positive effects of intentional over-forecasting on productivity are confirmed in a structural equations model. By following similar methodologies as described in this paper, warehouse managers can determine the amount of intentional forecast bias that works best for their situation. The information required for this kind of evidence-based labor management consists of historical data on order sizes, forecasts, and labor productivity, and the outcomes depend on the available hiring strategies and cost structures.
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Bibliographic InfoPaper provided by Erasmus University Rotterdam, Econometric Institute in its series Econometric Institute Report with number EI2013-15.
Date of creation: 01 May 2013
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time series; forecasting; decision support; case study; labor efficiency; wharehouse planning;
This paper has been announced in the following NEP Reports:
- NEP-ALL-2013-06-04 (All new papers)
- NEP-EFF-2013-06-04 (Efficiency & Productivity)
- NEP-FOR-2013-06-04 (Forecasting)
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