Quantifying costs of forecast errors: A case study of the warehouse environment

Our study evaluates the impact of forecast errors on organizational cost by simulating a labor-intensive warehouse environment using realistic cost data from a case study. Unlike past studies that measure forecast error in terms of forecast standard deviation, our study also considers the impact of forecast bias, and the complex interaction between these variables. Two cases of organizational cost curves are considered, with differing and asymmetric structures. Results find forecast bias to have a considerably greater impact on organizational cost than forecast standard deviation. Particularly damaging is a high bias in the presence of high forecast standard deviation. Although biasing the forecast in the least costly direction is shown to yield lower costs, sensitivity analysis shows that increasing bias beyond the optimum point rapidly increases costs. 'Overshooting' the optimal amount of bias appears to be more damaging than not biasing the forecast at all. Given that managers often deliberately bias their forecasts, this finding underscores the importance of having a good understanding of organizational cost structures before arbitrarily introducing bias. This finding also suggests that managers should exercise caution when introducing bias, particularly for forecasts that inherently have large errors. These findings have important implications for organizational decision making beyond the simulated warehouse, as high forecast errors are endemic to many labor-intensive organizations.