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Having a fair forecast where under and over forecasts are equally probable, 10% differences in two accuracies can be as a result of 5% under forecasts and 5% over forecasts. This means for 100 hours of labor, half of it would be under forecasting 5 hours. Under forecasts can be made up by an employee working extra hours, which means breaking compliance laws or missing on sales. Employees may stretch and cover up to 50% of the miscalculated (under forecasted) hours, however the other 50% of the extra labor remains unaddressed and will translate to missing sales. But, how much will those missing sales cost? If we assume a linear relationship between demand and labor, missing 2.5% of demand can be the result of 5% under forecasting the labor. The missing demand could be sales, which would result in 2.5% lost revenue. In other cases, it could be missing traffic, which for some retailers, like the food industry, could be converted to the same amount of missed sales. For other industries, the missing demand could be proportionally interpreted as missing sales because they predict , one out of every ten customers leads to a sale, for example in this case, instead of losing 2.5% sales, it will be 0.25% loss in sales.

As a result of under-forecasting, employees can be required to work extra hours which would result in fatigue and potential dissatisfaction. This not only causes employee attrition, but it also increases the risks and costs associated with breaking workforce compliance laws. With over forecasting, the company can be hit with unplanned and non-budgeted overtime, which would increase labor costs. This can be quantified based on the company’s monthly labor hours.