First, we need to have in place the relevant forecasts of various demand drivers impacting the workforce. In the retail and customer service context, for instance, we’ll want to forecasts the number of visitors and gain an understanding of what type of demand it generates. Similar visitor volumes may generate significantly varying demand for customer service personnel depending on the seasonality and time of the day. Respectively, typically after special events, there’s a spike in product returns which will put a strain in service personnel handling such returns – these demand drivers must be taken into account separately.
Although the conversion of certain demand drivers may be rather straightforward – some may require a bit of advanced modeling to define the optimal levels. In the case for sales-oriented customer service personnel for instance, the optimization logic should aim for maximizing overall profitability rather than simply seeking to minimize workforce costs. This can be done by modeling the impact of customer service personnel’s capabilities to convert sales potential into actual sales and with this information we can define the optimal staffing level that is most likely going to maximize overall profitability.