Retail store manager needs a forecast of her business to decide number of shifts she needs to staff. Most enterprise workforce scheduling softwares, as a pre-cursor to calculating labor needed in the store, provide a module to forecast sales ($sales, customers etc). Corporate teams configure this module to a great level of detail and expect the store managers to play their part in ensuring accurate business projections.

What no one attends to is the relative merit or demerit of an accurate forecast. Accurate forecast is a great thing to have. But one must realize that all enterprises have a separate sales forecasting software used by sales teams, meant purely for developing sales targets. It’s the job assigned to sales department, not labor planning. Albeit both the departments should work in harmony, but in principle, forecasting component inside a workforce scheduling software has limited role to play. It’s projection is only meant to help decide weekly labor needs. Often it’s projection is not even the same as sales projections set by sales teams using the sales planning process (This difference is a matter of confusion for stores. But, for another blog :)). Greater the accuracy expectations from forecasting in the workforce management, more the work one is expected to put in. The question is, is it worth the effort?

Suppose a forecasting module is operating at an average 95% accuracy. It means that, if the module forecasted a sale of 100K in a week, in reality the business would be between 95K to 105K. To most people, this sound unacceptable accuracy standard to plan their week with. Thus corporate teams engage in finer tuning of the module, end up making the software more complex and create more SOPs for the store managers to get to a better level of accuracy.

While you configure your forecasting module, following perspectives are worth bearing in mind: –

  1. All this need for greater forecasting accuracy is presumptuous. However accurate sales forecast one makes, the labor standards that drive the demand forecast are invariably flawed. Thus, no matter how much effort you put into sales accuracy, your labor forecast has inherent flaws of it’s own that often negate the sales accuracy.
  2. One has to be acutely aware of the sales per labor hour (SPLH). Labor investment in customer facing activities are primarily focussed on cashiering in a grocery shop. On the other hand, if you run a Pharmacy, each script order is like a manufacturing process that drives a lot of work in the store. Speciality retailers would fall somewhere in-between these two ends. Thus, your effort investment into accuracy of sales forecast should be directly proportional to how much sale each labor hour drives.
  3. Greater accuracy demands more microscopic tuning of the software and more ongoing time investment from store managers. The outputs must be worth that. And one must think objectively. It is unfair on software configuration teams and on the store managers to just say, “More accuracy is great. We will invest whatever it takes to get it.”
  4. Work incrementally towards better accuracy. Start with good and make it great over couple of years, not on the first day of your new software implementation

Accuracy is directly proportional to complexity of an implementation. It is often better to induce it over time, as one gets used to the cultural shift of using a workforce management software.

%d bloggers like this: