Strategically planning the composition of your workforce is a difficult task for retailers. Today, planning typically looks at the year-on-year (YoY) workforce trends, which effectively boils down to asking “did we grow or have we shrunk, and will this trend continue this year?” It’s a crude answer to the question of how you should plan your workforce, for two reasons.
Firstly, it lacks detail. It doesn’t take into account the more granular questions. How is your workforce structured? Do you prefer to hire employees permanently or on temporary contracts? What’s the cost of hiring, reallocating, and retraining employees? There are many questions that go unanswered if you purely look at YoY trends.
Secondly, today’s workforce planning ignores a key variable: demand. This leads to inaccuracy in your plans and is exactly the reason why it’s so difficult to strategically plan your workforce over a longer period of time. After all, staffing requirements fluctuate based on the demand you face, especially when faced with questions such as: what if you add a new store? Or change your product mix? Or introduce a new POS system?
With today’s technology, strategically planning your future workforce is easy, fast, and reliable. More specifically, a combination of demand forecasting and capacity planning algorithms can help plan your ideal staff mix and make staffing decisions over the next quarter or year, as well as help close the gap between your predicted demand and your current supply of employees.
The first part of the equation is to accurately and granularly predict your future demand. It’s best to have a demand forecasting algorithm that includes variables such as seasonality, trends, data patterns, projected growth, and holidays. Not only will these variables yield a more accurate demand forecast over the next quarter or longer of your business, but they’re also variables that might influence the composition of your workforce.
Now that you have an accurate demand forecast, you need to understand and fill the gap between the number of employees you’ll eventually need, based on demand, and how many employees you currently have for the next month or quarter. That’s the second part of the equation, and it’s calculated with a capacity planning algorithm.
The algorithm takes into account important business constraints such as attrition, employee efficiency, maximum staffing levels, and the physical location of your stores. It takes demand forecasts as input, as well as other variables that impact your workforce, such as the costs of hiring versus retraining as well as the optimal full-time to part-time ratio, and it gives strategic recommendations to help you plan your workforce on the basis of those variables. These recommendations are detailed and comprehensive, and can help you decide whether you’ll need to hire, reallocate, or retrain employees in the near future to cover the peaks in your demand without overstaffing for the rest of the year.
Accurate demand forecasts and staffing recommendations can help you strategically plan your workforce and influence all the other impactful business decisions you need to make. Instead of having to make decisions based on a simple YoY trend, you can make more accurate and more granular decisions, from deciding on your optimal staff mix for a particular store to whether you need to hire or re-allocate employees.
All in all, the combination of demand forecasts and capacity planning algorithms gives you a way to save money, save time, and gain control over what’s otherwise an uncertain part of your company.
Algorithm-based demand forecasts and staffing recommendations can help you make more accurate and more granular decisions, from deciding on your optimal staff mix for a particular store to whether you need to hire or re-allocate employees.
The benefits of algorithms are well-known and well-explained already. And as the CEO of Widget Brain, I’m stating the obvious when I say I’m a firm believer in the power of algorithms. But many of the articles that write about the benefits of artificial intelligence overlook an important fact. READ MORE.
Due to the crisis, most generic demand forecasting models in place today are no longer as accurate as they used to be and a relative approach has to be taken in order to find the “new normal” when compared to traditional historical patterns. Read more about the new normal in demand forecasting.