Since March, the job world reorganization has been one of the main defense weapons against the spread of Corona virus. Already from the first days of the health emergency, alongside the implementation of remote working, reducing crowding at work was, in fact, started through a different perception of work shifts. The goal was clear and precise right from the start: to be able to guarantee safety distances and minimize contacts – and therefore the opportunities for contagion – eliminating excessive crowding at the workplace. At first, those companies belonging to strategic supply chains for the country that continued business even during lock down were completely rethinking work shifts; then the same need was shared by all companies which, with phase 2, resumed production. Careful and precise forecasting staff needs for correct shift management has thus become doubly important for a wide range of companies. But how can this process be optimized to achieve maximum safety and effectiveness with a concrete reduction in costs?
The calculation of the staff needs starting from the analysis of previous years
How do you calculate staffing needs for individual company activities so that shifts can be organized without errors? There are, in fact, different methods. Until recently, the most reliable one consisted in the statistical analysis of the data collected in previous years, thus checking the history to identify driving indicators and setting shifts for the immediate future. Undoubtedly, this method makes it possible to organize shifts and working hours in a better way than what is done by those who estimate the need for personnel without any initial statistical data. However, it is true that we can do better to fully optimize this process and, therefore, business performance.
Calculating staffing needs taking into account several variables
There are many variables to be taken into consideration to calculate how many and which workers will be needed to carry out company activities safely and satisfactorily. Sure, historical data can help, but it’s true that situations rarely present themselves in the same way. Customer requirements, products currently in stock, new regulations, varying employee skills, the need to launch new products, the introduction of new machinery or new technologies, changes in demand and so on affect staffing needs over the upcoming weeks and months. As you can guess, mere historical data can do little in the face of these variables: thanks to the aid of artificial intelligence algorithms, however, it is possible to organize shifts more efficiently, starting from the analysis of all the above listed factors.
Optimizing staff shift management is now possible
With the advent of digital transformation, custom software has been developed to forecast staffing needs and organize work shifts without errors, without waste and in full compliance with current and future protocols, in a scenario that, as known, is inevitably subject to sudden changes. These software applications rely on artificial intelligence algorithms to reconcile staff organization with real demand and thus make shift management more efficient than ever, whether it be for a production company, retail store, large-scale distribution, call centers and much more.