A leading grocery supermarket chain wanted to reduce its inventory wastage and sales opportunity loss – the two main outcomes of inaccurate demand forecasting. It sought accurate predictions to optimize the inventory of its highly perishable items. Since sales of such items are highly dependent on price fluctuations, weather conditions, and seasonal demand patterns, among other factors, the company knew it needed an accurate prediction model to minimize its losses. With this goal in mind, it approached Netscribes to gain timely and accurate predictions to optimize its replenishment cycles across outlets.
Download the case study to find out how Netscribes used data analytics to provide highly-precise sales estimates that eventually the earned firm an annual savings of $1.8 million.