A major global manufacturer of breakfast foods and snacks with approximately $14 billion in annual sales wanted to improve its operational systems to fuel its growth. The company had three specific goals in mind: forecast demand more accurately, improve order fulfillment rate with inventory optimization, and shorten cash-to-cash cycle times. Traditionally, the company relied on an Excel-based solution for these strategic challenges, but that approach posed limitations in processing data from different markets and measuring KPIs accurately. It was also slow: getting feedback from existing planning tools and using that data to generate useful insights was taking too long with the Excel-based system. The company wanted a more advanced analytics solution that could address multiple scenarios connected with demand planning.
Wipro worked closely with senior stakeholders from the food manufacturer’s planning, operations, and finance teams to create an advanced analytics solution for demand planning. Together, the team developed blueprints for all requirements, formed new and enhanced KPIs for measuring and monitoring success, and built a new solution that consolidated multiple data sources from different markets to generate actionable, customizable insights for demand planning.
A key part of the solution was to deliver a user-friendly analytics capability that would enable decision makers to derive insights for demand planning in a fast, accurate, and timely fashion. By being able to generate and understand KPIs like forecast accuracy, and derive insights from them, the company was able to improve its forecasting capabilities, optimize inventory, and improve fulfillment rates. The new system automatically processes data from different markets, transforms it using different business rules, and generates insights. These insights help reduce instances of over-forecasting and under-forecasting, which optimizes inventory levels and prevents stockouts. As part of the initiative, Wipro helped train all users of the analytics solution, including the support team.
The new analytics solution enabled the company’s planning, operations, and finance teams to easily evaluate current situations and predict future demands with improve forecast accuracy: a 3% increase in forecast accuracy increased gross margins by 2-3%, a huge boost for a consumer-packaged goods company. Moreover, the new system helped improve inventory optimization by 15% by reducing bias, leading to an improvement in order fulfillment of 17%. The net result was that cash-to-cash cycle times shortened by 35%, which improves overall operating efficiency.