Demand planning and forecasting for an office products major
The client is a global leader in pressure-sensitive technology, self-adhesive base materials and self-adhesive consumer and office products and specialized label systems and ranks among the Fortune 500 companies. With sales of almost US$5 billion, the client is best known for its office automation and consumer products, self-adhesive materials, reflective and graphic materials, peel-and-stick postage stamps, industrial labeling solutions, radio frequency identification
(RFID) labels, label stock and related services and systems, automated retail tag and labeling systems, specialty tapes, and chemicals.
For the client, success required managing short product lifecycles, focusing on customer service and, most importantly, reducing supply chain costs. Optimized supply chain management is crucial for the success of companies in this industry and a key component of supply chain management is accurate forecasting and demand planning. The client was looking for a way to make its supply chain more streamlined and hence more cost-effective. Specifically, the client had been looking at forecasting the US sales for each stock keeping unit (SKU). It wanted to move towards a demand planning model that was based around collaborative and consensus forecasting. To facilitate this, the office products division of the client had already implemented i2 Demand Planning (DP), but poor forecast accuracy and suboptimal forecasting process took its toll on overall system efficiencies and diminished user confidence.
Infosys was engaged to conduct diagnostics to identify issues in DP implementation and to tune up the overall forecasting process. An Infosys team was deployed to identify the requisite improvements with a quick turnaround time of five weeks. The Infosys team conducted diagnostics to identify issues with current implementation and potential opportunities to optimize the system. The team then came up with 24 recommendations for improvement of utilization, user productivity and forecast accuracy. These recommendations were analyzed from two aspects - namely, ‘ease of implementation’ and ‘delivered business value’ - to arrive at the priority classification for implementation. In the second phase, the Infosys team was asked to implement eight of the statistical modeling initiatives, clubbed under four clusters, with an objective to improve forecasting process, user productivity (by making the system user friendly and flexible) and implementing various advanced features of DP that were not being used. The above scope was completed within a short span of four months leveraging the
Infosys Global Delivery Model with significant productivity/ process improvements.
- Enhanced version management capability, reducing hours of manual work
- Increased forecast accuracy – Error reduction by 2 percent and bias improvement by eight million units over the year
- Flexible process to manage historical data cleansing and accounting for future promotions
- Increased user confidence by making the process more transparent and flexible
- A sustainable and repeatable knowledge base through user education and training
- A roadmap for future enhancements