Improving supply chain planning with advanced analytics : analyzing lead time as a case study

Abstract

Over the years, supply chain management has continued to change and evolve to become a major component in competitive strategy to enhance organizational productivity and profitability. While considerable research has been done in formulating accurate and robust demand forecasts, many areas for improvement remain in supply chain planning. In particular, many planning parameters (e.g., lead time, waste, yield, run rate, capacity, etc.), which are vital inputs into the planning process, are often not given the consideration they deserve. Oftentimes, the planned values of these parameters were not scientifically derived in the first place, or their actual values may have changed since the planned values' original inception and now differ significantly from its planned value. This research examined one type of planning parameter in particular - lead time, and showed there is room for improvement in how lead time is managed and considered within the current planning process. The research showed that using predictive analytics to predict lead time (predictive lead time) can reduce the deviation between the planned and actual values in the lead time parameter..Moreover, the analyses showed that using predictive lead time can reduce the safety stock cost, the manual labor required in exception management (re-planning), and the manual labor in purchase order management.