We followed a two-step process:

                          • Impact analysis: The Tredence team put studied the inherent nature of sub-optimal ?eet transfers and understand its drivers. We were able to infer that 40% of the transfers occurred despite inventory availability, which was driven by anticipation of demand. However, about 35% of the transfers did not result in any rental in the short term, indicating inef?cient demand analysis.
                          • Solution development: We put together a demand forecast solution – a hybrid time series model that helped forecast short-term demand.

                          KEY BENEFITS

                          • The forecast model was scalable to predict demand at different levels, reducing suboptimal ?eet transfers
                          • It enabled optimized inventory planning for district manager, leading to better ?eet utilization


                          The impact of the solution was two-fold

                          • 35% reduction in sub-optimal transfers during the ?rst quarter of implementation
                          • Reduced stock out days through better anticipation of future demand