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Background
Our client is an American multinational delivery services company that ships over 18 million packages per business day. They operate with more than 680 aircrafts and 200,000 motorized vehicles to ensure overnight shipping service.
Challenge
Given the large amount of volume involved, it is business critical to have an accurate view on forecasted volumes in order to align all required resources. The strategic marketing department was looking for ways to automate analyzing market data and customer related insights. This would improve top-line forecasts and allow them to perform "what-if" scenarios and sensitivity analysis.
Approach
- Define main forecasting process challenges and bottlenecks. - Determine the key drivers and allocation principles that impact the forecast accuracy. - Design solution architecture. - Build a flexible, easily adaptable, and scalable forecasting model. - Test outcomes, compare with business goals, and refine model.
Key results
Benefits
By improving the level of automation, our client was able to: - Increase process efficiency by providing more insights into planning correlations, reducing cycle times and automating complex allocation principles. - Improve forecasting accuracy by bringing consistency to the forecasting process, allowing for more fine-grained forecasting and reducing manual interventions to decrease the risk of errors. - Realize real business impact by shifting their experts’ time from data crunching towards value adding recommendations.
Technology partners
SAS
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