About the Client: Fortune 100 Automotive Customer
The Fortune 100 Automotive Customer is one of the world’s largest and most important car manufacturers with 6 million market capture. It has 50 production locations across the five continents with 200000 employees across the globe. To be a leading, profitable volume manufacturer as well as playing a leading role in the new world of the automobility industry in the long term.
Maximizing the Dealer orders Fill percent or Fill Rate is always a key focus in the automotive after-sales stream. Increasing fill rate without piling excessive inventory at a plant requires not only a deep dive into fill rate and supply chain metrics across multiple dimensions but also deep learnings into data patterns and identify influencers impacting the fill rates.
A complete cloud-based reporting solution for tracking fill rate from Plant to the part level. Our consultants in partnership with the business developed a regression-based predictive model by taking the last 3 years’ dealer order history and fill rates into the account. The Model predicts future fill rates for the dealer odder demand data in the planning system.
- Gather data from supply chain history for drilling, rigging, pipeline materials, other inputs, part failure, and part usage history, lead time history (for reorders), history of disruptions to supply chain, planned and the actual history of ramping of capacity.
- Unified Historical and planning data along with master data within BW/4HANA
- Reporting Model which enables multiple drill-down levels and secured at region leve
- An easy-to-use yet very effective SAC-based visualization to track Fill rates historically and also drill down to material level with multiple end-user level capabilities including Ranking, Ad-hoc exploring.
- Considering 3 years dealer order history, created a regression model which predicts fill rate and quantities at a part level for demand planning data
Results and Benefits:
- The SAC story completely replaced excel based fill rate tracking. Users no longer need to perform any manual excel calculations to derive KPI’s which tremendously save time in analyzing.
- A very user friendly yet robust UI design
- A deeper insights into the fill rate data across multiple dimensions and
- 95% confident regression model trained from 3 years historical data which predicts the future fill rates, this will help users to maintain optimal inventory