A leading global supplier to the automotive and industrial sectors. It is one of the world’s largest family companies and has a global network of manufacturing, R&D, and sales facilities.
This customer faced high variability in the quality and accuracy of OEM forecasts. The company was unable to leverage external market drivers to predict demand and depended largely on Excel.
Forecast accuracy improvement reduces expedited logistics as well as excess inventory.
Business Scope Challenges
OEM Forecast Alignment
There was an absence of demand alignment across OEM forecasts, which were stored in multiple Excel sheets.
With o9, the company is able to automate the consolidation of OEM forecasts, on one single platform, including trend analytics, enriched with bottom-up sales forecasts. Moreover, intelligent triangulation of OEM forecasts are sent to multiple plants for the identification of mismatches.
The company had access to external market data from providers such as IHS, but was unsuccessful in leveraging this data to improve demand planning.
With o9, the company can easily incorporate data from external data providers (e.g. IHS) in the planning processes to streamline the calculation of take-rate forecasts for all produced parts.
Manual Capacity Checks
Capacity checks were done manually in Excel, which resulted in the lack of a layer of intelligence on top of the execution system (SAP APO).
With o9, Enterprise Knowledge Graph model, the company is able to run capacity checks and the full S&OP process directly on the o9 platform.
Key Functionalities Implemented
The o9 platform is used to run demand forecasts, high level S&OP, capacity checks, trend analytics and automate OEM forecasts, while leveraging o9’s machine learning forecasting models.
SAP APO and Excel.
Success Factors — 3 reasons why o9 was selected
Deep industry knowledge in the auto supplier and discrete manufacturing industry.
The future-proof planning platform allowing to leapfrog to best-in-class planning and decision-making.
Very intuitive UI/UX that users love, which boosts user adoption.
Forecast accuracy improved > 10 percentage points — from x7% to x8%.
Improvement in planner productivity, in line with previous experiences (15–25%).
Higher automation of tasks, estimated at 30% of initial baseline.
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