Automating Forecasting and Capacity Checks for a Global Automotive Supplier
Technology Category
- Analytics & Modeling - Machine Learning
- Platform as a Service (PaaS) - Application Development Platforms
Applicable Industries
- Automotive
- Transportation
Applicable Functions
- Discrete Manufacturing
- Sales & Marketing
Use Cases
- Additive Manufacturing
- Demand Planning & Forecasting
About The Customer
The customer is 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. The company was facing challenges in managing the quality and accuracy of OEM forecasts, leveraging external market data for demand planning, and conducting manual capacity checks. The company was heavily reliant on Excel and SAP APO for these processes, which were inefficient and lacked the necessary intelligence for effective decision-making.
The Challenge
The customer, a leading global supplier to the automotive and industrial sectors, was facing significant challenges in managing the quality and accuracy of OEM forecasts. The company was unable to leverage external market drivers to predict demand and was heavily reliant on Excel. This resulted in high variability in the quality and accuracy of forecasts. Additionally, there was a lack of demand alignment across OEM forecasts, which were stored in multiple Excel sheets. The company also had access to external market data from providers such as IHS, but was unsuccessful in leveraging this data to improve demand planning. Furthermore, 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).
The Solution
The company adopted the o9 platform to automate the consolidation of OEM forecasts, on one single platform, including trend analytics, enriched with bottom-up sales forecasts. The platform also enabled intelligent triangulation of OEM forecasts sent to multiple plants for the identification of mismatches. The company was able to 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. The o9 platform also allowed the company to run capacity checks and the full S&OP process directly on the platform. The o9 platform was 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. The systems replaced by the o9 platform were SAP APO and Excel.
Operational Impact
Quantitative Benefit
Case Study missing?
Start adding your own!
Register with your work email and create a new case study profile for your business.
Related Case Studies.
Case Study
Airport SCADA Systems Improve Service Levels
Modern airports are one of the busiest environments on Earth and rely on process automation equipment to ensure service operators achieve their KPIs. Increasingly airport SCADA systems are being used to control all aspects of the operation and associated facilities. This is because unplanned system downtime can cost dearly, both in terms of reduced revenues and the associated loss of customer satisfaction due to inevitable travel inconvenience and disruption.
Case Study
Integral Plant Maintenance
Mercedes-Benz and his partner GAZ chose Siemens to be its maintenance partner at a new engine plant in Yaroslavl, Russia. The new plant offers a capacity to manufacture diesel engines for the Russian market, for locally produced Sprinter Classic. In addition to engines for the local market, the Yaroslavl plant will also produce spare parts. Mercedes-Benz Russia and his partner needed a service partner in order to ensure the operation of these lines in a maintenance partnership arrangement. The challenges included coordinating the entire maintenance management operation, in particular inspections, corrective and predictive maintenance activities, and the optimizing spare parts management. Siemens developed a customized maintenance solution that includes all electronic and mechanical maintenance activities (Integral Plant Maintenance).
Case Study
IoT-based Fleet Intelligence Innovation
Speed to market is precious for DRVR, a rapidly growing start-up company. With a business model dependent on reliable mobile data, managers were spending their lives trying to negotiate data roaming deals with mobile network operators in different countries. And, even then, service quality was a constant concern.
Case Study
Digitize Railway with Deutsche Bahn
To reduce maintenance costs and delay-causing failures for Deutsche Bahn. They need manual measurements by a position measurement system based on custom-made MEMS sensor clusters, which allow autonomous and continuous monitoring with wireless data transmission and long battery. They were looking for data pre-processing solution in the sensor and machine learning algorithms in the cloud so as to detect critical wear.
Case Study
Cold Chain Transportation and Refrigerated Fleet Management System
1) Create a digital connected transportation solution to retrofit cold chain trailers with real-time tracking and controls. 2) Prevent multi-million dollar losses due to theft or spoilage. 3) Deliver a digital chain-of-custody solution for door to door load monitoring and security. 4) Provide a trusted multi-fleet solution in a single application with granular data and access controls.
