Automate Supply Chain Optimization
Project Lead: Concurrency
Partners: Brunswick Corporation
Member % Cost Share: 50%
CESMII % Cost Share: 50%
Duration: 3 Months
Brunswick is experiencing unprecedented volume for engines and fully produced boats, but the supply chain continues to be difficult to predict and presents interruption to the manufacturing process or brings fluctuations to inventory availability. Brunswick needs to minimize inventory volume and maximize availability of assets for the production process.
Reduction in unnecessary inventory and closer alignment of asset needs to the manufacturing process. This includes ensuring the availability of inventory where needed, without unnecessary overstock.
Leverage customer Machine Learning to analyze the production demand against anticipated assets for production to produce a more accurate inventory report. The SM platform will be integrated with a generic data model and create a SM profile that will be used to describe the inputs fields for the supply chain data, such as warehouse, quantity in hand, location, etc. The SM platform will also receive the AI model that predicts the desired supply chain demand using minimum amount of inputs. For accuracy to improve from the generic model, the user will need to provide industry specific data.
Key Tasks & Milestones
- Connected Data Environment – Cloud data environment prepared for data sets
- Machine Ready Datasets – Cloud datasets ready for ML workloads
- Data Model for SM Platform – Could dataset model ready for SM platform
- Forecasting Algorithm – Initial algorithm to start iterative ML process
- Predictive Analytics – Iterations on ML algorithm to provide predictive analytics
- ML Model for SM Platform – Availability of the ML model for the SM platform and project close
The goal is a direct reduction in the amount of inventory holdings in order to serve the necessary production process, as well as availability of the inventory necessary for the production process. The goal is to reduce inventory over $15 million, with an increase in availability to avoid $5m in losses due to non-production states.
CESMII members gaining access to data model for optimization
CESMII members access to SM profile for connected data model and demand sources for the model
CESMII members gaining access to AI model through open source
CESMII members gain manufacturing competitiveness domestically and internationally