SCORE: Supply chain optimisation for demand response efficiency
Background
The vision of this industrial research project is to bring the SCORE (Supply Chain Optimisation for demand Response Efficiency) system from TRL3 to TRL5. This system will enable Tier 1 and Tier 2 suppliers in manufacturing sectors to better manage their inventory through digital technologies and minimise the impact of sudden changes in demand and maintenance activities.
The main areas of focus in this project are on implementing the sensors for the track and trace of inventory and developing machine learning algorithms for the creation of demand forecast model and inventory change models. Although enterprise resource planning (ERP) systems take into consideration some factors, e.g. the scheduled maintenance activities, they are mostly generic tools, lacking specialist forecasting systems, and relying extensively on statistical methods for inventory control predictions. The innovation in SCORE lies in the application of machine learning to optimise supply chain management models which traditionally use statistical analysis methods, the integration of different models into one and the communication of the forecasts with the entire supply chain, leading to more precise control over the inventory, greater traceability of assets, and near elimination of delays in supply or overstocking of parts.
Our initial target market is the supply chain management (SCM) software market, with Tier 1 and Tier 2 suppliers the target users. This project represents a clear technological innovation for UK SCM, and major growth opportunity for the SME supply chain consortium. To successfully achieve this, the project consortium features the relevant expertise including track and trace system development, machine learning algorithm development, and inventory control expertise.
Objective
The key objectives in fulfilling this vision are to:
- Ensure smooth flow of materials between different nodes of supply chain
- Minimise waiting time to start production and avoid delays through tracking of materials at different stages
- Automate raw material demand according to production cell cycles for production lines to minimise 'on-floor' unused material
- Integrate continuous a learning-enabled model for prediction of demands and machinery breakdowns
Benefits
- Optimisation of inventory management.
- Elimination of waste with the establishment of clear and accurate demand signals to help them eliminate waste
- Greater traceability
- Near elimination of delays in supply or overstocking of parts
- Improvement in production planning efficiency by 20%
- 15% reduction in waste
- Reduction of CO2 emissions
Brunel Innovation Centre's Role
Brunel Innovation Centre of Brunel University will bring to the SCORE project its vast knowledge in machine learning algorithms and evolutionary optimisation algorithms.
Project Partners
Alford FE Ltd
Gestamp Tallent Ltd
Broadways Stampings Ltd
Brunel University London
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Related Research Group(s)
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Project last modified 12/10/2023