SCORE: Supply Chain Optimisation for demand Response Efficiency

这个工业研究项目的愿景是从TRL3到TRL5，SCORE：需求响应效率的供应链优化。这个系统将使制造业的一级和二级供应商能够通过数字技术更好地管理他们的库存，并将需求和维护活动突然变化的影响降至最低。实现这一愿景的主要目标是：1.确保供应链不同节点之间的物料畅通。通过跟踪不同阶段的材料，最大限度地缩短开始生产的等待时间，避免延误3。根据生产线的生产单元周期实现原材料需求的自动化，以最大限度地减少‘现场’闲置材料4。集成可持续学习的模型，用于预测需求和机械故障该项目的主要重点领域是实现用于跟踪和跟踪库存的传感器，以及开发用于创建需求预测模型和库存变化模型的机器学习算法。虽然企业资源计划(ERP)系统考虑了一些因素，如计划的维护活动，但它们大多是通用的工具，缺乏专业的预测系统，并且广泛依赖统计方法进行库存控制预测。SCORE的创新之处在于应用机器学习来优化传统上使用统计分析方法的供应链管理模型，将不同的模型整合为一个模型，并将预测与整个供应链进行沟通，从而实现对库存的更精确控制，资产的可跟踪性，以及几乎消除供应延迟或积压。我们最初的目标市场是供应链管理(SCM)软件市场，以第1级和第2级供应商为目标用户。该项目代表着英国供应链管理的一项明确的技术创新，也是中小企业供应链联盟的主要增长机会。为了成功实现这一目标，该项目联合体拥有相关专业知识，包括跟踪和跟踪系统开发、机器学习算法开发和库存控制专业知识。

The vision of this industrial research project is to bring from TRL3 to TRL5, SCORE: Supply Chain Optimisation for demand Response Efficiency. 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 key objectives in fulfilling this vision are to:1. Ensure smooth flow of materials between different nodes of supply chain2. Minimise waiting time to start production and avoid delays through tracking of materials at different stages3. Automate raw material demand according to production cell cycles for production lines to minimise 'on-floor' unused material4. Integrate continuous a learning-enabled model for prediction of demands and machinery breakdownsThe 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.