ECO-SPRINT: Environment Centred Optimisation of SME Productivity using Realtime INTelligence

以环境为中心，利用实时智能优化中小企业生产力(ECO-SPRINT) -专注于制造业中小企业的可持续发展和生产力需求。它汇集了一个专注于制造数据和智能的IDT提供商联盟，渴望在其产品中建立可持续性。该项目将首先关注能源密集型航空航天特殊精加工工艺（Poetons是英国最大的独立表面处理企业），然后通过金属组装有限公司在另一个领域的部署来展示可扩展性。认识到许多过程很难自动化，它将开发一种创新的视觉智能系统（VIOLET），结合可扩展和快速部署的工业物联网解决方案。它将把这一点与idt的创新组合结合起来，可持续智能能力将集成在其中，包括智能制造执行系统（MES）；共享需求波动情报的“客户/供应商门户”、高级调度系统、资产保护系统和可配置的商业智能（BI）解决方案。ECO-SPRINT的输出将使中小企业能够：-捕获有关人类操作的前所未有的商业智能，这将输入到联盟的调度软件中，并实现手动过程的优化，如铸造厂、电镀缸和热处理炉的利用-捕获工厂和能源利用数据，实现实时优化-将熟练的特殊工艺操作员的人类操作数字化，以提供自动化时间；动态和风险捕获数据-提取和分割工厂和公用事业的特殊工艺房屋优化过程中人类行为的视频，以通过数字化知识转移保留最佳实践-收集碳和材料足迹数据，这些数据可以跨供应链进行沟通和汇总。通过valuecchain的供应商和客户门户)

Environment Centred Optimisation of SME Productivity using Realtime INTelligence (ECO-SPRINT) - focuses on the aligned sustainability & productivity needs of manufacturing SMEs.It brings together a consortium of IDT providers that focus on manufacturing data & intelligence, eager to build sustainability into their offerings.The project will start with a focus on energy-intensive aerospace special finishing processes (with Poetons as the lead demonstrator - the UK's largest independent surface treatment business), and then demonstrate scalability through deployment in another sector courtesy of Metal Assemblies Ltd.Recognising that many processes are hard to automate, it will develop an innovative vision intelligence system (VIOLET) combined with a scalable and rapid-to-deploy IIoT solution. It will integrate this with an innovative combination of IDTs onto which sustainability intelligence capability will be integrated including a Smart Manufacturing Execution System (MES); a "customer/supplier portal" which shares intelligence on demand volatility, an advanced scheduling system, an asset protection system, and a configurable Business Intelligence (BI) solution. The outputs from ECO-SPRINT will enable SMEs to:-capture unprecedented business intelligence about human operations which will feed into the consortium's scheduling software and enable optimisation of manual processes such as casting plants, electroplating vats and heat treatment oven utilisation-capture plant and energy utilisation data enabling real-time optimisation-digitise human action from skilled special-process operators to provide automated time, motion and risk capture data-extract and segment videos of human actions on the special-process house optimisation of plant and utilities to retain best-practice through digitalised knowledge transfer-gather data on carbon and material footprint that can be communicated and aggregated across supply chains (eg. through Valuechain's supplier and customer portals)