AI-powered COVID-19 supplier risk index and demand planning toolkit

为了使卫生系统更好地为未来几个月做好准备和计划，Vamstar将创建一个基于风险的框架，以了解英国和欧盟医院层面的供应链缺口和不断变化的需求。此外，基于风险的框架将为情景预测提供动力，以便不仅确定相关风险，而且实时评估需求波动的影响，以便制定行动计划。风险评分矩阵、情景预测和需求规划工具包的目标是将重点放在欧洲卫生系统中最脆弱的部分，并尽快促进决策。有了供应风险框架，我们将能够预测管理COVID-19等危机所需的各种基本产品和服务的总体需求变化，并将供应商的重点引导到最需要的医疗服务部分。结合情景预测和需求计划工具包，Vamstar将能够评估哪些供应商已经完成了市场上当前的订单，并预测他们何时能够完全生产并再次销售。供应情景预测将能够预测当各国经历不同程度的不同类别的需求冲击时对供应的影响。此外，风险评分框架和需求规划工具包将帮助保健服务链中的利益攸关方快速评估已开发或正在开发预防性基础设施的国家，并分享调查结果。这一框架和连接的实时供应链分析将确保今后以更积极主动的战略管理此类危机，而不是目前在我们的卫生系统中普遍存在的被动供应链方法。通过人工智能分析该数据集，我们希望从宏观（能力）和微观（意愿）两个层面了解管理COVID-19等大流行所需的大流行-供应-风险和场景-预测。该工具包包括口罩等必要物品，但也包括数字平台，这些平台将有助于在这些国家经历快速转型的过程中对患者和人口健康进行管理。Vamstar提供了一个数据科学驱动的平台，用于预测和匹配医疗保健领域的公共合同，并将专门通过使用供应链风险评分矩阵创建一个自动化的“大流行准备工具包”，从而专注于受COVID-19大流行影响的卫生系统中最脆弱的部分。它将利用欧盟和英国关于公共和私人招标、定价来源和经济年度数据集的数据来实现这一目标。机器学习（ML）和深度学习将用于预测有闲置产能的供应商名单、短缺日期和关键供应品的价格等任务。这些预测和分析将成为报告和自动仪表板的一部分，这将使NHS医院和医疗保健供应商受益。

For health systems to better prepare and plan for the months ahead, Vamstar will create a risk-based framework to understand supply chain gaps and the evolving demand at a hospital-level in the UK and EU. Additionally, the risk based framework will power the scenario forecasts so that not only the relevant risks are identified but the impact of demand fluctuations assessed in real-time for action planning. The objective of the risk scoring matrix, scenario forecasts, and the Demand-Planning-Toolkit is to focus on the most vulnerable parts of the health systems in Europe and facilitate decision making as quickly as possible. With the supply risk framework, we will be able to predict changes in the overall demand for various essential products and services needed to manage a crisis like COVID-19 and direct the focus of suppliers towards the most needed parts of the care delivery. Combining this with scenario forecasts and the Demand-Planning-Toolkit, Vamstar will be able to assess which suppliers have fulfilled a current order in the market and make predictions about when they will become fully production-ready to sell again. The supply scenario forecasts will be able to predict the impact on supplies as the countries experience various degrees of demand shock across categories. Additionally, the risk scoring framework and Demand-Planning-Toolkit will help stakeholders in the care delivery chain quickly assess countries that have developed or are developing preventative infrastructure and share the findings. This framework and connected real-time supply chain analytics will ensure that in the future such crises are managed with a more proactive strategy vs a reactive supply chain approach currently prevalent in our health systems. By analysing this dataset through artificial intelligence, we want to understand the Pandemic-Supply-Risk and Scenario-Forecasts both from a macro (ability) and micro (willingness) levels needed to manage a pandemic like COVID-19. This toolkit includes necessary items such as face masks but also digital platforms that will aid in patient and population health management across these countries as it undergoes a rapid transformation. Vamstar offers a data science powered platform for predicting and matching public contracts in healthcare and will create an automated "pandemic preparedness toolkit" specifically by using supply chain risk scoring matrix so as to focus on the most vulnerable parts of the health systems affected by the COVID-19 pandemic. It will leverage EU and UK data on public and private tendering, pricing sources and economic annual datasets to do this. Machine learning (ML) and deep learning will be used for tasks such as predicting ongoing list of suppliers with spare capacity, the date of shortages, and prices of key supplies. These predictions and analysis will form part of reports and an autonomous dashboard that will benefit the NHS hospitals and healthcare suppliers.