Enterprise platform for dynamic product demand predictions in retail

在当前的Covid-19大流行期间，企业比以往任何时候都需要更多的支持；特别是，零售组织正在经历重大的颠覆。快速变化的环境和政府监管使得零售商越来越难以迅速调整库存和策略，以满足客户对主要消费品和药品等高优先级产品的需求。在危机期间，对这些组织来说，尽可能提高效率尤为重要。准确和及时的需求预测是最大限度地减少产品短缺和保持充足库存的关键，从而对客户和组织本身的财务稳健性产生巨大的积极影响。该项目将扩展causaLens平台。在目前的形式下，它是领先的时间序列平台，拥有独特的技术，利用最新的因果关系研究来自主构建适应新数据的动态模型。交付的产品将包含专门为消费者需求应用量身定制的技术，包括在固定时间表上加载新数据的能力，以在线方式发现和更新模型，支持小数据场景，以及提供跨多个位置的预测。开发的主要创新将是部署的机器学习模型能够在新数据到达时更新其参数，立即反映环境的变化并提供更准确的需求预测。在危机时期，关于产品需求驱动因素的假设可能会发生变化。不再能够使用正常业务操作时期的历史数据可靠地预测需求。因此，系统需要在小数据场景中运行，因为只有最近的数据才能提供准确预测的最大好处。这将通过实施对因果算法的最新研究来实现，这种算法能够利用数据中发现的因果关系，从比传统机器学习算法少得多的点进行学习。此外，该系统将允许用户结合来自不同零售地点的数据来预测每个零售地点的需求。能够学习这种新环境并以这种方式提供准确预测的系统以前从未使用过。

During the current Covid-19 pandemic, businesses require more support than ever; in particular, retail organisations are experiencing major disruption. The rapidly evolving conditions and government regulations are making it increasingly hard for retailers to adapt their stock and strategy quickly enough to meet their customer's demand for high-priority products, such as consumer staples and medicines. During a crisis, it is especially important for these organisations to be as efficient as possible. Accurate and timely demand predictions are key to minimising product shortages and maintaining adequate volumes of stock, resulting in a drastic, positive impact on their customers and on the financial robustness of the organisation itself.This project will expand the causaLens platform. In its current form, it is the leading time-series platform with unique technology that leverages the latest research into causality to autonomously build dynamic models that adapt to new data. The delivered product will contain technology specifically tailored for consumer demand applications, including the capability to load new data on a fixed schedule, to discover and update models in an online fashion, to support small data scenarios, and to provide predictions across multiple locations.The main innovation developed will be the ability of the deployed machine learning models to update their parameters as new data arrives, immediately reflecting changes in the environment and providing more accurate demand predictions. In a time of crisis, the assumptions about the drivers of a product's demand are likely to change. Demand is no longer able to be reliably predicted using historical data from a time of normal business operations. Therefore, the system will need to operate in a small data scenario as only recent data will provide the greatest benefit in providing accurate predictions. This will be achieved by implementing the latest research into causal algorithms, which have the ability to leverage causal relationships discovered in the data to learn from vastly fewer points than traditional machine learning algorithms. Additionally, the system will allow the user to combine data from various retail locations to predict demand at each one. A system that is capable of learning this new environment and providing accurate predictions in this way has never been used before.