Artificial Intelligence-based Tools for Fresh Produce Procurement Price Decisions as Applied to Canadian Distribution Centers

Loblaw Companies Limited(LCL)雇用了约200,000名加拿大人[年收入450亿美元]，从滑铁卢配送中心(DC)向他们在安大略省西南部的商店供应从427号高速公路以西延伸到温莎市的所有新鲜农产品(FP)。LCL的食品DC是非常大的仓库，有许多储存FP的温区，负责订购和分发到不同的商店地点。订货主要取决于对FP的需求，预计将满足需求，但采购价格也受到预期作物产量的影响。适当的时间和价格的订单为LCL带来了经济利益，同时最大限度地减少了浪费。例如，美国估计每年新鲜水果和新鲜蔬菜的损失分别为59亿英镑和61亿英镑。然而，价格取决于许多因素，这些因素受到采购FP的不同地区的影响，并因收入、劳动力和其他贸易问题等环境和社会经济影响而受到高度不确定性的影响。这些因素由于全球化和气候变化而变得更加不确定和难以预测，使得决定FP采购价格和数量成为一项极具挑战性的任务。这项重要的任务目前是根据相同产品的过去价格来完成的，这过于简单化了。由于巨大的交易量和货币价值(仅Loblaws FP一项就超过250亿美元)，每笔FP交易哪怕是微不足道的一分钱，都会为加拿大带来每年数亿美元的好处。此外，公平的价格带来了从种植者到消费者的全面繁荣，也给相关地区带来了普遍福利。LCL的迫切需求和支持，以及滑铁卢大学(UW)研究人员良好的研究记录，为开发、测试和使用先进的人工智能(AI)和机器学习(ML)工具提供了机会。这将导致改进FP“公平”的采购价格报价，使用将ML方法预测作物产量与经济模型相结合的模型。这一新的方法将为FP行业和加拿大社会提供更多的好处。

Loblaw Companies Limited (LCL), employing about 200,000 Canadians [$45B annual total revenue], supplies from Waterloo Distribution Center (DC) all fresh produce (FP) to their stores in South Western Ontario that extend from west of highway 427 to the City of Windsor. LCL's food DCs are very large warehouses with many temperature zones for storing FP and are responsible for ordering and distributing to various store locations. Ordering depends mostly on the demand for FP and is expected to meet demand but procurement prices are also affected by expected crop yields. Adequately timed and priced orders bring financial benefits to LCL and at the same time minimizes waste. For example, U.S. estimates an annual loss of 5.9 and 6.1 billion pounds for fresh fruit and fresh vegetables, respectively [1]. Prices, however, depend on many factors that are affected by the diverse regions from which the FP is procured and affected by high uncertainty due to environmental and socio-economic effects such as income, labor and other trade issues. These factors are becoming even more uncertain from globalization and climate change, and hard to predict, making decisions on FP procurement prices and quantities an extremely challenging task. This important task is currently done based on immediate past prices of the same produce, which is too simplistic. Because of the great volume of transactions and monetary value (over $25B just for Loblaws FP), an improvement of even a micro cent in each FP transaction transfers to benefits of hundreds of millions of dollars each year for Canada. Additionally, fair prices bring prosperity all around from growers to consumers and to the general welfare of the involving regions. The urgent need and support of the LCL and the well-established research record of investigators at the University of Waterloo (UW) have provided an opportunity to develop, test, and employ advanced artificial intelligence (AI) and machine learning (ML) tools. This will result in improved FP "fair" procurement price offers using models that combine ML methods predicting crop yields with economic models. This novel approach will provide increased benefits to both FP industry and Canadian society.