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

Loblaw公司（LCL）雇佣了大约20万加拿大人（年收入450亿美元），从滑铁卢配送中心（DC）向其位于安大略省西南部的商店供应所有新鲜农产品（FP），这些商店从427号高速公路以西延伸到温莎市。拼箱的食品配送中心是非常大的仓库，有许多温度区域用于存储FP，并负责订购和分发到各个存储位置。订购主要取决于对计划生育的需求，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给拼箱带来经济效益，同时最大限度地减少浪费。例如，美国估计新鲜水果和新鲜蔬菜每年分别损失59亿英镑和61亿英镑。然而，价格取决于许多因素，这些因素受到采购计划生育的不同地区的影响，并受到收入、劳动力和其他贸易问题等环境和社会经济影响的高度不确定性的影响。由于全球化和气候变化，这些因素变得更加不确定，而且难以预测，因此制定计划生育采购价格和数量的决策是一项极具挑战性的任务。这项重要的任务目前是根据同一产品的近期价格来完成的，这太简单了。由于巨大的交易量和货币价值（仅loblaw的FP就超过250亿美元），每笔FP交易哪怕提高一分钱，每年都会为加拿大带来数亿美元的收益。此外，公平的价格带来了从种植者到消费者以及相关地区的普遍福利的繁荣。LCL的迫切需求和支持，以及滑铁卢大学（UW）研究人员的良好研究记录，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。这将导致使用将ML方法预测作物产量与经济模型相结合的模型来提高FP“公平”采购价格。这种新颖的方法将为FP行业和加拿大社会提供更多的利益。

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.