New Data-Driven Methods for Managing Complex Inventory Systems in Global Supply Chains

生产各种商品的全球性公司（包括消费品（例如健康、个人和美容护理、食品和饮料）、药品、化学品和电子产品）面临三重威胁：（1）产品种类的增加，以满足日益缺乏耐心和挑剔的消费者基础，（2）由于创新速度的加快，生命周期缩短;（3）由于来自新的在线竞争者的激烈竞争，利润率缩小。 为数百个地点的数千种物品进行计划（并适当库存）的能力已经成为业务的一个越来越重要的方面，既可以获得收入（通过不因库存短缺而错过销售），也可以避免未售出材料的成本（包括环境成本）。与过去有足够的数据可用，规划范围更稳定不同，今天这些决策需要在更频繁的基础上使用少量历史数据（尽管有大量产品，地点和时间的复杂交互）。该研究项目将产生新的方法，以帮助这些公司承担以前无法使用的方法，并成功地应用于应用数学，统计和工程的其他领域。调查人员与多家公司密切合作，并获得了可用于测试新方法性能的代表性数据。在当今竞争激烈的全球供应链（努力以琳琅满目的产品、卓越的服务水平和低廉的价格取悦不耐烦的客户）的复杂数据环境中，有效的库存管理对于各行各业的业务成功至关重要。 在产品可用性上的竞争已成为一个关键的战略武器。这需要新的数据驱动方法，而不是依赖于分布假设的方法（这是目前实践的主流）。有五个方面使数据变得复杂。首先，购买历史数量有限，很难对已经不确定的消费者有一个完整的了解;因此，除了数据的不确定性之外，模型中的参数不确定性也必须考虑在内。其次，物品是成套购买的，或者作为替代品，因此需要一个可以用有限数据量建立的易处理的多变量模型。第三，由于消费趋势，购买行为存在时间方面的自相关和滞后。第四，并非每种商品都在每个时期被购买，也不是以固定的金额购买;除了购买之间的时间和购买数量之间的相关性之外，这种零星（间歇性）需求建模也是至关重要的。最后，由于季节性和生命周期阶段，购买行为是非平稳的。研究人员将使用一个考虑参数不确定性的框架，这是一种既稀疏又通用的多变量表示，并通过对数据生成过程进行最少的假设，从可用的有限数据开始开发快速算法。以前的工作是非常成功的，在处理单一产品，有限的历史情况下，没有间歇性的需求，和早期的测试显示非常好的性能与间歇性的数据。除了研究之外，研究人员还计划为教育工作者提供工具，以便这些工具可以成为高年级本科生和硕士生（包括技术MBA）的下一代教学工具的一部分。

Global companies that produce a variety of goods (ranging from consumer products (such as items in health, personal and beauty care, food and beverages), pharmaceuticals, chemicals and electronics) are facing a triple threat: (1) increasing variety of products for an increasingly impatient and discriminating consumer base, (2) shorter life cycles due to increasing speed of innovation and (3) shrinking profit margins due to intense competition from new on-line competitors. The ability to plan for (and stock appropriately) the thousands of items across hundreds of locations has become an increasing critical aspect of the business, both to capture revenues (by not missing sales due to shortage of inventory) as well as to avoid costs of unsold material (including the costs on the environment). Unlike in the past where sufficient amount of data was available, and the planning horizons were more stable, today these decisions need to be made with low number of historical data (albeit with complex interactions across large number of products, locations and time) on a more frequent basis. This research project will yield new methods to help these companies by bringing to bear previously unavailable methods developed from, and successfully applied in other areas of applied mathematics, statistics and engineering. The investigators have worked closely with several companies and have representative data on which they can test the performance of the new methods.Effective management of inventories in today's complex data environment of highly competitive global supply chains (fighting to delight impatient customers with a dazzling array of products, excellent service levels and low prices) is critical for business success across a variety of industries. Competing on product availability has become a key strategic weapon. This requires new data-driven methods rather than approaches that rely on distributional assumptions (which are the mainstay in current practice). There are five aspects that make the data complex. First, having a limited amount of purchase history makes it difficult to have a complete picture of the already uncertain consumer; thus, the parameter uncertainty in the model has to be accounted for, in addition to data uncertainty. Second, items are bought in sets, or act as substitutes, and so a tractable multi-variate model is needed that can be built with limited amount of data. Third, there are temporal aspects of purchase behavior, with auto-correlations and lags, due to consumer trends. Fourth, not every item is bought in every period, nor are they bought in fixed sums; this sporadic (intermittent) demand modeling is crucial as well, in addition to correlations between the time between purchases and purchase quantities. Finally, due to seasonality and life cycle phases, the purchase behavior is non-stationary. The investigators will use a framework that accounts for parameter uncertainty, a multi-variate representation that is at once sparse and general, and develop fast algorithms starting from available limited data by making the least amount of assumptions on the data generation process. Prior work was very successful in dealing with the single-product, limited history situation with no intermittent demand, and early testing shows very good performance with intermittent data. Beyond research, the investigators also plan to provide tools for educators so that these can be made part of the next generation of teaching tools for senior undergraduates and Masters students (including technical MBAs).