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T he benefits of good decision making by a distributor often have broad leverage across a supply chain, and data science provides a comprehensive framework for making this possible. We present a case study of an ongoing partnership between the authors and corporate managers at a distributor of heating, ventilating, and air-conditioning products. We describe in detail the "vertical integration" of our analytical tools through a long chain of data scientific activities, backward to raw data, and forward to visually appealing output, in an organization with legacy information technology infrastructure. The models are applied to a large-scale data set, and spreadsheet-based decision support tools that include useful visualization capabilities for the firm are illustrated. We also offer this case as a blueprint for building a collaborative research relationship between academia and industry.

In a storage-and-retrieval device, items are retrieved on demand from a storage bank by a picking mechanism. Many varieties of these robotic devices are in use in manufacturing, logistics and computer peripherals. In printed circuit board manufacturing, storage-and-retrieval is intertwined with component placement and product clustering. Under certain circumstances, the problem of assigning items by type to storage slots to minimize the expected retrieval time is a quadratic assignment problem. Although such models are very difficult to solve to optimality, an important special case considered here admits an easy solution, namely, the well known "organ pipe" arrangement of items.