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Data analytics key to supply chain optimization

BOSTON: August 21, 2017. The key to supply chain cost-reduction is using even more data, applying more advanced analytics and automating more processes says MIT professor David Simchi-Levi:

"High-tech businesses have spent the last decade implementing systems, processes and data-gathering capabilities to improve their supply chain operations. Within the past 12 months, we've seen renewed commitment to continue this quest.

"In my research, 60 percent of the Global 1000 have recently announced initiatives to reduce costs, with a big focus on supply chain efficiency.

"It starts with data: quality data, fast data, data from all nodes, and even data from points between nodes (from sensors on trucks and train cars, for example). Then, by applying mathematical models and analytics tools—including algorithms that can learn and improve over time—high-tech companies and their logistics partners can glean more insights to better manage risk and leverage opportunities to deliver customer value through flexible operations.

"My Ph.D. students and I recently developed a new approach to better manage supply chain disruptions. Using linear optimization, the model focuses on the impact—not the cause—of potential supply chain failures. This approach is more useful for planning risk management strategies for unforeseeable disasters, such as a tsunami or an earthquake.

"The computerized model helps supply chain professionals understand the financial and operational impact of failure at any node, estimate time to recovery and determine the best response. It provides a holistic view of existing and alternative sources of supply, transportation, inventory, warehousing, finished goods, work-in-progress and raw material, and production dependencies within the chain.

"Given the explosion of sensors, I could imagine a future in which this model is combined with live data from factories, warehouses, transportation hubs, shipments on trucks and the like, to predict where nodes might be weak, require maintenance or face other risk.

"My team is now collaborating with a few companies to make this vision a reality.

"Imagine how happy a customer might be to order a consumer electronics product in the morning, and have it arrive after lunch. With today's data analytics, it's possible for companies to identify the most popular products for a given geographic location. A logistics partner's delivery vehicle could carry some of these products. Then, when a customer submits an order, the driver closest to the neighborhood could deliver within hours.

"The data exists to make this happen: Popular purchases, individual consumers' preferences and purchase history, frequency and location, preferred delivery schedules, willingness to pay for expedited delivery—all these data points could feed into an analytical model that dictates which products each truck should carry.

"No business I know of is doing this. But at least one major retailer is opening warehouses in expensive large cities to store popular products as close as possible to a high density of customers to expedite next- or same-day delivery. Data and analytics are applied to identify the assortment of products that should be stored at these warehouses.

"High-tech companies and their logistics suppliers are particularly obsessive about using analytics, both predictive and prescriptive, to improve the last mile of delivery. Using both types of analytics, businesses are optimizing assortment, pricing and transportation flexibility by offering multiple ways to fulfill orders, wherever they are made, in whatever numbers."

David Simchi-Levi is professor of Engineering Systems at MIT and director of the Accenture-MIT Alliance in Business Analytics.

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