Do You Think a Firm Can Optimize Its Supply Chain

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The Answer is Answers will vary. To make the best use of its global supply chain, companies should develop supply-chain networks that map out a production process from beginning to end, including purchasing, production, warehousing, transportation, and inventory. They can then apply data analytics to optimize the supply chain by monitoring information about changing tax laws, new suppliers joining the market, foreign exchange rates, the locations of new customers in different countries, and other factors in the global landscape.
For example, Baker Hughes, a U.S. oil and gas equipment manufacturer, might have production facilities in Houston, Texas, that procure steel from mills in Indiana because they offer the lowest price. Then, if the United States and China enter a trade agreement to reduce tariffs against Chinese mills, it might become cheaper to procure steel internationally, even though the materials have to travel across the Pacific Ocean. Data analysis could run simulations that factor in traffic patterns, fuel prices, and the changing tariff situation to determine the most efficient place to procure raw materials. The most efficient method is often a form of conditional solution in which the best choice depends on factors such as time of day, fuel prices, currency exchange rates, prevailing weather patterns, and order size. Data analytics can help a firm implement an exact formula for procuring materials in the most efficient, cost-effective way possible and can update variables in real time to compensate for any changes in conditions.
Another area in which data analytics can prove invaluable is minimizing a firm's exposure to risk. Managers can gain insights about the risks they face by comparing real-time statistics across thousands of similar scenarios, paying special attention to the varying levels of success in those scenarios. For example, the consulting firm Deloitte has developed a prediction tool that helps consultants pinpoint weak links in projects by comparing a set of 28 characteristics drawn from a database of 2,000 projects. The data provided by these cases provide a basic framework from which to analyze the activities of Deloitte's clients, enabling consultants to deliver clear insights on how operations can be improved.
This or a similar tool could have been useful in analyzing the operations of Lululemon Athletica, a Canadian athletic apparel company. For its global retail operations, Lululemon was overly reliant on a single Taiwanese supplier for a fabric called luon-the primary material used in its branded clothing. Essentially sourcing all its products from one company was a risk that led to serious consequences when the fabric proved too sheer for customers' needs and products had to be recalled. Caught off-guard, investors filed a class-action lawsuit over the millions of dollars of lost revenue and the rapid 12 percent drop in the company's stock price. The right kind of data analysis would have warned Lululemon early enough to identify and reduce the risk it was taking.
While big data attempts to reduce risk, no analysis can remove risk completely. Data scientists use advanced tools to deliver new insights every day, but they are not fortune-tellers. Global supply chains will always experience problems, no matter how much they are optimized. For example, Toyota Motor Company, the largest car manufacturer in the world, is widely emulated for its efficiency as a multinational corporation. However, even Toyota and its world-class production system is immune to the chaos of random events.
The section "Improving Performance" on pages 341-343 discusses how a firm can optimize its supply chain by applying data analytics. Students can use this section to make their own interpretation and answer this question.

Do You Think a Firm Can Optimize Its Supply Chain

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