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Making sense of IoT and supply chain analytics

IoT could bring major changes to supply chain analytics. Here are ways businesses can think about the technologies and strategies involved in implementing IoT-based applications.

It's 3 p.m. Eastern on a Tuesday afternoon. Do you know where the shipment from the China plant is, or have any idea why shipments from there are consistently late? You might -- if you are using the new IoT-based supply chain analytics applications.

In fact, the internet of things is enabling business transformation in many ways, with new technologies to collect and analyze data. This new data analytics gives unprecedented visibility into the supply chain, by providing insight into the location, status and quality of goods throughout the process. But to realize the full potential of supply chain analytics, you need to understand the technologies and learn from business use cases.

For example, supply chain analytics is helping businesses to cut costs, gain efficiencies, offer better customer service and therefore get a leg up on the competition. Take The Bouqs Company, which uses supply chain analytics to enable its innovative business model. The cut-to-order flower retailer delivers flowers straight from its farm suppliers to customers, bypassing the warehouse middlemen typically found in the flower delivery business. This allows The Bouqs to deliver its goods in just four days instead of the typical 17 days, which is a huge advantage in a business that depends on product freshness. Analytics also helps the company by looking at historical data to predict the bestselling flowers in a particular month, giving a much more accurate picture of demand.

To better understand how supply chain analytics can help a business, it helps to take a look at the underlying technology that enables supply chain analytics. Two broad categories include sensors and devices, both of which are now cheap and ubiquitous. Sensors connect over the internet for functions such as measurement, counting and recording video. Devices, including smartphones and tablets, add context that helps make sense of all the data, such as GPS for location-based data. This combination of sensors that can measure virtually anything in the supply chain, such as location or temperature of goods, and a variety of devices to access, analyze and display the data, makes it possible to implement creative and informative supply chain analytics applications. 

Once you can make a business use case and understand some of the underlying technologies, you can begin to take steps to implement supply chain analytics. Think of supply chain analytics in two domains: operational and strategic. In the operational domain, which includes daily and real-time data, there are several areas where vendors provide analytics tools. These include inventory and logistics decision making, such as making more informed stocking decisions based on actual, at-the-moment demand; complex event processing, which tries to understand the causes of events and predict future outcomes; and risk management, where analytics can spot risks, analyze causes and provide potential alternatives. The strategic domain, which includes trend analysis, discovery and planning, provides two broad implementation paths. The is to use analytics within a larger-scale data management system, and the second is to use a business intelligence-focused tool from a BI provider.

IoT is making it possible to collect all sorts data in the supply chain. The challenge now is finding and using the tools to make sense it.

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