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How Big Data Solves Small but Costly Warehouse Problems

You don't need to go big to leverage big data. Forget for a moment the potential of adopting big data analytics throughout your entire supply chain. Numerous real-life examples showcase how big data can untangle and integrate seemingly unrelated masses of data to solve small but costly problems in any warehouse or distribution center.

Take the example of a distribution center in Florida that struggled with a high number of forklift truck impacts. Although a telematics solution (iWarehouse by The Raymond Corp.) had been installed, the cause of the impacts could not be determined. The company knew the time and location of impacts as well as the identity of the drivers involved, but still needed to pull in more data sources for an effective assessment.

By analyzing the link between environmental factors inside the distribution center and the forklift impact records, the culprit was swiftly identified: fast-moving thunderstorms that caused the humidity level to rise so quickly that the dehumidifiers could not keep up, increasing the risk of drivers losing control on the slippery concrete floor. That knowledge helped the company and Raymond prevent sliding accidents by using a function of the telematics solution to reduce the maximum speed of the trucks when the humidity hit a certain level.

As this example illustrates, a distribution center or warehouse presents an ideal environment — a microcosm — for big data applications. Modern facilities are loaded with sensors and detectors to track every pallet and every piece of material handling equipment in real-time. Managers see the benefits in increased productivity, improved inventory flow, optimized equipment usage, and more. However, for that Eureka moment, organizations should also apply big data analytics across these multiple sources of data to uncover patterns that will drive even more, and perhaps surprising, operational improvements.

Rather than looking at data in isolation, a holistic approach holds significantly more power. Managers typically keep careful track of the performance of lift trucks, batteries, and chargers. But it is not until those entities are reviewed as a single system and matched with data coming off the lift trucks that a new level of revelations can be had. As one provider of battery management technologies tells DC Velocity, “organizations may match up the activity of a truck powered by a particular battery with that battery's performance to find out whether one is affecting the other.”

Look for big data analytics to further raise the IQ of our “smart” warehouses and DCs.

Inbound Logistics sums it up this way: “Accessing the right information to make smart decisions in the warehouse is one main reason why the demand for big data has grown so much — and so rapidly — in the distribution sector.”

Do you think DC and warehouse managers do enough to leverage big data?

2 comments on “How Big Data Solves Small but Costly Warehouse Problems

  1. Susan Fourtané
    December 2, 2014

    In today's supply chain management looking at data in isolation is of no use. Integration is one of the key factors for succeeding in big data analytics.

    Also, considering both real-time and historical data is what will help supply chain management make the best decisions based on the value obtained of the data collected.

    -Susan 

  2. Daniel
    December 3, 2014

    “You don't need to go big to leverage big data. Forget for a moment the potential of adopting big data analytics throughout your entire supply chain. Numerous real-life examples showcase how big data can untangle and integrate seemingly unrelated masses of data to solve small but costly problems in any warehouse or distribution center.

    Fran, that's true, many of the issues can be solved by using big data analysis. But how and how is the big question? Eventhough most of the companies have these datas, they won't do any analysis and proper applications.

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