Take the Guesswork Out: Real World Examples

There are countless scenarios of companies and supply chain operations applying big data solutions that illustrate the wealth of process improvement opportunities available through the proper use of data. Here are just a few examples:

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  Many companies today utilize the Internet of Things (IoT) and machine learning for predictive asset maintenance to avoid unscheduled downtimes. The IoT can deliver real-time telemetry data via the use of high-tech sensors to reveal production process details. For example, Microsoft partnered with Powel, a water systems provider in Norway, to define an IoT system that detected water wastage. Within a 5 day hackfest, the team was able to build an MVP product that Powel could use to deploy across their entire system.
  
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  More and more organizations are taking advantage of machine learning algorithms – trained to analyze a company’s data – to more accurately predict pending machine repairs or fails. Losant Technologies is using Google Machine Learning to detect imminent failures through condition monitoring, which gathers millions of sound and vibration data points and analyzes them with machine learning.
  
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  Big data solutions can help minimize delivery delays by analyzing GPS data, as well as weather and traffic data, to optimize delivery routes better. UPS uses ORION, an internal dynamic route optimization system, and expects to drive 1 million miles less in 2018 due to the big data advancements.
  
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  Big data is also helping companies manage more responsive supply chains as they can better comprehend customers and market trends, and thus, are able to predict and proactively strategize supply chain-related activities. As an example, a dairy farm was able to use RFID and IoT sensors to detect up and down-stream issues regarding health of cows, quality of fodder, and variant temperature changes, all of which affected the overall quality of milk. The result was a more homogenized product of higher quality.