BIG DATA: CAN IT BE MANAGED?

Muma Case Review  •  Volume 1  •  2016  •  pp. 001-019
Justin Hurd, a regional manager for Marten Transport, Ltd. (Marten), hung up the phone feeling frustrated following a call with his Division VP. The mandate he received was simple--improve operational efficiencies and reduce operational costs. Justin saw his company do amazing things. He saw the company thrive by delivering loads quickly, efficiently and accurately to its customers. They always maintained a good work ethic and honest business practices, even in the face of high stress and publicity. He even saw his company make leaps and bounds in how much technology they utilized, from GPS tracking to data analytics.

However, as he considered his current challenges, he realized that finding and using the data to drive performance improvements was not going to be easy. Justin considered Marten a unique trucking company that stood apart from its competitors. But right now he needed to find the information on where Marten could improve. He contacted Marten’s Vice President of IT, Randy Baier to see what performance data was already collected. Randy informed him that by utilizing telematics to include ID Systems and StarTrak devices, Marten could monitor reefer settings and performance as well as trailer positions to uncover possible trailer abuse or operating inefficiencies. The data collected so far, identified clear inefficiencies, and indicated possible abuse by warehouse operators at Marten’s expense.

The challenge any firm faced with a big data problem was determining how they would convert relevant data into useful information. Because of the exponential rate at which data was produced, companies must practice ingenuity by proposing specific operations, and focusing their data gathering efforts on what they needed and what they could use.

Marten’s data gathering initiative had produced a wealth of information related to these issues, but how could this data be transformed into useful, actionable metrics that could reduce these inefficiencies and protect Marten’s bottom line? Was more data needed? If so, what kind of data and how should it be collected? Were there other technological control measures that could be applied by Marten to mitigate the impacts of their current issues? Surely other leading transport companies were facing similar issues. Perhaps there were existing solutions being implemented, but would they work for Marten? Justin Hurd was confident the answers were in the vast amount of data collected, but how could he best use that data to meet his cost reduction and performance improvement mandate?
Analytics, Case, Information Systems, Operations, Small Business, big data, logistics, operations, trucking
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