{"id":35990,"date":"2018-11-13T19:41:07","date_gmt":"2018-11-14T00:41:07","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/industrial-iot-and-machine-learning-in-caterpillar\/"},"modified":"2018-11-13T19:41:07","modified_gmt":"2018-11-14T00:41:07","slug":"machine-learning-in-caterpillar","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/machine-learning-in-caterpillar\/","title":{"rendered":"Machine Learning in Caterpillar"},"content":{"rendered":"
Caterpillar is one of the world\u2019s largest heavy industrial equipment company. Being the leader in the field for half a century, the company is facing more competition from its competitor John Deere. In 2017, John Deere acquired machine learning firm blue river [1]. \u00a0The trend in the industry is obvious, traditional heavy industry are also moving fast towards utilizing machine learning to improve performance of the equipment. How would Caterpillar response to this trend?<\/p>\n
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Machinery maintenance is always a big cost of operating the heavy machinery and if not properly maintained, the failure often leads to huge cost or worse, tragic outcome. Traditionally, product feedback or improvement happens in a much longer cycle, which involves user feedback or investigation after incidents happened. By utilising machine learning, this could be dramatically improved in the way that data can be collected in real time basics and failure could be predicted after enough data was collected and failure predciting models were establish. The other area that machine learning can help improve the performance is the human augmentation. By utilizing computer vision and machine learning, the system can warn the operator about potential hazard and other ground operators. But the problem to develop such algorithm is that to make such algorithm work, there needs to be a large amount of accurate labeled data. It took large amount of labor to tag the data and validate them.<\/p>\n
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It is important for caterpillar to develop the massive data collection and analytics capability for the above reasons. Caterpillar\u2019s management aims to utilize the machine learning to improve the automation\/maintenance of the machines as well as the data tagging process. Rather than looking outward, Caterpillar choose to look inward to grow their business. They had partnered with Matlab to develop an internal platform for aggregating data from different parties. [2] The data was then passed on to engineers developing the algorithms at the back end. The process was made efficient with the help of a lot of built in library made available by Matlab. Also the auto tagging function of Matlab helps reduce the amount of work that taggers need to do.<\/p>\n