Caterpillar is one of the world’s 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 . The 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?
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.
It is important for caterpillar to develop the massive data collection and analytics capability for the above reasons. Caterpillar’s 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.  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.
The company also utilize open machine learning platform called Kaggle to attract outside talents. Kaggle is an online community of machine learning enthusiast that competes on developing machine learning algorithm. Different companies provide labeled training and test data set on the platform. It is an online community of 1,000,000 developers . The winner of the Caterpillar challenge got a prize of 5-100K USD and provide them an excellence piece of algorithm for sorting objects on ground.
In the long run, the data analytics could be a standalone business itself offering as a subscription. This could be the next drive of their sales growth as the demand for machinery stagnate .
Besides the above-mentioned steps, I suggest Caterpillar to set up a centralized department inside the company to lead the data collection and learning effort. The efficiency of machine learning will be improved once the Following the same logic, the company should also dig deeper in the data to link it with fundamental science (metal fatigue failure etc.) to find out common patterns of failure mode so the same logic can be applied to other products lines. Another interesting approach is to form joint venture/spin out company to transfer knowledge to other heavy industry such as turbine/engine etc.
Moving ahead, there are two questions that matters to the implementation of machine learning in Caterpillar. First, what is the reliability of the product you need to get to before you release the product out to the market? All these heavy machineries are high stake in terms of work place safety, how reliable the algorithm should the company improve to until release to customer? Is the internal product development process adapted enough to check such compliance?
Second, with a long cycle of replacement for machinery, what are the key strategies to accelerate the big data and machine learning initiative to make it beneficial to most users? Are there after market kits to be developed so that customers can deploy them on their own machines? How would that solve the connectivity in the sites that the machine operates. These are challenges that the company needs to solve before the technology could be widely benefit to its customer.
Looking ahead, there are a lot of opportunities and challenge for Caterpillar Inc. in the machine learning age. (720 words)
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