{"id":33123,"date":"2018-11-13T16:16:01","date_gmt":"2018-11-13T21:16:01","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/machines-learning-caterpillar-inc-s-metamorphosis-into-big-data\/"},"modified":"2018-11-13T16:16:01","modified_gmt":"2018-11-13T21:16:01","slug":"machines-learning-caterpillar-inc-s-metamorphosis-into-big-data","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/machines-learning-caterpillar-inc-s-metamorphosis-into-big-data\/","title":{"rendered":"Machines Learning: Caterpillar Inc.\u2019s Metamorphosis into Big Data"},"content":{"rendered":"

Introduction:<\/strong> Today, there are 560,000 \u201cconnected\u201d Caterpillar machines \u2013 from back-office forklifts to high-powered 50-ton excavators \u2013 that have been outfitted with connectivity solutions designed to cut costs and drive productivity for customers. [1] Caterpillar, the leader in machinery innovation for the last century, is using machine learning to harness an \u201coverwhelming amount of data that [it] can now receive, process and feed back to [its] customers, dealers and factories in a way that [its] never been able to before\u201d. [2] The world\u2019s largest construction equipment manufacturer has begun its metamorphosis into a big data company.<\/p>\n

Opportunity: <\/strong>The volatility of production inputs (raw materials, labor costs, regulatory shifts, customer preferences, etc.) in the global manufacturing environment is a significant risk factor for most businesses. Stabilizing input costs and driving productivity improvements are perpetual strategies for industrial organizations to win. Today\u2019s confluence of digital technology and machine learning offers manufacturers and end-users of capital equipment an unprecedented opportunity to improve the performance of their assets. Predictive maintenance, a digital asset intelligence strategy that helps maximize utilization, minimize unscheduled equipment downtimes and predict failures, will be a key lever to lower operating costs. Manufacturers across the world are investing significant amounts of capital in predictive maintenance technologies to help transform their approach to maintenance, repair and overhaul (\u201cMRO\u201d). [3] Caterpillar\u2019s growing portfolio of \u201cSmart Iron\u201d products and services position the company well as a value-added partner and leader in the evolving MRO market. The MRO market is expected to grow to $4 billion by 2022 in North America and Europe alone, presenting an attractive opportunity for Caterpillar to continue its metamorphosis by providing customers with savings that result from big data. [4] Additionally, end-users are increasingly seeking upstream partners with an ability to serve as a single point of contact to fulfill all of their MRO requirements, a trend that Caterpillar can leverage through its scale as a one-stop-shop for OEM and aftermarket services. [5] While many other equipment manufacturers are still in the design or early adoption phases of their connected technology initiatives, Caterpillar is well-positioned to transform the maintenance strategies of its customers from reactive to predictive and preventive.<\/p>\n

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Source: http:\/\/s7d2.scene7.com\/is\/image\/Caterpillar\/CM20170719-40349-06707<\/a><\/em><\/p>\n

Management Actions: <\/strong>At Caterpillar, CEO Jim Umpleby emphasizes that priorities are shifting towards \u201cwhat goes around the iron\u201d. [6] Previously, only a portion of the equipment manufactured by Caterpillar was equipped with embedded connectivity. Now, every piece of machinery that leaves the factory is IoT-enabled. [1] In both the short- and medium-terms, Caterpillar must continue to refine its set of software and data analytics tools, as well as application user interfaces, to help customers understand how data and failure prediction can transform their MRO strategies. Caterpillar is also working to expand the breadth of its technology products and services to match customer needs. The company is creating a tiered offering of applications, ranging from \u201cvery basic on-machine GPS technology to fleet management and participation in a customer’s operations\u201d. [7] To help manage its strategic shift into technology, Caterpillar\u2019s management has formed a network of partners to provide clients with various options to process, analyze and store data. In 2015, Caterpillar took a minority investment in Uptake, a provider of big data analytics services, to jointly develop an end-to-end predictive diagnostic solution designed to help customers track and optimize fuel efficiency, uptimes, idle times and more. [8] Conversely, the company also appears to be building data analytics capabilities in-house. Its \u201cCat Asset Intelligence\u201d service leverages an internal team to provide predictive analytics and advisory services for marine clients. Cat Asset Intelligence offers the technical flexibility to integrate with existing data sources and combine new sensors, lowering the costs for potential customers of switching to Caterpillar. [9]<\/p>\n

Other Opportunities: <\/strong>Caterpillar management has done well staying ahead of the curve in capitalizing on the machine learning megatrend. However, there are still steps that can be taken to optimize its approach. Most notably, Caterpillar\u2019s pricing strategy for its technology products and services appears nascent. Key data points like tracking hours, location and error codes, among others, are offered free of charge when purchasing a piece of equipment, and comments from Caterpillar management have indicated a lack of understanding around customer willingness to pay [10]. Caterpillar should perform tests (e.g., A\/B) to better understand willingness to pay, which will inform a more sophisticated pricing strategy to capture growth. Additionally, the company should consider separating its new service offering from its traditional equipment manufacturing business.<\/p>\n

Closing Thoughts: <\/strong>Caterpillar\u2019s biggest challenge will be processing, analyzing and visualizing the sheer volume of data generated by its machines. This is not the company\u2019s core competency. Should Caterpillar focus on building out its in-house capabilities like Cat Asset Intelligence, or should it outsource data analytics to industry experts like Uptake? Which approach is better long-term for cultivating the explosive growth in machine learning?<\/p>\n

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