Oh Deere! Plowing Ahead Into the Future

What if your tractor was a transformer?

Like many well-known industrial companies, John Deere is adapting its business and operating model in numerous ways to the wave of digitization sweeping industrial manufacturing – also known as the “industrial internet of things”.  In the past few years, the company has been investing in and rolling out digital platforms and “smart” machines, which capture and transmit data on their performance to users in the field and at base, via internet enabled devices like computers and iPads.  The goal of these investments is at least two-fold: 1) to more closely bind customers to John Deere products and technicians by continuing to create differentiated value for customers, and 2) to keep pace with competitors like Caterpillar who are also investing significantly in connecting their machines to digital technology.  The company’s efforts appear to be paying off, at least in the sense that their service offerings communicate clear financial benefits to customers; however, the company will likely face additional challenges as they attempt to increase penetration of their new technologies and justify the investment going forward.

To begin with, John Deere has rolled out several digitally enabled technologies in its products.  At the most basic level, the company offers customers three benefits from these technologies: 1) machine optimization, 2) uptime optimization, and 3) jobsite optimization.  One tangible example is their “Forest Sight” system, which is embedded into new forestry machines.  Forest Sight relies on the “JDLink Machine Monitoring System” which allows customers to track vital stats being digitally transmitted from their machines – e.g., fuel usage, maintenance requirements, part failures, etc[1].  Customers presumably benefit financially from the Forest Sight system because they can better understand machine performance and improve metrics like capacity utilization and uptime.  Better leveraging the data and analytics that their machines are constantly sending out allows customers to minimize costly wasted time and fuel usage.  From a business model perspective, it therefore appears that Deere can defensibly advertise the positive impact of its new technologies and thereby drive increased sales vis a vis competition (at least against players who are not as advanced).

Additionally, a system like Forest Sight has the benefit of more closely aligning all the players in the Deere value chain – from Deere, to the service technicians who provide aftermarket maintenance, to the customer base.  Put differently, having access to real-time data from machines is changing the operating models of these companies across the value chain.  For Deere, with more robust information around how their products are functioning and where/when breakdowns occur, it can tailor its manufacturing processes and R&D spend accordingly.  Service technicians can improve their working capital and supply chain management through a more sophisticated understanding for what inventory they need and how frequently.  And customers can better plan for when they will need maintenance and downtime.  That is, more intelligent machines enabled by real-time data will minimize the impact of variability in supply chains, and lead times on product maintenance.

There are some potential downsides facing the various stakeholders in the chain however.  For example, from my experience working with aftermarket parts distributors, the ability to provide immediate service and stock the “right” part, no matter how obscure it is, commands a price premium.  As customers develop a better understanding of their maintenance needs (and which parts tend to fail), it’s possible that this increased supply chain transparency will erode pricing power for the service technicians.  In light of this challenge, Deere may believe that the ability to create value for customers is worth upsetting its service base.

There are also more opportunities for Deere to adapt its business and operating model to the rise of smart machines.  Like Deere, Caterpillar has also been investing heavily in integrating machines with data technology.  Unlike Deere, however, they have established Caterpillar Ventures, which invests up to $5M in new technologies – including energy, digital, robotics, and advanced materials – to ensure that the company stays at the cutting edge[2].  This is a business model decision Deere should consider to stay competitive with Caterpillar.

To close, it remains to be seen how successfully Deere will move into the new pervasively digital age, especially when faced with competitors who appear to be ahead, like Caterpillar.  Despite all the fancy ads the company puts out on its website, it’s not clear how well these new technologies are penetrating the customer base, and how receptive all the players in the value chain will be.  However, at the very least, Deere is modernizing to stay technologically relevant. (743 words)

[1] John Deere, “Forestry Technology Solutions,” https://www.deere.com/en_US/products/technology-solutions/forestry-technology-solutions/forestry-technology-solutions.page, accessed November 2016.

[2] Caterpillar, “Innovation”, http://www.caterpillar.com/en/company/innovation/caterpillar-ventures.html, accessed November 2016.


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Student comments on Oh Deere! Plowing Ahead Into the Future

  1. Kalumet,

    Thank you for helping us think about digital innovation within the context of agriculture, an industry many of fail to consider as a technological leader. I think the work John Deere and Caterpiller are doing is terrific, but I believe they could be doing so much more with their data. There are three levels of data analytics; the first is descriptive, which focuses on what happened, the second is predictive, which takes that information about the past to make predictions about the future, and the final is prescriptive, which provides probable outcomes for different actions (http://www.informationweek.com/big-data/big-data-analytics/big-data-analytics-descriptive-vs-predictive-vs-prescriptive/d/d-id/1113279). My sense is that John Deere has only made it as far as the descriptive analytics. If they were to advance into the predictive, and even the prescriptive levels of analysis, the maintenance needs can be better anticipated and planned for, or (in the prescriptive model) minimized as a result of the use or care of the equipment.

  2. Interesting perspective! I agree that digital transformation provides huge opportunities in agriculture to increase the productivity of equipment. I was thinking about how technology is changing mining operations (1), and wondering if John Deere can expand it’s delivery promise, not only offering products but also services such as sensors, software to analyze yields, robots, etc.

    (1) http://www.mckinsey.com/industries/metals-and-mining/our-insights/how-digital-innovation-can-improve-mining-productivity

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