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Scaling the industrial internet of things

Back in 2016, predictive maintenance was forecast to be one of the most promising uses of industrial IoT. It seemed like a no-brainer: Who wouldn’t want better information to prevent equipment failure?

So it’s somewhat surprising that predictive maintenance has failed to take off as broadly as expected. A recent Bain survey of more than 600 executives found that industrial customers were less enthused about the potential of predictive maintenance in 2018 than they were two years earlier. In our conversations with buyers, we heard that implementing predictive maintenance systems has been more difficult than anticipated, and it has proven more challenging to extract valuable insights from the data.

Predictive maintenance is just one of many IoT use cases that customers have had difficulty integrating into their existing operational technology and IT systems. As companies in the industrial sector have invested in more proofs of concept, many have found IoT implementation more challenging than they anticipated.

Because of this, we find that customer expectations, while still bullish for the long term, dampened slightly for the next few years (see Figure 1). Our 2018 survey found that buyers of industrial IoT services and equipment expect implementation to take longer than they thought it would back in 2016.

Figure 1: The IoT outlook for 2020 has dampened, but long-term targets remain bullish. Note: Red dashed lines indicate 2016 forecasts.

Bain’s 2018 survey also found that among industrial customers, concerns over integration issues — in particular, technical expertise, data portability and transition risks — have become more acute over the past two years (see Figure 2).

Figure 2: More experience with proofs of concept has shifted IoT customers’ concerns in the past two years.

  • In 2016, customers were most concerned about security, returns on investment and the difficulty of integrating IoT systems with existing IT and operational technology.
  • In 2018, security and integration were still top concerns, indicating that tech vendors haven’t made much progress in addressing them.
  • Fewer customers are concerned about ROI than in 2016, perhaps because they have been satisfied by the returns on their early implementations. Industrial IoT use cases are beginning to deliver vendors’ promises.

Customers are increasingly worried about issues that arise during implementation: technical expertise, difficulties in porting data across different formats, and the transition risks. Proofs of concept have revealed these challenges, and companies now realize that although the effort pays off, the devil is in the details.

Despite these barriers, industrial IoT remains a promising opportunity. Bain’s research indicates that the industrial portion of IoT — including software, hardware and system solutions in the manufacturing, infrastructure, building and utilities sectors — continues to grow rapidly, and could double in size to more than $200 billion by 2021 (see Figure 3).

Figure 3: The industrial IoT market could reach $200 billion by 2021.

To capture that opportunity, device makers and other vendors of industrial and operational technology need to dramatically improve their software capabilities — not a historical strength for most of them. Leaders are investing heavily in acquisitions to obtain the necessary capabilities and talent. Most of this M&A activity targets companies further up the technology stack — in the realms of software and systems integration — than the core capabilities of industrial companies.

As vendors and manufacturers work to build scale, four groups of actions can help position them for long-term success:

  • Concentrate your bets. Focus on select use cases and tackle the key barriers to adoption: security, ROI and integration with IT and operational technology. Learn from proofs of concept and develop repeatable playbooks. Package IoT solutions into scalable products that you then can roll out to customers.
  • Find good partners. Acknowledge your capability gaps and find partners to address them. Work closely with cloud service providers, analytics vendors or enterprise IT vendors. At the same time, avoid broad and unwieldy alliances with too many players; partnerships tend to be more effective with a selective approach based on the use case.
  • Understand it may take a while to break even. Building capabilities and forging strong partnerships takes time, so commit to long investment periods. Approach the effort with a realistic view of the funding, timeline and staffing changes needed to deliver results.
  • Identify new talent. Your best employees excel at their jobs, but new operating models may require different skills. Learn to identify, hire and retain the entrepreneurial talent to thrive in your evolving business model.

Finally, companies will need to be clear on where IoT fits into their operating model. Some executives worry about new products cannibalizing existing products and their revenue. Companies need to allow internal entrepreneurs to build new lines of business without alienating the rest of the organization.

All IoT Agenda network contributors are responsible for the content and accuracy of their posts. Opinions are of the writers and do not necessarily convey the thoughts of IoT Agenda.

This article was cowritten with Peter Bowen, Christopher Schorling and Oliver Straehle, partners with Bain’s Global Technology practice in Chicago, Frankfurt and Zurich, respectively.

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