Big Data Analytics / IIoT

IIoT survey results: Realizing the full benefits of the Industrial Internet of Things

IIoT adoption varies widely, but leaders push to get out in front.

By David White, ARC Advisory Group

Don’t look now, but the transformation of the industrial sector is under way. Whether you call it the Industrial Internet of Things (IIoT), Industrie 4.0, or digital transformation, companies have begun using new and existing technologies to completely reimagine their business models. For companies with the right organizational culture, Internet of Things platforms and technologies can bring significant opportunities to improve asset performance, deliver new service offerings, tweak operational efficiency, and create new products. Or, to put it another way, to grow revenues and cut costs.

Ushering in this new era requires two things of companies: a clear and compelling vision of the future state, and a dogged approach to incremental adoption. For most companies this isn’t a big-bang approach.

To help industrial companies make this transition, the ARC Advisory Group has created the IIoT Maturity Model. This helps individual companies understand where they stand, where they should aspire to get to, and the incremental steps needed to get there.

ARC’s maturity model aims to understand and examine a number of related factors at the individual plant level and across the entire enterprise. These factors include:

  1. Organizational culture: Specifically, how do decisions get made in production operations?
  2. Information: How well – and how widely – is information shared across the enterprise?
  3. Business processes: How well – and how richly - are production operations integrated with business need and demand?
  4. Fixed assets: How intelligent and connected are fixed assets within the plant?
  5. Systems and infrastructure: Is the technology foundation dated and inflexible or modern and agile?
  6. Cybersecurity: How well-secured are industrial information assets and data?

Leveraging this maturity model, the ARC Advisory Group recently surveyed industrial companies with an eye toward helping them understand how they measure up relative to their peers. Some of that data is presented here.

It was important first to examine how conceptually ready companies are to benefit from the IIoT – in other words, how ready they are to commit time and resources to making the IIoT a reality for them.

Benchmarking IIoT readiness

It seems that marketers still have some work to do. As Figure 1 shows, one in six survey respondents (17%) did not understand what the IIoT is – or of more importance, how it can help them. On a more-positive note, ARC Advisory’s survey data shows that at the other end of the spectrum, many organizations are quite advanced in their IIoT attitudes and approach.

Almost one-third (30%) of survey respondents already are actively using IIoT tools or investing in projects that will soon be live. What’s interesting is that the maturity of IIoT adoption that our survey shows maps closely with the classic technology adoption curve. If you take Figure 1, take out the 17% bar, and flip the axes, you have a classic technology adoption curve.

The 12% of survey respondents who have already deployed IIoT solutions are quite broad in their application and vision for the IIoT. However, there is currently a slightly keener focus on products (new or existing) than there is on tweaking existing services or introducing new services.

Cummins Engines provides a great example. Most manufacturers receive little or no meaningful feedback on how their customers actually use a product. And yet a product’s performance, life span, and total cost of ownership can be affected greatly by how a product is used. The environmental conditions in which the product is operated are also a major factor. Capturing such data can have a major impact on the design of future products.

For this reason, Cummins decided to implement a closed-loop feedback system to gather more data on real-world engine performance under a diverse range of operating conditions. The potential was there – the engine control module (ECM) already in use provided embedded intelligence in each engine. The ECM was used to collect data and transmit it to the cloud, via cellular networks or WiFi. This wealth of data will allow Cummins to improve engine design, gain a competitive advantage, and grow market share. As a secondary benefit, the data may also enable Cummins to offer predictive maintenance services. Product improvement is the first priority, but potential service offerings could expand Cummins’ business, too.

Among survey respondents, 27% are actively evaluating the IIoT’s potential for their enterprise. Overall, then, a majority of survey respondents – 57% – have already set the IIoT wheels in motion for their organization. This may seem like a big share for a concept whose ascendancy in the popular imagination has been so short. But while IIoT may be revolutionary in concept, it will be evolutionary in adoption for most companies. In other words, the Industrial Internet of Things is more of a journey than a thing.

Maturity of operational decision-making

There’s one thing that the IIoT is guaranteed to do: generate a large, rich data set. Sensors and devices will generate data about manufacturing processes. Meanwhile, at the far end of the supply chain, products will generate data about how customers use products in the real world. Other data, about the weather, about traffic conditions, and so on, will be harvested, too.

One thing that has always been true about data – and that will not change – is that data has no value unless you can understand it and act on it. To do this requires both operational intelligence and analytics technology. But more than anything else, it requires the right organizational culture and mindset.

In that light, and as Figure 2 shows, it’s a bit troubling that 23% of companies are making decisions based mostly on instinct or politics or without data. This prompts some key questions. First and foremost, how will such companies compete in the all-encompassing digital age of industry? The good news is that most companies who took part in the survey rely – at least in part – on data to drive production. In fact, half of all survey respondents are in a reasonable position to move forward with their industrial IIoT strategy. These companies are already incorporating feedback from customers and their supply chains to drive production decisions. For these companies, IIoT will serve to extend and enhance their decision-making.

To maximize their potential gains from the IIoT, industrial companies will need to develop the technical infrastructure to collect data from throughout the extended enterprise. That is, data from their own internal operations, data from the products and services they offer, and data sourced from beyond the walls of the company itself. Once collected, the data must be managed and processed and analytic tools employed to gain insight into potential challenges and opportunities. Of possibly greater consequence, industrial enterprises may need to shake up their decision-making culture. The Industrial Internet of Things will not just generate lots of data, but also it will generate fine-grained data and time-critical data. To take full advantage of this, companies must ensure that decisions are made at the right level within the organization. For some, that may require developing both decision-making skills and management culture to succeed in an IIoT world.

Maturity of production business practices

The Industrial Internet can also act as a big facilitator for Industrie 4.0, or smart manufacturing. As our survey data shows, however, many industrial companies are not well-positioned to take advantage of this (Figure 3).

Only 12% of survey respondents have production processes that are completely synchronized with demand signals from the business and that provide easy real-time access into that information. With such an infrastructure, processes, and habits already in place, this small minority of companies should be strongly positioned to capitalize on IIoT data. At the other end of the spectrum, 44% of companies rely on manual processes or have production processes that are largely driven by process control and safety requirements. In other words, they are not easily (or tightly) integrated with business demand at all.

And yet, the IIoT provides the potential to closely integrate operational technology (OT) within the plant and information technology (IT) within business management. This is a long-sought-after goal. The IIoT can provide timely business data from external sources to drive production processes with much greater efficiency and accuracy than before. This can result in productivity benefits, lower inventory levels, improved supply-chain efficiency, and ultimately higher market share.

Maturity of production fixed assets

The Industrial Internet of Things also provides a splendid opportunity to eliminate unplanned downtime and reduce maintenance costs for fixed assets. As Figure 4 shows, the majority of organizations instrument their fixed assets only for control purposes, if they are instrumented at all.

This represents a significant missed opportunity. ARC Advisory Group’s Ralph Rio has researched asset maintenance extensively. It turns out that the traditional approach to preventative maintenance works well for only 18% of assets overall. And yet it’s the most common approach to maintenance for industrial fixed assets. The remaining 82% of assets fail at random – hard to imagine, perhaps, but the data strongly suggest otherwise.

That’s where the IIoT can help. Data collected by sensors on fixed assets – combined with machine learning – can provide a powerful way to get an early warning of impending failure. With advance notice of an upcoming failure, the parts in question can be replaced at the next scheduled maintenance. Replacing parts before they fail can eliminate unplanned downtime and cut costly repair bills. Some companies – but just 16% of them – indicated that they already pursue this approach to maintenance of their fixed assets. An additional 16% are using conditioning monitoring. However, predictive maintenance is a significant step beyond simple conditioning monitoring, combining data from multiple sensors with advanced analytics techniques.

Maturity of systems and infrastructure

Overall, the systems and infrastructure used to support production operations are very mature. Unfortunately, they’re a little too mature to easily support the Industrial Internet of Things (Figure 5).

The existing infrastructure is based mostly on legacy architectures and technologies. At this point, only 10% of survey respondents are using cloud services or have migrated to a next-generation IIoT platform. But in many ways, the Industrial Internet of Things is the ultimate in distributed computing. Applications and services will span corporate boundaries and bridge geographies. Agility and flexibility will be the name of the game – and legacy technologies such as client-server will likely struggle in this respect. On the other hand, cloud-based solutions offer an appealing solution for connecting disparate technologies and parties. This can allow industrial companies to adapt faster to changing business needs.

Although a technology evolution, the Industrial Internet of Things promises a business revolution. Most industrial companies that took part in ARC’s survey are already analyzing the potential of or using IIoT tools. However, our data suggests that significant investments will be needed in assets, infrastructure, and management culture before the IIoT’s full benefits will be realized.