Table of Contents
• Executive Overview
• ARC’s IIoT Maturity Model
• Is Industry Ready to Embrace IIoT?
Call it what you will - the Industrial Internet of Things (IIoT), Industrie 4.0, or digital transformation – it really doesn’t matter. Pioneering enterprises have started their journey to reinvent and expand their business models using existing and emerging technologies. Companies with the right organizational culture will leverage Internet of Things platforms and associated technologies to grow revenues and cut costs.
Fortunately, for most companies this isn’t a big bang, all-or-nothing, do-or-die gambit. Most companies will require just two things: a powerful vision of their destination, and a resolve to pursue incremental adoption come what may.
To help industrial companies make this transition, ARC Advisory Group has created an IIoT Maturity Model. This helps individual companies understand where they stand, where they should aspire to get to, and the incremental steps to take to get there.
This report is based on survey data collected from 145 industrial organizations during the summer of 2015. In the survey, conducted in conjunction with PLANT SERVICES magazine, ARC asked respondents about the maturity of their technical capabilities in six dimensions. We also asked where companies were in their adoption of the IIoT. Overall, we identified a clear distinction in the capabilities of companies that are already using IIoT applications, compared to those that don’t.
ARC’s IIoT Maturity Model
ARC’s IIoT maturity model encapsulates a number of critical related factors, both at the individual plant level and enterprise-wide. As shown in Figure 1, these factors focus on capabilities in six key areas: decision making, digital information, business processes, fixed assets, systems & infrastructure, and cybersecurity.
Using a spider (or radar) chart like this allows us to easily gauge the capabilities of an enterprise, compare the capabilities of two different companies, or compare two different groups of companies. In practice, each capability is plotted on one of the six axes. The more mature the company is in a dimension, the further from the center that data point will be. We’ll use a completed chart of this type later to compare the capabilities of different groups of survey respondents.
But first let’s elaborate a bit on each of the capabilities measured.
- Decision making: This dimension is concerned with organizational culture as much as it is with technology. Specifically, how do decisions get made in production operations? The maturity of decision-making styles can range from decisions based on intuition or workplace politics, through to far more integrated approaches that tap data generated by customers or external events.
- Digital information: How well – and how widely – is information shared across the enterprise? IIoT will allow organizations to collect data and disseminate information more widely and more efficiently. But that will require cultural as well as technological changes.
- Business processes: How well – and how richly – are production operations integrated with business need and demand? At one extreme, processes may be entirely manually driven and orchestrated. At the other end of the spectrum, they may be fully synchronized and up to date with current business needs and demands.
- Fixed assets: How intelligent and connected are fixed assets within the plant? ARC anticipates that improving uptime and reducing maintenance costs will be some of the first applications for IIoT.
- Systems and infrastructure: Is the technology foundation dated and inflexible or modern, agile, and a solid foundation for change? A modern, agile infrastructure that embraces cloud technologies, for example, will be essential for widespread use of IIoT.
- Cybersecurity: How well secured are industrial information assets, data, and processes? Increased connectivity demands a broader, more robust approach to data security.
Is Industry Ready to Embrace IIoT?
While there is still some educational work to do, adoption of the Industrial Internet of Things is following a classic bell curve as shown in Figure 2. (The chart omits the 17 percent of survey respondents who did not understand what IIoT is, or how it could help them.) Almost one third of the organizations surveyed (30 percent) are either investing in IIoT projects, or have already moved such projects into production.
Among the 12 percent of enterprises that are already live with IIoT solutions, there is a distinct bias towards products rather than services. Sixty-three percent of survey respondents with live IIoT solutions have applied them to existing products, while only 38 percent are using IIoT to enhance existing services. That in itself is a sign of maturity. For most companies, it is surely easier to enhance an existing product, rather than having to modify all the moving parts required to deliver enhanced services.
Cummins diesel engines provide an example. Most manufacturers receive little or no hard data on how a product is really used by their customers in the field. However, the performance, useful life, and total cost of ownership of a product are all affected by exactly how a product is used. Environmental factors can also play a significant role in product performance. If such data were available, it could significantly impact the design of future generations of products.
Because of this, Cummins decided to implement a closed-loop feedback system based on IIoT technologies to gather data on real-world engine performance. The company leveraged an engine control module (ECM) already in use to provide embedded intelligence in each engine. The ECM collected data and transmitted it to the cloud using either cellular networks or Wi-Fi. This will allow Cummins to improve engine design, improve engine performance, and gain market share with this competitive advantage. In the future, Cummins may also offer predictive maintenance services based on this data steam. So, product improvement is the first step, with potential service offerings to follow later.
IIoT Adopters Have Stronger Capabilities
When it comes to the six dimensions of the ARC Advisory Group IIoT maturity model, early adopters of IIoT applications demonstrate stronger capabilities (Figure 3) overall.
Figure 3 shows how three groups of companies compare in the six key capabilities of IIoT maturity. The three groups are: companies with live IIoT projects (“Using IIoT”), companies investing in IIoT projects (“Investing in IIoT”), and companies that are still just monitoring or evaluating the industrial Internet of things (“Monitoring/Evaluating IIoT”). In this spider (or radar) chart, the further you travel away from the center along an axis, the stronger and more mature the company is in that capability.
Figure 3 clearly shows how, overall, companies currently investing in live IIoT projects (shown by the yellow area) have more mature capabilities than those who are not (the red area). Further, we can also see how companies currently using IIoT are even more advanced (shown by the green area). In the next few sections, we will see the range of capabilities included in the survey along each of the six dimensions. We will also see how industrial enterprises are measuring up to the opportunities offered and challenges posed by the Industrial Internet of Things.
Operational Decision Making
The Industrial Internet of Things will generate large, rich, complex data sets. However, data without analytics, insight, and actions has little value. Unfortunately, the complexity of IIoT will make gaining that insight harder still. As Figure 4 shows, industrial corporations vary greatly in the maturity of the decision-making capabilities.
Almost a quarter of survey respondents (23 percent) rarely or never use data at the heart of the production decisions. But that doesn’t tell the whole story of course. At the other end of the spectrum, 23 percent of all survey respondents collect data from many sources and use it rigorously to help drive decisions. However, that group of survey respondents is heavily skewed towards companies already using IIoT-based applications, with 50 percent of this group (see text box) almost always using comprehensive data to drive decisions.
To maximize gains from IIoT, industrial companies will need a technical infrastructure capable of collecting data from throughout the extended enterprise. That is, data from internal operations, products, and sourced from partners and customers. These data must be collected, managed, and analyzed to gain insight into potential issues and opportunities.
But critically, industrial enterprises may need to change their decision-making culture to take full advantage. The IIoT will generate detailed data, including data that may be more time critical than in the past. To take full advantage of this, companies must ensure that timely decisions take place at the right level within the organization. For some firms, that may require developing both analytical and decision-making skills in lower levels of management. Management culture may also need to change.
Use of Digital Information
Taking a step back from analytics, it’s also important to see how information is generated, collected, and used in a broader sense. For example, is information generally collected in a digital form, or is much data collection still dependent on trusty pencil and paper? As it turns out, the most common form of data collection is digital - but only just. As Figure 5 shows, although 47 percent of survey respondents collect digital control and time series data, they typically analyze the data using a spreadsheet.
While users can perform any number of calculations in a spreadsheet, the approach has many limitations. First, since spreadsheets allow data entry as well as analysis, they are prone to all kinds of data entry and transcription errors. Second, although you can perform calculations and generate charts in a spreadsheet, the tool wasn’t really designed for that purpose. And third, spreadsheets were definitely not intended to facilitate sharing and collaboration; two traits that are becoming increasing necessary in a highly connected industrial environment.
A number of companies (the 36 percent represented in the top three bars in Figure 5) have moved beyond spreadsheets. In these companies, more widespread data sharing and collaboration is common. Eighteen percent either take advantage of big data and advanced analytics, or at least have strong bidirectional information flows between the plant and the business. However, companies with those capabilities are far more abundant among early IIoT adopters. Seventy-percent of those companies already using IIoT applications possess one of those top two information sharing capabilities.
Production processes are built on a company’s information infrastructure and leverage decision-making capabilities. How well those processes are synchronized and coordinated can have a major impact on operating costs and profitability. Poorly coordinated processes can lead to errors and delays, raising costs unnecessarily. Conversely, close synchronization between demand and supply can reduce friction, lower costs, and keep customers happy. As Figure 6 shows, there is no strong trend in the way production processes are coordinated.
However, segmenting the data into three groups (those respondents using IIoT applications, investing in IIoT, and still pondering IIoT) provides some clarity. Among companies actively using IIoT applications, 31 percent have fully synchronized production processes. However, among companies still implementing the first IIoT solutions, only 17 percent are fully synchronized. For companies that are still monitoring IIoT developments but are not active, only 7 percent have fully synchronized production processes. Overtime, we will most likely see more companies migrate to a more synchronized approach due to the increased pressures and possibilities of emerging technologies and a digitally transforming ecosystem.
Managing Fixed Assets
The Industrial Internet of Things can also facilitate improved performance from large fixed assets by improving productivity, eliminating unplanned downtime, and cutting maintenance costs. As Figure 7 shows, this is a significant opportunity, since 57 percent of survey respondents only instrument fixed assets for control purposes.
But, there is a major opportunity here. ARC Advisory Group has researched asset maintenance thoroughly and discovered that the traditional approach to preventive maintenance only works well for 18 percent of assets. It is, however, the most common approach to maintenance for industrial fixed assets. The remaining 82 percent of assets fail at random intervals.
IIoT can help here. Data collected from fixed assets using low-cost sensors, combined with predictive analytics algorithms, can provide early warning of imminent failure. With advanced notice of an upcoming failure, necessary repairs can be scheduled into the next planned maintenance window. Replacing parts before they fail can improve machine uptime and greatly reduce repair bills. Sixteen percent of survey respondents (the top two bars in Figure 7) already pursue this approach for maintaining their fixed assets. A further 16 percent currently use conditioning monitoring. However, predictive maintenance is a significant step beyond simple conditioning monitoring, combining data from multiple sensors with advanced analytics techniques.
The systems and infrastructure used for production operations are very traditional and conservative (Figure 8). Most are unlikely to have the flexibility and agility required to easily integrate into large-scale IIoT applications. But, there is a way forward.
For most companies, their existing infrastructure is still rooted in legacy architectures and technologies. Currently, only 10 percent of survey respondents use cloud services or have adopted a next-generation IIoT Platform. Legacy industrial technologies are typically proprietary and monolithic. Interoperability is often an afterthought, not a priority.
Conversely, the IIoT promises to be a highly distributed computing environment. IIoT applications and services will readily span both corporate boundaries and national boundaries. Agility and flexibility will be key. Older technologies such as client-server solutions will likely struggle to keep up. Cloud-based solutions can offer an easier solution for connecting disparate technologies and parties across boundaries. This can enable industrial companies to adapt faster to address changing business needs.
The majority of industrial companies taking part in ARC’s survey have a very basic approach to cybersecurity (Figure 9). Almost half (46 percent) of survey respondents use basic firewall and antivirus technologies. Seventeen percent rely on physical isolation.
Such basic security measure may not be adequate to safeguard traditional production environments, even where connectivity is limited. However, taking full advantage of IIoT requires more openness, more complete and frictionless information sharing. For example, to drive closer coordination and synchronization between supply and demand requires free information exchange between many different entities – some beyond the traditional corporation. To fulfill this promise however, requires a more complete, robust, and integrated approach to cybersecurity to mitigate the additional risks incurred by such openness.
Although technology evolution that can be pursued incrementally, the Industrial Internet of Things promises a business revolution. Most industrial companies that took part in ARC’s survey are already actively evaluating or using IIoT. However, our survey data suggests that significant investments will be needed before the full benefits of the IIoT will be realized.
Based on ARC research and analysis, we recommend the following actions for industrial companies pursuing IIoT opportunities:
- Keep a finger on the IIoT pulse. Companies that are not tracking developments in IIoT – or have dismissed it as a fad – should think again. It’s difficult to imagine any corners of the industrial enterprise landscape that will not be touched by IIoT. Even organizations that are not ready to be a pioneer or invest in experimental IIoT projects should at least be ready to act when the opportunity or need arises. At a minimum, stay educated and keep up-to-date on IIoT concepts and technologies as they emerge and mature.
- Build a broad roadmap for your first projects. The IIoT maturity matrix in Figure 3 shows significant difference in the capabilities of three different groups of industrial organizations. Notably, companies that are already using IIoT projects are more mature in all six dimensions identified by ARC. In particular, the gaps are largest in the areas of system infrastructure, digital information, the intelligence of fixed assets, and cybersecurity. As industrial companies embark on their first projects, it’s important to understand that they may need to expand their capabilities on multiple dimensions at the same time.
- Future-proof your projects to mitigate risk. It is very early in the IIoT lifecycle. New technologies are being added seemingly on a daily basis – and vendors accumulate at a similar rate. Consequently, there is a risk in choosing a technology that later falls by the wayside – or a vendor that does not survive. Fortunately, we live in an era in which open standards are more widespread than ever before. Be sure to take advantage of that to mitigate your project risk. Choose open standards – and even open source – when it makes sense to do so.
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