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The Future of IIoT Predictive Maintenance

A Combined Study by Emory University and Presenso

Introduction
Summary of Findings
1.0 The Current State of Predictive Maintenance
2.0 The Outlook for Industry 4.0 Maintenance Technologies
3.0 Perspectives on IIoT Predictive Maintenance
4.0 Implementation of IIoT for Preventive Maintenance
5.0 Impact of IIoT Predictive Maintenance
6.0 Recommendations
Appendix A – Additional Research Data

Introduction

In April 2013, at the Hanover Messe conference in Germany, the guiding principles of Industrie 4.0 or Industry 4.0 were released. Over the past five years, Industry 4.0 has moved from German government policy to executive-level strategy across the globe. Today we are in the third wave: active implementation.

Industry 4.0 is considered the fourth Industrial Revolution, and industry analysts have forecasted a significant and broad economic impact. The application of the Industrial Internet of Things (IIoT), Artificial Intelligence and Machine Learning to industrial maintenance or Predictive Maintenance 4.0 is a core element of Industry 4.0.

The Emory University Future of IIoT Predictive Maintenance research study was designed to identify the gaps between the high-level strategic and business drivers of change and the reality of implementation. For this purpose, we interviewed Maintenance and Reliability professionals responsible for Predictive Maintenance in their organizations.

This study’s goal is to provide a field perspective on the following topics:

  • The current state of Predictive Maintenance in industrial plants
  • The level of satisfaction with current Predictive Maintenance systems
  • IIoT Maintenance systems most likely to be adopted within the next five years
  • The extent to which the Digital Twin is likely to be deployed
  • The disconnect between executives and O&M professionals responsible for implementation
  • Reasons for delays in investments in new IIoT Predictive Maintenance solutions
  • Factors blocking the implementation of IIoT Predictive Maintenance solutions
  • The likely impact of IIoT Predictive Maintenance on current O&M practices

For this study, 103 O&M professionals were surveyed across Europe, North America, and Asia Pacific. A combination of quantitative research (online survey) and in-depth interviews were used. In addition, feedback was solicited in public forums in Asset Maintenance LinkedIn groups.

Six Emory University students participated in the research and writing of this report:  Arnav Jalan and Nathan Brooks (project co-leads) and Dilsher Dhupia, Ian Goldstein, Hannah Laifer and Sabiha Officewala.

Summary of Findings

In 2017 and 2018 alone, significant advances in cognitive analytics have been applied to the discipline of Predictive Maintenance. In parallel, Industry 4.0 has been embraced by the senior management of worldwide industrial facilities.

Our research indicates a growing chasm between the potential for PdM4.0 and the reality in today’s industrial plants. We found no urgency to upgrade legacy Maintenance and Reliability practices from the 1970’s and 1980’s. Microsoft Excel is still the default analytics tool.

Concerns that are raised about PdM4.0 and Maintenance 4.0 stem from practical considerations regarding the feasibility of deployment and the lack of resources. O&M professionals view PdM4.0 positively but expect an incremental change in the form of improvements to existing systems and processes.

This report analyzes the following topics.

  1. Current State of Predictive Maintenance: IIoT for Predictive Maintenance is still in its infancy. Despite the promise of PdM4.0, there is little discontent with current Predictive Maintenance systems. Traditional Predictive Maintenance, including vibration monitoring, oil residue analysis, and thermal imaging, still dominates, and manual statistical modeling such as Excel has not been replaced by more advanced technologies.
  1. Outlook for Industry 4.0 Maintenance Technologies: O&M professionals expect that Automated Failure Reporting and Automated Repair Scheduling are most likely to be widely adopted over the next five years. There are limited expectations for the deployment of Robotics Assisted Repair and Drone/Robotics Assisted Inspection. The Digital Twin concept is not widely known by O&M professionals and is not forecast to play a major role in industrial plants within the next five years.
  1. Perspectives on IIoT Predictive Maintenance: O&M professionals are less enthusiastic about IIoT for Predictive Maintenance than is senior management. Part of this is attributed to the “hype” that resonates less with the Maintenance and Reliability workers who are responsible for implementation. Almost 40% of respondents in the online survey cite a lack of IIoT strategy as a reason for delays in adoption. In the long term, there is an expectation that the perceived ROI from IIoT Predictive Analytics will justify the expenditures.
  1. Implementation of IIoT for Preventive Maintenance: The most significant inhibitor of IIoT for Predictive Maintenance deployment is a skill shortage of Big Data Scientists and a lack of understanding of Industry 4.0. The complexity of software and access to sensor data are considered less significant factors affecting stalled deployment.
  1. Impact of IIoT Predictive Maintenance: Overall, O&M professionals have a positive view ofIoT Predictive Maintenance. Improvements to Operational Equipment Efficiency (OEE) are widely expected. Furthermore, most survey respondents believe that utilizing and analyzing the data in real-time will allow for better decision making. From an organizational perspective, there are only limited concerns that the roles and responsibilities of O&M professionals will change. In general, there was not much support for the outlook that IIoT Predictive Maintenance will force the convergence of Information Technology and Operational Technology organizations.

 

1.0 The Current State of Predictive Maintenance

IIoT for Predictive Maintenance is still in its early stage and O&M professionals see little impetus for adoption. The promise of PdM4.0 is recognized but little discontent exists with current Predictive Maintenance systems.

Traditional PdM including vibration monitoring, oil residue analysis and thermal imaging has not been displaced. Microsoft Excel is considered the default for predictive modeling.

1.1 Assessment of Current Predictive Maintenance Practices

Maintenance 4.0 may be a core element of an executive’s strategic vision. It could be included in an industrial plant’s technology roadmap. However, from a practical perspective, it is not widely deployed.

Today, the most common processes for Predictive Maintenance are traditional methods such as vibration monitoring, oil residue analysis, and thermal imaging. Industrial plants still rely on manual statistical modelings, such as Microsoft Excel for Predictive Analytics (44% of respondents), far more than they do on advanced statistical modeling (23%) and Machine Learning (12%).

Chart 1: Which of the following accurately describes the current state (s) of predictive maintenance in your facility and/or industry. (Select all that apply)

Why is Excel so prevalent? The reason is basic. Reliability & Maintenance professionals already use Microsoft Excel as opposed to other tools, such as MATLAB. Familiarity with Excel creates momentum; people start with a small table that grows exponentially and becomes the default due to a lack of viable alternatives.

O&M Practitioner Insight
Q. Manual statistical modeling is the most common method of Predictive Analytics (e.g., Excel). Do you think this is likely to change in the next few years? Why or why not?

Jack R. Nicholas, Jr., P.E., CMRP, CRL, IAMC

Statistical modeling is rarely used in predictive analytics, particularly at the local level. The most common method is Regression Analysis in various forms.

The second most common method is Pattern Recognition (e.g., for Infrared thermography and off-line and online motor circuit analysis) or visual analysis/recognition (of faults as indicated in images or graphical presentation of data).

The next most common method is Relative Comparison (from one machine to another or a group of machines).

After that comes Tests Against Limits or Ranges. This is related to regression analysis in that, while a trend may be developed and a sudden jump in parametric data occurs to a level above an alert or alarm presents, action will be based on the latter and not on the trend.

Statistical Process Control (SPC) is practiced in many programs and is common in SCADA systems. This is a visual/graphical analysis method that can be practiced by mill deck personnel and requires only simple mathematical and plotting skills if done manually (e.g., number of readings over time in an adverse direction towards upper and/or lower control limits).

The most powerful method is called Correlation Analysis. This involves using analysis results from two different technologies taken at the same time or under the same conditions of operation to confirm a fault or from the same family of technologies (e.g., Vibration Analysis and Shock Pulse Analysis) sequentially in time to track the same fault.

When advanced analytics are used (which is currently most effectively done using IIoT/Cloud Computing Technology), five (5) basic methods are prevalent at the present time. These are Visual Analysis (e.g., for outliers), Clustering Analysis (e.g., Density-Based Spatial, Hierarchical or other common methods such as k-Means), Data Mining (e.g., for Differentiating Analysis or Association Rule-based), Time Series Analysis and Statistical Analysis (which includes Regression, such as Partial Least Square and Multiple Linear Analysis).

Usman Mustafa Syed

Manual statistical modeling will gradually be replaced by Machine Learning & AI-based Predictive Analytics. With the current rate of technology advancement in the field of Machine Learning, AI, and Sensors & Connectivity, we will be entering an era of off-the-shelf Predictive Analytics tools that could be deployed economically and rapidly.

 

Reflecting an acceptance of the status quo, most survey respondents are relatively satisfied with the current options for Predictive Maintenance systems. Although only 2% were Very Satisfied with current systems, a larger percentage (46%) were Somewhat Satisfied and an additional 28% were Neutral. A minority of only 8% were Very Dissatisfied.

With respect to rules-based SCADA systems, 30% of survey respondents recognized that limitations are widely recognized, whereas 38% were neutral.

Chart 2: Level of satisfaction with current predictive maintenance systems

1.1.2 Review of Analyst & Industry Research

The following reports were considered as part of our evaluation:

Analyst Title Date
Plant Engineering Plant Engineering 2018 Maintenance Study 2018

Survey of technology use for PdM indicated the use of spreadsheets/schedules (55%), Computerized Maintenance Management Systems (CMMS, 53%) and paper records of maintenance reports (44%).

Analyst Title Date
PwC Predictive Maintenance 4.0: Predict the Unpredictable 2017

Survey of technology use for PdM indicated MS Excel/Access: 67%, WIFI: 34%, data warehouse: 18%, statistical software: 18%, conditioning monitoring software: 40%, cloud: 13%, data software 33%, mobile networks: 20%, IoT: 14%.

1.2 Metrics to Evaluate Maintenance Solutions

Unsurprisingly, O&M professionals view operational efficiencies/labor cost savings as the primary metric for evaluating the impact of Predictive Maintenance solutions.

However, there is also a recognition of the financial benefits of a proactive approach to maintenance. As depicted in Chart 3, in terms of the most important metric for a predictive maintenance solution, savings from lower downtime were rated almost as high as were operational efficiencies (7.3/9.0 versus 7.4/9.0). Furthermore, revenue from increased uptime (6.9/9.0) was ranked even higher than improvements in spare parts and other logistics management (6.3/9.0).

Even if O&M professionals do not maintain the same enthusiasm for PdM4.0 that senior management does, they understand the strategic value of increased uptime, higher production yield rates, and lower asset downtime.

Chart 3: Importance of the following metrics in measuring the impact of a predictive maintenance solution. (9 point scale)

 

2.0 The Outlook for Industry 4.0 Maintenance Technologies

The primary change expected from Industry 4.0 maintenance technologies is improvements to automated workflows and processes. In contrast, there is a lesser likelihood for the deployment of robotics or drone-assisted inspection.

The Digital Twin concept has not attracted much attention. Most O&M professionals do not expect to see significant deployment within the next five years.

2.1 Adoption Rates of Industry 4.0 Technologies

The primary change forecast is in the incremental adoption of the automation of processes and workflows. This is not surprising given that Computerized Maintenance Management System (or Software) or CMMS has been prevalent since the early 1990’s.

Adoption of industry 4.0 technologies within 5-year period (5-point scale)

A drill-down into the expected adoption of Automated Failure Reporting (Chart 4.1) indicates that 45% of respondents expect full deployment while an additional 22% expect a scenario of mostly deployment.

Chart 4.1: Expected adoption of automated failure reporting

With respect to the adoption of Robotics Assisted Repair, 35% do not expect adoption while an additional 17% expect only somewhat adoption. (See Chart 4.2.)

Chart 4.2: Robotics Assisted repair

O&M Practitioner Insight

Q. Do you think there will be a migration to IIoT Predictive Maintenance in the next few years? Why or why not?

Fred Schenkelberg

No. Gathering more data does not solve anything. We already don’t use the data we have nor know what to do to analyze and use the data today… until we learn how to gather the right data to help solve real problems (which may or may not include PdM), then IIoT is not useful.

We must get the basics right first.

Jørgen Grythe

Yes. Most organizations have at least one skilled maintenance person who can step onto the factory floor and sense, through sight, sound, smell, vibration and temperature, the conditions in the factory.

These human condition monitoring experts are retiring. There are not enough people to replace them. So, I think we are increasingly seeing a centralization of monitoring, either by remote sensing (not completely automatic, but you don’t have to be on-site to monitor the plant) or/and by automated condition monitoring with machine learning algorithms.

2.1.1 Review of Analyst & Industry Research

The following report was considered as part of our evaluation:

Analyst Title Date
McKinsey & Company Where Machines Could Replace Humans – And Where They Can’t (Yet) 2016

Following is a summary of the research:

Automation will eliminate very few occupations in the next decade. However, it will affect portions of almost all jobs based on the type of work they entail.

  • Predictable physical activities account for 1/3 of workers’ overall time in the manufacturing sector. Based on technical considerations alone, manufacturing is the second most readily automatable sector (after services).
  • Ninety percent of the tasks of welders, cutters, solderers, and brazers has the technical potential for automation.
  • Potentially59% of all manufacturing tasks are susceptible to automation.
2.2 Adoption Rates of the Digital Twin

In contrast to some analyst reports, the deployment outlook for the Digital Twin is relatively modest. Sixty-one percent of O&M professionals claim that they are not even familiar with the Digital Twin concept. An additional 20% of respondents do not expect that the Digital Twin will be deployed within five years. Only 4% of respondents expect complete deployment. (See Chart 5.)

Chart 5: Facility's plan for the digital twin within 5 years?

 

O&M Practitioner Insight

Q. The Digital Twin is not yet widely used. Why is this?

Georgi Kirilov

Even if there were support from the management, to implement such modeling, it would require local (facility) knowledge. Here, IT and consultants are not enough. The main drivers for this change would be reliability engineers, but incorporating their knowledge into software would threaten their jobs.

Usman Mustafa Syed

The Digital Twin requires a lot of expertise, which is scarce in the market; this is a big hurdle.

The bigger hurdle is the fact that GE has so aggressively associated itself with the name and concept of Digital Twin that it has somehow become a “pet brand” for it. This leads other competitors to avoid it. Also, the end users of non-GE assets tend not to consider it.

Jack R. Nicholas, Jr., P.E., CMRP, CRL, IAMC

Developing an accurate digital twin is expensive and time-consuming. This is because even in fairly new plants, configuration control is rarely practiced to the extent it should be. Drawings are usually out of date (even for newly commissioned assets) and specifications for performance are imprecisely documented over time and are, after a few operating cycles, ignored [because] assets are driven beyond the original design limits. To construct an accurate digital twin requires extensive research into current performance and condition requirements, not just referral to original plant specs.

Keeping a digital twin accurate over a life cycle is also expensive and requires at least periodic attention [from] data scientists, modeling specialists and business needs analysts – skills scarce in most organizations and usually available at high cost only from large service providers such as IBM, Accenture, and GE. Any plant modification affecting performance or required reliability conditions requires a change to the digital twin model and related algorithms.

Added to this is the fact that only a small fraction of available data is analyzed, and the data is rarely linked to functional failure modes (even if they are known by personnel who should be aware of them).

Added to that is the fact that brownfield plants haven’t provided connectivity from critical assets even with wireless technology to analysis centers in-plant or via the IIoT because the work to identify return on investment is seldom done and justified in a comprehensive plan containing all steps that must be accomplished to make cost-effective use of both analysis at the edge (near the machine), in the plant (before any firewalls) and in the Cloud.

In today’s cybersecurity climate, the safest approach is to have information and control functions on separate networks, the first allowing connectivity to the Cloud and the latter totally isolated from any external digital hacking. This, too, adds to cost.

2.2.1 Review of Analyst & Industry Research

The following reports were considered as part of our evaluation:

Analyst Title Date
Deloitte Industry 4.0 and the Digital Twin 2017
Gartner Prepare for the Impact of Digital Twins 2017

Following is a summary of the research:

  • Half of large industrial companies will use Digital Twins by 2021. The result is an expected 10% improvement in “effectiveness.”
  • ROI is expected to vary widely and will be based on monetization models that drive them.
  • “CIOs will need to work with business leaders to develop economic and business models that consider the benefits in light of the development costs, as well as ongoing digital twin maintenance requirements.” Alfonso Velosa, VP of Research, Gartner

 

3.0 Perspectives on IIoT Predictive Maintenance

There is a disconnect between the views of O&M professionals and senior executives. Executives recognize the potential represented by IIoT for Predictive Maintenance but the people responsible for implementation are less sanguine.

A lack of IIoT strategy is viewed as a significant factor in the delay of PdM4.0 adoption.

3.1 Attitudes Toward IIoT Predictive Maintenance

Senior executives are more likely to recognize the potential of IIOT Predictive Analytics than are facility staff.

Many O&M professionals still view PdM4.0 as over-hyped. In response to the statement “The hype of industry 4.0 and IIoT is exaggerated,” over 40% are Neutral. Twenty-three percent of online survey participants Agree with the notion and an additional 9% Strongly Agree. In contrast, only 6% Strongly Disagree and 18% Disagree. (See Chart 6.4, Appendix A.)

Almost 50% of O&M professionals surveyed say they Strongly Disagree or Disagree with the notion that facility staff recognizes the potential. An additional 15% are Neutral.

This contrasts with O&M professionals’ perception of senior executive. Fifty percent of online survey respondents Strongly Agee or Agree with the notion that senior executives recognize the potential of IIoT Predictive Analytics. Only 14% of respondents Strongly Disagree. (See Charts 6.1 and 6.5, Appendix A.)

Thirty-eight percent of online survey respondents indicate that they Strongly Agree or Agree with the statement that there are delays in new investment until a decision is reached related to an IOT strategy. Only 11% of respondents Strongly Disagree with this statement. (See Chart 6.2, Appendix A.)

Chart 6: attitude of reliability and maintenance professionals on IIoT predictive analytics (9-point scale)

O&M Practitioner Insight

Q. What is the reason for the disconnect between O&M professionals and senior management?

David Naus

There is a huge disconnect with upper management. I have talked with many decision makers who don’t care what happens on the maintenance side of their business, which is a huge asset to them and cost.

They will never take the time to learn how they could maintain their machines better and cut costs in doing it at the same time. They will always pass on to an employee in maintenance who is not qualified, and they won’t take the time to even discuss it.

Howard W Penrose, Ph.D., CMRP

I’m seeing something a little different. Many companies and maintenance organizations are championing IIoT devices, and some that you may not realize are IIoT, such as power monitoring, protective devices, and monitoring networks. When the device applications are presented at conferences, there is normally a significant turnout. However, [for discussions of] things like proper setup or, worse, security related to connected devices, the attendance is lacking. People see these issues as “someone else’s problem.”

Carlos De Leon

As Sundeep Sanghavi said in his article “Cognitive Predictive Maintenance Managed Data Flood”: The IIoT has the potential to transform this industry in a dramatic way, but this will be possible only if the volume of data is effectively leveraged by enhancing sensor connectivity.

3.1.1 Review of Analyst & Industry Research

The following report was considered as part of our evaluation:

Analyst Title Date
MIT Sloan/Boston Consulting Group Reshaping Business with Artificial Intelligence:

Closing the Gap Between Ambition and Action

2017

Following is a summary of the research:

  • Seventy-five percent of executives expect Artificial Intelligence to enable their organizations to move into new businesses.
  • Nearly 85% think that AI will help them obtain or sustain a competitive advantage. However, only one in five has incorporated AI in some offerings or processes.

 

4.0 Implementation of IIoT for Preventive Maintenance

A skill shortage of Big Data Scientists and a lack of understanding of Industry 4.0. are the two most significant factors negatively affecting PdM4.0 deployment. O&M professionals are significantly less concerned about the complexity of software and access to sensor data.

4.1 Factors Impacting the Implementation of PdM4.0

For O&M professionals, the biggest factor preventing the deployment of IIoT Predictive Analytics is a shortage of data scientists (4.2/5.0). Forty-four percent of online survey respondents say this skill shortage will have a Strong Impact. An additional 37% indicate a Moderate Impact. (See Chart 7.9, Appendix A.)

Chart 7: to what extent do the following negatively impact the deployment of IIoT predictive analytics (5 = strong impact, 1 No Impact)

The second-highest ranked negative impact on deployment is a lack of understanding of Industry 4.0 (3.8/5.0). Thirty-three percent of online survey respondents view this factor as Moderate, while an additional 30% indicate a Strong Impact. (See Chart 7.8 in Appendix A.)

Interestingly, the two factors that are least likely to impact deployment of IIoT Predictive Analytics are the complexity of the software and the inability to access sensor data. Both of these are ranked 3.2/5.0.

From a resource perspective, online survey participants are more likely to believe that their organization has the core infrastructure needed for IIoT Predictive Analytics (4.8/9.0) than that they have sufficient data scientists to deploy IIoT Predictive Analytics (3.3/9.0).

Chart 8: Availability of Internal resources to deploy IIoT predictive maintenance (9-point scale)

O&M Practitioner Insight

Q. Are the specific accelerators or blockers of IIoT Predictive Maintenance in your industry?

Fred Schenkelberg

Not just IIoT PdM.

PdM, in general, requires paying attention to the equipment in a systematic manner, identifying and modeling indicators, and having funding/support to implement.

Furthermore, this approach doesn’t address every cause of maintenance actions – for industries with a high percentage of failures due to predicable failure mechanisms, using IIoT or not first needs to prove to the organization that the approach works (adds value).

IIoT is a buzzword and may attract some in the market, yet unless they do the basics first, it will be an expensive experiment with little chance of success.

Jørgen Grythe

I cannot speak of the Predictive Maintenance industry as a whole since this is huge. We, however, focus on acoustic beamforming array and are using beamformed sound from machinery to classify a healthy or error state of a machine.

The biggest blocker for us right now is that every project is different, and there doesn’t seem to exist one universal black box solution to cover a wide variety of customers. So, if I were to generalize, I would say that it is difficult to really expand into a large-scale system that works for everyone. Kind of like if the iPhone had to be customized for each individual user; you can imagine the cost that would impose on the product and the lack of scalability.

Jack R. Nicholas, Jr., P.E., CMRP, CRL, IAMC

Current literature on Big Data, Advanced Analytics, Cloud Computing Technology and related subjects spells out the need for the merger of OT and IT, tying the mill deck to the boardroom.

While this is desirable in many ways, it often requires a significant change in culture in organizations with large if not dominant IT influence on digital transition. Many C-level executives (if not all by this time) are scared to death of a career-ending data breach.

Cybersecurity in most companies is assigned to IT personnel as a collateral duty and may be based on just a week or two of training – totally inadequate in today’s cyberspace climate. While methodologies such as reliability-centered maintenance and total productive maintenance have been around for decades, they still haven’t been adopted to the degree needed for personnel in plants to have firm knowledge of failure modes being experienced and [to] link them to data needed to provide advanced warning of onset at stages where economic mitigation can be planned and executed in an orderly fashion. Thus, there is no way for them to justify adoption of IIoT predictive maintenance.

Usman Mustafa Syed

In my opinion, many IIoT Service Providers (except for the big brands, mostly OEMs) come with a background in IT, Software and Business Analytics, with a bit of a disconnect from the Industrial Engineering fraternity.

They don’t speak the same language as the engineers managing the assets within different industries. This must change. Furthermore, there are concerns regarding cost, ROI, data security and leadership commitments in general.

Another important fact is that this wave of IIoT advancement came up during generally uncertain times. Global economies are sluggish right now, which has also impacted its success. But with the ongoing advancements and gradually improving economic conditions, things will improve.

Anonymous survey respondent

I was at a seminar last week with some very influential European organizations present. Some very telling facts were presented about the IOT in manufacturing.

As the two days progressed, there was a distinct split in the expectations of the data/tech guys and the “grease monkeys” responsible for day-to-day production. It seems that I4.0 is a dream of the tech guys that, at this team, the hardware cannot support.

Two key statistics stood out for me – there are 92% or 64 million machines in the world without network connectivity. The other statistic was the average of the machines in Italy is 13 years. This would lead to a gulf in desire versus ability.

It is my opinion that the large companies (BMW, Siemens) will have the resources to trial the new systems and machines and assess the ability to adopt across production facilities.

Small companies will have the flexibility to adopt and adapt.

This would then leave the medium size to invest in lower cost solutions and therefore experience [fewer] benefits from I4.0.

It’s clear that we will all be affected by IoT but in my opinion, this is likely to be driven by a consumer “need” versus a manufacturing readiness. It certainly will take longer than the predicted five to eight years to adopt.

4.1.1 Review of Analyst & Industry Research

The following report was considered as part of our evaluation:

Analyst Title Date
PwC Industry 4.0: Building the DigitalEnterprise 2016

Following is a summary of the research:

The biggest challenges or inhibitors for building digital operations capabilities were:

  • Lack of a clear digital operations vision and support/leadership from top management – 40%
  • Unclear economic benefit and digital investment–38%
  • High financial investment requirements – 36%
  • Unresolved questions about data security and data privacy in connection with the use of external data – 25%
  • Insufficient talent – 25%

 

5.0 Impact of IIoT Predictive Maintenance

Overall, O&M professionals have a positive view of IoT Predictive Maintenance.

In terms of change, most survey respondents do not expect that employment levels will change due to PdM4.0 but they do expect thatjob roles will change.

Improvements to Operational Equipment Efficiency (OEE) are widely expected. Furthermore, most survey respondents believe that utilizing and analyzing the data in realtime will allow for better decision making. From an organizational perspective, there are only limited concerns that the roles and responsibilities of O&M professionals will change. In general, there was not much support for the outlook that IIoT Predictive Maintenance will force the convergence of Information Technology and Operational Technology organizations.

5.1 Impact of IIoT Predictive Maintenance on Financial Results

Regarding the financial impact of PdM4.0, the strongest belief that O&M professionals hold is that the perceived ROI from a Predictive Analytics solution will justify the costs. Forty-eight percent Agree with the statement that perceived ROI will justify the cost, while an additional 11% Strongly Agree. Only 4% of respondents Strongly Disagree with this statement.

Chart 9: which of the following reflects the cost considerations for predictive analytics solutions? (9.0 scale)

5.2 Impact of IIoT Predictive Maintenance on Employment

Most O&M professionals do not think that employment levels will change dramatically with the adoption of Industry 4.0 practices such as PdM4. Twenty-four percent of survey respondents believe that employment levels will fall and only 6% believe they will rise. By far, the prevalent view is that employment levels will stay the same but job roles and functions will change (57% of respondents).

5.3 Impact of IIoT Predictive Maintenance on O&M Practices

Almost all O&M professionals expect that standard O&M practices will change due to PdM4.0, although opinions are split as to the extent of this change.

More than half (54%) of online survey respondents expect moderate changes to standard O&M practices, while an additional 5% expect no changes to practices. A minority of respondents (41%) expect major changes to standard O&M practices.

Chart 11: the impact on standard O & M practices due to industry 4.0 and IIoT predictive maintenance

5.4 Overall Impact of IIoT Predictive Maintenance on Plant/Industry

Most O&M professionals expect that PdM4.0 will result in positive change. There is a significant consensus that Overall Equipment Efficiency (OEE) will increase. In fact, 54% of respondents Strongly Agree with this statement, while an additional 36% Agree. Similarly, regarding the statement that utilizing and analyzing data in real time will allow for better decision making, 38% of respondents Agree and 54% Strongly Agree.

There is less support for the notion that PdM4.0 will force the convergence of Information Technology and Operational Technology.

Chart 12: Impact that IIoT predictive maintenance will have on plan and/or industry

O&M Practitioner Insight

Q. What changes do you see happening in the Reliability & Maintenance discipline over the next five years?

Fred Schenkelberg

I see an increased emphasis on establishing consistent processes, gathering and using data in a meaningful manner (manual first, then, where useful, automating it).

I also expect atenfold increase in the collection of data that goes under-utilized. Currently, many factories collect tons of data and there is little or limited use of it. Adding more sensors adds more data, not information.

 

6.0 Recommendations

6.1 Recommendations to Accelerate PdM4.0 and Industry 4.0 Deployment

The following high-level recommendations for accelerating the deployment of IIoT Predictive Maintenance are provided based on research in this report.

  • Include O&M professionals in developing the organization’s PdM4.0 strategy. Not only does this help secure organization-wide buy-in, but their inclusion can provide practical insight into adoption.
  • Implement Proof of Concepts with multiple vendors and evaluate results based on pre-defined criteria.
  • Include O&M professionals in the process of creating functional and specification documents, Requests for Proposals (RFPs) and Proof of Concepts.
  • Create Centers of Excellence for the Application of Machine Learning.
  • Develop and nurture an Industry 4.0 vendor and solutions ecosystem so that complementary technologies can be rapidly evaluated and adopted when needed.
  • Design a company-wide IIoT Technology Roadmap that is vendor-neutral.
  • Form a working group representing Operational Technology and Information Technology to explore technological solutions.
  • Develop operational metrics to measure the adoption of PdM4.0. These should not only include deployment (number of assets covered), but also relate to the enabling infrastructure (people, process, technology,etc.).
  • Include elements of Artificial Intelligence and Machine Learning in O&M Skills Development programs.
6.1.1 Review of Analyst & Industry Research

The following reports were considered as part of our evaluation:

Analyst Title Date
McKinsey & Company Notes from the AI Frontier: Insights from Hundreds of Use Cases 2018

Following is an abstract of recommendations included in the report:

  • There are steps to be taken before embarking on pilots or PoC’s. McKinsey suggests a “holistic approach”: creating a prioritized portfolio of initiatives across the enterprise, including both AI and the wider analytic/digital techniques.
Analyst Title Date
PwC Netherlands Predictive Maintenance 4.0: Predict the Unpredictable 2017

Following is an abstract of recommendations included in the report:

  • Set up an IIoT infrastructure including “choosing the right protocols for wireless connectivity, data encryption and security.”
  • Install feedback loops. The ML solution may provide insights into the cause of machine asset failure. “Perhaps the PdM 4.0 business case for a particular asset type needs to be re-evaluated: it may be more expensive or yield worse returns than initially thought. Or the criticality of assets may change over time and warrant new feasibility studies.”

 

Appendix A – Additional Research Data

This section contains additional graphics not included in the main report.

1.0 Attitudes Towards IIoT Predictive Maintenance

Chart 6.1 facility staff recognize the potential of predictive analytics

Chart 6.2 we are delaying new investments in predictive analytics until we decide on an IOT strategy

Chart 6.3 adopting industry 4.0 is a priority for our organization or facility

Chart 6.4 the hype over industry 4.0 and IIoT is exaggerated

Chart 6.5 Senior executives recognize the potential of IIoT predictive analytics

2.0 Factors Impacting the Deployment of IIoT Predictive Maintenance

Chart 7.1 Impact on deployment: software too complex

Chart 7.2 Impact on deployment: inability to access sensor

Chart 7.3 Impact on deployment: budgetary constraints

Chart 7.4 Impact on deployment: lack of need or interest

Chart 7.5 Impact on deployment: concerns about sharing

Chart 7.6 Impact on deployment: other priorities

chart 7.7 Impact on deployment: skill shortage (engineers)

Chart 7.8 impact on deployment: lack of understanding of industry 4.0

Chart 7.9 Impact on deployment: skills shortage (data scientists)

Chart 8.1 we have sufficient staff of data scientist to deploy IIoT predictive analytics

Chart 8.2 my organization has the internal expertise to deploy IIoT predictive analytics

Chart 8.3 My organization has the core infrastructure needed for IIoT predictive analytics

3.0 Impact that IIoT Predictive Maintenance Will Have on Plant and/or Industry

Chart 12.1 impact: the overall equipment efficiency (OEE) will improve

Chart 12.2 Impact: utilizing and analyzing the data in real-time will allow better decision making

Chart 12.3 Impact: the capture, storage and use of operational data will be a high priority

Chart 12.4 Impact: it will lead to a meaningful extension of asset useful life

Chart 12.5 Impact: there will be an improvement in operational safety and health

Chart 12.6 Impact: it will allow my organization to become more competitive in our market

Chart 12.7 impact: roles and responsibilities for maintenance and reliability professionals will change

Chart 12.8 Impact: force the convergence of information technology and operational technology organization

 

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Arnav Jalan

Arnav Jalan

Arnav Jalan is an undergraduate sophomore at Emory University, majoring in Computer Science and Mathematics.