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Do you need help building a business case for investing in IIoT Predictive Maintenance software?

As more industrial plants seek solutions for IIoT Predictive Maintenance software, many turn to publicly available analyst reports.This blog article summarizes the most important analyst reports, provides the highlights in terms of key data and findings and evaluates the reports’ usefulness. We have included relevant statistics that one can use to build the business case for IIoT Predictive Maintenance software or other Industry 4.0 solutions.

Please note that our analysis focused on PdM4.0 / IIoT for Predictive Maintenance.Other parts of research reports that are not related to this topic were not assessed.

Industry 4.0: Building the Digital Enterprise

PwC

2016

Industry 4.0: Building the digital enterprise

This most extensive global survey on Industry 4.0, containing estimates for expenditures on digital solutions and expected costs savings on a per-industry basis.

Topics Covered A series of predictions from 2015/2016 on Industry 4.0’s impact on various aspects, including financial performance, customer relations, etc.
Geographic Coverage Worldwide
Research Methodology Survey of 2,000 senior executives in 26 countries
Link to Report https://www.pwc.com/gx/en/industries/industries-4.0/landing-page/industry-4.0-building-your-digital-enterprise-april-2016.pdf
Report Summary A perspective, written in 2015, indicating that as Industry 4.0 moves from buzz to reality, internal competencies in new areas are necessary. The report suggests focusing on people as enablers of change and building enterprise-wide data analytics competencies.
Key Statistics • Expectations of US$421 billion in cost reductions and US$493 billion per annum over the next five years.
• Industry 4.0 cost reduction for industrial manufacturing: 3.6% per annum until 2020.
• Percentage of revenue that industrial manufacturers expected to spend on digital operations solutions until 2020:5%.
• Maturity of data analytics capabilities (across all industries):Medium – 52%, Advanced – 18%, Poor – 22%.
Evaluation of Findings
Pros Cons
The report provides a holistic view of all Industry 4.0 technologies and their impact on multiple sectors. Because this report is from 2016, the data forecasts are somewhat outdated.

Relevance of report for IIoT Predictive Maintenance Business Case

New/Unique Perspective Usefulness of Data Practical Guidance

Review of Research Reports on Industry 4.0 and IIoT Predictive Maintenance

Predictive Maintenance 4.0: Predict the Unpredictable

PwC Netherlands

June 2017

Predictive Maintenance 4.0

The PwC report on Predictive Maintenance provides insights into both the current state of PdM4.0 and future plans for deployment.This report includes practical advice.

Topics Covered Current state and future state of Predictive Maintenance (PdM 4.0) and Big Data Analytics
Geographic Coverage Europe: Belgium, Germany and the Netherlands
Research Methodology Anonymous phone survey of 280 individuals; additional in-depth interviews also conducted
Link to Report https://www.pwc.nl/nl/assets/documents/pwc-predictive-maintenance-4-0.pdf
Report Summary • Four levels of maturity in predictive maintenance (1= visual inspections, 4= PdM).
• There is widespread acceptance of IIoT Predictive Maintenance, even if deployment lags behind the strategic vision.
• Companies with similar assets have more predictive maintenance than companies with unique assets.
• The need for governance when building in-house Big Data capabilities.Creating internal competencies requires more than just hiring people.
Key Statistics • Primary goal for the adoption of PdM: 47%: Uptime improvement, 17%: Cost reduction, 16%: Lifetime extension of aging asset, 11%: Reduction of safety, health, environment & quality risks, 8%: Higher customer satisfaction, 1% New revenue stream: 1%.
• Small minority (11%) at level 4 / PdM deployment.
• About half of respondents have plans for PdM deployment. Breakdown is as follows: 20%: yes, we are working on it; 6%: yes, we start next year; 6%: yes, we start in 3 years; 17%: yes, no start date; 51%: no.
• Tools (hardware and software) currently used for PdM:MS Excel / Access: 67%; WIFI: 34%; datawarehouse: 18%;statistical software: 18%;conditioning monitoring software: 40%; cloud: 13%; data software 33%; mobile networks: 20%; IoT: 14%.
• 27% currently employ reliability engineers in predictive maintenance and 8% employ data scientists.
Evaluation of Findings
Pros Cons
• Use of case study examples provides an interesting context for the report.
• The report offers a thorough analysis of organizational and infrastructure issues.
• Accuracy of findings. According to the report, PdM 4.0 is not popular in Germany (only 2%), whereas in Belgium it is popular (23%).The disparity between these estimates raises concerns about the sampling methodology.
Relevance of report for IIoT Predictive Maintenance Business Case
New/Unique Perspective Usefulness of Data Practical Guidance

Review of Research Reports on Industry 4.0 and IIoT Predictive Maintenance

Notes from the AI Frontier: Insights from Hundreds of Use Cases 

McKinsey & Company

April 2018

Review of Research Reports on Industry 4.0 and IIoT Predictive Maintenance

McKinsey & Company provides a well-researched report on AI, generated through an analysis of more than 400 use cases.The report is strategic in nature, with little in terms of practical guidance.

Topics Covered Mapping AI techniques to problem types, insights from
use cases, sizing the potential value of AI, the road to impact and value
Geographic Coverage Worldwide
Research Methodology Analysis of more than 400 use cases across 19 industries and 9 business functions
Link to Report Link available here
Report Summary • Deep Learning’s ability to analyze very large amounts of high-dimensional data can take predictive maintenance to a new level. Layering in additional data – such as audio and image data – from other sensors’ neural networks can enhance and possibly replace more traditional methods.
• The ability to predict equipment failure can lead to a reduction in downtime.
• McKinsey expects cost reductions and increased production yields.
• Realizing AI’s full potential requires a diverse range of data types.
• AI systems require constant data acquisition and model refreshers. One-third of use cases require model refreshers at least monthly. In some cases, daily refreshers are required.
Key Statistics • Predictive Maintenance has the potential to create $0.5–0.7 trillion in incremental value.
• In 69% of use cases, deep neural networks can enhance performance beyond that provided by traditional analytics approaches.
Evaluation of Findings
Pros Cons
• The report contains a thorough analysis and offers a perspective across multiple industries. • The report provides insufficient practical information to help with deployment.
Relevance of report for IIoT Predictive Maintenance Business Case
New/Unique Perspective Usefulness of Data Practical Guidance

Review of Research Reports on Industry 4.0 and IIoT Predictive Maintenance

Finding Europe’s Edge in the Internet of Things

Bain & Company

2017

High-level report about the differences in attitude and progress in the deployment of IoT and analytics solutions in the US and Europe.

Review of Research Reports on Industry 4.0 and IIoT Predictive Maintenance

Topics Covered How European and US executives view the IoT potential
Geographic Coverage Europe and the US
Research Methodology Survey of 500 executives in Europe and the US
Link to Report http://www.bain.de/Images/BAIN_BRIEF_Finding_Europes_Edge_in_the_IoT.pdf
Report Summary • European firms are more advanced in their deployment of IIoT than are their US counterparts.
• US executives see IoT / IIoT primarily from an operational / cost savings perspective, whereas European executives value more strategic factors such as competitive superiority.
Key Statistics Implementing / already implemented IoT and analytics use cases:27% – Europe, 18% – US.
75% of US executives expect IoT / IIoT Predictive Maintenance for cost savings versus 35% of European executives.
Evaluation of Findings
Pros Cons
• The report demonstrates a deep understanding of European versus US trends and provides relevant examples from other industries. • The guidance is at a very high level.

Relevance of report for IIoT Predictive Maintenance Business Case

New/Unique Perspective Usefulness of Data Practical Guidance

Review of Research Reports on Industry 4.0 and IIoT Predictive Maintenance

Other Research Reports:

The following reports can provide additional information about Predictive Maintenance but are less useful for the development of a business case for IIoT Predictive Maintenance.

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Avi Nowitz

Avi Nowitz

Avi Nowitz writes about the financial impact of Machine Learning on the industrial sector. Avi started his career on Wall Street as an equity analyst, providing institutional investor research on manufacturing companies. After completing his MBA in Finance from New York University, he joined the consulting division of Peppers and Rogers Group where his international clients included Bertelsmann, Ford Motor Company and Doğuş Holdings. For more than a decade, Avi led the Microsoft Consulting Practice at New Age Media and managed the execution of initiatives in EMEA and APAC.