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Peter Drucker famously stated that the “greatest danger in times of turbulence is not the turbulence; it is to act with yesterday’s logic.”  The industrial world is entering a period of disruptive change. For some, Industry 4.0 represents a break in the past that may lead to catastrophic results. Others view Industry 4.0 as an opportunity to break into new markets. At Presenso, we speak to groups of executives from the largest industrial plants. We’ve summarized some of the most common myths about Industry 4.0 and outlined our response.

Myth 1: Industry 4.0 requires a massive investment in infrastructure.  Only large and deep pocketed manufacturers will survive.

Response: Let’s put all cards on the table. There is a good reason that GE, Siemens, SAP and many others are investing billions of dollars in IIoT R&D. Simply stated: we are living in the digitalization era.  Technology is the enabler of change and there will be entities lacking the flexibility to adapt.

This does not necessarily mean that you should rush to invest in facility-wide IoT capabilities.  Many industrial plants prioritize Industrial Analytics for Predictive Maintenance and can implement solutions by simply accessing existing Historian databases.

The smart approach is to define what Industry 4.0 means for your competitive environment. Are wearables, augmented reality, 3D printing and advanced robotics part of the future landscape? Are business models evolving with Industry 4.0? Start with a strategy and work your way back to infrastructure.

Are you looking for a Machine Learning solution for IIoT Predictive Maintenance, consider Automated Unsupervised Machine Learning? To schedule a Free Solution Demo, Please Click Here.

Myth 2: Predictive Maintenance is primarily an operational efficiency play to cut costs.  This makes ROI difficult to justify, especially since there is no obvious revenue impact.

Response: Revenue is overlooked in ROI calculations for IIoT Predictive Maintenance. The reason is technical. For an oil refinery, back-of-the envelope revenue calculations are based on commodity prices set by external markets. These are relatively easy to estimate.

However, in a manufacturing environment, it’s difficult to assign a revenue number to asset failure. There are numerous variables that are hard to calculate:

  • What is the product mix within the factory?
  • What is the factory’s production rate?
  • What is the wholesale price of each product?

If you are looking for average industry data, there is no published data for lost revenue for a food factory or paper mill.

Instead of performing the hard (perhaps impossible) work of estimating revenue lost to downtime, there is a tendency to focus on cost savings from reducing Operations and Maintenance (O&M) budgets.

In reality, machine and asset failure cuts into top-line revenue and the opportunity cost is tangible. Downtime reduces an average of at least 5% of a factory’s productive capacity.   Is it extra work to calculate the revenue from ROI?  Perhaps.  At the same time, even if you need to rely on high level/ballpark estimates to forecast additional revenue, this is not a calculation that can be excluded from ROI.

Myth 3: Industry 4.0 is transformative and requires a dedicated Chief Digital Officer (CDO) to own the implementation.

Response: Agreed. Industry 4.0 is transformative. However, there is more than one model to implement change and the office of the CDO is not the only approach. This is how Deloitte defined the four faces of the CDO:

  • Catalyst
  • Strategist
  • Technologist
  • Operator

At his or her core, the CDO is an evangelist and champion, but not a business unit owner. The success or failure of implementing Industry 4.0 is dependent on numerous factors including executive buy-in and the ability of Operational Technology (OT) and Information Technology (IT) to collaborate.  In fact, the CIO or COO can lead the digital transformation. Formalizing a CDO role does not guarantee the implementation of Industry 4.0 and even organizations that lack this role can succeed.

Myth 4: Only companies that can hire Data Scientists and Big Data Engineers will be able to use Artificial Intelligence and Machine Learning for Predictive Maintenance

Response: The dearth of Big Data professions is well-known and industrial plants are not likely to compete with hi-tech industry when it comes to recruitment of data scientists. At the same time, there are workarounds to mitigate against a lack of talent.

Just because a technology is based on Big Data and Machine Learning does not mean that the end-user need to develop expertise in these domains. At Presenso, our end-user interface with dashboards visualizes analytics in an easy-to-read format.  All the information that is needed for the end-user is self-contained and the data science work is performed with the solution. In the future, we expect predictive maintenance vendors to target their solutions to so-called citizen data scientists, the term that is used to describe an individual that “generates models that use advanced diagnostic analytics or predictive and prescriptive capabilities, but whose primary job function is outside the field of statistics and analytics.”

Myth 5: Industry 4.0 is overhyped and is similar to the dotcom bubble

Response: There are similarities between the dotcom era and Industry 4.0, but few warnings signs of an inflated bubble.  Even though brick-and-motor stores never disappeared, the internet revolutionized commerce irreversibly. Some analysts expect that Internet of Things (IoT) will disrupt the industrial world with a similar force.

We refer to a “bubble” when financial valuations are based on unsustainable growth projections. It is true that there is significant interest in the IoT category from the investor community.  However, although the stock market is at an all-time high, we are not seeing valuations on Industry 4.0 equities that were reached during the late 1990’s.

In summary, the comparisons between the Internet and the Internet of Things are valid, but the concerns seem overblown.

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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.