The business philosopher 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 undergoing a period of disruptive digital change, which has been accelerated by Covid-19’s impact on ways of working.
For some, Industry 4.0 represents a break with the comforting familiarity of the past and that may lead to catastrophic results. Others view Industry 4.0 as an opportunity to expand into new markets or as an adaptive approach to current and future market uncertainties.
At SKF, we are in constant dialogue with groups of executives from the largest industrial plants around the world. We’ve summarized some of the most common myths about Industry 4.0 that we’ve encountered and outlined our response.
Myth 1: Industry 4.0 requires a substantial investment in infrastructure. Only large and deep pocketed manufacturers will survive.
Let’s put all cards on the table: We are living in the digitalization era. There is a good reason that legacy players are investing billions of dollars in IIoT R&D. Technology is the enabler of change, and entities lacking the flexibility to adapt will find themselves unable to compete at some point.
This does not necessarily mean that you should rush to invest in facility-wide IoT capabilities. Many industrial plants utilize their sensor data to take advantage of the savings and optimization offered by predictive maintenance. In the right hands, your existing Historian databases can provide valuable, actionable insights into machine health and operations.
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 your future landscape?
- Are business models evolving alongside Industry 4.0?
Start with a strategy and work your way back to infrastructure.
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.
Revenue is overlooked in ROI calculations for IIoT Predictive Maintenance. The reason for this 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 simply 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 or 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.
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:
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. At SKF, for instance, our CEO Alrik Danielson has taken on the responsibilities of CDO to demonstrate how critical digital transformation is for our organization. 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 AI and Machine Learning for Predictive Maintenance.
The dearth of Big Data professions is well-known, and industrial plants are not likely to compete with the hi-tech industry when it comes to recruitment of data scientists. At the same time, there are workarounds to mitigate this lack of talent.
Just because a technology is based on Big Data and Machine Learning does not mean that the end-user needs to develop expertise in these domains. At SKF Enlight AI, our end-user dashboard 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 within the solution.
Gartner predicted that citizen data scientists, individuals who “generat[e] models that use advanced diagnostic analytics or predictive and prescriptive capabilities, but whose primary job function is outside the field of statistics and analytics” would become more prevalent by 2020. From what we’re seeing, this is not the case within most factories, nor are we headed in that direction. If your end-user interface is doing its job properly, there is no need for maintenance technicians to take on data science roles.
Myth 5: Industry 4.0 is overhyped and is similar to the dotcom bubble
There are similarities between the dotcom era and Industry 4.0, but there are 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 comparable 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, 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.
Have an Industry 4.0 strategy in place
The above myths speak to a fear or fears that manufacturers associate with Industry 4.0. The best way to overcome these fears is to address the variables and come up with a strategy to navigate the various new scenarios that Industry 4.0 might send your way. Arming yourself with facts and a roadmap is a productive and responsible way to prepare management and plant workers for the inevitable changes that Industry 4.0 will bring.
Industrial plants generate terabytes of process data. SKF Enlight AI is a SaaS Predictive Maintenance solution that uses Automated Machine Learning to identify emerging asset failure patterns within this data. It provides early warnings and sensor-level intelligence to help avert unplanned downtime and meet production goals. For more information on how SKF Enlight AI can improve performance and reliability, click here.