From IIoT theory to practice

Five risks for designing the Smart Factory

If you're looking for reasons not to invest in Predictive Maintenance, you won't have to look hard: There are numerous risks associated with IIoT solutions, and addressing these risks is critical. However, the biggest risk for plants is not pursuing Predictive Maintenance at all.

The Smart Factory is moving away from being a boardroom strategic initiative to a factory floor implementation. At every industry and customer event that I have attended in recent years, there is a receptiveness to new technologies that support digitization and Industry 4.0. Industry analysts are predicting astronomical growth rates and companies are reinventing themselves to capture a share of the Smart Factory category. 

Nevertheless, when I speak to C-level executives there is a shift in tone. Although there is a recognition that the Smart Factory is inevitable, there are some real concerns that the costs may not be justified and that smaller and medium size factories may not accrue the same benefit as larger industrial plants with more resources. Ironically, some of the larger factories with significant sunk capital are concerned about how to adjust to digitisation within a reasonable time frame and budget.  

Apart from the generic concept that extracting insights from big data and applying them to factory production and machine maintenance, there is almost no common definition of the Smart Factory or a roadmap for how to develop one. 

Based on some of my more recent conversations, I would like to share some of the concerns that are often missed by the vendor-sponsored industry publications: 

The cybersecurity threat is palpable 

Executives have a lot on their plates these days, but cybersecurity issues should still be top of mindCovid-19 has been the catalyst for an increase in cybercrime, with manufacturing joining retail as one of the main targets. However, even before the pandemic hit it seemed like every day another article appeared in the media about state sponsored cyber warfare or a data breach.  

A 2019 Smart Factory cybersecurity study by Deloitte and the Manufacturers Alliance for Productivity and Innovation (MAPIprovides a helpful overview. The study found that 4/10 manufacturers had been impacted by cyberattacks over the past year, and that the average cost of an IoT-related cyber incident amounted to $300,000. 

After the cloud concept was first introduced, the initial wave of objections to it was based on security considerations: when the organization’s internal staff maintains its data, it controls access to unauthorized third parties. The assumption underlying this argument is that enhanced control results in better security. 

Because the cloud is a critical component of the Smart Factory, the debate about onpremise versus cloud has been reignited. In some ways, the increasing adoption of the Digital Twin in factories with IoT deployment can be attributed to the perceived advantage of storing factory data within the onpremise virtual clone of the machine. 

For industrial plants considering using a cloud-based solution for predictive asset maintenance, it is prudent to inquire about how a company transfers, stores and accesses a customer’s data.  

For instance, in the case of SKF Enlight AI, our stringent security model is the same as used in the online banking industry. Furthermore, we do not record machine asset information: our advanced algorithm searches for patterns of abnormal sensor data, and is therefore an unlikely target for state-sponsored or criminal cyber-attacks. 

Conversely, one should not simply assume that an on-premise solution is more secure. Cybersecurity is a function of data policies and protocols, employee training and various other factors. In my experience, there are many industrial plants that lag behind other industries and therefore are more vulnerable to attack.  

Betting on a technology vendor is risky

Change is the new normal. The Smart Factory requires a significant investment in technology ranging from automation tools to business intelligence. The issue is that while a Smart Factory strategy requires flexibility and rapid adoption of new processes, some of the underlying technologies require longterm commitments. 

Specifically, the list of vendors that now offer the Digital Twin is growing. The recent addition of new companies providing Digital Twin capabilities to the mix has created a great deal of confusion. Why? Because these industry behemoths are asking factories to make a long-term investment in their platform without sharing or committing to a technology roadmap. 

I am not suggesting that established vendors will disappear. At the same time, it is not clear which, if any, entity will dominate the category. As companies rush to build entire vendor ecosystems around their platforms, plant owners are concerned that they will be locked into a digital platform that will limit their own flexibility long-term. 

The digital factory is not always feasible 

Over the years, I have heard vendors argue that if you don’t purchase their solution, your business may suffer the consequences. Although I shy away from this type of hyperbole, there is some validity to the concern that companies that do not embrace digitalisation and the Smart Factory could lose their competitive advantage. 

Ironically, there is a more acute (and contradictory) concern that over-investing in the Smart Factory could hurt a factory’s bottom line. In the traditional factory environment, one buys machinery and can amortise the cost over an extended period. But with the Smart Factory, virtual equipment may not last that long and can become redundant relatively quickly. 

Therefore, there is a risk of over-investing in technologies with a short-term shelf life. Miscalculating the lifetime of an asset could lead to distortions in ROI. 

SKF Enlight AI is cloud based and does not require investments in hardware and other infrastructure. However, many other Smart Factory solutions that require hardware and software implementation can be cost prohibitive, especially for smaller plants. 

Organisational change is hard to implement 

The Smart Factory is not a technology solution or suite. The Smart Factory is a new mindset. The ability to find new ways to monetise Big Data is not simple, and there is often a lag between the promise of digitalisation and the organisational infrastructure that needs to operationalise it. 

In the age of the Smart Factory new skills are required that are both technical and business oriented. The good news is that industrial plants will have access to massive amounts of valuable data. At the same time, if frontline employees are not trained and empowered to act on the insights in close to real time, the facility will not realise the value from its Smart Factory investments. 

Conclusion: Change should be incremental 

If there is one thing that I have learned from my experience in IT management, it’s that change does not need to happen overnight. Another term for an early adopter is a beta customer. The Smart Factory can be adopted carefully and incrementally. A new and flexible mindset is needed for the Smart Factory, but the same level of prudence that is required before physical capital expenditures should be extended to Smart Factory investments. 

 

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