If disruptive technologies and new business models have become the “new normal,” is strategic planning for Maintenance 4.0 even irrelevant? The logic is simple and recognized in a Harvard Business Review article:
“In an environment of rapid change, events can render market forecasts obsolete almost overnight. Having repeatedly experienced such frustrations, planners begin to lose their faith in forecasting and instead try to understand the basic marketplace phenomena driving change.”
Before reaching a conclusion, keep in mind that the publication date of this article is July 1980 – several decades before the digital revolution.
Getting Back to the Basics of Strategic Theory
Let’s start with a refresher on strategy by reviewing two of the most prominent thinkers on the topic: Michael Porter and Henry Mintzberg.
Deliberative, Michael Porter: A top-down, position-driven method for planning based on analytics. This approach advocates for a consistent, company-wide commitment to a focus strategy such as production differentiation or low price.
Emergent, Henry Mintzberg: Traditional strategic planning is a “fallacy” because there are changing and unknown variables, such as innovation, competitor actions, and price fluctuations. Mintzberg’s approach is based on a strategy that “can develop inadvertently, without the conscious intention of senior management, often through a process of learning.”
Even in the era of digital transformation, it would be a mistake to arbitrarily assume that Mintzberg is more relevant than Porter. We should not discount the need for management to develop a distinct position (Deliberative), and not all organizational cultures can support an adaptive strategy (Emergent).
Guidelines for Maintenance 4.0 Strategic Planning
Even if we adopt the position that there are no unique approaches to strategy in the Smart Factory era, there are still critical considerations that should inform and affect its development:
- Make ROI-Based Investments, Not Bets: Given some of the stratospheric predictions from analysts about the economic potential of Maintenance 4.0, it may be tempting to make big bets. However, these could be risky, and even deep-pocketed players can overextend themselves with Industry 4.0.
- Benefiting from Industry 4.0 can be done conservatively: With so much uncertainty, there are risks associated with completely overhauling a plant’s Information Technology (IT) and Operational Technology (OT) infrastructure and committing to an IoT platform. Investments must still be justified from an ROI perspective.
- Incrementalism is Key: Even small reductions in unscheduled downtime can lower operating costs and improve yield rates. The good news is that, in the Maintenance 4.0 arena, opportunities are available that do not require large-scale investments. There is a significant installation base of older equipment with legacy monitoring systems in industrial plants built on 1970’s technologies. As machinery is replaced, new equipment should contain sensors that generate Big Data for AI-based Industrial Analytics.
- Consider External Maintenance Delivery Capabilities: In the last couple of years, more OEMs are offering their hardware as a service (HaaS). In this way, industrial equipment is not sold but, instead, leased to plants, while the OEM maintains ownership (and maintenance and reliability responsibilities).
In an era of disruptive change, plants should consider moving non-revenue O&M functions to external service providers. OEMs are increasingly starting to include industrial analytics as part of their HaaS offering. SKF Enlight AI’s cloud-based solution, for example, allows companies to access innovations in Machine Learning and Artificial Intelligence without building internal competencies in these areas.
At a time of disruptive change, there is no one-size-fits-all approach to strategic planning. Industrial plants do not have the luxury of waiting on the sidelines while their competition adopts Maintenance 4.0 technologies such as Machine Learning, automation, and 3D printing of spare parts. However, at the same time, over-investing in new technologies can be risky.
In summary, invest carefully and consider incremental opportunities and external maintenance delivery capabilities.
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.