For some industrial plants, the sprint towards Industry 4.0 has slowed. A report by McKinsey has identified the “pilot purgatory” scenario: Industry 4.0 pilots do not demonstrate the expected benefits, thereby limiting the momentum towards full-scale deployment. The industrial sector is grappling with the challenges of adopting transformative change. Many have underestimated plants’ ability to adopt new technologies, processes and even business models without jeopardizing existing revenue streams.
Turbomachinery is critical to the operations of many industrial sectors, and unscheduled downtime has a meaningful impact on production yield rates and profitability. This article explores the evolving role of Turbomachinery OEMs adopting a service model and the implications for plant owners.
The Underlying Economics of Maintenance 4.0
Maintenance 4.0 refers to the Reliability and Maintenance activities that form the basis of Industry 4.0. The primary goal is to maximize production uptime by eliminating unplanned, Reactive Maintenance. As depicted in the following graph, with Maintenance 3.0 / Reactive Maintenance, an alert is triggered only close to or after the occurrence of the failure incident. Once this alert takes place, parts must be ordered and maintenance repairs scheduled. Repair crews typically lack detailed Root Cause Analysis (RCA) and therefore rely on trial and error. Most significantly, the equipment is inoperable during the repair process, which can have a cascading effect on production activities.
Let’s compare this approach to Maintenance 4.0. The following graph depicts the repair process when AI-driven industrial analytics are applied to data generated from sensors embedded in the machinery. Through the use of Machine Learning, evolving machine failure can be detected before incident occurrence.
Because plant technicians receive early warnings of evolving failure, repairs can be scheduled and parts ordered while the machinery remains operational. Furthermore, Maintenance 4.0 industrial analytics can provide detailed RCA information, which helps eliminate guesswork on the part of the repair crews.
The shift to Maintenance 4.0 has a wide impact on both cost and revenue. From a cost perspective, when jobs are scheduled in advance, repair crews spend less time waiting idly. In addition, advance planning can eliminate the need for excess spare-part inventories.
Finally, because the occurrence of repair activities such as scheduling and inventory management shifts from downtime (when production halts) to uptime (when production levels are maintained), plant yield rates improve.
The combination of operational cost savings and revenue from higher yield rates is the reason why Maintenance 4.0 has become a focus area for industry executives.
Why Maintenance 4.0 Implementation Fails
Although justifying investment in Maintenance 4.0 is relatively easy from a strategic perspective, the reality on the plant floor is more complex. The following insights were gained through research conducted by Presenso and Emory University:
- There is a widely held perception that the industrial sector lacks qualified experts in the science of Machine Learning and Artificial Intelligence – experts necessary to operationalize the insights from the Big Data generated within plants.
- Plants have not instituted practices and processes to support deployment. For instance, the value of operational data may be recognized at a high level, but the data itself is often inaccessible or the organization lacks data governance. Turf wars between Operational Technology (OT) and Information Technology (IT) groups can slow deployment.
- The transformative nature of Maintenance 4.0 is underestimated. Change management requires both sustained executive-level sponsorship commitment and dedicated evangelists at the plant level.
- A surge of new technologies and vendors in the Maintenance 4.0 market adds confusion to those tasked with building internal solutions roadmaps. The disappointing performance of GE’s Predix offering highlights the risk of investing heavily when market leadership has not been established. Many plants have adopted a wait-and-see approach and prefer evaluation to full-scale deployment.
A New Equipment Purchase Model
In the early 1960’s Rolls-Royce launched its “Power-by-the-Hour” service for jet engines. Instead of selling the jet engine, the company provided a fixed hourly usage cost and assumed responsibility for maintenance activities. Today, we are seeing a new iteration of this model in the form of Hardware as a Service or HaaS.
Turbomachinery Hardware as a Service combines the pricing/service model of “Power-by-the-Hour” for engine jets with the following Maintenance 4.0 processes:
- Embedded in the machinery are sensors generating data that is continuously streamed into an industrial analytics platform. Advanced Machine Learning algorithms are applied to the data that are trained to detect abnormal behavioral patterns.
- Based on the sequence of abnormalities, a Root Cause Analysis is traced.
- A remote monitoring center staffed with technicians monitors the analysis and manages the activation of repair scheduling and spare parts ordering.
Will HaaS Become the Default for Turbomachinery Maintenance?
The recognition on the part of many industrial plants that they lack the resources and expertise to implement Maintenance 4.0 makes the OEM-driven HaaS a compelling option.
Turbomachinery OEMs benefit from economies of scale and are more likely to develop deep expertise in Artificial Intelligence (internally or via partnerships). The cost of remote monitoring centers can be amortized across an install base that includes multiple industrial plant customers.
An overlooked factor increasing the likelihood of HaaS is the fact that Maintenance 4.0 is in its infancy and there are nascent technologies such as 3D printing of spare parts and automated repairs. Industrial plant internal O&M groups must spread their capabilities across multiple equipment types. In contrast, Turbomachinery OEMs can develop deep product-related maintenance competencies that can evolve as new Maintenance 4.0 solutions are introduced.
What are the risks?
An industrial plant that chooses to lease critical equipment from an OEM forms a long-term dependency on that OEM. The practical implication is a scenario of insolvency or other financial conditions that prevent the OEM from executing obligations that are part of Service Level Agreements.
There are additional risk factors for OEMs that must carry Turbomachinery assets on their balance sheets.
Although it is premature to forecast the ultimate winners and losers from the shift to HaaS, the new model brings risk to all players.
Summary and Conclusion
The adoption of Hardware as a Service on the Turbomachinery market is likely to change the ways in which machinery is maintained. Although OEMs will likely benefit from economies of scale from operational efficiencies, there are risk factors that should be carefully weighed before industrial plants pursue this approach.