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Your Quick Guide to 5 Maintenance-as-a-Service (MaaS) Business Models of Predictive Maintenance

by John Soldatos on January 15, 2018

Predictive maintenance is without doubt one of the major trends in enterprise maintenance. With the advent of Industry4.0 and the expanded digitization of industrial processes, plant operators are now able to collect and analyze vast amounts of historic data about their equipment. This enables them to predict and anticipate when a failure is about to occur, which reveals unprecedented opportunities for cost savings and improved quality management. Enterprises can capitalize on these opportunities via predictive maintenance programs offered by equipment vendors and maintenance solution providers.

Predictive maintenance technologies such as automatic data collection and data analytics are maturing at a fast pace. However, the wider deployment and use of predictive maintenance requires the identification and validation of viable business models, which leads to tangible benefits for all stakeholders.

In other words, it is important to identify proper ways of selling and operating predictive maintenance solutions, in ways that benefit solution providers, plant operators and Original Equipment Manufacturers (OEMs).

Predictive maintenance stakeholders

Before we can explore the business of predictive maintenance, it’s important to understand the key stakeholders in the ecosystem of this revolutionary form of industrial maintenance, what roles they play, and what matters to them:

  • Plant Owner or Operators: Predictive maintenance solutions are deployed in a plant. Their primary focus is to optimize Overall Equipment Efficiency (OEE) and alleviate the limitations of conventional preventive maintenance. Plant owners are typically in charge of maintaining the equipment they deploy and operate in their plants. As such, their personnel (e.g., maintenance engineers, IT experts, plant worker) engage actively in enterprise maintenance processes.
  • Original Equipment Manufacturers: Equipment vendors provide and support their products. In several cases they already provide a range of digital services, including data collection and analytics. In the light of predictive maintenance, equipment vendors will be gradually transforming their products to Cyber-Physical Systems (CPS), which will provide interfaces for accessing digital data about their equipment.    
  • Solution Integrators: Predictive maintenance solution providers integrate the different elements of turn-key predictive maintenance solutions, including data collection and data analytics technologies. The role of solution integrators is not, however, confined to technology deployment. Rather, solution integrators also collaborate with plant owners towards blending data-driven knowledge extraction in the plant’s maintenance processes.
    As a result of the predictive maintenance market momentum, there is already a long list of integrators of predictive maintenance solutions.
  • Supply Chain Partners: Supply chain participants play a significant role in predictive maintenance, as they can have access to maintenance insights e.g., OEE or End of Life (EoL) information, to optimize interactions with other stakeholders. For example, EoL information can drive the optimized procurement of spare parts, while OEE influences the planning of product or service delivery to the end-customer.

These stakeholders can leverage the benefits of predictive maintenance in various ways using different business development strategies. For example, solution integrators opt usually for a customer-driven approach to meet plant operators’ needs. On the other hand, machine vendors are also looking at utility-driven approaches that change the scope of their sales from selling equipment to selling maintenance services (i.e. Maintenance-as-a-Service).

Likewise, the various stakeholders set different goals for their predictive maintenance projects. For example, plant operators and supply chain partners target optimized productivity based on OEE improvements, while solution integrators emphasize customer satisfaction. At the same time, OEMs are also interested in building long-term relationships with their business partners that purchase and use their products.

 5 MaaS predictive maintenance business models

Based on the above-listed considerations, a variety of novel business models are possible, which are in most cases changing the common ways in which maintenance services are offered. Most of these business models promote a Maintenance-as-a-Service (MaaS) paradigm, which is in-line with the Service-as-a-Product model that is currently trending in industry.

These models emphasize plant owners’ and solution providers’ interactions with OEMs after the sale of the equipment i.e. in the form of after sales services. Such interactions unveil opportunities for new recurring revenue streams as part of added-value services that are offered to customers. 

These are five of the primary MaaS business models.

1. Equipment health monitoring and maintenance recommendations as a service

This business model offers plant owners online tools that enable them to monitor the status of their equipment and obtain predictive maintenance recommendations. These tools are offered as cloud services. Hence, the model involves sales of cloud subscription with every purchase of equipment. It is naturally offered as a complementary feature by OEMs to plant owners.

In this context, this model can be perceived as an up-selling opportunity for OEMs, while enabling plant owners to increase their productivity. It is also possible for solution integrators to provide custom health monitoring solutions to plant owners. However, they may be unable to provide detailed insights on status of the machines unless they collaborate with OEMs.

2. OEE risk as a service

An interesting variation of the maintenance recommendations as a service is the OEE risk as a service model. The latter provides higher level maintenance recommendations (e.g., when to schedule maintenance of a part), accounting for the overall OEE risk. It can be offered by the equipment vendor in a way similar to the recommendations service outlined above.

However, this model can be also built as a custom service that exploits data from multiple machines and equipment in the plant. In this case, it will be integrated by the solution provider and offered to the plant operator as a service.

3. Uptime as a service

In-line with the MaaS concept, OEMs could opt to sell uptime of their equipment instead of selling the product itself. This leads to a utility-based concept, where the deployer of a machine or part (such as an engine) doesn’t pay for the product, but for the time that the machine/part is used based on a per-hour-of-operation charge. In this model, the OEM undertakes all needed service and maintenance activities, which are covered based on the pay-per-use charges.  

The uptime as a service model is quite different from the health monitoring service outlined above. Specifically, it is destined to replace the conventional equipment purchase model, rather than being a complementary value-added service.

In cases where the OEM’s machine can be shared across many end-users, the uptime-as-a-service model could be used to move industrial maintenance to the realm of the “sharing economy” like many other popular IT-based services like Uber and Air BnB. This could become possible, if plant owners become more interested in operating rather than owning the machine or equipment.

4. Warranty as a service

MaaS can also change the relationship between OEMs and plant operators, by redefining the traditional warranty. Warranty claims can create problems and result in finger pointing, as OEMs and plant operators tend to refuse responsibility for failures and malfunctions. But, with operational data collection and analysis, equipment vendors will be able to prove claims about the operation of the equipment using real data rather than assumptions.

Hence, both plant owners and OEMs will be able to prove whether the equipment has been operated correctly. This will greatly facilitate agreements on warranty claims, which helps reduce costs and build trusting relationships between stakeholders.

Consequently, OEMs can offer many different varieties of “warranty as a service”, such as warranty based on time (e.g., 2 years warranty) or based on usage (e.g., warranty for 5,000 hours of operation), or even based on combinations of time and usage based schemes. All these services can be based on the collection and analysis of historical data in order to identify and verify the cases where the equipment was not operated correctly.

5. Supply chain management information as a service

The primary purpose of predictive maintenance is to minimize critical and unexpected failures. To serve this purpose, information can be shared with business partners to plan relevant supply chain management processes and ensure the availability of spare parts as needed.

This business model is implemented between the plant operators and its supply chain partners. Plant operators can automate orders and other related supply chain processes, which help eliminate the hidden costs of reactive maintenance such as inventory costs and higher prices for spare parts.

Such information can be shared by enhancing conventional supply chain management systems with additional automation (e.g., orders triggered by predictive maintenance insights).

Challenges and trade-offs

As predictive maintenance technologies mature, the implementation of the above models becomes technically possible. Nevertheless, there are still several challenges to be confronted prior to their wider deployment and use, including:

  • Data ownership: All five models rely on the collection of data about the operation of the equipment and on the subsequent processing of this data by plant owners, solution integrators and OEMs. This raises a data ownership challenge, as data collected within a plant cannot be freely and easily provided to third-parties like integrators and OEMs. Rather, the process of data sharing should be negotiated as part of non-trivial Service Level Agreements (SLAs). But this can raise barriers to the implementation of the listed business models.
  • Conflicting interests: In several cases, the deployment of MaaS business models leads to conflicting interests for OEMs. Many OEMs generate profits by providing frequent (and often costly) maintenance services to their customers. Hence, the introduction of new MaaS services that are destined to reduce these profit margins should be performed with caution and in a way that delivers additional benefits to compensate for the reduction of maintenance revenues. Cross-selling or up-selling MaaS services can help in this regard.
  • Distribution of the benefits between stakeholders: Some of the presented models seem to balance the benefits towards specific stakeholders, which may be a set-back against wider adoption of predictive maintenance. It’s not only a matter of finding win-win scenarios, but also of balancing them appropriately to incentivize all stakeholders to engage in the predictive maintenance deployment.
  • Complexity of return-on-investment (ROI) calculations: All of the listed business models can be associated with tangible ROI. Nevertheless, these calculations can be challenging, as they are based on predictions and sometimes need to quantify intangible benefits. The complexity of ROI calculations makes stakeholders skeptical when it comes to deploying such models.
  • Practical limitations: Some of the presented models have practical limitations. For example, the “uptime as a service” model is hardly applicable in some industries and business cases (e.g., in cases where equipment and plant is not easily accessible for business, technical or even policy reasons.) For instance, outside access to plant data and equipment is in several cases prohibited by a plant’s security policy.

It’s important to understand the benefits and technical implementation of predictive maintenance. To succeed with predictive maintenance, it’s equally relevant to reflect on viable business models that drive sustainable deployment and use in the context of your operations.

Are you planning on deploying predictive maintenance techniques in 2018? Let us know in the comments.

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John Soldatos

John Soldatos holds a Phd in Electrical & Computer Engineering. He is co-founder of the open source platform OpenIoT and has had a leading role in over 15 Internet-of-Things & BigData projects in manufacturing, logistics, smart energy, smart cities and healthcare. He has published more than 150 Read More..