Maintenance 4.0: managing risk

4 ways to limit investment risk in Maintenance 4.0

Investing in Maintenance 4.0 is always a delicate balancing act, as executives struggle to promote innovation while trying to persuade stakeholders of this new technology's ROI. Avoid this type of conflict in the first place by minimizing the amount of risk involved in any new maintenance venture.

In April 2019, we participated in the annual ritual for industrial plant executives: Hannover Messe. Against the backdrop of an “Industrie 4.0 meets Artificial Intelligence” theme, approximately 215,000 visitors trekked through the tradeshow of 6,500 exhibitors. On display were impressive technological breakthroughs: robots that simulate human behaviors and advanced applications of Machine Learning.

However, the initial excitement from events such as these wears off when executives return home and need to balance the potential opportunity from innovation and the need for fiscal discipline.

There are two parallel realities. Viewed from a high-level strategic perspective, executives recognize that the digitalization of production assets can upend business models and result in massive changes in the fortunes of today’s industrial plants. At the same time, these executives are responsible to shareholders who expect investments to be based on financial criteria that create long-term value.

Within this context, let’s explore the following topics:

  • What are the risks in overinvesting in Maintenance 4.0, given that many industrial plants lack the perquisite infrastructure, technology and organization resources?
  • How can industrial plants mitigate these risks?

Is Maintenance 4.0 a risky investment?

We’ve been here before. The rapid growth of internet access in the late 1990’s jeopardized multiple industries including retail, travel and music. Entire product categories were eliminated (fax machines and encyclopedias), and brick-and-mortar stores were challenged by e-commerce companies with new competencies and business models.

Although the threat and opportunity the of the emerging internet was widely recognized, there were no risk-free choices. With vast sums of capital invested in dotcoms, existing market players could not wait indefinitely. At the same time, there was no clear path for digital transformation or obvious strategic options. Executives that had previously relied on ROI criteria were forced to consider large bets in promising but nascent technologies.

How is this relevant?

The hype surrounding digitalization is at fever pitch. As in the 1990’s companies understand the need to commit financial resources to new solutions when faced with uncertain outcomes. Let’s review each factor contributing the potential of a risky investment.

How to mitigate the risk of overinvesting in Maintenance 4.0

Although there is no formula or silver bullet to eliminate investment risk entirely, there are numerous fail-safes to limit the risks.

#1 Business case based investment decisions

It is a misnomer that business cases need to be highly structured and complex. If formal business cases are mandatory for investments over a pre-determined threshold, it imposes a discipline on the purchase process that may otherwise be ignored. The critical element of a business case, regardless of the format and size, is that an investment be based on solid rationale and backed up with an expected financial return. Devising a business case forces the decision makers to make the investment justification based on specific expected outcomes that should be high priority. For instance, a Maintenance 4.0 solution that is expected to improve production yield rates may receive higher priority than a solution to save costs due to energy efficiencies.

#2 Test before you buy

Whereas a business case is somewhat theoretical and based on assumptions about expected behavior, a pilot or Proof of Concept (PoC) requires the industrial plant to evaluate the actual results of a solution in a scenario that simulates its own environment before making the purchase decision.

The importance of conducting a PoC cannot be understated.

First, it provides the business decision maker with objective data that can be used as a benchmark of solution benefits relative to the current state. This becomes a critical component in comparing multiple vendor offerings and can be used as input for calculating the expected investment return.

For example, in a PoC scenario two Predictive Maintenance solution providers are given access to historic sensor data from a gas turbine. If one solution provides evidence of evolving asset failure 5 hours before occurrence and a second solution provides evidence 72 hours, this data can be used to justify a selection of the vendor with more accurate results.

There are some elements in the buying decision that are not only based on data-driven results. Are service requests addressed in a timely fashion? Is information communicated effectively? Are end-user dashboards easily accessible? A PoC provides the plant with the opportunity to asset a multitude of factors that require first-hand experience working with the vendor in real time.

#3 Develop internal expertise to evaluate new solutions

The transformative nature of Maintenance 4.0 increases the likelihood of organizational disruption and political rivalries. There are multiple stakeholders involved in purchasing Maintenance 4.0 solutions from disciplines including data science, asset maintenance and Information Technology. We are not advocating for specific responsibilities for a Chief Digital Officer or Maintenance 4.0 Evangelists because each organization will define roles based on internal considerations. However, there are some best practices that can be applied to most situations.

First, dedicated resources should be assigned to Maintenance 4.0 innovation scouting. This is not a part-time job or an entry level position. This person needs to be responsible for evaluating new solutions that are consistent the organization’s overall Maintenance 4.0 strategic and roadmap. The second factor is that there is a cost associated with evaluating and onboarding new solutions. Budget should be allocated for these activities, including for PoC’s described above.

#4 Minimize long-term investments

Organizations can limit their exposure to solutions that become redundant by replacing capital expenditures with asset licensing models. This applies to both hardware and software solutions.

How does it work?

Let’s start with Hardware as a Service (HaaS). In the 1960’s, Rolls-Royce offered its airline customers a subscription payment model so that they could lease jet engines instead of purchasing them. Instead of a one-time transaction, Rolls-Royce maintained ownership of the jet engines and was responsible for maintenance and repair activities. Although not yet pervasive, OEMs are starting to offer industrial plants the option to lease equipment without the upfront financial commitment.

Software as a Service (SaaS) is an established licensing model for the software, whereby the end user purchases a license for software and the vendor is responsible for back-end infrastructure and software updates. Solutions such as Machine Learning based Predictive Maintenance can now be purchased as a service so that the plant does not need to invest in building internal Machine Learning competencies or the resources to manage scalable deployment across multiple plants.

By licensing Maintenance 4.0 HaaS and SaaS solutions, industrial plants do not need to make large bets in emerging technologies and can share the risk with the solution vendor.

Summary and conclusion 

We are still within the first decade of the fourth industrial revolution. In comparison, the first three industrial revolutions spanned over 200 years. With respect to Maintenance 4.0, given that uncertainties vastly outweigh certainties, industrial plants must balance the need to innovate with responsibility and accountability to their shareholders.

Even without a complete solution for the challenges faced by industrial plants, there are incremental steps that can be used to improve planning and purchasing processes.

SKF Enlight AI

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.

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| 2020-02-06 | 06:45
Agradecemos tu comentario, efectivamente tienes razón; el Mantenimiento 4.0 es el presente y el futuro del mejoramiento continuo en la industria, equilibrando la necesidad de innovar con la responsabilidad. Si quieres saber más a fondo de cómo podemos ayudarte a ti o a tu empresa a comenzar a implementar el Mantenimiento 4.0; puedes dejarnos tus datos, y con gusto nos comunicaremos contigo.
roosevelt avella
| 2020-21-05 | 15:51
Excelente articulo , gracias por la informacion, es el dia a dia, dentro de las plantas a un nivel micro y macro ,como ser mas competitivivos y eficientes y estas tres herramientas" IITo, Machine learnig y industrias 4.0, son la actualidad y se necesita un apoyo extermno para logfar un analisis claro efectivo y basado en el benefico costo y asumiendo riesgos cuantificables, pero sobre todo, asumiendo el riesgo, no hay excusas para no hacerlo, la industria necesita seguir en mejoramiento continuo si deseamos seguir siendo competitivos. Gracias