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Written by Bhoopathi Rapolu
on 09 Nov 2019

Businesses collect substantial volumes of data every day from fielded assets and sensors. Several forward-thinking players in the industrial and heavy equipment space are using this data to drive predictive maintenance (PM) for a more significant competitive advantage. By predicting maintenance needs ahead of time, asset and equipment availability can be increased significantly, reducing overall maintenance costs.

However, as companies start to implement a predictive model framework, they need to be aware of challenges that can lead to a failed execution. To ensure safer, smarter, and more reliable assets, equipment, and operations, your PM solution should address five key challenges.

Challenge 1: Limited Understanding of Outcomes for Business Impact

Understanding the behavior of an asset should be in the context of business outcomes. Some of the metrics to consider include machine uptime, throughput, defect rate, history of critical failures, and operating life of the asset. A top-down approach where business priorities are identified and carefully mapped to machine outcomes is more desirable than having the operations teams select metrics based on asset performance objectives (bottom-up approach). This allows OEMs to effectively move away from a responsive maintenance outlook toward a predictive maintenance strategy with prescriptive actions.

How you can address it: Build predictive models that address metrics relevant for business. Identified behavior can then be modeled into an algorithm to monitor and even predict future equipment behavior. Leverage and process enterprise data to offer significant insights, improve decision-making and customer experience, and maximize revenue. 

 

Challenge 2: Unclear Translation of Business Problems to Technical Problems

When translating the business problem into a technical solution (e.g., predicting equipment failure), one may not think to collect sensor data, or there may be budget constraints for a predictive maintenance solution, and a sub-optimal solution (e.g., analytics on past failures) could be selected. While documenting the wish list of the technical solution that addresses business needs, the available data or budget does not need to be considered. Once a technical solution is identified, it becomes easier to fit that solution into the available budget, given the reduced cost of data acquisition.

How you can address it: Identifying the right solution demands strong business acumen and technical expertise. A consultant with a neutral or unbiased preference can provide the necessary capability and support. Many companies first engage a high-level consulting company to build the strategy and solution outlined and then partner with an IT vendor for realization. When the right solution is identified, a partnership with the right vendor can build the solution within the budgetary constraints.

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Challenge 3:  Absence of the Right Data

Effectively harnessing the right data to realize its real value can be a complicated task. Often, available data may not necessarily lead to the desired outcomes. So, a data outcome mapping exercise is needed to identify any missing data. Getting accurate input data is undoubtedly one of the most critical, but not necessarily expensive, steps to ensure successful predictive models.

How you can address it: The right technical solution identified in the previous step should be used to outline the missing pieces in the detailed design, such as architecture gaps, technical or development tools, and of course, the missing data. In case new data is required, it can be produced cost-effectively by leveraging the emerging sensors or other means. With predictive analytics, predictive models can be derived for optimized business performance. The systematized data is processed into meaningful bits with advanced analytics, but now the information needs to provide actionable intelligence. By applying models and algorithms to the data, organizations can identify and predict performance issues, enabling informed decisions that improve asset performance.

 

Challenge 4: Inadequate Combination of Technical and Domain Skills

A team with domain experts as well as data scientists is the recipe for predictive analytics success. With the data scientist building cutting-edge algorithms and the domain experts providing experience to guide the build of the right algorithm, there is a synergy needed for an efficient PM solution.

How you can address it: The right team with the right technology stack and interactive analytics approach will be able to build on what has been learned from the data collected and seamlessly integrate within an organization’s business workflow. For the right mix of technical and domain skills, the following must be kept in mind:

  • Predictive modeling is a multi-disciplinary area that requires a complex interplay of niche skills
  • Unlike a typical software development project, this involves significant research-based iterative experimentation enabling the domain and technical experts to minimize the number and increase the speed of trials
  • Given the nature of this development process, the overall project may find additional business benefits depending on insights uncovered by the domain expert 

Challenge 5: Weak Change Management and Implementation Strategies

Factors such as change management, user training, and continuous support on the solution are critical. An intelligent solution may end up being under-utilized or even lost if it is adopted by the operations teams without a sound understanding.

How you can address it: Professional change management practices need to be leveraged by the business and operations teams to drive the adoption of predictive models. Unlike software tools, predictive models are evolving solutions. They can enhance over time when appropriately built. Active support from the technical and domain teams is necessary to ensure the models improve over time and help companies gain a competitive advantage.

Conclusion

The rate of failure in PM projects is very high. However, it is possible to systematically identify the reasons behind the failure and understand how to prevent them so that a successful PM solution may be developed. Leadership commitment to moving toward PM will help businesses identify the prioritize outcomes and map them to a technical solution without worrying about available data or budget. The leadership buy-in will also ensure a seamless adoption of the new solutions across the organization and support its continuous evolution.

An effective PM solution goes beyond just predicting an asset failure. It also includes identifying business scenarios and the associated prescriptive actions to drive operational objectives such as:

  • Anticipating and prioritizing upcoming maintenance action(s)
  • Tactically planning the just-in-time inventory for replacement parts, systems, or subsystems
  • Proactively suggesting asset design upgrades where recurrent performance issues exist

The above is possible if businesses understand why implementations fail and categorize the reasons into action buckets. Early identification of potential issues with a specific machine or component is vital to a successful maintenance strategy. Only then can maintenance and repair services be deployed to minimize or prevent equipment downtime. 

Implementing a robust predictive maintenance solution will support asset replacement as well as provide insights into the health and utilization of assets. An advanced analytical approach, supported by a unified data strategy, is critical to effectively manage risks, optimize operations, and propel business growth.

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