One of the pioneering applications fueling the resurgence of manufacturing in the digital era has been the rise of Predictive Maintenance as the poster child within the suite of digital transformation technologies. Estimates from analysts vary widely but the overall Predictive Maintenance market is forecasted around $5 to10 billion by 2020. With Global Assets under operation amounting to roughly 2.5 times the world GDP, the economic impact which is forecasted to be truly transformative clearly extends to trillions of dollars.

Maintenance in the past was traditionally relegated to the status of a support function within the realm of manufacturing and was considered a necessary evil and a pure play cost center. Predictive Maintenance flips this status over and elevates the maintenance function from a cost-centric role to a prime strategic role within the organization. 

With the potential that Predictive Maintenance promises, there is an excessive hype surrounding the topic and enough content peddling the power of algorithms as a magic wand to realize maintenance nirvana. The reality, unfortunately is a lot more nuanced than that. Predictive Maintenance is not a pure play technology gig. Maintenance as a function can be a distinct competitive differentiator (whether you are competing on cost, customer service or innovation) and can truly bring business value to your organization.

Let’s peep under the hood to gain perspective on the application of Predictive Maintenance. Here’s a list of 10 tips to consider as you step into your journey of implementing Predictive Maintenance.

Top 10 Tips for practical application of Predictive Maintenance

  1. PoCs are a good start, but there’s more to it
  2. Busting the lone ranger attitude
  3. There is no “one size fits all"
  4. You may have a hammer but everything is not a nail
  5. The great horizontal vs. vertical divide
  6. It’s not just in the asset!
  7. Predictive is not the end all
  8. Measurement is Key
  9. Deployment and sustainability is the end game
  10. The future beckons, so embrace it!


  • PoCs are a good start, but there's more to it: It is easy to get enamored by a new shiny toy and dive straight in. The risk of falling into the trap is that the technology may work just fine but fail to deliver the goods. Instead, begin with a clear understanding of your business goals. It is imperative to have a clear understanding of the specific KPIs that you wish to impact and more specifically by how much (OEE, MTTR, MTBF, OTIF, Maintenance Effectiveness etc.). An overall maintenance function audit is a wise strategy in this start-up phase.
  • Busting the Lone Ranger attitude: Functional maintenance specialists have a tendency to be enamored with the fruits of their toil and believe that they are equipped to independently deliver the goods. This induces a silos mentality and is counter-productive. It is imperative to bust the silos and have an integrated thinking when considering a Predictive Maintenance initiative. Predictive Maintenance is a team sport. Maintenance should work as an inter-dependent function and balance considerations related to other functions including production, inventory, human capital and customer service to optimize overall performance.
  • There is no “one size fits all”: Don’t paint all your assets/asset classes with the same brush. Before diving deep, it is imperative to classify your assets into distinct classes, each with its own maintenance strategy suited to maximize business value. Some situations need reliability-centered maintenance for critical high value assets but a reactive approach may be sufficient for some simple non-critical assets. The segmentation should enable classifying assets into distinct classes, each with its own strategy but ultimately delivering the best business outcomes.
  • You may have a hammer but everything is not a nail: It is important to have an in-depth understanding of the physics of each asset, its potential failure modes and root causes. Vibration monitoring may be a great technique but it may not be the right strategy for what you are trying to monitor (e.g. loose electrical connections). An effective understanding of failure modes and how they can be effectively pre-empted by measuring what characteristics is absolutely essential. Knowing where to apply what sensing technique (thermal imaging, ultrasound, infrared, spectral analysis, vibration analysis etc.) is absolutely key.
  • The great horizontal vs. vertical divide: One school of thought contends that maintenance is a horizontal function and given enough historic data (hopefully labeled), smart algorithms can figure out all underlying patterns and correlations, delivering near perfect insights without an iota of understanding on the vertical or asset class. The other school believes that an in-depth understanding of the asset, its constituent components and its functioning modes is absolutely essential to a good maintenance strategy. The reality lies somewhere in between. Having domain expertise on the pertinent asset classes and the contextual environment is definitely useful, but there is truly a bit of magic behind the data science algorithms. While they do uncover interesting and often counter-intuitive insights that are humanly impossible in the end, the objective should be to balance the two perspectives to derive the most optimal value from your implementation.
  • It’s not just in the asset!: Birds of the same feather may flock together but Assets of the same make and model don’t always perform similarly. An inordinate focus on just the asset data (based on sensors) can trip us up. The sensor data needs to be blended with the context data (ambient conditions, operational environment, asset operation mode, general asset upkeep etc.) to deliver the right insights. Context is really the king and needs to be accounted for.
  • Predictive is not the end all: According to Gartner, Predictive Maintenance is a strategy on a continuum from reactive to financially optimized.” In that sense, Predictive Maintenance is a cog in the wheel that powers higher end objectives. The insights gleaned through Predictive Maintenance should be made actionable in a sustainable way for translating them into real tangible value for the organization. The end goal is clearly either financial optimization or driving new innovative business models. Maintenance should work as an interdependent function and balance considerations related to other functions including production, inventory and human capital to optimize overall financial performance.
  • Measurement is Key: Very often, strategic transformation initiatives like Predictive Maintenance get a bad rap for not delivering the goods. A major issue is often the lack of effective communications on the benefits achieved (e.g. by averting major breakdowns through Predictive Maintenance, avoiding truck-rolls etc.) and quantifying the financial impacts. An effective baselining and measurement strategy is absolutely key to ensuring that the benefits realized through Predictive Maintenance are adequately captured and communicated to gain a wider traction within the organization and keep the initiative.
  • Deployment and sustainability is the end game: Creating standalone algorithmic models and proving their efficacy may get all the attention but is not the end game. The real value is derived once those models have been deployed in a production context and are integrated into the business applications landscape. It is imperative to refresh models periodically to avoid model fatigue and ensure that the model incorporates new contextual information to sustain high levels of accuracy.
  • The Future beckons so embrace it! Predictive Maintenance is a fast evolving function and will greatly benefit from its confluence with technologies like AI and AR. McKinsey forecasts productivity increases of up to 20% and reduction of maintenance costs greater than 10% through application of AI in Predictive Maintenance. The field of Computer vision as a subset of AI is making rapid advancements and will likely enhance sensor based optimization. AR applications will enhance maintenance worker productivity through wearables for applications like guided repairs. The era of self-healing machines is also not too distant. The field will continue to make rapid strides and it is imperative to stay plugged in and start piloting with new emerging technologies on an experimental basis. 


Delivering true benefits from the implementation of a Predictive Maintenance program calls for a holistic perspective and requires the right blend of domain, consulting, technology and analytic skills.