Your machines are giving you signals every second. Are you listening? Learn how the Progress DataRPM CPdM platform can boost the health of your assets.
According to expert analysts, there will be more than 30 billion digitally connected devices by 2020 which equate to a trillion dollar economic opportunity waiting to be tapped. What happens when this opportunity suddenly dwindles due to feeble asset maintenance? That could hurt a lot! Especially to your bottom line. Manufacturers typically deal with around 800 hours of downtime annually and this translates to $22,000 per minute which is $1.3M per month! How many of us can afford these kinds of losses owing to poor asset maintenance strategies?
Your assets are your industrial armor and adequately maintaining them should be among your topmost priorities. While maintaining machines and equipment at optimal performance levels can be quite a struggle, choosing from a gamut of maintenance strategies to ensure the upkeep of your critical assets, can definitely prove to be a daunting task.
The future well-being of your assets lies in looking beyond preventive maintenance and clearly, a proactive approach can boost your bottom line than adopting a reactive strategy.
As a production-centric manufacturer in the digital age, one of the main tasks when it comes to asset maintenance is to ensure that you take good care of your factory equipment by extracting maximum value from your machines.
However, the twist in the tale in this complex ecosystem is due to the fact that every asset and its roles are interdependent and equally disproportionate as well. Organizations therefore not only need a clear asset maintenance strategy which clearly lays out each assets’ scope but also needs a viable plan to derive maximum value from high-stake assets.
Most asset-intensive industries focus on minimizing unexpected outages, managing asset risks and maintaining assets before failure strikes and depending on a preventive maintenance plan may make matters worse when it comes to avoiding asset failures completely.
Secondly, since typical asset data patterns are extremely volatile and change too often, it becomes critical to have a cognitive predictive maintenance solution which can help with lightning fast speed in picking up flickering data patterns.
Thirdly, asset data is complex due to its veracity, velocity, volume and variety all of which are quite impossible to comprehend with a manual maintenance approach. With a sound automated asset maintenance plan, data scientists can differentiate the true signals hidden in the data scattered over millions of data points across various sensors and generate valuable insights required to maintain an asset across its lifetime
The digital revolution sparked by the Industrial Internet (IIoT) has unlocked new opportunities for manufacturers to challenge conventional norms and re-think how their assets are being managed and optimized over a period of time. The competitive market environment today requires manufacturers to lay the foundation of their success by consistently improving asset performance. The IIoT is fueling this transformation to ensure critical asset availability, thus delivering on customer promises.
Imagine if you could stay one step ahead of your asset maintenance needs, leveraging efficiencies by forecasting failures…
Imagine if you could accurately schedule asset maintenance process and times…
Imagine enabling intelligent manufacturing by automating maintenance and thus slashing operational costs…
One of the biggest challenges that prevents manufacturers from making the above possibilities a reality is that a significant mass of data generated by industrial assets is never really analyzed, particularly the ‘unstructured’ data. It is the fact that only 20% of the world’s data produced is structured and accessible by the internet and the remaining 80% is wholly unstructured. Further, manual data analysis and modeling are unsuitable for today’s IIoT environment with only 20% of asset failures being common and predictable and the remaining 80% being extremely complex, random and unpredictable, which require individual predictive models for every asset.
Therefore, manufacturers need the supreme intelligence of cognitive technology enriched with the right amount of predictive maintenance which is a great way to throw some light on this dark and dense data to extract value and generate insights. So, the ultimate goal here is to reduce unplanned downtime and achieve greater asset availability by using cognitive predictive maintenance (CPdM).
With our Cognitive Predictive Maintenance platform you can slay down asset failures with the ultimate power of cognitive machine learning and automated maintenance to achieve absolute nirvana in asset maintenance. This is how manufacturers can benefit from this wonder platform: Addressing specific user requirements with the benefits from predictive maintenance. Addressing specific user requirements with the benefits from predictive maintenance
The Progress DataRPM platform provides user groups with unified insights by integrating operations and analytics on a single platform. To help users with continuous asset maintenance and improvement, insights can be derived from both structured and unstructured data sources to help predict asset malfunctions before they occur and thus save you millions in damage control. The platform automates predictive maintenance in a way such that asset operators of connected equipments can easily analyze patterns and trends and hence manage risks while aligning with organizational goals.
Strategic decision makers across industries are constantly facing pressures of automating and streamlining asset maintenance processes. If one can predict equipment deterioration in advance, business efficiencies can be multiplied easily. This is where our predictive analytics and cognitive insights make a business case as data anomalies are picked up in real time which helps identify deviances from normal trends and hence predict potential failures. This enables deriving new insights and continuous business innovation while providing very little scope for latency and boosting operational performance
Using our cognitive machine-learning algorithms, manufacturers can detect fault patterns and monitor equipment health to take necessary action before the asset efficacy erodes. This helps reduce the overall maintenance budget and avoid loss of resources due to unplanned maintenance events. Finally, the platform provides a single consolidated overview of a humongous mass of structured and unstructured data to improve overall visibility of the past and future asset performance. The result? Increased asset reliability, higher asset uptime and reduced maintenance periods for all kinds of equipment.
Syncing your asset maintenance strategy with the DataRPM CPdM platform can significantly enhance internal production quality, cut down scrap and rework rates. By using our meta-learning technology, manufacturers can predict the remaining useful asset life and hence plan for optimum inventory levels of spare parts. While the asset lifespan is kept in check, necessary actions to plan for contingencies can be taken even before the failure occurs.
With the CPdM suite, your asset maintenance strategy can present you with the most astounding business outcomes. We are talking about scratching down of maintenance costs by up to 25%, cutting asset breakdowns by up to 70% and downtime by 50%, slashing unplanned outages by up to 50% and eliminating scheduled repairs by almost 12%!
Pay attention to your machines… They are giving you signals every second
Your manufacturing equipment is capable of giving you signals of distress and breakdown even before a failure occurs. All you need to do is to steer in the right amount of intelligence using machine learning based predictions. Our DataRPM CPdM platform is the perfect solution to help you drive asset maintenance decisions which can extremely favorable for your bottom line. So, are your ready to immunize your assets’ health with our predictive acumen?
Anita Raj is a Product Marketing and Growth Hacking Strategist on the Progress DataRPM team, with over 10 years of experience working in the field of big data, cloud and machine learning. She brings a deep expertise in running growth marketing leadership experience at multi-billion-dollar enterprise companies and high growth start-ups.
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