Unplanned downtime is the single biggest enemy of productivity.
Forbes estimates the cost of downtime alone is over $50 billion each year for industrial manufacturers. This is more than substantial and can often be the difference between a company making or losing money. The article points toward predictive maintenance gaining prominence to significantly reduce downtime, to learn faster and predict failures long before stoppages can ever happen.
There are multiple technologies making this improved hyper-accurate maintenance paradigm a reality. First and foremost it is IIoT, which through advanced sensors installed on the equipment, to detect even the slightest change in critical components. Then, it is IT/OT platforms, which capture and communicate this data to other higher-level applications, that in turn compare historical data and create trends, predictions, and alerts.
But before we understand how one graduates from the current maintenance management system in place to the ‘next generation’ predictive maintenance model envisioned by Forbes, let’s understand what needs to be fixed in the existing maintenance models to initiate this desired optimization.
Understanding Maintenance as it Exists
For manufacturing in general and other capital-intensive industries like the energy sector, asset utilization is the key to making a better profit margin. Unplanned downtime can mean loss of revenue at best and disgruntled customers and permanently damaged reputation at its worst.
The predictive maintenance model as propagated by the Industry 4.0 paradigm evolves from the basic inspection, repair, and prevention model of maintenance. Traditionally in any given plant maintenance, workers and teams inspect equipment on a fixed schedule, with assistance from asset operators, to evaluate the current conditions of any given asset. Inspection, up until recently, was a predominantly condition-based activity, where maintenance professionals and operators would respond to either a planned or unplanned maintenance event. This ‘run to failure’ mode was seen as a pragmatic activity, for it was based on equipment ‘failure’ or a degrading of performance to trigger a maintenance activity (repair/overhaul/service). It also meant you weren’t spending time maintaining an asset that still had ‘life’ in it.
Repair itself would mostly represent a status where there had been a breakdown, and the given asset needed to be worked on to make it operational. Repairs, in cases where successful inspection happens, would ideally be a planned activity. Preventive maintenance would be the planned and pre-emptive servicing of mission-critical equipment/assets to avoid untimely breakdowns. With the advent of IT applications and Enterprise Asset Management tools such as SAP-EAM, these activities became highly organized, graduated from paper to digital as things progressed.
Important considerations for a predictive maintenance model
For organizations aiming to have a predictive maintenance model, there are a few important points to consider:
- Is the inspection activity in the plant organized, or better yet, digitized?
- How much of the repair work is handled in-house, and how much is outsourced? How efficiently does the maintenance cycle execute from the time a failure is detected to the time it is corrected?
- What does the PMC (preventive maintenance compliance) metric look like? Is there a detailed plan in place for preventive maintenance activity, orchestrated and controlled through an existing EAM or MES application?
- How much unplanned downtime does the plant currently have? What are the MTBF & MTTR metrics like?
- Are the basic foundational IIoT and/or Industry 4.0 technologies available to create conditions for maintenance optimization?
- Is the workforce executing the production and maintenance process in sync and performing in a collaborative manner?
- Are enterprise applications connected to workforce management tools to enable smooth maintenance management cycles?
The answers to the questions posed above will help you gauge your existing maintenance management environment. If your operation still relies on paper-based forms or task lists, where a maintenance request or breakdown is reported to a supervisor, who then requests an inspection or repair, which is then executed after receipt of requisite spares, you are at the very first rung of the maintenance optimization ladder.
According to a paper by industry analyst ARC, only 18% of assets have an age-related failure pattern, while a full 82% of asset failures occur randomly.
However, even if you have an amazing EAM application in place, with sensors installed on your assets that communicate key parameters as transactions that happen in real-time, and where any out-of-spec incident raises alarms and triggers actions through alerts and automated work orders, there is still much room for improvement. Let’s understand how.
A Glimpse of Optimized Maintenance
For companies still having a corrective model for maintenance, where actions are triggered by failure, the first step would be to move towards a planned maintenance model to move from corrective to preventive maintenance. In a study by Oniqua Enterprise Analytics, researchers found that a full 30% of maintenance activities are carried out too frequently, which was a main driver of waste. This move alone may have the potential of saving millions of dollars, depending on the scale of a given operation. From a planned maintenance management to a predictive one, the journey can lead to a truly optimized maintenance management program.
The Maintenance Optimization Continuum
Maintenance optimization progresses from paper-based or spreadsheet-based to a digitized system. However, it is extremely important to understand that this journey towards an optimized maintenance management system is not a step change and requires careful and deliberate planning.
Based on the analysis of your existing maintenance management process, the first and most important step towards optimization is to determine a way to digitize activities that are currently executed manually. Whether it is recording a failure or creating a purchase order, digitizing all activities related to maintenance should be the first priority.
Once maintenance transitions from paper to electronic forms, the journey towards optimization begins. Remember it is a continuum, which will then involve the incorporation of better IT and collaborative tools to enable preventive maintenance to save costs, progressing to predictive maintenance to enable process excellence and optimized asset utilization.
Collaboration is the Key to Optimized Maintenance
For any maintenance management program to be successful, it is not sufficient to simply have EAM applications and assets with modern sensors in place. What is most important is to have a workforce that is mobilized, based on the alerts created, and a maintenance workflow that executes automatically to predict a repair event or an actual failure on the shop floor.
Webalo is the platform that fills in the gaps, by bringing maintenance, operations, IT, and vendor personnel together, facilitating collaboration between teams to ensure overall efficiency. As a workforce management and optimization platform, Webalo connects all process personnel to the enterprise, as it integrates with EAM applications like SAP and process execution and automation applications like MES and data historians. This integration bridges the gaps that can exist when these systems remain disconnected, removing communication mishaps and delayed maintenance activities.
With Webalo in place, machine operators and maintenance professionals can utilize sensor-related data, on a cohesive platform, which enables workers with a complete digital experience—from notifications to work order generation, to task instructions and mobile data gathering. Real-time event notifications, and activity dashboards become part of the day-to-day process, enabling workers to view real-time data in the context of both ideal and breakdown scenarios. With precise metrics at their fingertips, maintenance can graduate from corrective to preventive as process personnel is able to compare data and determine whether an asset needs repair now or later. The same platform also allows them to re-route production flow, create work orders, order spare parts, and execute/log maintenance activity, all the while receiving instructions on how to execute activities.
With EAM applications and Webalo acting as a foundation for planned preventive maintenance, the maintenance activity graduates from preventive to predictive. And that activity serves the bottom line, bringing improvements in asset utilization, and increasing uptime and productivity.