Predictive Manufacturing as a service – gamechanger?

Predictive Manufacturing as a service – gamechanger?

It is estimated that top fortune 500 manufacturing enterprises lose almost $1 trillion to unplanned downtime every year, a sum that represents nearly 8% of their annual revenue. Here we will look at how the introduction of Predictive Maintenance as a Service might prove to be a gamechanger in the world of manufacturing & asset maintenance to curb unplanned downtime.

What are the current forms of Asset Maintenance in Manufacturing?

Reactive Maintenance: Reactive Maintenance means allowing your machines to run unchecked up until the moment they fail. In this scenario, maintenance occurs post-failure, as a reactive approach after the anomaly. Reactive maintenance may well save unnecessary downtime & maintenance costs for parts that don’t require servicing but it also means you risk machine failure anytime as we have no idea at all of the machine’s health.

Planned Maintenance or Preventive Maintenance: Since the reactive maintenance approach led to constant fire-fighting for plant managers, maintenance then became a time-based activity, i.e., annual, bi-annual, based on their own and peer’s experiences. Despite this scheduled maintenance it was often noted that a planned downtime, although revealing nothing wrong with the asset, would still result in loss of productivity & profits. In addition to this, the machines would sometimes fail even before the planned period, so the problem of machine failure persisted.

As these two approaches had not succeeded in curbing machine failure and unplanned downtime in time, the industry subsequently looked forward to solutions like IoT & AI to power up maintenance with real-time insights.

Predictive Maintenance as a Service: Predictive Maintenance (PdM) is dependent upon carrying out real-time monitoring of machine health using smart technologies like edge-computing, IIoT, data science, and analytics. Whenever an anomaly (w.r.t vibration, temperature, or acoustics) is detected, it is flagged off to the plant supervisor to take immediate action. This enables maintenance activity to be scheduled if something does go wrong while the maintenance expert can also decode the exact problem. PdM ensures that the maintenance teams have the necessary controls to extend equipment lifecycles, optimize the cost of maintenance, maximize machine uptime and amplify factory performance.

Which Industries are most impacted by Predictive Maintenance as a Service?

Any manufacturing plant- whether discrete or process-based can accrue huge benefit from Predictive Maintenance as a Service measure but process-based manufacturing plants can truly thrive because of their unique workflow of interconnected processes.

Since the output of the process manufacturing plant depends on the previous steps being completed in tandem, the stoppage of even a single machine can bring the entire production process to a grinding halt. This is where predictive maintenance as a service comes into its own by helping ensure that the machine health issues are taken care of before they become serious.

Some examples of plants where Predictive Maintenance as a Service can have a huge impact:

  • Cement plants
  • Steel plants
  • Metals & Mining
  • Oil & Gas Refineries
  • Power plants
  • Chemical plants
  • Pharmaceutical plants
  • Petrochemical plants

Predictive Maintenance as a Service, a game changer for Manufacturing?

Predictive Maintenance as a Service brings all the benefits of cutting-edge technology without the financial downside of requiring huge capital investment and sustainability. Here are some of the benefits of Predictive Maintenance as a Service for manufacturing plants:

Asset health & performance: In an asset-intensive industry like manufacturing, where the equipment is both costly and used to the extreme, equipment & component replacement costs are, as a consequence, prohibitively high. The use of Predictive Maintenance as a service can help boost asset life, RUL (Remaining Useful Life), and Machine Uptime & Reliability by tackling asset issues before they get serious.

EHS & Compliance benefits: Manufacturing plants feature some of the most demanding working environments with toxic gasses, material, and dangerous machines operating furiously. This in turn leads to stringent regulatory guidelines and a risky working environment for the operators and other employees. Predictive maintenance as a service policy should help ensure that there are no untoward accidents or compliance issues.

OEE: Overall Equipment Effectiveness (or OEE) is a globally recognised metric that measures the productivity of a manufacturing asset. Calculated as a product of equipment availability, performance & quality of output produced, OEE is a benchmark for comparing the productivity of plants since the availability & performance of the machine is highly dependent on maintenance & servicing. A study carried out by Deloitte showed that regular PM results in high OEE, Uptime & Reliability, compared to all other forms of maintenance.

Quality & Brand reputation: Regular Asset maintenance and machine health analysis can also be used to ensure that a machine performs at the top of its capacity. This in turn guarantees high quality of the overall output while a fully functional plant producing quality output with minimal disruptions also ensures good brand image & reputation in the ecosystem.

Increased Employee Productivity: A well-functioning asset means that employees are not forced to fight fires caused by last-minute machine failures. It also means quality and timely output, allowing employees to remain productive at what they do and not distracted from their normal tasks by dealing with unplanned machine failure.

What are the benefits of Infinite Uptime’s Predictive Maintenance as a Service?

The end-to-end Predictive Maintenance as a service by Infinite Uptime involves collecting data & computing the triaxial vibrations, temperature, and noise of the mechanical equipment in real-time via its patented edge computing system. The data is then monitored and analyzed in real-time, and a machine health score is assigned. A machine with a lower health score is flagged to the plant supervisor or plant engineer with a diagnostic assessment score and the probable cause for the anomaly and a recommendation on improving the machine. This in turn helps the maintenance teams to plan better and to avoid critical downtime of machines which clearly has a positive impact on the overall factory performance and productivity.