In terms of equipment maintenance, there are several paradigms or ways of approaching the topic. In today’s article, we propose to examine how Swarm can support each level.

According to a Gartner study, “Quick Answer: What Is the Scope of EAM in Maintenance and Reliability Technology?” dated February 28, 2022, “Maintenance and reliability is a core competency for asset intensive industries and begins with a solid foundation in enterprise asset management. This research highlights the first four levels of asset maintenance that are enabled by EAM.”

In broad terms, we may start the discussion around reactive, preventive and condition-based maintenance. Each paradigm has its own use cases, and ideally, an organization needs to consider a balanced approach to maintain asset health, longevity and reduce costs associated with spare parts and repairs.

First paradigm – reactive maintenance or run to failure is a type of maintenance strategy that involves repairing equipment or systems after they have already experienced a failure or breakdown. This approach is typically used in situations where the cost of preventive maintenance is deemed too high, or where it is difficult to predict when a failure will occur.

In reactive maintenance, the focus is on responding quickly to problems and restoring equipment or systems to their normal operating condition as soon as possible. This can involve replacing faulty parts, performing emergency repairs, or taking other corrective actions to address the immediate issue.

The key term here is “fix the issue immediately”. While the detection plays an important role here, we will focus on what happens after the inspection is performed. To be more precise, we need to log what happened with the equipment so that we can reduce the likelihood of a similar occurrence in the future. Once a repair person is dispatched to the equipment, they will quickly receive a smart form that is prefilled with essential historical information about the equipment. The insights will help the maintenance personnel address and log the issue faster. 

Swarm offers maintenance people a variety of inputs such as sliders, location inputs, the ability to add pictures, tables and much more. After the work order is filled and validated by a supervisor, the data is transferred to a preferred enterprise system such as SAP or IFS.  In time, organizations that use Swarm, will be able to take advantage of the collected insights and build sensible preventive (time or condition based) maintenance plans.

To summarize, Swarm offers organizations a way to gather data from reactive maintenance activities and use the data to streamline preventive and even predictive scenarios.

Second paradigm – preventive or planned maintenance can either be based on specific conditions or based on time. Preventive maintenance is a maintenance strategy that involves regularly scheduled inspections, upkeep, and repairs of equipment or systems to prevent breakdowns or failures. This approach is based on the idea that regular maintenance can identify potential problems early on and fix them before they become serious issues.

In preventive maintenance, the focus is on performing maintenance activities based on a predetermined schedule or set of criteria. This can include activities such as lubrication, cleaning, inspection, calibration, and replacement of parts. By following a regular schedule, maintenance activities can be performed during planned downtime, reducing the likelihood of unexpected equipment failures, and minimizing the risk of safety hazards.

Maintenance done at fixed time intervals is recommended when we have assets with linear degradation and constant usage (example: an inkjet printer on a canning line). On the other hand, there’s maintenance based on metrics such as hours and cycles. Condition based maintenance is best for scenarios with variable equipment usage (example: a back-up generator or a pump).

In any given scenario, within the same factory or environment, there are different asset classes of equipment that require one approach or the other. Even among assets that are best to be inspected at timed intervals, we can argue that a different type of smart form is required based on historic data and equipment type.

Regardless of the scenario, Swarm is able to collect granular equipment data and help organizations build an inspection time table for each factory or site. Moving forward, the precise insights enable the transition towards a predictive type of maintenance.

Third paradigm – condition-based maintenance is a maintenance strategy that involves monitoring the condition of equipment or systems in real-time to determine when maintenance is needed. This approach is based on the idea that equipment failures can be predicted by monitoring certain indicators or performance parameters, such as temperature, vibration, or fluid levels.

In condition-based maintenance, sensors or other monitoring devices are used to collect data on equipment performance. This data is then analyzed to identify patterns or anomalies that may indicate the need for maintenance. Maintenance activities are then scheduled based on the data analysis, rather than on a predetermined schedule.

The goal of condition-based maintenance is to optimize equipment performance by performing maintenance only when it is needed. This can help reduce downtime, lower repair costs, and extend the lifespan of equipment and systems. It can also improve safety by identifying potential safety hazards before they cause harm to employees or the environment.

This approach is recommended when used “when equipment has telltale physical signs and measures of extreme usage or parametric limits, for example, a predetermined level of vibration, sound, pressure or temperature has been reached.” (“Quick Answer: What Is the Scope of EAM in Maintenance and Reliability Technology?”)

With this approach, a higher level of sophistication is required and closer monitoring of each asset. Here we’re looking at equipment data and trigger an inspection if the pattern is disrupted.

In the same fashion as above, we have a model for maintenance (condition-based this time) and we are looking to validate the model by intertwining historical data with human input. We can have a false alarm based on a power fluctuation of the equipment or an unplanned event may lead us to mistake it for something else. Building an accurate model takes time and requires a substantial amount of data, and this is perhaps the main reason for using a tool such as Swarm.


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