CMMS and KERN IoP: operating evidence for measurable reliability indicators

A computerised maintenance management system (CMMS) organises work orders, preventive plans, spare parts, costs and technical resources. KERN IoP complements this management with information acquired directly from operation: effective operating hours, cycles, states, failures and asset conditions. Together, these records provide a consistent operational basis for reliability indicators.

In many plants, maintenance and operating histories are kept

separately.

The CMMS records the request, intervention, technicians, spare parts used and closure of the work order. At the same time, the PLC, sensors, meters and operators hold information about what was physically happening in the equipment.

Keeping those records separate makes fundamental questions harder to answer:

• How long had the asset operated before failing?
• What state was it in?
• What condition preceded the stoppage?
• How long was it actually unavailable?
• When did it recover its function?
• How long did it operate before the failure recurred?

Rigorous maintenance engineering needs to connect both histories.

The CMMS documents the work performed. KERN IoP contributes the operating evidence needed to assess asset behaviour before and after the intervention.

What a CMMS manages and what KERN IoP contributes

A CMMS supports the planning, execution and control of maintenance. Its typical functions include:

• creating and tracking work orders;
• scheduling preventive tasks;
• managing technicians and resources;
• controlling spare parts;
• recording costs;
• storing procedures;
• documenting interventions.

These functions organise the work consistently.

CMMS information often begins when someone reports a condition or creates an order. The reporting time may differ from the time the asset physically stopped. Similarly, the administrative closure of an order and the restoration of the equipment’s productive function may occur at different times.

KERN IoP can supplement the CMMS with information obtained from:

• PLCs and control systems;
• sensors;
• meters;
• digital and analogue signals;
• interfaces used by operators and technicians.

This information supports the recording of actual operating hours, cycles, starts, states, alarms, stoppages and process variables on a synchronised timeline.

CMMS	KERN IoP
Manages work orders	Records when the asset actually stopped
Schedules preventive activities	Accumulates actual hours, cycles and starts
Manages technicians and resources	Identifies operating states and failure periods
Controls spare parts and costs	Retains variables associated with each event
Documents the intervention	Verifies when the asset recovered its function
Preserves the work history	Builds the equipment’s operating history

The operating history required by maintenance engineering

In technical terms, reliability describes an asset’s ability to perform a required function under defined conditions for a specified interval.

Assessing reliability therefore requires the following to be defined first:

• the function the asset must perform;
• the condition that constitutes a failure;
• the operating regime;
• the actual operating exposure;
• and the period covered by the analysis.

A pump may keep rotating after losing its required function if it no longer delivers the specified flow. An injection moulding machine may remain energised while production is stopped. A compressor may be switched on and run unloaded for long periods.

The asset’s effective function must be assessed alongside its electrical state.

KERN IoP provides context for signals through states such as:

• production;
• operation under load;
• unloaded running;
• waiting;
• failure;
• maintenance;
• testing;
• return to service.

Signals can also be linked to variables such as temperature, pressure, flow, power, current, vibration or cycle time.

This context allows asset behaviour to be interpreted under comparable conditions.

Operating data for measurable reliability indicators

KERN IoP’s contribution is to supply the data required to build clearly defined, traceable indicators. Assigning a reliability rating requires the corresponding engineering assessment.

Effective operating hours

Operating hours should be calculated by adding only the intervals during which the asset met the condition defined as operation.

Effective operating hours = sum of the intervals classified as operation

Depending on the equipment, exposure may also be expressed as:

• cycles;
• starts;
• hours under load;
• movements;
• quantity processed;
• time at a specified thermal condition.

Mean time between failures — MTBF

For repairable assets, MTBF can be calculated as:

MTBF = effective operating hours / number of functional failures

The calculation requires consistent failure classification, separate from stoppages caused by material shortages, cleaning, product changes or a lack of demand.

The calculated value describes the behaviour observed over the period analysed. A probability assessment or forecast of future asset behaviour requires further analysis and assumptions.

Mean time to repair — MTTR

MTTR = sum of active repair times / number of repairs

Correct interpretation requires a distinction between:

• response time;
• diagnosis;
• active repair;
• waiting for spare parts;
• testing;
• release to production.

KERN IoP can help identify the event’s time boundaries. The CMMS retains the technical and administrative details of the intervention.

Operational availability

Operational availability (%) = [(required time − unavailable time) / required time] × 100

Required time must correspond to the period during which the asset was expected to perform its function.

A stoppage outside production hours has a different operational significance from a failure during planned production.

CMMS and industrial monitoring for consistent data

The quality of the indicators depends on the quality of their source data.

ISO 14224, developed for the petroleum, petrochemical and natural gas industries, provides principles for collecting and structuring reliability and maintenance data using a common taxonomy of equipment, failures and interventions. ISO 22400 sets out the need for clearly specified definitions, formulae, units and time behaviour for industrial KPIs.

In practice, a plant needs consistent criteria for:

• identifying assets;
• defining states;
• classifying failures;
• marking the start and end of each event;
• accumulating operating exposure;
• relating interventions to results.

KERN IoP can automate a substantial part of this recording through asset signals. Operators and technicians can add causes that require human interpretation.

For example, the system may detect a stoppage while a person confirms whether it resulted from:

• an electrical failure;
• a mechanical problem;
• a material shortage;
• a blockage elsewhere on the line;
• scheduled maintenance;
• a quality condition.

This approach combines automatic records with the technical knowledge of plant personnel.

Preventive maintenance based on actual asset use

Many preventive plans are triggered by the calendar: every week, month or year.

This is appropriate for certain tasks. When wear depends on use, actual exposure can provide a more representative basis.

Two items of equipment installed on the same day may accumulate very different exposure. One injection moulding machine may complete hundreds of thousands of cycles while another works for only a few hours. Two compressors of the same age may have very different numbers of loaded hours or starts.

KERN IoP can provide counters such as:

• production hours;
• hours under load;
• cycles;
• starts;
• movements;
• time at a specified temperature;
• cumulative production;
• operating time since the last intervention.

The CMMS continues to manage the preventive work order, resources and procedure. KERN IoP supplies the counter associated with actual use.

Together, they allow suitable interventions to be scheduled against asset exposure as well as calendar dates.

Verifying the outcome of an intervention

Closing a work order confirms that an activity was completed. Assessing whether asset behaviour improved requires operating evidence after the intervention.

After an intervention, KERN IoP can continue to record:

• operating hours until the next failure;
• event recurrence;
• number of alarms;
• stability of variables;
• recovery time;
• duration of minor stoppages;
• energy behaviour;
• cycle performance.

This information enables comparisons before and after maintenance.

The plant can distinguish between:

1. an intervention that temporarily restored service;
2. an action that reduced the frequency or duration of the problem;
3. an intervention followed by no observed recurrence over a defined period of operating exposure.

That last formulation matters. A short period without another failure provides limited evidence about whether the cause has been removed. The assessment must consider how much the asset operated, under which conditions and for how long it was observed.

Connecting work orders and reliability indicators

Combining a CMMS with KERN IoP helps answer questions that require information from both sources:

• How many hours did the asset operate before failing?
• What condition preceded the stoppage?
• Which failures recur most frequently?
• How much time was spent repairing and how much waiting?
• When did the equipment actually return to production?
• Did the intervention increase operating time between failures?
• Is preventive maintenance scheduled according to actual use?
• Which assets account for most unavailability?

The CMMS manages maintenance administration, documentation and logistics.

KERN IoP complements it with data on actual asset behaviour.

The CMMS documents maintenance work. KERN IoP contributes the operating history needed to build measurable reliability indicators.

This relationship supports maintenance engineering based on consistent data, known operating conditions and verifiable results, with intervention records linked to the equipment’s actual behaviour.

References

International Organization for Standardization. (2014). Automation systems and integration—Key performance indicators for manufacturing operations management—Part 2: Definitions and descriptions (ISO 22400-2:2014).

International Organization for Standardization. (2016). Petroleum, petrochemical and natural gas industries—Collection and exchange of reliability and maintenance data for equipment (ISO 14224:2016).

International Organization for Standardization. (2018). Condition monitoring and diagnostics of machines—General guidelines (ISO 17359:2018).

International Organization for Standardization. (2024). Asset management—Asset management system—Requirements (ISO 55001:2024).

Lopes, I., Senra, P., Vilarinho, S., Sá, V., Teixeira, C., Lopes, J., Alves, A., Oliveira, J. A., & Figueiredo, M. (2016). Requirements specification of a computerized maintenance management system—A case study. Procedia CIRP, 52, 268–273. https://doi.org/10.1016/j.procir.2016.07.047

Shaheen, B. W., & Németh, I. (2022). Integration of maintenance management system functions with Industry 4.0 technologies and features—A review. Processes, 10(11), 2173. https://doi.org/10.3390/pr10112173