PCML Systems
Bring equipment, maintenance, production, energy, and cost information together to investigate operating losses and evaluate possible interventions.
Under development
Our approach
A production loss rarely appears in only one dataset. An equipment problem may show up in maintenance work orders, throughput variations, energy consumption, quality records, and delivery performance. PCML is developing ways to connect these observations using existing ERP, SCADA, and other plant information.
The development roadmap includes predictive maintenance use cases, digital twins, scenario modelling, and predictive cost intelligence. Each use case starts by defining the equipment or process boundary, the decision to support, the available history, and the evidence needed to validate the analysis.
Starting with the evidence
The precise scope depends on the use case and the information available.
PCML Systems
Explore the work within this capability, the decisions it can inform, and the information needed to develop a useful result.
Bring together failure history, inspections, operating hours, maintenance records, and available condition signals. Investigate recurring issues and identify assets that deserve a more detailed reliability assessment.
Explore patterns that may precede equipment degradation or failure. Model development would include event labelling, comparison with historical maintenance outcomes, and evaluation of missed events and false alerts.
Relate stoppages to duration, affected equipment, production schedules, and lost throughput. Distinguish recorded production losses from estimates that depend on assumptions about normal operating capacity.
Develop a digital representation of a defined asset or process for monitoring and analysis. The modelling approach, level of detail, and update frequency would depend on the use case and available engineering data.
Explore questions such as changing a maintenance interval, rescheduling an asset, or adjusting a process parameter. Compare potential outcomes under explicit operating constraints and assumptions.
Translate operating conditions into estimated maintenance, interruption, energy, or resource costs. Expose the assumptions behind the calculation so technical and financial teams can review them together.
Assess electricity, fuel, water, and material use in relation to production volumes and operating conditions. Examine deviations and opportunities for investigation across equipment, shifts, or process stages.
Map tags, timestamps, asset identifiers, work orders, and production records across systems. Identify missing history, inconsistent naming, and integration requirements before defining an analytical pilot.
Defining the work
We agree the boundaries, responsibilities, and intended outputs at the start. Depending on the assessment or pilot, the scope can include the following.
Illustrative applications
Connect failure records, operating conditions, and lost production to assess whether maintenance planning or a process review deserves priority.
Compare energy consumption at similar production levels and identify the operating periods or equipment groups that need investigation.
Putting the work in context
The intended result is a clearer path from plant data to an operational decision, supported by evidence that engineering, operations, and finance teams can examine together. Advanced predictive models and digital twins remain part of the development roadmap.
A closer look
CCTS includes a compliance mechanism for obligated entities and an offset mechanism with separate project requirements.
Source · BEEThe ILO recognised a safe and healthy working environment as a fundamental principle and right at work in June 2022.
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Explore applicability, current status, key preparation tasks, and primary sources for Indian requirements and selected international regimes.
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