Based on a consulting project done for one of the largest global chemicals’ corporations – names and specifics sanitized to maintain client confidentiality.
Everyone who has ever worked at or with heavy industry facilities knows just how important the equipment maintenance is for plant economics. Efficient maintenance is one of the most significant drivers of both the top line (through increased capacity utilization) and bottom line (through cost savings on maintenance labor and spare parts/materials) of a manufacturing plant, especially in industries such as O&G, mining, chemicals, metals etc. that rely heavily on continuous processes often running 24/7 and involving highly destructive environments leading to rapid equipment degradation. Equipment breakdowns in these industries are not unexpected emergencies, but a daily reality they are dealing with. Yearly maintenance materials’ costs can reach 3-5% or more of the total asset base, and maintenance labor might contribute 30-50% of total labor costs on the plant. Efficient maintenance can also easily make a 5-10% difference in equipment utilization and the plant output. Coincidentally, many of these industries also produce commodity products which compete mainly on cost, and output volume is the only revenue driver the plant can control.
Digital revolution has enabled the Client to dramatically increase both maintenance effectiveness (as measured by equipment uptime) and efficiency (cost savings) through a number of initiatives, of which I will focus only on the most important two: the “reliability-centered maintenance strategy” (RCM) managed by an automated planning application, and the Industrial Internet-enabled predictive/condition-based maintenance.
Where old pre-digital maintenance function relied on either reactive maintenance (“fix it when it breaks down”, which leads to massive losses of productive time and possible spread of the damage to other valuable equipment) or ad hoc preventive maintenance scheduled by the supervisors based on their personal experience and/or observations of the production staff (“this sounds like it’s gonna blow!”), the automated RCM operates in a much more systematic way. The entirety of equipment in the facility was cataloged in the application (in this case, SAP-based) in a hierarchical manner, from the entire production shops down to individual pieces of equipment and further down to individual components such as bearings and mechanical seals. For each component, equipment piece and so on, a list of potential failure types was compiled, and each failure type was assigned a criticality level, ranging from unimportant failures that do not lead to lost productive time and/or additional costs, to those that can have catastrophic consequences for the facility and its workers and cannot ever be allowed to happen. Depending on the specifics, an equipment component can be assigned through a decision-tree algorithm to one of the maintenance types:
- “Reactive-by-design” maintenance, where preventive or predictive maintenance is not worth the effort (and labor)
- Regular preventive maintenance, where a component is simply replaced every X days depending on the historical statistical data on failure rates and a probabilistic cost-benefit analysis.
- Condition based-maintenance through equipment check-ups done by the previous worker shift or, importantly, data obtained from sensors installed on the equipment and digitally linked to the planning system. The sensor complexity can range from a basic thermometer to elaborate systems of vibration and pressure detectors, capable of identifying dangerous metal fatigue or vibration patterns associated with specific failures. Depending on the cost efficiency and criticality, sensor systems can be installed on the equipment itself and report monitoring data 24/7, or built into the workers’ handheld devices and used during equipment check-ups scheduled depending on the “P-F interval” (period of time between the first instance when future failure can be detected and the expected moment it occurs, which is also programmed into the planning system).
- Predictive maintenance capable of synthesizing data from the sensors mentioned above with the production statistics and other information, and identifying correlation between various system states and failures caused by them after a period of time.
The system then generates maintenance orders automatically at the beginning of every shift, compiles them into working plans for the shift and distributes them to individual maintenance teams. As a nice add-on, it allows monitoring the current production parameters, equipment downtime statistics and other important information which is helpful in managerial decision making with regards to those calls that cannot (yet) be made by a machine.