In any industrial setting, reducing downtime of equipment in productionplants as much as possible has always been the primary concern for maintenance managers and asset managers, with the ultimate goal of maintaining, and if possible improving, the value and competitiveness of the company Often, to perform their work, typically, those responsible for the management and maintenance of industrial assets must combine quantitative measurement techniques, such as the analysis of parameters and indicators of the operating status of the plants, with qualitative assessment practices. The functions of the asset manager are fundamental for the extent of the impact that a malfunction is capable of determining in the production activity: today poor maintenance strategies, estimates the consulting company and professional services Deloitte, can reduce from 5 20% of a plant’s total production capacity, and recent studies indicate that unexpected downtime is costing industrial manufacturers around $ 50 billion annually.
OEE, the factors that influence productivity
To maximize the overall efficiency of an industrial plant, or OEE (overall equipment effectiveness), as a rule, the asset managers must analyze three key indicators: one is there availability of the equipment, which is calculated taking into account planned and unexpected stops and is the percentage of the actual operating time compared to the established production times. The other indicator is represented by the performance, i.e. the level of efficiency of the system, which shows whether it is operating at full speed, at the maximum possible speed, or not. The third indicator is quality, i.e. the percentage of non-defective parts compared to those produced. In the measure of the OEE indicator, an asset manager therefore takes into consideration all the factors that generate productivity losses, such as malfunctions, blockages, configuration problems, defective parts and rework.
As mentioned, unexpected downtime costs a lot. However, even today, most managers of asset management and maintenance, when deciding how often to stop and disconnect the machinery to perform the necessary interventions, are faced with a decision-making crossroads: that is, choosing whether it is better to maximize the useful life of the equipment, opting for a ‘run-to-failure’ strategy, in which maintenance is performed on the asset only when it fails, or if it is convenient to maximize the uptime, through the early replacement of certain components which, however, potentially can still be in good condition.
Asset manager, a role that changes
Constantly committed to maximizing the productivity of often outdated equipment and infrastructures, today asset managers, taking advantage of the current technological evolution, have the opportunity to avoid the previous decision dilemma, planning and programming as much as possible the maintenance interventions intended for optimize the OEE indicator. The opportunity is, in fact, to overcome the limits of approaches such as reactive maintenance – performed only when the components or equipment experience a failure or break down – or the preventive maintenance, aimed at preventing the breakdown of equipment through the ‘time-based’ replacement of some parts, although they can still provide a certain duration. This becomes possible by implementing technological paradigms such as the Internet of Things (IoT) and Industrial IoT (IIoT), which by collecting directly on the field, through distributed sensor networks, large amounts of data relating to the operation of connected equipment, assets and machinery network, allow you to implement predictive maintenance models. The latter, thanks to the intelligent data analysis made possible also by the latest generation artificial intelligence (AI) algorithms, allows you to predict when and where malfunctions could occur, and therefore, potentially, to maximize the efficiency of components and machinery and reduce unnecessary downtime.