Equipment maintenance approaches have gone a thorny path from reactive to predictive in the Industry 4.0 concept. This term means the fourth industrial revolution, which is not left from the lips of engineers of giants like Toyota, Siemens and General Electric.
In this article, we will talk about how the approaches to equipment maintenance changed from the first to the fourth industrial revolution. All described methods are part of RCM (Reliability-centered Maintenance) – a strategy aimed at preventing critical deviations of equipment parameters.
Industry 1.0 – Reactive approach
The first industrial revolution is the transition of mankind from manual to mechanical labor. In those days, the equipment was serviced according to the principle “breaks down – we repair”.
The units were running out to failure, and the equipment availability indicators barely reached the 50% mark. The reactive approach handled maintenance of non-critical equipment that was cheaper to replace than regularly maintained.
The reactive approach has several disadvantages. Repairs were not planned ahead of time, and huge delays occurred as critical equipment failed. Enterprises did not always have the necessary spare parts in their warehouses, so they spent huge sums on urgent delivery.
British philosopher Hobbes called life in that era “difficult, brutal and short.” In the language of equipment maintenance, the reactive approach is “accidents, downtime and costs”.
Industry 2.0 – Proactive Approach
During the Second Industrial Revolution, electrification was in full swing. Together with it, preventive approaches to equipment maintenance appeared. Their essence: replacement or repair of a working asset at a specific time interval.
The proactive approach relied on statistics to determine the average uptime or lifecycle of an asset. For example, some units were serviced weekly and monthly. In other cases – every 100 cycles, 1000 hours, etc. Since in those days there were no CRM systems for competently accounting for the repair schedule, enterprises did not keep pace with changes in equipment characteristics and suffered financial losses due to untimely repairs.
Industry 3.0 – Proactive Approach
This stage is a rehearsal for digitalization. To ensure the reliability of equipment, factories began to take a proactive approach. Its purpose is to ensure the maximum overhaul life of the units, excluding the causes of failures.
The proactive approach is made possible by:
Thanks to new technologies and the gradual development of the Internet, enterprises have been able to analyze equipment indicators in real time. Preventing accidents and monitoring the “health” of critical units has become much easier. A proactive approach keeps hardware availability at 75%.
Industry 4.0 – Predictive Approach
The fourth industrial revolution (Industry 4.0) is happening right now and combines previous advances with industrial innovation.
Thanks to IІoT and Big Data analytics, computers can exchange information and make effective decisions without human intervention. This gave a new round of development to “smart factories” – the concept of enterprises, where most of the routine is performed by robots, and people are concentrated on solving business problems.
So humanity came to the predictive approach (Predictive Maintenance). He predicts possible malfunctions long before the unit or unit fails. Thus, the availability of equipment reaches 90%, and the company saves on the purchase of components and storage.
Intelligent data collection and analytics save money and improve production here and now, as happened at the Bosch plant in China. With big data analytics and Industry 4.0 methodologies, the Bosch plant has secured longer uptime and increased production by more than 10%.
The future of Industry 4.0
According to experts, the Industry 4.0 market will reach $ 1 trillion by 2030. At the same time, enterprises are increasingly practicing predictive methods, where any decision to stop or repair equipment is based on Big Data. A system like SmartEAM automates the collection of asset information, allows tracking micro-trends in their operation, more accurately planning repairs and attracting less labor. Taking Interpipe as an example, we see the result in figures: an increase in efficiency by 15% and 20% savings on repairs from each ton of products.
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