Why transform operations?

At present, some productive enterprises have implemented digital applications and gradually realized the value of factory production data. However, for most factories, the biggest use of these data is to generate reports submitted to various departments, and value-added data services have not been realized. For operation and maintenance personnel, operation and maintenance is still daily routine inspection, shift scheduling, maintenance equipment, these activities occupy a lot of working time, and the work load is high. For operation and maintenance management personnel, operation and maintenance is to monitor the operation performance of the site at all times, cope with unexpected situations in production, and ensure the stable operation of production.

With the innovation of Internet technology and the wide application of big data, under the dual role of current industry development trends and policies, each production enterprise is exploring its own development direction and development measures. In this process of thinking, we will naturally ask a question, whether the production company believes that it needs to continue to follow the above operation and maintenance concept, and whether there are new changes in focus?

According to Roland Berger’s survey, for manufacturing enterprises, more than 85% of respondents believe that O&M service providers should pay more attention to digital planning capabilities, and 97% believe that O&M service providers should provide active and complete digital O&M service solutions. From the perspective of production enterprises themselves, traditional maintenance methods have been unable to meet the requirements of the current digital transformation and development, and new operation and maintenance solutions that integrate the concept of the Internet of things and apply advanced digital means are the future development trend. This brings a new question, where is the direction of operation and maintenance.

Direction of O&M mode transformation

At present, the operation and maintenance methods adopted by production enterprises are usually divided into three types: passive maintenance, preventive maintenance and predictive maintenance. The comparison of the three O&M modes is shown in the following table:

Through the above table, we can see that traditional passive maintenance, preventive maintenance, it is difficult to accurately grasp the equipment has no fault, fault type, fault location and fault degree. In addition, due to the repeated disassembly of good parts, the mechanical properties are often not ideal, even lower than before maintenance. Therefore, the use of these two maintenance methods has caused huge maintenance costs, and the parking loss caused by failure to deal with it in time is immeasurable.

Predictive maintenance is one of the key innovation points proposed by “Industry 4.0”. It takes the status as the basis for continuous online status monitoring and data analysis of equipment, diagnoses and predicts the development trend of equipment failures, and users make predictive maintenance plans and confirm maintenance work based on such information. It covers the whole process of equipment condition monitoring, equipment fault diagnosis, fault state prediction, maintenance decision support and maintenance content confirmation. Predictive maintenance can help organizations minimize the risk and financial impact of unplanned downtime, so digital transformation of operations can realize cost savings and efficiency gains.

How to achieve predictive maintenance?

In order to help enterprises better achieve maintenance management, the operation and maintenance team needs to consider the four aspects of personnel, management processes, technical assistance tools and maintenance data, and transform the operation and maintenance platform, no longer adhering to the concept of “operation and maintenance = maintenance” in the past.

It is recommended that production enterprises consider the following dimensions to achieve close cooperation between people, technology and process, realize data-driven maintenance decision making, and integrate the value of data into operation and maintenance process:

1. Ensure the accuracy of data

More and more production enterprises realize that a large number of data accumulation is necessary, which can be used as the basis of modeling and data support for decision-making. However, through the investigation of some enterprises, some on-site equipment selection does not match, resulting in reduced data accuracy, lack of reference, storage and analysis value, unable to develop a reasonable operation and maintenance plan. For production enterprises, ensuring the accuracy of data is the basic requirement for the implementation of various functional modules of digital factories, and it is also the basis for laying the foundation of digital-driven production.

2. Build a predictive data platform

The purpose of building the platform is to realize the flow of data. It is necessary to consider the characteristics of data interface and comprehensiveness of data types, establish scene classification labels, form standardized asset labels and other identifiers closely combined with business, and finally provide visualization support at the application data layer, such as factory-level equipment visualization cockpit. The cockpit can provide electronic inspection, ledger management, alarm cause analysis, reports and other images.

3. Introduce machine learning

Machine learning can increase the value of predictive maintenance, helping operators and maintainers quickly identify data anomalies that indicate equipment problems. Machine learning is capable of generating a device model that identifies and simulates the operation of the device, thereby predicting the remaining useful life of the device and the failure of the device within a specific time.

4. Plan the intelligent O&M process

According to the actual operation and maintenance requirements, provide KPI statistical reports and analysis reports, provide optimization measures and suggestions, and automatically execute optimization policies to improve operation efficiency.

5. Training of compound talents

Production enterprises summarize production related experience into a variety of knowledge bases, constantly update and optimize the knowledge base, help professionals improve professional quality and comprehensive training, and provide compound talents for future digital transformation.

Production enterprises customize various application scenarios based on actual production to realize data collaboration, sharing, and reuse. For example, build a simulation environment to improve the insight of the process, operators and other on-site workshops, and provide auxiliary tools for various professionals to communicate better, learn faster, and make more efficient decisions.

Closing remarks

The winning basis of digital transformation is the transformation of operation and maintenance mode. Data platform and optimization analysis tools have become an important way to improve the economic efficiency of enterprises. It is believed that more and more production enterprises will realize digital operation and maintenance in the future.