5 Common Misconceptions about Equipment Management

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Images: As equipment becomes more automated and digitalized, factories urgently need to improve their equipment management capabilities. Driven by the wave of intelligent manufacturing, the manufacturing sector is also undergoing active transformation, with improvements in automation, digitalization, and intelligence levels. However, although most manufacturing enterprises invest heavily in highly advanced equipment, equipment management and maintenance, as well as the knowledge level of their staff, remain at a low level: data collection relies mainly on pens, processing relies on human effort, and analysis relies on guesswork (experience). It can be said that industrial 4.0 equipment has been purchased, yet management practices from industrial 2.0 continue to be used. From the perspective of the efficiency and output generated by equipment assets, there is significant room for improvement in the overall equipment efficiency of China’s manufacturing sector. According to statistics, the OEE of most discrete manufacturing industries in our country is around 40%, meaning there is still at least a 30%-40% improvement potential compared to developed countries. At the same time, many companies do not pay enough attention to the meticulous management of equipment maintenance, resulting in hidden losses such as unexpected shutdowns and waste of spare parts. Amid the current impact of the pandemic and the increasing uncertainty in the global landscape, improving equipment management levels can provide valuable opportunities for businesses to survive and thrive as well as to enhance their competitiveness. How to build management capabilities for new types of equipment is a problem and challenge facing China’s manufacturing industry at present. To this end, we have identified 5 common misconceptions among manufacturing companies and provided corresponding strategic recommendations, in the hope of helping them avoid unnecessary detours. For most enterprises, when building new factories or purchasing new equipment, they focus solely on the acceptance and handover of the hardware, while neglecting the operation, maintenance, and service standards of software systems. They fail to explicitly require equipment manufacturers to provide data collection interfaces and define ownership rights over the equipment data. According to relevant statistics, the average digitization rate of production equipment in Chinese enterprises is currently 47%, the CNC utilization rate for key processes is 51%, and the networking rate of key equipment is 41%. Embedded software, human-machine interfaces, data monitoring models, and management platforms are all important components of smart devices; they should also fall under the scope of device management. Equipment data acquisition faces challenges such as the abundance of industrial field protocols, the lack of openness and support from manufacturers, and issues related to the ownership of equipment data; as a result, it remains one of the biggest obstacles in advancing digitalization in production environments. ●Equipment management has gone through four stages of development: from 1.0 Corrective Maintenance (CM), 2.0 Preventive Maintenance (PM), 3.0 Reliability-Centered Maintenance (RCM), to 4.0 Predictive Maintenance (PHM). Essentially, it is an evolution centered on Equipment Health Management (EHM), shifting from treating existing problems to preventing them in the first place. Through EHM, the health status of equipment is no longer simply categorized as normal or abnormal. We can use new technologies and tools to analyze the accumulated basic data, assess the sub-healthy condition of equipment, and carry out maintenance in advance, **thereby reducing equipment maintenance costs. Equipping smart inspection tools with vibration sensors is akin to providing doctors with \"smart stethoscopes\"; by monitoring vibrations over a few seconds and using built-in spectral analysis models, it is possible to accurately and quickly determine the health status of equipment as well as the causes of any fault symptoms, thus serving as a valuable aid for equipment engineers in fault diagnosis. In this way, the responsibilities of equipment managers shift from fixing things once they break down to carrying out professional maintenance tasks aimed at ensuring the proper operation of the equipment, thus creating a positive cycle. Although TPM has been in use for many years, many managers still believe, both in terms of mindset and actions, that when equipment malfunctions it is the responsibility of the equipment department; as a result, the production department shows no concern for such malfunctions and does not pay enough attention to equipment maintenance that affects output and quality. Equipment maintenance engineers often refer to themselves as watchdogs and scapegoats due to their low status and low salaries; on holidays, while others can take time off, they cannot leave because it is precisely the best time to repair the equipment ； Whenever a problem occurs, whether it’s equipment downtime, production halts, or quality defects, it is always related to the equipment; the personnel in charge of the equipment are almost always held responsible. The Equipment Department has become the place where top talents are least willing to work. This vicious cycle requires production managers to take proactive action, establish correct concepts of equipment management, and create a self-maintenance system involving all employees with production as the core. Only when managers in the production department pay attention will the operators of production equipment change their indifferent attitude toward the equipment, enabling effective equipment maintenance. The Equipment Department alone cannot handle this. Zero failures are unattainable; when companies face difficulties and need to cut costs, they often target the expenses associated with equipment maintenance, even setting targets to reduce the maintenance budget by a certain percentage each year. From a traditional financial perspective, funds for equipment maintenance are generally classified as costs and expenses. In fact, as early as 30 years ago, when Germany hosted the International Conference of European Maintenance Organizations, it introduced the theme of “Maintenance – An Investment for the Future.” As an investment, there needs to be a clear input-output ratio. The costs associated with maintenance include the labor costs for daily equipment upkeep, various protection measures, spare parts expenses, as well as the investment in equipment management systems. What is the output? It is to avoid losses resulting from equipment downtime, accuracy or quality defects caused by inadequate equipment maintenance and poor equipment management. If the value of these losses is misjudged, it can easily stifle investment in improving equipment maintenance techniques and management systems. Reducing the value of 1 hour of equipment downtime consists solely of the labor costs associated with those employees during that 1 hour. The loss resulting from a one-hour shutdown of the equipment must be assessed from the factory’s perspective as a whole; this includes the opportunity cost of lost production capacity, that is, the value of the products that could not be produced during those 1 hour – and this is what constitutes the actual loss for the factory. With this approach, many small-scale improvements that can help reduce downtime, cut waste, and implement lean practices at the factory can be carried out. This stimulates the creativity of frontline employees and indeed brings tangible benefits to the factory. In actual practice, there is another problem: the replacement of spare parts is largely in the hands of maintenance workers, and there are many factors related to “human control” involved in this process. Most maintenance technicians rely on experience to determine the degree of damage to spare parts. For components suspected to be faulty, they usually replace them with new ones even when they are still functional, in order to avoid hassle, which leads to hidden waste from excessive maintenance. This invisible loss can be turned into “profit” by implementing more accurate spare parts life management. Equipment maintenance has shifted from previously focusing on serving production and striving to achieve high equipment availability rates, to now being centered around the enterprise’s economic benefits. This shift requires equipment management to pay greater attention to the management and control of maintenance costs, so as to find a balance where the highest possible equipment availability is achieved at the lowest possible maintenance costs. Senior corporate managers should view maintenance and equipment management from an “investment” perspective in order to shift the mindset behind equipment management. Implementing predictive maintenance is more difficult than expected, as relying solely on data to derive explainable logical principles of industrial processes proves to be far more challenging than anticipated. ●There are mainly two reasons. ● 1 Many companies still lack a proper accumulation of basic data. For example, records related to routine inspections of equipment, maintenance activities, and fault analysis are still scattered across various papers and Excel files. There are no digital archives for the equipment; basic maintenance data, records of spare parts replacement, as well as data on faults and repairs, including information on the characteristics of equipment faults, have not been systematically collected. Without this, it is impossible to carry out model training and validation ； 2 Many manufacturers attempt to rely solely on data analysis approaches, ignoring the integration of the existing expertise and experience of equipment engineers. Relying only on mathematics and AI algorithms can lead to statistical pitfalls; such methods only yield correlations, making it difficult to develop interpretable and predictable causal models. Therefore, we recommend that factories prioritize the development of basic digital capabilities such as digital records of equipment, routine maintenance, repair work orders, and fault trees. Secondly, for key high-value devices that cause significant losses when out of service, an approach is adopted that combines empirical models with data-driven models; the output of these models is intended to assist maintenance personnel, but ultimate decision-making still requires human judgment. To develop new equipment management capabilities, factory managers need to recognize that equipment forms the basis of a factory’s core competitiveness, and they should actively transform equipment management and operational methods to move toward digitalization and intelligence.