What is the TPM?
Machines’ defects or failures during production have negative effects on the production schedule as well as on employee morale. Total Productive Maintenance (TPM) is at the origin of the equipment reliability. It is essentially machine-centric and its productivity measured by the OEE (Overall Equipment Efficiency, also called TRS for Synthetic Efficiency Ratio). *
The focus is on reducing the 16 OEE generalized losses, classified into three groups: equipment losses, labor-related losses, and material-, tool- and energy-related losses.
The JIPM (Japan Institute for Plant Maintenance) has defined in 1989 the eight pillars of the TPM management approach :
Autonomous management and maintenance of equipment
This is the part of the maintenance supported by production operators to make production units more autonomous and flexible. Two types of activities autonomous maintenance (AM): daily routines (inspection, cleaning, operation control, workstation management (provision, declarations...)) and the treatment of hazards (reboots, anomaly detection, hazard treatment, defined repairs). The roll-out of the AM counts 7 steps.
Waste elimination / Improvements on a case-by-case basis
All wastage and loss of efficiency causes are covered. The search and elimination of the root cause by improvement activities (Kaizen) must definitively eliminate dysfunctions.
Improvement of knowledge and know-how:
Safety, working conditions and environment
This is the zero accident and zero pollution principle.
Quality control or maintenance
The objective is to eliminate causes of non quality on products in order to reach zero defect.
Mastering product design and associated equipment
It is a question of capitalizing on past errors, taking into account all lessons learned during the problem solving, integrating improvements of the design equipment, making them "Maintenance-free", etc.
Efficiency of related services or "TPM at the office"
It is a question of developing the contributions of the support services to the TPM activities in production, it is necessary to have coherence in the management within services and between them.
These eight pillars will remain the industry fundamental. The question is simple: "How will the 3.X consolidate and ensure ever more efficient maintenance? ".
What does Industry 3.X bring us?
The analysis of Big Data, the Internet of Things, artificial intelligence, virtual and augmented reality, and cyber-physical systems are considered the main levers of the next digital upgrade.
- Big Data analysis
The term "big data" refers to large, technically complex data sets for typical data analysis and processing applications. Big Data analysis allows companies to "predict". Companies can predict events using in-depth analysis of large data sets that will be actively monitored. The amount of information stored is growing four times faster than the global economy, while computing speeds are growing nine times faster. This remains very important because from the beginning of the digital data creation until 2003, there were 5 exabytes of information created and now the same amount of information is created every two days. A study with over 2,000 business participants from 9 major industry sectors and 26 countries, shows that 80% of global CEOs have recognized the importance of data mining and analytics for their organizations. Large data can extract new data from existing data, providing important technical and business information that helps make clearer and more efficient decisions.
Cloud computing and machine learning are also leading technologies in Big Data analytics, cloud computing is a scalable platform that helps to use IT resources more efficiently, helping automate and reducing system costs. isolated. Cloud manufacturing is the concept that reflects the idea of a smart factory, that is, the collaboration of advanced production models with cloud computing technology to achieve computer-based and service-oriented production. (Givehchi, Tresk and Jasperneite, 2013).
- Internet of Things (IoT)
The idea of the Internet of Things appeared for the first time in the 1980s to meet the needs of automated teller machines (Shon, 1996). Many of these devices have been networked. The Internet of Things (IoT) is the terminology used for physical devices or components that can be connected via the network and have the ability to communicate with each other via Radio Frequency Identification (RIFD) or intelligent sensors (Gilchrist, 2016b ). According to ISO / IEC JTC1 (2015), IoT is an infrastructure of interconnected objects, people, systems and information resources as well as intelligent services to enable them to process information from the physical and virtual world and react. However, it is a concept similar to physical cyber systems (CPS). According to an estimate, around 25 billion devices / objects will be interconnected and will communicate with each other, and this will be used by 2020. The Internet of Things allows businesses to add transparency to processes and make it analytically measurable. These sensors can be worn by the operators, placed on the production lines, on the machines directly, in the warehouses ...
Similarly, the new RFID type solutions make it possible to guarantee a perfect products traceability on the value chain and also to measure everyday performance through the collection of relevant data. IoT's ability to offer enhanced intelligence helps organizations optimize decision-making capabilities, ensure efficient data collection, and generate the right reports for their specific environment. IoT helps the company gain "intelligence" by giving them the ability to analyze their physical processes that were not measurable before. All of this contributes to better strategic and operational capacity and, in some cases, to a competitive advantage (Kopetz, 2011).
- Artificial Intelligence
AI was introduced as a field of research in the late 1950s. Artificial intelligence is a sub-domain of computer science, whose sole purpose is to give machines or robots a human intelligence as they become independent platforms and able to make intelligent decisions autonomously (McCarthy, 2007). There are two types of AI: Artificial Narrow Intelligence (ANI) which is related to single-task applications in a very specific area that we are witnessing today (ie Go game), then there is Artificial General Intelligence (AGI) which is still developing. The concept of AGI is broad, deep and contains features that surpass human intelligence in many dimensions such as analytic speed, memory, multitasking, pattern recognition and adaptability with new auto information. Learned (Muehlhauser, 2013). According to Hawking et al. (2014), the success in creating the AGI would be the biggest event in human history, but they are not sure whether it is also the last, unless one learns to avoid risk, hence the experts’ hesitant vision in relation to AI.
How many jobs will be replaced by robots and AI in the coming years? And how many new jobs and positions will there be created? The answer is not given but, as an example, Japan's largest bank, Mitsubishi UFJ Finance, has recently installed robots for its customer service operations and IPsoft, a call center, is using an AI robot called "Amelia" able to self-learn outside preprogrammed knowledge. "Amelia" can now process more than 60% of all incoming requests.
- Virtual and Augmented Reality
The Smart Factory will be supported by advanced human resources programs in "Virtual Reality and Augmented Reality". Virtual Reality (VR) is a computer-simulated environment. It is presented to the user as a real environment. It can support employee training programs and business process support with compatible digital devices. On the other hand, Augmented Reality (AR) has a head start and allows the user to interfere with a simulated environment (Boud et al., 1999). According to Jason Ganz, CEO of Agora VR, "Internet has allowed us to learn anything - VR and AR will allow us to experiment everything". Experts suggest that some of the management tasks will be held virtually as, for example, meetings and strategic conferences. In addition, the RV and RA will assist the Human Resources Department in training and the continuous coaching system for transition in a digital environment.
- Cyber-physical systems
In order to make the smart factory operational or functional, we will need "Cyber Physical Systems (CPS)". CPS’s are intelligent systems that bridge virtual and physical components used in production, logistics and products. It is the concept that combines with the Internet of Services (IoS) to make 3.X industry possible, opening up new possibilities for innovative applications and processes. CPS’s will facilitate the paradigm shift from business models and market models to all stakeholders in the value chain including suppliers. All these technologies combined to other technologies such as additive manufacturing, for example 3D printing, selective laser sintering, cobotic, AGV, etc., are the basis for the future factory, which brings together virtual and physical systems via physical cyber systems. Such a fusion of technical processes and business processes will be a gateway to the concept known as the "Smart Factory" (MacDougall, 2014).
All these technologies already known, how will they contribute to production sites? And especially as part of a TPM approach? In the next article, we will present the contributions of the digitized environment to the TPM and more broadly, practical solutions that we put in place within our customers’ companies.
Boud, A. C., Haniff, D. J., Baber, C., & Steiner, S. J. (1999). Virtual reality and augmented reality as a training tool for assembly tasks. In Information Visualization, 1999. Proceedings. 1999 IEEE International Conference on (pp. 32-36). IEEE.
Gilchrist, A. (2016b). Middleware Industrial Internet of Things Platforms. In Industry 4.0 (pp. 153-160). Apress.
Givehchi, O., Trsek, H., & Jasperneite, J. (2013). Cloud computing for industrial automation systems—A comprehensive overview. In Emerging Technologies & Factory Automation (ETFA), 2013 IEEE 18th Conference on (pp. 1-4). IEEE.
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McCarthy, J. (2007). WHAT IS ARTIFICIAL INTELLIGENCE? Retrieved March 15, 2017, from http://www-formal.stanford.edu/jmc/whatisai/
Muehlhauser, L. (2013, September 15). What is AGI? Retrieved March 26, 2018, from https://intelligence.org/2013/08/11/what-is-agi/