IT and OT Integration Technologies Are Available to Accelerate the Development of Smart Manufacturing

The Era of Digital Transformation

Taiwan is a major foundry country for 3C products in the world. In addition to having sufficient manufacturing knowledge and technology, manufacturers are also actively embracing digital transformation under the trend of Industry 4.0 and factory intelligence, introducing emerging technologies to accelerate production capacity and increase international competitiveness. However, the transformation of factory intelligent manufacturing does not mean success by introducing AI or IoT solutions. To build a suitable and applicable intelligent manufacturing system, in addition to hardware and software, whether it has practical experience in solution introduction is the key to success.

Taiwan's manufacturing industry is facing changes in digital transformation. How to accelerate the implementation of smart manufacturing and promote large-scale applications will be a major challenge. The general manager of IBM Taiwan said that due to the lack of overall goals or long-term vision, many smart manufacturing projects are facing the dilemma of ineffectiveness or stagnant progress, and it is difficult to expand deployment to cross-plant or cross-field applications. After the manufacturing industry has defined its core competitiveness, it wants to integrate new technologies, but also finds that companies cannot rely on their efforts to implement the goal of smart manufacturing. IBM pointed out: "In a fast-competitive world, it is no longer feasible to go it alone. We must create and cooperate with our partners!" To get rid of fragmented and partial investment in the manufacturing industry, it is necessary to use a full-scenario approach during the evaluation and proof-of-concept operation stages. From a perspective, to find the most directly beneficial application scenarios can it have the scalability that can quickly expand the scope of applications, which is helpful to the digital transformation of enterprises.

Therefore, Taiwan IBM has joined hands with industry partners to create a complete ecosystem of smart manufacturing, integrating operating technology (OT), information technology (IT) and AI, and working together to provide complete solutions and solutions from OT, IT and AI to enterprise application systems and hybrid clouds. Professional services enable smart manufacturing to be truly implemented as application scenarios, creating achievable investment benefits, and a scalable operation structure. Accuracy, yield, and utilization rate are where the competitiveness of the manufacturing industry lies. It covers the four major elements of the production line including personnel, machines, materials, and processes. Connecting OT and IT will be an indispensable key and a prerequisite for the implementation of AI in manufacturing. The core proposition of an enterprise is how to improve quality, reduce costs, and create maximum profits. The biggest challenge for companies in the next 10 years is people. How to reduce the risk of human error and make personnel operations more efficient will be the key. Therefore, it is imperative to import AI. The structure of OT must be solved first, and then it will make sense to send data to IT! Smart manufacturing can't just be a single point application. It must integrate OT and IT. The key is the "five Rs", that is, making the right decisions and taking the right actions with the right data, the right time, the right location.

Manufacturers start thinking about smart factory construction from the end. For example, a production line has several processes. Can the quality be judged when each process is completed? Are key machines operating normally 24 hours a day and properly utilized? Is it possible to optimize the workflow of employees? If you want to automate operations, you must think about how data is calculated on edge devices and make decisions directly. There are four levels to achieve smart manufacturing. The first is the machine level. If you want good and correct data, you must start with machine design to obtain effective data, present the state of the machine through digital methods, and design Parts, devices, and equipment can be reused, increasing the utilization rate of the machine, and reducing the cost of machine updating and upgrading intelligence in the face of future changes. The second is the production line level. In the face of a small number of diverse product changes, it is necessary to make good use of flexible construction and dynamic reorganization of modular simulation technology to reduce the production cost of flexible manufacturing. The third is at the factory level. Faced with market demand, it uses smart planning and execution system scheduling, understands the data and the relationship between the overall system, and allows intelligence to be injected into the factory to develop smart decisions that support each stage; to achieve increased production capacity and Process optimization. The fourth is the enterprise level. In the process of developing smart manufacturing, enterprises of any size need to have a platform that supports improvement and can continue to evolve and optimize and continue to bring value to the enterprise with a flexible structure and sustainable competitiveness. Finally, the key to success is to use the four levels of Manufacturing on Demand, supplemented by integrated ecosystem collaboration, to further effectively achieve the goal of continuous transformation of smart manufacturing.

IT and OT Convergence Technology

It is extremely difficult to integrate OT and IT because OT does not have a consistent standard. Therefore, factories should think from the concept of Data Fabric, build a data structure platform before production, and how to make different models from the interface of data integration. The yield rate is maximized. In production, we must make good use of hybrid cloud AI applications and think about the efficiency of AI from the perspective of data. After the integration of IT and OT, use the optimized IT architecture of different production lines to find a model suitable for AI to improve the efficiency of the production line. IBM has many AI training models used by international companies that can be directly introduced into the manufacturing industry. In the industry's time-rush competition, the manufacturing industry can quickly find a suitable AI architecture.

The next generation of manufacturing needs to use cloud-based SaaS and containerization to provide different services at the right time. The key is how to design its data structure. The data structure allows factories to face different challenges when developing visual dashboards and developing DevOps in the future. For digital transformation, companies must first clearly define their core competitiveness and core values. Don't use technology for the sake of technology. They must set return on investment and set goals and key results (OKR) with each employee. As an aggregator of Internet of Things solutions (Aggregator), the World Peace Group can assist customers in quickly matching solutions that can be deployed immediately, providing multiple options without being restricted by a single brand, and assisting customers in introducing the most cost-effective solutions. Reduce the time and cost required for enterprise trial and error. And cooperate with IBM to develop the IoT ecosystem, jointly promote the establishment of application standards, and shorten the time for the industry to market products. IBM builds a smart manufacturing ecosystem and works with strategic partners to solve the challenges of equipment networking and data extraction at the IoT connection layer in the manufacturing industry, as well as edge application scenarios, helping customers break through the technical bottleneck of converting OT data into IT data. After data extraction, AI data application scenarios and AI platforms will be further created, and the subsequent three stages of smart manufacturing competitiveness including dynamic simulation, smart factory, and dynamic customization will be gradually implemented to accelerate the advancement of Industry 4.0 and specifically realize the benefits of smart manufacturing.

In the past few years, the industrial environment of the manufacturing industry has undergone substantial changes, and these changes have brought severe challenges to the electronics assembly industry. The external environment, due to the Sino-US trade war, forced the manufacturing industry to change its production strategy, and successively began to adjust the production model concentrated on the mainland manufacturing base to be scattered around the world. However, setting up factories in different countries will face different geographical, cultural, and construction challenges. Not only can the existing production model not be fully replicated, but it may also delay the expansion of the factory, resulting in reduced production capacity.

The challenges within the industry come from manpower. Taiwan’s population continues to grow negatively, coupled with the fact that manufacturing is not a priority employment choice for young people. These two factors make Taiwan manufacturing industry under-worked. Besides, the gap in the quality of operators is also the main reason for the inefficiency of production lines. Especially in the electronic assembly industry that requires multiple processes and high precision.

To solve the two major challenges internally and externally, the manufacturing industry inevitably needs to introduce an automated production system. Through the standardized production mode of automated equipment, the production line can operate smoothly and the product quality tends to be consistent, thereby solving the problem of human variation. However, for the electronic assembly industry, the structure of electronic products is complex, and the unavoidable production line has many processes that must be completed manually. Therefore, it is difficult to introduce an unmanned factory solution to a fully automated production line. Is the most appropriate way of implementation.

The absence of 100% automation does not mean that the electronics assembly industry cannot be transformed intelligently. In consideration of competitiveness, the demand for smart manufacturing in the electronics assembly industry is becoming stronger. The reason is that while the manufacturing industry is expanding and expanding, only intelligence can accurately manage production lines. The digital system can respond more quickly to the changing external environment. For example, after the pneumonia epidemic, many factories will gradually resume work. The factory can shorten the resumption time and quickly restore production through remote monitoring or intelligent systems for production mode adjustment. Also because of the extensive use of smart devices, the dependence on labor is greatly reduced and the impact is reduced.

Three key points need special attention in the construction of related intelligent manufacturing systems. The first is the setting of the system's return on investment, the second is the integration of the two systems of OT and IT, and the third is the control of the system's online schedule. Among them, the most difficult and time-cost investment for customers is the system integration of OT and IT.

The barrier between IT and OT is not only the technical level but also the organizational structure. The planning blueprint proposed by IBM must first communicate with senior management, define key performance indicators (KPI) and return on investment (ROI), and then ask colleagues to start implementation, otherwise, it will only be a single point of breakthrough or innovation. Cannot bring higher value. As for the implementation, it can rely on aggregators to integrate IT and OT technologies to create ready-to-use application solutions for enterprises, using virtualization, security protection, artificial intelligence and other existing IT field-related technology deployment architectures. The gears moving towards digitization must begin to roll. The overall solution of the industry can shorten the time of trial and error, and then accumulate big data, explore the potential of data, convert it into information that can assist decision-making, gradually accumulate into knowledge, and finally evolve into wisdom, Take the initiative to make actionable decisions.