KSC Conference:
The future of manufacturing with Symbol

Last Tuesday, June 20, 2023, we participated in the KSC Manufacturing Conference 2023 along with other participants such as ASML, Thermo Fischer, Mitutoyo and Jingdiao. This year’s event focused on innovation, collaboration and the future of manufacturing. The conference brought together experts, industry professionals and enthusiasts to explore the latest developments and challenges within manufacturing. Symbol is an expert partner in the manufacturing world, of course we were also present.

 

Future business requires redesign collaboration

 

The KSC event focused on the topic “The Future of Business Requires Redesigning Collaboration,” highlighting the essential role of collaboration in driving innovation and success among manufacturers. In particular, the focus during the event was on the meaning of collaboration in relation to production technology, processes and quality management. In these aspects, consultancy is quite important. Because this is precisely where many manufacturers experience complications in communication and coordination between their own suppliers and clients. With the Lean approach, collaboration can be streamlined. Through Value Steam analysis, Visual Management and multidisciplinary teams, collaboration is encouraged. Continuous learning and adaptation ensure future-oriented collaboration.

 

Defectivity in an ASML machine

 

During a lecture by ASML, it discussed how defectivity plays a crucial role in the performance and reliability of their machines. In the context of ASML, “defectiveness” refers to the presence of defects or imperfections in the machines.
ASML’s machines are highly complex and precisely designed to create intricate patterns on semiconductor wafers. Even minor defects can have significant effects on the functionality and yield of the semiconductor chips produced. Therefore, minimizing defectiveness is critical to ensure the optimal performance and reliability of ASML machines. It was briefly discussed what efforts and strategies ASML is trying to apply to reduce defectiveness in their machines. This can include rigorous quality control measures, advanced inspection techniques, process optimizations and continuous improvement initiatives.
To manage this, Lean Six Sigma is a good approach. Lean Six Sigma helps reduce defectiveness in machines by providing a structured methodology for process improvement and quality control.
Notably by:
– Process Analysis and Identification of Defectivity: By identifying and quantifying defectivity in each process step, potential sources of defects can be identified and analyzed.
– Problem analysis and root cause identification: Six Sigma methods, such as the DMAIC approach, can be used to analyze the root cause of defectiveness.
– Process optimization and reduction of variation: Using statistical process control techniques, Six Sigma can help identify sources of variation and develop improvement strategies to reduce variation and increase process stability.
– Quality assurance and standardization: Lean Six Sigma emphasizes creating standardized work practices and ensuring quality in every aspect of production.
– Continuous improvement and monitoring: Lean Six Sigma promotes a culture of continuous improvement. It includes monitoring process performance, measuring critical process indicators and regularly reviewing processes to identify and implement continuous improvements.

 

Application of AI

 

AI is becoming an integral part of manufacturing technology. It can help automate processes, reduce costs and improve efficiency. Machines with AI can quickly and accurately detect and react to changes in the environment, allowing manufacturers to produce high-quality products faster. Although the power of AI is well known, companies sometimes struggle to give AI a place in their process. AI development is rapid, but how can you best implement AI in your own production process and to what extent should you blindly trust AI?
An interesting comment came from Thermo Fisher Scientific: “I dont really trust AI, I trust my suppliers.” This is because AI can still have a degree of unpredictability, and sometimes the lack of human judgment in a process is necessary, human judgment and intuition may be needed to respond appropriately to contingencies during a process. There are also concerns about security and implementation challenges.

At Symbol, we understand where the challenges lie and where the opportunities can be exploited. Find out more about AI and Industry 4.0

 

Solutions for the manufacturing industry

 

Lean transformation
Interim management
Green Belt Training
Black Belt Training

 

 

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