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Tianyin Shen, a Communication & PR Supervisor at NIO, possesses 4.5 years of experience in marketing/ branding for industrial consumer goods, and 1.5 years of experience in marketing/PR in automotive industry. He has good relationship/social network with media in lifestyle and automotive firms. As a versatile, motivated, and organized person, Tianyin has good marketing and PR knowledge with 6 years of working and internship experience.
1. In light of your experience, what are the trends and challenges you’ve witnessed happening to the Manufacturing Intelligence space?
Trend 1: the first trend for the future of manufacturing will be data intelligence. It includes data analysis for R&D, manufacturing processes, and user & marketing. Intelligent data analysis improves design efficiency and manufacturing quality, reduces cost, provides insights into potential users’ consumption pattern, and facilities OEMs to achieve operation and management innovation and transformation.
Trend 2: Artificial Intelligence (AI) technology, such as recognition, sensing, algorithm, and model analysis, will be increasingly used in manufacturing. With AI, expert know-how and experience from traditional manufactures can be applied to train an expert system that enables the self-learning, self-adaption and self-decision-making of manufacturing process parameters and the automatic prevention and prediction of manufacturing process quality.
Challenge 1: A company should eliminate the managerial silos within the organization and build an organizational structure adaptive to manufacturing intelligence. It requires a top-down momentum.
Challenge 2: The existing project-based cooperation mode featuring a Party A and a Party B is not ideal for the long-term sustainable development of Manufacturing Intelligence.
Challenge 3: The return on investment in smart manufacturing may become apparent over a long period. Yet the management of manufacturers would like to see the benefits in the short term, which contradicts the long-termness of smart manufacturing. Therefore, many companies with a focus on short-term interest cannot achieve good results from smart manufacturing projects.
2. Could you elaborate on some interesting and impactful project/initiatives that you’re currently overseeing?
The top-level design and planning of smart manufacturing. It covers zero-defect process and technique development system, IT system architecture planning for the end-to-end value chain of make-to-order process, full-lifecycle quality management system for quality issue prevention and digital quality issue tracking, digital equipment management system, and transparent factory operation and management system.
3. What are some of the points of discussion that go on in your leadership panel? What are the strategic points that you go by to steer the company forward?
Green, sustainable and smart factory, intelligent driving, best joyful user experience, and best product delivery quality.
4. How do you see the evolution of the Manufacturing Intelligence arena a few years from now about some of its potential disruptions and transformations?
The introduction of intelligent manufacturing elevated by, for example, AI, industrial internet, cloud computing, big data analysis and 5G, to some extent will transform the R&D, manufacturing, and user service in traditional OEMs. For instance, mass production will be shifted to mass customization and then to personalization. Companies will leverage synergies not just from inside of the company, but also outside. Traditional OEMs focus more on the synergy between R&D and manufacturing, manufacturing and sales. But in the future, OEMs will put more focus on the synergy between OEM and suppliers, suppliers and suppliers, OEM and other manufacturers, which represents the transformation from traditional eco-chain to eco-system.
5. What would be the single piece of advice that you could impart to a fellow or aspiring professional in your field, looking to embark on a similar venture or professional journey along the lines of your service and area of expertise?
Manufacturing intelligence is systematic engineering. Whether it is adopted by a new OEM planning to set up a smart system from scratch or by an established OEM planning to upgrade its existing system to a higher level of intelligence, it is important to integrate it into corporate strategies and objectives and take the characteristics and current situation of a company into consideration. Companies should make practical and feasible targets, scope and roadmap to achieve intelligent manufacturing, instead of blindly making manufacturing intelligence godlike or making unachievable targets. The realization of manufacturing intelligence is a lengthy process, and it won’t take shape overnight. A company will need to go through a series of processes of standardization, automation, informatization and digitalization before it starts introducing the cutting-edge intelligent manufacturing, exploring intelligent use cases and improving the actual value of a company in operation and management.