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"The Future Of" Series - Smart Manufacturing In Hong Kong

Interview with Dr. Benny Drescher & Mr. Boris Wong – Smart Manufacturing In Hong Kong

Industry 4.0 is the digitalisation of the way goods are produced, with an emphasis on automation, machine learning and data. The Smart Manufacturing Leadership Consortium (SMLC) defines smart manufacturing as “the ability to solve existing and future problems via an open infrastructure that allows solutions to be implemented at the speed of business while creating advantaged value”.

In the first interview of Robert Walters’ “The Future of” series, we are delighted to have Dr. Benny Drescher, Chief Technical Officer, and Mr. Boris Wong, Senior Manager of Hong Kong Industrial Artificial Intelligence and Robotics Centre (FLAIR) as our guests. With a primary focus on industry 4.0 technologies, Benny and Boris collaborate closely with manufacturers across Greater Bay Area to enhance their manufacturing operations and digitise their processes.

Benny and his team are dedicated to advancing numerous technologies from the realm of applied research to practical implementation within companies. Their focus encompasses a diverse range of technologies, including computer vision, digital twin, and smart machinery. In this interview, Benny and Boris share how they drive innovation as well as how they see the future of the industry with Dicky Leung, Manager of Construction, Engineering & Supply Chain and Hazel Leung from Tech & Transformation at Robert Walters Hong Kong.

In a nutshell, what is FLAIR's approach to applying data analysis and AI in the manufacturing industry?

In the context of the manufacturing industry, the application of data analysis and AI requires a combination of domain knowledge and technical expertise. Simply handling data is insufficient without a deep understanding of shop floor operations and specific machines. Our approach involves helping data science professionals gain insights into manufacturing processes, while also educating individuals from a manufacturing background about the potential of data-driven solutions.

One of our current projects focuses on planning assembly lines in a more efficient and automated manner. Typically, the process involves CAD designers determining the product and engaging in extensive discussions with manufacturers to decide the assembly sequences, design the workstations, and balancing the assembly lines. To simplify this complex process, we have developed a solution in collaboration with multiple industrial companies.

Our algorithm automatically derives the assembly sequence from Computer Aided Design (CAD) data, integrating seamlessly with the CAD software. With just a click, the CAD designer receives an automated sequence that can be adjusted as needed. The integration reduces time-consuming discussions between planners and designers, expedites the sequence determination, and serves as a decision support system for an optimal product assembly. The implementation of the system can potentially decrease lead time, minimise human involvement, and enhance overall efficiency for designers and production planners.

What are the competitive advantages of FLAIR?

Our strength lies in data science modeling and building AI models based on data. While our focus is primarily on data science modeling, we remain open to incorporating various collaboration partners as needed. The emphasis lies in building accurate and robust models for an industrial application.

Our team consists of diverse members, including postdocs with doctoral degrees in fields related to data science and engineering, individuals with master's degrees, and professionals with industrial experience who bring valuable domain knowledge. We also have software engineers who contribute to architecture development and software modeling.

We maintain flexibility depending on the requirements of different projects. We don't have a one-size-fits-all approach to cloud platforms. The choice depends on the project, client, and industry partners we collaborate with. For instance, we have used PTC Onshape or AWS as cloud platform for certain projects.

What are the key impacts of smart manufacturing compared to traditional practices in Hong Kong – apart from evident time and cost savings, are there other notable benefits that come with embracing smart manufacturing methodologies?

With automation comes higher efficiency. For example, real-time monitoring from overseas headquarters enables effective management, even during times of restricted access. Smart manufacturing empowers us to transform operations, enhance products, and stay ahead in an evolving industry.

It presents tremendous opportunities to innovate and excel. Working in manufacturing is no longer just about factory level operations. You have more opportunities to interact with the products, say by integrating sensors into products, building cloud platforms behind it. Some people may relate smart manufacturing to the so-called "dark factories", which are usually highly automated in high-volume production. However, you can see that the trend of manufacturing is going low-volume, high-mix production. In such cases, human involvement remains vital for maintenance, changeovers, and data analysis.

Overseas experience and having an MBA are not necessary requirements

How do you envision the manufacturing market in five years?

Over the past 8 years, the initiative to digitalise factories and logistics has gained momentum. Some companies in Hong Kong have already reached a certain level of maturity in connecting machinery and gaining insights. This maturity will continue to grow, along with the integration of AI technologies that go beyond visualising data to modeling and decision support systems. As manufacturers see the value and benefits of these solutions, they will increasingly adopt them.

Currently, many companies are focusing on building data hubs and collecting data through IoT devices as a first step towards digitisation. The next steps involve filtering and categorising the data, analysing it, and applying AI algorithms for specific purposes. This step-by-step approach will become more well-known and widely adopted in the industry as the government continues to drive and promote these initiatives. Investing in data infrastructure first and then implementing AI holds promise for the future of manufacturing.

What do you think is the biggest challenge of finding suitable talent and how do you address this considering the competitive nature of the job market?

Finding right talent with a manufacturing or industry background, particularly data engineers, is a significant challenge. Many data science professionals tend to gravitate towards the financial industry, making it difficult to attract them to engineering field. However, it is crucial for us to hire individuals with expertise in both hardware and software aspects for the sake of operational efficiency.

Hong Kong is a very candidate-short market. To address this challenge, we are open to hiring talent from various locations and nationalities, including Italy, USA, UK, Mainland China and India. Despite the presence of passionate candidates, it is observed that many are drawn to other industries, especially the financial industry, due to various factors.

Many data professionals may not be aware of the production industry. In fact, the manufacturing field offers a unique appeal, as tangible products are created, and the sense of purpose is evident. This aspect can be particularly exciting for engineering students and professionals. Occasionally, passionate candidates with construction market backgrounds can be attracted towards research and development in manufacturing sciences.

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