The problem of intelligent manufacturing ITOT integration can be solved through this model

For traditional industrial enterprises that need to improve quality and efficiency, one question that cannot be avoided is - "Where to find the most suitable technology and solutions?" Industrial giants can certainly provide one-stop complete solutions, but at a time when new technologies are changing with each passing day and iterating rapidly, the power of small and medium-sized scientific and technological enterprises that master digital means such as cloud computing, the Internet of Things, and artificial intelligence cannot be ignored.

If we compare the production process in a factory to the veins in the human body, we can easily find that although it is at the basic level and the tasks are fragmented and complicated, any "blockage" anywhere will affect the overall operating efficiency.

For example, in the palletizing scenario of the logistics link, faced with a large number of small-batch orders of multiple varieties, if the required number of pallets and the optimal mixed palletizing rules cannot be calculated in real time based on the categories and specifications contained in the order, efficiency will be greatly affected; for another example, defect detection is an important link in ensuring product quality. A seemingly insignificant defect or flaw can cause a company's reputation and property to suffer losses, or even cause casualties. Traditional manual detection methods alone cannot guarantee efficiency and accuracy. In addition, there are many challenges in industrial scenarios such as production scheduling, material addition, and operation and maintenance...

Faced with these challenges, in the context of the development of the digital economy, from industrial giants deeply engaged in OT to Internet giants specializing in IT, they all claim to "help traditional manufacturing achieve transformation and upgrading". The essence of this is to help industrial enterprises solve the challenges existing in these actual production scenarios one by one. In this process, enterprises are not pursuing "high-end" but "most suitable" solutions.

Build a bridge for IT/OT integration

When the industry talks about digital transformation, the most frequently mentioned concept is the integration of information technology (IT) and operational technology (OT). The integration of the two promotes the mining of data value, opens the door to the development of the industrial Internet, and brings advanced information technology to the forefront of industry.

For traditional industrial enterprises that need to improve quality and efficiency, one question that cannot be avoided is - "Where to find the most suitable technology and solutions?" Industrial giants represented by Schneider Electric can certainly provide one-stop complete solutions, but at a time when new technologies are changing with each passing day and iterating rapidly, the power of small and medium-sized scientific and technological enterprises that master digital means such as cloud computing, the Internet of Things, and artificial intelligence cannot be ignored.

This is exactly the original intention of the "Green Smart Manufacturing Innovation Plan" jointly launched by Schneider Electric, Amazon Web Services, Lenovo Group, and Tsinghua University Global Industry Research Institute, that is, to unite start-ups with leading technologies to enable them to play an important role in digital transformation, and thus work together with Schneider Electric and ecosystem partners to expand and enrich the digital ecology in China's industrial field.

Taking the mixed palletizing scenario as an example, Entropy Intelligence Technology, an innovative technology company dedicated to the research and application of innovative artificial intelligence technologies, has developed an advanced artificial intelligence algorithm for this scenario. It can perform mathematical modeling on all cartons, calculate the number of empty pallets required according to the order, and the specifications, quantity, picking and placement of the cartons on each pallet, and find the optimal path for picking and palletizing. However, the generation of the palletizing model is very dependent on the quality of the data. In theoretical verification, the sizes of the boxes are all equal, but in actual scenarios, due to extrusion deformation, the sizes of the boxes will obviously be slightly different. Only with a large amount of real data can the model be trained more accurately. And the real data is hidden in the production lines and equipment of end customers represented by Jingkelun.

In the past, there was a "gulf" between the industrial enterprise Jingkelun and the start-up Enzhi Technology. They needed each other but could not see each other. The "Green Intelligent Manufacturing Win-Win Plan" is like building an IT/OT integration bridge between the two, communicating with each other in an ecological model.

Similar gaps also exist between other industrial customers and start-ups. As a leading company in electrification, digitalization and smart manufacturing, Schneider Electric hopes to use a series of professional training and certification to enable technologically advanced SMEs to have a deep understanding of typical industrial application scenarios. While helping SMEs to quickly grow into smart manufacturing solution providers, it also helps industrial customers solve pain points at the end of industrial scenarios, thereby truly opening up the "last mile of digital transformation."

A win-win ecological model

The "Green Smart Manufacturing Win-Win Plan" has created a typical win-win model for all parties.

For the enterprises participating in the program, the process of participating in the Win-Win Program is like taking a digital "private lesson" for several months. The sponsors, such as Schneider Electric, Amazon Web Services, Lenovo Group, and Tsinghua University Global Industry Research Institute, are like "mentors" from different professional directions. Amazon Web Services is committed to teaching the way to cloud computing; Lenovo focuses on the edge and the end side; Schneider Electric has deep insights in the field of automation. The three complement each other's capabilities and fully cover the three-layer architecture of the Industrial Internet from the bottom to the top.

At present, there are many various "training camps" and "incubators" on the market, but the "Green Smart Manufacturing Winning Plan" is unique. It is a plan carefully selected by Schneider Electric for 6 segmented scenarios, based on customer needs, to unite everyone to think about how to solve the problems in these actual scenarios.

After entering the camp, Schneider Electric invites SMEs to visit customers on site to gain a deeper understanding of customer needs and then conduct PoC drills. Afterwards, the mentor team and event supporters, including the Smart Factory Research Institute and experts from Chuangyebang, will provide professional consulting and guidance to the shortlisted companies through online and offline training, PoC process management, one-on-one guidance, and other forms.

During the coaching process, many problems of small and medium-sized enterprises will be exposed. A typical example is that start-ups often have their own core technologies in the niche areas they are good at, but there is still a long way to go from technology to products and from products to commercial realization. Having a product does not mean that everything is fine. How to price the product, how to divide the market segments, how to leverage more customer demand, how to face competition from peers... these are all "must-answer questions" in the process of product commercialization. Start-ups may have expertise in technology, but they do not have the mature system capabilities and rich commercialization experience of large enterprises.

After the systematic training and drills in the "training camp", start-ups will gain more talents, more scenarios, more applications and more practical experience. At the same time, if they can do a good job on a certain solution, there will be a large number of industrial customers' needs in the future.

From the perspective of end customers, joining the Win-Win Program not only has no additional cost, but also allows customers to choose a more suitable solution in the PoC.

On November 6, 2020, the nine companies that entered the finals of the event demonstrated their PoC development results on site and provided solutions and models for various difficult scenarios, including:

Group photo of 9 companies at the "Green Smart Manufacturing Winning Plan Finals"

In the cold chain warehouse order picking and palletizing scenario, Entropy Intelligence Technology relies on its core intelligent sorting and palletizing AI technology to verify the stability of its palletizing model with 120,000 boxes of goods, ensuring the global optimization of orders and the local optimization of pallet shapes; the solution of automatic picking by order on the stacker may become the first in the industry, which can not only greatly improve the palletizing efficiency, but also reduce energy consumption and eliminate quality risks.

In the scene of lithium battery raw material research and development and production workshop, Ruifu Time provides a complete and truly implementable overall industrial solution - XR production line inspection and quality control auxiliary system set with its rich MR (mixed reality) engineering experience, and introduces ReID (double identity authentication) technology and IoT smart shelves. Under 3D multi-dimensional monitoring, front-line workers can greatly reduce labor intensity and error probability, realize the seamless process of inbound and outbound storage, autonomous route planning in the workshop, and obtain visual assistance and guidance throughout the business process, ultimately achieving the goal of reducing costs and increasing efficiency.

In addition, several other companies also demonstrated PoC solutions for scenarios such as equipment operation and predictive maintenance in the cement industry, production data mining, automatic dosing in the water industry, and portable MES that can view production and manufacturing information at any time.