In Japan, both the population and the birthrate are declining while the population is aging. In the manufacturing industry that supports the Japanese economy skilled workers are also aging and in short supply. For this reason, manufacturing companies are increasing their productivity by implementing digital technology in order to make up for this shortage of skilled workers.
The advanced case of Nitto Denki Seisakusho
Until now, in general, most of the work using Robotic Process Automation (RPA) technologies occurred in indirect departments such as office work. Most of it is bank-related, and it is thought that it is difficult to apply to technical work, but Nitto Denki Seisakusho (of the Gunma Prefecture, with 150 employees, and 3 billion yen in sales), which is a manufacturing company, introduced RPA to the direct department instead of the indirect department
When the design work is broken down, it is divided into work that uses the brain and a work that uses the hands. Hand-based work includes, for example, duplicate input work, or simply copying a drawing. The purpose of introducing RPA into the design process is to let humans concentrate on the work that uses their brains and let the work that can be automated be RPA.
Specifically, the company has automated the work of outputting and printing design drawings from CAD data of electric circuits. Before using RPA, the same work of opening data, printing, and pressing a stamp was repeated 20 to 30 times for each blueprint, which took a considerable amount of time. By leaving this work to RPA, human work can be completed simply by saving the drawing data that one wants to draw in the RPA folder, which is a shared folder. RPA is managed by the task scheduler. At 15:00, RPA starts automatically, and the drawing is opened and printed. With regards to the stamp-pushing work, the stamp is converted into data and automatically pushed onto its designated place by RPA. The task scheduler is the most important part of this system. Humans do not have to print as long as they enter the data by 15:00, but they have to print by themselves after 15:00. As a result, designers work to be punctual. The printing time for each print is about 20 minutes, but a considerable amount of time is used for a month’s worth of drawing work.
By introducing RPA at Nitto Denki Seisakusho, it was possible to reduce a person’s work by about 20 hours per month. The company is one of the nine model SMEs that participate in the "Study Group for Strengthening the Competitiveness of SME by IoT and AI" that the author has hosted since April 2016.
Model SME’s of a Study Group
The purpose of the study group is as follows. Even now, it is extremely rare for a full-scale introduction of wide-ranging Internet of Things and Artificial Intelligence (IoT and AI) technologies to be achieved across the factory sites of Japanese SMEs. The reason is simple. "I'm not sure what the benefits of my company are," thinks the president of any SME.
In my experience, there are few SMEs presidents who decide to invest in IoT and AI just by looking at the final appearance of successful introduction cases of other companies. This is because even if the president of a SME sees only the final output of other companies, he may feel he cannot take that step because he is worried that "that company may have been good in that way, but my company is different". The anxiety is endless, and presidents cannot decide to invest in IoT and AI unless that anxiety is resolved.
This is why, in April 2016, I started the "Study Group for Strengthening the Competitiveness of SME by IoT and AI" in which nine model companies participate. The study group discloses all corporate know-how from the start to the end of the examination by the model company. The goal is to create a successful case. If everyone sees a successful case, everyone is convinced that the method is effective.
IoT and AI investments have already been made, and the results have been measured by some companies. For instance, Nitto Electric had a 20% increase in sales, Tokyo Denki had a 10% increase in sales and a V-shaped recovery, and Daiichi Fabtech had a 30% increase in sales, too.
As a result of the efforts of the study group so far, the "introduction procedure manual" for introducing IoT and AI into SMEs has been almost established. It can be said that the "introduction procedure manual" is the greatest result of the know-how obtained at these study groups. Having been modeled by the SMEs of nine companies, it became clear what kind of discussions and steps the SMEs would need to take in order to successfully introduce IoT and AI, and where there might be a bottleneck.
In addition, similar study groups were launched in 2018 in local governments that have participated as observers of the study group and absorbed the know-how; meanwhile, the number of local governments is gradually increasing as this initiative spreads nationwide.
At the start of the study group, there were no experts in this field in Japan, and the members of the study group were not specialists. Over the years, however, they became experts by dealing with the nine model companies.
The result of this study group is that it is effective to train experts in this field, dispatch them to companies for a long period of time, and provide specialized consulting from the beginning to the end. It can be said that this method has proved successful.
In Germany, as in Japan, the manufacturing industry is the country’s main industry, with 99.7% of it being SMEs. The German population and the birthrate are also declining while the population is aging. Accordingly, the German Ministry of Economics and Energy is implementing the Mittelstand 4.0 project to introduce IoT and AI technologies into SMEs at 25 locations nationwide. The German method is a testbed method in which mechanical equipment is installed and specialists are trained on-site.
I conducted a field survey of the most successful case, the Technische Universität Darmstadt Competence Center. The annual budget is about 5.5 million euros, of which two-thirds come from businesses. Local companies around the university enter into a paid consulting contract with the university. The role of the Competence Center is to train specialists in digital implementation within SMEs and dispatch them to contracted companies for consulting work.
From the example of Technische Universität Darmstadt, it can be seen that the success factor of IoT and AI implementation in SMEs is to train experts in this field, dispatch them to companies for a long period of time, from the beginning to the end, in order to provide professional consulting. This is because, much like in Japan, there were no experts in this field in Germany.
As aforementioned, the biggest issue in introducing IoT and AI to SMEs is how to train experts in this field.