Improving Productivity and Increasing Visibility of Strengths Through Academic Collaboration and IoT TechKazuyuki Motohashi
＜Series 3 / complete＞
Part 2 discussed how SME can survive the shift to open innovation. Can management recognize the movements of their lead customers, fully understand their own company’s strengths and form appropriate strategies? These important challenges will test their true value to the company. Their judgement will make or break improvements in productivity. Next, Part 3 discusses improving productivity in closer detail.
Implementing IoT to Improve Productivity
There are so many things that can be done to improve productivity, but I really think that using IoT is going to be a huge key to success. I’m going to use an example from a major company.
Komatsu Ltd. is a major manufacturer of construction equipment. They moved beyond the old model of just making and selling things, and successfully increased their added value by collecting and analyzing post-sales data.
Komatsu’s reputation for “smart construction” comes from the sensors built into different parts of the construction equipment that it sells and leases. These sensors gather data and enable AI to learn about each machine. For example, these sensors monitor specific values like the frequency and temperature of the engine, allowing them to gather data about how high values can reach before damage is likely. This seriously helpful system alerts users when the data values approach abnormal levels during equipment use, allowing users to respond to a dangerous situation before damage occurs.
Parts with a strict “2 year replacement policy” are monitored according to measurements that indicate poor condition like wear and warping. The system also provides notifications about replacement periods, which generally reduces maintenance costs.
Other smart construction features offered by Komatsu include driver training support and site survey services. Thanks to these expanded added value practices, Komatsu successfully escaped the limits of price competition (commoditization). As a result, Komatsu both increased its profits from sales and leases and improved productivity in the Era of Solutions.
These successful results were made possible by multiple sensors, that is to say, that IoT was the hero.
We can see how implementation of IoT can help boost productivity, but gathering proper post-sales data will be much harder for SME. Yet, SME have a particular incentive to improve productivity. How should they start making plans to achieve it?
SME & IoT to Improve Productivity
I think that IoT is also the key to success for SME.
But I’m sorry to say that very few SME have already implemented IoT systems. A November, 2015 implementation survey by the Research Institute of Economy, Trade & Industry showed that only around 10% of all participating companies could say that they had already introduced IoT into their work. Another 10% of participating companies rejected it completely, stated that they knew about IoT but that it had no place in their company. The majority of companies recognized IoT as a tool that could contribute greatly to their business. Yet, more than half of the participants admitted that although they have heard about IoT, they have taken no actions towards using it.
（“Survey of Big Data Use and Innovation in Japanese Manufacturing Firms”, October 2016.）
So let’s start thinking about implementing IoT for SME. I understand that it’s difficult for them to collect user data in like in my previous example with Komatsu Ltd. So I think that SME could try getting some data from a closer source, like doing it within their own company.
In Part 2 I mentioned the need to dig down and uncover their company’s strengths. In order to get really deep and give their technology and management processes visibility, they need to gather the right data. For example, if they can add sensors to their production line, then they can get data about about production efficiency and how often defects are occurring.
It’s clear that IoT is important, but even if SME manage to install sensors within their own facilities, the next step of data analysis is probably a huge hurdle for most companies.
IoT & Academic Collaboration
But I think that many SME will have trouble following through with data collection and analysis. In those cases, it would probably help to borrow the brains of a university of public research institution. Universities and public research institutions are great at taking a scientific approach, they already have links to local companies, and they can help create a network of minds.
Right now, major companies are already escaping the limitations of self-sufficiency. There’s no reason for SME to insist on doing all the data collection and analysis by themselves. Any task that one company struggles with can be fixed by collaboration with someone who excels at it.
Let me tell you a fantastic example of SME collaborating with a university. There’s a company in Nagano called Komatsu Seiki (precision machinery), and their specialty was putting together precision parts. They sensed an upcoming crisis as their industry began to face decline, so they took steps to bring visibility to their company’s technical skills.
Komatsu Seiki asked a university to investigate and analyze their unique processing technology from a fundamental scientific perspective, and they wrote an academic paper together. This process of clarifying the scientific basis of their technology revealed many benefits.
First, by clarifying the details of their technology, they were able to make technical advances, and fortify their existing strengths. This led directly to a wide improvement in productivity. Next, this academic recognition earned their technology a higher level of trust and respect. In addition, this full understanding of fundamental science gave them the power of persuasion, enabling them to explain their technology easily when they reach out to previously untouched industries. All of this made Komatsu Seiki successful at approaching new industries and building new business. Overall, this project both contributed to more sales, and greatly improved their productivity.
But I don’t think that this is a unique case example. There are many cases where SME with unique technology manage to expand their work and make new business connections thanks to bringing visibility to their strengths in a scientific manner through collaboration with universities.
Interview Date: January 15, 2019
Improving Productivity and Increasing Visibility of Strengths Through Academic Collaboration and IoT Tech
Department of Technology Management for Innovation at the University of Tokyo
Joined the Ministry of Economy, Trade and Industry after finishing his Master’s degree at the School of Engineering, University of Tokyo in 1986. After working for the OECD, he accepted an assistant professor position at the Institute of Innovation Research, Hitotsubashi University in 2002. Next, he returned to the University of Tokyo as an assistant professor at the Research Center for Advanced Science and Technology in 2004. In 2006, he accepted his current professor position at the Department of Technology Management for Innovation, University of Tokyo. Meanwhile, he has also served as a World Bank consultant, OECD consultant, Research Institute of Economy, Trade & Industry faculty fellow, Japan Fair Trade Commission Competition Policy Research Center visiting researcher, National Institute of Science and Technology Policy (NISTEP) chief researcher, Stanford University Asia-Pacific Research Center visiting fellow, and East China Normal University visiting professor. MBA Cornell University & PhD Keio University School of Business and Commerce. Specialties include econometrics, industrial organization theory and technology management theory.
・“Alliance Management” Hakuto-Shobo Publishing Company, April 2014.
・“We Still Have Time: Reviving Industrial Competitiveness” Nikkei Publishing, February 2014.
・“Global Management Strategies” University of Tokyo Press, March 2013.
・“Japanese Bio-Innovation” Hakuto-Shobo Publishing Company, November 2009.
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