Manufacturing DX Report Part 2: The State of Automation and Digitalization Responses to Manufacturing Challenges Arising from Management Changes

The aims of the recently conducted “Survey on the Current State of Smart Factories in Japan – Responding to Manufacturing Challenges Arising from Changes in the Business Environment” (hereinafter referred to as the Survey) are as follows.

1. Ascertaining the state of and reasons for the factors hindering the implementation of smart factories up to the model factory level, revealed in the Manufacturing DX Report Part 1
2. Extracting issues in the supply chain management (SCM) domain, which fell outside the scope of the first survey, and searching for manufacturing responses aimed at solving those issues

As part of the Survey, we conducted a questionnaire targeting individuals working in the manufacturing sector in order to ascertain the present state of smart factory projects and interviews to dig deeper into the responses we received (see Figure 1).

As part of the questionnaire, we received responses from 513 companies (with no duplicates from single companies) with a wide range of sales.
Breaking down respondents by industry, 74.4% were in discrete manufacturing (plastic product manufacturing, production machinery manufacturing, electronic component, device and circuitry manufacturing, etc.), 9.2% were in process manufacturing (food product manufacturing, chemical manufacturing, steel manufacturing, etc.), while 16.4% were in other manufacturing (see Figure 2). 

In this section, we will summarize the manufacturing challenges we saw in our questionnaires and interviews thus far, and the changes in business environment and management circumstances that lie behind this. We will then go on to cover some of the directions in which responses to manufacturing challenges are going.

The concept of Industry 4.0 was first introduced in Germany in 2011. It has also been nearly a decade since Japan’s Ministry of Economy, Trade and Industry (METI) released the “Smart Manufacturing Roadmap Survey” in 2017. In this time, progress has been made on visualizing and automating simple processes. However, the issues facing the manufacturing industry and people on the ground at factories have grown more complex. As has been frequently commented on in our interviews, this includes challenges such as addressing moves towards manufacturing more diverse products in smaller volumes and towards variable-mix variable-volume manufacturing (through production planning, manufacture and procurement), skill succession employing the digitalization of technique transfer and implicit knowledge, and dealing with the growing complexity of global forces in the SCM space.
Addressing any of these issues through the use of simple IoT sensing or application of AI to formalized knowledge is impractical. Instead, companies need to address these challenges by combining digital technology, improvements in machine technology and manufacturing, and strategic and organizational response (see Figure 7).

The optimal form of production differs intrinsically depending on whether a company is practicing high volume production of limited items or low volume production of a variety of items. As companies continue to move toward variable-mix variable-volume production, it is thus becoming impractical to approach this using existing line production and integrated production. In particular, there are limits to what can be done using traditional line production and job shop production approaches in variable-mix variable-volume production. We believe companies are thus at a point where they should consider moving towards new forms of production that support concepts such as reconfigurable manufacturing systems (RMS) (see Figure 8).

In interviews, some companies have even responded that they are considering RMS because, as products diversify further and variable production levels become more common, it is becoming impossible to deal with this using approaches such as line production. This, while such companies are still a minority, we are already seeing companies achieve the implementation and operation of forms of production such as RMS (see Figure 9).

There are multiple factors impeding the formalization of knowledge in operations involving a large portion of implicit knowledge. Examples that have been raised include perceptions using all five senses, judgments coming from experience and ad hoc devices.
Such operations are made up of implicit knowledge, and represent the value of experience that comes from an accumulation of on-the-ground improvements. This has made them a source of competitive edge in Japanese manufacturing. On the other hand, they carry issues that make digitalization and automation difficult, because knowledge of such operations is not formalized.
For this reason, companies need to undertake initiatives to collect these implicit factors as data and extract multiple knowledge formalization patterns, while performing analytic simulations (knowledge digital twins; see Figure 10).
While the original problem is one of how to capture data, applying sensing technology to the problem is not enough. Instead, it is important for companies to gather and store non-sensing data such as the utterances of experience operators and traces of their lines of sight.

The circumstances of supply chains in major countries and regions differ widely, and the changes they are undergoing are gathering pace.
Amidst this, globally integrated SCM operations have become more difficult. This has made it necessary for companies to have national and regional SCM operations organizations, as well as frameworks for supporting the integration of suppliers and the streamlining of deliveries spanning nations and regions through the integration of data.
While there are companies that have already implemented such frameworks, beginning with automotive OEMs, there are also many companies facing concerns over shortages of personnel and know-how in terms of internal operation of SCM.
In circumstances where it is difficult for a single company to secure and deploy resources with an awareness of global conditions, companies will need to work to move towards shared SCM intelligence through joint responses using regional clusters or through outsourcing to SCM contractors (as exist in the MRO space, for example; see Figure 11).