acatech DISCUSSION

Revitalizing Human-MachineInteraction for the
Advancement of Society
Perspectives from Germany and Japan

H. Kagermann, Y. Nonaka (Eds.)

2

acatech DISCUSSION

Revitalizing Human-MachineInteraction for the
Advancement of Society
Perspectives from Germany and Japan

H. Kagermann, Y. Nonaka (Eds.)

1

The acatech DISCUSSION seriesThis series documents symposia, working groups, workshops andother events organised by the National Academy of Science andEngineering. The relevant editors and authors are responsible forthe contents of the publications in this series.
All previous acatech publications are available for download from:www.acatech.de/publikationen.

2Outline

Foreword ................................................................................................................................................................ 5
Executive summary .............................................................................................................................................. 7
Project team........................................................................................................................................................... 9
1.

Introduction ................................................................................................................................................. 11

2.

Emerging issues in smart manufacturing .............................................................................................. 123.

The future of work ...................................................................................................................................... 13

4.

5.

6.

3.1.

Population change, labor market, productivity and quality of work ............................................... 13

3.2.

Estimation of changes in labor structure due to digital transformation ......................................... 21

Gap emerged due to diversities & approaches of sustainable innovations ..................................... 33
4.1.

Gap emerged due to diversities .................................................................................................. 33

4.2.

Managing the real-world and the cyber-world ............................................................................. 37

4.3.

Sustainable innovations with diversities ...................................................................................... 38

Challenges to the new paradigm of human-machine collaboration ................................................... 45
5.1.

Current situation ........................................................................................................................ 45

5.2.

Future development ................................................................................................................... 46

5.3.

New paradigm of human-machine collaboration .......................................................................... 47

Conclusion ................................................................................................................................................... 60

Bibliography ........................................................................................................................................................ 613

4

Foreword

Foreword
Prof. Dr. Henning KAGERMANN, Project Leader
Chair of the Board of Trustees, former President,
acatech – NATIONAL ACADEMY OF SCIENCE AND ENGINEERING
Global Representative & Advisor,
Plattform Industrie 4.0
Industrie 4.0 has been conceived as reaction to the global economic crisis of 2009 and its far-reachingeconomic and societal consequences. The intention was to use the opportunities of the second wave ofdigitalization in order to ensure competitiveness and resilience of the economy through increased adaptabilityof products, processes and organizational structures.
In addition, a phase of sustainable growth should be established, in which ecological (e.g. circular
economy), and social or societal challenges (e.g. the future of work) were addressed equally to the economic
impact. Of course, the transformation process of the fourth industrial revolution must first of all provide specificbenefits to individual users and citizens. Human-machine interaction (HMI) plays a central and crucial role inthis process, particularly due to the increasing intertwining of natural and Artificial Intelligence.
In 2016, the acatech Impulse Innovation potential of human-machine interaction already clarified: “Thefocus of HMI is shifting to the human user”.1 Therefore, we were pleased to follow our Japanese partners’
suggestions and examine HMI in the context of solving social and societal issues.
While there are similar challenges the Japanese and German society is confronted with (e.g. aging
population, skills shortage, etc.) there are also diverging societal trends and culturally related different
problem-solving approaches that must be analyzed and understood.
One important outcome of this paper is to clarify and highlight the similarities and difference s between thetwo country-specific perspectives in the context of HMI in order to identify common areas of cooperation for
future German-Japanese initiatives.

1

acatech 2016, p. 9.

5

Dr. Youichi NONAKA, Co-Project Leader
Senior Chief Researcher,
Center for Technology Innovation - Production Engineering,Research & Development Group,
Hitachi, Ltd.
Member of Robot Revolution & Industrial IoT Initiative
Social subjects such as resource depletion, environmental burdens, declining birthrates and aging areglobal-wide challenges for the establishment of sustainable societies. Japan also focuses on those subjects
since we have real affections and incidents due to operational errors and/or by machine deterioration in oursociety.
Furthermore, even in this 4 th industry evolution era, we feel it is not enough to define the problems andimpacts of those social subjects for our society of current and of the future. Many publications tend to
introduce new technologies such as Artificial Intelligence (AI), Software Defined Control (SDC), 5G
communications, etc., not to focus on challenges and policies based on sociological point of view andorganizational point of view. This is the motivation of the German and Japanese colleagues gathered for thisproject.
Based on this motivation, this paper focuses on challenges and policies with some quantitative researches
and assumptions with some use cases delivered from Germany and Japan. It was a quite valuable experience
for us to feel the similarities and differences between the German and Japanese perspectives on how to grasp
the problem, which is caused by the historical background and social structure of Germany and Japan, eventhough we aimed the same goal.
We expect that this paper will provide an opportunity to connect social subjects with the everyday issues
confronting us. And we hope that our daily activities will contribute in a positive and better way in striving toestablish a sustainable society.

6

Executive summary

Executive summary
A maturing workforce and aging machines and infrastructure have the potential to cause social challenges
in countries such as Germany and Japan. In order to solve these problems, maintain a high level ofmanufacturing efficiency, and establish a sustainable society, these countries are increasingly turning todigital technologies, for example Cyber-Physical Systems (CPS), AI, and robotics. These next-generation
growth engines are being supported by initiatives like Industrie 4.0 in Germany and Society 5.0 in Japan.
In digital society, interacting with machines is becoming more and more similar to interacting with people.
As a result, the gap between humans and machines is permanently narrowing. This transformative power incurrent technological developments applies to all areas of life and has significant consequences oninteractions between humans and technology. General work routines and employment conditions are also
being impacted. This paper explores distinct questions on how Japan and Germany are responding to thesedevelopments:
 How will work (and the workplace) potentially evolve in the future?
 How do companies need to evolve their workforce?
 How do companies need to evolve their organization and processes?
In order to answer these questions, this paper is structured as follows:
Chapter 1 begins with a short introduction that describes the context and the focus of this paper.
In Chapter 2, we point out emerging issues in smart manufacturing, specifically by referring to societal
boundary conditions and circumstances in Germany and Japan.
In Chapter 3, we examine the future way of working in the context of Industrie 4.0 and social issues
regarding employment, working style, and human resource development. Statistical data on labor population,
quality, and productivity are brought in, as well as estimates of changes in labor structures from German andJapanese perspectives.
Chapter 4 sheds light on the anticipated increase in diversity – in regard to both humans and machines –in the near future, and it presents solution policies with sustainable innovations across the life cycle ofproducts and services. In this context, the chapter examines the emerging gap due to diversity and dis cusses
the opportunities and challenges of managing the real and cyber worlds. It also points out sustainable
innovations with diversity, drawing on country-specific case studies.
In Chapter 5 we focus on augmented human and self-learning machines collaboration – which is also
referred to as human-machine collaborated smart manufacturing – and the impact it will have on
manufacturing work in the future. Potential solutions may give rise to new styles of interaction. Additionally,
integrating human and self-learning machines will call for operations and management to be flexible andadaptable. A possible result could be the creation of better and more satisfying work.
Finally, in Chapter 6 we present our conclusions.

7Generally, digital technologies are expected to solve social challenges. Yet they will also require socialtransformation, since they will perform routine cognitive tasks that were previously the domain of humans.
Essentially, to establish a sustainable society, it is necessary for humans to be able to create high-valueadded work continuously and to be able to shift from work that potentially becomes obsolete to high -valueadded work at any time. Also, it is necessary for machines to not only carry out non-high-value-added work,
but to also be a mechanism of creating high-value-added work by interacting with humans. According to theserequirements, the digital transformation can enable a novel, human-centered manufacturing system in whichhumans concentrate on life-long skill improvement and continuously create high-value-added work.
Essentially, this system revitalizes human-machine interaction, allowing both humans and machines to playa role in shaping digital society.
Ultimately, we should strengthen the public interest in digital society, share knowledge acquired frominteractions between humans and machines, and work to establish a sustainable society that focuses onhuman well-being. We encourage members of the public to start considering and discussing the scenarios
and actions together for the future.
This collaborative project between Germany and Japan is valuable, as it takes into consideration
recognized similarities and differences between the two countries on how to deal with the issues at hand.
Although both countries are aiming for a similar goal, their historical backgrounds and social structures are
impacting their methodologies and focal areas in finding solutions. In closing, we encourage open discussions
among multiple countries in the effort to establish a sustainable society.

8Project teamProject team
Leader / Co-Leader•

Prof. Dr. Henning KAGERMANN

acatechGlobal Representative & Advisor of the Plattform Industrie 4.0•

Dr. Youichi NONAKA

Hitachi,Member of Robot Revolution & Industrial IoT Initiative

Core Project Team•

Prof. Dr.-Ing. Reiner ANDERLTU Darmstadt,
Head of the Research Council of the Plattform Industrie 4.0,
Member of the Steering Board of the Plattform Industrie 4.0•

Prof. Dr. Elisabeth ANDRÉUniversity of Augsburg•

Ms. Megumi ARATAME

Meiji University•

Prof. Dr. Andreas DENGEL

DFKI

•

Ms. Sarah HAAS

Infineon•

Mr. Koichi IWAMOTO

RIETI

•

Prof. Dr. Jun OTA

The University of Tokyo•

Dr. Matthias PEISSNER

Fraunhofer IAO•

Prof. Dr. Sabine PFEIFFERFriedrich-Alexander-Universität Erlangen-Nürnberg,
Member of the Research Council of the Plattform Industrie 4.0•

Dr. Carsten POLENZ

SAP,
Member of the Steering Board of the Plattform Industrie 4.0•

Prof. Dr. Peter POST

FESTO,
Member of the Research Council of the Plattform Industrie 4.0,
Member of the Steering Board of the Plattform Industrie 4.0•

Mr. Joachim SEDLMEIR

acatech•Dr. Xiaonan SHI

Mitsubishi Electric•

Ms. Tanja SMOLENSKI

IG Metall•

Mr. Daisuke TSUTSUMI

HitachiContributors•

Dr. Sheraz AHMED

DFKI

•

Mr. Mohammad AL-NASER

DFKI

•

Mr. Takenori BABA

Mitsubishi Electric,
Member of Robot Revolution & Industrial IoT Initiative•

Dr. Martin ECKERT

Hitachi•Mr. Mitsushiro FUJISHIMA

Mitsubishi Electric,
Member of Robot Revolution & Industrial IoT Initiative
9

10

•

Mr. Josef HAID

Infineon•

Mr. Kazuya KAWAI

Information-technology Promotion Agency, Japan (IPA),
Member of Robot Revolution & Industrial IoT Initiative•

Emeritus Prof. Fumihiko KIMURAThe University of Tokyo,
Member of Robot Revolution & Industrial IoT Initiative•

Dr. Hitoshi KOMOTONational Institute of Advanced Industrial Science andTechnology, Japan (AIST),
Member of Robot Revolution & Industrial IoT Initiative•

Mr. Matthias LIESKE

Hitachi•Prof. Dr. Michiko MATSUDA

Japanese Standards Association (JSA),
Member of Robot Revolution & Industrial IoT Initiative,
Kanagawa Institute of Technology•

Mr. Kazuo NAKASHIMA

Robot Revolution & Industrial IoT Initiative•

Dr. Takehiro NIIKURA

Hitachi•Mr. Tobias PAULUS

Infineon•

Ms. Natalie SCHNELLE

SAP,
Member of the Plattform Industrie 4.0•

Dr. Kiyotaka TAKAHASHI

Hitachi•Ms. Yumiko UENO

Hitachi•Prof. Dr. Yasushi UMEDA

The University of Tokyo

Introduction

1. Introduction
Globalization has accelerated in each of the three industrial revolutions that have occurred up to now, andmany people have moved across national and international borders for employment. This readiness torelocate became the foundation for rapid economic growth in both Germany and Japan. When the proportionof children (0 to 14 years of age) in the total population is less than 30 percent, and the proportion of seni ors(65 years and older) is less than 15 percent, an economy is expected to grow dramatically. Such a period of
growth potential is called the demographic window of opportunity. According to the National Intelligence
Council (2012), Germany’s window started before 1950 and ended in 1990; in Japan, it was from 1965 to
1995; in the US from 1970 to 2015; and in China from 1990 to 2025. These figures show that developed
countries such as Germany and Japan are currently positioned in a post-window phase. What is required isan economic growth initiator separate from the window.2 In reality, these societies face issues that could
cause social challenges associated with a maturing workforce and aging machines and infrastructure. Also,
productivity declines can be expected in an older population due to decreased vision, lowered reactive ability,and diminishing physical strength. In Japan, for example, accidents increasingly occur as a result of operator
error and/or machine deterioration.
In order to solve these problems, maintain efficiency, and establish sustainable societies, these countries
are turning to digital technologies, such as CPS, AI, and robotics. These next-generation growth engines arebeing supported by initiatives like Industrie 4.0 in Germany and Society 5.0 in Japan. Both initiatives aim tosolve social problems and stimulate economic growth with the ultimate goal of creating a human -centeredsociety that integrates cyber and physical space to achieve the United Nations (UN) Sustainable Development
Goals (SDGs).3 As a higher degree of digitalization also correlates with increasing energy consumption,
related environmental consequences (e.g., carbon dioxide emissions) must also be taken into account for thesustainable development of societies.
This paper focuses on aging employees, declining birth rates, skills of young workers, and the resulting
challenges for the factory of the future. The discussion seeks to establish how these problems should be
defined and what new approaches in HMI can contribute to achieving the goal of sustainable societies. In
interactions with machines, human behavior and individual capabilities are often limited by traditional
structures and systems, based on standardized machine interfaces. Against the background of a qualitativechange of humans and machines, the hypothesis is that a novel form of human-machine symbiosis has to be
defined that involves different population groups – such as older and younger people, disabled people, andpeople from different cultural backgrounds – along with old and new machines, and machines with different
specifications. In addition, new strategies and technologies are needed to harmonize and revitalize HMI.
In cooperation with partners from science and industry in Germany and Japan, this study intends to addressthese questions, presents several aspects for discussion, and offers different approaches to solving the
problems from the perspectives of these two countries.

2

See National Intelligence Council 2012.

3

See Akaishi 2018, UN 2019.

11

2. Emerging issues in smart manufacturing
Hardly any technological development is currently changing our lives and thinking as much as the processof digitalization. This phenomenon is impacting entire economies and how we work .4
The far-reaching, technology-induced changes in the production sector are subsumed under the term
Industrie 4.0. The integration of Cyber-Physical Systems (sensors, actuators) leads to a comprehensive(inter-)connection of single components and whole production systems. As a result, it is not only possible toleverage optimization potential in traditional production routines, but also to realize flexible, value -added
networks and innovative business models. This process of change is further accelerated by AI – and machinelearning (ML) in particular.5
Nevertheless, today we are still at the beginning of this journey, which will lead to a powerful and disruptive
transformation process that also impacts our relationship with the machines around us. At the heart of thisdevelopment, machines must be seen as serving humans and society. The central focus of HMI is shifting to
the human user, whereby (self-)learning machines adapt more and more to the individual competencies, tasks,
and needs of the users, instead of requiring them to stick to rigid and fixed control schemes.6
As a consequence, interacting with machines becomes increasingly similar to interacting with humans.
Therefore, the gap between man and machine is permanently narrowing. Every one of us is alreadyconfronted with HMI – even in our private lives. Hearing aids, wearable sensors, and collaborative robots are
just few typical examples for the ubiquitous interaction with technical devices in daily life .7
It is expected, however, that the transformative power of the current technological developments applies
to all areas of life. This particularly includes developments in the field of AI, as well as innovative concepts
and applications of HMI. Equipping previously analogous (production) facilities or systems with integrated
capabilities for processing and communication affects interaction between technical systems and machines(M2M). Additionally, it also has significant consequences on the interaction between humans and technology.
That, in turn, impacts employees’ general working routines and conditions.8
All these consequences depend highly on societal boundary conditions and circumstances. This study
portrays the situation in Japan and Germany with a variety of examples and case studies to highlight how
these two societies are facing the upcoming changes.
In each of the next three chapters, we will explore distinct questions and look at how Japan and Germany
are responding to them: How will work (and the workplace) potentially evolve in the future?
 How do companies need to evolve their workforce?
 How do companies need to evolve their organization and processes?
4

See Allianz Industrie 4.0 Baden-Württemberg 2017.

5

See also PLS 2019.

6

See acatech 2016.

7

See acatech 2016.

8

See Gorecky et al. 2014.

12

The future of work

3. The future of work
In the context of digitalization and Industrie 4.0 9, working methods are referred to as “Arbeit 4.0” or “Work4.0” in Germany, in which other trends and drivers like globalization, demographic, cultural and societal
changes are also considered, including aspects of training and co-determination.10 As a consequence of thedigital transformation, it can be assumed that employees are being confronted with significant changes in theway they work and collaborate – both with humans and with machines.11 In Germany, social partners, suchas work councils and management or trade unions, have realized that the future of work has a significant
impact on employees, collaboration and on the company’s organization. Moreover, it also affects the qualityof work, job satisfaction, health, qualifications along with interaction between humans and machines bycreating new forms of collaboration.12 Therefore, in this chapter, we focus on social issues revolving aroundemployment, working methods and human resource development and provide some statistical data aboutlabor population, quality and productivity, as well as some forecasts about changes in labor structure that are
being induced by the digital transformation in society.
3.1. Population change, labor market, productivity and quality of work
In Japan and Germany, a decrease in population of productive-age has already begun. In addition, a rapid
advance in population decrease and aging of society by lower level of total fertility rate compared with aworldwide level is observable (Table 3-1).13
Table 3-1. Changes of populations in major countries (Source: Atkinson 2019, p.24).
Population [Unit: in thousands]
Country name
Increase-decrease
2016
2060
rate [%]
USA
322,180
403,50425.2
China
1,403,500
1,276,757
-9.0
Japan
127,749
86,737-32.1
Germany
81,91571,391-12.8
UK
65,78977,255
17.4
France64,721
72,06111.3
India
1,324,171
1,745,182
31.8
Italy
59,43054,387-8.5
Brazil
207,653
236,014
13.7
Canada36,29045,53425.5
Korea
50,79247,926-5.6
Russia143,965
124,604-13.4
Australia
24,12635,78048.3
Spain46,348
43,114-7.0
Mexico127,540
166,111
30.2
World wide
7,466,964
10,165,231
36.1
9In terms of innovation policy, this development is addressed in Germany with the "Strategic Project Industrie 4.0", see also

Kagermann et al. 2013.
10

See BMAS 2017, Hoose 2018.

11

See Allianz Industrie 4.0 Baden-Württemberg 2017.

12

See Botthof/Hartmann 2014.

13

See Atkinson 2019.

13

As the background, we observed the following: Germany has been demonstrated a trend of relatively small or even negative population growth since
the early 70 th.14
 Japan was the first country where the number of people of 64 was more than 20 percent. 15
 Over time, both countries have learned to deal with the related social challenges.
 One of the differences between the two countries is the migration background. In Germany, more than
23 percent of the people have a migration background.16 In Japan, however, less than two percent havea migration background. 17Generally, the process of the digital transformation changes our society rapidly, but according to theeconomic outlook published by OECD, the overall labor productivity growth decreased over the last few years
in most OECD countries. A slightly decrease can also be identified both for Germany as well as for Japan(see Figure 3-1).
There are various interlinked causes for this productivity slowdown including, especially, the effects of thefinancial crisis and the subsequent slow recovery of the global economy.18

Figure 3-1. Labor productivity growth in OECD countries(Source: OECD 2019, p. 57).

14

See World Bank 2019.

15

See UN 2017, p. 238.

16

See Destatis 2018a, p. 35.

17

See MIC 2019a.

18

See OECD 2019, p. 56f.

14

The future of work

Figure 3-2 indicates the ranking of unit labor costs in the manufacturing industry. 19 It is a comparativeindex (Germany is standard = 100). It reveals the high unit labor costs in Germany, whereas in Japan, thesecosts were about 18 percent below the German level in 2016. Even the quite high level of productivity inGermany fails to fully compensate for the drawback of these high labor costs, that, in turn potentially threatensthe competitiveness.20 The Japanese manufacturing sector, however, saw a drop in productivity and declined
steadily in a global view – in times of a weaker yen and the growth of technological advances in othercountries.21

Figure 3-2. International Comparison of unit labor costs in manufacturing(Source: Schröder 2018, p. 78).

19

See Schröder 2017, p. 78.

20

See Schröder 2017, p. 75; 78.

21

See Nikkei Asian Review 2017.

15

In addition, Table 3-2 indicates the ranking of quality of human resources. 22 Hereby, Japan took the fourth
place while Germany is ranked no. 11.
Table 3-2. Quality of human resources (Source: Atkinson 2018, p. 80).
Ranking
Country
Rating1
Finland85.86
2
Norway
84.54
3
Swiss
84.51
4
Japan
83.44
5
Sweden83.29
6
New Zealand
82.79
7
Denmark
82.47
8
Netherland
82.18
9
Canada81.95
10
Belgium
81.59
11
Germany
81.56
12
Austria
81.52
13
Singapore80.94
14
Ireland80.79
15
Estonia
80.63
16
Slovenia
80.33
17
France80.32
18Australia
80.08
19
UK
80.04
20
Iceland
79.74
24
US
78.86
32
Korea
76.89
34
Italia75.85
44
Greece
73.64
45
Spain
72.79

Risk of change and risk of automation
According to Arntz et al. 2016, OECD, there are two types of risks for workers in 2016: 50 -70% with “change
in tasks”, and 70 to 100% with “automation of tasks”.23 Nedelkoska and Quintini (2018) published the new
estimation of risks for 70-100% and 50-70% in OECD countries (Figure 3-3). The portion of employment
substitution risk is 70-100%, for 16% in Germany, 9% in US, 13% in Japan, and 14% in OECD average. 24
This estimate was a consensus by experts throughout the world. As for Japan, according to the Work Force
Survey 2017 by the Ministry of General Affairs, the number of work force was 67.2 million. As the risk of
70-100% is 13%, calculation of 67.2 million×0.13=8.7 million.

22

See Atkinson 2019.

23

See Arntz et al. 2016.

24

See Nedelkoska/Quintini 2018.

16

The future of work

Figure 3-3. Cross-country variation in job automatability, %age of jobs at risk by degree of risk
(Source: Nedelkoska/Quintini 2018, p. 49).

When we see these tables, it turns out that when it comes to productivity, Japan and Germany have agreat deal of room for improvement. Improving human skill-sets and the related changes in the quality ofhuman work are an area of high potential. Such human skills can be developed by digitalization as an enabler
for the competitiveness of industries in the situation of a decreasing population and a changing workforce.
In order to achieve real sustainable growth, it’s important to raise the potential growth rate by increasing
the investment and innovation.
For Germany and Japan, boosting digitalization is a favorable option to gather thought potentials.
Digitalization can help offer potential employees more high-value-added jobs and add higher value to theindustry itself in that human beings can assign to machines that can be better performed by those mac hines.
Society has to drive these changes so that humans and machines can develop together in a harmonized
way in terms of sharing of work-related tasks and supporting them. We have to ask ourselves what such achange in the world of work might look like. The following case studies describe the situation in the laborstructure of both countries and the required skill changes.

17Case study in Japan: Economic disparity by ICT investment – implications from US studies
For the US, Prof. David H. Autor of Massachusetts Institute of Technology revealed the mechanisms
and processes that lead to a structural change in the world of employment . The process of thereplacement of human workers by machines and economic disparity resulting therefrom are caused by
active investment in information and communication technology (ICT, see Figure 3-4).25 In Japan, there
is no comparable analysis, but the mechanism and process that structural change of employment, theprocess of the replacement of machine to human and economic disparity is thought to be almost thesame as US. This research output was the most significant output of a series of “the future of work”
research works, and a great deal of subsequent research has been strongly influenced by this research .

Figure 3-4. Smoothed employment changes by occupational skill percentile, 1979-2012(Source: Autor 2015, p. 20).
Data; Autor (2015) calculated using 1980, 1990, and 2000 Census Integrated Public Use Microdata Series (IPUMS)
Autor defined “skill percentile” as follows. The figure plots by “skill percentile” rank using a locally weighted smoothing regression(bandwidth 0.8 with 100 observations), where “skill percentiles” are measured as the employment -weighted percentile rank of an
occupation’s mean log wage in the Census IPUMS 1980 5 percent extract. The sample includes the work ing-age (1–64) civilian noninstitutionalized population with 48+ annual weeks worked and 35+ usual weekly hours. Weekly wages are calculated as annual
earnings divided by weeks worked. And in the followings, we call “low skill” or “ inexperienced” as meaning of that the skill percentile
is low, and so on.

25

18

See Autor 2015.

The future of work

Autor explains that the active ICT investments changed employment structure for more than thirteen
years. “Routine cognitive tasks” have been replaced by computers, but in the future, the same
phenomenon will continue to be implemented across the board. Even if “routine cognitive tasks” arehighly difficult, or many years of training for human is necessary to learn, it can be programmed as it isbased on logic. (Note: Academically, routine tasks are divided into cognitive tasks and manual tasks.
The former is being replaced by computers and the latter is being replaced by robots) . In the U.S.,
employees with middle skilled occupations are replaced by computers due to ICT investment. The
boundaries of continuing decrease of employment loss is moving towards higher skill positions.
As the number of inexperienced workers continues to grow, so too will the speed. The number ofhighly skilled workers will continue to increase, but the speed of the increase will decline. It is possible
to analyze this as follows. As technology progresses, the demand by companies for highly skilled workers
grows rapidly, but the supply of highly skilled workers for the labor market that fulfill company needs
declines. The increasing speed of highly skilled workers declines and wage increase. Most middle -levelskilled workers who lose their jobs transfer to inexperienced jobs. The total number of inexperienced
jobs, however, remains constant, and as a result, wages continue to stay at a low level and theemployment status becomes unstable.
Autor also explains that “(…) automation also complements labor, raises output in the ways that leadto higher demand for labor and interacts with adjustments in the labor supply” 26. He argues that thepolarization of the labor market is “(…) unlikely to continue very far into the foreseeable future” 27.

Case study in Germany: The future of employment
Frey and Osborne (2013) derive in their well-known study “the future of employment” that routinizedtasks will be substituted in the US. 28 Moreover, they estimate that 47% of the total US employment isalready in more than 70% risk category, which could potentially be automated in the next decade(s) .29
The result by Frey and Osborne has two important conditions. The first is to show the technical possibility.
For example, if automatic driving technology is available, it would take decades until all professional
drivers in the U.S. may be substituted. Nevertheless, these cases were counted as a possibility for
substitution by machine. The second condition is new employment by new industries were notconsidered. Frey and Osborne’s estimate extends to a practical number, based on calculation, of
employed workers who exist present and who may lose jobs in the future. They did not include thenumber of new employments that will be created by new future industries.
Once the Frey and Osborne estimate was published, The Ministry of Labor and Social Affairs ofGerman Government started the “Arbeiten 4.0” or “Work 4.0” project to confirm the numbers of theestimation in Germany. The Ministry commissioned the analysis to Zentrum für Europäische
Wirtschaftsforschung GmbH (ZEW). ZEW recalculated under the same condition of Frey and Osborne.

26

See Autor 2015, p. 5.

27

See Autor 2015, p. 5.

28

See Frey/Osborne 2013, Hirsch-Kreinsen 2018, p. 17.

29

See Ittermann/Niehaus 2018, p. 43.

19

Their paper indicated that the risk in US was 9% and in Germany 12% as of June 2015.30 The estimation
by Frey and Osborne was 47% but 9% by ZEW. How did this difference occur? The reason was thedifference of estimation method.
Frey and Osborne presumed a substitution of humans by machines in the context of jobs. However
ZEW distinguishes between jobs, work and tasks where jobs are broken into a series of tasks.They
assume that the portion of tasks done by machine will gradually increase and finally a single human job
may be replaced by machine. The estimation by Frey and Osborne differed because they presumed thata single human job will be substituted by machine in a single stage, which is considered as overestimated.
As for the process of substitution of humans by machines, the Institute of Arbeitswirtschaft undOrganisation, Fraunhofer (Fraunhofer IAO) has conducted a detailed research and analysis. Fraunhofer
IAO first surveyed various numbers of German companies, but could not find consistency. Second, when
Fraunhofer IAO surveyed the trends of German companies, they found consistency of the same activities
as follows: 1) Machines are used to support humans. 2) Humans will make efforts to increase their skills,
and companies provide skill training to employees. However, as the progress of technology is rapid,
several companies will substitute humans with machines in order to cut costs. 3) In the last stage, bothhumans and companies will cease making efforts to increase human skills.
Case study in Japan: The future of employment
A phenomenon happening in reality is a separation of a series of tasks divided into tasks done bymachine or human. As the separation progress, the human role becomes to do tasks which must be
done by human: It may be very detailed, delicate, non-routine, and creative. Human will be requested tohave more and more high skills. For these several years, AI engineers are developing the technologies
to replace the human artistic, cultural, and sensitive activities. These activities were thought to be an
output of human creativeness. But by monitoring these activities carefully, it was found that these
activities contained many routine tasks.
As technology progressed, routine tasks and physical strain and hard labor once had to be done byhuman, were eventually substituted by machine. This is the history of technology and will continue inthe future. To prepare for these changes, human must self-train during their entire life-time.
In Japan, Senior Researcher K. Iwamoto, the Research Institute of Economy, Trade and Industry(RIETI) has the following hypothesis regarding the process of substitution of humans by machines, after
many interviews of Japanese companies. 1) The first stage is, hard labor disliked by human is substitutedby machines. The aim of these introductions of machines is to support human, then human raise theirmotivation. 2) The introduction of machine progresses. Then humans are requested to perform morehigh skilled work and companies promote human resource development investment . 3) The rate ofmachines being introduced increases. Managers start to replace humans and layoffs will start. 4)
Ultimately, all humans are replaced by machines.

30

20

See Bonin et al. 2015.

The future of work

In a global perspective, according for the estimation by McKinsey Global Institute (2017), 400-800
million jobs will be automated and 75 million to 375 million workers (3 to 14 percent of the global
workforce) will have to retrain or learn to do new jobs or learn a new skill by 2030 .31
It is surprising that the hypothesis RIETI has and results of research output by Fraunhofer IAO are
the same, that is, the process of machines replacing humans is the same in both countries .
Overall, the research and analysis of estimation for employment in the future was drawn from three parts.
1) The decreasing tendency of routine tasks will continue. As technology progresses, more high skilled routine
tasks will be replaced by machines. 2) The number of unexperienced workers will continue to increase, but
as the technology progresses, the volume of tasks replaced by machines will increase. We must argue thatas machines’ abilities increase over time and at a faster rate than the increase in that of inexperienced
workers, it’s likely that workers will be replaced by machines. Therefore, we have to desig n harmonizedsystems for humans and machines, wherein machines support inexperienced workers who are striving to
improve their skills. By the support of the technology, the worker can follow the skill growth and grow fasterthan machines or other workers. This will lead to a situation where humans grow side by side with machines.
3) The number of highly skilled workers will continue to increase, but the need for highly skilled workers bycompanies will also grow. However, since the supply of highly skilled workers in the employment market will
be unable to match companies’ needs, wages will continue to increase.
3.2. Estimation of changes in labor structure due to digital transformation
According to the research and analysis worldwide, there is a trend that as t echnology progresses,
machines will replace humans for routine tasks. Measurements for the rate of routine tasks in each country
are conducted actively by experts around the world. Such a rate is expressed by what is referred to as Routine
Task Intensity (RTI). De la Rica and Gortazar (2016) published the results of RTI of OECD countries. 32
Hereby, the RTI is defined as follows:
𝑅𝑇𝐼𝑖 = 𝑅𝑖 − 𝐴𝑖 − 𝑀𝑖
Where 𝑅𝑖 ; 𝐴𝑖 ; 𝑀𝑖 correspond to the Routine (cognitive and manual routine), Abstract (cognitive andinterpersonal non-routine) and Manual (non-routine manual) tasks indexes respectively. 33
The RTI differentiates the countries, focusing on different stages in the de-routinization process. The U.S.,
for example, represents a highly de-routinized country (RTI=-0.39). Similar to other central European
countries, Germany (RTI=-0.12) is characterized as medium de-routinized country. Japan (RTI=0.26),
however, is positioned in the group of so called low de-routinized countries. 34

31

See McKinsey Global Institute 2017, p. VI.

32

De La Rica/Gortazar 2016.

33

De La Rica/Gortazar 2016, p. 7ff.

34

De La Rica/Gortazar 2016, p. 10.

21

The study of De la Rica and Gortazar (2016) also shows that e.g. workers in the U.S. have adopted
technology more intensively in comparison to central European workers. Moreover, the ICT-adoption in Japanis slightly faster compared to the PIAAC (Programme for the International Assessment of Adult Competencies)
sample.35
As a result, the total number of workers does not seem to change much in Japan. Howev er, when looking
at the type of labor, there is a possibility that the required skills will not correspond to the supply and demandof labor. For example, many workers may lay off before the emergence of new labor demand due to the riseof new industries.
It is a case of whether the total number of workers of a company will increase or decrease, and if humans
doing routine work are ultimately replaced by machines. There is a possibility that as the competitiveness ofthe company and sales increase, the company may expand. Then the total number of workers may increaseby introducing machines. On the other hand, even when technology progress, a company may continue tokeep the old-fashioned work style by not introducing new technology. Then the productivity and thecompetitiveness of the company may decrease, and a large scale of layoff may happen.
When new technology is introduced, the phenomenon of workers loss is mainly focused. But introducing
new technology has another effect that creates new jobs. For example, a young person who did not have theskill to work under the old technology, such as using an oil tooling machines in a factory, may be able to work
under the new technology, such as development of application software by personal computer. For theconclusion of this matter, the most serious problem is how to deal with the person who never caught up with
the new technology, after a series of re-training and re-learning. For this problem, Japan, same as Germany
has not yet reached to an answer.
Case study in Germany: Potential substitution of parts of the human workforce
The assumptions of the potential substitution of parts of the human workforce have also been taken
up in macroeconomic labor market studies for Europe, concluding that employment will decrease as ashort-term compensation through novel activities wouldn´t be feasible.36
Similar results can be found in the German context.37 The risk of substitution generally falls with therequirement level of the task. 38 Figure 3-5 shows which requirement levels of human work are most
likely to be substituted by technical systems. 39 Between 2013 and 2016 that the gap between high andlow levels of requirement had become even larger.

35

De La Rica/Gortazar 2016, p. 11.

36

See Ittermann/Niehaus 2018, p. 43, Bowles 2014.

37

See Dengler/Matthes 2018.

38

See Dengler/Matthes 2018, p. 1.

39

The requirement levels are defined as follows Unskilled: Helpers with no vocational training or one -year vocational training;

Skilled: Professionals with at least two years of vocational training or a professional qualifi cation at a vocational school orcollege; Specialists: Master or technician education or advanced technical school/bachelor’s degree; Experts: University degr eeof at least four years (Dengler/Matthes 2018, p. 5).

22

The future of work

Figure 3-5. Substitution potentials by requirement level in %, 2013-2016 (Germany)(Source: Dengler/Matthes 2018, p. 1).

Various studies also anticipate – at least from a short term perspective – a potential risk of substitution
for qualified, non-routinized occupations, characterized by a high level of creativity and interaction – notonly on the shop-floor in the industrial sector but also in the context of administration, development andmanagement.40 There are even scenarios, which forecast a potential decrease of nearly 60% of theemployment (mostly routine jobs) in Germany.41 For an overview of estimated substitution potentials by
occupational segments, see Figure 3-6. Again, it can also be seen, that not only the occupations within
the production processes in the context of Industrie 4.0 are affected but also the service sector.42

40

See Hirsch-Kreinsen 2018, p. 17.

41

See Ittermann/Niehaus 2018, p. 43, Brzeski/Burk 2015, p. 3.

42

See Allianz Industrie 4.0 Baden-Württemberg 2017.

23

Figure 3-6. Substitution potentials by occupational segments in %, 2013-2016 (Germany)(Source: Dengler/Matthes 2018, p. 6).

In the course of the digital change, however, companies are also able to react faster and more
precisely to changed and changing customer needs and new market conditions. It is already wellunderstood that a fast implementation of data-based business models and a high level of willingness tochange, flexibility and adaptability of organizations and its employees is crucial for success in the faceof global competition.43 Key factors in the successful introduction of Industry 4.0 include the acceptanceof new technologies by employees and the design of attractive forms of work. 44
At the same time, the higher degree of flexibility, in turn, opens up the potential ch ance for workers
also to achieve a higher level of work-life balance and to safeguard their long-term employability bypersonalized re- and up-skilling measures. In this context, the ability of workers to learn (and retrain)throughout the span of their careers is a key to ensuring their future employability (lifelong learning). For
this, both companies that provide the corresponding educational and training offers and their employeesthat are meant to benefit from these measures have a shared responsibility.45
The phenomenon that it is not only routine occupations and tasks that require minimal qualifications arepotentially the ones that bear the risk of replacement is evident in the following Japanese case study, whichrefers to the application of AI in the Japanese health care sector.

43

See Jacobs et al. 2017, p. 9, Lanza et al. 2018.

44

See Abel et al. 2019.

45

See Jacobs et al. 2017, p. 9.

24

The future of work

Case study in Japan: AI applied into medical careThis case study describes an AI application to medical hospitals in 9th Academic Promotion Council
Report by Japan Medical Association Academic Promotion Council.46 AI learns treatment results fromall over the world and advises doctors of the treatment policy for their patients. AI plays an “advicegiving” role for humans, whereby humans are responsible for the final assessment.
The report stated that Japan, which has an unprecedented aging population, may contribute to theworld by pioneering cutting-edge medical care using Artificial Intelligence. There is a report that themisdiagnosis rate has decreased by 85% due to cooperation between doctors and AI. In the 2016
metastatic breast cancer diagnosis contest (Camelyon Grand Challenge), the misdiagnosis rate ofArtificial Intelligence was 7.5% and the misdiagnosis rate of pathologists was 3.5%. The misdiagnosis
rate decreased to 0.5%.
The report also explained “even if individual AI technology is developed, it should not be put topractical use unless the effectiveness and safety of AI are sufficiently secured. In particular, the healthcare field is related to human life, and ensuring effectiveness and safety is extremely important. And nomatter how skillful it is, the presence of a doctor will continue to be necessary. But what doctors do mayvary greatly” 47. In the report, Prof. Tsumoto, Shimane University mentioned that it is noticed that Artificial
Intelligence to support the business is changing the way of the business itself.
The same can be said for the factories, where the cycle of receiving “advice” from AI, with the final
assessment being made by humans, could establish a good relationship between AI and human.
When the above-mentioned AI or robots replace the even “more skilled routine cognitive tasks”
performed by human beings, economic disparities in society will grow. Then the phenomenon occurs
where the total number of employees increase in low skilled low wage labor market, wages do not rise,and employment becomes unstable. This happens when jobs for medium-skilled employees are lost,and fall into the lower skilled positions, although the total amount of low skill jobs remains unchan ged.
The number of employees employed in occupations that require low skills has been rising continuously,
as well as accelerating.

46

See JMA 2018.

47

See JMA 2018, p. 38.

25

Case study in Japan: Potential substitution of parts of the human workforce
As for Japan, the Cabinet Office of Japanese Government published Economic and Financial White
Paper 2018 with a corporate consciousness questionnaire survey in February 2018. According to theresults, concerning the work expected to be reduced when IoT (Internet of Things) and AI are introduced,
the order of “general affairs, receptionist, secretary”, “general affairs, personnel management,
accounting”, “manufacturing, production process, management” (Figure 3-7).

Figure 3-7. Expected increase and decrease in professions in terms of the progression of AI and IoT implementation(Source: Cabinet Office 2018, p. 141).
*administrative profession refers to survey analysis or legal etc. Technical spec ialist refers to research and development, system design
etc. As for administrative specialist, customer service, interpersonal service, companies responded both to expectations for increase
and decrease. Therefor the categories appear in both.

26

The future of work

The business that the company is considering to substitute for AI is in the order of “Accounting,
Finance, Taxation”, “Constant Document Production”, “Labor Management Relationship”, “Adjustment of
Schedule etc.”, “Manufacturing and Assembly” (Figure 3-8). When the introduction of IoT and AI progress,
the management of Japanese companies expect to reduce skilled workers in the factories second to
office clerks, in regard to personnel reduction.

Figure 3-8. Occupations likely to be replaced by AI (in%)(Source: Cabinet Office 2018, p. 142).

According to Gini coefficient of Japan, 48 the time-change for economic disparity has been relatively
small until now in the trend of developed countries. But as the era of full dissemination of RPA (Robotic
Process Automation) arrives, there is a possibility that the economic disparity will expand at once .
Case study in Japan: Required skill change in office workers
Currently, the replacement of routine cognitive task to RPA is in progress in the administrative divisionof Japanese company’s offices. In particular, the banking and finance industry is most active in thisrespect. From recent press announcements by three megabanks, all three megabanks reported thatabout 30,000 personnel were to be reduced by ICT investments. Those who take part in the routine
cognitive task are primarily regular employees responsible for clerical jobs and non -regular employees.
As RPA progresses, presumably, Japanese companies will protect employment by realign personnel for
regular employees where as non-regular employees will be dismissed. Just by taking a look at
recruitment of new graduates in the spring of 2019, we can see that the ba nks have greatly reduced therecruitment of regular employees for clerical jobs.

48

See METI 2017.

27

In addition to the banking and finance industry, local governments are also active in implementing
RPA. Local governments have many routine cognitive tasks that deal with pres cribed forms, many ofwhich could be replaced by RPA. Local government have strong needs to cut employment wages due tobudget shortfalls.
RPA is now extending to manufacturing companies in Japan. For example, Nitto Denko manufacturesswitchboards and has 150 employees; design engineers have many time to print out design drawings
currently, but Nitto Denko has introduced RPA into the printing process, amounting to 20 hours cut inprinting time per month.49 As it can be seen in this example, even in manufacturing companies, someprocess are routine office work which can be replaced by RPA.
Thus, in Japan, the banking and finance industry and the local government are now leading ahead in
the implementation of RPA and eventually expanding to manufacturing and other industries, the “higher
skilled routine cognitive office work tasks” are about to be replaced by computer.
These employees are expected to enter the labor market looking for unskilled low wage jobs just to makea living, and in this low skilled low wage labor market, the unemployed Japanese will be competing forjob opportunities against the resident foreign workers. In Germany, more foreign people are employedthan in Japan.
Case study in Germany: Required skill change in field workers
Although autonomous systems potentially relieve the human operators in certain areas, such as inquite monotonous daily job scheduling operations, the volume of complex tasks is increasing at the sametime. Consequently, there is a higher demand for willingness to learn – e.g. via various assistive systems
or further qualification programs and concepts in order to keep pace with the changed requirements. All
employees involved in the production process must increasingly adopt interdisciplinary forms of work.
This is due to the fact that co-operation is required, especially with IT. Moreover, it is obvious thatdemand is likely to shift from foremen to IT specialists.50
In the past, nearly every technological development or progress has been accompanied by new skills
needs. In the context of the digital transformation, Industrie 4.0 and new forms of HMI, companies areespecially challenged by the fact that these requirements are now changing at an accelerated rate and,in addition, almost all activities are affected.51 As a consequence of the short innovation cycles and thechanging requirements, precisely tailored employee training measures are necessary. Therefore, acomprehensive analysis of the skills an individual worker or employee possesses is crucial. In a secon dstep, those skills that are needed have to be identified. All learning measurements with respect toqualifications and education should be modified in order to guarantee systematic and individually tailored
professional development measures that both meet the needs of the individual worker and the whole
company.52 While conceptualizing an adequate (re-)qualification and learning strategy, the potential

49

See Iwamoto 2018.

50

See BMWi/BMAS 2016, p. 14.

51

See Jacobs et al. 2018, p.27.

52

See Jacobs et al. 2017, p. 9.

28

The future of work

diversity (esp. between younger and older people) has to be considered.53 In terms of the technologyrelated skills, developing skills across IT, and especially AI is a necessity.54 This insight, however, hasbeen far from acknowledged across the entire German economy and across the board in the workforce.
While the spread of the term “Industrie 4.0” certainly lends an enormous amount of attention todigitalization, there are still companies that are failing to systematically grasp the need for action whenit comes to fostering development in terms of skills and qualifications. The fast and well-founded training
and retraining is, however, crucial for a successful transformation into deployment, employability and“good work”.55
In order to enable and provide the workforce with greater qualifications (and students as a future
workforce), there are various strategies and measures that can be undertaken (e.g. seminars, workshops,
business games or practice-related case studies). Moreover, during the last years, a promising approachhas been developed, called learning factory 56 . Especially in Europe, a variety of different learning
factories has been established that have taken specific forms, focuses and purposes. All learning
factories, however, have in common, that a hands-on qualification for the human worker is pursued.57
A key advantage of a learning factory is the fact that certain training and demonstration activities can be
carried out without the risk of potential production downtime, as would be possible in the real production
environment. Recently, more and more learning factories or labs have been focused on Industrie 4.0related topics. The approaches, however, mostly fail to combine both learning aspects of humans andmachines.58
Case study in Japan: Required skill change in field workers
In Japan, there is a history of cherishing skilled field workers in the factories. The new digitaltechnology now being introduced to the factories isn’t replacing working with computers but makes thebest use of these skilled field workers. When IoT is introduced, the workers acquire the new skill while
receiving little training, so the problem of re-education or retraining of field workers has not become
obvious in Japan, rather, the issue of AI that is to be introduced in a few years. The new digital technology
now introduced to the factories in Japan is the “visualized” system. Therefore, skilled field workers arestill responsible for looking at the displayed visualized data, exploring the cause of the failure of
production machines and devising countermeasures. However, the operations that is based on the past
precedent, such as learning the precedent, then judging measures from the displayed data, should be
replaced by AI in the near future.
In Japan, the number of large manufacturers that are fully introducing new technologies to theirfactories and proving the output results is still a few. According to Fujitsu executives,59 emphasis is53

See Majkovic et al. 2018.

54

See also PLS 2019.

55

See Jacobs et al. 2018, p.27.

56

See Schallock et al. 2018, p. 28, see also the “Case study in Germany: Learning Factory” in Chapter 0.

57

See Schallock et al. 2018, Elbestawi et al. 2018.

58

See Ansari et al. 2018a, Schallock et al. 2018.

59

See Iwamoto 2018.

29

placed upon the purpose of new digital technology to “empower humans” and lessen the burden of theemployees, the company’s IoT system support a lack of skills of field workers due to skilled field workers’
population decline and support a decline the level of skills of field workers due to aging, and support
field workers as many-various and small-volume products is increasing. Mitsubishi Electric's executives
say that in the 1990’s investment for mechanization, 60 automation and labor saving in the factories wereactive, but now the atmosphere is introduced to leave jobs suitable for machine (human) work to machine(human), which is said as “harmony between human and machine”. The executives of DENSO, 61 (asupplier of Toyota) stress the fact that “the concept for the IoT system of DENSO is human-centered”.
DENSO positions the skilled field workers as company treasure to create competitiveness. It is worth
pointing out that visualization by digital technology alone is insufficient for the skilled person to provide
sufficient performance in the field. And they state that it’s important to actually measure shortcomings in
production equipment and to formalize how the shortcomings are solved by skilled workers. Both
Mitsubishi Electric’s e-F@ctory and Hitachi’s Lumada,62 which are representative IoT platforms in Japan,
are designed on the same idea. In addition, the executives of Nippon Steel Co. emphasize 63 “wecontinue to invest in issues where the number of skilled workers is rapidly decreasing in factories and it
is difficult to ensure manufacturing quality".
As can be seen from these cases, maintaining and expanding human capabilities is one of the main
targets of digital investment in Japanese companies.
RIETI conducted the questionnaire survey to comprehend the trend of IoT in Japanese industry from
August to October 2017 targeting 10,075 Japanese companies, and collected responses from 1,372
companies (recovery rate 13.62%).64 Through the introduction of new digital technology, 34 companies
replied that the number of employees is “decreasing”, and 43 companies answered that it is “increasing”.
Technical experts, such as data engineers, are needed to operate new digital system. The survey
revealed, not only the technical experts but also the needs for administration who manage them andoffice clerks who support the technical experts are increasing. Meanwhile, in the administrative division sof the banking and finance industry etc., computerization of routine cognitive tasks is ongoing andclerical office workers is reduced continuously. When calculating the present increases and decreases,
the increases are greater than the decreases, which means that, Japanese companies are more eager
to digitalize factories in the manufacturing sector than the office. This may apparently be the case, wewill need to monitor this trend carefully in the future.

60

See Iwamoto 2018.

61

See Iwamoto 2018.

62

See https://www.mitsubishielectric.co.jp/fa/sols/ and https://www.hitachi.co.jp/products/it/lumada/index.html.

63

See Iwamoto 2018.

64

See Iwamoto/Tanoue 2018.

30

The future of work

Case study in Germany: A positive scenario against the previous discussions
In Germany, there is a scenario that the substitution potential can not immediately be equated withthe probability of occurrence. In addition, the current implementation of modern technologies or Industrie
4.0 solutions and automated projects in the area of HMI shows that today's activity c annot be fully
automated at reasonable costs. 65 In contrast to an pessimistic view, that i.e. refers to potential
substitution effects of human workforce and a devaluation of industrial work, the positive scenario
highlights the potential aspects or consequences of the implementation of modern technologies andIndustrie 4.0 systems from an optimistic perspective, e.g. by referring to improvements incompetitiveness, quality of living, work-life balance etc.66
Therefore, in the context of this optimistic scenario, positive labor market effects are expected. This
is i.e. forecasted in a study by the Boston Consulting Group, which assumes employment growth of 6%
in Germany within the next ten years. The positive effects are primarily based on the increasing demandfor highly qualified industrial work, in mechanical engineering, in the automotive sector and in electricalengineering.67 A rather moderate technological change in the industry and the successive enrichmentof work and production processes by new technologies is often regarded as basis for this development .68
Moreover, it is argued that even the low skilled workers are still needed and that nearly all kinds ofroutine work entail non-routine elements like experience-based knowledge that is hard substitute bytechnical systems.69 In addition, a general overestimation of technological potentials and implications
is stated, that in turn lead to potentially misleading conclusions as described in the pessimistic or
negative scenario.70.
This positive scenario also features a new look or perspective at the interactions between humans
and machines and the related distribution of control and/or responsibility of the technical and personal
systems. As orchestrators of technical systems, highly qualified and skilled people are in the center ofindustrial production system. In case of problems or breakdown, they are able to intervene, thanks totheir previous experience. All in all, the qualified workers are supposed to be fully in control over theentire work and production processes. If necessary, a flexible and adequate support by technicalassistance systems is provided.71
Together with the change in the interactions of humans and technology in novel forms ofcommunication and collaboration, the restructuring of work and lifelong learning that is necessary tocontrol the also changing Cyber-Physical Systems (CPS), such assistance systems create theprerequisites to maintain and continuously improve the physical and mental performance capabilities ofhumans.72
65

See Allianz Industrie 4.0 Baden-Württemberg 2017.

66

See Ittermann/Niehaus 2018, p. 39f.

67

See Ittermann/Niehaus 2018, p. 39f., BCG 2015.

68

See Ittermann/Niehaus 2018, p. 40., Vogler et al. 2016.

69

See Pfeiffer/Suphan 2015, See Ittermann/Niehaus 2018, p. 53.

70

See Hirsch-Kreinsen 2018, p. 17.

71

See Ittermann/Niehaus 2018, p. 41f.

72

See Becker 2015, p. 25.

31

Today, it is generally accepted that the individual productivity of the people does not primarily dependon the chronological, but from the biological age. In particular, assistance systems allow machines thatinteract with people to make the work demographically sensitive and to reduce strain. 73 Together withspecific measures of work design and initiatives for competency development, an interactive
collaboration between humans on the one side and technological systems on the other side creates newopportunities for companies even to benefit from the process of demographic change. In times of aprevailing shortage of skilled workers and an increasing diversity of employees (age, gender, cultural
background), Industrie 4.0 enables diverse and flexible career models and thus sustainable productivity
through work.74
In summary, one can conclude that the positive scenario is also supported by forecasts, which suggest
an upgrading of activities and qualifications. According to Ittermann/Niehaus (2018, p. 40), upgrading
can be understood as a process of enriching activities and abilities that tends to affect all groups of
employees.
Case study in Germany: Hybrid teams of humans, robots & virtual agents in a production setting 75[Concept video available from below link;
A concept video is available from below link:bremen.de/de/mediathek/videoarchiv/hysociatea-ausgewaeh.html]

https://robotik.dfki-

In Germany, the project “Hybrid Social Teams for Long-Term Collaboration in Cyber-physicalEnvironments” (HySociaTea) demonstrates that – in contrast to any risks of substitution – recent
developments in the human-machine interaction are enabling new forms of cooperation in hybrid teams.
In this project, funded by the German Federal Ministry of Education and Research (BMBF), research ers
at the German Research Center for Artificial Intelligence (DFKI GmbH) aim at realizing and examining
the collaboration of augmented humans with autonomous robots, virtual characters and SoftBots in order
to work out common tasks and to meet future production requirements that are characterized by a highdegree of complexity and flexibility.
Hereby, not only the technical feasibility is analyzed, but also the development of (robotic) team competencies and intelligent multi-agent behavior. By proactively assisting the humans in the production
process, they are seen as partners without being directly instructed. This, in turn, has important
implications on (future) decisions on team organization and configuration.
Characterized by the high level of cognitive abilities and flexibility, humans are at the center of thehybrid teams, while robots are responsible for the physically demanding tasks. The digitally available
information, moreover, is provided on an interface of the virtual agents and the SoftBots76 aggregate
the data that is produced by the other team members, update databases and are responsible forproviding meaningful, refined data.
73

See Becker 2015, p. 26.

74

See acatech 2016, p. 16.

75

This case study is described in Schwartz et al. 2016.

76

SoftBots can be defined as purely software-based agents or computer programs that can act autonomously in virtual worlds.

32

Gap emerged due to diversities & approaches of sustainable innovations

4. Gap emerged due to diversities & approaches of sustainable innovations
This chapter discusses the anticipated increase in the diversification of workers and machines in the near
future and promotes solution policies with sustainable innovations across the life cycle of products andservices.
4.1. Gap emerged due to diversities
Since the industrial revolution, companies have striven to standardize production in order to achieve highproduction volumes at high speeds. However, nowadays, standardization has reached its limits. The declining
population has led to a diversified workforce with varied capabilities in the manufacturing fields. Even in thecase of machines, old machine tools are being used alongside AI robots (Figure 4-1).

Figure 4-1. Possible changes on workers and machines in factories.
(Source: https://www.youtube.com/watch?v=fKjN3PPbUhk).

In manufacturing, increasing diversity has created gaps that have been causing imbalances between theplanned task and actual capabilities of workers and machines. This imbalance might exhaust workers and/or
machines or lead to an opportunity loss.

Regarding to growth in diversity, in Japan, the declining population has been boosting workforce
diversity. Table 4-1 shows increasing trends of diversity, such as woman, elderly people and nonJapanese employee. This diversity comes along with new requirements for the working environment.

33

Table 4-1. Trends on employment in Japan (Source: MIC 2019b, MHLW 2019).
Trends of the times
66 [%]
72.7 [%]
Employment ratio of woman
61 [%](2010)(2016)(range of age: 25 to 44 years)(2000)+0.5 [%]/year+1.1 [%]/year
36.4 [%]
44.3 [%]
Employment ratio of elderly people
36.2 [%](2010)(2017)(range of age: 65 to 69 years)(2000)+0.02 [%]/year+1.1 [%]/year
718k
1279k
486k
Number of non-Japanese(2013)(2017)(2008)+50k/year+140k/year

Additionally, many companies have been extending the life of their facilities annually. Especially, after
1990, the average life of a manufacturing facility was increased to over 16 years, as shown in Table 4-2.
Table 4-2. Trends on the average life of facilities in Japan (Source: Hamagin Research Laboratory 2016).
Trends of the times
10 [years]
16 [years]
8 [years]
Average life
(1991)(2016)(1971)+2 [years]+ 6 [years]

Germany is also facing several social and technical challenges, as illustrated in Table 4-3. This
compels companies to explore new ways to utilize the hidden potentials of humans and machines. Such
potentials include people with disabilities, the diversity of people with migration background and theextensive knowledge base of elderly people.
Table 4-3. Trends on employment in Germany(Source: Eurostat 2019, Destatis 2018b, Federal Employment Agency 2018).
Trends of the times
69.7 [%]
75.2 [%]
Employment ratio of woman
60.8 [%](2010)(2017)(range of age: 20 to 64)(2000)+0.89 [%]/year+0.79 [%]/year
5.9 [%]
7 [%]Employment ratio of elderly people
3.9 [%](2015)(2017)(range of age: 65 to 69)(2010)+0.40 [%]/year+0.55 [%]/year
Number of non-German2219k(2012)

2919k(2015)+233k/year

3470k(2017)+275k/year

As mentioned above, the competitiveness of Japanese and German manufacturing sectors could be
impacted due to a decline in the skilled workforce as well as demographic and technological trends
challenging the hitherto state-of-the-art production processes and workflows.
Both Germany and Japan are facing a shortage of skilled workforce as well as an aging workforce pool;this scenario has been leading to more diversified workforce structures in industry in terms of age, gender,and skills. At the same time, currently, advanced automation in the manufacturing sector is transforming job
profiles and the way humans and machines interact at the workplace. Owing to the rapid pace of this34

Gap emerged due to diversities & approaches of sustainable innovations

development, discrepancies between skills demanded for planned tasks and actual capabilities of factory
employees have emerged leading to a skill gap. In Germany, there is too much of a shortcoming in science,technology, engineering mathematics (STEM) and care qualifications, but this trend has been ongoing forsome time now, starting long before Industrie 4.0 was introduced.
While job profiles and requirements may change, this – at least in the context of the German manufacturingsector – that does not automatically mean that vocational qualified workers are obsolete and people withnecessary qualification are not to find. However, it’s important that workers learn what is new to be learned.
In addition, often there is only a slight change necessary in the vocational profiles. 77
Relating to the importance of interactions between humans and smart systems, it also seems to benecessary to not only focus on how humans or machines can learn but also how they improve their skills.
Therefore, the question is how smart, autonomous devices or systems and humans can learn from each other.
In the context of HMI, there are basically two groups of learners (humans and machines) that, in turn, follow
distinctive but interacting learning concepts. For the HMI, however, hybridization of the learning concepts isnecessary in order to achieve mutual learning success between humans and machines. This mutual learning
is potentially affected by different potential capacity of humans and machines in performing different tasks
such as mechanical jobs and decision-making roles. Quality and performance variation in carrying out a task
are regarded as key indicators, which identify and ultimately distinguish the capability of human and machineon performing the assigned task. Table 4-4 provides a comparison of quality and performance variation of
the learner groups with respect to an assigned task.
Table 4-4. Comparing capability of human and machine (learner groups),
based on quality and performance variation (Source: Ansari et al, 2018a, p. 119).

The already discussed differences in the capabilities of the (individual and technical) learners reflect
enormous learning potentials both for human workers and smart machines or devices. The respective learning
process may either be independent (distinctive training for each group) or dependent (co-occurrence oflearning by executing a common task). At present, there is still a lack on exploring co-occurrence of humanmachine learning. Hence, we have a strong tendency to differentiate between learning approaches.
77

See Pfeiffer et al. 2016.

35

Ansari et al. 2018a define the mutual learning as “a bidirectional process involving reciprocal exchange,dependence, action or influence within human and machine collaboration, which results in creating newmeaning or concept, enriching the existing ones or improving skills and abilitie s in association with eachgroup of learners”78.
Case Study: “Learned non-use phenomenon” in manufacturing
In future, AI robot technology is supposed to be increasingly introduced into manufacturing systems inorder to obtain high performance from the viewpoint of high quality and low cost. After all, what will be
the extent of the influence of AI robot technology on humans in the long term? Taking the easy way of
introducing in AI robot technology may have a negative effect on the proficiency of humans . This occurs
because the humans might be accustomed to the situation that they do not need to do any skills in the
manufacturing process. We need to consider the possibility of this emerging vicious cycle of AI robot
introduction and a corresponding decline in the skills of human beings (left cycle on Figure 4-2).

Figure 4-2. Vicious and virtuous cycle in humans and AI robot systems(Source: The University of Tokyo).

In the field of brain science, substantial neurological injury usually leads to produce a vicious downward
spiral that results in “learned nonuse” of the affected extremity.79 Similar discussion has been conducted
as for the relationship between automation and humans.80.
As for the methodology to overcome learned non-use phenomenon in brain science and rehabilitation
medicine, constraint-induced movement therapy (CI therapy) has been proposed. Here, training
procedures can be used to reward patients systematically for using the affected arm for a period ofconsecutive weeks. In addition, use of the uninvolved limb can be restricted, so much so that the subject
78

Ansari et al. 2018a, p. 119.

79

See Taub et al. 2002.

80

See for example Carr 2014.

36

Gap emerged due to diversities & approaches of sustainable innovations

is rendered virtually helpless unless he/she tries to use the affected limb. A similar approach might bepossible for manufacturing sites. So that humans and AI robots work together in the future manufacturing
sites, two requirements need to be fulfilled: (a) The efficient achievement of tasks with the cooperationof humans and AI robots and (b) the enhancement of skills in human workers through applying t raining
process. One possibility is to construct an excellent management system (might be AI -based) to
implement a systematic harmonization of HMI that can arbitrate the above two problems (right cycle on
Figure 4-2).
4.2. Managing the real-world and the cyber-world
Manufacturing companies are traditionally dominated by classical engineering roles from mechanical orelectrical engineering, especially in R&D. Mechanical and electrical engineering have been at the core ofinnovation activities in manufacturing since the 19 th century, co-evolving over the last 100 years and being at
the core of the unprecedented development driving what we call industry today.
Nevertheless, over the past few decades, the importance of software as a driver for innovations inmachinery or electric products has increased ever since. These software innovations have mainly occurred
within embedded systems, physically confined to the single machine and still driven by the physics.
Meanwhile the software industry has grown significantly over the last decades at an ever-increasing speed,
while becoming ever more present in various aspects of daily life and business processes.
Software itself is an abstract entity and understanding its inner workings requires a higher level ofabstraction than a problem in mechanical engineering. Together with an increasing complexity and additionof new layers of software this has established the field of software engineering which developed its own
methods and tools as well as unique processes and organizational procedures.
The emergence of Industrie 4.0 now signifies the fact that both worlds get connected and merged. This
leads to a whole new arena of innovation in this overlapping world which leverages the methods andprocedures of the cyber-world on top of classical engineering how-to.
Manufacturing companies are now faced with methods like agile development, scrum development andpair programming that are different to waterfall approaches or detailed engineering specifications. On top
organizational principles like teams of ten or flat hierarchies are typical for software outfits and manufacturing
companies need to adapt these in order to be (and be perceived by potential software talents) agile. Given
the traditional approaches and the long and highly successful history of these compani es this is a hugechallenge for them. But these changes won’t go away, companies must actively address them in theirworkforce, the organizational principles and management procedures.
Software development itself is a “desktop-based” activity, confined to the virtual world of computers andservers. This in itself opens up careers to people who have hitherto been excluded from the workforce to alarge degree, but who in the virtual world have no impediment any longer. We will give one example of this in
the following chapter. Further, the development of software which is embedded in machinery requires not only
software skills but also a deep understanding of those production technologies, in which the software is to be
integrated. Therefore, qualified workers with insights and decades of experience into machinery, production
technology and its hurdles, would bring a special expertise into software development if they have been
additionally qualified.
37

To sum it up: the real-world and the cyber-world merge within Industrie 4.0 into a new domain of innovations
where a new set of tools, methods and principles are the foundation to success, but have to be combinedwith existing processes, technological paths and worthful manufacturing qualifications and experiences.
Manufacturing companies must actively make themselves acquainted with new challenges and adopt them,
but activate and innovate their unique manufacturing expertise.
4.3. Sustainable innovations with diversities
Both Germany and Japan face a shortage of skilled labor and an aging labor force pool, leading to morediversified workforce structures in industry in terms of age, gender and skills. At the same time the advanced
automation that is currently taking place in the manufacturing sector transforms job profiles and the way
humans and machines interact at the workplace. Inclusive working environments will therefore become aneven more significant competitive differentiator and a driver of business success, as the y attract the mosttalented employees in a tight labor market. Inclusiveness has proven to boost engagement and creativity
among employees. Moreover, when employee structures reflect the societal strata of customers, their needs
can be better understood and met. And after all, collaboration with colleagues with different viewpoints
generates a greater mix of ideas that spurs innovation.

Case Study in Germany: SAP “Autism”[Concept video available from below link;
https://www.sap.com/corporate/en/company/diversity/differentlyabled.html?source=social-atw-mailto&sharedId=9e6909ee-6a7c-0010-82c7eda71af511fa]

This case study describes how an organizational culture based on diversity and inclusion appreciation
at Europe’s largest software company SAP has laid the foundation for integrating people with autism
into the workforce. SAP’s program can serve as an example for other companies on how to expand thetalent pool. Furthermore, it prepares manufacturing companies for the type of employee structures they
may find in the software companies such as SAP – a key partner for the transformation to Industry 4.0.
The software industry has been a sector exposed to fast-paced change and characterized by speedy
growth rates from the very beginning, making talent acquisition a crucial factor for success. In this “warfor talents”, SAP has been faced with the prospect of labor shortages in the ICT sector early on. The
need of both addressing volatile market trends as well as obtaining talented employees, motivated thecompany to create an inclusive and accessible work environment for a diverse workforce. Diversity inemployees, offering a broad range of perspectives, is seen as an important source of innovation and an
unquestioned necessity for dealing with complex technological challenges. What is more, as SAP
software is used by a wide breadth of societies around the globe, the benefit of having the workforce
reflect the diversity of customers with different abilities and needs was an additional business driver forthe recruitment strategy.

38

Gap emerged due to diversities & approaches of sustainable innovations

This open mindset was a crucial precondition for launching the Autism at Work program, which
integrates people with autism into the company's workforce. It allows the company to tap into a talentpool, which thus far has remained largely unaddressed by the labor market, adding to its over allcompetitiveness.
The Autism at Work Program was officially launched in 2013 and so far, as per Q2 2019, SAP hasover 155+ employees with autism working in 26 different roles from 13 countries with a retention rate ofabove 90%. The program was initiated through a local project at SAP India, where first experiences withpeople on autism spectrum were gathered. Their outstanding performance convinced the company of
the superb contribution to software development that people with autism can make.
Autism is a neurological disorder that can create challenges with social, emotional and communication
skills. It is estimated that an approximate 1% of the global population is affected, while 90% of them are
un- or under-employed. People with autism are often attentive to routines and sameness and havediﬃculty adjusting to unfamiliar surroundings or changes in routine. But this is only one side of the medal
as SAP’s own experiences with colleagues on the autism spectrum have shown. Creating anenvironment, where the needs of employees with autism are addressed, helps bring out their uniquestrengths and skills, including among others:• a meticulous attention to detail and pattern recognition,• the ability to remain focused on a task for a prolonged duration of time,• analytical and logical thinking,• and the skill to detect irregularities.
Stable and open work conditions are an important prerequisite to make employees with autism feel
welcome. This is achieved, amongst other things, with a set group of internal and external people ateach program location who hire, on-board, support and coach their colleagues with autism while at work.
While this requires investment, most colleagues quickly become independent in their new workplace,creating a true return on investment – not only for the company, but for society at large, benefitting fromsaved social welfare costs and strengthened social cohesion. After all, the stable structures are crucial
for running the program in a sustainable and scalable way.
But necessary adjustments already start in the recruitment phase: Mainstream processes frequently
look for people who have good communication skills and who are team players - categories thatcandidates with autism typically do not tick. For that reason, SAP has attuned its recruitment practices,focusing on the passions of candidates with autism and incorporating practical tasks in the interview
process

39

Figure 4-3. SAP’s Autism at Work Program(Source: SAP)

In order to help the program succeed, SAP has built a network of partners, including NGOs, research
institutes and government agencies, supporting the company in reaching potential employees with
autism. These stakeholders help identify possible candidates and advise the company on supportingcolleagues with autism to integrate smoothly. With the future stream of employees already in mind, SAP
has also initiated cooperation with select high schools and universities to make students with autism
aware of job opportunities at the company. Since many of people with autism are used to being left out
by conventional recruitment programs, the fact that their skills are sought after must be put on their radar.

The “harmonization” in the interaction between variety of human and machine is different from thecomplementation that simply compensates for the lack of functions, or the synchronization that issynchronized with either. Harmonization indicates a relationship that grows together by respecting andhelping each other, leading to attractive results. In order to realize the above, it is first necessary to recognize
individual characters and to respect and enhance each other.
That is, the world is becoming increasingly diverse in terms of HMI in the real world, and real-cyber world
relationships. To ensure sustainable economic growth, we must realize a world in which human find fulfillment
in their work while evolving hand-in-hand with machines. For the establishment of sustainable management
system with above diversities, transition of HMI should be discussed as shown Figure 4-4.

40

Gap emerged due to diversities & approaches of sustainable innovations

Figure 4-4. Transition of interaction models(Source: Hitachi).
Former model (left on Figure 4-4) is a technology tradition model, basically applied to humans, in which amaster teaches a disciple. In this model, scalability of technology tradition would be a potential challenge due
to necessity of appropriate masters all the time.
Case Study in Japan: Technology traditions
In Japan, the technical tradition of traditional crafts such as lacquerware is still performed mainly byusing the so-called apprenticeship system. Not only that, for example, but since the brazing work of theheat exchanger (which is the main component of the air conditioner) is accomplished by seali ng thincopper tubes together, formalizing this technology is very difficult, and for skilled masters to train
beginners efficiently has become a major issue.81
Today’s model (middle on Figure 4-4) is a machine assistance model aiming at addressing the above
mentioned potential challenge of the traditional technology transition model. Machines would provide relevant
instructions and support automatically for compensating the potential gap in human skills for completing giventasks. However, excessive computer aids would lead to an increase of a learned -non-use phenomenon, andhumans would in turn lose a skill they need to have.
Case Study in Japan: Issues of excessive assistance
In Japan, many machining processes such as drilling and turning are operated by Numerical Control(NC). Sometimes it occurs that an operator can hardly understand the reasoning behind the parametersthat have been set by the skilled operator or by the implemented AI, for example, the feed amount andcutting amount of the cutting tool. 82 Although industrial robots are used in many cases for assembling81

See Hitachi Brand Channel 2018.

82

See Nonaka 2019.

41

home appliances and spot welding of automobile bodies, sometimes it has occurred that the change ofrobotics program leads to a bottleneck in the event of a process change because it’s difficult for an
operator to understand the reasoning behind the program parameters set by the skilled operator or by
the AI.83
So, the future management system for a systematic harmonization of HMI would have a multiverse
mediation process among humans and machines (right on Figure 4-4). In this future model, the mediation
would adjust interactions as mutual aids between human-human, human-machine, and machine-machine
based on digitalized knowledge for inspiring sustainable growth of both humans and machines respectively.
This future model also observes interactions among human-human, human-machine, and machinemachine. Moreover, it accumulates that observed information as digitalized interactions between thoseentities. If a human is a skilled worker, the mediation process analyzes interactions for identifying key tips,
produces adjustment for mutual aids to stimulating relevant actions.
Focusing on the interaction of (1) human-human and (2) human-machine based on this future model, thecompanies need to adopt the following strategies:(1) Revisit their traditional workforce structures and create an inclusive environment for a wider set oftalented workers whose characteristics may differ from the majority in the current employee base.
(2) Systematically measure the skills and capabilities of human and machine in the same plant and,
subsequently, harmonize task plans and adjust the programming of machines/robots so that they can
assist their human counterparts optimally.
The strategies we propose have the potential to cause organizational disruption as they involve
restructuring of internal processes and responsibilities. Some stakeholders may have an interest in
maintaining the status-quo to secure their level of influence. Such dynamics may inhibit the implementationof diversity programs and new production processes based on tailored human -machine collaboration. In afirst step, the mediation process and sharing of data should happen within a company. Then, in a second
step, this procedure should be transferred to the whole society as long as no intellectual property is harmed.
That is, for a sustainable society with well-being, knowledge acquired from interactions among human andmachine should be formalized in public, transformed other interactions among human and machine as themediation for continuous, efficient operations in the society.
According to these strategies solution scenarios are promoted in the following.
Case Study in Japan: Hitachi “Multiverse Barrier Free”[Concept video available from below link;
https://www.youtube.com/watch?v=NWq6wQ_XzwQ]

This case study describes Hitachi’s activity of a systematic harmonization of HMI,
called Multiverse Barrier-Free.84 In Japan, the diversification of workers is progressing, mainly due tothe declining birthrate and aging population. In addition, diversification is also progressing in machines,
83

See Hitachi Brand Channel 2019.

84

See Hitachi Brand Channel 2019.

42

Gap emerged due to diversities & approaches of sustainable innovations

which indicates that old machines introduced during the period of high economic growth and latest robots
introduced in recent years are used together in the factory. Hitachi is promoting the development of aflexible production system utilizing human and robot cooperation 85 that optimizes work allocation
between human and robot.
At conventional work sites, workers are taught by an expert, and perform according to the expert’s
model. However, as workers diversify, even if they receive uniform work and instructions, the gap withworkers’ existing ability increases the stress and workload of workers. As a result, they cannot work as
instructed, and therefore, problems such as a decrease in productivity and loss of growth opportunities
may occur.
In factories where diversification of machines progresses, there is a potential risk that human work is
substituted by machines, that in turn could lower the workers’ motivation. In addition, there is also the
problem that the machine supports the workers excessively, which causes the workers’ ability to fall andprevents the growth opportunities, as mentioned in the case study: “learned non-use phenomenon” inmanufacturing.
Given these problems, the concept of “Multiverse Barrier Free” has been proposed by Hitachi as amechanism to promote sustainable growth rather than over-supporting each other in order to takeadvantage of the individual characteristics of diverse human and machine. In this concept , a mediation(which is mentioned in Figure 4-4) is created and performs for the purpose among interactions between
human-human, human-machine, and machine-machine with following three steps shown in Figure 4-5.
• First step: Measuring
The characters of human-machine interaction are digitalized and a private mediation is created foreach interaction based on these characters. Hitachi assumes the HMI consists of 4M (Man, Machine,
Material, and Method) entities, and a cross analysis of 4M behavior can extract key tips of HMI.
With this policy Hitachi collaborates OKUMA to extract key tips of machine tool manufacturing forthe manufacturing efficiency maximization. 86• Second step: Harmonizing
In this step, the mediation generates appropriate policy of HMIs for the performance bottleneck
compensation. For example, the mediation promotes an optimized organization plan of humans andmachines to achieve a performance, an appropriate support menu from machines to humans, anappropriate operation policy from humans to machines with paying attention to machine’s condition,etc. With this policy Hitachi has developed technology to optimize both production line configurationand product design.87 Since product design specs require some production capabilities to workers,
robots, etc., and since capabilities of workers and robots limit product design specifications such as
weight, fineness, etc., a harmonization technology was established and examined in an automotive
device manufacturing.

85

Hitachi R&D Group 2017.

86

See Nonaka 2019.

87

See Tsutsumi et al. 2018.

43• Third step: Sustainable growth
For the purpose of achieving growth of human and machine respectively, it is required to provide
mutual aids for each human and each machine precisely. With this policy Hitachi is collaboratingwith Daikin for the sustainable growth of HMI for brazing.88 In the collaboration a new processvisualization function was added to existing brazing system for the brazing of copper tubes of heatexchangers in air conditioners, and an improving program of worker’s brazing skill with the function
was established.

Figure 4-5. Key three steps of Hitachi’s Multiverse Barrier Free
(Source: Hitachi).

That is, the essential value of Hitachi’s Multiverse Barrier Free is;1.

Help human and machine develop their creativity continuously

2.

Replace operations which becomes obsolescent and versatile with automatic control as much as
possible to free humans from having to perform tedious tasks

3.

This continuous expression of creativity contributes to maintain the maintenance anddevelopment of machine in the sustainable society, too.

Hitachi has launched this Multiverse Barrier Free concept at the Hitachi Social Innovation Forum2018.89and has released concept videos on YouTube.90 Hitachi contributes to the creation of a society
where diverse humans and machines grow together.
88

See Hitachi Brand Channel 2018.

89

For more information regarding the Hitachi Social Innovation Forum, see http://hsiftokyo.hitachi/en/.

90

See Hitachi Brand Channel 2019.

44

Challenges to the new paradigm of human-machine collaboration

5. Challenges to the new paradigm of human-machine collaboration
The impact of the digital revolution, such as IoT and AI, on industry and society is a lready evident. With
the projected growth in population and increase in prosperity, an increase in overall global market volume for
products and services will offer tangible opportunities. Such demand even develops towards increasingly
customized products. On the other hand, manufacturing labor decreases globally.
Addressing the societal trends mentioned before, industry in general is changing its ways and becoming
increasingly diverse and complex, as we have become aware today. The new paradigm of industrial
manufacturing can rapidly adjust the physical and cyber system (CPS) to changes in customer requirements.
Responsiveness to the customer is most visible in the case of using the human-machine collaboration which
is appealingly more productive presented opportunities to gain a new competitive advantage for the enterprise.
However, how will humans and machines interact with each other, and what role will our thoughts play?
In this chapter, we focus on the augmented human and self-learning machines collaboration work in future
manufacturing which is referred to as human-machine collaborated smart manufacturing. Potential solutions
are described to play out the new ways of interaction and moreover the integration bet ween human and selflearning machines so as to facilitate both in operation and management to be flexible and adaptable, which
can also lead to creating a more satisfying work style as a result.
5.1. Current situation
On the work site, many efforts have been taken in trying to make machines function in a more intelligent
way and self-learning with features such as sensing technologies and AI. For machines on a worksite,
technology barriers exist with respect to routine cognition of frequently changing work tas ks and theirfunctionality restriction. Traditional machines require more technical effort to realize the routine cognitive,
where advanced machines or robots are more efficient in adapting routine cognitive in manufacturing process.
For workers, technology barriers exist on both routine and non-routine cognitive and the interaction alongwith the behaviors change at the machine. Management barriers exist on problem-solving and innovativeskills according to the fast change manufacturing process for workers on worksite as well. As shown in Figure
5-1, on the one hand, basic skilled workers are not able to do as many tasks as professional skilled ones,
and on the other, the support provided by machines depends on the different levels of intelligent function thata machine is implementing.

45

Figure 5-1. Current situation in manufacturing(Source: Mitsubishi Electric Corporation).

Furthermore, taking companies as a whole into account, various situations present a wide range ofdifficulties for management on a worksite. Thanks to the efficient, increasing usage of manufacturing
information, this will help make the right decision at the right time in the future workplaces. It is clear thatgauging and communicating the readiness of every individual worksite is becoming more important all thetime.
5.2. Future development
Taking the current speed in the integration of CPS, machines are oriented on the basis of having availability
through the connectivity for all instances participating in the manufacturing process.
Such dividends should not be only reflected in the machines by taking advantage of CPS, but the role ofhuman is also expected to expand, leading to an optimization of the manufacturing proc ess in both theworkplace and management.
Given the above situations, to properly address the barriers, the following two aspects of strategic
approaches can be emphasized:

46

Increasing the intensity of cognitive technology and thus working to a greater ext ent toward humanmachine collaboration, by enabling features such as collecting human intelligence on shop -floor alongwith knowledge-sharing management, merging the human intelligence with CPS and additionally thelearning in process management, collaborative robotics and activity recognition for highly skilledworkers, including the overall interface design.

Increasing the intensity of supervising skills, thus raising the returns reflected in human capital bymaking a systematic assessment of enterprise management possible.

Challenges to the new paradigm of human-machine collaboration

5.3. New paradigm of human-machine collaboration
The new paradigm of human-machine collaboration in manufacturing, as shown in Figure 5-2, can make a
big leap in terms of a company’s competitive abilities. This figure can be interpreted as a generalization of
Figure 4-5.

Figure 5-2. New paradigm of human-machine collaboration(Source: Mitsubishi Electric Corporation).

In the workplace, workers involved in the technical systems must adopt statistical optimization as well as
dynamical optimization increasingly with the self-learning machines in an appealing way. The reason behind
this is the fact that collaboration is required, especially with the support of digital technologies. It is also
obvious that demand is likely to shift to the intensive margin of total capability that reaching out the enterprisemanagement with such a new paradigm of human-machine collaboration as a whole. Companies can expand
their implementation of flexibility as well as adaptability to the emerging sustainability that matches its
strategic business competitive environment.
The following case studies introduce different aspects of the key elements for the new paradigm of human machine collaboration implication.

47

Case Study in Japan: Mitsubishi Electric “Collective Intelligence”
Collective intelligence, which is based on lessons-learned and ideas gathered by participants in acompany, organization or society contributes not only Kaizen, successive improvement of products andproductions, but also innovations, as shown in lateral thinking 91 and TRIZ92.
However, the reduction in the productive population leads to the deceleration of Kaizen and innovation
due to less amount of knowledge, lessons-learned, and ideas collected from the worksites. In the future,
workers in the workplace will have to come to terms with increasingly complex manufacturing processes;
their adaptability of skills and efficiency of operation are more important when it comes to manufacturing.
In addition, the less productive populations prevent companies from providing for long -term skills
development in their workplaces. This also concerns, first and foremost, striving to establish a culturethat will be more accommodating in the different approaches implemented in workplace itself. However,considering the drastic and dramatic changes occurring throughout current business environments, it isa key to success to have employees’ latest feedback about their working environment and to ensure that
cutting-edge training is introduced as quickly as possible.
In order to overcome the barriers, collective intelligence (Figure 5-3) is a goal worth striving for, whose
purpose is ensuring that the lessons-learned and collected ideas in the workplace can continue to be
implemented and explored through a platform that reflects the increasing diversity of manufacturing andaccelerating the processes in actual manufacturing environment. Such a collective intelligence platfo rm
can also assist utilizing data and leveraging skills and knowledge efficiently to contribute to thecollaboration and the training of human on worksite to enable them to focus on more creative and value adding tasks.

91

See de Bono 1967.

92

TRIZ is a Russian acronym for the “Theory of Inventive Problem Solving”. See Altshuller/Shapiro 1956.

48

Challenges to the new paradigm of human-machine collaboration

Figure 5-3. Collective intelligence platform(Source: Mitsubishi Electric Corporation).

As shown in Figure 5-4, the collective intelligence platform gathers data not only from machine
systems, but also from the human by communication platform that state the lesson-learned idea,
suggestion and recommendations with the increasing knowledge-intensive data & knowledge base.
Exploiting the diversity of manufacturing, AI aids collective intelligence platform to evaluate thecorrelation of data, the similarity of methods, and the effect to prototyping ideas. The information gained
from AI analysis is highlighted as skills and knowledge and stored as use cases to the data & knowledge
base. These skills and knowledge will be shared by humans who are involved as members via
communication platform for on-demand education, idea evaluation, simulations. As a result, members’
developed ideas will be accelerated. Furthermore, categorizing skills and knowledges regarding the key
performance indicators in the collective intelligence platform, can provide guidance apparently andaccommodate the actual transformation cycles of skills and knowledge with the goal to exploit higher
smart manufacturing maturity as successfully as possible.93

93

See Shi et al. 2019.

49

Figure 5-4. Meaning of using collective intelligence platform(Source: Mitsubishi Electric Corporation).

Enabling the diversity of manufacturing with collective intelligence exploits breakthroughs in thecompany, such as cell style production system for electromagnetic switch assembly,94 is successful. In
this example, one worker collaborates with robots takes charge of a broader range of the manufacturing
processes to accomplish the assembly, where robots compensate for the weaknesses of the worker andthe worker’s value lies in process judgment. The knowledge gained from one cell can be put to practice
faster through collective intelligence management board can evaluate the return on investment in timeplan and make more manufacturing happen.
With the support of collective intelligence platform, humans on the worksite can leverage skills andknowledge effectively for creation of innovative product manufacturing.

94

50

See Mitsubishi Electric’s concept of the e-F@ctory. URL: https://us.mitsubishielectric.com/fa/en/solutions/efactory.

Challenges to the new paradigm of human-machine collaboration

Case study in Japan: The University of Tokyo “Digital Triplet”
An important approach to promote the human-cantered manufacturing is to encourage on-siteengineers and technicians in developing new ways of executing engineering activities on their o wn
initiatives, in addition to routine works specified by manuals and supervisors. This is the traditional way
of Kaizen in Japanese manufacturers. While this means that CPPS (Cyber-Physical Production
Systems) should be equipped with this functionality, we view that this has not been realized. Digital
Triplet discussed in this subsection is the concept to add this functionality to CPPS.
Although continuous improvement (Kaizen) for pursuing lean production done by manufacturing
engineers and shop floor technicians is an important key for high-quality manufacturing, activities ofcontinuous improvement are not digitalized to be “smart” in CPPS 95 , since many engineers andtechnicians cannot follow the digital transformation. With the growing digitalization of manufacturingsystem, the question is what kind of next-generation system we need for supporting engineers in creating
maximum values in manufacturing, rather than just “automating” engineers’ activities.
This section deals with the situations, as seen in typical Japanese manufacturing companies, where
manufacturing system engineers are always stationed at the shop floor and continuously improv e the
manufacturing systems with workers. They conduct engineering activities including operation of
manufacturing systems, problem solving when some troubles occur, continuous improvement (Kaizen)
for pursuing lean production, and new development of manufacturing system.
In order to solve this problem, we are proposing “Digital Triplet”. 96 Digital Triplet aims to support
engineers to solve troubles and to create various values throughout a product life cycle. While Digital
Triplet is a kind of Cyber-Physical Production System, 97 Digital Triplet adds the functionality toencourage engineers in developing new ways of executing engineering activities on their own initiatives
to CPPS.

95

See Monostori et al. 2016, Geisberger/Broy 2014.

96

See Umeda et al. 2019.

97

See Monostori et al. 2016, Geisberger/Broy 2014.

51

Figure 5-5. An engineering cycle on Digital Triplet(Source: Mitsubishi Electric Corporation).

As can be seen in Figure 5-5, an engineering activity consists of data collection, information analysis,
decision by the engineer, and execution of a plan in the physical world. We call this cycle “engineering
cycle”. When an engineer executes a cycle, this creates some values from the d ata. These roundrectangles in Figure 5-5 are represented in the traditional CPPS. But important things for supporting the
manufacturing system engineers to construct the engineering cycles are rectangles in this figure suchas selection of data to be collected selection of analysis methods and tools. They are not described in
the traditional CPPS but the main players in Digital Triplet. In other words, the manufacturing systemengineers targeted in this section execute Kaizen by constructing their own engi neering cycles basedon their own initiatives. Digital Triplet aims to support this process. Among others, human -centered
CPPS98 is a good reference of this framework; the main difference of human-centered CPPS and Digital
Triplet resides within the subject who develops engineering cycles; while CPPS developer in humancantered CPSS, manufacturing system engineers in the field in Digital Triplet.
We are going to describe these engineering cycles as reusable process knowledge. In other words,
we would like to formalize, collect and archive the process knowledge of manufacturing systemengineers, support human activities by reusing and deploying the knowledge, and employing theknowledge to education and generalizing the knowledge. By utilizing this process kno wledge, we can
improve engineering cycles, including the HMI method. This is a new form of knowledge transfer from98

52

See Ansari et al. 2018b, 2018c.

Challenges to the new paradigm of human-machine collaboration

experts to novice engineers and training of novices in that, namely, an expert engineer develops anengineering cycle for a specific problem (e.g., to increase productivity of a manufacturing system) byhimself/herself. Digital Triplet records this engineering cycle as a process of data collection, usage ofseveral AI tools, simulators, and other software, and execution of some actions, with an notations of theengineer. By tracking this process record, a novice engineer can study expert’s thinking approach andhow to solve this problem.
Digital Triplet is an extension of CPPS, focusing on the integration of continuous improvement(Kaizen), which encourages engineers to construct and execute various engineering cycles based on
their own initiatives, into CPPS. This also facilitates and enables knowledge transfer from experts tonovice engineers and training of novices in the era of smart manufacturing. In other words, Digital Triplet
makes a smart manufacturing system possible that fully utilizes the varied abilities of diverse
manufacturing system engineers to create values in a respectful manner, thus improving their skills andabilities.
Case study in Germany: Infineon “Collaborative Robotics”
The latest generation of collaborative robots (CoBots) demonstrates that the benefits outweigh therisks of humans collaborating with robots. Moreover, these risks can be minimized or even nearly
eliminated with the right combination of semiconductor solutions and advanced algorithms. First andforemost, CoBots need advanced safety and security capabilities as well as the ability to scan theirsurroundings with utmost precision.
Unlike conventional industrial robots, CoBots operate without safety cages and interact directly withhumans. The lack of cages, however, significantly increases the safety risks for humans and production
material. If you want to liberate robots from their cages, you also have to ensure that human beings areprotected from entering the critical reach of a robot operating at high speed in the first place and be
injured, either through their own negligence or a malfunction. Making robots sufficiently sensitive only
works with sophisticated sensor technology. Basically, the area between humans and robots, but also
between the robots themselves must be made safer. The aim is to make protection zones more flexible
so that, for example, a much smaller protection zone moves dynamically with a moving robot arm. A
zone concept is used to implement the virtual fences. If a human enters the outer perimeter, a warning
signal is emitted, but the robot continues to operate at full speed. If the human gets closer, the speed isreduced, and a warning is issued accordingly. The CoBot stops if the human is directly about to enter
the danger zone.
Although both terms, safety and security, sound similar at first, there is a clear distinction: While safety
deals with the protection of human, security embraces data protection and thus aims at protecting rob ots
from cyber-attacks. Only systems that are appropriately safe in terms of data security can also befunctionally safe – an aspect that is becoming increasingly important in the context of Industrie 4.0 and
IoT. Encryption systems protect the commands sent to the robot from manipulations whereas
authorization mechanisms protect the robot from executing wrong commands. Security mechanisms also
aim to prevent the robots as part of the production process against manipulation during wired or remote
software updates. Secured authentication of users and newly added components is also required toprevent unauthorized access.
53

In order to function correctly, robots must also be calibrated. However, if an attacker manipulates thecalibrations, for example, the robot could exceed its programmed motion limits. This is where safety andsecurity converge – without efficient security protection, functional safety doesn’t exist . This is a key
requirement for future systems and is being addressed by special security controllers or microcontrollers
with functions such as Hardware Security Module (HSM). Since the security functions are implemented
in the hardware, users need only minimum detailed knowledge of encryption tec hnologies. In addition,
the impact on existing software implementations is extremely low.
The automation equipment and robots employed can only get work done when they fully function. In
order to do so, they need to be maintained regularly. However, turning these machines off for performing
maintenance takes away valuable time from production activities. In addition, automation teams might
be replacing parts that are still fully functional because of the specific load profile within the production
process. This may cause significant costs that could have been avoidable in the event that users of themachine had known better.

Figure 5-6. Structure of a CoBot including sensing and security components(Source: Infineon).

Robots provide us with a wide range of signals that we can use to diagnose their “health”. For instance,a change in their whirring sound or a slight shuddering motion – these are two examples of signs that a
motor, gearbox or bearing is in need of repair or maintenance. For instance, if power consumption goesup slightly but steadily, perhaps there is some increased mechanical resistance. This can result fromclose-to-fail bearings causing the electrical system to draw more current than usual. The semiconductor
industry has used the unique properties of silicon to create cheap and tiny sensors to measure a wide
variety of signals. With this wealth of low-cost but high-quality sensing capability, there is little reason
not to integrate a range of sensors into robots to monitor their health. For example, temperature, vibration,
54

Challenges to the new paradigm of human-machine collaborationnoise, positioning and motion, acceleration and force sensors could be integrated into the joints of arobot. Many of them can be found there already, as they are required to enable the intended function of
the robot or any other machine. But in addition, monitoring all these sensors together would, like anintuitive human, recognize signs of potential early failure.
If this continual sensing is to be of use, it also needs to be evaluated immediately in order to highlightpending failures. Microcontrollers can be used to collect, pre-process and evaluate the data. Featuring
all the necessary data interfaces to connect to such sensors, these tiny number crunchers can a lso be
integrated inside robotic joints. This fusion of sensor information can be evaluated on -chip or passed on
to a central computer using Industrie 4.0 data networks. By continuously reviewing the robot’s signals,
an Artificial Intelligence could later easily highlight when some of these parameters are starting to falloutside their expected range. This would be the trigger for the maintenance team to inspect the robot
for excessive wear. The combination of continuous sensing and data analysis by AI systems enables
condition monitoring and predictive maintenance solutions in robotic as well as other production systems,
thus enabling a high availability for all types of equipment. Therefore, robots need to be equipped withsensors that detect their surroundings. Technologies like radar or 3D imagers based on the time of flight
principle that might function as “eyes”. MEMS microphones might give “ears” to machines and pressure
sensors providing a kind of “feeling” to them. This reliable “understanding” of the environment improves
the safety of human co-workers.
Case study in Germany: Learning Factory
Learning factories typically are factories that are designed and implemented to educate and to train
workers for creating the new skills that are necessary to create an advanced production environment.99
Production technologies rapidly adopt the concepts of information and communication technologies aswell as new production paradigms such as additive manufacturing or collaborative robotics. In the future,
the frequency of changes in production will increase. Competence building in a production environmentis required increasingly, that, in turn, has also implications to new forms of human-machine interaction.
Learning factories provide a new education and training approach, complementary to the traditional
education and training concepts of higher education and industrial training. The approach combines bothconcepts and integrates research driven state of the art solutions with industrial strategies forcompetitive production. The main goal is to strengthen human´s role in advanced value chains. As
information and communication technologies impact production technologies more and more it becomesincreasingly important to train workers 4.0 for keeping track with the high deve lopment dynamics in an
ICT-driven production environment and with new forms of human-machine interaction.
For society 4.0, lifelong learning and a high level of flexibility in education or up-skilling play a key
role. Lifelong learning, however, also requires places where skills are being updated and further
competences are created. Such places need to be established either physically or as in cyber space or
as a combination of both as a cyber-physical education and training environment. Learning factories
represent a cyber-physical education and training environment, dedicated to support lifelong learning.

99

See Abele et al. 2018.

55

One practical example of a learning factory is the “ETA factory”. It has been initiated by theinterdisciplinary research group “Energy Technologies and Applications in Production” (ETA) of theInstitute of Production Management, Technology and Machine Tools at TU Darmstadt. As a real researchtool, this ETA factory offers not only excellent opportunities for research, but also serves as a learning
environment in which the insights gained in industry and teaching are transferred. The production hallof the ETA factory consists of two complete value chains in which small-scale, market-ready products
can be produced (see Figure 5-7).100

Figure 5-7. Cyber-physical education and training environment(Source: ETA-factory, Darmstadt).

In recent years, more and more learning factories or labs have been established. In Germany, too,
the concept of learning factories has already been taken up an implemented into practice. For example,
the “Map Industrie 4.0”, that is provided by the “Plattform Industrie 4.0”, lists already realized use cases,
test beds, and support services in the context of Industrie 4.0. It demonstrates that various initiatives,
activities and implementations of learning factories that have been initiated by companies (e.g. SAP,
Festo) or scientific and public institutions (e.g. wbk [Institute of Production Science] of the Karlsruhe
Institute of Technology [KIT], Fraunhofer IGCV).101
Generally, the concept of learning factories is not restricted to qualification measures on production
sites, as it also can be transferred i.e. to so-called learning labs, learning laboratories or learning groups.
Therefore, the term can be generalized to learning places where human-machine interaction topics play
an important role.

See further https://eta-fabrik.de/bildung/lernfabrik/.
For the “map Industrie 4.0” of the German Plattform Industrie 4.0, see https://www.plattformi40.de/PI40/Navigation/Karte/SiteGlobals/Forms/Formulare/karte-anwendungsbeispiele-formular.html.
100
101

56

Challenges to the new paradigm of human-machine collaboration

Case study in a collaboration of Germany and Japan:
DFKI and Hitachi “Worker activity recognition, evaluation, and transfer”
The aging society and declining birth rate are one of the biggest social issues in mature societies
such as Europe and Japan. Especially for manufacturing companies, the retirement of the experienced
workers, who have been supporting strong industry, is a serious problem. Therefore, there is an immense
need for a technology that can benefit from experts to support novice workers. Worker (either human or
machine) activity recognition can play a vital role to support workers in the factories for instance qual ity
control, reducing risk of accidents, maintaining workers' health, and knowledge transfer from experts tonovices and/or to/from machines.
The main difficulty in applying an activity recognition model to different factories is the diversity of theactivities between these factories. Since the important activities to be recognized are different fromfactory to factory, the target activities are different depending on the factories. In addition, even if thetarget activities are the same among some factories, their representation in each factory may be different.
For example, the activity of “check manual” may refer to a situation where a worker is reading papers infactory A, but in factory B it may be the situation where a worker keeps looking at a displ ay (to read
digitalized manual). In this case, the conventional methods used need to create separate recognition
models for these factories because the model created for one factory cannot be used for the other factoryeven though the target activity has the same name of “check manual”. In other words, with conventional
methods it is required to create a customized model for every individual customer.

Figure 5-8. Conventional model vs easy to deploy activity recognition model(Source: DFKI).
57

In addition, even if a customized model is successfully developed, it lacks the flexibility of further
modification. For example, if a display to read digitalized manual is introduced in factory A after acustomized model was developed, a lot of data for new “check manual” activity needs to be collected,
and new customized solution needs to be developed. Similarly, any decision taken by these systems is
not supported with the explanation, i.e. why a particular decision was taken, which is critical, especiallyin environments where human-machine collaboration is indispensable.
The goal of this research is the development of an easy-to-deploy human-activity recognition
model, 102 which can be applied to different factories as standard solution with limited or no
customization needs, therefore deployable with low cost. In addition, explaining machine’s decisions tohuman is a vital component of this approach.
The key idea is to recognize complex activities based on the combinations of simpler components,103
like the actions and objects involved in the activities and explain the recognition to human in bothvisual104 and textual form 105.
We used two wearable sensors; one is a smart glass or eye-tracker with camera providing theegocentric vision of the worker, and the other is motion-tracking sensor (armband sensor or full-bodysensor) on the body. These sensors are utilized to recognize gazed objects and basic actions
respectively.106 Although many conventional systems use fixed cameras as sensors, wearable sensors
are more appropriate especially in a complex industrial environment because fixed cameras often sufferfrom the difficulty of occlusion and view angle.
We assume that even though the representation of the activities may be different from factory tofactory, what’s different is just how to combine actions and objects (hereinafter these are collectively
called “basis”). In other words, we assume that the recognition modules for the basis, namely action andobject, can be commonly used in different factories.
Figure 5-9 provides an overview of the proposed model.107 This framework enables recognizing a
new activity without time-consuming retraining process assuming that a new activity can be represented
by a combination of predefined basic actions and objects. Here, “action” is defined as a simple motionof body parts such as “raise arm” or “bend down”, while “activity” is defined as a combination of basic
action and object. Recognitions decisions of both actions as well as gaze guided object detection aresupported both with visual 108 and textual explanations 109. These explanations play vital role to build atrust on machines where human and machine are in working in close proximity.

102

See Al-Naser et al. 2018.

103

See Al-Naser et al. 2018.

104

See Palacio et al. 2018, Siddiqui et al. 2018.

105

See Ohashi et al. 2017.

106

See Al-Naser et al. 2018, Ohashi et al. 2017, 2018.

107

See Al-Naser et al. 2018, Ohashi et al. 2017, 2018.

108

See Palacio et al. 2018.

109

See Ohashi et al. 2017.

58

Challenges to the new paradigm of human-machine collaboration

In the case of above-mentioned example of “check manual” activity, the possible action is “look at”,
and the possible object is “paper” in factory A, and “display” in factory B. By decomposing the complex
activity into simpler actions and involved objects, the intermediate recognition module of “look at” action
recognition can be used for both of the factories. This framework enables t o recognize many activities
in various factories without customization with sufficient level of explanation.

Figure 5-9. Overview of the framework(Source: DFKI).

This system introduces a deep neural network (DNN) based explainable action recognition methodthat utilizes wearable sensors and a gaze-guided object recognition method110 to recognize activities.
The experimental results showed comparable performance to the conventional methods with anaccuracy of over 90%.
By developing easy-to-deploy explainable activity recognition system, we will support many workers
in various manufacturing factories in terms of training, guiding, quality control, and reducing risk.

110

See Al-Naser et al. forthcoming, Munir et al. 2019.

59

6. Conclusion
This paper focused on social challenges associated with factors such as a maturing workforce and aging
machines and infrastructure, discussed how these problems should be defined, and explored new approaches
in HMI through use cases in Germany and Japan. Digital technologies like CPS, AI, and robotics are expected
to help solve these social challenges. Applying these technologies also impacts social transformation, since
the technologies perform routine cognitive tasks that were previously the domain of humans .
In order to establish a sustainable society, it’s necessary for humans to be able to continuously createhigh-value-added work, and to be able to shift from non-high-value-added work to high-value-added work at
any time. It’s also necessary for machines to not only carry out non-high-value-added work, but to also be amechanism to create high-value-added work by constant interaction with humans. According to theserequirements, the digital transformation can enable a novel, human-centered manufacturing system in whichhumans concentrate on lifelong skill improvement and continuously create high-value-added work. Essentially,
this system revitalizes human-machine interaction, allowing both humans and machines to play a role indigital society.
Ultimately, we should work to strengthen the public interest in digital society, share knowledge acquired
from interactions between humans and machines, and establish a sustainable society that focuses on humanwell-being. In this paper we called for a mediation process, and we encourage society start considering anddiscussing the scenarios and actions together for the future.
Based on various country-specific case studies, we observed how Germany and Japan are changing
traditional HMI with digital technologies. It appears that a new HMI model – a social mediation process that
adjusts for mutual aids, as explored in the previous chapter – is under development. However, a mechanism
for storing the experiences of HMI as social collective intelligence and attempts to improve the performanceof humans and machines using the collective intelligence have not been explored in both countries. In orderto realize this potential, it is necessary to establish a mechanism and rules to share experiences at various
levels, such as the individual, regional, factory, company, country, world, etc.
This collaborative project between Germany and Japan is quite valuable, as we have recognizedsimilarities and differences among the two countries on how to deal with the issue at hand. Although bothcountries are aiming for a similar goal, their historical backgrounds and social structures are impacting their
methodologies and focal areas. In closing, we encourage keeping discussions open among multiple countries
in the effort to establish a sustainable society.

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acatech – National Academyof Science and Engineering
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