People, Competencies, and Capabilities Are Core Elements in Digital Transformation: A Case Study of a Digital Transformation Project at ABB

Introduction

Digitalization or the widespread adoption of digital technologies, industrial Internet of Tilings (IIoT), artificial intelligence (AI), and advanced analytics is driving digital transformation in various industrial sectors. The major drivers for this development have been energy and industrial revolutions, as illustrated in Figure 11.1. Industrial revolution is a major driver for digitalization, IoT, and AL The transformation will disrupt several industrial sectors including, for example, energy production, industrial manufacturing, transportation, and infrastructure.

As one real-life example, electric utilities are facing both opportunities and challenges in the emerging energy revolution. Some key questions are how to boost productivity out of aging and regulated assets and how to manage distributed renewable energy resources, along with the future massive integration of electric plug-in vehicles and the complications and changes in the regulatory landscape. At the same time, the decline in demand and revenue from the regulated asset base has them thinking about digitalization, monetization of data, and alternate sources of revenue (ABB, 2018a).

Based on our analysis related to the use of digital technologies in industry, there is a remarkable potential in the use of digital, industrial IoT, analytics, and AI

Industrial revolution is a major driver for digitalization, loT, and Al (ABB, 2019a)

Figure 11.1 Industrial revolution is a major driver for digitalization, loT, and Al (ABB, 2019a).

technologies in different industries as an S-curve, which is illustrated in Figure 11.2 (Jouret, 2017).

Digital transformation will disrupt existing business models, create efficiencies, and enhance customer experiences in different industrial sectors. The digital transformation of industrial sectors like energy management and automation lies at the core of this journey, enabling a paradigm shift for the industry (World Economic Forum, 2018).

Both competitiveness and productivity of industrial companies can be increased with AI, IoT, and digitalization technologies. Based on the business analysis of the World Economic Forum and Accenture (2019) and data from 16,000 companies, there is a strong positive return on investment, although most of the gains

S-curve of digital technologies in different industrial sectors (ABB, 2018; 2017)

Figure 11.2 S-curve of digital technologies in different industrial sectors (ABB, 2018; 2017).

are clustered among industry leaders. These early adopters saw a 70% productivity increase, compared with just 30% for industry followers.

Several national level R&D and innovation programs (e.g., Industry 4.0 in Germany, Made in China 2025, and Manufacturing USA) have a strong focus in the development in IIoT, AI, advanced analytics, and intelligent technologies to enable the fourth industrial revolution (World Economic Forum, 2018; CSIS, 2019; Wubbeke et al., 2016; Manufacturing USA, 2019).

Based on McKinsey, there will be remarkable value migration opportunities in digital services, which are illustrated in Figure 11.3.

Objectives and Research Approach

The objectives of the research are:

  • 1. To develop a value-based framework for industrial digital solutions which can be used in the ramp-up of digital, IoT, and AI solutions in organizational digitalization programs.
  • 2. To find out the impacts of a digital solutions framework for people, competence, and capability development needs in organizations.
  • 3- To find out what the major customer benefits are of digitalization in industrial solutions.

The research and development efforts reported here wish to serve both as useful theoretical insights and as practical solutions to the described overall challenge and to the problems of particular cases. The report is based on the results from the project “TAKEOFF innovation, talent and competence development for industry 4.0.”

Figure 11.3 Business opportunities related to digitalization (ABB, 2018).

This research has been partly funded by EIT Digital and done in collaboration with Aalto University, Aalto Executive Education, and some partner companies of ABB.

Challenges Related to the Use of Digitalization and AI

Despite all the potential benefits of digitalization, many industrial companies have been facing challenges in how to utilize all opportunities and benefits of digitalization, IoT, analytics, and AI. According to Furr and Shipolov (2019), there are myths and challenges in the digital transformation, which are summarized in Table 11.1.

Also based on the findings of Fountaine, McCarthy, and Saleh (2019), technology is not the biggest challenge for building Al-powered organizations—it is culture, people, and competencies. Based on the McKinsey research of several thousands of companies, AI and advanced analytics support core practices are in widespread use in only 8% of the companies, while in most of the companies, ad-hoc pilots or applying AI and analytics in single process pilots are the norm. Only 23% of companies have budgeted resources for non-technical development, for example, training and adoption of new ways of working. The reason for this is because of cultural and organizational barriers but also myths and unrealistic expectations of AI as a plug and play technology with immediate returns.

Many organizations have failed to realize that digital transformation and AI cannot be done solely through innovation and technology. Transformation also requires investment in people and culture (Furr and Shipolov, 2019). People need upskilling in order to apply new skills and ways of working, as well as using the latest technology in their daily work. Regardless of how good the new technology is, investment will be partly wasted if the people do not accept the change (Fountaine et al. 2019). Unlearning of old habits and upskilling of competencies are needed to realize the benefits of digital, IoT, and AI tools.

 
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