1. Introduction to digital transformation in Business
Dr Alex Zarifis, University of Nicosia, University of Cambridge
Prof. Despo Ktoridou, University of Nicosia
Dr Leonidas Efthymiou, University of Nicosia
Prof. Xusen Cheng, Renmin University of China
The introductory chapter at the start of the book explains why each chapter was chosen, the common threads and some key findings.
Each case brings a different piece of the puzzle, but it also serves to test and verify our current understanding of digital transformation. There are common lessons across all cases, along with the specific lessons some cases offer for those specific sectors of the economy.
2. Insight and Control over Personal Data for Swedish local Government: A citizen-centered model for public-private data sharing
Theo Anderson, Business Intelligence Officer / Omvärldsbevakare at DIGG – Swedish Agency for Digital Government
In July 2020, the Swedish Public Employment Service, the Swedish eHealth Agency, the Agency for Digital Government (DIGG) and the Swedish Tax Agency were collectively tasked with showing how an individual's ability to have insight and control over data that have been stored about them by the public sector – and, in the long term, in the private sector – can be enhanced.
Large quantities of data about individuals are processed by public administration, for which there is support in statutes or agreements. The public sector often not only has the right but also an obligation to process personal data in order to perform its duties.
It is widely believed that it would make things easier for individuals, and introduce efficiency improvements for the public sector, if it were possible for another party to reuse information already possessed by an authority as the basis for making decisions or taking certain measures. It is however unclear exactly how this can be achieved and what prerequisites need to be in place for such user-centricity to be achieved in the design of these services.
The initial external monitoring and analysis of these issues focused on various publications from amongst others, the EU, UN and OECD in order to determine if there exists a consensus regarding whether individuals are or should be, entitled to increased insight into and control over the data stored about them. After confirming that indeed there seemed to be such a consensus, we were interested in how well technological developments and discussions on policy, data management and public agencies instructions agreed with the vision for how this insight and control will be achieved and then given to, and managed, by the individual.
3. Virtualizing New Product Development in Heavy Asset Industries
Mads Jørgsholm Bierrings, Implement Consulting Group, Copenhagen Business School, Denmark, mabi@implement.dk
Nico Wunderlich, IT University of Copenhagen, Denmark, nicw@itu.dk
Reliability and validity have been long-term quality criteria in heavy asset industries. Due to the capital-intensity and long product cycles, companies traditionally invested extensively in expensive physical test rigs to carry out long-winded prototype trials preparing for the product launch. The technological trend of modularization as well as increasing client demands for customization and non-disruptive on-site maintenance force even heavy asset industries to accelerate and flexibilize their new product development. Industries less dependent on physical elements in their products and service, such as the financial and media sector, have been already facing strong shifts in market demands, which unleashed transformational forces and initiated processes of infinite constant change. In this chapter, we study the case of one of the world's leading manufacturers of highly reliable, sustainable energy systems and how this company digitally transformed their new product development by virtual prototyping to become more cost and time efficient and more agile from scaling up the number and configurations of tested and validated models.
In the case company, the emergence of big data from real-time streaming of digital twins and other industry 4.0 developments allow to evaluate model designs before building a physical prototype of the product. Designers and engineers are now capable of detecting design problems and errors early in the product development process (Carulli et al., 2013). Virtual prototyping is digital technology that enables processes of constructing and testing digital prototypes. New product development in the company adopted virtual prototyping due to the improved availability and lowered prices of this digital technology (Choi et al., 2015). While computer-aided design tools have been commonly used in product design for decades, the case company transformed their new product development to verify their models in the virtual hemisphere. Shifting the physical validation at the latest possible time of the development process can be subsumed as the ability of front-load verification, “in front” of the physical prototyping stages. Virtual prototyping thus limits physical iterations and increases the confidence in the models before proceeding to the physical test stages, while integrating experimental test data. The opportunity to involve customers in the early design phases is another benefit of virtual prototyping. This activity gathers input from external sources and allows to access knowledge beyond the organizational boundaries. Embedded in a data landscape of industry 4.0 and digital twins, the case company adopted principles of digital business from influential software companies and emphasizes virtual prototyping as major technology for digitizing new product development in heavy asset industries. Testing and validation in the digital hemisphere provides scale, scope, and learning effects at low cost, in short time, and enriched by early collected customer feedback which utilizes the advantages of digital technology for the sustainable evaluation of traditionally heavy weight models in the light-footed, agile virtuality.
Data collection:
16 semi-structured interviews with managers and executives from new product development units and secondary data collection (ethnography, team meetings, guidelines, documents) over a four months' period in 2020
References:
Carulli, M., Bordegoni, M., & Cugini, U. (2013). An approach for capturing the Voice of the Customer based on Virtual Prototyping. Journal of Intelligent Manufacturing, 24. https://doi.org/10.1007/s10845-012-0662-5
Choi, S., Jung, K., & Noh, S. D. (2015). Virtual reality applications in manufacturing industries: Past research, present findings, and future directions. Concurrent Engineering, 23. https://doi.org/10.1177/1063293X14568814
4. What are we automating? On the need for vision and expertise when deploying AI systemsDr Alex Rast, Oxford Brooks University
Abstract: The emergence and availability of powerful, easy-to-use frameworks like TensorFlow or PyTorch appears to make implementing in-house AI simple and attractive. Neural networks,
in particular, are often seen as a sort of 'cure-all' panacaea for any difficult problem or opportunity businesses across numerous sectors may face. However, such systems have subtle and often very surprising behaviours that require considerable domain expertise in order to implement a functional system not vulnerable to unexpected pathologies. More fundamentally, they need to be deployed with a clear sense of what the AI system is going to achieve. Careful attention must be paid at the outset to draft a clear and concrete design specification that indicates the intended function, and equally, draws a line under capabilities that are out of scope. Likewise, an effort needs to be made either to identify in-house people with the required skill sets to develop the system,
