Digital Technology Deployment in Multi-National Enterprises

Digital Technology Deployment in Multi-National Enterprises

Jose Irizar
DOI: 10.4018/978-1-7998-7712-7.ch002
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After the longest period of continuous growth in its history, the automotive industry is experiencing a most dramatic downturn. The challenge for automobile companies is not just to cope with the three converging trends of vehicle electrification, autonomous driving, and shared mobility, but also to make the best judgement on how and where to invest in a declining market. Digital is becoming the de facto way of operating along the value chain. Advanced automation, artificial intelligence (AI), and additive manufacturing will reshape traditional processes. This chapter reports upon the implementation of new digital technologies and related critical success factors in two multi-national industries, with major interests in the automotive sector. It takes an empirical approach, analysing use cases, projects, and input from experts. The findings assess the repercussions for IT strategy and changes in business processes impacted by the use of new technologies and illustrate how people skill requirements have evolved, both within the IT organisation and in other company departments.
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Background And Technology Concepts

This research focuses on two large multi-national groups. Although a large part of the companies’ activities is in the automotive sector, around a quarter of the business in both companies concerns other industry sectors, such as consumer goods (power tools, power-tool accessories), measuring technology, energy and building automation technology, wind power and escalator drives. Some of the use cases discussed in this chapter were developed and implemented within these non-automotive industry sectors. The introduction of many disruptive new technologies follows a different pattern to the traditional adoption of previous techniques and applications. While in the past, most technology implementations followed a classic project management approach, current projects tend to follow an agile, or at least hybrid, approach. Instead of meticulous planning, projects are now more about cross-disciplinary collaboration and adaptive planning for rapid delivery and early benefits (Gemino et al., 2021).

This chapter deals with the concepts of use cases and projects, and it is relevant to point out how these two concepts differ yet overlap. While projects have, until recently, typically been formal, structured, complex and, in most cases, timely endeavours with several stakeholders, use cases tend to be smaller in scope, and often encompass iterative development cycles, involving end users in the implementation of a system or technology. Use cases can thus be seen as agile projects, and are a small part of a long-term product road-map with a “start small – but think big” approach, characterized by the use of diagram structures to describe and communicate required functionalities (Krasadakis 2019; Larson & Larson, 2015). Use cases often become test cases with acceptance criteria, and can be understood as prototypes or minimum viable products. While many projects in the automotive and consumer goods industries are run using traditional project management methodologies, use cases are mostly performed applying agile project management approaches.

Key Terms in this Chapter

Digitalization: The use of digital technologies to change a business model and provide new revenue and value-producing opportunities; it is the process of moving to a digital business.

Data Lake: Storage of large volumes of unstructured data in their original format.

Analytics: Refers to data-driven analysis using various methods such as data mining and statistical techniques to provide better decision making.

Agile: Methodologies and approaches mainly applied to software development which place an emphasis on delivering working code and downplay the importance of formal processes. It is suggested, therefore, that the software development process can adapt and react promptly to changes that occur in user requirements.

Data Fusion: The process of integrating multiple data sources to produce more consistent, accurate, and useful information than that provided by any individual data source.

Artificial Intelligence (AI): AI methods attempt to make a computer system behave in the same way as a human being. One application for AI is in natural language processing, where users can communicate with a computer system using English-like statements.

Industry 4.0: The idea of a fully integrated manufacturing industry enabled by digital transformation and the integration of information technology and automation systems in manufacturing.

Cloud Computing: Cloud computing is the delivery of computing services – hardware such as servers, storage, databases and networking and software – over the Internet (‘the cloud’).

Big Data: The large datasets that are enabled by IT systems which support, capture, and disseminate these data.

Machine Learning (ML): A type of artificial intelligence that enables self-learning from data and then applies that learning without the need for human intervention.

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