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Managing Industry 4.0 integration - the Industry 4.0 Knowledge & Technology Framework

Freund Lucas, Al-Majeed Salah

LogForum

2021

Vol. 17, no. 4

569--586

Background: This paper has the aim to address the key area of managing complex Industry 4.0 production systems to support a successful adoption and integration of Industry 4.0. This is achieved by approaching methodological research challenges of Industry 4.0 in the form of lacking reference models and the need to establish common definitions of fundamental concepts. The general underlying challenge this paper aims to contribute to solve can therefore be defined as how the technological advances, like CPS, IoT, Big Data or CC can be best linked with each other on different levels of perspective and how they can be used by decision-makers to generate economic value and to improve existing processes. This is achieved through the introduction of the Industry 4.0 Knowledge & Technology Framework (IKTF). Methods: The Industry 4.0 Knowledge Framework (IKTF) is based on the concept of the micro-meso-macro analysis framework and consequently is representative for the approach of micro-meso-macro analysis in managerial practice. It proposes three categories of factors and places them in three basic levels layering them on top of each other. The macrolevel includes the financial, political and sociocultural factors that influence Industry 4.0. The meso-level includes the technical and organizational factors. The micro-level refers to individual factors, particularly individual companies’ intention to use Industry 4.0 in practical economic contexts. Results: The Industry 4.0 Knowledge & Technology Framework (IKTF) provides guidance to corporate decision makers by providing a comprehensive, multi-level sequential integration framework for Industry 4.0 based on a sequential micro, meso and macro perspective analysis of the individual corporate context. The aim of the IKTF is to support an informed and successful managerial decision-making process and therefore enable the integration of Industry 4.0 in a corporate context. Conclusion: As a first step, the structure, and contents of the IKTF are sequentially introduced and described. In a second and final step the functionality and applicability of the IKTF are demonstrated and discussed on a theoretical and practical level with the help of a case study.

Hypotheses concerning complexity surges in modern and future industrial information systems

Freund Lucas, Al-Majeed Salah

LogForum

2021

Vol. 17, no. 3

321--329

Background: This paper has the central aim to provide an analysis of increases of system complexity in the context of modern industrial information systems. An investigation and exploration of relevant theoretical frameworks is conducted and accumulates in the proposition of a set of hypotheses as an explanatory approach for a possible definition of system complexity based on information growth in industrial information systems. Several interconnected sources of technological information are investigated and explored in the given context in their functionality as information transferring agents, and their practical relevance is underlined by the application of the concepts of Big Data and cyber-physical, cyber-human and cyber-physical-cyber-human systems. Methods: A systematic review of relevant literature was conducted for this paper and in total 85 sources matching the scope of this article, in the form of academic journals and academic books of the mentioned academic fields, published between 2012 and 2019, were selected, individually read and reviewed by the authors and reduced by careful author selection to 17 key sources which served as the basis for theory synthesis. Results: Four hypotheses (H1-H4) concerning exponential surges of system complexity in industrial information systems are introduced. Furthermore, first foundational ideas for a possible approach to potentially describe, model and simulate complex industrial information systems based on network, agent-based approaches and the concept of Shannon entropy are introduced. Conclusion: Based on the introduced hypotheses it can be theoretically indicated that the amount information aggregated and transferred in a system can serve as an indicator for the development of system complexity and as a possible explanatory concept for the exponential surges of system complexity in industrial information systems.