A strategy guidance model to realize industrial digitalization in production companies

Management and Production Engineering Review Volume 11 • Number 3 • September 2020 • pp. 14–25 DOI: 10.24425/mper.2020.134928 A STRATEGY GUIDANCE MODEL TO REALIZE INDUSTRIAL DIGITALIZATION IN PRODUCTION COMPANIES Andreas Schumacher1,2 , Wilfried Sihn1,2 1 2 University of Technology Vienna, Austria Fraunhofer Austria Research GmbH, Austria Corresponding author: Andreas Schumacher Fraunhofer Austria Research GmbH Dept. Advanced Industrial Management Theresianumgasse 7, 1040 Vienna, Austria phone: +43 676 888 616 35 e-mail: Received: 15 January 2020 Accepted: 27 July 2020 Abstract The realization of digitalization in production companies – currently also referred to as Industry 4.0 – aims for reduction of internal value creation costs as well as costs for intercompany collaboration and plays a key role in their current strategy development. However, related strategy research still lacks to provide operationalized digitalization methods and tools to practitioners with scientific rigor as well as real-world relevance. To challenge this status quo, we present a scientifically grounded 14-step procedure model including 11 practically tested tools, developed specifically for real-world application. The model leads practitioners from their first contact with industrial digitalization, through the maturity assessment of 143 digitalization items, until the implementation of a KPI-monitoring system and a continuous improvement process. We applied and re-worked the procedure model during three years of application. Validation and Feedback from practitioners and scholars indicate, that the model drives strategy development towards objective and data-based decision making and increases stakeholder engagement in organizations considerably. Keywords Industry 4.0, industrial production systems, strategy development, digitalization, automation. Introduction and problem definition By focusing ID on the industrial enterprise sector, for which the EU aims at a target value of 20% of the total value added to secure prosperity, the concepts of ID are currently summarized within the industry 4.0-approach. Which pro-claims the Fourth Industrial Revolution by pursuing three target states [9, 10]: 1) horizontal integration via value creation networks; 2) digital consistency of engineering across the value chain; 3) vertical integration and networked production systems. The fundamental goals of production companies in the era of Industry 4.0 does not differ compared to goals defined during traditional production- optimization approaches, such as increased productivity through efficiency and flexibility as well as reduced costs and complexity [11, 12]. However, our own re- Increasing digitalization of data, thus information, as well as the automation of formerly manual processes, had a decisive influence on the development of society and economy in the last decades [1, 2]. For example, a country’s degree of digitalization [3] correlates positively with the prosperity and life satisfaction of its inhabitants [4]. Or in the economic sector, the use of digitalizing technologies and the automation of processes have a positive effect on the company’s performance [5–8]. In this paper we build on these positive effects of Industrial Digitalization (ID). However, we acknowledge, that the digital transformation of companies holds various challenges and that pre-conditions such as educated workforce, broadband internet or supporting laws have to exist to manifest these positive effects. 14 Management and Production Engineering Review search and project experience in the field of Industry 4.0 [13–15], as well as expert interviews of the last four years lead to the definition of two main challenges during implementation: • the abstraction level of Industry 4.0 is too high for operational implementation; • guidance to operationally implement Industry 4.0 is missing. As a result, Industry 4.0-concepts lack transfer into industrial companies, thus the intended competitive advantage cannot be claimed. The basic motivation of this paper therefore lies in the reduction of the abstraction level of Industry 4.0-strategy tools. We aim to reduce abstraction via the development of more easily understandable strategy tools of ID that operationalize abstract Industry 4.0-tools. Fig. 1. Industry 4.0-operationalization through DAVO. At this stage, we state some working definitions to transfer developments to an operational level. Firstly, we present our interpretation of Industry 4.0 composed by an integrated framework of digitalization and automation applied on organizational and value creation factors – short DAVO [16]. Following this framework, we argue that all concepts related to Industry 4.0 such as smart factories or smart objects, and subsequently smart processes can be composed of integrated digitalization and automation elements (Table 1). Table 1 Working definitions for Digitization, Digitalization and Automation [17]. Digitization Digitalization Automation Describes the conversion of continuous analog, noisy and smoothly varying information into clear bits of 1s and 0s. Describes the social implications of increased computerassistance, new media and communication platforms for economy, society and culture and working environments. Describes the implementation of technology, software and programs to accomplish a procedural outcome with little or no human interference. Although these concepts are distinct, yet in practice they manifest in combined and integrated manners to realize modern production paradigms – such as Industry 4.0. In the following, we refer to the utilization of these concepts in a combined manner as Volume 11 • Number 3 • September 2020 Industrial Digitalization (ID). The concept of ID receives increased scientific attention as research contributions e.g. in the scientific database Science Direct show a 300%-increase between 2015 and 2019 or results in the scientific search engine Google Scholar show a 2100%-increase during the same time. However, we conclude that regarding strategic guidance research related to ID, two fundamental research issues remain: • frameworks, methods and models are developed on a generic level with a lack of operationalization; • a lack of holistic metrics to measure the implementation of ID in real production environments. Moreover, interviews with practitioners revealed: • a lack of tools that allow for evaluation of the own operational ID development status in the company; • missing operationally relevant approaches for systematic strategic planning of the implementation of ID. Based on these problems, we define two research goals: 1) development of a practically applicable and holistic strategic guidance tool towards ID; 2) development of an ID-assessment tool on an operational level using quantitative measurement metrics. In order to define our research requirements and build on the current state of the art, we carry out an extensive literatur (...truncated)