The impact of Industry 4.0 on the reconciliation of dynamic capabilities: evidence from the European manufacturing industries

Figures & data

Table 1. Literature review taxonomies.

Author(s)	Cluster	I4.0 Coverage	Type of planning	Focus
Business and operations impact
Nagy et al. (2018)	Business and operations impact	IoT in manufacturing and logistics	Strategic/operational level	Logistic services, efficienc processes, higher market and financial performance, competitiveness – increase of economic sustainability
Porter and Heppelmann (2014)	Business and operations impact	Smart connected products	Strategic level	Impact on industry structure and industry boundaries, trade-offs
Strange and Zucchella (2017)	Business and operations impact	I4.0 Competition and corporate strategies in global value chains	Strategic level	Global value chain impact
Rossini et al. (2019)	Business and operations impact	I4.0 and lean production practices	Operational level	Operation, logistics and supply chain processes
Tortorella and Fettermann (2018)	Business and operations impact	I4.0 and lean production practices	Operational level	Performance improvements
Mladineo, Veza, and Gjeldum (2017)	Business and operations impact	Virtual production space and more data based decision making	Operational level	Better control, surveillance, transparency and efficiency of production processes
Technological and environmental impact
Santos et al. (2019)	Technological and environmental impact	Plug & Clean approach based on lean-green methods	Operational level	Improving productivity and environmental performance, waste reduction
Wang, Wan, et al. (2016)	Technological and environmental impact	Big data based coordination system	Operational level/tactical level	Interconnectivity between assets, massive data collection from smart objects for better feedback and coordination
Lee, Bagheri, and Kao (2015)	Technological and environmental impact	CPS architecture	Operational/tactical level	Better product quality, system reliability, intelligent and resilient manufacturing equipment
Social Impact
Whysall, Owtram, and Brittain (2019)	Social impact	Talent management practices	Strategic/operational level	Closing gap between existing capabilities of employees and the changing requirements
Pacaux-Lemoine et al. (2017)	Social impact	Intelligent manufacturing and complexity	Operational/tactical level	Solution to retain humans in the process control loop (automation level)
Longo, Nicoletti, and Padovano (2017)	Social impact	Augmented reality Intelligent tutoring systems	Tactical level	Enhancing operators capabilities, more flexible and dynamic working environment

Figure 1. Research framework Based on (Teece, Pisano and Shuen 1997, Slack and Lews 2002, Beer and Liyanage 2012).

Table 2. Data collection details.

Case	Type of company	Respondents	Data collection	Type of data	I4.0 specifics
A	Semiconductor manufacturer (Portugal) Cyber Security unit of semiconductor manufacturer (Germany)	Senior Researchers Production manager Project manager Digitalisation manager	Stage 1: Self-Assessment by case owners Stage 2: Review of self-assessments by authors Stage 3: Semi-structured Interviews with case owners to revise content	Qualitative and quantitative data based on self-assessment and interviews. KPIs (qualitative statements)	Area: Cyber security for legacy systems Case: Intrusion Detection System for cyber security
B	Component supplier of ECS industry (Austria)	Supply chain manager Manager Process Service Centre Project manager Senior scientists	Stage 1: Self-Assessment by case owners Stage 2: Review of self-assessments by authors Stage 3: Semi-structured Interviews with case owners to revise content	Qualitative data based on self-assessment and interviews. KPIs (qualitative and quantitative statements)	Area: Agile manufacturing and maintenance Case: Implentation of unified unit number (UUN) Control and optimisation algorithm ERP and MES system
C	Printed circuit board assembler (Austria)	Project manager Production Engineer Supply Chain Manager Senior scientist	Stage 1: Self-Assessment by case owners Stage 2: Review of self-assessments by authors Stage 3: Semi-structured Interviews with case owners to revise content	Qualitative data based on self-assessment and interviews. KPIs (qualitative and quantitative statements)	Area: Machine learning for improved decision making Case: Digital full unit traceability
D	Semiconductor manufacturer (Germany)	Manager factory integration Project manager Senior Researcher Material manager	Stage 1: Self-Assessment by case owners Stage 2: Review of self-assessments by authors Stage 3: Semi-structured Interviews with case owners to revise content	Qualitative data based on self-assessment and interviews. KPIs (description and quantitative statements	Area: Fab digitalisation and virtualisation Case: Automated quality inspection system Implementation degree: 100%
E	Aerospace manufacturer (UK)	Logistics Operations Executive	Stage 1: Self-Assessment by case owners Stage 2: Review of self-assessments by authors Stage 3: Semi-structured Interviews with case owners to revise content	Qualitative data based on self-assessment and interviews KPIs (qualitative statements).	Area: Interconnectedness of supply chain participants Case: IT-based order to delivery system
F	Aerospace manufacturer (UK)	Enterprise Architect	Stage 1: Self-Assessment by case owners Stage 2: Review of self-assessments by authors Stage 3: Semi-structured Interviews with case owners to revise content	Qualitative data based on self-assessment and interviews. KPIs (qualitative statements)	Area: Smart factory with digitally connected machines Case: Smart manufacturing setup

Table 3. Case descriptions.

Case	Description
A	Secure production networks are a central prerequisite for implementing Industry 4.0 applications in real production environments. In today's production plants, many different machines communicate intensively over the network. Compared to an office IT network, the communication behaviour of nodes in a Machine-to-Machine (M2M) communication network is generally relatively stable. A human intervention (from outside) in this communication violates this stability. The use of Intrusion Detection Systems (IDS) is a way to detect such deviations from normal behaviour in the production network. However, classical IDSs require the definition of rules for the execution of applications on a host machine or the communication behaviour in the network. These rules are manually defined by experts based on their understanding about the known signatures of the running applications on a host or the traffic exchanged over the network. If one of these rules is violated, an alarm is triggered, and an analysis can begin to identify the possible causes. For large systems such as production networks, however, such rule-based IDSs are very complex to configure and maintain requiring extensive domain knowledge to map the entire production network and its acceptable behaviour and to keep these rules or signatures updated.
B	The production of sputtering targets is a powder metallurgical process that involves many critical production steps and tight requirements. The sputtering targets are treated in production lots of up to 10 pieces. Not only a few process parameters are recorded along the supply chain, but the combination of these data with the production lots was either impossible or only possible to collect by hand. The main production step - hot deformation - was planned by hand and the heating times are determined on the basis assumptions. The challenge is to integrate single piece traceability into the entire production chain and to optimise and more accurately design the main production process step. Single piece tracking is achieved by a unified unit number, which will be implemented in the ERP/MES and on each single material ingot. With new tailored process models and advanced model-based control tools, the production step deformation will be controlled more efficiently and accurately. In the final production steps, single piece production is installed together with almost paperless production.
C	Embedded components directly in the printed circuit board (PCB) increase the complexity of PCB production, especially the copper plating and photo (etching) process. The general trend in the electronics industry is towards smaller, more compact and more powerful devices. To meet the new requirements, the case company developed and patented its technology several years ago. The aim was to integrate the active and passive components attached to the outside of printed circuit boards inside the boards in order to reduce the overall size and protect the components. The current quality control of the copper plate thickness is performed after each individual copper process. The complete production process for a lot size of 30 working panels can be traced in the collected data based on manual operator input into Excel documents. These Excel documents are not standardised for all users and process steps and are stored on local storage devices.
D	In addition to cost and time, effective quality assurance is one of the most relevant parameters for efficient manufacturing in the semiconductor industry. Up to now, the detection and classification of quality issues has mostly been carried out by skilled employees. Over the last years, the increasing level of fab automation requires enhanced and fully automated quality controlling methods with extensive measuring instruments and tools. The implementation of automatic control serves to overcome the limits of the human operator’s performance to detect defects. A central IT platform must capture and process quality data from a highly automated semiconductor manufacturing process in real time. Based on this data, especially trained employees will only have to inspect wafers that are marked as defective.
E	The visibility of items from thousands of global and local suppliers increases the complexity of the case company’s supply chain, especially when it comes to prioritising shipments from these suppliers to fulfil the material requirements. This poses serious challenge to the supply chain operators of the case company who are responsible for ensuring that suppliers are making and supplying parts in due time. Besides visibility and prioritising shipments, there has been a problem of measuring on-time delivery as one of the important performance metrics. Over the past few years, this has been carried out through the manual booking & scheduling process, which was disjointed, inefficient and relies on tacit knowledge and relationships. The aim was to establish an end-to-end, event-driven information technology (IT) solution that gives full visibility, via the internet, to planned and actual events during the order-to-delivery process. The new process replaced the manual system with an IT-based solution. The solution linked together demand signal and transport booking to prevent early shipment of unwanted materials. This provides suppliers with a booking portal that sends advance shipment notifications to the case company. It then enables supply chain operators to monitor their shipments. In addition, the solution provides a consolidated cost and delivery management dashboard that allows the case company to quickly analyse performance and identify opportunities for improvements.
F	A leading aerospace company in the UK, which used to have numerous manufacturing facilities originally developed in 1950s and lacking the ability to further expand in terms of capacity as well as in technological advancements. Besides that, the company anticipated a strong growth in terms of output. Moreover, the challenges on quality, changing customer requirements and flexibility of operations were questionable taking into account the old manufacturing facility and, hence, it was considered as a single point of failure in their supply chain. This triggered a need for a much more automated, integrated and systemised production process, which takes the shape of building a new digital connected smart factory equipped with cutting edge, connected machine tools, 3D printing and big data driven digital devices. The company attempted to amalgamate the knowledge and expertise of a leading advanced manufacturing research centre in the UK with the company’s own manufacturing engineers’ competencies to setup a state-of-the-art digital manufacturing facility.

Table 4. Case coding example “Traceability”.

NVIVO node	Original text (coded data) [Case]
Impact on traceability	Early detection of an anomalous network traffic can support to trace any changes in processes or harm induced by cyberattacks [Case A].
	Each product can be traced through single piece tracking along the production chain, ERP and on the shop floor by implementing marking via engraving machines [Case B].
	Enable full unit traceability this will in turn enable further opportunities to enhance processes and faster pinpoint and tackle problems. Traceability allows full accountability in production. Data is no longer transferred manually (transfer happens directly from one equipment to a data base and the MES) [Case C].
	All data can be tracked using the new solution. Data is automatically stored in the database, no more lack of data traceability [Case D].
	Event delays are catalogued according to an agreed criteria which allows quick analysis of where, when and why failures occur. This provides a rich source of data to aid problem solving and traceability [Case E].
	The traceability of the product has significantly increased as they have reduced the amount of inventory as well as the lead times, which leads to much better control over tracing products. Moreover, the single manufacturing setup improves process visibility too. The staff gains access to live data generated by machines. [Case F]

Table 5. Description of KPIs.

Performance measure	Description
Economic
Level of automation	Impact on the factory/plant automation level, transformation from manual to automated/digital processes and decision-making and support.
Process quality	How well the process of manufacturing/servicing a product is working; the degree of realisation of specified requirements and process capability.
Product/service quality	To what extent the product/service is free of any deficiencies or defects, the degree of realisation of specified requirements by the product/service.
OEE	Overall Equipment Effectiveness index contains the used availability, the effectiveness of the work unit, and the quality rate on performance and quality losses.
Traceability	The capabilities to track and trace changes in processes and products.
Transparency	The continuous access to information about production processes and production planning status in the context of the information system.
Reduction of operating costs	Expenses associated with the operation, maintenance and administration of the firm’s production capabilities.
Social
Workforce satisfaction	The extent to which the assessed use case empowers the workforce to identify potential for improvement and/or solve issues.
Innovation and problem-solving skills	The extent to which the assessed case empowers the workforce to identify potential for improvement and/or solve issues.
Knowledge development/gain	The expected level of relevant knowledge gained by the workforce and organisation from implementing the case.
Environmental
Energy efficiency (Impact on CO² emissions, fuel efficiency and empty running)	Energy consumption is the amount of energy or power used for plant operation, manufacturing process, or manufacturing equipment.
Material resource efficiency (Impact on waste)	The extent of material usage in production considering scrap ration and the relationship between scrap quantity and produced quantity.

Based on (ISO 24000-2 and GRI G4 standard).

Table 6. Cross-case analysis of economic performance measures.

Economic performance measures	Impact
Level of automation	Reduction of manual intervention Self-learning networks, self-diagnose of machines Automated decision-making over supply chain processes Increase of automated information and data exchange Interconnection of different process steps Interconnection of SC participants Increase of process speed (lead time, throughput)
Process quality	More resilient manufacturing processes Standardised manufacturing processes Improved process data collection Continuously monitoring and control of process Increase of process reliability Reduction of lead-time Increased flexibility and efficiency
Product/service quality	Making system more secure and resilient leads to better product quality Increase of defect detection Improve overall product quality based on real-time data Improved quality by reducing faults and scrap Stable quality level based on algorithms Zero-failure culture High precision in products through high quality processes
OEE	Improved overall system availability through automation Improved utilisation of production resources/systems Reduction of downtime of production systems Decrease of quality losses Improved overall process quality less downtimes and maintenance OEE in overall system is better Automated quality inspection lead to less quality losses Better process planning Better inventory accuracy due to less rework Full utilisation of IT Processes more streamlined Highly standardised facility
Traceability	Early fault detection Full unit traceability along supply chain and production chain No more manual transfer – no human interaction Holistic data tracking Automatic data storage Reduction of lead time and inventory Better control over tracing products
Transparency	Transparency of systems increase Actual planning status and availability of equipment evident Transparency of all production processes Constant monitoring of processes by collecting data in a timely manner Better access to live data for staff Improved supply chain coordination
Reduction of operating costs	Reduction of manual work (less low level work) Level of automation reduces staff – reduction of overall operating costs Increase of utilisation of resources and reduction of quality losses Reduction of scrap rate Paperless production Reduced energy costs Less rework, less waste Better transport infrastructure (internal and external) New infrastructure is more efficient (both modern buildings and integrated machine tool units).

Table 7. Cross-case analysis of environmental performance measures.

Environmental performance measures	Impact
Energy efficiency (Impact on CO² emissions, fuel efficiency and empty running)	Energy saving based on automated capacity requirement forecasts Algorithm based models can estimate ideal utilisation of system Fewer samplings, fewer rework, fewer process adjustments and fewer measurement cheques with equipment can reduce energy consumption Less electric energy and pure hydrogen Automation of whole company processes can be enhanced, and then a significant energy reduction will be reached Heating and lighting much more effective and energy efficient compared to old factories Footprint of physically factory floor and number of machines is reduced resulting in reduced energy consumption Reduced footprint, as the machine tools are fewer in quantity
Material resource efficiency (Impact on waste)	Improved process stability leads to improved product quality further leading to a reduced scrap rate Reduced scrap rate reduces material consumption Fewer samples – less material Less tool downtimes lead to less maintenance resources and material Increased production visibility reduce surplus production and inventory holding Consumption of raw material improved Reduction of consumables

Table 8. Cross-case analysis of social performance measures.

Social performance measures	Impact
Workforce satisfaction	MES is focussing workforce Improved usability and acceptance through fab virtualisation (screens) Real-time exchange between human and machine Increase in IT effort, which needs to be handled Problems are now more visible to employees Less monotonous work, higher satisfaction Improvement of worker condition Workforce efficiency is increased Fear of job loss increases Workforce is satisfied to have a reliable manufacturing process People want part of technological change Increase of retirements
Innovation and problem-solving skills	General positive impact on innovation and problem solving skills Knowledge based approach can result in low false positive rates Automatic self-improvement of employees Reduced study time for e new workers Increase of operator convenience Higher skill-set and process knowledge for employees Increase of managerial support Change of skill-set due to new process environment New setups enhance people skills
Knowledge development/gain	General high impact on Industry 4.0 knowledge gain System knowledge gain along whole value chain Knowledge gained can be transferred to other production lines and facilities Common understanding and language within fab Higher knowledge in individuals leads to higher qualification Development of new automation knowledge Significant learning for business Replacing old facility with new manufacturing creates a lot of knowledge Technical knowledge and skill enhancement Introduction of educational measures

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