Key performance indicators and industry 4.0: A socially responsible perspective

Žižek, Simona Šarotar; Nedelko, Zlatko; Mulej, Matjaž; Čič, Živa Veingerl Article Key performance indicators and industry 4.0: A socially responsible perspective Naše gospodarstvo / Our Economy Provided in Cooperation with: Faculty of Economics and Business, University of Maribor Suggested Citation: Žižek, Simona Šarotar; Nedelko, Zlatko; Mulej, Matjaž; Čič, Živa Veingerl (2020) : Key performance indicators and industry 4.0: A socially responsible perspective, Naše gospodarstvo / Our Economy, ISSN 2385-8052, Sciendo, Warsaw, Vol. 66, Iss. 3, pp. 22-35, https://doi.org/10.2478/ngoe-2020-0015 This Version is available at: https://hdl.handle.net/10419/290474 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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Key Performance Indicators and Industry 4.0 – A Socially Responsible Perspective. Naše gospodarstvo/Our Economy, 66(3), 22–35. DOI: 10.2478/ ngoe-2020-0015 DOI: 10.2478/ngoe-2020-0015 UDK: 005.32:005.35 JEL: M11, M12 RECEIVED: MARCH 2020 REVISED: MAY 2020 ACCEPTED: AUGUST 2020 Vol. 66 2020No. 3 Key Performance Indicators and Industry 4.0 – A Socially Responsible Perspective Simona Šarotar Žižek University of Maribor, Faculty of Economics and Business, Slovenia [email protected] Zlatko Nedelko University of Maribor, Faculty of Economics and Business, Slovenia zlatko[email protected] Matjaž Mulej University of Maribor, Faculty of Economics and Business, Slovenia [email protected] Živa Veingerl Čič Doba fakulteta, Maribor, Slovenia ziva.[email protected] Abstract The main aim of this contribution is to outlinethe role and importance of key performance indicators in the frame of Industry 4.0 implementation. These key performance indicators are presented as a cornerstone for industry 4.0 implementation in organizational practice, since they represent key input for needed data in digitalized organization. In that framework, the contribution first exposes some of the essential characteristics of “Industry 4.0”, followed by the methodology of key performance indicators (KPI). Next, thecontribution outlined a proposed methodology for implementing KPIs in frame of Industry 4.0 adoption in organizations. Another section of the paper is dedicatd to the linkage between corporate social responsilbty and KPIs in frame of Industry 4.0. The paper also outlines implications, limitations and further research directions are outlined. Keywords:Industry 4.0, key performance indicators (KPI), social responsibility Introduction The term “Industry 4.0” was first introduced at the Hannover Messe Fair in 2011. Industry 4.0 (I4.0) can be defined as »real-time, intelligent, and digital networking of people, equipment and objects for the mangement of business processes in organizations« (Dombrowski et al., 2017). Since the emergence of this new phenomenon, there has been a constant increase of literature on Industry 4.0. It addresses theoretical discussions about the phenomenon of Industry 4.0 (Drath & Horch, 2014; Weyer et al., 2015); case studies on the implementation of Industry 4.0 principles in various industries (Oliff & Liu, 2017; Caricato & Grieco, 2017; 23 Kuo, 2017); the role and importance of lean management for implementation of Industry 4.0 (Sony, 2018; Mayr et al., 2018); and linkages between implementation of Industry 4.0 and sustainable development (Varela et al., 2019; Duarte et al., 2020). Despite growth of the body of literature, several issues need to be addressed with regards to the implementation of Industry 4.0 into the practice of organizations. One such challenge is the role and importance of key performance indicators (KPI) in the process of Industry 4.0 implementation. There is literature on KPI, but it is not linked to the Industry 4.0 implementation. Thus, literature offers definitions of KPI (Ballard, 2013; Bishop, 2018, ISO 22400), case studies of implementation of KPIs in organizations, etc. The role of KPI in implementing Industry 4.0 was neglected in the literature, although KPIs are of huge importance when implementing Industry 4.0 principles. The role of KPI is crucial when organizations prepare blueprints for implementation of Industry 4.0 practices, i.e. defining KPIs, which are foundation for measuring key points in the process and are thus building blocks for measures established in frame of digitalized organizations. The main aim of this contribution is to outline the role and importance of key performance indicators (KPIs) in frame of Industry 4.0 implementation, while also considering the linkage between corporate social responsilbty and KPIs in frame of Industry 4.0, which has not yet been addresesed in the literature. The paper contributes the following: First, it highlights the role and importance of KPIs in the process of Industry 4.0 implementation. Second, it outlines the theoretical framework for implemenation of Industry 4.0, from identification of KPIs to their implementation. Third, it establishes the linkage between corporate social responsibility and KPIs in frame of Industry 4.0. Finally, it offers recommendations for implementaiton, as well as some directions for further research in this area. Methodology and Research Approach In line with identified challenges in Industry 4.0, we proposed the following research question: How can KPIs contribute to a healthy and socially responsible implemenation of Industry 4.0 in organizations? The methodology used is M. Mulej‘s Dialectical Systems Theory. The structure matches the above overview of the main issues. Based on a systematic literature search strategy, the databases dLib.si, ProQuest and Cobbis.si were reviewed in 2018. The literature was searched using the following keywords: “Industry 4.0,” “KPIs,” and “social responsibility.” We broaden our search of the literature on the management and systems theory (in conjunction with requisite holism by systemic approach). The limitation resulted from outflow year for the search, because the study covered only publications since 2010; such restrictions were deliberately set, because we wanted to obtain the latest and must current information on the issues. We focused on articles published in Slovenian and English. There were no further restrictions. Authors researched in the databases of the University of Maribor. Qualitative research methodology, including desk research, which was based on systems theory (Šarotar Žižek & Mulej, 2015), Mulej’s Dialectical Systems Theory (Mulej & Dyck, 2014) and the law of requisite holism was used. The search in the databases of the University of Maribor resulted in 1.850 hits. We selected and included 54 sources and researched them; see Figure 1. Figure 1. Research process flowchart Quality score review and description of the data processing The selected sources were published between 2010 and 2018. We excluded the sources that were duplicated or where we estimated the content was not sufficiently connected to Simona Šarotar Žižek, Zlatko Nedelko, Matjaž Mulej, Živa Veingerl Čič: Key Performance Indicators and Industry 4.0 – A Socially Responsible Perspective In databases n = 1.850 Potentially relevant sources n = 1.125 Appropriate sources n = 400 IdentificationRelevanceInclusion Filtering Selected sources n = 54 In other sources n = 100 Excluding duplicates n = 725 Excluded n = 346 Included n = 54 24 NAŠE GOSPODARSTVO / OUR ECONOMY Vol. 66 No. 3 / September 2020 the subject, purpose, or objective of our research. For the analysis of the technical and scientific content, we synthesized the results and took into account the availability content and contextual relevance. We chose 54 sources that were appropriately connected with our topic and objectives and contribute with high quality to our research. Industry 4.0 (I4.0) I4.0 symbolizes the beginning of the fourth industrial revolution, which is the first revolution that has been announced ahead of its inception. Based on concepts and technologies that include cyber-systems, the internet of things (IoT) and the internet of services, processes in I4.0 include interconnections of the virtual, digital and physical worlds and the learning in production. These connections include machines, products, services, information and communication systems, and staff. The result of I4.0 is a more efficient, adjusted and individualized production. The essence of I4.0 is a comprehensive and structured use of the digital networking of the creation, logistics and use of products and services. The promoters of I4.0 expect this will lead to significant improvements in industrial processes in manufacturing, engineering, material use, supply chain, and life cycle management. The essence of the joint program – the platform of the German government and the representatives of its industry sector – I4.0 (in German: Industrie 4.0) lies in a comprehensive and systematic digital networking of the creation, logistics and use of products and services (Hennies & Raudjärv, 2015), aimed to gain power in global production (Sanders et al., 2016). I4.0 is often described as an incentive for the fourth industrial revolution (Hennies & Raudjärv, 2015), or equated with it (e.g. Kamensky, 2017; Dais, 2014). After Hermann and co-authors (2016), I4.0 presents two aspects: 1. this industrial revolution was the first one announced a priori, and not observed ex post facto (Drath & Horch, 2014); 2. one expects a large economic impact from this industrial revolution, because I4.0 promises increased operational efficiency as well as the development of entirely new business models, services and products (Kagermann et al., 2013; Hair et al., 2014). Other authors (Alexopoulos et al., 2016; Qin, Liu, & Grosvenor, 2016; Li, 2017) have mentioned thad Industrie 4.0 is also called Industry 4.0 which symbolises the beginning of the Fourth Industrial Revolution. Li Da Xu and coauthors have summarized many authors (Hermann, Pentek, & Otto, 2016; Jasperneite, 2012; Kagermann, Wahlster, & Helbig, 2013; Lasi et al., 2014; Lu, 2017a, 2017b) who have said that Industry 4.0 represents the current trend of automation technologies in the manufacturing industry, and it mainly includes enabling technologies such as cyber-phys- ical systems (CPS), the Internet of Things (IoT), and cloud computing. For our research, GTAI‘s definition (2014) is also important, as it reveals that Industry 4.0 represents the technological evolution from embedded systems to cyber-physical systems. Rüßmann and the other authors (2015) define nine technologies of I4.0 (Figure 2): 1. Big data and analysis 2. Autonomous robots 3. Simulation 4. Horizontal and vertical integration systems 5. Industrial internet of things 6. Cyber-security 7. The cloud 8. Additive production 9. Virtual reality Figure 2. Technologies of I4.0 Dalenogare and coauthors (2018) have mentioned these technologies of the Industry 4.0: (1) Computer-Aided Design and Manufacturing (CAD/CAM), (2) Integrated engineering systems (ENG_SYS), (3) Digital automation with sensors (SENSORING) (4) Flexible manufacturing lines (FLEXIBLE), (5) Manufacturing Execution Systems (MES) and Supervisory control and data acquisition (SCADA), (6) Simulations/analysis of virtual models (VIRTUAL), (7) Big data collection and analysis Technologies of I4.0 Big data and analysis Autonomous robots Virtual reality Additive production The cloud Cyber-security Industrial internet of things Horizontal and vertical integration systems Simulation 25 (BIG DATA), (8) Digital Product-Service Systems (DIGITAL-SERV), (9) Additive manufacturing, fast protoyping or 3D impression (ADDITIVE) and (10) Cloud service for products (CLOUD). The concept of I4.0 describes various changes in production systems, which are mostly supported by information technology (IT). These changes have not only technological but also organizational effects. They will mean a change in orientation from production to service in the whole traditional industry. The concept of I4.0 refers to the set of current concepts, which cannot be clearly classified and, in particular, cannot be accurately distinguished in individual cases. These concepts are shown in the Figure 3 (Lasi et al., 2014; summarized after Čančer 2018): • Smart factory: smart technology will be used to operate a smart factory, which will support the management of complex systems and processes. The production will be equipped with sensors and autonomous systems. Communication between machines, products, people and other resources will take place in a similar manner as in social networks. It will be supplemented by communicating of customers with facilities in a smart factory and by communicating with the supply chain. • Cybernetic-Physical Systems: This is a combination of physical and program levels. After inclusion in production, the systems will no longer suffer from a strict separation between software and hardware. • Self-organization: Existing production systems are becoming increasingly decentralized and self-organ- ized. This coincides with decomposition of the usual production hierarchy. • New approaches in distribution and ordering: Distribution and ordering will be increasingly individualized. • New approaches to the development of products and services: The development of products and services will be individualized. • Adapting to human needs: The new production systems will be designed to follow human needs, and not vice versa. • Corporate social responsibility is increasingly at the core of the design of industrial production processes. Components of I4.0 are after Hermann and coauthors (2016): • Cyber-Physical Systems (CPS), • Internet of Things • Internet of Services • Smart Factories In order to support companies in the definition and construction of I4.0 systems, the general principles for the design of I4.0 (Hermann et al., 2016) are as follows: • Interoperability: the ability of machines, devices, sensors and people to connect and communicate with each other through the Internet of things or the Internet of people. Figure 3. Concepts of Industry 4.0 Source: Lasi et al., 2014; summarized after Čančer, 2018 Smart factory Cybernetic- Physical Systems Concepts of Industry 4.0 Corporate social responsibility Selforganization Adapting to human needs New approaches in distribution and ordering New approaches to the development of products and services Simona Šarotar Žižek, Zlatko Nedelko, Matjaž Mulej, Živa Veingerl Čič: Key Performance Indicators and Industry 4.0 – A Socially Responsible Perspective 26 NAŠE GOSPODARSTVO / OUR ECONOMY Vol. 66 No. 3 / September 2020 • Information transparency: Information systems must be able to create a virtual copy of the physical world, enriching it with data derived from sensors. This requires the implementation of raw data obtained from sensors into higher value information. • Technical assistance: The ability of support systems to support decision-making for people by combining and visualizing data. The data are processed so as to be understandable to the people / employees and make it easy to make informed decisions in the shortest possible time. The technical assistance is also the ability of cybernetic-physical systems to physically support people in carrying out tasks that are unpleasant, too hard or too dangerous for humans. • Decentralized decisions: The ability of cybernetic-physical systems to make decisions about their own systems and the decisions that are necessary for the autonomous performance of the tasks envisaged. Aside from some exceptions, disruptions or conflicting objectives, the decision-making requirement is transferred to a higher level. Hermann and coauthors (2016) have prepared a table in which are six design principles that can be derived from the I4.0 components. Table 1. Design principles of each Industry 4.0 component Cyber- Physical Systems Internet of Things Internet of Services Smart Factory Interoperability x x x x Virtualization x - - x Decentralization x - - x Real-Time Capability - - - x Service Orientation - - - x Modularity - - x - Source: Hermann et al., 2016 Regarding the challenges of the I4.0, employees are expected to: • have the necessary knowledge on processes and their use; • have specific competences to perform work in I4.0. The company must define the required competencies according to the specificity in accordance with strategy 4.0; • become even more flexible in terms of working time and location, and also in terms of how they face tasks and problems; • assume much greater responsibility for work and self-initiated knowledge, and collaborate with each other effectively; • perform a number of tasks (the type of work will be important, not the location - companies will have to consider modifying job descriptions). The number of routine physical tasks will be (markedly) reduced, while on the other hand, there will be more jobs that require flexibility, problem solving and creativity. In order to manage and control I4.0, performance indicators are necessary. In the following, we highlight the methodology of key performance indicators. Key Performance Indicators (KPI) Why should organizations implement key performance indicators? There is a permanent need to monitor efficiency and effectiveness and a quick and clear overview of the current situation. The requirements of digitization and I4.0 indirectly compel us to do so. We also use key performance factors because a wide range of indicators for comprehensive monitoring of the situation is expanding, as well as the need to integrate fragmented data, ensuring data compatibility across different systems in organizations and in different databases (Matlab, Ms Access, SQL). A performance measurement system is important. It consists of a set of procedures and indicators that precisely and constantly measure the performance of activities, processes and the organization as a whole, and is a vital aspect in regard to the management of companies (Neely et al., 2005; summarized after Varisco et al., 2018). Lohman (2004; summarized after Varisco et al., 2018) mentioned that a performance measurement system should be able to provide data for monitoring both past and the future performance, to strengthen the strategies and avoid introducing the conflicting indicators, and to support providing data for benchmarking. Therefore the performance measurement system focuses not only on financial procedures and indicators, but also on consumers’ aspects or internal processes. Parmenter (2007) connected a performance measurement system with key performance indicators (KPI). He says that key performance indicators are considered the core of the performance measurement system: they are defined as a set of measures that focus on the main critical activities. Key performance indicators (KPi) are critical to understanding the performance of organization and to the decision-mak- ing. They are used by almost all types of businesses by managers, to evaluate effectiveness in achieving strategic and operational goals (Bishop, 2018). KPI are not only financial but also non-financial indicators that organizations use in order to estimate and define how successful they are, aiming at previously established longterm lasting goals (Velimirović, Velimirovic & Stankovič, 2010). Velimirovič and co-authors (2010) mentioned that 27 KPI are static and stable indicators that carry more meaning when comparing information. Therefore KPI help to remove the emotion from object of the business, and allow workers focus on the things that joy is really about, and that are making benefit. „Quantitfiable level of achieving a critical objective. KPI are derived directly from or through an aggregation function of, physical measurements data and/or other key performance indicators.“ (ISO 22400-part I). »ISO 22400 defines a KPI by giving its content and its context. • Content: a quantifiable element with a specific unit of measure (including the formula that should be used to derive the value of the KPI); • Context: a verifiable list of conditions that are met«. The selection and implementation of KPI is influenced by the organizational structure (line-line or process organizational structure or some other), as well as the type of production process, such as non-serial or serial production. Management in a production company is of utmost importance. The extensive and complex production processes can be managed in a transparent and efficient manner with a proper management hierarchy, which includes, in addition to the process and business levels, the production level of management. Management in a production company is based on a system for managing and controlling production processes. An example of such a system is MES, which is usually also computerized and includes classification, data transfer and optimization, allocation and resource status, and document management. Zorzut (2009, 27) points out that the indicators are at different levels of corporate governance. The lowest level covers individual devices, control loops, process cells, etc. This is followed by the production level, on which one monitors the entire production line or plant. At the highest level, there is the business level, where the business of the whole company is managed. The dimensions of indicators are as follows (Lohman 2004; summarized after Zorzut, 2009, p. 26): • The name of the indicator. • Objective: Describes the meaning and purpose of using the indicator so that the user knows what a particular indicator represents. • Unit of measure: this is the metric used to calculate the indicator. • Scope: Defines the range in which the indicator values may be located. • Level: which level in the hierarchy of implementation priorities the indicator belongs to. • Frame (detailation): determines how far the company wants to go by measuring the indicator (eg. production line, plant, individual machine, ...). • Measurement type: absolute or recalculated; the indicator can indicate the total quantity (for example, the total energy consumed in one week in kWh) or the calculated quantity (energy consumed per unit of product / service per week). • Period: the period of tracking and calculating the indicator (eg. week, day, shift). • Sources of data: which data are needed to calculate the indicator, where they are captured / measured and who is responsible for them. • Owner: Each indicator also has its own administrator, who is responsible for its calculation, as well as evaluating and making decisions based on the information obtained. An example of KPI is presented in Table 2. Table 2. Example of KPI KPI DEFINITION CONTENT Name Availability ID Description Availability is a ratio that shows the relation between the actual production time (APT) and the Planned busy time (PBT) for a work unit. Scope Work unit, product, time period, product Formula Availability = APT / PBT Unit of measure % Range Min: 0% Max: 100% Cotext Timing On-demand, periodically Audience Supervisor, management Production methodology Discrete, batch, continuous Effect model diagram See A. 10 Notes Availability indicates how strongly the capacity of a work unit for the production is used in relation to the available capacity. The term availability is also called degree of utilisation or capacity factor Source: Johnsson, 2006 It is important that each KPI is defined through a formula, a time model and an effect model. In ISO 22400 the following is mentioned: • »The formula presents the equation that should be used for deriving the numerical value of the KPI. The equation is an aggregation function of physical measurements, data and/or other key performance indicators. Simona Šarotar Žižek, Zlatko Nedelko, Matjaž Mulej, Živa Veingerl Čič: Key Performance Indicators and Industry 4.0 – A Socially Responsible Perspective 28 NAŠE GOSPODARSTVO / OUR ECONOMY Vol. 66 No. 3 / September 2020 • The time model is used to visualize information about physical measurements used in the aggregation functions. The time models visualize start/stop time for specific measurements, as well as its relationship to other physical measurements etc. • The effect model can be seen as a root-cause diagram. Each KPI has its own effect model. The effect model is a picture that highlights the relationship between the KPI and its parameters«. KPI and their values can be presented in different ways (Zorzut, 2009, p. 27): • Presentation with absolute value (priority: the indicator has a unit known to the user and directly related to the measured quantity, eg. productivity given by the number of pieces of product at the time of production). • Linear scale - based on a classical evaluation from 0 to 10 or from 0 to 5. The expected value of the indicator is, for example, rated at 8 and represents 80% of the value of the indicator, so the score 10 corresponds to 100% of the value of the indicator. • Presentation with a normalized value (usually the indicator is 1 or 100% when one assumes the expected value and it represents a percentage improvement of the indicator relative to the expected value of the indicator). In standard ISO 22400-2: 2014, 34 KPI for production companies are listed, presented in Table 3. It is very important that KPI be definable at different levels of company management: at the process, production and business levels (Johnsson, 2006; Zorzut, 2009). The process level means that KPI are installed for individual devices, control loops and process cells. With KPI at the production level, one monitors the production line or the production plant. The business level covers the business of the entire company and is also focused on the success of the business. KPI on the business level The most influential framework for measuring organizational performance (KPI on business level) is the balanced score card (BSC) proposed by Kaplan and Norton (2000). The BSC responds to the limits of traditional accounting criteria and seeks to translate the strategy into quantitative criteria that uniquely communicate the organizational vision. Based on the BSC, business performance can be measured (Kaplan & Norton, 2000): • from a financial point of view, with the following indicators: operating profit, profitability of assets and capital, return on investment, economic value (EVA), revenue growth, and the creation of cash inflows; • from the point of view of business processes, with the following indicators: market share, share of preservation of old clients, share of new clients acquisition, customer satisfaction, and profitability of clients; • from the point of view of customers with indicators that include quality, productivity, time cycle, and cost measurement; from the point of view of learning and growth and employee satisfaction, maintaining employees in the organization, productivity of employees, intellectual property of the organization, market innovations, and the ability of the organization to develop new skills. Table 3. KPI after ISO 22400 Worker Efficiency Production process ratio Finished goods ratio Allocation Ratio Actual to planned scrap ratio Integrated goods ratio Throughput rate First pass yield Production loss ratio Allocation efficiency Scrap ratio Storage and transportation loss ratio Utilization efficiency Rework ratio Other loss ratio Overall equipment effectiveness index Fall off ratio Equipment load ratio Net equipment effectiveness index Machine capability index Mean operating time between failures Availability Critical machine capability index Mean time to failure Effectiveness Process capability index Mean time to restoration Quality Ratio Critical process capability index Corrective maintenance ratio Setup Rate Comprehensive energy consumption Technical efficiency Inventory turns Source: ISO 22400-2, 2014, p. 34 29 KPI on production and process levels KPI systems have been developed to support business management at the highest levels of business. In the last decade, indicators on the process and production level of management - pPI are being implemented. Optimal operation of the management systems can be achieved by automatically collecting process data and mapping these data into pPIs, and by forwarding pPIs to interested users. pPIs show a genuinely useful value when users are able to quickly understand the information contained in the submitted production process data; eg. with these data, pPIs detect problems that arise in production or deviate from the set goals and, with timely action, correct the situation. It is understood that in organizations there is a link between process-level and business-level indicators. Therefore intermediate direct-level production data is consolidated for each end user separately and is transmitted to it. In a process-oriented approach, the data represent a means to achieve the goal, that is, better implementation of processes, as defined in the process organizational structure. We now present some examples of indicators at the procedural level (Ruel, 2004; Kinney, 2004; Haji-Valizadeth, 2005; Gerry & Buckbee, 2005, 2006; Gordon, 2006; summarized by Zorzut, 2009, p. 30): • Variance indicator • Oscillation indicator • Usability indicator • Saturation indicator • Expert tune indicator • Exit at the border • Standard output exit • Average absolute error • Crossing the reference value • Absolute integral error • Robustness • Efficiency • Variability • Reliability • Time in emergency mode, etc. The pPIs on the production level of managing are collected within five groups: safety and the environment, production efficiency, production quality, staffing, and implementation of the plan. The pPIs are as follows in the framework of each group (Zorzut, 2004; Rakar et al., 2004): • Safety and Environment: – Number of accidents per DM – Number of alarms – Freshwater consumption – Production from recycled waste – Number of exceedances of limit concentrations of harmful substances • Efficiency of production: – Employee/Infrastructure Efficiency (OEE) – Consumption of raw materials and energy – Product flow time – Efficiency of services – Production jam • Quality of production: – Percentage of finished products/raw materials/materials that do not meet quality criteria – Waste – Quality of services • Implementation of the plan: – Realization of the plan – The proportion of delayed production – The proportion of production that triggers penalties due to delays – The proportion of production that was prematurely realized • Employees: – Lost work days due to injuries and/or illnesses – Number of suggestions for improvements and other innovations – Number of training sessions per employee – Fluctuation on working places/employee performance – Realization of goals – Degree of absenteeism by location/employee performance The introduction of Ppi is based on its three-level structure, which allows the organization to use indicators in three groups per levels according to the priority of implementation: • Level 1 are indicators that are related to regulatory requirements for safety and environmental protection and should be implemented first. • Level 2 are indicators of quality, tracking the work plan and efficiency. • Level 3 are indicators that describe different aspects in relation to employees. Depending on the objectives and importance set, the company begins by defining key or implementing simple indicators and moving towards more complex or less influential indicators. The use of indicators is a continuous process that consists of setting goals and measuring effectiveness in achieving these goals. Simona Šarotar Žižek, Zlatko Nedelko, Matjaž Mulej, Živa Veingerl Čič: Key Performance Indicators and Industry 4.0 – A Socially Responsible Perspective