**1. Introduction**

Industry 4.0 is a platform combining a variety of advanced modern technologies to meet today's challenges. Industry 4.0 elements are increasingly emerging as one of the main strategic managemen<sup>t</sup> goals in recent years. The use of new technologies raises the need for long-term and strategic investments, intended to increase the competitiveness of the enterprises in the future. Most of the enterprises are already implementing smart technologies and smart processes. Some of the enterprises indicate their experience with such technologies practically. They use the new technologies at least partially. It means that they successfully completed the implementation, and they are now looking for the added value that these technologies offer. However, their use is still partial, used to deal with certain issues and probably without the overall interdependence of all the systems.

Finally, outside this area, there is a small group of innovators, looking for new developments and looking for ways to integrate them into their well-functioning organizations. They have in common mastering the basic and higher levels of Industry 4.0 brought by the Fourth Industrial Revolution, now preparing for further global changes brought about by advances in artificial intelligence, digitalization, computer science, robotics, complexity, and network theory. They include the enterprises that are at the heart of these changes, drawing their energy and position from these processes.

The current challenges of Industry 4.0 force managers to discuss whether they are prepared for such changes. They often wonder what the situation is in their own organizations and then which technology they should invest into in the near future.

Lower absorption capacity of the enterprises is related to the issue of resource constraints. Right now, the managements of a number of enterprises consider the future strategy and the steps to be taken to be competitive in the future. For this reason, this paper discusses how to assess and analyze the current state of business in the context of Industry 4.0. Based on the research, the authors sugges<sup>t</sup> a methodology, with an index for easy evaluation of the preparedness of the enterprise for the future.

The structure of this paper is organized as follows: Section 2 defines the theory as used in the paper; Section 3 describes the methodology and methods used; Section 4 summarizes the most important results, including the evaluation of the hypotheses; Section 5 discusses the results with other authors; finally, Section 6 summarizes the most important conclusions of the research.

## **2. Theoretical Background**

The cornerstone of Industry 4.0 is based on machines, equipment, logistics, and humans who are connected to each other to exchange data, process data, and make decisions, appropriately coordinating the ever-present machines [1]. Industry 4.0 is characterized primarily by digitization, robotics, and artificial intelligence. Kelkar [2] emphasized that 79% of the manufacturing enterprises (any size) perceive Industry 4.0 as very important for their development (research was conducted in 227 enterprises). Similarly, in Computer Science Corp (2015), 63% of United States (US) manufacturing companies (900 in the sample) identified Industry 4.0 as necessary for their further development. Consistent with these findings, there is a study of 235 German enterprises carried out by PricewaterhouseCoopers [3], reporting that the enterprises plan to increase digitization between 24% and 86% in the next five years. Dörfler [4], who stated that 94% of companies perceive digitization as important for their development, reported the highest percentage. This research was carried out regardless of the size and area of business using a sample of 1849 German enterprises [5].

The subsequent sub-sections describe theoretic background of the main technologies and processes which are necessary to create future intelligent factories and enterprises based on the conception of Industry 4.0.

#### *2.1. Use of Sensors*

Sensors are sources of information for the control system (computer, brain) and technical devices, which measure certain physical and technical quantities and convert them into a signal that is remotely transmitted and further processed. These are various global positioning system (GPS) sensors, cameras, and microphones, forming the digital nervous system. These devices acquire information on position, distance, motion, speed, displacement, temperature, drought, humidity, sound, vibration, gases, chemicals, flow, strength, load, pressure, level, electricity, acceleration, tilt, light, etc. The use of sensors in smart factories has many facets, as discussed by many authors [6–8]. To make full use of the sensors, the availability of efficient and affordable sensor networks (such as radio frequency identification, RFID) is a prerequisite [9]. Based on this, intelligent objects and devices are created, enabling real-time communication between computers, work resources, and application systems. Together, this technological development provides the basis for the introduction of new production processes and business models in smart factories [10]. As they are able to retrieve and process data, they can check certain tasks and communicate with people through an interface [6]. The importance of sensors for Industry 4.0 is also illustrated by the fact that, as mentioned by Reference [7], by 2020, nearly 20.8 billion devices will be connected and RFID will be fully utilized. Such a shift will have an impact on most industrial sectors and, in particular, manufacturing industries. RFID technology is used to identify various objects in warehouses, production halls, logistics companies, distribution centers, retail outlets, and disposal/recycling stages [11]. Analysis of monitored activities will be used for fault detection and predictive maintenance [12]. Based on the information gathered and also using machine-to-machine (M2M) communication, the resources will be remotely controlled to improve industrial processes [13].

#### *2.2. Data Collection and Analysis*

The process of data collection process includes retrieving, searching, selecting, and generating. As more and more business activities are digitized, new data sources arise, and the equipment to process these data becomes increasingly cheaper; we are entering a new era [14]. The volume of stored data is growing four times faster than the world economy, and computing power is increasing nine times faster. Interestingly, in 2000, only one-quarter of the world's information volume was stored in digital form; today, the figure is close to 95% [15]. The creation of large volumes of data is supported by digitization, aiming to convert all possible information and media—text, sounds, photos, videos, and instrument and sensor data—to the natural language of computers. Big data is a versatile term for any collection of datasets that are very large and complex. Big data are quantified in petabytes (1015), as it is not possible to receive, store, secure, process, and visualize them with common hardware and software in a reasonable time [15].

Big data are defined as a cultural, technological, and scientific phenomenon that rests on interplay [16] of technology and analysis. Laney [17,18] defined big data through three characteristics: volume, variety, and velocity. The Gartner company introduced the term big data. Gantz and Reinsel [19] complemented the big data characteristics with a fourth Vs value. Reference [20] classified big data through a data life cycle that includes data, process, and managemen<sup>t</sup> activities. Ge, Bangui, and Buhn [21] classified big data in eight areas: healthcare, automation, transport, energy, smart cities, agriculture, industry, and military.

Data analysis can be expressed through di fferent terms such as data mining, clustering, classification, analytics, aggregation, annotation, combining, extraction, evaluation, and filtering. Data analysis is performed either directly through a variety of cloud computing services (PaaS — platform as a Service; SaaS — Software as a Service), or in a conventional way on the user's end computers [19]. The main advantage of cloud services is e ffective integration with other applications, scalability, performance, multitasking, and configurability [22]. According to Tsai et al. [23], data analysis methods have the following limitations for big data usage: centrality and unscalability, dynamics (inability to analyze data on the fly), and data structure format (data inconsistency).

#### *2.3. Information Technology (IT) Infrastructure and Mobile Terminals*

In particular, Industry 4.0 includes a radical shift in how IT infrastructure works, defined as the overall transformation of the manufacturing industry through the introduction of digitization and the internet.

These transformations mean a revolutionary improvement in the design and manufacturing processes, operations, and services of manufacturing products and systems. Tjahjono [24] defines Industry 4.0 requirements for IT infrastructure in terms of device automation, auto-driving, increased need for reality, an extremely large number of monitored and managed devices, and process automation.

The enterprises using the Internet of things (IoT) cannot rely solely on wireless networks such as WiFi, ZigBee, and low-power wide-area network (LPWAN) for their future critical related systems [25]. They demand more and more functionality now unavailable according to Rao and Prasad [26], particularly including very low latency, very high reliability, and very high bandwidth and bit rate.

Many of the current network technologies (2G, 3G, 4G — 2nd, 3rd, 4th Generation of mobile telecommunications technology, NFC — Near Field Communication, ANT — Adaptive Network Topology, Bluetooth, GSM — Global System for Mobile communication, WMAX — Woldwide Interoperability for Microwave Access, etc.) are not really good for the future. Future flexibility is o ffered by the 5G (5th generation of mobile telecommunications technology) standardization as recommended by Sriganesh [26] for infrastructure. Future infrastructure will lead to vertical and horizontal network connectivity and the use of the industrial internet concept. For example, General

Electric's practice Leber [27] described it as connecting industrial sensors and drives to local processing and the internet. Furthermore, links with other important industrial networks can independently create value. The main di fference between consumer/social internet and industrial internet lies in the value created. For consumer/social websites, most of the value is created from ads [28].

## *2.4. Cloud Storage*

Data storage includes recording, transportation, replication, compression, cleaning, indexing, stream processing, integration, and transformation of data. Given the increase in data volume (big data), the main question is how to store all data and where. Data warehouses and centers are the most often used. A data warehouse (DW) is an integrated collection of subject-oriented decision support data [29]. Data warehousing (DW) is a specific type of information system and should enable the acquisition of business data, its transformation into appropriate strategic business information, and subsequent storage of data in a format that facilitates business analysis [30]. The cloud is currently the hardware and software solution of the data center providing the services [31]. The cloud is a parallel and distributed computing system consisting of a collection of interconnected and virtualized computers that are dynamically delivered and presented as one or more unified computing resources based on service level agreements negotiated between a service provider and a customer [32]. Such resources are dynamically transformed to adapt to variable load, enabling the optimal use of resources [33]. Clouds are hardware services o ffering computing, networking, and storage capacity [34]. Mostly, clouds are operated on a cloud deployment models basis [35]: public cloud, private cloud, hybrid cloud, and community cloud [36–38]. The cloud inherently includes the concept of cloud computing, based on the provision of services or programs stored on servers and the internet. Cloud computing distinguishes three types of distribution models [22]: IaaS (infrastructure as a service), PaaS (platform as a service), and SaaS (software as a service). For the purposes of data storage, there are IaaS services, i.e., the use of virtualization, providing only infrastructure and hardware. Block chain technology [39] is emerging significantly in the architecture of the internet and is pushing out the standard model of client–server architectures. The point is that individual transactions between di fferent entities are transparent to everyone, but no one other than the two entities that took the action can influence and change this record. Block chains remove the third-party distribution of information flow [40]. Block chain is basically a data structure that is used to create a digital transaction ledger that is not stored by a single provider but is shared across a distributed network of computers. Block chain is, therefore, a special type of distributed decentralized database in which records are stored.

#### *2.5. Information Systems and Learning Software*

The implementation of Industry 4.0 uses the concept of an automation pyramid in connection with information technology. The pyramid is closely related to the vertical integration of information systems in an enterprise. Typical solutions and technologies in this vertical integration include data acquisition sensors: programmable logic controllers (PLC) that control production processes and take control levels, supervisory control and data acquisition (SCADA), which allows managing di fferent levels of support processes and supervision, manufacturing execution systems (MES) controlling production processes, and intelligent enterprise resource planning (ERP) managemen<sup>t</sup> for the enterprise level, the highest level in this hierarchical image [41–43]. ERP supports enterprise-wide planning such as business planning, supply chain management, sales and distribution, accounting, human resources management, and the like. These are usually commercially available solutions.

German SAP SE (Systems, Aplications & Product in Data Processing Service) is currently the leading SAP solution. In traditional ERP tools, the decision-making process is centralized at the highest level; most available ERP solutions do not support rapid adaptation in production planning due to unplanned events. MES supports reporting, scheduling, dispatching, product tracking, maintenance, performance analysis, workforce tracking, resource allocation, and more [44]. Most systems work with their own way of storing data and, often, with their own data format. The use of a production system

(MES) as a central database is a solution to such an issue [45]. In the future, a decentralized IT solution in smart factories might be used. In order to give the employees the right data in time, a support system is needed [46]. As Haddara [47] noted, it is the right time to check the readiness of ERP systems to meet the demands of the factories of the future. SAP developed its predictive maintenance module, based on firm integration of robots, machines (to be maintained), and ERP. Predictive maintenance is based both on the integration of data from ERP data sensors and the predictive algorithms.

Learning software includes pattern and machine learning (ML), which embodies some of the aspects of the human mind that allow us to deal with an extremely complex solution to the problem with the speed overcoming even the fastest computers [48]. Wen et al. [49] reported the most commonly used techniques: case-based reasoning (CBR) [50], artificial neural networks (ANN) decision trees (DT) [51], Bayesian networks (BN) [52], support vector regression (SVR) [53], genetic algorithms (GA) [54], genetic programming (GP) [55,56], association rules (AR) [57], rule induction (RI) [58], and fuzzy algorithms [59].
