4.1. Analysis of the Spread of IoT Tools and Solutions
The survey looked at the prevalence of IoT tools and solutions among Hungarian companies, exploring their future plans and the factors which hinder them. The tools and solutions surveyed in the questionnaire included sensors, RFIDs, cloud storage, large data analytics, CPSs, CPPSs, robot arms, AGVs (Automatic Guided Vehicle) and other smart devices, as well as smart products.
The first question was “Please indicate whether your company is using the following tools and solutions?” Table 2
shows that CPS is the most widely used tool (67.4%), especially in production (53.5%), and it can also build on data analysis (62.8%). In spite of the high rate of CPS use—and what is somewhat contradictory—the integration of sensors and RFIDs is relatively unusual. With these technologies, large companies are leading, with 18 of the 24 responding large companies using CPS, 15 using CPPS, and 15 using big data analyses. As far as industry sectors are concerned, CPS, CPPS, sensors, robotic arms and cloud storage are most common in the mechanical engineering industry, while in the rest of the industrial sector they occur on an ad hoc basis. This is also in line with international trends, where among the leading companies of the fourth industrial revolution, automotive industry companies are the most prominent.
In the questionnaire, the following questions were raised in order to identify the benefits of introducing the tools listed in Question 1. Their utility was evaluated on a scale of 1 to 5. The questions were the following (numbered on the questionnaire from Q1 to Q7):
The efficiency of the company’s internal logistic processes (higher level of logistic service) (Q1).
The efficiency of processes with the ordering partner in the supply chain (Q2).
The efficiency of processes with the supplier partner in the supply chain (Q3).
Cooperation between certain functions of the company (e.g., marketing, finance, logistics) (Q4).
Market performance of the company (e.g., ensuring greater market share (Q5).
Financial performance of the company (Q6).
Competitiveness of the company (Q7).
Essentially, sophisticated methods are needed to analyze the ways in which we can estimate the usefulness of IoT tools in terms of the increased logistics service level, the efficiency of the processes of the business partners, cooperation among certain logistics functions, financial and market performance, and the competitiveness of firms. Therefore, independent sample t-tests are used to determine whether those companies applying one of the IoT tools tend to estimate their utility more highly than firms without them. Here, as the methodology is assumed, the utility responses are normalized.
According to our results (Table 3
and Table 4
) a significant difference appeared between the two samples examined, in which those logistics companies applying IoT tools are proven to be more efficient and have better performance. In the case of CPS and CPPS, firms seemed to have a higher level of logistics service, more efficient processes with their partners, better cooperation among certain logistics functions, and higher financial and market performance and competitiveness. When using big data, we found a higher level of service, efficient logistics processes and greater competitiveness. In other cases, we found no substantial differences among the mean differences.
Nevertheless, this study has its limitations which also need to be emphasized. The main limitation of our estimations is that these empirical findings were only able to demonstrate one empirical aspect of IoT devices. Meanwhile, other determinants which may affect the utility of IoT tools and solutions have not been included due to restricted access to data, so the validity of our conclusions is limited by the bias caused by the exclusion of these variables and method.
In order to demonstrate the validity and reliability of our results, an additional chi-square test for independence, also called the Pearson’s chi-square test or the chi-square test of association, was used with SPSS to discover if there is a relationship between two examined categorical variables. From this perspective, there was a statistically significant association between, for example, CPS, CPSS and the efficiency of the business performances of IT companies.
Disincentives for development were also surveyed in the questionnaire. Respondents were asked to indicate what factors hold them back from using IoT solutions. The main inhibitory factor (21 responses) is, of course, the unknown level of costs. Companies feel that the new technology has uncertain costs, which do not, at present, have guaranteed returns, not to mention the lack of standards and the risk of rapid obsolescence.
A quarter of respondents identified data security as a risk factor, especially when it comes to external data or data sources. Equally, a labor force with inadequate qualifications was considered a barrier. It is an important hindering factor for the spread of Industry 4.0 features that standards, norms and certificates are not yet available to ensure the interconnection of different systems. According to the respondents, the basic technological tools of digital infrastructure are currently spreading slowly, and are not necessarily available in every supplier or customer organization, so cooperation may also be limited. A total of 14% of companies also fear losing control of the company’s intellectual property.
Many respondents regard the fear of organizational resistance as a deterrent. While high cost is an obvious reason, it is not only fear of organizational resistance which can paralyze the development of a company in terms of its IoT assets. The task of a serious management is to make workers understand the inevitability of change, because it facilitates the work of the employees, and they can be involved in work which is more creative and has higher added value.
4.2. Results of Expert Interviews
The purpose of this section is to present the experiences of the four companies that were interviewed during the research in 2017. The purpose of the interviews is to illustrate all the technologies and solutions described above. Among the companies, there is an SME belonging to a majority owned Hungarian holding company in the electronics industry, two multinational large automotive companies operating in the automotive industry and a multinational automotive company functioning as a system integrator. The value chain approach seems to be appropriate since the companies have a functional structure, even though they have realized that Industry 4.0 developments have to be handled at a cross-functional or company group level. Generally speaking, Industry 4.0 has been more noticeable in the international companies, while the Hungarian SMEs—although it has begun development—prefer to wait until the technologies that are the most successful are revealed and until the purchase price falls.
4.2.1. Approach to Industry 4.0
The surveyed companies had opinions on what Industry 4.0 meant to them:
“An information revolution in the industry.”
(V1 interview, 2017)
“To use and interpret the enormous amount of data, and use it to predict the future. This is the secret of success.”
(V2 interview, 2017)
“Industry 4.0 Data and Behavior. Everyone gets all the relevant information, enabling them to react and decide on it in different ways.”
(V3 interview, 2017)
“Linking to a smarter network that encompasses the industry”
The companies interviewed have differing degrees of affinity to Industry 4.0. One company makes the necessary improvements but does not move forward, does no piloting, and takes advantage of what is afforded by cheap and easily accessible technology. There are two companies where the Hungarian factories in the group of companies are pilot factories creating ideas, transformations and developments, and what works is transferred to other factories. These are local, stand-alone initiatives, and it can be stated that a lot depends on dynamic leadership. The innovative activity of one company has led to the development of digitalization before the launch of the German High Tech Strategy.
Among the companies asked, we identified three behavioral patterns. In one approach progress can be achieved by taking only the “low-hanging fruit”; alternatively, there can be intense internal motivation, which can even help a subsidiary of an international group of companies; thirdly, the company can socialize into an innovative environment where a high degree of innovation is expected, in which all members of the group participate and the results are applied globally.
There was a question in the interview about what phases a company goes through to actually exploit Industry 4.0’s potential.
The first step is that the companies start collecting data. They install the technological tools or software that can capture the desired observations and collect data: “performance immediately increases by 30% if we start observing a process by cameras and sensors” (V1 interview).
The second step is to transform this data into decision support information: “we need to build up a serious IT infrastructure to store and handle the data produced in the manufacturing processes” (V2 interview). This is a critical point; in many places there is lack of capacity for data analysis and interpretation, so there is a lack of professional skills in this area. It is not only necessary to run the analysis; other tasks—from the preliminary cleaning of the database, through to the knowledge of algorithms, the recognition of errors and distorting effects, to the transparent presentation of the results—are important.
The third level is the use of the results gained from data: “we have specialists at group level who develop machine connectivity and big data analytics software” (V2 interview). To do this, well-trained personnel are needed who can re-program software and hardware, and write new software or algorithms, and who are able to further develop the systems. There is also a need for decision-making algorithms and decision-makers who can build this information into their decisions and achieve goals using real-time data access and analysis (V1 and V2 interviews).
4.2.2. Industry 4.0 in Production
From the interviews, it became clear—as it does from the international literature—that Industry 4.0 exerts its greatest impact on production, and that the companies surveyed have also developed varied methods and procedures. These can be termed first category when sensors are built into a machine, and sensors are incorporated into the process of monitoring a production process and indicating deviations from it. The V2 example shows how a company incorporates a sensor in the injection molding machine which indicates if the tool needs to be replaced soon, and so the new tool should be prepared by a worker. It also indicates if something stops production. It then sends a notification to the operator’s mobile telephone that an intervention is required. On reaching the scene, the worker observes the machine’s performance and the perceived problem on the monitor mounted on the machine. If the solution is known, the worker can intervene and restore the production process. If the problem cannot be solved, the supervisor is informed (V2 interview): “to do this we need clear escalation routes and protocols” (V2 interview). A similar system was reported in the V3 interview, where the sensor installed in the assembly station can track the worker’s work speed, and if it detects a pause, reports this to the worker responsible for intervening. If it does not detect an intervention within a certain time it will notify the next level of the chain. Thus, it becomes clear that something has held up the production process and a solution can be found within a short time (V3 interview).
Sensors can be used to maintain the condition of the production line. The Czech brother firm of the V4 interviewee monitors a tool with the help of sensors. If the tool gets dirty, it may require a month to repair. The vibration-sensing sensor notifies the maintenance staff of the slightest deviation, so that the dirt can be removed before the tool is damaged. With this assistance, they have saved thousands of Euros because they have not had to buy new tools, and the old ones have not been taken out of production; in this way, unplanned maintenance has fallen by 12% (V4 interview).
The second level and category of digitalization of production processes occurs when machines are a coherent network, as, for example, in an easy-to-adapt, flexible manufacturing system (FMS). A member of the V4 Group’s international group of companies is working on a rapidly networking, flexibly configurable line of machines. If the production so requires, a new machine is inserted into the production line. The machine also connects to the network—without the need to reprogram the machine—and thanks to the rapid transfer of data, the production program runs on it and production can continue (V4 interview): “We call this a Plug and Produce system” (V4 interview).
The third level is support for production in an expanded sense. During the production process, one of the interviewees cited the use of augmented reality as an example of support for quality assurance. The French member of V4’s group of international companies uses ActiveGlass in quality assurance. There are steps in the production process that must be carried out in a specific order. Previously, checklists were used, whereas now the glasses are used. The lists are compiled and saved on a computer and connected to the glasses. At the beginning of the work, a small video (with sound and image) shows the wearer what work phase is next, and when and how it will occur. He/she can point to a QR code, and nod or press the button when he/she has completed the task. Meanwhile, both hands are free to work. Where glasses are used, the time required for quality control falls by half, and the error rate decreases significantly. The glasses are manufactured by an external supplier to the group, but the software is self-developed (V4 interview).
When discussing the options of Industry 4.0, interviewees also mentioned that surveillance by sensors can help identify the machines that need maintenance, and identify the replacement parts, so that the maintenance team can already bring them out, shortening the standby time caused by installation work. This can be supported by augmented reality, when the mechanic uses glasses to understand what to replace in the device and how to replace it (V4 interview).
The advantage of the solutions built into production is not only that the data can be accessed immediately, but also that the intervention protocol can be worked out in advance and the information available shortens both the decision making process itself and forced downtime (V2, V3 interviews).
4.2.3. Data Analysis, the Critical Point
The criticality of collecting and analyzing data was stressed by all interviewees. Even if they have not completely overhauled their production systems, some of their machines are already equipped with sensors, scanners, and 3D cameras to get a fuller picture of the processes taking place and to gather the data. Most of the data is stored with their own company, or (also) at the level of a group of companies. In one case, an interviewee referred to a cloud at the group of companies level, and in one case there was an outside cloud service provider (V1, V2, V3, V4 interviews). Software used for analysis and information, such as ERP system interfaces and the platforms and applications used by workers, are developed in-house, or with the involvement of consulting firms: “We develop our own interfaces and data mining and simulation software with the help of a consulting company. It costs a lot of money” (V3 interview). All interviewees referred to this knowledge as critical within the organization and expressed their concerns about ensuring a supply of professional expertise (V1, V2, V3, V4 interviews).
A data mining department is operating in the Madrid unit of the V4 group, which systematizes the production of ultrasonic sensors. Data is generated in three places in the production: In the MES, in the testing and in the production of machines. This produces 170 GB of data per day, in this section alone. They process data into a special computer cluster, and accelerate the analysis using search engines in web browsers. The system is able to connect virtually all information with everything, so very complex cause-and-effect relationships can be observed (V4 interview).
Information generated as a result of data analysis should be used in decision making: “It is a huge task to select which data to analyze, which will be useful in a given decision” (V2 interview). This can support not only the decisions made by humans, but those by autonomous robots, which is another example of the benefits of an electronic network linking everything with everything. Such an autonomous robot is already used in Hungary, in the V4 company. The machine in question engages with a metal plate. The 3D scanner detects when the disk is moved and if it is anticipated that the coating will not completely cover the next disk, it informs the robot, which adjusts it (closed loop M2M—the two machines communicate on the basis of data and then intervene) (V4 interview).
4.2.4. Human-Machine Connections, DIGITAL Ecosystems
As mentioned earlier, the development of interfaces represents significant work and investment, but quite simple equipment can be used as a platform.
The V3 firm employs a variety of simple platforms to connect machines and people. Workstations performing the final assembly of the end product after welding are equipped with tablets where the work of the workstation and workstation can be traced. This is where the worker logs in, gets the job done, and indicates when it is ready or has a problem (V3 interview).
The other day-to-day device is the mobile phone that the sends an SMS to the production line when it detects a problem. Here, there is an escalation pyramid, so first the operator, then the shift manager, the production manager, and the operating leader will be notified if there is no confirmation of the problem at each level within a given time. In addition to the SMS, an application developed for the company can be used to display individual machines, production lines, and daily production performance data (V2, V3 interviews).
The question of digital ecosystems divided the respondents. Three of the surveyed companies consider it a very distant future, but one feels it is feasible within five years, as the group of companies is in effect already working within it. Of the four companies, the latter is the largest and has a basic operational model for joint product design and development with customers. Its suppliers are members of the same group, so sharing information in this direction is less risky. Basically, those who believe it will come in the future, argued that this kind of deep, real-time data sharing would require a degree of trust between organizations and people that does not exist at present—and not just in Hungary: “I can imagine this if the company can derive a clear business value from it that can be expressed in cash” (V3 interview). Digital trust [28
] is a critical factor in corporate cross-border data sharing and collaboration, and is created when there is a high level of security in terms of cyber security, data reliability and intellectual property (V1, V2, V3, V4 interviews).
4.2.5. Human Resource Issues
Negotiating the relationship between digital evolution and human resources is important for two reasons: On the one hand, many people are afraid that digital solutions and robots will lose them their jobs. This is possible, but it is necessary to show the possibilities for learning and doing a higher level of work, and the fact that the new technology makes work easier. On the other hand, other opportunities must be found for the workforce released due to robotization and automation, and not necessarily (only) at company level.
Most interviewees have tried to introduce new technologies to their employees, and the need for them. There were cases where this proved to be sufficient—workers accepted and used the new tools and the new technology. However, there were also respondents who experienced resistance. Employees damaged the sensors and interface devices, or refused to follow the instructions. Due to the termination of the signal transmission, this emerged very quickly, and turned out to be a major cost. Therefore, in this company, they have shifted to an autocratic approach: Those who are unwilling to work with new tools, must find new jobs. Despite the resistance, large scale resignations have not occurred, even though it is an area with plenty of other industrial facilities (V1, V2, V3, V4 interviews).
Informing employees should also mean telling them that their work is more closely monitored, and their performance directly affects (or will affect) the level of their salary. Good performance is therefore recognized, and poor performance can be analyzed and changed (V1, V3 interviews).
According to PwC [28
], the lack of digital culture and training is the biggest challenge for companies: “We need creative people and people with strong analytical skills” (V3 interview). One major challenge in the human resource area is to find and retain these workers. It is also very important that these disciplines develop dynamically, that is, training will also have to keep employees up to date: “We have to add people to continuous learning” (V2 interview).
In the opinion of the leader of the V4 group, Industry 4.0 will also change organizational structure. Since much work is automated, mechanized, and robotized, trained workers will only be able to service these machines. Above them are machines directors, who program and maintain the machines on a daily basis. The next level is specialists who are experts in a process, analyzing data, looking for patterns, and writing algorithms and software for optimization. Above these will be a narrow leadership layer that will coordinate and steer processes, and this layer is expected to be less extensive than it is today (V4 interview).
Investigating the relationship between human resources and digitization is also important for another reason, since humans can also be a resource from whom we want to collect data; not only about performance, which we then display in performance pay, but also on the characteristic ways they carry out work. This also raises serious data protection issues.
At one of the factories in question, a pilot project is being planned, where workers are equipped with a wrist watch or a built-in sensor that always tells when and where they are in the plant and what they do (V3 interview). This method may present a legal problem in terms of the protection of personal data.
According to the V2 system, a worker who logs into a machine for work immediately sees whether or not he/she has the right to perform a given production activity, and whether he/she has the necessary ability. If not, the system will direct him/her to an e-learning interface to quickly learn, for example, how to handle the machine in question: “We call this the Digital Education Platform” (V2 interview). Both methods serve to increase the productivity and efficiency of human labor, and the machines increase the opportunity to use Industry 4.0 possibilities.
4.2.6. Smart Products
In the companies investigated—as we mentioned earlier in the previous sections—we find smart machines, but smart products have not yet really appeared. In fact, none of the factories can be considered a wholly smart factory, since high-level digital technologies have only been used on certain product lines.
A British member of the V4 group of companies is working with the Centrica gas group to develop so-called IQ boilers. These devices will also be able to connect to the Internet and can be controlled remotely. If something goes wrong, it can be remotely fixed quickly, and the engineer can come to the repair site with the appropriate parts. This can significantly shorten customer waiting time and downtime (V4 interview).
The experiences of the interviews presented here do not reflect the level of development of the entire Hungarian industry, but they help to understand the steps taken and the directions in applying the technology. It should also be noted that the companies surveyed focus very heavily on their production processes, and they have not yet addressed the impacts on any other element of the value chain. A comprehensive evaluation of companies was carried out in the following section [28
4.3. Determining the Level of Development of Companies
Many of the solutions and technology described above do not spread like lightning. Companies have to go through a number of steps in order to adopt all the achievements of the Fourth Industrial Revolution. It is not necessary for everyone to achieve everything, the possibilities of digitization and integration are available at different levels, and it also depends on what can and cannot be achieved in the given industry. PwC [28
] set up a model that differentiates the four levels of development of the companies in Industry 4.0, and gives a breakdown of the development levels in seven dimensions. These are shown in Table 5
below, and on this basis we listed the companies that were investigated during the research.
lists the four companies where the interviews were made. For each dimension, we have evaluated the present stage of development. The color of each stage of development is indicated by the appropriate color in Table 5
The results show trends that prove that automotive and electronics companies operating in Hungary have also started towards Industry 4.0 development. The direction of these developments is primarily production and the motivation is either the stimulus from the foreign parent company, and its newly adapted solutions, or the Hungarian leadership’s aspiration to become agile and innovative. Developments can be found in the Digital Novice and Horizontal Integrator levels.
The companies surveyed are at the beginning of the exploitation of the Fourth Industrial Revolution. Companies belonging to the international group of companies have the advantage of the inspiration and encouragement of their parent company, and the not infrequent explicit expectation that a pilot project be started. In two cases, the agility of the Hungarian leadership turns the Hungarian factory into a pilot plant (V2, V3, V4). Each company has a smaller or larger Industry 4.0 team, who is looking for opportunities and selecting projects for which it is worth starting a pilot. As a result, projects are usually island-like, they are slow to link and to extend to the level of the enterprise group, but there are also examples of this (V2, V3, V4). As a first step, production lines are installed with the tools needed for data collection, and no examples were mentioned of products being converted into smart ones. As the pilot projects are island-like, the basic integration of these developments is underway and most of them are moving towards horizontal integration. There is an example of vertical integration, and a pilot project carried out at a supplier (V3, V4), or even with a customer (V4), but this is not routine. All the business leaders were already aware that data use was the key to digitalization and a new source of competitive advantage. They all perceived the ability to analyze as a key competency, and try to attract skilled workers themselves, for example, by organizing joint (dual) courses with universities (V4). All companies are making great efforts in this area, trying to grow the software development team, develop software for data analysis, and develop applications (V3, V4). The core IT system is developed at each company, and is complemented by a number of systems developed by the companies in the group. Many interviewees have pointed out that digitalization will be successful if suppliers and customers are also members of the digital ecosystem, which can only be achieved through standard platforms and interfaces, which is not yet the case (V3, V4). Data collection and management, and above all security and risk issues, are key areas for all companies. Most place more trust in their own server space or group cloud, but there are those who use a global cloud provider. Since this issue is also a sensitive one with partners, it is dealt with through continuous mutual agreement. The organizational culture is changing. Most adopt a method based on common involvement, and there are cases where manual workers themselves are involved in generating ideas, and in exploring the possibilities of digitization (V2). The international background here also has a positive influence, and ideas originating from foreign affiliates, and competitions to generate ideas also inspire workers in the Hungarian factory (V4).