Smart Manufacturing (Industry 4.0)

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Table of Contents

Introduction
The rationale of the Study
Results and Discussion
Conclusion
Recommendation
References

Introduction

The characteristic features of Industry 4.0 are fully automated production facilities where all processes are controlled in real-time and taking into account changing external conditions. Cyberphysical systems create virtual copies of objects in the physical world, physical control processes and make decentralized decisions. They are able to unite in one network, interact in real-time, self-adjust, and learn. An important role is played by Internet technologies that provide communications between personnel and machines. The core of the Industry 4.0 is made up of digitalization and robotics, in particular, the use of collaborative robots and MES systems, such as the TrakSys platform.

MES (manufacturing execution system) is a specialized system designed to solve the problems of synchronization, coordination, analysis, and optimization of production. MES is a specialized software system intended to solve the problems of operational planning and production management. Systems of this class are designed to solve synchronization problems, coordinate, analyze, and optimize production output within a specific production (Govindaraju & Putra, 2016). At the same time, MES systems are subject-oriented; they fully reflect the features of technological processes in mechanical engineering, electrical engineering, woodworking, etc.

As to collaborative industrial robots, they are directly connected with smart manufacturing, being an integral part of today’s integrated production environment. The active development of robots designed for physical interaction with people in a collaborative work environment provides unprecedented new opportunities. Collaborative robots (cobots) are economical, safe, and versatile (Dinwiddie, 2018). Cobots are safer than regular robots and can interact with workers. Among the industries that use collaborative robots in production, there are aerospace, medical, automotive, pharmaceutical, small, and medium enterprises in the food industry.

Innovation in business models and innovation in technology can both be disruptive. In this context, disruptive innovations can be considered as the most important element of a disruptive economy as an innovative system of relations between business entities regarding the organization and management of production, concentrated use of combined knowledge. Given that MES systems and collaborative robots can be classified as breakthrough innovations, it seems relevant to consider the relationship between their large-scale implementation and the corresponding change in business models.

The rationale of the Study

In connection with the above-mentioned, the task of studying the technological features and economic implications of the use of the TrakSys platform and robots, in terms of their impact on business models, seems to be urgent. It is supposed to find an answer to the question of what impact the technologies under consideration have on the effectiveness of disruptive innovations. To achieve this goal, it is necessary to solve the following tasks:

To analyze the data on the process of functions and application of MES platforms and collaborative robots in the industry, in frames of Industry 4.0;
To determine patterns and key technological parameters of the technologies under consideration;
To consider the impact of the use of TrakSys platform and collaborative robots on the development and prospects of business models in frames of the disruptive economy.

Our research is limited to considering MES (TrakSys) platform and collaborative robots as an innovation in the industry. In this context, a potential limitation should be noted ‑ disruptive innovations rarely appear to be a priority right at a time when investments in them are especially important; therefore, the so-called “common sense” of managers of ‘mature’ firms is the barrier that is being erected between entrepreneurs and investors (Kawamoto & Spers, 2019).

The question arises whether these technologies represent a breakthrough technology or a consequence of the progressive technological development of the industry. There is an opinion that this is not a breakthrough technology (disruptive innovation) but just the next stage in the automation of production processes (Kawamoto & Spers, 2019). Obviously, organizations must be careful because major business collapses are often associated with radical innovation.

This means that, in reality, the problem lies not in the field of generating ideas but in the algorithm that underlies the implementation of one or another innovation in mass production and attempts to achieve competitive advantages. This discussion naturally determines the significance of the presented study.

The significance of the study lies in the identification patterns of development for MES platforms (TrakSys) and collaborative robots in order to determine the nature of the innovations associated with the technology under consideration. The existing implementation framework of these technologies potentially can help in understanding not only the prospects of this technology and its long-term socio-economic effect but also shed light on specific features and challenges of disruptive innovations as such.

Also, the scientific and practical significance of the study is due to the fact that the theory and methodology of changes in economic systems, like market practice, do not have the necessary developments in the content of a ‘disruptive economy’ (Christensen, Raynor, & McDonald, 2015). In this context, disruptive innovations can be considered as the most important element of a disruptive economy as an innovative system of relations between business entities regarding the organization and management of production, concentrated use of combined knowledge.

Results and Discussion

The new industrial system architecture can be introduced gradually through digital upgrades to existing facilities. This means that this concept can be implemented not only in completely new enterprises but also gradually deployed to existing enterprises in the process of evolutionary development. In an industry built around the Industry 4.0 concept, manufacturing equipment, and products will become active system components that manage production and logistics processes.

They will include cyber-physical systems that connect the virtual space of the Internet with the real physical world. At the same time, they will differ from existing mechatronic systems by the ability to interact with their environment, plan and adapt their own behavior according to environmental conditions, learn new models and behaviors, and, accordingly, be self-optimizing (Ustundag & Cevikcan, 2017).

Industry 4.0 involves the creation of robotic systems in conjunction with Internet technologies in the format of “smart” enterprises, in which MES systems represent an integral part. The Fourth Industrial Revolution aims to exploit (smooth) the differences between the physical, digital, and biological realms.

In particular, the complication of technological processes and the challenges facing modern production required the development of special programs for the automation of production, which include MES systems that have been actively introduced into production processes since the 90s of the last century. Currently, they have become an integral tool for managing production at enterprises of various scales in all industries. The MES system and appropriate platforms such as TrakSys cover the following tasks (OECD, 2017):

Distribution and control of the status of resources (building a production model, centralized storage, quick and convenient search for data on specifications of raw materials, semi-finished products, finished products, and packaging, suppliers, quality standards, legislative documents, etc.);
Dispatching of production processes (management of production orders, management of raw materials and semi-finished products, monitoring of the plan implementation, control of balances);
Data collection, quality management (data collection from process control systems, data quality and reliability verification, data collection and archiving, long-term storage, laboratory data management);
Maintenance management;
Performance analysis (statistical and mathematical analysis, process performance control, calculation of TEC, accounting for equipment uptime and downtime, reporting);
Preparation of production schedules;
Control of documents (electronic document management);
Human resources management;
Coordination of technological processes and tracking of finished products.

Without affecting automation issues at the hardware level, i.e., at the level of SCADA systems (control of meters, sensors, and other devices and equipment), MES systems concentrate their efforts on supporting the planned and organizational components of the production process itself. They can carry out real-time process corrections, unlike other information systems. When performing tasks, MES systems operate according to the actual state of production.

In some cases, systems of this type can schedule not only machine tools but also vehicles, set up teams, and maintenance devices. Using MES as a special industrial software can significantly increase the capital productivity of technological equipment and, as a result, increase the company’s profit even in the absence of additional investments in production (Mantravadi & Moller, 2019).

In particular, the TrakSYS platform is designed to create effective solutions for operations management in real-time, based on data-driven manufacturing. The implementation of this system allows the company to manage its current production activities with maximum efficiency, reducing production costs and increasing labor productivity. Unlike ERP systems, primarily focused on solving financial, economic, and managerial problems, these systems focus on the production process directly (TrakSYS, 2019). They provide more complete and accurate information about production processes, answering the question: how is the production really carried out.

TrakSYS provides unprecedented customization capabilities by enabling building-specific solutions with the use of pre-configured frameworks of application. Also, it is possible to create customized, powerful, and highly effective built-in configuration tools (TrakSYS, 2019), not creating unique requirements for personnel and not increasing the requirements for qualifications and level of training.

As for cobots, they are designed for physical interaction with people in a joint working environment in a variety of industrial fields. While the automation of production and technological processes is far from new, its implementation is still determined by such an economic indicator as ROI, and namely, it plays the main role for many manufacturers. One of the components of the cobots concept is that helping to optimize operations; they work together with people and not replace them. The introduction of such robots allows achieving the following significant results (Dinwiddie, 2018):

Reduce risk. Collaboration ‑ combining robots and humans in the production process ‑ can significantly reduce risks. This is especially noticeable in enterprises where personnel work in a hazardous environment or, if necessary, have to carry out certain production and technological operations in too unfavorable areas.
Improve quality control and increase labor productivity. People tend to make mistakes, while robots can be programmed to perform similar work with higher productivity and without risk to quality.
Increase flexibility. The growing tendency to use collaborative robots in the short term will lead to their greater flexibility compared to traditional robotics.
Cut costs. The integration of cobots in a specific facility has become more economical. This is partly due to the fact that small robots can be maintained and controlled by special mobile applications.

It is assumed that the market for collaborative robots will grow rapidly. A study on the development of the collaborative robot market says that by 2025, investments in this area will be increased several dozen times – from $ 373 million to $ 12.3 billion (Pascual, Daponte, & Kumar, 2019). It is estimated that the number of cobots sold will come close to 1 million pieces annually by 2025 (Pascual, Daponte, & Kumar, 2019).

The success of the first cobots produced by Universal Robots gave rise to real cobotomania. Cobots not only do not pose a threat to the employment of people, but they can also create opportunities for further training and career growth of workers (Dinwiddie, 2018). Robotics does not replace humans but makes it possible to expand the set of skills of an employee: thus, having learned how to program robots, a worker can become a technician for their maintenance.

Moreover, previously, the means of protecting people consisted in separating them from the robotic equipment with physical protective devices, for example, special fences, etc. With the advent of collaborative robotic systems, this paradigm has shifted: now, joint work of robots and humans is possible ‑ cobots can work in close proximity to people. This increases productivity in industry, which means it has a positive effect on the economy as a whole.

Apparently, appropriate business models must correspond to all these breakthrough studies aimed at “jumping” to the next technological level. Usually, the true disruptive power of innovation lies not in the features and functions of the innovative proposal itself but in the business model. Researchers at disruptive innovations emphasize that the most common mistake is to believe that any leap in development is synonymous with ‘undermining’ the market (Christensen, Raynor, & McDonald, 2015).

Many innovations have become breakthroughs for companies ‑ the quality of products has improved significantly compared to previous versions, and to create some of them, multimillion investments were required. When properly managed, such innovative products bring great returns; however, this is not disruptive innovation.

All companies are characterized by certain distortions in their perceptions of the market: those who have an advanced engineering direction usually appreciate the opportunities associated with technological development; famous brands look at the world through marketing glasses. Such distortions force companies to make a fundamental mistake: to assume that a task that they themselves have never tried to solve before will be disruptive to the entire market.

However, the fact is that technology can be disruptive for one company and support for another. Therefore, a true understanding of the impact of innovations requires their assessment from the point of view of the entire market and applied business models. Sometimes, some approach seems really disruptive to individual company representatives, but for an already formed consumer audience and competitors it will be just supportive ‑ such projects are doomed to failure.

All this must be taken into account when assessing the impact of the technologies in question on the market, business models, and economic development within the framework of the modern “disruptive economy.” This is a set of economic relations that provide the process of social production, a scientific description of the method of its implementation, as well as management methods (Christensen, Raynor, & McDonald, 2015; Kawamoto & Spers, 2019). The latter have no analogs in the world; they lead to real revolutionary changes, if not in all spheres of human life, then in many of them.

However, disruptive management methods arise in a certain environment, which can be formed on the basis of knowledge sharing and coopetition mechanisms. Companies that not only make this model basic for themselves but also combine the most complex external network of projects and relations with their internal processes, culture, and key performance indicators, have the highest chances of prosperity in the emerging disruptive economy.

Industry 4.0 provides for digitalization and vertical integration of processes throughout the organization, from product development and procurement to production, logistics, and after-sales services, as well as digitalization and horizontal integration of several value chains. Obviously, the advantages and challenges of all new smart manufacturing technologies, both production and management ones should be addressed in a complex, system manner.

Conclusion

As part of the development of technologies in Industry 4.0, the use of MES systems (TrakSys platform) and collaborative robots have a number of significant advantages, allowing the creation of robotic systems in conjunction with IT technologies within the framework of the concept of “smart” enterprises in the Industry 4.0. The described systems and technologies make it possible to synergistically combine human capital, high-tech assets, and advanced management methods.

At the same time, despite the excellent technical features and advantages of the technology under consideration, as well as its positive impact on reducing the cost of production and enhancing sustainability, the study led to the conclusion about the economic role of these technologies in the context of business models, which represent the necessary element for these technologies to become really disruptive.

Moreover, it is necessary to understand that the emergence of disruptive technology attracts many market players ‑ this leads to lower prices for products, which means a reduction in producers’ profits and the appearance of surplus goods. Over time, industries that produce high-margin products will be forced to yield to market pressure (competition) and will no longer be able to influence the economy. As a result, original innovation becomes a mass commodity, part of economic relations.

The foregoing, however, does not exclude the possibility of turning technologies of MES platforms and collaborative robots into truly disruptive innovation, provided that the knowledge-sharing and coopetition practices in the industry are widely developed.

Recommendation

It can be argued that the modern information space will be effective if it becomes open, which, in turn, will make it possible to realize agreed interests on the one hand and will rely on the latest information technologies on the other hand. The concept of a single information space is changing the approach to building information systems.

Today, the principle of building an information system is the fragmentation of the information field into components, while a single information space, on the contrary, covers the entire information field, which, in turn, requires the development of change management capabilities (that is, to go back a few steps, change what was selected earlier, and then update the result without having to do all the work again), as well as interfaces with the most common platforms within the philosophy of openness. It seems advisable to study the possibilities of implementing these principles in the development, implementation, and improvement of the TrakSys platform and collaborative robots.

Moreover, it is advisable to create software based on the principle of modularity while taking into account the industry-specific solutions focused on specific industries. In today’s world, they’re increasingly less room for the principle “One size fits all.” Systems based on a model-oriented approach will reflect the dynamic interaction of subsystems. Such an approach, as well as large-scale knowledge sharing, can expand the scope of application for the technologies under consideration ‑ , for example, to the sphere of medicine, etc.

References

Christensen, C. M., Raynor, M., & McDonald, R. (2015). What is disruptive innovation? Harvard Business Review, 93(12), 44-53.

Dinwiddie, K. (2018). Industrial robotics. San Francisco, CA: Cengage Learning.

Govindaraju, R., & Putra, K. (2016). A methodology for Manufacturing Execution Systems (MES) implementation. Materials Science and Engineering, 114, 1-10.

Kawamoto, C. T. & Spers, R. G. (2019). A systematic review of the debate and the researchers of disruptive Innovation. Journal of Technology Management & Innovation, 14(1), 73-82.

Mantravadi, S. & Moller, C. (2019). An overview of next-generation manufacturing execution systems: How important is MES for Industry 4.0? Procedia Manufacturing, 30, 588-595.

OECD (2017). The next production revolution implications for governments and business. OECD.

Pascual, D., Daponte, P., & Kumar, U. (2019). Handbook of Industry 4.0 and SMART systems. Boca Raton, FL: CRC Press.

TrakSYS (2019). Parsec. Web.

Ustundag, A., & Cevikcan, E. (2017). Industry 4.0: Managing the digital transformation. New York: Springer.

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