Evolution of Industrial Revolutions

Introduction

Industrial revolution has evolved over centuries from Industry 1.0 to 4.0. As early as the 1800s, water and steam powered machines were introduced as the new ways of aiding workers. Next, electricity was developed for use with machines to replace the archaic steam powered machines. In fact, machines could now even have their own sources of power hence portability (Almada-Lobo, 2016). Then came the advent of innovative technologies in manufacture of electronic devices like circuit chips and transistors. Today, there is the Industry 4.0 Industry 4.0 generally refers to the developmental process in the managing of manufacturing and chain production. It involves the use of cyber-physical production systems which merges the real world and virtual worlds (Barreto, Amaral and Pereira, 2017). The fourth industrial revolution also known as smart manufacturing, has changed the use of automation as previously perceived in the third industrial revolution. This stage is characterized by extreme automation of manufacturing processes through customized and flexible mass production technologies. At this level machines operate independently and communicate with each other, with the ability to maintain itself through software updates instead of physical maintenance.

Fundamental Concepts

Cyber-Physical Systems

This is an integrated system of computation networking and physical processes. Computers, with aid of networks are able to monitor and control the physical processes in manufacturing (Lee, Bagheri and Kao, 2015). Feedback is received from reaction of the physical processes which is analyzed and interpreted with the ability to track results. Systems are vital components of integrating the two processes as stated above. Cyber-physical systems performance is dependent on certain factors or phases. In manufacturing, unique identification is used a basic language of communication between the machines and computers. A common identification method is the use of Radio-frequency identification. For machines to operate, there must be an integration of sensors and actuators that can store and analyses data (Lee, Bagheri and Kao, 2015). These sensors and actuators are then connected to a network to allow communication and exchange of information between machines. Fire alarms operate within the above background such that there is exchange of data between machines, sense of variations in the environment and subsequent action by the computer to sound an alarm.

Internet of Things

Human beings are capable of independently making decisions and performing tasks on their own. Similarly, Internet of Things enables different machines to communicate with one another in performance of given tasks. However, the role of human beings in the operations of these systems and machines cannot be dismissed or be dispensed with (Madakam, Ramaswamy and Tripathi, 2015). The intervention of human beings is still important in the integration of technologies to enable machines work and provide solutions independently. In Industry 4.0, network machines and sensors are incorporated to into the supply chain. Computing technology is attached to devices that can communicate with other devices just like people do with aid of internet (Lee, Bagheri and Kao, 2015). This has been demonstrated and applied in supply chain and manufacturing control systems by allowing exchange of data between them.

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Internet of Services

In light of the current proliferation of electronic devices, their connectivity is becoming even more important. In many cases these electronic devices are either connected to other devices or to the internet for full enjoyment of its services (Madakam, Ramaswamy and Tripathi, 2015). The issue of connectivity of the ever evolving electronic and smart devices has brought about complexities and inhibition of the utility of a new device added to the system. The role of internet of services in all these is to deconstruct the complexities by creating a system that connects all the devices seamlessly so that each device is fully utilized fully.

Smart Factory

Smart factories can be fed with soft information relating to a product being manufactured be it specific models or drawings. Hence, it can be said to be a factory where cyber-physical systems exchange information over via the Internet of Things and aid people and machines in completion of their tasks. The Siemens Electronic Works facility in Germany has applied smart machines to coordinate production and global distribution in a process that involves 1.6 billion components.

Features of Smart Factory

Connected nature

Smart factories needs to have processes that are interlinked to generate data so as to make real-time decisions. Machines are thus fitted with smart sensors that collects information from both new and traditional sources, analyses and updates the same as necessary (Weyrich and Ebert, 2016). Supply chain is thus enhanced in such a way that there is integration of data form business and operation systems, suppliers and customers.

Optimization

The aim of optimization in smart factories is to execute operations with limited manual intervention while ensuring high reliability. It is also a way of enhancing reliable and predictable production capacity. A highly automated production and handling of materials in a smart factory ensures that human intervention is minimized to monitoring and occasional service of the systems (Wang, Wan, Li and Zhang, 2016). Additionally, there is reduced cost of quality and production.

Transparency

Data collected in smart factories are usually transparent due to their real-time nature. These data are thereafter used for autonomous decision making by the systems or for human decision making. The end result of a transparent data is that there is expeditious and seamless decision making (Almada-Lobo, 2016). Again, accuracy in decision-making is guaranteed to a certain extent through the employment of tools like real time notifications, alerts, monitoring, and time tracking.

Proactivity

Just like people in an employment setting need to anticipate certain actions and take steps to respond, systems can also perform the same in smart factories. It is the opposite of a reactionary attitude that entails waiting till an issue arises then steps are made to address the same. Interconnected systems have now been developed to identify glitches in production processes including issues of restocking, inventories and quality (Wang, Wan, Li and Zhang, 2016). Further, the same system is capable of tracking safety and maintenance issues in a production process with the ability to forecast future challenges to the processes.

Agility

A smart factory has the unique ability to adopt to product changes with little or no manual involvement. This flexibility allows self-configuration of machines to produce specific products from given materials with the results being real-time (Weyrich and Ebert, 2016). In essence, these applications cut time spent for changeover in equipment as well as reduced cost as a result of a seamless self-configured system with minimum manual interference.

Strengths, Weaknesses, Opportunities and Threats

Ericsson has implemented Internet of Things technology in its factories in Stockholm, Sweden. The telecommunications equipment manufacturer, having a number of factories around the world, is currently using networks to offer real-time management of is plants. Industry 4.0 technologies like motion sensors are being used by Ericsson on high end screw drivers to capture data for quality monitoring and provide predictive maintenance.

Strengths

Increased productivity has been realized by organizations that have adopted smart manufacturing and in this case Ericsson. They have implemented the use of robotics in the high end manufacturing of telecommunication equipment. This has been possible because these robotics are self-configured with intelligence in private cloud hence provides accurate testing and expeditious handling of material. Studies suggest that smart factories have the potential to increase productivity between 15-25% without material costs and 5-8% with material cost factored in (Russman et al., 2015). Ericsson has been able to drop the use of cables in favour of wireless sensors, which can last up to 30 years. As a result of these smart technologies a higher level of productivity has also been realized. Production costs are minimized since there is less manual intervention and implementation of predictive maintenance. A company will experience industrial growth due to increased productivity as well as increased competitiveness with regard to the products produced. In this case Ericsson gets to enjoy competitive advantage against other companies in the same sector owing to the ability to seamlessly produce high-tech products. The production cycle is substantially reduced due to the automation and self-configuration of equipment (Khusnullova and Absalyamova, 2016). For stakeholders, it means an increase in shareholder value and probably more dividends.

Weaknesses

Smart factory technologies have the general effect of reducing manual intervention in the various process. As a consequence, low skilled employees are likely to lose their jobs since their functions shall have been taken care of by integrated machines. However, high skilled employees like IT technicians, mechanical engineers and software developers remain indispensable since their services are crucial for the running of the machines (Schmidt et al., 2015). Therefore, reduction in employment is one of the challenges and weak points in adopting the smart factory technologies for companies like Ericsson. Given that smart factories are almost always fully dependent on resilience of networks and technologies in their operations, miniature disruptions can have serious consequences for the company hence inconveniencies. During the first stages of introduction of new technologies, significant costs are involved. Technological innovations can easily disrupt the relevance of an existing and old technology currently being used by a company, hence, companies have to spend a lot in remaining technologically relevant.

Opportunities

There is a possibility for Ericsson to extend the application of smart technologies in other factories around the world to achieve the similar milestones as evident in the Stockholm factory. Once technology has been tried and tested like in the Ericsson case, it is likely that the same can be replicated to capture new markets with similar or new products. Improved customer satisfaction and new markets presents an opportunity for a company to invest more in customization of products and maintaining a product variety (Stock and Seliger, 2016). Some scientists have contended that smart companies have the potential, in future, to transcend Industry 4.0 to the sixth technological structure of the economy. It means that smart factories is merely a scratch on the surface and there exists a lot of untapped potential in the industry that remains to be exploited in future with the continued advancement in technology. It is also possible that industrial enterprises and research organizations can cooperate further to achieve synergies in furtherance of technological advancement (Stock and Seliger, 2016). Ultimately, sustainability of the whole process must also be considered even as stakeholders warm up to the opportunities present in this era.

Threats

Political and legal factors like taxation policies can be untenable for companies that have intentions to establish smart factories within certain jurisdictions. Again, the adoption of smart technologies in smart factories is capital intensive at first instance hence may be difficult for organizations that do not have access to long term loan facilities (Russman et al., 2015). Ericsson has been able to successfully implement smart factory technology since it has an established brand with financial muscle, however, for small capitalized companies it is a different story. Some barriers to spread of technologies may preclude implementation. This may arise where there is poor digital infrastructure as a result of government indifference or neglect of such matters. This can make it not only difficult but also expensive to adopt such technology in production. In others cases, it could be that there is an inadequate formation of the Industry 4.0 ecosystem hence problems in development of applications services that support the technology (Rubmann et al., 2015. Also, the reception by the potential customers if not gauged correctly may lead to product failure or poor response.

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The future of workforce

In light of the advent of smart factories, future work force will be substantially different from the present. Already, smart factories are in need of employees with certain type of skills and low skills either being dropped or retrained to fit into the new system. Automation in the workplace has necessitated the expansion of digital and soft skills. Human skills such as creativity, people management and critical thinking are increasingly becoming a requirement in automated companies (Bonekamp and Sure, 2015). Digital skills have also become essential since integrated machines have replaced manual processes and humans performing repetitive tasks. Workers are also increasingly relying on digital tool boxes to effectively complete their tasks and ensure effective communication. In the end, the future looks transformative even for people in applicable professions. Eventually, people must adapt and learn skills relevant to the smart technologies or risk being irrelevant in the job market.

Conclusion

In as much as smart factories have presented a substantial disruption in manufacturing, it will not greatly affect the labour market for some years to come. This is because different countries and companies are not at par in technological advancement standards. It is a progressive realization.

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References

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Bonekamp, L., & Sure, M. (2015). Consequences of Industry 4.0 on human labour and work organisation. Journal of Business and Media Psychology, 6(1), 33-40.

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Erol, S., Jager, A., Hold, P., Ott, K. & Sihn, W. (2016). Tangible industry 4.0: A scenario-based approach to learning for the future of production. In the Proceedings of the 6th CIRP Conference on learning factories, 13-18.

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