Case studies – Sustaining Global Competitiveness with Industry 4.0

R. Subhaa, R. Sudhakara Pandian, Leos Safar, and Jakub Sopko


In the digitalised world, small and medium enterprises (S.M.E.s) are facing day- to-day challenges regarding their sustainable development, especially in the highly competitive global market environment. Numerous companies are working on digitalisation processes with the aim of implementing the Industry 4.0 concept. Industry

4.0 is the fourth industrial revolution enabled by advances in technology. Hence, it requires high levels of technical skills and technology implementation strategies which are still very far away as far as S.M.E.s are concerned. On the other hand,

S. M.E.s play a vital role in any economy and thus it is required to equally and simultaneously develop S.M.E.s along with large enterprises for a balanced development.

The cost of automation, complexity of the technology and non-awareness of new paradigms of Industry 4.0 have led the S.M.E.s to avoid or postpone implementation of Industry 4.0 (Cheng-Ju Kuo et al., 2017).

On this concern, this chapter looks at the cases of implementing Industry 4.0 with the focus on S.M.E.s. This will be useful for strategic planners and designers to help them identify the challenges and issues of implementing Industry 4.0.

Challenges and Issues of Industry 4.0

The Industry 4.0 production model generally requires fast and dynamic decisions based on online data. This requires complex technology with high computational efficiency for implementing Industry 4.0. The problem with available technologies like linear discriminant analysis, Markov analysis and decision trees is that they are time-consuming, complex and they employ offline data (Cheng-Ju Kuo et al. 2017). But in the Industry 4.0 scenario, batch production, once done, may not be repeated, and thus all the data collected will not be needed anymore. And also, even if batch production continues, the data will again be useless as the more time will be consumed in formulation and analysis and much time has already elapsed. Further, most of the S.M.E.s are still using traditional machines which need a lot of investment for the changeover to Industry 4.0. Such S.M.E.s will be reluctant to move to Industry

4.0 unless we provide proven cost-effective solutions to such problems.

The production scenario develops a lot of data like quality data, machine capabilities data, machine health data, maintenance data and production process data. Under Industry 4.0, such data pose a big storage problem and also in processing and analysing the data. Apart from size, such data need to be retrieved and analysed quickly for taking any rapid decisions for a faster response for Industry 4.0 (Ateeq & Klaus, 2016).

Often, such data are to be shared with customers or suppliers for the effective functioning of Industry 4.0 and its achievement. These data are to be integrated with technologies for the development of Cyber-Physical Systems (C.P.S.). The systems are to be integrated both widely and deeply for the successful implementation of any technologies. Often, integration is a highly challenging task as it requires major changes in data structure and also requires methods for transformation of data.

The mass customisation concept of Industry 4.0 needs the processes to be flexible to cope with the changing demand and shorter product life-cycle (Ateeq & Klaus, 2016). This flexibility should also be quick and high-quality, with more effective and efficient processes. This further requires flexibility in all departments of the company. Above all, these flexibility and adaptations are to be done in a cost-effective manner.

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