The industrial application of data and communication technology is called Industry 4.0. The third Industrial Revolution is essential for the growth of Industry 4.0. Koh et al. (2018) define industry4.0 as a combination of technologies. However, they also refer to future consumer needs, sharing of resources, information, owning, using, regenerating and exploiting information. In order to make a product with a lower cost, greater sustainability, higher efficiency, and faster production, Koh and colleagues (2019). Telukdarie et al. (2018) claim that Industry 4.0 encourages autonomy in production equipment. Automated planning and programming can be done without the need for a machinist, allowing for more efficient manufacturing. Industry 4.0 has had a profound impact on logistics and the supply chain, but many companies are struggling to implement it.
The supply chain and logistics network are negatively affected if the technology chosen is not the right one. Businesses have difficulty choosing technology that is high-performing. A business’ economic viability could be affected if it chooses the wrong technology (Telukdarie, et al. 2018, 2018). A second concern concerns the industry rate as a result of the integration of information theory (IT) and its development. Facilitating Industry 4.0 could have negative repercussions in areas with poor education systems, like Africa. The impact of Industry 4.0 on education could lead to a decrease in societal sustainability. A third problem is the constant need to upgrade skills and network. In order to keep up with industry 4.0’s rapid growth, there must be ongoing training programs available for sub-suppliers, consumers and suppliers. Failure is for those who don’t update their knowledge and systems.
Fourth, operational problems are involved, including the execution of final stage operations, or the convergence between conducting final stage operation and taking part in economic models. (GarayRondero, et al. 2019, 2019). A fifth problem is difficulty in analyzing and harmonizing data during the shift from an insistent distribution strategy to a predictive last stage.
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