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Identified IntelliAuto company employees issue by using R to generate (1) Descriptive Analytics, (2) Visualization and Predictive Analytics, and (3) Prescriptive Analytics

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CASE STUDY: ITELLIAUTO (Fictional company)

Industry 4.0 has poised to modernise manufacturing sector ad boost the industrial competitiveness. Specifically, it couples with emerging technology such as Internet of Things (IoT), automation and robotics, to increase productivity and reduce costs and thus digitally transformed the manufacturing sector into the “smart factory”. Although Industry 4.0 has great benefits, it also has significant impact on the manufacturing workforce. Potentially it disrupts the manufacturing processes and also the future of manufacturing workforce and leads to controversial debate such as fear of jobs being taken by robots, ethical choices between job losses and profit, etc.

IntelliAuto is an automobile parts manufacturer with 5000 employees. The organisation has plan in place to move towards a digital manufacturing factory in next 5 years. Although the digital plan is at its infant stage, IntelliAuto wishes to study its full-time workforce by developing profile of employees that measures factors such as income, job satisfaction, career progress, etc. The current aim is to better understand the employees of the organisation and avoid the Employee churn. Additionally, the study also helps to better prepare for negotiation with unions in future when the organisation is in transition to digital manufacturing factory.

A survey of a random sample of 1000 employees was conducted. In this case study, we play the role of a data analyst to identify some of the issues related to IntelliAuto employees including "Working hours" and "The years of employment" in order to help the HR team in planning they own company strategies.

Notes: Employee churn is expensive for companies. When an employee leaves he/ she takes his experience with them. Recruiting new employees are expensive and time consuming.

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Identified IntelliAuto company employees issue by using R to generate (1) Descriptive Analytics, (2) Visualization and Predictive Analytics, and (3) Prescriptive Analytics

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