How will industry jobs be affected by the increasing application and integration of analytics. With all the buzz around new technologies in AI/ML, ChatGPT, etc. this topic is “ever” relevant.
Everyone is speculating and considering what this impact will be (some interesting links below)
https://www.whitehouse.gov/wp-content/uploads/2022/12/TTC-EC-CEA-AI-Report-12052022-1.pdf
https://www.mckinsey.com/featured-insights/future-of-work/ai-automation-and-the-future-of-work-ten-things-to-solve-for
https://www.wsj.com/articles/how-ai-change-workplace-af2162ee?mod=Searchresults_pos4&page=1
https://www2.deloitte.com/us/en/insights/focus/technology-and-the-future-of-work.html
In fact, what does Chat GPT think....
The increasing application and integration of analytics in manufacturing will have a significant impact on manufacturing jobs. Here are some ways in which manufacturing jobs may be affected:
Automation and Robotics: Analytics can be used to optimize production processes, identify bottlenecks, and improve efficiency. This often leads to the implementation of automation and robotics technologies in manufacturing facilities. As a result, certain manual and repetitive tasks previously performed by humans may be automated, reducing the need for labor in those areas.
Upskilling and Reskilling: With the integration of analytics, manufacturing jobs will require a higher level of technical proficiency. Workers will need toacquire new skills to effectively operate and maintain advanced machinery, analyze data, and interpret insights derived from analytics platforms.
Upskilling and reskilling programs will become essential to ensure the existing workforce remains relevant and adaptable.
Data Analysts and Data Scientists: The integration of analytics in manufacturing will create a demand for professionals skilled in data analysis and data science. Manufacturers will need experts who can collect, analyze, and interpret large volumes of data generated by various systems, such as sensors, Internet of Things (IoT) devices, and production equipment. Data analysts and data scientists will play a crucial role in optimizing processes, predicting maintenance needs, and making data-driven decisions.
Quality Control and Predictive Maintenance: Analytics can improve quality control processes by monitoring production data in real-time, detecting anomalies, and identifying potential defects early in the manufacturing process. This can lead to a reduction in the number of manual inspections required, but it will also create a need for skilled technicians who can oversee and maintain the analytics systems used for quality control and predictivemaintenance.
Decision Support Systems: Analytics can provide valuable insights to support decision-making in manufacturing, such as optimizing inventory levels,forecasting demand, and identifying cost-saving opportunities. This can lead to more efficient resource allocation and strategic decision-making.
However, decision support systems may also result in a shift in job roles, with a greater emphasis on data-driven decision-making and a reduced need
for manual planning and forecasting.
It is important to note that while some manufacturing jobs may be affected or replaced by automation and analytics, new job opportunities will also emerge as companies adapt to these technologies. Workers with the ability to embrace and leverage analytics, as well as those involved in designing, implementing, and maintaining the analytics systems themselves, will likely find new avenues for employment in the evolving manufacturing landscape.
