How Data and AI Apps Can Help the Blue-Collar Employees on the Shop Floor

Applying Lean manufacturing methodologies to human resource functions can create employee satisfaction. Happy and self-motivated workers on the shop floor positively impact the organization's productivity. Read to find out how AI/ML technologies can take a load off the shoulders of Blue-collar workers.

“Employee engagement is an investment you make

for the privilege of staying in business.”

Ian Hutchinson – Human capital management strategist, Australia

Employee satisfaction is one of the most important factors that leads any manufacturing company to success. Many studies have demonstrated job satisfaction has a considerable impact on the motivation of workers. That, in turn, has an immediate effect on productivity, which affects the performance of business organizations. [1]  

A blue-collar worker is thus an equally important foot soldier in this era of Industry 4.0. Although the manufacturing industry has awakened to the fact, it still struggles to prioritize employee satisfaction. Consider AI and Machine Learning strategies that can help augment human resources. 

In manufacturing, the shop floor is where the magic happens. It is where products get made. Employees on the shop floor often work long hours and do the heavy lifting. The working conditions are challenging, and the pressure of getting optimum turnover is high. 

Production processes optimized by AI apps and ML technology can help the manufacturing teams: from quality control to production planning and scheduling to inventory management. That primarily makes the work-life of employees easier by reducing workload and taking the stress of decision-making off their shoulders. Workers provided with a supportive factory environment feel motivated to do more for the company. 

AI/ML and other data-driven technologies can not only facilitate lean manufacturing but help blue collared employees on the shop floor to do their jobs better. Consider how AI apps enable monitoring of production lines and provide a real-time prediction of problems, which helps the production planners prevent process deviations. 

Maximized capacity utilization, optimum inventory management, and efficient utilization of resources are vital to any given manufacturing process. However, it is humanly impossible to forecast events that can cause process deviations, such as unexpected machine breakdown, change in production demand, unavailability of raw materials. 

Reinforcement Learning utilizes historical and LIVE production data. Powered by RL and integrated data analytics, our AI apps identify the parameters influencing the process workflow and causing variations. By providing actionable insights about constraints to be updated, the AI apps let the planning team optimize the processes for increased efficiency. When the production planners or shop floor workers get accurate recommendations, the scope for human errors gets considerably reduced. If your organization believes in imbibing empathy in your company culture, you can imagine how an employee will feel relieved to get guidance in the decision-making process. 

In the recent past, blue-collared employees on the shop floor felt threatened by the arrival of AI technologies. It was a growing fear that AI initiatives could disrupt manufacturing methods and replace manpower on the shop floor. That is now a proven misconception! 

Skilled workers are as vital as technology for manufacturing. With advancements in AI and machine learning technologies, the manufacturing industry could optimize the use of available human resources. Machine learning algorithms can also help the entire production facility get clarity on the realities of the shop floor, beginning with the availability of human resources. The production team can then customize a product campaign considering the over or under-utilized workforce on the shop floor. Avoiding bottlenecks or ineffective distribution of any manufacturing resource is important for running efficient production lines. Flexible production planning can also transfer flexibility into your blue-collar workers’ life.  

Future Forum reported that workers crave flexibility in their jobs. Those with schedule flexibility feel 3.2X better about their work-life balance and 6.6X better about their work-related stress. [2]

Employee retention should be the number one priority for all SMEs and big manufacturers. You must engage your employees and create a desirable company culture with long-term profitability in mind. Leveraging production data analysis with the help of AI Apps is the best way to ensure your blue-collared employees are happily engaged on the shop floor.

References:

[1] Lavanya, V. (2017). A Study on Employee Job Satisfaction in Manufacturing Sector. International Journal of Engineering Technology, 5(10). http://www.ijetmas.com/admin/resources/project/paper/f201710261509008553.pdf

[2] Hartman, J. (2021, December 8). 19 Employee Retention Statistics for 2022. Fit Small Business. https://fitsmallbusiness.com/employee-retention-statistics/

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