Shortages of workers, especially skilled workers, make recruitment and retention a major focus for protein processors. Retention has assumed even greater urgency as the average tenure of manufacturing workers in the United States has plummeted from 20 years in 2019 to three years today, according to Technology & Workforce: Using Smart Technologies to Bridge the Skills Gap, a new white paper from PMMI Business Intelligence, a division of PMMI, The Association for Packaging and Processing Technologies. This more rapid turnover negatively affects organizational productivity and training costs and spurs demand for wider implementation of automation and smart technologies.

Tactics to encourage employee loyalty include automating changeovers, adopting app-based troubleshooting, and embracing digital solutions. Unfortunately, many hurdles to implementation exist, including cost, doubts about return on investment, lack of knowledgeable and skilled staff, substantial training needs, lack of access to machine data due to firewalls and classified machines, challenges in updating legacy technology, internal resistance to change, and the organizational difficulties associated with any transition to a new system. 

According to the 2024 State of the Industry, another report from PMMI, machines that minimize manual intervention and are designed to be simpler to operate with an easy-to-use human/machine interface (HMI) can help overcome the skills gap, facilitate training, and forestall turnover. 

The Technology & Workforce white paper also mentions the importance of advanced HMIs and the availability of resident tools such as personal assistant avatars, which provide an individualized user interface capable of presenting tailored instructions and feedback according to the operator’s language preference, skill level, and capabilities. Another HMI-resident resource, exploded views of the machine, can deliver realistic, 3D simulations showing how the machine works. This information helps operators locate and fix problems and expedite training. Another tactic, HMI standardization, offers a way to reduce training time by eliminating the need for operators to learn multiple systems. 

Training programs are changing, too, with greater emphasis on fostering employee empowerment and wider use of advanced tools such as video, virtual reality, augmented reality, and radio frequency identification. Furthermore, there’s interest in shifting from operational to situational training so workers know how to operate and troubleshoot equipment without necessarily understanding precisely how each system works. In other words, they can drive the car without knowing exactly how the engine operates. 

Another growth area is AI, including AI copilots/assistants, digital twins, connected worker platforms, and predictive maintenance, according to another PMMI report, The AI Advantage in Equipment: Boosting Performance and Bridging Skills Gaps. AI copilots/assistants can reduce the time it takes to perform laborious work and free employees to spend more time accomplishing higher-value tasks. Digital twins and connected worker platforms can optimize operation and training. In addition, connected worker platforms can help gauge the effectiveness of training courses and track what information an employee needs to know about a specific machine or process. According to the report, “AI copilots and generative AI predictive maintenance solutions allow users to ask questions regarding issues they are having on machines, further enabling [staff] members … to upskill independently and reduce the risk of human error.” Perhaps more importantly, due to the nature of AI, the longer these technologies are used, the better the outputs. Improvement is continuous.   

The Technology & Workforce white paper also mentions the benefits of other smart tools such as intelligent illumination that can direct operators to the correct door or compartment, halo lenses or machine-mounted cameras that provide real-time visibility inside the machine to monitor performance, and AI-based large learning models that can make troubleshooting, predictive maintenance, and changeovers more efficient. 

Finally, effective collaboration, potentially supported by AI, is seen as crucial in streamlining operations and enhancing job satisfaction. 

With satisfied employees more likely to become long-term and increasingly valuable employees, automation and digital tools, which free them for more high-impact responsibilities and give them more control over their time and career development, will continue to be in demand. 

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