- By Steve Mustard, P.E., Eur Ing, C.Eng, CAP, FIET, GICSP, CMCP
- June 26, 2023
- The Final Say
Summary
If this were not enough, Industry 4.0 assumes the use of newer technologies and concepts such as augmented reality (AR), the industrial Internet of Things (IIoT), and digital twin. Various organizations are attempting to understand what the future workforce will need to look like to embrace all the benefits of Industry 4.0, also called “smart manufacturing.”
An article by Deloitte, The Future of Work in Manufacturing, examines “what future manufacturing jobs will be like in the digital era.” To help explain their predictions, they created 10 personas, each describing a future job in manufacturing. Four of the personas are conventional manufacturing jobs (e.g., “Quality Assurance (QA) manager”) with the word “smart” added. The remainder are a mixture of new job titles for existing roles that use new technology in that role (e.g., “digital twin” engineer) and very narrowly defined jobs that may exist or be part of another role (e.g., "drone data coordinator”).
MxD (Manufacturing x Digital) is a non-profit organization where major manufacturers, in partnership with the U.S. Department of Defense, are identifying the tools and expertise they need to enhance their capabilities. Its report The Digital Workforce, Succession In Manufacturing, identifies 165 roles, although each role is narrowly defined, and the skills and knowledge overlap with several others. For example, there are eight roles with IT/OT in the title (application developer, systems analyst, systems architect, systems technician, etc.). In reality, there are probably two roles here (technician, engineer), and a variety of company-specific names or activity-specific roles (e.g., during a project).
A more likely reality
The approaches by Deloitte and MxD can certainly stimulate good discussion, but manufacturing organizations are unlikely to change so dramatically. The real benefit of Industry 4.0 technologies is that organizations can achieve their core mission more efficiently and deliver better quality output. As a result, the skills and knowledge requirements for existing roles will need to change and personnel in those roles will need additional training.
A simple example is the QA engineer. Today’s QA engineer’s toolkit includes 2D drawings, manufacturers data books, and a tape measure. The role has developed over the years and has moved further away from hardcopy toward electronic documents, including 3D models. Future QA engineers will continue to develop and be able to utilize:
- A digital twin that combines an accurate 3D model of equipment with the manufacturer’s data fully integrated
- Smart glass technology to allow the overlay of a 3D model on the actual equipment, simplifying the process of tie-in point validation and dimensional control
This is the Deloitte Smart QA Manager role—although it would be more helpful to focus on the new skills and knowledge the QA manager needs, rather than dwell on the name of the role. This is a minor issue. The more significant concern is that Deloitte and MxD both foresee manufacturing organizations creating roles such as digital twin engineer and virtual reality/augmented reality software engineer. Digital twin and augmented reality systems are no more core business for manufacturing organizations than are manufacturing execution and enterprise resource planning systems.
Of course, manufacturing organizations will employ personnel who can support the business with technology, most likely in their IT departments. But even then, these roles cannot be so specialized that they only support one technology. As technology inevitably evolves, the role needs to be one that can keep up (or ahead) and continue to evolve. Therefore, organizations should update the skills and knowledge requirements for their relevant roles to include the new technologies that need supporting.
Automation competency model
ISA, representing the automation profession, has been working with the U.S. Department of Labor since 2008 on the development and ongoing maintenance of an automation competency model (ACM). The model identifies the knowledge and skills needed in the automation profession. The ACM defines the totality of skills and knowledge needed in the automation profession. To identify a specific profile for a role, users are able to employ the needs analysis matrix. This can be used to screen applicants for roles, as well as to assess gaps in skills and knowledge in the existing workforce.
With the ACM skills and knowledge descriptions, it is possible to define a training curriculum. ISA’s existing training portfolio covers a significant portion of the ACM skills and knowledge, and work is underway to review the gaps. This will also include identifying the gaps in new skills and knowledge areas, such as Industry 4.0, and identifying a strategy for closing those gaps.
This may involve the development of a new training (or certification) program or partnering with other like-minded organizations that are better suited to a particular set of skills and knowledge. Either way, ISA will be able to offer all the relevant training and certification needed for the future automation professional and help manufacturing organizations achieve their ambitions to deliver better-quality output more efficiently through the use of Industry 4.0 technologies.
A version of this column originally appeared on the ISA Interchange blog.
Reader Feedback
We want to hear from you! Please send us your comments and questions about this topic to InTechmagazine@isa.org.