This Q&A with experts from consulting firm Deloitte discusses how manufacturers can use AI and digital transformation tools to create a connected workplace culture and ease the labor strain on manufacturers.
Tim Gaus, (Left) principal and leader of the Deloitte Smart Manufacturing Business, Deloitte Consulting LLP, Faruk Muratovic, principal and Deloitte engineering leader, Deloitte Consulting LLP. (Images: Deloitte)
Labor issues in the manufacturing sector are nothing new, with difficulties finding and retaining skilled trades workers seemingly stretching over a couple of decades. But the current labor crunch in manufacturing is different. It’s no longer just the machine operators, welders and other technical workers that are challenging to find—engineers and workers with advanced technical specialties are becoming increasingly difficult to bring on board.
With this trend showing no sign of receding, manufacturers and their engineering managers must adopt some novel ways to augment and support their current teams without adding new people.
To get a better idea of how manufacturers can approach this dilemma, Engineering.com enlisted the help of Faruk Muratovic and Tim Gaus, two principals within advisory and consulting firm Deloitte. Muratovic leads Deloitte’s Software and Platforms industry practice and has more than 18 years of global experience in advising companies on evolving their business models for scalable and profitable growth. Gaus is the Leader of the Deloitte Smart Manufacturing Business and got his start in the manufacturing sector programming industrial controls for high speed manufacturing, leading large scale engineering programs and running 24/7 production operations. Their responses are edited for clarity.
Engineering.com (Eng.com): How can you create an empowered, connected culture in the workplace to foster engineering innovation?
Faruk Muratovic (FM): By providing a consistent, common experience across multiple engineering teams and allowing engineers to speak a common language with each other, such as applying a consistent recruiting approach, onboarding, training, methodology, engineering frameworks and tools. Also, by encouraging innovation, exploring new ways of doing things when working with clients, and developing engineering platforms and assets to drive efficiency. Keep open channels for every engineer to bring their point of view to the table and provide the freedom to prototype and experiment. Furthermore, use consistent messaging around the engineering vision, culture, and work from all levels of leadership.
Eng.com: There is a new trend of viewing talent and technology as one connected entity. How does this transition away from siloed work environments benefit organizations and their employees?
Tim Gaus (TG): Work today is no longer performed by just humans and the workplace now spans digital, physical, and virtual environments. As a result, rather than just a collection of employees, a manufacturing workforce today is better described as a connected ecosystem of workers and technology across these various environments.
By viewing the manufacturing workforce in this way, organizations can rapidly respond to changes in the overall supply chain as well as coordinate digital and external contributors to optimize cost and talent needs. Deloitte found that 83% of digitally maturing organizations employ cross-functional teams, compared to 71% of developing organizations and 55% of early-stage organizations. Cross-functional teams enjoy greater autonomy in maturing digital organizations and 73% of these organizations foster an atmosphere conducive to their success.
By continuously incorporating new technology into manufacturing and engineering processes, employees will have access to detailed data and tracking, shifting their roles from more manual work to using data collected by technology to make real-time decisions. Strategically pairing people with emerging technology allows companies to construct “super teams” that combine the benefits of human employees and machines to optimize engineering and manufacturing processes. Yet, getting human-machine collaborations to succeed is not easy. While workers around the world are increasingly collaborating with smart machines, these machines have not yet been optimized for people, and people haven’t yet figured out how to maximize the value of these collaborations.
Eng.com: How do you see this continuing to develop through the next year?
FM: Increased democratization of software development, driven by the increased clout of business buyers, low-code/no-code platforms and AI-powered tools. Increased convergence of the physical and the virtual, such as digital twins and the shift to cloud-enabled connected devices. We will see the continued launch of transformative new offerings via generative AI, with some failures, as well, due to monetization challenges.
Eng.com: How is generative AI impacting the product development process and software lifecycle?
FM: We are seeing a step function change in the ways software development is conducted, driven by widely available generative AI ‘co-pilot’ platforms. There is a significant increase in the velocity at which innovative new features can be predicted, and in the velocity at which they can be built. We are also seeing a huge and broad improvement in efficiency, particularly in the areas of test, test automation and requirements, developments and prioritization.
Eng.com: Manufacturing engineering is one area where AI is bound to have dramatic impacts. Not only does it touch almost every aspect of engineering, but it is also colliding with the profession faster than almost any other advancement. How can Engineering managers prepare their departments and engineers for what’s to come?
TG: Manufacturing and engineering organizations have implemented AI and advanced digital technologies to better manage the complex variables reshaping the market. Combined with the fact that 2.1 million manufacturing jobs are estimated to go unfilled by 2030, organizations are now taking an even closer look at how integrating AI into operations can help to attract a new workforce, upskill current employees, and differentiate their business from competitors.
AI can help attract new talent and will evolve the way we upskill them and the tasks that they are responsible for, making it a capability that all manufacturing engineering leaders must prioritize. Deloitte itself is developing its current workforce via a $1.4B investment into “Project 120,” a plan for Deloitte employees to develop critical tech and leadership skills.
The engineering leaders who will take their companies to the next level are those that harness the power of AI and human integration, which can lead to many notable benefits and use cases for advanced AI, including process optimization to maximize operational efficiency, improving maintenance intelligence to create OEM-recommended preventative maintenance guidelines, and factory layout and design to create a dynamic space.
Eng.com: In what aspects of engineering will the next generation of engineers shine? How will that generation leave its mark on the field of engineering?
FM: We are at the beginning of the era where co-creation with AI/GenAI tools will become imperative across all engineering disciplines, particularly in software engineering—and every business is a software business now. The next generation of engineers will also deliver innovation and new products/solutions at an unprecedented velocity, which will create completely new markets.
Eng.com: Succession has been a big concern for companies for years but losing the “tribal knowledge” of a long-time engineer can cripple a company’s ability to maintain productivity. What are the latest trends you are seeing in how companies mitigate against this and what technology is being applied to the issue?
FM:One strategy for retaining great talent is providing them with the ability to work on things they love to do and shaping a culture of engineering in which engineers can thrive. Engineers in these environments can typically make good succession and transition plans. So much of that tribal knowledge is accumulated due to lack of proper documentation, and evolving GenAI tools, for example, can provide the opportunity to auto-document and “train the enterprise” on engineering requirements, procedures and processes, reducing the amount of tribal knowledge and turning it into documented and easily accessible knowledge exchanges.
