How AI Will Transform the Future of Manufacturing

How AI Will Transform the Future of Manufacturing

The introduction of artificial intelligence (AI) into the manufacturing value chain is significantly changing the nature of that sector's workforce, according to new research from the MAPI Foundation. But rather than robots taking human jobs, new hybrid roles are emerging where humans enable machines and AI augments human capabilities.

Robert Atkinson and Stephen Ezell of the Information Technology and Innovation Foundation (ITIF), and both the authors of "How AI Will Transform Manufacturing and the Workforce of the Future" say that within the next five years manufacturers will see significant growth in AI through machine vision, intelligent products, machine learning, and cobots, both within factories and throughout the supply chain. They project this will lead to a myriad of new types of AI-related jobs in manufacturing.

The research was conducted in partnership with ITIF and included a survey of MAPI members to understand the current and future state of AI for manufacturing.

According to this survey of U.S.-based manufacturers, currently almost three-fourths of them have not yet introduced new types of AI-related jobs into their companies. In addition, only 20% have comprehensively re-evaluated job roles, titles, levels, and pay scales in recognition of the need to attract employees with AI skills. However, Atkinson and Ezell note this is changing quickly.

The study emphasizes that more than 40% of manufacturers have already created "data scientists/data quality analysts" in their workforces, and 35% more expect to do so within the next five years. A sizable proportion of manufacturers are also creating "machine learning engineers or specialists" (33% today, 70% within five years), "collaborative robotics specialists" (29% today, 27% within five years), and "data-quality analysts" and "AI solutions programmers/software designers" (26% today, 40% within five years).

"Manufacturing is already facing a worker shortage, and advanced technologies create additional technical and workforce challenges to find and retain talent with the necessary digital skills," observed Stephen Gold, president of the MAPI Foundation. "Companies that acquire and cultivate new digital-related skills will have a distinct advantage as AI reshapes the industry, including identifying new roles for AI-focused jobs such as leading AI strategy and supervising implementations."

Six Recommended Elements of the Manufacturing Evolution

Atkinson and Ezell share recommendations for business leaders as they integrate new AI-related strategies and technologies:

1. Create teams to drive digital transformation in the enterprise.

  • The digitalization of manufacturing, including the application of AI-based solutions, heralds the most significant transformation to manufacturing in a generation.

  • Manufacturers will need dedicated teams to navigate this transformation, such as digital command centers and digital business teams tasked with leading the deployment of emerging digital technologies.

2. Define an "AI governing coalition" for AI transformation.

  • Manufacturers should define their own AI transformation strategy, with a core mission of assessing the company’s current processes, procedures, production systems, and operations and evaluating how the application of AI-enabled systems could transform and improve them.

  • Companies should establish an “AI governing coalition” of business, IT, HR, and analytics leaders who own responsibility for activities such as setting the direction of AI projects, analyzing problems to solve with AI, and managing internal change.

3. Evaluate AI and workforce transformation readiness.

  • A workforce transformation strategy should consider what AI-specific jobs need to be created and how to provide relevant AI training to employees at every level of the organization.

  • Manufacturers need an inventory of what AI skills the company will need, to ascertain to what extent internal resources can fill these needs, or skills that need to be acquired externally, and develop a plan to train and upskill workers.

4. Set measurable objectives for digital and AI transformation.

  • Companies shouldn’t deploy AI technologies for technology’s sake – all implementations of digital technologies should address a clear business need and be supported by a reasonable return on investment rationale.

  • Manufacturers should define annual objectives for how the application of AI can help meet key performance indicators such as overall operating efficiency and productivity growth.

5. Redefine digital and physical product innovation processes.

  • The advent of digitally-based innovation creates a need to speed time to market, but this presents a challenge to the stage-gate models used to manage product development and innovation cycles.

  • Companies will have to modify their product development processes to accommodate digital transformation while still meeting key safety, reliability, and product quality standards for their finished products.

6. Overinvest in communication for change management.

  • Effective practices include developing a communications process to explain the implications of AI applications and solutions to employees, customers, and partners.

  • Some companies have already set up worker councils to facilitate dialogue between the front office and the front line about how the advent of AI will change workers’ roles and responsibilities in the AI era.

"Most manufacturing companies are only beginning to realize the opportunities possible with AI," said Ezell. "Businesses that want to remain on the cutting edge of manufacturing innovation need to implement policies that support and enable the use of the technology throughout their organizations."

Learn more about "Building the Future of Work in the Age of AI: How New Jobs and Technologies Will Transform Manufacturing" at MAPI's annual executive summit ManufacturED held Sept. 16-18, 2019, in Chicago.

Article published by icrunchdata
Image credit by MAPI Foundation
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