Prioritizing Profitability and Sustainability in Manufacturing with AI
Learn how AI-driven sustainability strategies can attract new talent and increase manufacturing profits.
James Newman, head of product and portfolio marketing at Augury, discusses how AI for sustainability initiatives can position Manufacturers as attractive employers for a new generation of talent while simultaneously promoting the upskilling of their current workforce.
Core keywords: Manufacturing, AI, Sustainability, Profitability, Production Health, ESG, Technology, Upskilling,
How are manufacturers balancing production and profitability targets with sustainability goals? Are we reaching a tipping point where technology can finally end zero-sum game thinking and help industrial companies tackle all three at once? The State of Production Health Report uncovered some compelling trends among 500 manufacturing leaders.
Misconceptions and Untapped Opportunities in Sustainability
Can sustainable business practices and profitability stop acting like oppositional forces and become team members rallying for the same cause? The answer is yes if they adopt the right playbook. AI-powered production health strategies can help business KPIs align with sustainability targets while also helping manufacturers boost ROI by up to 10 times.
However, before adopting this powerful solution, industrial leaders must understand the true relationship between profitability and sustainability and view the demands of each as complementary rather than competing.
There are many untapped opportunities for prioritizing sustainability in business goals, yet most in the industry have yet to grasp the win-win scenario it presents. The report findings indicate that 71% of manufacturing executives think sustainability targets either hurt or have no positive impact on their production goals.
Deploying AI to advance ESG campaigns helps organizations drive healthier, cleaner production, attract new talent, reduce wasted materials and energy, and optimize machines for efficiency, safety, uptime, and capacity.
The Power of Robust AI Analytics for ESG
The report finds that manufacturers’ top ESG goals are leveraging environmentally smarter technology (44%), reducing byproduct waste (37%), and producing more environmentally friendly products (35%). With all this investment, one would expect significant progress in quantifying ROI and progress; however, using an example above, of the 44% attempting to leverage environmentally smarter technology, only 18% can quantify how AI is moving the needle on this objective. Companies are struggling to quantify the impact of their AI efforts on goals at the same cadence as they implement solutions.
This gap is baffling and unnecessary.
AI provides better insights and data visibility so stakeholders can see how their digital transformation efforts are paying off, even for reaching sustainability targets. So, why can’t these organizations fully quantify the impact of AI?
This gap stems from a lack of visibility and involvement, an issue no technology can fully solve, but communications can. Many organizations strategize sustainability and technology improvements with a top-down approach instead of involving plant floor workers, leaving those on the frontlines confused about the goals they’re striving to meet. With more relevant data and insights, manufacturers can get better visibility into the before-and after-picture of technology adoption and illustrate the critical importance of AI-driven sustainability to the end users interacting with the technology on the factory floor.
By fully taking advantage of the timely AI-provided insights, manufacturers can see where their investments in ESG are going and better steer production. In turn, this will strengthen their bottom line with more reliable production schedules and anticipate repair costs with regular machine checks.
See More: How 5G is Driving the Future of Sustainability
Attracting the Next-gen Talent
Sustainability and AI also help manufacturers combat a phenomenon known as the silver tsunami, which refers to manufacturing’s aging workforce. Younger workers are more selective when pursuing prospective employers, seeking organizations that align with their core values, provide a positive work-life balance, and empower them with opportunities to learn innovative or disruptive technologies. By adopting an AI-driven sustainability strategy, industrial organizations open the door to a new generation of talent while upskilling their current teams.
However, there is still work to be done to prove technology’s worth in addressing these skill gaps. The report shows that 80% of respondents say technology adoption will positively impact upskilling efforts, and 19% still say that it will have no impact. The industry must continue to evolve by identifying and deploying the emerging technologies needed to boost working conditions, empower current employees, fill skill gaps, and appeal to new workers.
The Future of Manufacturing with ESG and AI
While the future of anything is hard to predict, using AI to quantify ESG and sustainability initiatives will help the industry realize that significant changes are required for a healthier future. Just saving 3M metric tons (MT) of CO2 annually is the equivalent of taking about 750K gas-powered cars off the road, making a notable impact all around. We have the potential to see at least 3M MT reduced a year by 2040 just by using the right technology to improve production health and make machines run more efficiently than before.
There are undeniable challenges in the manufacturing industry today, but technology can make positive improvements and help the industry achieve scalable success. Through data-driven insights, skill enhancements, and improved production health, technology, and clear ESG goals can make the industry better for people, profits, and the planet — building a future where all three win.
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