Global warming and the associated reality of climate change are the most discussed outcomes of unsustainable human behaviours. However, global warming is just one of the problems precipitated by the overuse of our natural resources. Other sustainability issues include water stress; depletion of forests, rare natural resources and unrecoverable materials; geopolitical stress on supply chains; and inequitable labour. All of these must be addressed urgently, in addition to mitigating the cascading effect of global temperature shifts.
Many manufacturers that have committed to net-zero targets produce environmental, social and governance (ESG) reports to measure their efforts in improving sustainability. However, our survey of 3000 executives across industries calls out two stark data points:
• Over 40% of respondents admitted to a lack of clear alignment between ESG disclosures to stakeholders and traceable actions in their business or product strategy.
• In over 60% of companies, ESG data is primarily consumed by external stakeholders rather than used in the business to guide strategy.
For most companies, ESG reporting relies heavily on standardised and aggregated data. This information is too broad and often too late to bring about meaningful sustainability-related shifts. As a result, it doesn’t significantly help bring about sustainability-related shifts. That needs to change. Just as manufacturers require real-time financial controls, they also need their ESG data to be a reliable facsimile of their business operations.
This is where artificial intelligence comes in. AI-driven ESG data can bridge the gap between manufacturers and their stakeholders. AI can identify financial incentives to drive sustainable change, resulting in myriad welcome outcomes, including the following four.
Reducing material waste: Global warming potential – a reliable quantification of the amount of material waste that human society creates – is estimated at $40 trillion, and the manufacturing industry generates 40% of this. Manufacturers can and must address this in the following ways:
• Remove hazardous and impacting materials through planned obsolescence.
• Reduce use of single-use materials and excessive material in general.
• Design products and services with sustainability, circularity, and reduced planetary impact in mind.
Each of these goals introduces opportunities for manufacturers to create new revenue, reduce spending, and develop new product and application pathways that could amount, we believe, to a $4 trillion market opportunity. AI-enabled data is critical here, as the technology can identify inefficient material use even before a product is on the production line. AI is equally critical in enabling precision sourcing operations for raw materials, energy management, and the design of new service models.
Driving energy transition strategies throughout the supply chain: Nearly 60% of human-induced carbon dioxide emissions come from manufacturing and its associated transportation and logistics operations. One reason for these high emissions is the siloed nature of the supply chain, which prevents manufacturers from visualising an integrated approach to reducing fossil-based emissions and transitioning to renewable sources.
Here again, AI can play a role. The technology can create global performance models using data volumes that were unimaginable just a few years ago. Using AI, manufacturers can analyse their spending models and work in partnership with the maritime and logistics sectors – breaking down those silos.
To reduce emissions, manufacturers must collaborate with their logistics partners, particularly ocean liners. The maritime logistics industry transports over 90% of the world’s commerce. Only by working together can they optimise operations, reduce emissions, improve sustainability, and boost profitability.
As noted previously, advancements in AI are paving the way for manufacturers and supply chain partners to reduce emissions by analysing large data sets, including data on shipping routes, weather and traffic patterns. At Cognizant, we’ve created an AI-enabled advisory system for one of the world’s leading maritime logistics companies. The system helps the company optimise fuel consumption across a fleet of more than 70 vessels, improving efficiency by over 7%. The model also optimises cargo booking and port operations management, reducing cases in which ships rush to a port but find themselves waiting in the harbour for dockage to become available. These gains benefit the logistics company and the manufacturers that rely on it.
Increasing consumer awareness and demand: When measuring and reporting on Scope 3 emissions, manufacturers are primarily responsible for increasing their products’ recyclability, and generating more consumer awareness. It’s critical for manufacturers to reduce reliance on single-use plastics in a world that produces 400 million tons of plastic waste a year and recycles not even 21% of it.
With AI-driven models, manufacturers can visualise product impact and end-of-life models by analysing data across customer lifecycles. Analysis of market trends, brand guidelines and product lifecycles enables manufacturers to visualise waste streams and other product attributes, which can help drive competitive differentiation and create more sustainable usage models.
Manufacturers also directly educate consumers about what makes products more sustainable and how to recycle them after use.
We worked with an apparel and toys manufacturer to create an integrated ESG data strategy to quantify its supply chain sustainability attributes. This strategy will help the manufacturer better substantiate product claims and increase awareness through marketing and advertising.
Reducing exploitation: Traditional manufacturing economics – buy cheap, make more, sell high – invariably leads to resource and labour exploitation. AI and other digital technologies have shown promise in developing new product and service models that are commercially viable, but fundamentally disruptive. We’ve worked with clients to reduce resource and labour exploitation in the following ways:
• Precision-use models: Systems based on AI, remote sensing and IoT have reduced the use of energy and chemicals in agriculture and aquaculture by over 30%. This has allowed feed and fertiliser suppliers to transition from volume-based to yield-based models.
• Beyond-the-bottle models: Using AI, IoT, and real-time fleet management, beverage companies have reduced emissions from refrigeration, glass and water shipments by creating new dispensing strategies for hospitality and residential use.
• Connected equipment fleets: An integrated solution for managing surgical procedures and associated medical supplies has reduced hospital waste by capturing real-time inventory insights during surgery. The result is a 70% reduction in ordering and inventory management transactions.
A sustainable future manufacturing with AI
Real change toward a circular production and consumption process will only happen when manufacturers implement a long-term sustainable business model. Ultimately, it isn’t policy that drives sustainable change, but the free market that creates new ways of doing business. Applying AI to foundational enterprise data will drive the discovery of opportunities that limit exploitation and reduce costs while creating a healthier planet – and strengthening the potential for new avenues of business growth and performance.
Originally published on the World Economic Forum at www.tinyurl.com/mpspcbkm
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