Driving sustainable—and profitable—manufacturing with AI

Using AI, manufacturers can identify operational changes that result in quantifiable improvements to both sustainability and the top and bottom lines.

Global warming and the associated reality of climate change are the most discussed outcomes of unsustainable human behaviors. However, global warming is just one of the problems precipitated by 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 labor. 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 3,000 executives across industries calls out two stark data points:

For most companies, ESG reporting relies heavily on standardized and aggregated data. This information is too broad, and often too late, to bring about meaningful sustainability-related shifts. This 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 changes, including four we’ll explore here.

Four AI-led opportunities for sustainable manufacturing

1.    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 40% of this is generated by the manufacturing industry. 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 material, energy management and design of new service models.

2.    Driving energy transition strategies throughout the supply chain

Nearly 60% of human-induced C02 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 visualizing 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 analyze their spending models and work in partnership with the maritime and logistics sectors—breaking down those silos.

To reduce emissions, it’s critical for manufacturers to collaborate with their logistics partners, particularly ocean liners, as the maritime logistics industry accounts for the transportation of more than 90% of the world’s commerce. It’s only by working together that they can optimize operations, reduce emissions and improve sustainability in a way that also boosts profitability.

As I wrote previously, advancements in AI are paving the way for manufacturers and supply chain partners to reduce emissions by analyzing 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 optimize fuel consumption across a fleet of more than 70 vessels, improving efficiency by over 7%. The model also optimizes cargo booking and port operations management, reducing cases in which ships rush to a port—but find themselves waiting in the harbor for dockage to become available.

These gains are beneficial not just to the logistics company but also to the manufacturers that rely on it.

3.    Increasing consumer awareness and demand

When it comes to measuring and reporting on Scope 3 emissions, manufacturers have the primary responsibility of increasing the recyclability of their products 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 only 21% of it, at least in the US.

With AI-driven models, manufacturers can visualize product impact and end-of-life models by analyzing data across customer lifecycles. Analysis of market trends, brand guidelines and product lifecycles enable manufacturers to visualize waste streams and other product attributes, which can help drive competitive differentiation and create more sustainable usage models.

Manufacturers also have a direct role in educating 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 so it can better substantiate product claims and generate more awareness through marketing and advertising.

4.    Reducing exploitation

Traditional manufacturing economics— “buy cheap, make more, sell high”—invariably lead to resource and labor exploitation. AI and other digital technologies have shown promise in developing new product and service models that are commercially viable but are fundamentally disruptive.

We’ve worked with clients to reduce resource and labor exploitation in the following ways:

1. Precision-use models: Systems based on AI, remote sensing and Internet of Things (IoT) have reduced the use of energy and chemicals in agriculture and aquaculture by over 30%. This has allowed feed and fertilizer suppliers to transition from volume-based models to yield-based models.
   
   
2. 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.
   
   
3. 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: a 70%+ reduction in ordering and inventory management transactions.

Looking ahead at a sustainable manufacturing future

Real change toward a circular process of production and consumption will only happen when manufacturers put a long-term sustainable business model in place. In the end, 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.