Opportunities for industrial manufacturers at the Intersection of sustainability, AI and ethics

Photo:© leowolfert / Adobe Stock

The quest to uncover new sources of revenue, value and growth in a world that is increasingly digitally driven and resource-constrained has brought manufacturers to a critical confluence (or some might say, collision) of three factors: sustainability, artificial intelligence and ethics.

As resource-dependent as their businesses tend to be, industrial manufacturers now must also factor sustainability — specifically, carbon reduction and the reuse-repurpose-reduce waste principals of the circular economy — into their operations and their strategies to meet their customer, employee and even shareholder expectations. “By 2029, the circular economy will be the only economy, replacing wasteful linear economies,” Gartner said in 2019.

Artificial intelligence, meanwhile, will be instrumental in helping companies turn their sustainability and circular economy initiatives into lastingly profitable ventures by effectively informing how circular products, components and materials are designed, how circular business models are operated, and how circular infrastructure are optimized. As industrial manufacturers increase their use of AI, not only in a sustainability context but also in a variety of operational and supply chain applications, as well as in their own equipment, they need to be conscious of the ethical implications with how their products are sourced, manufactured and used.

In a 2020 webcast, Lian Jye Su, a Principal Analyst at ABI research in Singapore, commented on the business ethics challenge embedded within the convergence of sustainability and AI.

“Humans are such an intelligent being that we tend often to focus on the promises behind all these scientific discoveries, be it sustainable manufacturing methods, or AI, or robotics automation in general,” he said. “Then we tend to further extrapolate what we see behind all these possibilities and potentials. But often, we tend to ignore the pros and cons that come with it.”

Customers will ultimately be the catalyst for companies to integrate sustainable practices into every stage of the product lifecycle, and to have visibility into their own processes as well as those of their suppliers.

The Business (and Broader) Case for Sustainability
There is both a business case and an ethical case to make for an industrial manufacturer to put sustainability at the centre of its strategic thinking, as it applies not only to its own operations, but also to the entire value chain of which it is part of.

In a recent report from Oxford Economics and SAP based on a survey of executives from companies across the manufacturing landscape, two-thirds said having a clear purpose and mission is a necessity to the long-term success of their business. Ultimately, the report asserts the sustainability of a company’s extended supply chain and operations “may determine financial performance and company survival, not to mention creating a more hospitable world for future generations.”

Customers will ultimately be the catalyst for companies to integrate sustainable practices into every stage of the product lifecycle, and to have visibility into their own processes as well as those of their suppliers.

“I think in the not-so-distant future, we will see all types of products and processes having CO2 and other sustainability tags on it,” our colleague Georg Kube predicted during the aforementioned 2020 webcast. “The powers of the consumer will drive behavior. Not only with [consumer products] companies, but all the way up to the manufacturers of the equipment that the [consumer products] companies use, and into the supply chain of products.”

Fulfilling customers’ growing appetite for sustainable products, and doing so cost-effectively, depends heavily on running automated, data-driven digital processes that use Industry 4.0 best practices, leveraging AI and the Internet of Things. For industrial manufacturers, that means designing and engineering products for more resource-efficient performance, with the ability to predict the cost tradeoffs involved in developing and operating these products.

It also means minimizing waste and environmental impact in the factory by extending asset life, monitoring and managing energy usage as a function of production volume, measuring CO2 emissions and ensuring employee safety. So, it’s vitally important that companies have the ability to track, measure and reduce emissions across the entire product lifecycle.

It’s also important that manufacturers look beyond their own walls by leveraging their supply chain relationships and their business networks to further their sustainability goals. By working together, global supply chains and networks can collaboratively work to improve environmental impact, and to ensure more inclusive economic growth through ethical business practices. End-to-end supply chain visibility is key to this effort, from raw materials sourcing, to last-mile logistics, and even to product usage, returns and recycling processes.

Photo:© BillionPhotos.com/ Adobe Stock

Working together, manufacturers and their supply chain partners are modeling and developing logistics processes and pathways that optimize loads to reduce mileage, emissions and carbon footprint, for example, along with CO2- and energy-optimized warehousing and transportation.

A Growing Role for Artificial Intelligence
AI and Industry 4.0 best practices are enabling manufacturers to evaluate and develop more sustainable products cost-effectively, and to optimize the new business models they’re developing around these products.

Manufacturers have only begun to scratch the surface of AI’s potential for helping them create products, components and materials specifically for CO2 reduction and the circular economy. Companies are using AI algorithms to design products to meet their own sustainability goals and those of their customers, and to illuminate simpler, more cost-effective circular pathways for repurposing, recycling and/or reusing materials and products at end-of-life — pathways they may otherwise have overlooked.

Industrial manufacturers have begun to leverage AI generative design to quickly propose solutions to design challenges, such as reducing the weight of machines. When parameters are entered, generative design programs are able to design, prototype and simulate testing of properties simultaneously – and propose many possible options – by using the power of computing to complete many iterations quickly.

Like all AI platforms, generative design depends heavily on input data and parameters. Here’s where engineers will need to think more about the objectives/outcomes of their designs — essentially, their potential human impact. They also will need to parse the data and algorithms that inform their AI systems, and to identify bias in those systems. In a broader sense, companies and their developers will need to understand and explain how they expect an application of AI will impact stakeholders, employees, customers and the public at large.

Industry’s growing reliance on AI raises weighty questions — about the future of human beings in the workplace, about intended and unintended consequences, about the handling of sensitive data, about organizational and individual values — the list goes on. Instead of waiting for regulators and lawmakers to answer those questions, now is the time for manufacturers to proactively start pursuing answers for themselves, so they can be part of a dialogue that results in policies that protect people while also preserving a company’s ability to innovate, run a more sustainable business, and do so profitably.
________________
Judy Cubiss is the Director of Industry Marketing and Chao Yi is a Solution Manager in SAP’s Global Industrial Manufacturing team. The team is responsible for developing and bringing solutions to market for industrial manufacturers.