Can AI Help Supply Chains Be More Environmentally Conscious?
By Ariadnna Garcia
I am pleased to introduce this WEC blog post by Ariadnna Garcia. As we explore challenging issues for sustainable development, we want to feature diverse perspectives. Ariadnna is an undergraduate at UC San Diego and one of our first Climate Ambassadors in partnership with the University of California Alianza Mexico. Ariadnna explores the potential of AI as a tool for sustainability in supply chains. We hope you find her ideas thought-provoking!
– Glenn Prickett, President and CEO of WEC
Supply chain sustainability, which refers to the management of the environmental, social, and economic impacts of goods and services throughout their lifecycle, is a pressing challenge facing businesses today due to the worsening condition of our planet. As the climate crisis approaches the point of no return, it becomes essential for corporations to ensure their business practices are sustainable and protect the environment from further harm.
Supply chains are complex and global in nature. Thus, it is no surprise that businesses may struggle with measuring their sustainability performance, managing their carbon footprint, and reducing their waste. Also challenging is ensuring that their ethical and social responsibility mitigate their environmental impact. According to a report conducted by McKinsey in 2016, 80% of a business’s emissions are produced from the supply chain. Businesses understand the cruciality of sustainability within their supply chains and rank this among their most urgent concerns.
Can AI address sustainability challenges in supply chains?
A solution that may address these challenges is that of artificial intelligence (AI) which is the future of data organization, analysis, and complex decision-making. By incorporating AI into the process of supply chain sustainability, businesses may contribute to a more equitable and environmentally conscious global economy by further developing tech-smart approaches to corporate sustainability.
Sustainability with AI- collecting and synthesizing sustainability data
One of the most significant barriers facing businesses in achieving supply chain sustainability is the complexity of modern supply chains. With hundreds of thousands of suppliers spanning worldwide, it is difficult to track, collect, and measure environmental data, specifically their sustainability performance.
The increased number of regulations implemented by governments adds additional complexity in the management of supply chain data. All this data must be synthesized into a human-readable format so that businesses make data-driven decisions, implement the most effective sustainability initiatives, and analyze their current progress.
This is where AI has the potential to transform the way in which businesses interact with data. Such uses include:
- managing sustainability data- by examining this large volume of data from a variety of sources
- identifying patterns and trends that can improve predictability
- providing insights and suggestions for improvement
AI works quickly, frequently outperforming humans and accomplishing tasks that might take minutes in a matter of seconds. The complexity and globality of supply chains can benefit from technology like AI that can sort and process through large data sets, producing insights and visualizations with fewer errors than those committed by humans.
Tracking data- advanced technologies can decrease environmental harm
This transformative technology can monitor a business’s current sustainability performance by providing continuous feedback on highly polluting actions executed by businesses daily, such as:
- carbon emissions
- waste generation
- water consumption
- energy usage
Using AI may improve communication among the supply chain networks as businesses share their real-time data providing fast feedback to decision-makers so that errors can be addressed, and improvements implemented.
The scope of AI insights and suggestions can extend to assessments of risks and opportunities. AI may be programmed with technology that can advise where a business can improve its sustainability performance and flag areas where a business may be failing. For example, AI can provide businesses with options for the most optimal transport routes based on traffic and other data, suggestions on the most sustainable suppliers and materials, and methods for the most efficient way to package items – all of which improve supply chain sustainability.
AI may also have the ability, through its analysis of patterns and trends, to predict future risks and ways in which a business can best mitigate or eliminate these sustainability endangerments. The collection and synthesis of this data can then be used to boost a company’s sustainability transparency and communicate to stakeholders their commitment to mitigating their environmental impact.
Maximizing profit while reducing environmental footprint through AI
AI can also be a valuable tool for optimizing supply chain sustainability, and one prominent way is through predicting and managing inventory levels. With projective capabilities, AI can analyze inventory levels, sale patterns, and supply chain trends to help predict the supply and demand a business requires and should expect.
Such insights may help with the overproduction of items, reducing waste, and ensuring that products are delivered to customers safely through its analysis of transport routes. According to the company, Avery Dennison, overproduction and waste can account for approximately $163 billion worth of lost inventory. If businesses wish to maximize their profits while simultaneously losing fewer products and increasing customer satisfaction, AI can potentially help achieve this.
Navigating the risks of AI integration
Although AI brings substantial benefits to supply chain sustainability, the risks and challenges of working with large language models should not be ignored. Companies should consider the risk before implementing this sophisticated technology. These risks can arise from how AI is implemented within a business to the technology itself.
Discrimination expands to AI-powered supply chain sustainability
The most prominent issue that has gained international attention is that of biases and discrimination within AI technology. AI works by taking in copious amounts of data, sifting through them, and generating an output. Because AIs are training on large, unfiltered data sets from the internet, biased language in the AI output may occur.
According to McKinsey, even the most perfectly crafted AI systems can generate biases despite efforts to avoid it during development. For example, if using AI for hiring new suppliers, biases present in the language model may favor certain demographics of candidates as opposed to others who may have more sustainability qualifiers. Businesses should be aware of these risks, rigorously assess the data used to train the AI systems, and develop alternate approaches in case of biases or output errors.
Mitigating security risks and privacy concerns of AI technologies
Another potential risk of using AI technology is the impact on IT security and data privacy. AI systems may collect a sizable amount of confidential data and IT infrastructure that must be adequately protected. If the data security systems fail or if the AI system is poorly designed, data breaches could occur and negatively impact businesses. Security and privacy measures, like bias and discrimination checks, should be rigorously implemented to ensure that the AI system is appropriately designed to protect the vast amount of information collected and mitigate the occurrence of breaches.
Understanding the limitations and fallibility of sophisticated technologies
A third risk is that of over-relying on the AI system and fully entrusting it to produce infallible information. Although AI produces fewer mistakes than humans, a business cannot without review expect the technology to produce no errors. Implementation of an AI system should ensure that human decision-makers work alongside the programs to provide quality assurance checks that data and outputs are accurate.
Recognizing the possibility of job displacement
On this same note, an issue that also prominently is discussed is the rise of unemployment due to the integration of AI systems. While using an AI tool does not directly translate into job losses, new jobs can even be created while old jobs can be improved. AI requires input from humans, especially to inspect its accuracy; therefore, companies should be inspired to create these new jobs and train their employees to handle these machines instead of simply dismissing them. Training programs for employees can equip the evolving workforce with the skills to use AI to increase productivity and decrease time wasting administrative tasks. Job losses are a valid concern for individuals, and optimistically, with the right approach and design of AI, the potential job losses can be partially offset.
Exploring the future of AI in supply chain sustainability
Supply chain sustainability is an urgent and complex challenge facing businesses. Those challenges should be met with technology that is equipped with the correct tools to handle the complicated and international nature of supply chains. Fortunately, AI is the current technology available that may be able to live up to the responsibility of ensuring companies are on a sustainable route placing the betterment of the environment at the forefront of their agenda.
An AI system can
- Gather, analyze, and simplify a significant amount of data collected through the supply chain
- Generate insights, patterns, and projections along with suggestions for sustainability improvements.
- constant reporting of information gathered coupled with the predictive analysis performed provides AI the ability to spot ways in which a business can improve as well as where it is failing
- can assess future risks that pose a threat to the business
- Optimize supply chain sustainability through data-driven decision making
- Enhance production and increase efficiencies that reduce overproduction and the production of waste
AI does include potential risks, such as the presence of bias and discrimination, security and privacy breaches, fallible technology, and job losses. Companies can mitigate these risks through a series of strategies that focus on human-reviewed safety and security features along with fallback systems.
Final Thoughts on How Businesses Can Use AI to be More Environmentally Conscious
In this era of widespread technological advancements, the prevalent use of sophisticated technologies, like AI, is bound to affect our lives. Therefore, here I start the conversation about the potential effects of AI in sustainable supply chains. Become a WEC member and join the discussion on AI with industry leaders. Already a member? Log into the forum now to join the conversation.
“Artificial Intelligence vs. Human Intelligence.” Simplilearn, November 17, 2022. https://www.simplilearn.com/artificial-intelligence-vs-human-intelligence-article#:~:text=The%20processing%20of%20data%20and,operation%20of%20AI%2Dpowered%20devices.&text=When%20it%20comes%20to%20speed,for%20artificial%20intelligence%20or%20robots.
Atsmon, Yuval. “Artificial Intelligence in Strategy.” McKinsey & Company. McKinsey & Company, January 11, 2023. https://www.mckinsey.com/capabilities/strategy-and-corporate-finance/our-insights/artificial-intelligence-in-strategy.
“Big Data Analytics.” IBM. Accessed April 25, 2023. https://www.ibm.com/analytics/big-data-analytics.
Blšták, Miroslav. “Even Artificial Intelligence Can Make Mistakes.” KInIT, May 9, 2022. https://kinit.sk/even-artificial-intelligence-can-make-mistakes/.
Bové, Anne-Titia, and Steven Swartz. “Starting at the Source: Sustainability in Supply Chains.” McKinsey & Company, November 11, 2016. https://www.mckinsey.com/capabilities/sustainability/our-insights/starting-at-the-source-sustainability-in-supply-chains.
Ferrer, Xavier, Tom van Nuenen, Jose M. Such, Mark Cote, and Natalia Criado. “Bias and Discrimination in AI: A Cross-Disciplinary Perspective.” IEEE Technology and Society Magazine 40, no. 2 (2021): 72–80. https://doi.org/10.1109/mts.2021.3056293.
Gülen, Kerem. “Ai and Ethics: Balancing Progress and Protection.” Dataconomy, January 16, 2023. https://dataconomy.com/2023/01/16/artificial-intelligence-security-issues/.
Jackman, Jonathan. “Supply Chain Sustainability: 3 Pillars for the Future of Business.” Logility, January 25, 2022. https://www.logility.com/blog/3-pillars-of-supply-chain-sustainability/.
Kasparov, Garry, and David De Cremer. “Ai Should Augment Human Intelligence, Not Replace It.” Harvard Business Review, August 30, 2021. https://hbr.org/2021/03/ai-should-augment-human-intelligence-not-replace-it.
“Real-Time AI.” DataStax. Accessed April 26, 2023. https://www.datastax.com/products/real-time-ai.
“The Secret Weapon for Sustainable Business? AI.” The Atlantic. Atlantic Media Company, 2022. https://www.theatlantic.com/sponsored/ibm-2022/the-secret-weapon-for-sustainable-business-ai/3741/.
Silberg, Jake, and James Manyika. “Tackling Bias in Artificial Intelligence (and in Humans).” McKinsey & Company. McKinsey & Company, June 6, 2019. https://www.mckinsey.com/featured-insights/artificial-intelligence/tackling-bias-in-artificial-intelligence-and-in-humans.
Srivastava, Sudeep. “Optimizing Supply Chain with AI and Analytics.” Appinventiv, March 22, 2023. https://appinventiv.com/blog/ai-in-supply-chain-analytics/#:~:text=According%20to%20a%20study%20from,%25%20and%2065%25%2C%20respectively.
Stühler, Gregor. “Council Post: How Ai Is Paving the Way for More Sustainable Supply Chains.” Forbes. Forbes Magazine, January 19, 2021. https://www.forbes.com/sites/forbestechcouncil/2021/01/20/how-ai-is-paving-the-way-for-more-sustainable-supply-chains/?sh=401447f65896.
“Supply Chain Crisis Made Worse as 8% of Stock Ends up as Waste.” RFID. Accessed April 26, 2023. https://rfid.averydennison.com/en/home/news-insights/press-releases/supply-chain-crisis-made-worse-as-8-percent-of-stock-ends-up-as-waste.html.
Weitzman, Tyler. “Understanding The Benefits And Risks Of Using AI In Business.” Forbes. Forbes Magazine, March 2, 2023. https://www.forbes.com/sites/forbesbusinesscouncil/2023/03/01/understanding-the-benefits-and-risks-of-using-ai-in-business/?sh=67ff2e916bba.
“What Is Artificial Intelligence (AI)?” IBM. Accessed April 30, 2023. https://www.ibm.com/topics/artificial-intelligence.
“What Is Predictive Analytics?” IBM. Accessed April 26, 2023. https://www.ibm.com/topics/predictive-analytics.
Zen. “Artificial Intelligence and Its Impact on the Supply Chain Industry.” Demand Planning & Forecasting Software | Supply Chain Management Tool, April 19, 2023. https://www.demandsolutionseurope.com/artificial-intelligence-and-its-impact-on-the-supply-chain-industry/.