Unleash the Potential of AI in Circular Economy: Businesses Potentials
By: Americana Chen
Tags: Economics, Big Data, Sustainability, AI, Artificial Intelligence

Business Potential of AI in promoting circular Economy

1. Environmental impact analysis and monitoring

From CSR to ESG, most businesses today have good intentions to stand up to their environmental and social responsibilities. Nevertheless, in order for them to reduce its environmental impact, they need to first estimate the effect of their activities in quantitative means. Producing such estimates could be challenging, especially for large businesses involved in multiple chains of production and distribution. This can be resolved by the use of IOT ( Internet of Things) and smart sensors. IOT allows automation of accurate and reliable data collection, which could then be fed into data analytic algorithms that outputs analysis of the different sources externalities or pollutions. Businesses could gain insights from these analysis and take actions. For example, by identifying the greatest source of residuals or pollutants in a manufacturing procedure businesses could invest in developing or switching to more efficient production methods to cut their impact.

image via pixabay under Pixabay License (Free for commercial use)

2. Big data powered product innovation

Using big data, product development can be done in a more evidence based manner. Two of the main product features that would be desirable for circular economy are modularity and durability. The benefits of greater durability is obvious by itself, on the other hand, modularity means to decompose a complex product engineering process into simple subparts. Greater modularity could then make product remanufacturing and recycling more convenient, it “permit the arrangement of components in a manner that can be easily modified, enhanced, exchanged, or proliferated.” (Tucker J. Marion, 2010) During the process of product development, data can be collected from prototyping and testing of the product. These data can now be analyzed iteratively through machine learning algorithms, which assists with the evaluation of these desirable sustainable product features and could be used to improve upon current design of products

3. Blockchain and cryptographic anchoring for supply chain management

Another challenge in tracking and recognizing environmental impact of businesses is the difficulties in reliably following and trace back to the source of its inputs. What blockchain could do is to make the whole supply chain “transparent”, where each stage in the chain is recorded in an immutable way. Imagine you operate a small restaurant, to provide tasty dishes to customers, you would need to purchase the ingredients. To make it simpler, let’s only focus on the meat supplier. In order to know whether the meat supplier is operating in an ethical and sustainable production process, you need to know which livestock farm did they get their “inputs” from. If you would like to dig deeper, you might also want to know the source of the food they fed to the livestock. This could quickly get complex and difficult to track, and that is indeed a general concern of many businesses, especially for those that have to manage a large variety of suppliers, such as Walmart. Indeed, Walmart was one of the earliest adopters to test the application of supply chain management using blockchain by using it to trace pork in China, to authenticate transactions and facilitate accurate and efficient record keeping.

Example of blockchain digital supply chain (Oliver Wyman) — Copyright © 2018, Oliver Wyman

4. Smart inventory management

image via pixabay under Pixabay License (Free for commercial use)

Smart inventory management mainly concerns with the accurate prediction of customer demand to efficiently produce the right amount of product at the right time. Stockpiling could be extremely wasteful and costly for a business, especially in cases where the product cannot be stored for a long period of time, either because of the nature of the product (fresh food), or because of the decrease in value of product over time (fashion products). By using internal data such as records of past sales, customer preferences and external data such as competitor’s performances, market demand fluctuations and patterns, AI’s prediction capabilities can be utilized to prevent stockpiling and excess inventories. This not only reduces the inventory rental cost for companies, but also greatly lowered the amount of waste and unused products that may impede for transition to a circular economy. There are already wide use of smart inventory management using Internet of things and machine learning, an example of such service provider is “Zenventory”.

5. Automated Optimizing Delivery and Shipping

The use of AI have enabled us to improve the logistics of shipping and delivery by designing the fastest route. On one hand it could analyze customer order data to make best plan for shipping in different regions, on the other hand real-time traffic data can be used to produce efficient scheduling of deliveries. Moreover, the scope of AI usage in delivery extends beyond backend planning to autonomous shipping. In 2018, Rolls Royce and Finferries launched the first fully autonomous car ferry. The cost advantages of using autonomous truck in B2C (business to customer) last mile delivery is substantial, with potential of reducing delivery costs by 10% comparing to traditional delivery method ( McKinsey,2018). The design of the fastest route reduced the amount of pollutants created by shipping vehicles , especially for oversea shipping, while autonomous delivery act as a solution to effective, low manual input delivery, allowing more funds and human resources to be devoted to more productive usage.