Carter’s: Sustainability + AI in Supply Chain
By: Briana Corcoran, Brooke Leeder, Drew Cutright, Myung Ki Min, Stephanie Kinsey
Carter’s Inc. is the largest branded marketer of children’s clothing in the U.S. Their goods are purchased from third party suppliers overseas and imported. Carter’s has three distribution warehouses across the U.S., one on the west coast (Los Angeles) and two on the east coast in Georgia (Braselton and Stockbridge). All imported goods are transported on ocean vessels from the overseas port to the U.S. port, and then the shipping containers are routed from the U.S. ports by truck to warehouses where the goods are unloaded for storage. They then ship out the goods to their end customers as needed, whether it be large retailers like Belk, Macy’s, Kohl’s, Walmart, Target, etc. or their own Carter’s retail stores all over the U.S. by truck or rail. The last mile delivery routing is currently decided manually by people, primarily based on the lowest cost option or the fastest option for time sensitive orders.
In discussing Carter’s we use the term AI (Artificial Intelligence) to refer to the use of advanced computational algorithms and techniques to automate decision-making processes and optimize operations within the logistics and distribution industry. AI technologies could be utilized at Carter’s to enhance the efficiency and sustainability of routing processes for imported goods from distribution warehouses to end customers, such as within large retailers or in company-owned stores.
In recent years, supply chain disruptions have made headlines due to a rise in labor shortages, delays, and complicated networks servicing multinational companies; however, AI can be used to simplify and improve supply chain networks to increase efficiency and sustainability. AI can be used to identify the most efficient transportation route for goods and can be used to make sure that stops are routed in the most efficient grouping, thereby reducing carbon emissions from routes that are unnecessarily complicated.
AI can also be used in purchasing departments to monitor inventory levels in distribution centers and notify purchasers of the most ideal time to replenish inventory. This is especially useful as Carter’s does not own their own products and relies on third-party manufacturers for product supply. AI systems may be better able to estimate demand than human counterparts, helping to reduce supply shortages and excess inventory. AI systems can also use data analytics to help select the locations of future distribution centers based on consumer demand and increase operational efficiency.
In addition, the emergence of smart technology can also reduce manual operations and improve data collection. For example, advances in and implementation of smart tags and smart pallet systems collect data regarding the location and efficiency of existing routes; however, AI can be integrated into these systems to recommend improvements and automate manual processes.
The use of AI models to improve Carter’s last mile delivery would positively impact Carter’s sustainability goals by helping them to reduce their Scope 3 GHG emissions. In 2022, 28% of their Scope 3 GHG emissions came from their Tier 1 suppliers and logistics providers. Scope 3 GHG emissions can often be difficult to measure; however, by using AI to develop their own sustainable last-mile delivery routes, they would be able to better track the data for these emissions and further reduce these Scope 3 emissions by choosing optimal routes. AI would also positively impact Carter’s bottom line because ultimately, they might be able to hold less inventory if they are more able to accurately predict supply and demand patterns and base their future buying decisions from their third-party suppliers on this information.
AI may negatively impact Carter’s employees, as they are currently relying very heavily on human expertise and human labor to make many of their logistics and purchasing decisions. With the future use of AI, they may require only a few employees at a management level to review the AI models for accuracy and some lower-level jobs may be eliminated. Also, when an unforeseen supply chain disruption does occur, it may be more difficult to quickly adjust their supply chain and incorporate alternative supply solutions if they are running their business on a just-in-time inventory model with little inventory or too few employees to help with the supply chain crisis.
In the Nature article “The role of artificial intelligence in achieving the Sustainable Development Goals,” Vinuesa, Azizpour, Leite, and co-authors indicate that AI may impact the SDGs in three categories: environmental, economic, and social. AI applications for supply chains primarily impact environmental and economic SDG indicators, though social impacts are possible if AI technology is used to adjust labor practices and is developed and shared with those in least developed nations.
We are certain to see both short-term (in next five to 10 years) and long-term environmental impacts of using AI to find optimal routes and distribution timing through reduced use of fossil fuels. These technologies should result in fewer miles traveled, and other AI transit technologies, such as self-driving delivery vehicles, could also reduce fossil fuel usage through more energy-efficient driving. (Indicators: 7.3.1 Energy intensity measured in terms of primary energy and GDP; 9.1.2 Passenger and freight volumes, by mode of transport; 9.4.1 CO2 emission per unit of value added; 11.6.2 Annual mean levels of fine particulate matter (e.g. PM2.5 and PM10) in cities (population weighted; 13.2.2 Total greenhouse gas emissions per year).)
Aside from transportation, AI technologies used to optimize material usage and circular economy technologies, particularly in fashion logistics chains, could result in lower use of virgin natural resources and in recycling of synthetic fabrics. These effects would benefit indicators 12.2.1 Material footprint, material footprint per capita, and material footprint per GDP and 12.2.2 Domestic material consumption, domestic material consumption per capita, and domestic material consumption per GDP.
On the economic side, AI technologies for efficiencies in supply chains would long-term likely contribute to 8.2.1 Annual growth rate of real GDP per employed person and (in short and long-term) to 9.b.1 Proportion of medium and high-tech industry value added in total value added.
In addition to these important economic and environmental gains, there is a real possibility for transformative social change. The fashion and consumer goods industries often rely on low paid and sometimes underaged labor in least developed nations. If AI technologies could be used to track and prevent these practices or to automate some of the assembly roles, this could contribute to 8.8.2 Increase in national compliance of labor rights (freedom of association and collective bargaining) based on International Labour Organization (ILO) textual sources and national legislation, by sex and migrant status and 8.7.1 Proportion and number of children aged 5-17 years engaged in child labor, by sex and age. If AI technology was available to those in developing countries, long-term it could also help increase output and capital in these industries, advancing indicator 17.11.1 Developing countries’ and least developed countries’ share of global exports.
Sources:
Vinuesa, Ricardo, Azizpour, Hossein, Leit, Iolanda, et al. The role of artificial intelligence in achieving the Sustainable Development Goals. Nature Communications. 2020.
https://doi.org/10.1038/s41467-019-14108-y
https://www.ibm.com/thought-leadership/institute-business-value/en-us/report/cognitivesupplychain
