Top AI Application for Supply Chain Optimization in 2023:

Artificial intelligence (AI) is one of those solutions that is bringing advancements to almost every industry, including the logistics & supply chain. However, according to the researchers despite the efforts, supply chain leaders have not been able to truly harness the power of AI in the sector. They found that the fault does not lie in the technology but in what, where, how and what extend it is applied. 

To address this issue, I have curated this article to highlight the top 12 AI applications in supply chain management and how supply chain leaders can implement them.

Global AI adoption rate in supply chain and manufacturing businesses (2022 and 2025)

# Supply Chain Automation:

Modern supply chain automation is not possible without AI. AI gives supply chain automation technologies such as digital workers, warehouse robots, autonomous vehicles, RPA, etc., the ability to perform repetitive, error-prone tasks automatically.

Through AI, the following supply chain tasks can be automated:

1. Back-office automation

Tasks such as document processing can be automated thanks to intelligent automation or digital workers that combine conversational AI with RPA.

2. Logistics automation

Efficient logistics in a supply chain can also be achieved through AI & automation. Companies like Amazon, Tusimple, and Nuro are extensively investing in transport automation technologies such as autonomous trucks and distribution by drones.

3. Warehouse automation

AI-enabled technologies such as cobots are helping drive efficiency, productivity, and safety through automated warehouse management. Ocado is one of the leading warehouse automation market players.

4. Automated quality checks

AI-enabled computer vision (CV) systems can help automate quality checks for products. Since these systems do not tire, they can help improve productivity and accuracy in production lines. For instance, AI-powered computer vision systems can automate and improve the quality assurance of finished products.

5. Automated inventory management

Bots enabled with computer vision and AI/ML can be used to automate repetitive tasks in inventory management, such as scanning inventory in real time. Such inventory scanning bots can also be implemented in retail stores. However, while implementing such solutions, you need to ensure their feasibility and calculate their long-term benefits; otherwise, such initiatives can lead to failure.

# Predictive Analytics/Forecasting:

A supply chain manager’s Holy Grail would be the ability to know what the future looks like in terms of demand, market trends, etc. Although no prediction is bulletproof, leveraging machine learning can help managers make more accurate predictions. 

AI-enabled demand forecasting applications can significantly increase forecast accuracy. The benefits of high-level accuracy include, but are not limited to, the following: 

6. Inventory optimization

AI-powered tools can help determine optimal inventory levels by analyzing historic demand and supply data and trends. This can help avoid over-production and storage costs 

7. Region-specific forecasts

Supply chain AI can also provide detailed region-specific demand to help business leaders make better decisions. For instance, each region has its own events, holidays, trends, etc. By using region-specific parameters, AI-powered forecasting tools can help customize the fulfillment processes according to region-specific requirements. 

8. Bullwhip effect prevention

The bullwhip effect is a major pain point in supply chain management. This phenomenon occurs when small fluctuations at one end of the supply chain are amplified as they move upstream/downstream.

AI-powered forecasting tools can help reduce demand and supply fluctuations to control bullwhip by leveraging data collected from customers, suppliers, manufacturers, and distributors. This can help reduce stock outs and backlogs. 

# Enhanced Supplier Relationship Management:

Many of the current issues we face in global supply chains are related to weak supplier relationship management. Due to a lack of collaboration and integration with suppliers, many supply chains, such as food and automotive, faced serious disruptions during the global pandemic of 2020.

AI can help improve supplier relationship management (SRM) by making it more consistent and efficient. 

9. Improved supplier selection 

AI-enabled SRM software can aid in supplier selection based on factors such as pricing, historic purchase history, sustainability, etc. AI-powered tools can also help track and analyze supplier performance data and rank them accordingly.

10. Improved supplier communications

AI-powered tools such as RPA can also help automate routine supplier communications like invoice sharing and payment reminders. Automating these procedures can help in preventing silly hiccups caused, for example, by failing to pay a vendor on time and having a negative knock-on effect on shipment and production.

# Improved Sustainability:

Sustainability is a growing concern of supply chain managers since most of an organization’s indirect emissions are produced through its supply chain. AI can help improve supply chain operations to make them greener and more sustainable.

11. Greener transport logistics

AI-powered tools can help optimize transportation routes by considering factors such as traffic, road closures, and weather to reduce the number of miles traveled. For instance, DHL uses AI to optimize vehicle routes and reduce fuel consumption, resulting in lower emissions and improved sustainability. Watch the video below to learn more:

12. Greener warehousing

Since AI-powered forecasts can help maintain optimal inventory levels, carbon emissions attached to storage and movement of excess inventory can be reduced. Smart energy usage solutions can also reduce carbon emissions related to warehouse energy consumption.

AI-powered with big data can help the supply chain become not only sustainable but resilient at the same time.