A supply chain is a web that interconnects business activities, making it one of the most crucial elements of any business.
The 2020 pandemic and other geopolitical disruptions have demonstrated how weak supply chains can bring down entire organizations. Many companies are, therefore, investing in digital solutions to optimize their supply chain operations to get ahead of the curve.
Artificial intelligence (AI) is one of those solutions that is bringing advancements to almost every industry and department, including the supply chain. However, according to a survey by BCG, 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 where and how it is applied.
To address this issue, we have curated this article to highlight the top 12 AI applications in supply chain management and how supply chain leaders can implement them.
Figure 1. Supply chain AI adoption rate1
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.
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.
To learn more about supply chain automation, check out this comprehensive article. You can also read our article on hyperautomation efforts for supply chain autonomy.
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.
Watch how BMW uses computer vision to scan car models as they move on the assembly line.
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.
Watch how Sam’s club, an Amazon-convenience store, uses cleaning robots installed with a computer vision system to scan inventory levels in its stores and warehouses.
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 stockouts and backlogs.
Watch how AI can utilize data generated from customers to create accurate demand forecasts and adjust them in real-time to make the supply chain smarter and more robust.
The global furniture brand Ikea has also developed a demand forecasting tool based on AI, which uses historic and new data to provide accurate demand forecasts.
To improve demand planning in your business, check out our data-driven list of Demand Planning Software.
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.
PwC explains the benefits of AI-powered SRM:
To learn more about how to improve supplier relationship management, check out this quick read.
And to enhance your supply chain visibility, check out our data-driven list of Supply Chain Visibility Software.
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. Watch the video below to learn more.
To learn more about how AI and other technologies can help improve supply chain sustainability, check out this quick read. You can also check our comprehensive article on 5 ways to reduce corporate carbon footprint.
You can also check our data-driven list of supply chain software to find the option that best fits your business.
Further reading
- No Code Supply Chain: Benefits & Vendor Benchmark
- 3 Ways DX Can Strengthen Supply Chain and Logistics
- Top 5 Ways Digital Twins Transforms Supply Chain
- 7 Ways to Improve Your Supply Chain Sustainability
If you have any questions, feel free to contact us:
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