The modern era of working is all about speed and efficiency. Businesses are turning to automation solutions for all their operations. AI in the supply chain is dominating the race for development and advancement, transforming the production and supply of goods worldwide. According to Statista, the AI adoption rate for the supply chain is growing and is expected to dominate the manufacturing sector by the end of 2025.
But how exactly is AI playing a prominent role in the technical aspects of business, such as supply chain management? And how do businesses incorporate AI solutions across their networks? In this blog, we will discuss all these sorts of topics in detail, highlighting key aspects and challenges throughout the business cycle.
What is AI in Supply Chain?
AI models are significantly evolving with efficient algorithms and modern infrastructure advancements, enhancing the data processing capabilities overall. These models are coming closer to mimicking human-like intelligence when it comes to optimizing supply chain operations. Simply put, AI is the goods supply manager that coordinates the flow of products, plans finances, and predicts the consumption. It also optimizes the efficiency of supply chain operations planning to reduce costs while enhancing customer satisfaction. The key areas where AI enhances the supply chain are:
- Planning
- Sourcing
- Production
- Distribution
- Returns
AI in Supply Chain: Key Industrial Developments
- The number of supply chain businesses is increasing every year. In the US, an Executive Order on America’s Supply Chains came into action in 2021 to ensure the US supply chain resilience in critical ICT and semiconductor industries. This order insists on improving the implementation of AI technologies to minimize the risks of geopolitical and cyber threats.
- Later in 2023, the Whitehouse USA released a progress report to create resilient supply chains to promote safe, secure, and trustworthy artificial intelligence-powered solutions.
- As per reports, around $52.7 billion is set to empower the domestic semiconductor production that is essential for AI development. The investments will lead to more focus on workforce training and innovation across the supply chain sector.
- Key partnerships are happening across the globe to enhance the business supply chain. For instance, the international coordination of the Indo-Pacific Economic Framework promotes a secure and resilient supply chain for ICT products. The vulnerabilities can also be reduced to foster AI-enabled innovation.
- According to the survey from McKinsey, AI enhances the supply chain efficiency and inventory management by 5%.
Key Applications of AI in Supply Chain
Predicting Demands and Forecasting Sales
One of the major applications of AI in supply chain is demand predictions and sales forecasting. This AI feature allows businesses to stay ahead of the competition and plan their resources and budgets as per AI predictions. The accuracy of this model is high, as it uses data from legitimate sources, including historical sales, economic factors, customer response, and, most importantly, market trends. In contrast with the traditional models, AI demand predictions are more adaptive to new patterns and perform real-time data analysis to ensure dynamically adjustable sales forecasting.
- AI in supply chain uses predictive AI models just like ChatGPT, which uses LLMs. These predictive models are trained on neural networks to perform time-series analysis and identify complex demand patterns. This helps in accurate predictions.
- Real-time adjustments are key to identifying the market trends and demands, such as promotions, price hikes/lows, and promotional events.
- The demand prediction feature is key in reducing overstocks of goods, minimizing the expenditure. Moreover, accurate demand forecasting helps in restocking goods at the right time to prevent stockouts and lags. This ensures customer satisfaction and overall experience.
Models like the following can be used:
- Seasonal ARIMA
- Random forests
One great example of AI in this context is Amazon’s application of AI to forecast daily shipping demands for over 400 million products during the Holiday season of 2023.
Intelligent Inventory Management
AI in supply chain allows intelligent and efficient inventory management by analyzing sales, demand & supply dynamics and stock levels. This allows business owners to achieve their goals by striking a balance between inventory and stock requirements. Here are some key features AI in inventory management systems bring to the table:
- Automated Inventory Replenishment
- Demand-driven restocking and optimization
- Improves warehouse efficiency
Inventory management serves as a wonderful application of AI in healthcare, especially when it comes to the rapid consumption of many disposable materials.
Route & Logistics Optimization
Another key aspect of AI in supply chain is route and logistics optimization. This process is key and requires attention from the contractors and route specialists. AI can provide production planning to the logistics operators. Similarly to other operations, AI analyzes route data here as well, providing real-time touchpoint information. This helps the logistics team to make informed decisions and plan production and transportation schedules intelligently. Here are the three logical operations of AI in route optimization:
- Transportation Route & Shipment
- Intelligent Production Resource Allocation
- Real-time Visibility
Enhancing Supplier Network Relationships
The network of suppliers needs to be highly coordinated and well maintained for every business to grow in the supply chain. AI can also prove fruitful in this regard across several aspects. It provides several collaboration opportunities that develop key relationships between suppliers. AI analyzes the large-scale enterprise data to strengthen the SRM (Supplier Relationship Management). AI in supply chain enhances these key aspects:
- Suppliers KPI monitoring
- KPIs Risk assessment
- Network Collaboration
The main use case of these aspects falls in the e-commerce industry, where multiple partners collaborate across the business network.
Informed Decision Making
The best result from AI in supply chain is making informed and intelligent decisions across the different business stages. AI, feeding on the real-time data, provides valuable insights that drive companies to a more proactive approach for making timely decisions. It impacts different stages and cycles of the business, from supply chain to marketing. AI plays a pivotal role in making large shifts in the market trends and economic stability of the business. Here is a more detailed breakdown of good decision-making across operations through AI.
- Overall Predictive Analytics
- Scenario Planning
- Faster response to changes
Top Challenges for AI in Supply Chain
While AI provides a whole lot of advantages to the supply chain business, there are still different constraints and limitations that pose challenges for businesses. The three main concerns for AI implementation in supply chain businesses are:
Privacy
As AI feeds on the data, privacy and security issues arise, making the business prone to giving out sensitive information to AI. While AI is trained on compliance and regulatory grounds, there is still a chance of leaking data, and companies have to ensure that higher management data handling is done solely on private networks.
Complexity
AI algorithms shine when they’re backed by great data! It’s important for companies to keep their data accurate, relevant, and fresh to prevent any pesky errors in predictions.
Cost
There is a large cost upfront to AI implementation across the supply chain networks. Companies have to carefully evaluate the costs and ROIs to assess the potential benefits. It also requires hiring professionals who know how to work with AI technology.
Final Thoughts
AI in supply chain is making businesses big profits due to its capabilities of analyzing data in real-time, allowing the business owners to make informed decisions. Firms can keep their systems up-to-date with AI systems. In a nutshell, AI in supply chains is becoming pivotal to stay relevant in the market, and businesses need to capitalize on the exciting developments to stay ahead of the competition. However, the costs and constraints of AI implementation should be kept in mind before approaching automation for the supply chain.
