Insights

Fifteen years ago, it was predicted that artificial intelligence (AI) would improve supply chain performance using three key characteristics: 

• Instrumented: Instead of relying on humans to create information, machines generate it through sensors, RFID tags, meters, GPS, and more. For example, inventory will be able to count itself and containers can detect their contents.
• Interconnected: The entire supply chain, including customers, suppliers, IT systems, and smart objects used to monitor the supply chain, will be connected. This extensive connectivity will enable supply chains worldwide to plan and make decisions together.
• Intelligent: The decisions made by supply chains will be much smarter. Advanced analytics and modeling will help decision-makers evaluate alternatives against complex risks and constraints. Smarter systems can make decisions automatically, reducing the need for human intervention and increasing responsiveness.  

Those predictions have become reality.

Many COOs and CSCOs are turning to AI, machine learning, and cognitive computing to overcome supply chain challenges, especially for forecasting new product demand and optimizing inventory levels. Many companies are already applying AI and cognitive technologies to their products and daily operations, while others are just beginning to explore their potential in these and other areas:  

• Forecasting: AI algorithms can analyze historical sales data, market trends, and other relevant factors to predict future demand accurately. For example, a company may use AI-powered demand forecasting software to decide how much inventory to order and when to reorder it. This helps optimize inventory levels, reduce stockouts, and improve supply chain efficiency.
• Demand sensing involves using AI algorithms to analyze various data sources, such as social media trends, customer reviews, and market research, to gain real-time insights into customer demand. By accurately predicting demand patterns, organizations can optimize their production and distribution processes, ensuring they have the right amount of inventory at the right time. This reduces the risk of stockouts or excess inventory and improves customer satisfaction by meeting their demands more effectively.
• Risk management is an essential consideration in supply chain planning, and AI can help organizations identify and mitigate potential risks. AI algorithms can analyze various data sources, such as historical performance data, weather forecasts, geopolitical events, and supplier information, to identify potential risks and their impact on the supply chain. By proactively identifying risks, organizations can develop contingency plans, make informed decisions, and take appropriate actions to minimize disruptions and maintain supply chain continuity.
• Supplier risk assessment: AI can analyze vast amounts of data from various sources, such as news articles, financial reports, and social media, to assess the risk associated with different suppliers. This helps supply chain managers make informed decisions about supplier selection and mitigate potential risks.
• Demand sensing: AI can analyze real-time data from various sources, such as social media, weather forecasts, and online search trends, to detect changes in consumer demand patterns. This enables supply chain managers to respond quickly to fluctuations in demand and adjust production and inventory accordingly.
• Route optimization: AI algorithms can optimize transportation routes by considering factors such as traffic conditions, delivery schedules, and fuel costs. This helps reduce transportation costs, improve delivery times, and minimize carbon emissions. By finding the most efficient routes, organizations can reduce transportation costs, improve on-time delivery performance, and enhance overall logistics operations.
• Inventory optimization is another area where AI can make a significant impact. AI algorithms can analyze historical data, customer demand patterns, and other relevant factors to determine the optimal inventory level to maintain at different supply chain locations. Organizations can minimize carrying costs, reduce stockouts, and improve overall supply chain efficiency by dynamically adjusting inventory levels based on real-time demand and supply information.
• Sales and operations planning (S&OP) is a collaborative decision-making process that companies can use to manage various aspects of their supply chain, such as demand volatility, supply constraints, production scheduling, and distribution.  

These examples demonstrate the diverse applications of AI in supply chain management, highlighting its potential to enhance efficiency, accuracy, and decision-making in forecasting and inspection processes. 

By incorporating AI into various aspects of supply chain planning, organizations can enhance their supply chain planning capabilities, improve operational efficiency, reduce costs, and deliver better customer service.

CONCLUSION 
The use of AI in supply chain planning offers many benefits to organizations, including improved decision-making, increased efficiency, and enhanced customer service. AI technologies such as predictive analytics, demand sensing, and route optimization can optimize various aspects of the supply chain, leading to cost savings and improved operational performance. Additionally, AI can automate processes such as quality inspection and supplier risk assessment, reducing human errors and mitigating potential risks.

However, organizations may face challenges and limitations when implementing AI solutions in supply chain planning. One challenge is data quality, as AI algorithms rely on accurate and reliable data to make informed decisions. Organizations need to ensure that their data is clean, consistent, and up to date to maximize the effectiveness of AI applications. Integration challenges may also arise when integrating AI systems with existing supply chain management systems, requiring careful planning and coordination.

Another challenge is the need for skilled AI professionals who can develop and maintain AI models and algorithms. Organizations may need to invest in training or hiring AI experts to effectively implement and manage AI solutions in supply chain planning.

Despite these challenges, the benefits of AI in supply chain planning outweigh the limitations. By leveraging AI technologies, organizations can optimize their supply chain operations, improve decision-making, and gain a competitive edge in an increasingly complex and dynamic business environment. It is crucial for organizations to carefully assess their specific needs and capabilities to determine the most suitable AI solutions and develop a comprehensive implementation strategy. With proper planning and execution, AI can revolutionize supply chain planning and drive significant improvements in efficiency, accuracy, and overall performance.