AI in Supply Chain: Key Applications and Examples

For those involved in the supply chain industry, the impact of AI is becoming increasingly evident. Industry data indicates a significant rise in AI adoption within supply chain operations, with a large percentage of companies leveraging AI by 2025. The advantages are clear, with organizations reporting reduced costs and increased revenue in areas where AI, including generative AI, has been implemented.

Given the proven transformative potential of AI for the supply chain, understanding its specific use cases and applications in supply chain management is crucial. This exploration will delve into these applications and provide practical insights into AI's role in optimizing supply chains, including how industry leaders are implementing this technology.

Let's begin by examining the broader impact of AI on the supply chain landscape.

Businesses are integrating artificial intelligence into their supply chains to enhance efficiency, accelerate distribution, and proactively address potential disruptions. By utilizing advanced analytics and decision intelligence, AI in supply chain management enables companies to make faster and more accurate decisions across strategic, operational, and tactical levels.

Notably, the adoption of generative AI is rapidly increasing, with a substantial percentage of supply chain organizations regularly using it. Companies are deploying generative AI in areas like marketing, sales, product development, and IT functions to streamline workflows, boost production efficiency, and facilitate virtual logistics management.

For example, some companies are using generative AI for combining truck loads. This approach can significantly reduce manual effort and improve efficiency by lowering shipping costs, reducing emissions, and enhancing on-time shipments.

AI's capability to quickly process extensive datasets, generate predictions, and respond to natural language queries is driving its widespread adoption. Early implementations focus on optimizing logistics operations, improving decision-making processes, and enhancing customer support functions. AI also contributes to supply chain resilience by addressing disruptions and navigating complex global operations. Furthermore, AI is being adopted across diverse industries, from food and beverage to pharmaceuticals and agriculture.

Use cases and applications of AI in the supply chain with examples

We will explore the potential of AI across the entire supply chain lifecycle, dividing the use cases into key stages: planning, sourcing and procurement, manufacturing, and supply chain automation.

A. Planning

#1. Demand forecasting

Traditional demand forecasting methods, such as time series analysis and regression models, primarily rely on historical sales data to identify trends and seasonality. These methods often do not fully account for dynamic factors like changing consumer preferences, market disruptions, and the impact of external variables such as weather events or economic shifts, which can lead to less accurate forecasts.

AI addresses these limitations by employing machine learning algorithms to analyze vast amounts of data from various sources, including online search trends, website traffic, purchase histories, social media sentiment, competitor activities, and macroeconomic indicators. This allows companies to generate more accurate, detailed, and adaptable demand forecasts, even in volatile and uncertain market conditions.

One AI-powered solution for demand planning and forecasting utilizes advanced AI and machine learning algorithms to predict consumer demand across multiple time horizons. The system generates highly localized forecasts for every product and location by incorporating factors like local events, seasonality, pricing, and promotions. Subsequently, its AI-powered segmentation categorizes forecasts into different levels of required human intervention to streamline the planning process.

#2. Pricing optimization

Setting optimal prices is a critical priority for businesses today, especially with customers having easy access to resources for comparing product prices. Here’s how AI is assisting organizations with pricing optimization:

Dynamic pricing: By continuously monitoring factors like competitor pricing, customer behavior, and market conditions, intelligent algorithms can adjust prices in real time to capture the optimal price point.
Personalized pricing: Machine learning models can identify distinct customer segments based on purchasing history, price sensitivity, and demographic data. Businesses can then determine the ideal pricing strategy for each segment and maximize revenue without alienating price-conscious buyers.
Promotional strategy optimization: AI can simulate the impact of various promotional strategies and recommend the most effective discounts or bundle deals to drive sales and profitability. This goes beyond simple rules-based discounting, using advanced algorithms to predict the complex interplay between pricing, demand, and customer behavior.

Some AI-powered pricing solutions utilize advanced AI and machine learning algorithms, including deep learning and game theory, to optimize pricing strategies in real-time. These solutions offer capabilities like base-price optimization, discount personalization, and deal margin optimization across multiple industries. They also include a pricing recommendation engine and multi-lever simulation tool, which help businesses quickly test and implement optimal pricing scenarios to drive profitability.

#3. Sustainability planning

AI is enabling organizations to make data-driven decisions that enhance environmental, social, and governance (ESG) performance. Here’s how:

Energy management: AI algorithms can monitor energy consumption patterns, identify inefficiencies, and recommend targeted interventions to reduce energy usage. For example, AI-enabled predictive maintenance can predict equipment failures to minimize downtime and optimize energy usage.
Waste reduction: AI can forecast demand more accurately, enabling organizations to align production with actual needs and minimize overproduction. Additionally, AI-powered supply chain traceability and transparency solutions can track the flow of materials, allowing businesses to identify and address sources of waste and inefficiency.
Product lifecycle management: By providing insights into the environmental impact of products, from sourcing to disposal, organizations can make informed choices, implement eco-friendly design practices, and collaborate with suppliers to improve sustainability throughout the product’s life cycle.

One company is developing an AI-powered waste management solution to predict, prevent, and proactively handle waste. The solution analyzes data to identify key waste reduction opportunities and drivers, then recommends ways to reduce waste, such as adjusting pricing, relocating products, returning them to suppliers, or donating them. The AI also identifies which items contribute the most waste in a particular store, considering factors like stock availability and seasonality. This information is presented to store associates through an easy-to-use dashboard, allowing them to make informed decisions and learn from best practices in other locations.

B. Sourcing and procurement

#1. Supplier relationship management

AI-powered supplier relationship management solutions leverage machine learning, natural language processing, and data analytics to help organizations select and manage the right suppliers for their products and services.

Some ways AI can optimize supplier relationship management:

Supplier selection: ML models can analyze a vast amount of data on suppliers, such as quality, delivery, pricing, and customer reviews, to help you rank and select the best-fit partners.
Supplier performance prediction: AI in supply chain can forecast potential supplier issues like financial troubles or quality problems before they occur. Predictive analytics on factors like past incidents, supply chain risks, and market volatility can generate supplier risk scores to guide proactive interventions.
Contract compliance: Natural language processing techniques can scan supplier contracts to detect non-compliance with regulations, ensuring you stay compliant with legal requirements.

A good example of AI in supply chain is how a leading manufacturer implemented an AI-powered supplier portal to streamline its supplier network. It enabled the automation of supplier pre-screening and self-registration, ensuring that only qualified suppliers get added to the database. Moreover, the portal allowed the company to digitize and streamline various supplier management tasks, such as certificate tracking and profile updates. These and more AI features in the portal, have helped the company eliminate manual processes from their supplier relationship management operations.

#2. Procurement

AI and generative AI hold immense potential to transform procurement allocation processes. Here are some critical ways through which you can leverage these cognitive technologies:

Global sourcing insights: AI can aggregate and analyze data from various third-party and public sources to identify shifts in global supply trends across commodities and products.
Invoice data extraction: AI-powered invoice data extraction solutions can automatically extract relevant data from invoices using computer vision and NLP. This can reduce fraud, improve processing times, and provide better spending insights.
Automated compliance monitoring: AI-powered solutions can automatically compare payment terms, identify non-compliance rates between contracts and invoices, and detect duplicate invoices.
Contract data extraction: NLP can extract and structure critical information from unstructured contract documents, such as start and end dates, payment terms, and references to master agreements.
Contract lifecycle management: AI is being integrated into contract lifecycle management (CLM) tools to streamline the contract generation, negotiation, and risk identification process.

With a high volume of purchase orders and invoices processed annually, a company's procurement division sought to improve the accuracy and efficiency of assigning general ledger accounts. They developed an AI-powered General Ledger Recommendation solution that analyzes historical purchase and invoice data to suggest the most appropriate general ledger account at the point of purchase. This solution was integrated directly into the company’s procurement platform, which now helps buyers assign correct accounts and improve accuracy, efficiency, and cost of downstream accounts payable.

#3. Spend analysis and cost optimization

AI-powered spend analysis tools can rapidly categorize and analyze vast amounts of purchasing data across an organization. These systems use NLP and machine learning algorithms to automatically classify spend data into standardized categories, regardless of how individual vendors or departments may label items. This granular categorization allows procurement teams to identify consolidation opportunities, negotiate better contracts, and uncover maverick spending.

For cost optimization, AI models analyze historical pricing data, market trends, and supplier performance to recommend optimal sourcing strategies. These systems can predict future price fluctuations, suggest the best time to make purchases, and even automate routine procurement tasks.

C. Manufacturing

#1. Inventory management

Traditional inventory management relies on manual processes and historical data, while AI-based systems leverage real-time data and predictive analytics for smarter inventory management. Here are some of the applications of AI in inventory management:

Sales forecasting: By analyzing historical sales data, current market trends, and changing customer behavior, AI-powered models can generate accurate sales forecasts, enabling companies to plan their inventory levels to meet fluctuating demand proactively.
Inventory optimization: Reinforcement learning helps determine optimal reorder points, safety stock levels, and replenishment strategies, effectively minimizing the risks of overstocking and stockouts.
Customer segmentation: Clustering algorithms and ML models can categorize customers based on their purchasing patterns, choices, and demographics, enabling businesses to tailor their inventory strategies according to different customer groups.

Optimizing inventory management for a global retailer:

A consumer electronics retailer partnered with a technology firm to optimize inventory management across a large number of stores in multiple countries. They faced challenges with stockouts, overstocking, and inefficient order fulfillment. The technology firm developed a custom AI-powered inventory management solution. Key components of the system included:

Predictive demand forecasting: Machine learning models were created to analyze historical sales data, seasonal trends, and external factors to accurately predict demand for each product across all stores.
Dynamic inventory allocation: The system continuously adjusted inventory levels across the network in real-time, ensuring optimal stock distribution.
Real-time supply chain visibility: A dashboard was implemented, providing the company with comprehensive views of inventory levels and potential issues across their entire network.
Data processing and storage: Data storage and management systems were used to store and manage vast amounts of inventory and sales data, while streaming services handled real-time data streaming for up-to-the-minute analysis.

#2. Risk management

Supply chain risk management involves identifying, assessing, and mitigating potential disruptions and vulnerabilities across the supply network, from raw material sourcing to final product delivery. AI helps by enhancing capabilities to predict, prevent, and respond to supply chain-specific risks.

Real-time data analysis and risk detection

AI systems can process vast amounts of data from diverse sources such as weather reports, geopolitical news, and transportation logs in real-time. For instance, an AI model might analyze satellite imagery and weather forecasts to predict flooding risks in key manufacturing regions, allowing companies to proactively adjust production schedules or secure alternative suppliers.

Predictive analytics for proactive risk mitigation

By leveraging historical data and current trends, AI can forecast potential supply chain disruptions. For example, an AI system might predict a shortage of components based on global production data, consumer demand trends, and geopolitical factors, enabling companies to secure inventory or develop contingency plans months in advance.

Enhanced supply chain visibility and rapid response

AI-powered tracking systems provide granular, real-time visibility into the movement of goods across the supply chain. If a shipment of perishable goods is delayed due to a port congestion, AI can automatically recalculate delivery times, assess the risk of spoilage, and suggest alternative routing or storage solutions to minimize losses.

#3. Predictive maintenance

As enterprises navigate the challenges of rising costs and supply chain disruptions, optimizing the performance and reliability of physical assets has become increasingly crucial. Powered by AI, predictive maintenance helps you extract maximum value from your existing infrastructure.

Here are the top use cases of AI in predictive maintenance:

Predictive failure analysis: AI models can analyze sensor data, maintenance logs, and operational data to identify patterns and anomalies that indicate an impending equipment failure. It allows you to intervene proactively before a breakdown occurs.
Optimized maintenance scheduling: By understanding the health and performance of your assets, AI can recommend the optimal maintenance schedule to maximize uptime and efficiency. It can also automatically trigger maintenance work orders.
Prescriptive maintenance: AI can go beyond predicting failures. It can also recommend specific actions to address an issue, such as which parts to replace or adjustments to make, helping your maintenance team work more effectively.
Asset life extension: AI-driven predictive maintenance can extend the useful life of your equipment by preventing unnecessary wear and tear, allowing you to extract more value from your capital investments.

Streamlining manufacturing capabilities with predictive maintenance for a global company:

A technology firm partnered with a global industrial equipment manufacturer to develop an AI-powered Asset Performance Management solution. The team utilized cloud services to create a comprehensive system which included the following:

A predictive maintenance model built to analyze real-time sensor data from thousands of machines to forecast failures with high accuracy up to two weeks in advance.
An asset health scoring algorithm that considers numerous variables, including operational data, maintenance history, and environmental factors.
A real-time anomaly detection system using streaming data processing, capable of identifying unusual equipment behavior within seconds.

The solution integrates data from multiple different internal systems and IoT devices, processing a significant volume of data daily. Key results after implementation included a reduction in unplanned downtime, a decrease in maintenance costs, and significant annual savings from extended equipment life and improved operational efficiency.

#4. Warehouse automation

Some of the critical methods and techniques through which AI can facilitate warehouse automation are:

Autonomous Mobile Robots (AMRs) leveraging deep learning algorithms can navigate warehouses autonomously, transport goods efficiently, and adapt to changing environments.
Language and image generation models can generate accurate product descriptions, labels, and process documentation, streamlining warehouse workflows.
Computer vision enables automated item picking and packing, while NLP powers intelligent warehouse management systems and chatbots for customer support.

A major logistics company has partnered with a technology firm to develop an innovative fleet of AI-powered warehouse robots, starting with a collaborative mobile robot for material handling. This solution leverages advanced AI to optimize picking processes, adapt to real-time warehouse conditions, and generate data for improving layouts, staffing, and inventory management. The AI-driven robots are designed to enhance efficiency while complementing human workers, aiming to create a smarter, safer, and more reliable supply chain.

#5. Production planning and scheduling

Production planning and scheduling are crucial in supply chains for optimizing resource utilization, reducing costs, and ensuring timely product delivery to meet customer demand. Artificial intelligence enhances these processes in many ways:

Real-time adaptability: AI systems can quickly adjust production schedules in response to unexpected events like machine breakdowns or supply chain disruptions.
Efficient resource allocation: AI algorithms intelligently allocate resources by considering factors such as equipment availability, worker skill levels, and material availability.
Integration with IoT and automation: AI-driven production scheduling integrates seamlessly with Internet of Things (IoT) devices and automation technologies. This integration enables real-time data exchange between machines and scheduling systems, enhancing production efficiency and agility through continuous feedback and adjustment.

A large processing company implemented an AI-based solution to optimize production schedules and minimize manufacturing costs at their plant. The company integrated disparate data sources like demand forecasts, customer orders, production costs, and inventory into a unified data image. Based on these data, the machine learning models predict customer demand and configure optimization algorithms to generate optimal production schedules. This improved demand forecasting accuracy and incorporated numerous operational constraints from various categories.

Optimizing production planning and scheduling for a global car manufacturer:

A technology firm partnered with a leading car manufacturer to optimize production planning and scheduling across operations in multiple countries with a wide range of models in production. The team leveraged cloud services to create a sophisticated AI-powered solution which included:

A demand forecasting model that analyzes historical sales data, macroeconomic indicators, and other data sources to predict demand with high accuracy up to several months in advance.
A supply chain optimization algorithm that factors in numerous variables, including supplier lead times, inventory levels, and production capacities across all facilities.
A dynamic scheduling system that can re-optimize production plans in real time based on unexpected events like supply disruptions or sudden demand spikes.

The solution integrates data from multiple different internal systems and external sources, processing a significant volume of data daily. After implementation, key results included an increase in overall production efficiency, a significant reduction in manual planning hours, and substantial annual savings from improved resource allocation and reduced waste.

#6. Quality control

Conventional quality control using machine vision relies on expert-defined rules. The process typically involves:

A quality control expert identifies relevant features in camera-captured images, such as edges, curves, and colors.
They create a rule-based system to classify objects based on these features.
The system then automatically decides if a product meets quality standards using these predefined rules.

While effective for many applications, this method has limitations.

This is where AI-powered machine vision overcomes these limitations. AI systems can autonomously learn which visual features are essential for quality inspection by analyzing large datasets of good and bad product samples. This self-learning capability, enabled by deep learning algorithms, allows the AI to adapt to a wide range of quality scenarios without the need for extensive manual programming by experts. The AI can identify complex, nuanced patterns that human experts may overlook, leading to more accurate quality control solutions.

D. Supply chain automation

#1. Fleet tracking and monitoring

AI-powered telematics solutions can help with real-time fleet tracking and monitoring in several ways:

Instantaneous fleet monitoring: AI-enhanced telematics help monitor vehicle status, including location, speed, and fuel usage, in real-time. This helps make timely, informed decisions and rapidly address emerging issues.
Driver behavior analytics: By leveraging telematics and AI, organizations can analyze driving habits and identify potentially unsafe behaviors.
Asset tracking and monitoring: AI-powered asset tracking solutions use cameras, sensors, and GPS to provide real-time visibility into the location and status of vehicles, equipment, and cargo. This facilitates strategic routing and load-matching decisions.
Fuel efficiency optimization: AI algorithms analyze vehicle sensor data, fuel information, and historical consumption patterns to identify opportunities for improving fuel efficiency.
Driver coaching and training: By monitoring driver behavior data, AI can identify areas for improvement and provide targeted coaching and training programs.

#2. Route optimization

One key benefit of AI in route optimization is real-time traffic analysis. AI systems can provide up-to-the-minute information on traffic conditions by processing vast amounts of data from GPS, traffic cameras, and mobile apps. This allows route optimization algorithms to dynamically adjust routes and avoid congestion, saving time and reducing fuel consumption.

Moreover, ML models can leverage historical patterns and external factors like weather to anticipate traffic bottlenecks and suggest alternative routes before they become problematic. AI also enables personalization, allowing route optimization to be tailored to individual preferences and needs, such as delivery time windows, customer instructions, and vehicle characteristics. This level of customization improves both efficiency and customer satisfaction.

Efficiently managing deliveries for a global logistic giant:

A technology firm developed a sophisticated route optimization AI system for a global logistics provider operating in many countries. At its core, the solution uses machine learning to dynamically plan and adjust delivery routes. Advanced AI techniques were combined to represent and navigate complex road networks efficiently. The system processes a variety of data inputs, including historical delivery patterns, real-time traffic updates, and weather forecasts. By analyzing this diverse data set, the AI can predict potential delays, identify optimal routes, and suggest proactive adjustments to delivery schedules. Cloud services were leveraged to build and deploy this solution, providing the tools for developing and training machine learning models, while scalable computing resources enabled the creation of a responsive system that can handle real-time data processing and decision-making.

#3. Order fulfillment

AI-powered analytics can analyze real-time data on inventory levels, sales trends, and customer demand to forecast future requirements accurately. This enables companies to optimize inventory positioning, minimize stockouts, and avoid costly excess stock, ensuring the right products are available at the right time and location.

AI algorithms can also automate and streamline critical warehouse operations, such as order picking, packing, and shipping. These systems can dynamically allocate resources, optimize workflows, and rapidly adjust to changing conditions, leading to improved throughput and reduced fulfillment times.

A retail company has adopted AI and robotics to streamline its Buy Online, Pickup In-Store (BOPIS) service. AI robots retrieve online orders from the warehouse to address customer queues and waiting times. These robots can handle a large number of packages, scan barcodes, and deliver items to designated pickup points. The automated system lets customers quickly retrieve their orders by entering a PIN and scanning a barcode. The company has improved its online order fulfillment speed and efficiency by leveraging AI and robotics.

#4. Supply chain visibility

AI enhances supply chain visibility by enabling real-time tracking, monitoring, and data analysis across the network. Some critical applications include:

Mapping supply chains: Large language models can synthesize data from product orders, customs declarations, and freight bookings to map a company’s multi-tier supply chain network.
Demand sensing: AI systems can analyze data from point-of-sale systems, customer reviews, social media, and other sources to detect changes in demand patterns.
Supply disruption detection: AI tools can monitor supply chain data to identify potential disruptions caused by events like port congestion, worker shortages, or natural disasters.
Simulation and scenario analysis: AI can simulate different scenarios and evaluate the effectiveness of various strategies to mitigate supply chain disruptions, helping design optimal solutions.

A beverage bottler in Latin America aimed to improve its supply chain visibility. The company built an internal app that provides real-time inventory, distribution, and delivery insights across its plants and distribution centers. The app pulls data from data storage systems. The company also harnesses the power of ML, using models that predict delivery availability, reducing wasted attempts. This has led to a reduction in out-of-stock and fewer undelivered orders.

Overall, AI in supply chain has the potential to transform the industry holistically, from planning, sourcing, and procurement to quality control and supply chain automation. However, leveraging AI for supply chain is easier said than done. Like any other technology implementation, this too comes with its challenges.

Challenges of implementing AI in the supply chain

Here are the major challenges that you may face while incorporating AI in their supply chain ecosystem:

Data quality and accessibility
Integration with existing systems
Lack of skilled talent
Ethical considerations and bias
Implementation complexity
Cost of investment
Change management and adoption

Make your supply chain smart and future-ready with expertise

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