THE POWER OF AI IN SUPPLY CHAIN MANAGEMENT

Introduction

In this blog, we’ll explore how AI is revolutionizing supply chain management, offering solutions to old problematic processes, and paving the way to a more efficient supply chain. We will examine how AI has advanced key features of supply chain management, including demand forecasting, inventory management, logistics, and supplier management.  

Supply chain management can be defined as the control and management of the flow of goods, data, and finances related to a product or service, from the procurement of raw materials to the delivery of the final product. It involves comprehensive oversight of a product’s journey from raw materials to the end customer. 

Artificial intelligence (AI) involves the simulation of human intelligence processes by machines, particularly computer systems. The main goal of supply chain management is to improve efficiency, ensure timely delivery, and reduce costs, which ultimately enhances customer satisfaction (Chopra, 2016). 

AI technologies, such as machine learning, process automation, and advanced decision-making, have significantly improved efficiency across various sectors. “The integration of AI in various industries enhances efficiency and operational effectiveness” (Norvig & Russell, 2020). Integrating AI into supply chain management has profoundly transformed operations by increasing both efficiency and effectiveness. This synergy promotes better decision-making and provides a competitive advantage (Wang et al., 2016). 

Forecasting demand 

A method identified as one-hot encoding is practiced for the pool of data to interpret the data discreetly subsequently with the application of an ideal k-value as per the elbow technique. Additionally, to ensure the comparability of the data characteristics from each and every listing clustering analysis is conducted to segregate time series data composed about the distributers, commodities and the stockrooms having significant or poor demand. 

 

Furthermore, the determination of time series data correlation is computed by encrypting and finding out the mean of suppliers, goods and warehouses as well as their similar feature data. While processing data for vast time series of data usually during a half year cycle an ARIMA time model is best suited. The autocorrelation graph highlighted concludes by showcasing how the demand prediction data is comparatively more accurate with already existing values. Consequently, this attested data was designated for the time series approximation aided by the implementation of the ARIMA model. 

 

 

 

Inventory Management 

For a company to have successful operation one of the important aspects that must be looked at is the effective management of inventory. Effective inventory management ensures that a company has the right amount of stock at the right time which not only minimizes costs but also maximizes efficiency. Initially, inventory management was carried out manually, but with the introduction of AI, there has been a shift. This shift to AI has brought about many benefits. Some include the improvement of demand forecasting, Stock optimization and automated reordering.  

AI systems can easily analyze complex datasets to predict the future product demand. As Dhaliwal et al (2023) highlights, AI improves demand forecasting accuracy, which is crucial for maintaining crucial optimal inventory levels. AI plays an important role in stock optimization, by analyzing the sales data, market trends and other factors that are relevant, it can determine the most efficient levels of stock required by a company. As noted by Naik (2023) in his research on an Inventory management carried by AI, this optimization leads to reduced costs and improved operational efficiency. Automated reordering is another benefit provided by AI. AI systems can freely order stock based on the factors mentioned earlier such as predicted demand and stock levels. As discussed by Eldred (2023) this not only saves time but reduces human error as well. 

Logistics and Transportation 

Logistics is the planning and implementation of moving goods from supplier to customer. It encompasses warehousing, inventory management, order fulfillment and transportation. Diving into logistics is the aspect of transportation. Transportation simply focuses on movement of the goods by land, sea or air. 

Tracking transported shipments and packages was quite challenging before AI was introduced into the supply chain. Customers often struggled to get real-time updates on their goods, which could negatively impact customer satisfaction. Today, AI enhances visibility by providing accurate tracking systems and up-to-date information on the current location of packages. 

  

Another issue was planning optimal delivery routes. Suppliers would typically plan routes manually or with basic software, leading to high fuel costs and delayed arrivals. AI has revolutionized this process by analyzing factors such as traffic conditions, weather, and optimal travel routes, resulting in more efficient and timely deliveries. 

 

An example is UPS, a major carrier in the USA, which has implemented the ORION system to optimize delivery routes when drivers are delivering to multiple customers in an area. This system has greatly improved delivery efficiency and reduced fuel costs for the company. 

Supplier Management 

Supplier management frequently encounters challenges such as inefficient supplier selection, inconsistent performance tracking, and risk management difficulties. Regular approaches tend to be slow and reactive, which can result in disruptions and higher expenses. 

Supplier Selection: AI improves the supplier selection process by analyzing past performance data, market trends, and supplier capabilities. For instance, platforms like IBM Watson leverage machine learning to evaluate and rank suppliers on criteria such as reliability, cost, and performance, facilitating more strategic and informed decisions. 

Performance Monitoring: AI systems provide continuous monitoring of supplier performance through real-time data analysis. SAP Ariba, for example, utilizes predictive analytics to track important metrics like delivery accuracy and quality, notifying managers of potential issues before they impact the supply chain (McKinsey & Company, 2024). 

Risk Management: AI enhances proactive risk management by assessing various risk factors, including geopolitical, financial, and operational risks. Riskified employs AI to foresee and address risks related to suppliers, allowing companies to diversify their supplier base and prevent disruptions. 

For instance, Amazon has used AI for over 25 years to evaluate and select suppliers. Suppliers that do not meet certain standards lead to deactivated accounts. Recently, Amazon introduced the OTDR policy, which requires suppliers to deliver on time and maintain high customer satisfaction. (ShipSage Blog) 

 

CONCLUSION 

In light of the above, it can be established that integrating AI with supply chain management can lead to improved demand forecasting, enhanced inventory management, logistics, and management of suppliers. Businesses can project demand patterns with more accuracy with the help of AI to analyze huge datasets with a high level of precision. Inventory levels are optimized while waste is reduced as a result. Cost minimization and timely delivery can both be achieved simultaneously as advanced algorithms make the logistics process more efficient. Relationships with suppliers are strengthened as AI enables improved communication and decision-making based on data.  

The evolution of AI will only make its role in supply chain management more crucial, powering efficiency, sustainability, and profitability across the entire supply chain. 

 

REFERENCES 

• McKinsey & Company (2024). How AI Transforms Supplier Performance Monitoring. https://www.mckinsey.com  
• Shipsage Blog. (2024, August 15). Why Amazon’s OTDR is Key to Keeping Customers Happy. https://shipsage.com/why-amazon-otdr-update-is-key-to-keeping-customers-happy 
• Singh, N., & Adhikari, D. (2023). AI in inventory management: Applications, Challenges, and opportunities. International Journal for Research in Applied Science and Engineering Technology, 11(11), 2049-2053.     
• Supply Chain Digital. (2023). AI in Supply Chain Management: Enhancing Supplier Management. https://www.supplychaindigital.com 
• UPS. (2020, January 29). UPS To Enhance ORION With Continuous Delivery Route Optimization. https://about.ups.com/us/en/newsroom/press-releases/innovation-driven/ups-to-enhance-orion-with-continuous-delivery-route-optimization.html 
• Wang, G., Gunasekaran, A., Ngai, E. W. T., & Papadopoulos, T. (2016). Big data analytics in logistics and supply chain management: Certain investigations for research and applications. International Journal of Production Economics, 176, 98-110. https://doi.org/10.1016/j.ijpe.2016.03.014 
• Xun, H., & Li, W. (2024). The supply chain demand forecasting model based on LSTM and multiple clustering techniques. Highlights in Science, Engineering and Technology, 101, 390–394. https://doi.org/10.54097/y5c6pb47