The machine learning market is growing exponentially and experts predict continued growth. A McKinsey report shows that AI has great potential to become a key driver of economic growth. Amid fierce competition, organizations are turning to machine learning to improve business efficiency and reduce costs.
Supply chain management is one of the key areas that affect a company’s bottom line. Organizations can gain a competitive edge and maximize profits by harnessing the power of technology to improve the efficiency of their supply chain operations. By harnessing the power of ML, businesses can reduce costs and increase profits while delivering a better customer experience.
This article explores common applications of machine learning that provide great solutions in supply chain management.
What is machine learning
Machine learning is a form of artificial intelligence (AI) that enables computer systems to learn from data, identify patterns, and make decisions without programming. By analyzing large amounts of historical data, machine learning algorithms can identify patterns and trends that are difficult or impossible for humans to perceive. Businesses can use these insights to make more informed decisions quickly and accurately about their supply chain management processes.
supply chain management
Core competencies for most companies include supply chains. A supply chain consists of all the steps necessary to get a product or service from the beginning to the final consumer. People, information, channels, resources, and means of transport, as separate groups are all part of and connected to the supply chain. Supply Chain Management integrates all supply chain activities. From the original supplier of procurement through fulfillment to the end user.
Problems in supply chain management
There are several problems facing supply chains that machine learning algorithms can solve. Distinct challenges include:
• Poor supply chain relationship management
• Poor resource planning
• Maintenance of poor quality and safety standards
• High transportation costs
• Unmet customer needs
• Cost inefficiencies
How machine learning techniques can help
Many studies have explored various applications of machine learning in parts of the supply chain. These applications include supplier selection, financial and supply chain risk forecasting, and SCM framework automation. ML applications can help improve the operational efficiency of your supply chain. This reduces costs, minimizes delays and increases customer satisfaction.
Let’s look at some standard uses of machine learning applications in supply chain management.
1. Automation of the SCM framework. ML can automate certain supply chain tasks such as inventory management, demand forecasting, and order processing. Task automation helps reduce costs and improve efficiency by streamlining processes and eliminating manual work. ML algorithms can help automate customer service tasks like order tracking and query resolution, freeing up staff resources for more value-added tasks like marketing and product development.
2. Predictive analytics. One way supply chain management can apply machine learning is through predictive analytics. By analyzing historical data and customer trends, ML algorithms can predict and forecast customer demand and optimize production plans. Businesses can more accurately forecast future orders and plan inventory levels. When organizations adopt intelligent forecasting systems, they can expect to optimize performance, reduce costs, and increase sales and profits.
3. Risk management. ML algorithms can analyze historical data to identify potential supply chain risks, such as delivery delays or product defects, before they occur. Organizations can take proactive steps to mitigate these risks before they disrupt supply chain processes.
Machine learning algorithms can also predict financial risk by issuing fraud alerts. Business managers can increase security by setting up alerts such as duplicate payments to suppliers. In this way, the chances of potential fraud charges can be reduced.
4. Optimization of supply chain processes. Organizations can optimize the entire supply chain process from inception to delivery to the end user. ML algorithms help identify areas that need improvement to increase efficiency and reduce costs. Companies that optimize their supply chains are more efficient because they can choose the best options.
5. Optimization of transportation and logistics. Machine learning algorithms can be used to optimize transportation routes and schedules. For example, real-time traffic data can be analyzed to determine the most efficient delivery routes. Businesses can reduce fuel costs and ensure delivery deadlines are met. ML algorithms can also track goods in transit. Historical data allows you to accurately forecast lead times and reduce errors.
By predicting accurate delivery dates, supply chain managers can control and improve operations and increase customer satisfaction.
6. Inventory management. Inventory management is one of the key areas of ML applications in the supply chain. Machine learning improves inventory management by predicting demand for specific products and predicting when items will need replenishment. Inventory planning is essential for tracking and optimizing demand and supply schedules. Planning helps prevent overstocking or out of stock of unwanted products. Inventory planning ensures that customers always have access to the products they need, when they need them.
7. Supplier Selection. One of the main functions of the supply chain is choosing the best vendor for your business. Finding the right vendor can be time consuming and costly. Machine learning techniques can be used to find the right factors in vendor selection and evaluation. Organizations can use historical data, market performance, and seasonal fluctuations to find the right factors in vendor selection and evaluation.
Adopting AI and machine learning
Machine learning techniques are used in different industries in different parts of the supply chain. It’s important to note that ML has several applications, depending on the nature and type of industry and the amount of data your business has. All these factors have a great impact on choosing a suitable algorithm. Machine learning techniques will certainly see increased use in the future. As more companies adopt AI and ML to improve their supply chains, their capabilities, knowledge, and business insights may improve.
About the author:
Arindam Mukherjee is an IT Supply Chain Architect and author of leading supply chain publications.he can be reached at (JavaScript must be enabled to view this email address).