Machine learning techniques in supply chain management

The machine learning market is growing in leaps and bounds, and experts project continued growth. A report by McKinsey indicates that AI has a large potential to be a significant driver of economic growth. Amid relentless competition, organizations are turning to machine learning to improve business efficiencies and reduce expenses.

Supply chain management is one of the key areas that affect businesses’ bottom lines. Organizations can gain a competitive edge and maximize their profits by leveraging the power of technology to increase efficiency in their supply chain operations. By leveraging the power of ML, businesses can reduce costs and increase profits, all while providing a better customer experience.

This article looks at the common applications of machine learning that offer excellent solutions in supply chain management.

What is machine Learning?

Machine learning is a type of artificial intelligence (AI) that enables computer systems to learn from data, identify patterns, and make decisions without being programmed. By analyzing large amounts of historical data, machine learning algorithms can identify patterns and trends that would otherwise be difficult or impossible for humans to recognize. Your business can use these insights to make more informed decisions, quickly and accurately, about your supply chain management processes.

Supply chain management

Most firms’ core competencies include their supply chains. The supply chain consists of all the steps needed to get a good or service from its beginnings to its final consumers. People, information, channels, resources, and means of transportation, as separate groups are all part of the supply chain and connected. Supply chain management integrates all supply chain activities; from original suppliers in procurement through fulfillment to the end users.

Pain points in supply chain management

There are a few problems faced by supply chains that machine learning algorithms can solve. Some of the distinct challenges include:

• Poor supply chain relationships management

• Inferior resource planning

• Low quality and safety standard maintenance

• High transportation costs

• Unmet customer needs

• Cost inefficiencies

How machine learning techniques can help

Many studies have investigated the various applications of machine learning in parts of the supply chains. Some of these applications include supplier selection, predicting financial and supply chain risks, and automating SCM frameworks. ML applications help improve the efficiency of supply chain operations, thus reducing costs, minimizing delays, and improving customer satisfaction.

Let’s examine some standard uses of machine learning applications in supply chain management.

1. Automation of SCM framework. ML can automate certain supply chain tasks such as inventory management, demand forecasting, and order fulfillment. Task automation can aid in reducing costs and improving efficiency by streamlining processes and eliminating manual labor. ML algorithms can help automate customer service tasks such as order tracking and query resolution, freeing up staff resources for more value-adding tasks such as marketing or product development.

2. Predictive analytics. One way in which supply chain management can apply machine learning is through predictive analytics. ML algorithms can predict and forecast customer demand and optimize production planning by analyzing historical data and customer trends. Companies can better predict future orders and plan their stock levels. Once your organization adopts an intelligent forecasting system, you can expect optimized performance, reduced costs, and increased sales and profit.

3. Risk management. ML algorithms can analyze historical data to identify potential risks in the supply chain, such as delivery delays or product defects way before they occur. Organizations can take proactive measures to mitigate these risks before they cause any disruption in the supply chain process.

Machine learning algorithms can also predict financial risks by raising alarms about fraudulent activities. Business managers can tighten security by setting alerts including duplicate payments to suppliers. In this way, they can reduce the chances of potential fraud charges.

4. Optimization of the supply chain process. Organizations can optimize the entire supply chain process from the start to the end-user delivery. ML algorithms can help identify areas where improvements should be made for greater efficiency and cost savings. Businesses that optimize their supply chains can select their best options and in turn improve efficiency.

5. Transportation and logistics optimization. Machine learning algorithms can be used to optimize transport routes and schedules. For instance, you can analyze real-time traffic data to determine the most efficient delivery routes. Companies can reduce fuel costs and ensure that deliveries are on time. ML algorithms can also track goods during transit. Historical data can precisely predict the lead times and reduce any errors.

Supply chain managers can control and improve operations and enhance customer satisfaction by having an accurate delivery time prediction

6. Inventory management. Inventory management is one of the critical areas of ML applications in supply chains. Machine learning improves inventory management by predicting demand for certain products and forecasting when items need restocking. Inventory planning is essential to track and optimize the demand and supply schedule. Planning helps prevent overstocking products that are not needed or running out of stock too quickly. 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 supply chains is to select the ideal vendors for your business. Getting the correct vendors takes a lot of time and is also costly. Machine learning techniques can be used to find the correct factors in selecting and evaluating your vendors. Organizations can use historical data, market performance, and seasonal variations to find the correct factors in selecting and evaluating vendors.

Embracing AI and machine Learning

Machine learning techniques are used across industries in various areas of the supply chain. It is important to note that there are several applications of ML depending on the nature of the industry, the type, and the volume of data the business has. All these factors have a significant effect on selecting a suitable algorithm. Machine learning techniques will definitely increase in use in the future. As more and more businesses embrace AI and ML to improve their supply chains, they will likely increase their capacity, knowledge, and business insights.

About the author:

Arindam Mukherjee is an IT supply chain architect and published author in leading supply chain publications. He can be reached at [email protected].

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