Role of Artificial Intelligence and Machine Learning in Supply Chain Management 

Introduction to Artificial Intelligence and Machine Learning in Supply Chain Management 

Artificial Intelligence (AI) and Machine Learning (ML) are revolutionizing the Supply Chain Management domain. These technologies are helping companies streamline their supply chains, reduce costs, enhance customer satisfaction, and increase revenue. They also help companies achieve faster business results by automating manual processes and providing valuable insights to improve customer experience. 

Supply Chain Management is an essential part of any business. The initial stage of the supply chain process is ensuring that the right goods are delivered at the right place and time to meet consumer demand. Supply Chain Management is also responsible for optimizing costs by reducing waste while maximizing efficiency. 

In the past, few tools were available to help companies manage their supply chain. But now, Artificial Intelligence and Machine Learning have revolutionized this domain by improving efficiency and reducing costs. 

What Is Artificial Intelligence? 

Artificial Intelligence (AI) is the branch of computer science that studies how to make computers do things that require intelligence when done by humans. AI research has many practical applications, including medical diagnosis, speech recognition, and translation between languages. 

AI systems can be trained to learn from experience, which means they can improve their performance based on what they’ve previously seen or done. In this way, AI machines are different from traditional computers that perform only according to tasks programmed into them beforehand by people. 

AI is a broad field and includes many different types of systems. Globally, the market for Artificial Intelligence is expected to reach over $136 billion by the year 2022. Some AI systems are designed to solve specific problems like playing chess or diagnosing diseases. Other systems attempt to mimic human thought processes by learning from experience. 

Which Business Cases Does Artificial Intelligence Solve Better? 

Let’s take a look at some of the ways that AI is being used today. 

• Games: AI helps create a more immersive gaming experience by providing realistic human-like behavior in games like Starcraft, Dota 2, and League of Legends. For example, Artificial Intelligence can be used to create characters with personalities that react to each other and their surroundings in various ways (e.g., buffering). 
• Business: Companies are using Artificial Intelligence as customer service support agents so customers can talk to an intelligent system instead of waiting on hold with a representative from their company or submitting an email or message form. AI is a proactive process to predict potential risks efficiently. An example of this application is IBM Watson Assistant, which provides businesses with tools for building conversational applications that allow customers to interact with different aspects of their services from one interface (e.g., product comparison). 
• Healthcare: AI can be used to predict medical outcomes and identify patterns in medical data that are not humanly possible. For example, IBM’s Watson has been used to help doctors diagnose cancer by analyzing symptoms and providing them with treatment options. 
• Education: AI creates personalized learning experiences for students based on their needs and interests. For example, companies like Tute Genomics are using AI to help students learn more effectively by providing them with targeted resources based on their interests and needs. 
• Financial Services: AI is also used to automate tasks like fraud detection and credit scoring, which will free up resources for human employees to focus on more important tasks. A good example of this is how companies like FinTech are using Machine Learning to help businesses secure their data by identifying patterns in the data that may indicate a security breach. 

Machine Learning Meaning and Importance 

What is Machine Learning? It’s a subset of Artificial Intelligence. Machine Learning is the science of getting computers to act without being explicitly programmed. Algorithms are used to make predictions about future data, such as what movie you might like or what price you should pay for your next car. 

Machine Learning algorithms are often used for classification and prediction problems. For example, a computer can be trained—through Machine Learning methods—to identify specific types of images (e.g., dog breeds) with high accuracy based on a training set of labeled images (i.e., dogs). 

ML is used in many industries, including supply chain and logistics, healthcare, finance, and insurance. It’s used to predict which customers are likely to churn from a loyalty program and what premiums insurers should charge for certain policies. Machine Learning algorithms can help companies make more informed business decisions without human intervention by using data from past experiences (i.e., historical data). 

Various types of Machine Learning algorithms are applied to different fields like image recognition, speech recognition, natural language processing, robotics, and others. Some examples of Machine Learning are: 

• Image Recognition is the process by which machines can identify objects in a digital image or video frame. The most common application is finding faces in images. Face detection using Machine Learning was introduced by Viola–Jones at an ICCV workshop in 2001. Since then, it has been widely used for surveillance systems and driverless cars where cameras continuously capture images from surrounding environments. 
• Speech Recognition: In this method, computers learn to understand human voice commands with the help of deep neural networks (DNNs), which have shown promising results in recent years. This is due to rapid advancements in computing power and large amounts of training data available today compared to previous decades. 
• Natural Language Processing: This is the process of using computers to understand human speech. It is a powerful tool for applications such as customer service chatbots and voice assistants like Siri or Alexa. 
• Robotics: In this field, Machine Learning algorithms are used to allow robots to perform tasks without being explicitly programmed for each specific situation. The most common types of deep learning algorithms used in robotics are convolutional neural networks and recurrent neural networks, which are also popular for image recognition and speech recognition applications. 

Benefits Of Artificial Intelligence And Machine Learning In Supply Chain Management 

As the name suggests, Supply Chain Management is a process of managing the flow of goods and services from supplier to consumer. These days, suppliers are using Artificial Intelligence and Machine Learning to automate their operations and improve their productivity. 

Automation of Order-to-Cash Process 

Businesses can reduce manual errors and speed up order processing by automating the order-to-cash process. This will also help them improve customer service as they can process orders faster and reduce time spent on processing payments, refunds, and other requests. 

Automating the order-to-cash process can also help businesses improve their financial management. For example, they can reduce their risk of fraud and theft by monitoring payments more closely. 

Predicting Demands and Preparing for Shifts 

There are many benefits of using predictive analytics for Supply Chain Management. One of the biggest advantages is its ability to improve customer service. Predictive analytics can predict demand, allowing companies to plan in advance and make sure that enough inventory is available when needed. This also helps reduce costs by ensuring that excess supplies are not purchased unnecessarily. 

Another benefit is increased efficiency. Predictive analytics can help optimize production schedules and delivery routes, so products arrive at the right place at the right time without delays or high transportation costs incurred from unnecessary stops. It also optimizes inventory levels based on past usage patterns, such as seasonal sales demand fluctuations or product popularity changes over time. 

Improved Inventory Forecasting 

Artificial Intelligence (AI) and Machine Learning can help you increase forecasting accuracy, which is critical for Supply Chain Management. 

Demand planning is a process that helps organizations understand their future demand, make decisions on how much to produce or order, and determine the optimal inventory levels. AI and Machine Learning tools can assist with demand planning by automating repetitive tasks, being proactive in suggesting potential solutions to problems that arise during the process, or identifying data quality issues quickly, so they don’t hinder effective decision-making. 

Supply chain optimization uses data about activities across an entire supply chain to predict outcomes for each step of the process—from supplier selection to delivery—and then recommends actions that result in better outcomes for each step. Machine Learning algorithms are particularly well-suited for predicting the effects of changes in certain variables because they allow you to experiment with different scenarios without having any prior knowledge about what will work best (such as which suppliers perform best). 

Smarter Operations with Data-driven Manufacturing 

The advent of Machine Learning and Artificial Intelligence in the supply chain has made it possible to predict demand, optimize inventory management, and reduce order-to-cash cycles. 

When it comes to forecasting, predictive analytics helps with the development of new products by analyzing past sales data. This can be extremely helpful for companies that sell seasonal products or those with limited resources for forecasting sales performance. With Machine Learning algorithms in place, you can make better decisions about what products to launch next or which ones will perform well based on historical data from previous launches. With these insights from AI technology, companies are able to prioritize their product strategy so they don’t waste time or money developing items that won’t sell as well as others within their portfolio. 

Conclusion 

Artificial Intelligence and Machine Learning in Supply Chain Management have many benefits. They help companies improve their operations, increase productivity, and reduce costs. While these technologies have only been applied to Supply Chain Management in recent years, they are already making a big difference for businesses around the world. To understand the importance of Artificial Intelligence and Machine Learning clearly, one must pursue the Executive PG Diploma in Management & Artificial Intelligence by IIM Indore in collaboration with UNext Jigsaw.