7 Ways Machine Learning Can Help Improve Your Supply Chain

Machine learning is a powerful technology making waves in supply chain management. As a type of artificial intelligence (AI), machine learning helps algorithms continuously improve without any explicit programming. It builds off of historical data and recognizes patterns. Supply chains have massive amounts of data, making them a natural fit for machine learning.

With machine learning in supply chain management, you can drastically improve efficiency and accuracy and meet diverse goals through the following capabilities.

1. Predictive Analytics

Supply chains are particularly vulnerable to interruptions. This fast-paced industry can benefit from machine learning’s ability to predict upcoming events, such as shipping delays, demand spikes, and equipment failure. Sophisticated algorithms can continuously monitor for disruptions and offer suggestions or automated behaviors to address the problems. They’re so useful that four out of five supply-chain executives plan to use or are already using AI and machine learning in planning.

Machine learning is exceptionally good at identifying patterns, so it can use your organization’s unique historical data to create tailored predictions. You could use it to optimize your inventory levels or determine the most cost-effective time to replace equipment. Machine learning can even improve predictive maintenance, identifying when breakdowns are likely and helping you extend the life of your equipment with less downtime and fewer costs.

One particularly useful way that machine learning can help is through the Internet of Things (IoT). IoT devices, such as sensors and telematics devices, collect huge amounts of data. Machine learning and AI can use these data points to create actionable insights and build on them over time, improving the accuracy and capabilities of predictive models.

2. Streamlined Plans and Reports

While machine learning excels at predicting problems, it can also find existing ones. AI can train algorithms to look for inefficiencies and waste that increase costs and downtime. It can help you streamline different processes and more readily adapt to problems. Supply chain machine learning considers countless constraints and variables to create more detailed, accurate planning insights.

AI does all of this with incredible accuracy. Forecasting efforts can incorporate many different data points, from IoT sensor measurements to purchasing data from an enterprise resource planning (ERP) tool. You can feed these sources into a machine learning system and eliminate error-prone manual forecasting. For example, vendor managed inventory (VMI) can use your rules and goals to generate recommended orders that help you find the right balance of inventory levels.

3. Real-Time Visibility

Today’s supply chains are diverse and complex. Adding end-to-end visibility can help you manage every asset and shipment. One McKinsey survey found that supply chain leaders who increased end-to-end visibility were twice as likely as their colleagues to avoid supply chain challenges in 2022. From deep analytics to real-time, proactive monitoring, visibility efforts can ripple throughout the business.

A machine learning system supports visibility by analyzing historical data and making new connections. For instance, you could use machine learning to assess your suppliers’ performance and find ways to improve resiliency. The system could suggest changing suppliers to one with a better track record or finding a regional supplier to minimize transit costs.

Visibility is especially important in regulated industries, such as pharmaceuticals and food and beverage. Real-time analysis and reporting from machine learning can make a significant difference and save countless hours on otherwise manual processes.

Last-mile tracking is another area where improved visibility can help. Machine learning can improve this phase for better on-time delivery and more satisfied customers. With access to vast amounts of ever-changing data, machine learning factors many different variables into its calculations to speed up delivery, reduce damage from transit, and provide more communication to buyers. For instance, if a package is delayed, AI could calculate the expected arrival date and automatically communicate the change to the customer.

4. Advanced Inspections and Quality Control

Machine learning’s pattern recognition capabilities extend to visual data. It’s an excellent fit for inspections and quality control. Machine learning can allow AI to take over these tasks, eliminating error-prone and time-consuming manual processes. It can offer more detail to find minute defects and minimize the chances of defective products reaching the client. Machine learning could flag and classify damage and recommend the most effective remediation.

5. Reduced Costs

All these benefits, such as efficient operations, faster fulfillment, and improved communications, ultimately support your bottom line. Machine learning is keyed into your organization’s unique historical information to analyze the best solution for your needs and goals. It can help prevent expensive problems, such as unexpected downtime and damaged shipments, while providing optimization opportunities by identifying gaps in your operation. It also eliminates manual requirements for fewer errors and labor costs.

Whether your most troublesome costs come from transportation, labor, customer retention, or some other expense, machine learning can help.

6. Improved Resiliency

In the modern supply chain, adapting to the unexpected is paramount. Machine learning can help you get ahead of the competition and stay prepared for whatever comes next. It can predict issues and problem-solve independently, allowing you to focus less on putting out fires and more on reaching business goals. Since they can access so much data, AI and machine learning tools can analyze the situation in a way that humans can’t and promote unparalleled resiliency, helping you stay productive and efficient in the face of challenges.

7. Simplified Automation

An efficient supply chain typically relies on many automated processes, from information sharing to robotics in a warehouse. Machine learning’s unique problem-solving capabilities can further eliminate manual work by suggesting or taking corrective action.

Say you receive notification of a delayed order. AI could run different scenarios to see whether you need to expedite another shipment, reorganize manufacturing processes, or resolve the problem with a buyer. The solution may be as simple as sending an automated apology email to a customer, but a sophisticated machine learning system could even place new orders on its own.

Go Further with an AI-Powered Supply Chain

AI and machine learning are exploding. Experts predict the market will grow from $21.17 billion in 2022 to $209.91 billion in 2029. It’s especially important in industries that rely on large amounts of data, such as supply chain management. As you work toward your business goals, keep AI and machine learning in mind. The applications are endless and growing more sophisticated every year.

One of the most pressing applications for machine learning is inventory management. A VMI platform can use AI to deliver intelligent, accurate suggestions and support for optimized inventory forecasting, ordering, and management. At TrueCommerce, we put machine learning to work in our VMI solution, backed by our globally recognized, fully managed services. To learn more about how our machine learning technology can help you strengthen your supply chain, reach out to us today!