Building a New Supply Chain Strategy with Machine Learning
There has been plenty of talk in the news recently about worldwide supply chain problems contributing to runaway inflation. That has led to a serious discussion about the weaknesses of the current global supply chain and how business leaders can create a new system that is designed to meet the demands of the 21st century.
Machine learning in supply chain management is a leading concept in these discussions and will likely shape how global trade is conducted in the post-Covid-19 era. There are plenty of supply chain strategy examples where AI can be used to make faster, more informed decisions, improving the overall health and efficiency of the system. Here is a look at how machine learning can prop up the struggling global supply chain.
Benefits of Machine Learning in Supply Chain
There are many benefits to machine learning in supply chain management. From predictive analysis to optimizing delivery logistics, AI can aid human beings in creating a more streamlined approach to global trade. Here are some ways AI-based tools can make supply chains more efficient and resilient to change.
Get a Bird's Eye View of the Supply Chain
Global supply chains are becoming increasingly more intricate and complex, which creates challenges for managers who may struggle to understand precisely how the system functions at every stage. This creates a greater possibility for errors and bottlenecks that may slow distribution and create tension within the system. Machine learning tools can help supply chain managers collect data to better understand how the supply chain operates from a bird's-eye view.
Managers can implement complex ERP and business intelligence solutions to better handle the health of the supply chain at a larger scale. Plus, machine learning in supply chain management can predict potential risks and downstream consequences of a particular action that humans may struggle to see, allowing managers to make faster, more informed decisions without fearing the consequences of unforeseen errors or delays.
Improved Demand Forecasting
One of the biggest challenges that supply chain managers face is predicting demand. To keep the system running, you must keep shipments flowing at a rate that can keep up with the needs of the public. But especially in times of change, it becomes increasingly difficult to predict consumer appetite, leading many brands to overcompensate or underdeliver. Both scenarios can lead to potential inventory problems that cut into a business’s profits.
Machine learning in supply chain management is excellent at identifying patterns and making accurate predictions about human behavior. This is done through a “constraint-based modeling process,” where every decision's outcome is restricted to a minimum and maximum limit.
This helps reduce the possibility of grossly over or under-predicting demand, causing inventory shortages or a glut of unused products. While nothing can accurately predict human behavior 100% of the time, machine learning in supply chain management can be a helpful tool to avoid missing the mark in a way that would lead to a loss of profit.
Reduce Costs and Increase Revenue
There are countless supply chain strategy examples where AI has significantly reduced costs and increased revenue. Machine learning takes the guesswork out of creating complex shipping and distribution systems.
A McKinsey study found that 61% of business managers who use machine learning in supply chain management saw a decrease in costs, and 50% saw an increase in profits. AI helps reduce this margin of error by creating powerful predictive models for companies to make informed decisions.
Even if the error is small, the costs increase over time when considering the scale of many global supply chains. Maybe you're only losing a few cents per shipment due to an oversight. But if you’re sending out thousands of shipments daily, the amount of money you’re losing annually quickly multiplies. Machine learning programs can help you spot these potential errors faster or avoid making them in the first place.
Increase Efficiency in Delivery Logistics
Delivery logistics is a sector that all businesses should monitor closely. Most customers shopping online expect their product within a short window, and if you can't reasonably deliver, they will likely move on to another company that can. So, your delivery logistics must be carefully mapped out to prevent potential delays and backlogs.
Supply chain managers are wise to use AI-powered mapping software that can be used to plan delivery routes and respond to any significant changes in the terrain. That way, if a major highway is shut down temporarily due to construction, you'll be quickly alerted and able to find an alternative route in time to prevent any significant delays.
Analyzing geospatial data is just one of the ways that machine learning in supply chain management can help predict potential hazards and offer fast solutions that will prevent shipping delays and delivery problems.
Make Up for Labor Shortages
Labor shortages worldwide are a significant weakness in the global supply chain. With such a quickly changing labor market, it can be difficult for businesses to find enough skilled workers to keep up with the demand. Automation can not only replace roles that a human once performed but also help fill in gaps where human workers may be lacking in skill or knowledge.
For instance, machine learning can be used for quality control if workers must be trained quickly and don't have the time to master a particular skill before shipments are sent out. This allows workers to move faster without the fear that the quality will suffer.
Automation can also fill in the gaps where human beings may be inefficient or obsolete. So if you're experiencing a labor shortage because there aren't enough workers looking to fulfill that role, it may make sense to invest in an AI-based application that can take on some of the excess workload.
AI Supply Chain Strategy Examples
Machine learning in supply chain management has a variety of applications for businesses across many sectors of the economy, from manufacturing to healthcare. Here are a few common supply chain strategy examples that use AI to improve efficiency and logistics.
Safely Transport Cargo: Many companies, from automobile manufacturers to big box stores, use machine learning programs to transport cargo from warehouses to distributors or retailers safely.
Create Maps for Delivery Drivers: AI-based GPS mapping programs can be used to help delivery drivers plan their routes and pick up shipments on time.
Managing Inventory: AI algorithms can predict when it's time to order or manufacture more products and when to pause production to avoid overstocking.
Plan Shipping Routes: AI-based programs are great for planning complex shipping routes and can be used to predict delays and identify potential shortcuts
Autonomous Vehicles and Delivery Bots: Rather than using machine learning to assist human delivery drivers, many companies opt to deliver specific shipments using autonomous vehicles and delivery bots built to respond to challenges.
Predictive Analysis Forecasting: Many companies are using machine learning in supply chain management to create software that predicts challenges before developing problems, rather than waiting to respond to errors.
Learn More About Your Customers: Machine learning in the supply chain isn't limited to backend logistics. You can also use machine learning algorithms to analyze customer behavior at the point of sale, which can help make smarter decisions about inventory and shipping.
These are just some of the most common supply chain strategy examples already employed by large corporations. But the practical usage of machine learning in supply chain management is virtually unlimited. So if you have a unique problem you're facing with your business, chances are that machine learning can help you find a solution.
But to get the most out of the available technology, you need a trusted partner who can help you navigate this complex landscape. For everything from machine learning to mobile app development, Soluntech can help you use technology to streamline your business processes and better serve your customers. Give us a call or quote your project today to see how we can assist.