The world of supply chain management is changing fast.
According to Gartner, a global research and advisory firm, supply chain companies will invest heavily in machine learning and artificial intelligence (AI) capabilities over the coming years to maximize productivity and reduce uncertainties of every kind.
From autonomous vehicles to predictive analytics software, these technologies are expected to help companies operate faster, leaner, and smarter. Supply chain managers may no longer have to estimate supply and demand. Instead, these systems will provide them with end-to-end visibility of their supply networks and help them forecast demand across all of their customer segments.
In this article, we look at how AI and machine learning can help firms boost their materials management capabilities.
- AI and machine learning technologies help machines and systems within the supply network operate autonomously using data, reducing the need for human intervention and increasing efficiency.
- By implementing AI-powered systems in your organization, you could optimize inventory management to keep just the right level of stock you need. You could also make more accurate demand predictions based on market insights generated by AI.
- Supply chain companies that use machine learning systems have been able to save time, reduce waste, and cut costs in their daily operations.
Artificial intelligence and machine learning: What’s the difference?
AI and machine learning are two hot buzzwords gripping the imagination of the supply chain industry today. Sometimes used interchangeably, the two terms are used to describe different technological functions.
AI, the older and better known of the two technologies, refers to the general ability of a system or a machine to complete tasks autonomously with a high level of accuracy. Machine learning, on the other hand, is a subset of AI that focuses on programming machines to analyze data and create actionable insights on their own.
Both technologies are making supply chain management more efficient and cost-effective.
The basics of AI
Since the late 1950s, the field of AI has progressively made everyday machines more capable of making intelligent, autonomous decisions. AI systems teach machines to mimic human behaviors and thought processes to enable them to carry out tasks as humans do.
AI can be broken down into two main categories: augmentation and automation. Augmentation is what we normally experience in our data analysis software and virtual assistant apps – it assists us with our day-to-day tasks by providing insights and suggestions based on available data to help us reduce errors caused by human bias.
Automation goes a step further by programming machines to operate without human intervention. Robotic arms that lift and move heavy items in warehouses are one such example of automated AI.
The basics of machine learning
In the world of supply chain management, machine learning is where most advancements are occurring. Engineers and data scientists are creating machines that can take outputs from multiple sources, analyze the accuracy of the data, and develop their models to make better, informed decisions. In other words, these are machines that are capable of autonomous learning and improving with experience.
Supply chain companies have used machine learning to forecast demand and shipping times since the early 2000s. As algorithms have improved over the years, machine learning systems have been able to take advantage of big data to identify the most effective solutions for supply chain managers, whilst continuing to learn to make better analyses in the future.
What are the potential applications of AI and machine learning?
Although AI and machine learning have been around for several decades now, they’ve yet to be widely adopted by most manufacturing and supply chain firms. The high cost of machine learning systems as well as their huge appetite for data are often cited as the key factors hindering small- and medium-sized companies from adopting them.
But if these obstacles can be overcome, AI and machine learning can provide several competitive advantages to firms big and small. These technologies can help supply chain companies handle internal legal compliance, detect procurement fraud, and better allocate budgets across departments. AI-powered machines assemble can products and pack them faster, creating shorter lead times.
With less than 10% of global supply chain companies using AI and machine learning, the potential for these technologies to transform the global marketplace is huge.
How to implement AI & machine learning in materials management
If your organization is planning to acquire AI-powered tools and machine learning systems to optimize your supply chain, here are some ways to put them to good use starting from day one.
Optimize warehouse management and cut costs
Every supply chain manager knows how inventory-related problems such as overstocking and understocking can become hugely time-consuming and expensive. But with AI-driven tools, you can access your data, your suppliers’ inventory and even customer demand forecasts to accurately predict supply and demand and find the right balance.
Machine learning systems can pore through current and historical market trends to give you insights into new customer habits. They can also analyze ongoing disruptions to warn you about shipment delays and supply shortages. With AI software, all of this information gets pulled into one place so you can optimize which supplies come in and which products go out in real-time, saving time and money in the long run.
Analyze data to facilitate predictive analytics and planning
Companies today produce more data than ever before and AI can help them make sense of it very quickly. Within the supply chain industry, AI systems are used to aggregate data from multiple sources, including internal production capacity as well as competitors’ prices. The systems then apply algorithms to the data sets to forecast supply and demand across multiple functions of the business.
Demand planning, in particular, has benefited well from predictive analytics. According to research from McKinsey, AI-powered forecasting can reduce about 65% of lost sales due to inventory out-of-stock situations and decrease warehousing costs by up to 40%.
Enable autonomous vehicles, factories, and processes
One of the most exciting changes happening in supply chain management now is the rise of autonomous vehicles and smart factories. These innovations have major potential to keep operations running 24/7 in today’s interconnected world, primarily because of their abilities to make independent decisions and interact with other production equipment and delivery systems in the network.
Analysts predict that the widespread adoption of these technologies could double the output of the U.S. transportation network at just a fraction of the cost. Industry giants are so convinced about the potential of this technology that they’ve even started designing autonomous ships, like Google and Rolls-Royce’s latest project.
AI could also help your firm enable autonomous maintenance processes for key supply chain assets such as engines and production machinery. Using Internet of Things (IoT) sensors, these machines could be programmed to analyze their performance data and alert managers to malfunctions or upcoming repair works, thereby reducing the time that a piece of equipment is out of order.
Benefits of utilizing AI and machine learning in your supply chain
Several firms that utilize AI and machine learning in their supply chains reported seeing increased revenue and decreased costs in just a few years, and in some cases months, after switching to these new technologies.
From enhanced supply chain forecasting to better customer service, AI is helping supply chain companies solve problems in all areas of their business. Here’s a look at how AI and machine learning can transform your organization.
Optimize the data you already have
AI and machine learning systems can help you sift through large sets of data that your firm has collected over the years and analyze them to generate insights into your operations. They can help visualize the seasons where demand is high and supply is low, and vice versa. They can also bring inventory information from different locations together to give you an overview of current inventory excesses and deficiencies.
You may also be able to link your existing SAP supply chain systems to powerful AI-based inventory management platforms to further strengthen your supply network’s resilience.
Increase supply chain efficiency and resiliency
No supply chain network is immune from disruptions. Natural disasters, cyberattacks, and trade disputes have shut down factories and grounded aircraft time and time again. But AI and machine learning systems can help you better predict these disruptions to help you minimize losses.
They can help supply chain managers anticipate bottlenecks at certain parts of the supply network, allowing them to act fast and put in place contingency measures to keep materials flowing.
AI-powered tools may even be used to make supplier assessments based on their annual performance data, audit reports, and credit scores. This ability can help you periodically review different suppliers and make changes as necessary to keep your supply chain running efficiently.
Predict demand and respond ahead of time
Machine learning systems can find data patterns in customer demand with very little need for human intervention. This frees supply chain managers to focus on more strategic decision-making tasks. It also provides managers with deep insights into customer behavior, which can be used to conduct more accurate supply planning.
Some supply chain companies go as far as using machine learning applications to predict demand at the store level. They feed their systems store traffic data, weather data, and even prices at competing stores to enable the applications to make better predictions.
Save time, reduce waste, and cut costs
Perhaps the most valuable benefit of a machine learning system is its ability to help you reduce waste. Logistics, as any supply chain manager can attest to, can feel like trying to keep under control a thousand moving parts. Waste occurs when supply delays cause time loss on the factory floor or when poor planning creates an inventory surplus that no one wants to buy.
Machine learning systems log all of these inefficiencies and constraints. With the aid of IoT sensors and advanced algorithms, they give supply chain managers end-to-end visibility of their network as well as insights into the areas where waste is occurring. Managers can then make the appropriate adjustments to save time and cost where possible.
Improve your materials management with an AI-driven platform
Verusen is a cloud-based materials management solution that helps you reduce risk in your supply chain and optimize your inventory. Using both AI and machine learning, our platform harmonizes data across all functions, including that of your suppliers, to bring you actionable insights for all users across procurement, sourcing, and operations.
Our platform can be set up without any need for coding or consultants. Insights are delivered straight to real-time dashboards that are personalized to your firm’s needs. Try Verusen today to build a more resilient supply network.