Businesses are always striving to increase profit margins in a highly competitive market. Disruptive technologies such as Machine Learning (ML), Artificial Intelligence, (AI) provide some great opportunities to increase customer satisfaction, lower costs, and reduce costs.
Machine Learning in Supply Chain
Artificial Intelligence, Machine Learning, and Machine Learning are buzzwords in different verticals. But what does this actually mean for modern supply chain management?
Machine learning can be integrated into supply chain management to automate many mundane tasks, and enable enterprises to concentrate on more strategic and important business activities.
Supply chain managers can optimize inventory and find the best suppliers using intelligent machine learning software. This will allow them to maintain a smooth-running business. Machine learning is gaining popularity among businesses. This is due to its many benefits and the ability to fully leverage the massive amounts of data that are collected through warehousing, transportation, industrial logistics, and other systems.
Enterprises can also use it to build machine intelligence-powered supply chain models to reduce risk, increase insight, and improve performance. This is crucial for establishing a globally competitive supply chain model.
Gartner’s recent study also indicates that new technologies such as Artificial Intelligence and Machine Learning (ML), would significantly disrupt current supply chain operations models in the future. ML technologies are considered one of the most beneficial and cost-saving technologies. They also allow for more profit.
Before we get into the details about Machine Learning and the success stories of companies using ML to improve their supply chain delivery, let us first briefly discuss Machine Learning.
What is Machine Learning?
Machine learning is a subset of artificial intelligence that allows software, algorithms, or systems to learn and adapt without having to be programmed.
ML uses data or observations to train a model computer. The different patterns in the data, combined with predicted and actual outcomes, are analyzed to improve the technology’s performance.
Machine Learning (ML), models based on algorithms are excellent at analyzing trends and spotting anomalies. They can also derive predictive insights from large data sets.
It is a powerful solution for addressing some of the major challenges in the supply chain industry.
Challenges In Logistics and Supply Chain Industry
These are some of the problems that supply chains and logistics face. Machine Learning and Artificial Intelligence can help solve them.
Inventory management: Supply chain management relies on inventory management to allow enterprises to adjust and deal with any unexpected shortages. A supply chain company would not want to stop production and launch a search for a new supplier. They wouldn’t want overstock to cause profit losses.
Supply chain management is all about finding the right balance between managing inventory and ensuring that the order processing runs smoothly. However, they don’t overstock items that they won’t use or need.
Quality and safety: Supply chain companies are under increasing pressure to deliver products on schedule to maintain a supply chain assembly line. This makes it difficult for them to maintain a double check on safety and quality. Accepting substandard parts that do not meet safety or quality standards could pose a safety risk.
Additionally, issues such as environmental changes, trade disputes, and economic pressures can quickly turn into problems and risks in the supply chain, causing serious problems.
Problems due to scarce resources: The scarcity of resources is a problem in logistics and supply chains. The implementation of AI and machine learning in logistics and supply chains has made it much easier to understand various aspects. After studying many factors, algorithms can predict demand and supply and allow stocking and planning to be made in a timely manner. ML provides new insight into different aspects of the supply chain. ML also makes it super easy to manage inventory and team members.
Inefficient supplier relationship management: Logistics firms face another problem: a shortage of supply chain professionals can make supplier relationship management difficult and inefficient.
Artificial intelligence and machine learning can provide useful insights into supplier data that can be used to help supply chain companies make immediate decisions.
Why is Machine Learning Important to Supply Chain Management?
Some of the most renowned and well-known companies are beginning to look into machine learning to improve their supply chains efficiency. Let’s see how machine learning in supply management addresses these issues and what the current uses of this powerful technology are.
Machine learning has many benefits for supply chain management, including:
- Machine learning drives efficiency and waste reduction, while systematically improving quality.
- Optimization in product flow without supply chain firms holding too much inventory
- Streamlined supplier management thanks to proven administrative processes that are simpler, quicker, and more efficient
- Machine learning allows for fast problem solving and continuous improvement.
Top 9 Use Cases of Machine Learning in Supply Chain
Machine Learning is an interesting and complex subject that can help solve many problems across different industries.
Machine learning is a popular application in supply chain management, which is heavily dependent on data. Below are the top 9 use cases for machine learning in supply chain management that can drive efficiency and optimization.
1. Predictive Analytics
Accurate demand forecasting can have many benefits in supply chain management. They include decreased holding costs and optimal inventories.
Companies can use machine learning models to benefit from predictive analytics for forecasting demand. These machine learning models can identify hidden patterns in historical demand data. Machine learning in supply chains can be used to identify issues in the supply chain before they cause disruptions in business.
A robust supply chain forecasting system ensures that the business has the intelligence and resources to deal with emerging threats and issues. The speed at which the business responds to problems determines how effective the response is.
2. Automated Quality Inspections For Robust Management
Logistics hubs often conduct manual inspections to check containers and packages for damage or loss during transit. Automation of quality inspections throughout the supply chain has become easier with the advent of machine learning and artificial intelligence.
Machine learning-enabled techniques enable automated analysis of industrial equipment defects and the detection of damages using image recognition. These automated power quality inspections have the advantage of reducing the chances of customers receiving defective or faulty goods.
3. Real-Time Visibility To Improve Customer Experience
According to a Statista survey, visibility is a constant challenge for supply chain businesses. A successful supply chain business relies heavily on visibility and tracking. We are constantly looking for technology that will improve our visibility.
Machine learning techniques can help improve supply chain visibility significantly. This is possible by using a combination of IoT, deep analytics, and real-time monitoring. Businesses can transform customer experience and get faster delivery times. This is done by analyzing historical data and discovering connections between processes throughout the supply chain.
Amazon uses machine learning to provide exceptional customer service to its customers. This is an excellent example. ML allows the company to see the correlation between customer visits and product recommendations.
4. Streamlining Production Planning
Machine learning can be used to optimize production plans’ complexity. The use of machine learning techniques and models can help identify inefficiencies and waste in production.
It is also noteworthy to note the importance of machine learning in supply chains in creating an environment that is more flexible and able to deal with any kind of disruption.
5. Reduces Cost and Response Times
B2C companies are increasingly leveraging machine learning to trigger automated responses and manage demand-to-supply imbalances. This reduces costs and improves customer experience.
Machine learning algorithms can analyze and learn from historical delivery records and real-time data to help supply chain managers optimize the route for their vehicles, resulting in reduced driving time, increased productivity, and cost savings.
Furthermore, improving connectivity with different logistics service providers and integrating freight- and warehousing processes can reduce administrative and operational costs within the supply chain.
6. Warehouse Management
Efficiency in supply chain planning can often be equated with efficient warehouse and inventory-based management. Machine learning, which uses the most current demand and supply information to improve the company’s efforts towards achieving the highest level of customer service at the lowest possible cost, can be used to enable continuous improvement.
With its models, forecasting tools, and techniques, machine learning can solve both overstocking or understocking problems in the supply chain and transform warehouse management.
With AI and ML you can analyze large data sets faster and avoid common mistakes.
7. Reduction in Forecast Errors
Machine Learning is a powerful analytical tool that can be used to assist supply chain companies in processing large amounts of data.
Machine learning in the supply chain not only processes such large amounts of data but also ensures that there is the most variety and variability. This is all thanks to telematics and IoT devices as well as intelligent transportation systems and other powerful technologies. This allows supply chain companies to gain better insight and forecast more accuratelyMcKinsey’s report also shows that AI and ML-based supply chain implementations can reduce forecast errors by up to 50%.
8. Advanced Last-Mile Tracking
Last-mile delivery is critical to the supply chain because it can impact multiple verticals such as customer experience, product quality, and customer service. Data suggests that 28% of all delivery costs is due to last-mile delivery in the supply chain.
Machine learning can be a great tool for improving supply chain efficiency. It takes into consideration different data points such as how people enter their addresses and how long it takes to deliver the goods. ML can be a valuable tool in optimizing the shipping process and providing more detailed information to clients about the status of their shipment.
9. Fraud Prevention
Machine learning algorithms can be used to improve product quality and reduce fraud risk by automating inspections, auditing, and then performing real-time analysis to identify anomalies and deviations from the norm.
Machine learning tools can also prevent privileged credential misuse, which is one of many causes of supply chain breaches worldwide.
Companies Using Machine Learning to Improve Their Supply Chain Management
These are the top companies that use machine learning to improve the productivity of their supply-chain management.
a) com – eCommerce
Amazon is a well-known supply chain leader in the eCommerce sector. It leverages innovative and technologically advanced systems that are based on machine learning and artificial intelligence, such as drone delivery and automated warehousing.
Amazon’s strong supply chain is able to control key areas such as packaging, order processing, and delivery. This is due to significant investments in software systems, transportation, and warehousing.
b) Microsoft Corporation – Technology
Microsoft’s supply chain system heavily depends on machine learning and business intelligence to provide predictive insights.
A large product portfolio generates a lot of data. This data needs to be integrated at the central level in order to allow predictive analysis.
The company has been able to use Machine Learning to create a seamlessly integrated supply chain system that allows them to collect data in real-time and analyze it. The company’s strong supply chain uses proactive and early warning systems that help them to reduce risk and quickly resolve queries.
c) Alphabet Inc.– Internet Conglomerate
Alphabet, a well-known technology company and technological innovator, relies on a flexible Supply Chain that can work seamlessly across different regions.
Alphabet’s Supply Chain uses machine learning, AI, and robotics to become fully automated.
d) Procter & Gamble – Consumer Goods
P&G is consumer goods giant with one of the most complicated supply chains and a large product portfolio. P&G uses machine learning techniques to optimize its product flow management, including advanced analytics and data application.
e) Rolls Royce – Automotive
Rolls Royce and Google have teamed up to create autonomous ships. Instead of replacing one driver in a self-driving car, machine learning technology and artificial intelligence technology can replace the entire crew.
The ships of the company use algorithms to determine what is in the water and to classify items according to their danger to them. AI and ML algorithms can be used to monitor engine performance, security, and load and unload cargo.
Any enterprise must improve the efficiency of its supply chain. Any process improvement can make a significant impact on bottom line profits, even if the business is operating with tight profit margins.
Machine learning and other innovative technologies make it easier to manage volatility and accurately forecast demand in global supply chains. Gartner predicts that by 2023, at least half of all global supply chain companies will be using AI and ML-related transformational technologies. This is a testament to machine learning’s growing popularity in the supply chain industry.