How Machine Learning Can Improve Supply Chain Management Efficiency

How Machine Learning Can Improve Supply Chain Management Efficiency

Global competition is fierce in today’s economy. Every organization strives to increase business efficiency and decrease expenditures. Supply chain management has become a critical task for business owners. It is crucial to know how to implement a supply chain management system that works. Some of the most innovative and disruptive technologies, such as Artificial Intelligence or Machine Learning, can provide excellent solutions.

These AI andML solutions can be used to help businesses predict a reliable demand prediction model (also known as demand sensing). Old predictive forecasting methods are losing their value as they are no longer able to learn continuously and make informed decisions like the AI-driven demand sensing model.

This article will discuss how AI and ML can be used to improve modern supply chain management.

What are AI and ML?

Artificial intelligence can be described as a combination of many processes and algorithms. Artificial intelligence can mimic aspects of human intelligence such as problem-solving, self-learning, and responding to input. Subsets of AI solutions include machine learning and deep learning (DL).

Machine learning falls under the “limited memory” category. This means that the AI solution can learn over time and evolve itself. To improve efficiency, different ML algorithms can be used in AI solutions.

These strong AI/ML solutions, such as those developed by AltaML, are used to address some of the problems faced in the supply chain.

Also read: What is Supplier Relationship Management?

Supply Chain & Supply Chain Management

The supply chain is the combination of all activities necessary to transport a product/service, from its conception to its final destination. The supply chain encompasses people, information, and transportation modes. These entities all work together to complete the supply chain, from procurement to fulfillment. Also, reverse logistics is important. Waste management and recycling are two examples of how this can be used. It is not just a supply-chain, but a circular process.

The supply chain management process combines all activities necessary to meet the demand and sustain the supply life cycle. The global supply chain has been severely affected by the Covid-19 pandemic. Companies that have been focused on lean supply chain management to maximize cost efficiency and meet end-user demand for years are now required to think about risk management and mitigation. High levels of efficiency and visibility can be achieved in supply chain management with the help of technologies such as AI/ML.

The Supply Chain Management and Logistics Pain Points

Supply chain management is very complicated. The global pandemic created many uncertainties in the supply chain. This set of challenges includes logistic and transportation issues, higher customer expectations, unexpected demands, lack of visibility, and operational complexities.

Let’s try to sum up these pain points.

  • Demand planning and supply planning: An unexpected increase or decrease in demand can lead to speculation and excess inventory storage. An effective inventory management system will help the organization maintain a balance between supply and demand, which helps to reduce the ” bullwhip effect”, where small fluctuations can be amplified as they travel downstream.
  • Reactive Management: Management is always ready to react to unplanned events or uncertain notifications. Instead of planning in advance, it is better to plan. During the pandemic, it was clear that a lack of scenario planning had grave consequences.
  • Planning of supply networks: Insufficient planning downstream and upstream in a network can lead to inventory shortages or excess. This can lead to network deployment problems. Insufficient inventory can lead to long wait times, and customer loss.
  • Safely and quality: Inefficient supply chains make it difficult to deliver goods/services on time. As a result, Maintaining safety and quality is a difficult task.
  • Inadequate information management: It is difficult to find critical and important information when you need it. This can lead to a loss of sales and a decrease in profit margin.
  • Supply chain problems: Logistics and supply chains cannot function efficiently when there is a shortage of resources.
  • Inefficiency in cost: It is vital that any supply chain has financial planning. Financial planning is essential for any organization.
  • Technical downtime: Any technical problem can impact the supply chain. To support downtime, it is important to have a fail-over and backup strategy.

Role of machine learning in supply chain

The question remains: How can you make your supply chains less susceptible to uncertainty? Changes in work processes and volatile demands have had a significant impact on the market dynamics of supply-chain management. The supply chain is not a linear, predetermined process. Instead, the workflow follows a predetermined sequence. The supply chain is no longer a linear, predefined process. The sequence can be altered to optimize the process. Automation will help you manage your supply chain better.

Integrating AI/ML into the supply chain can automate many repetitive and common tasks. An intelligent ML model is a tool that can be used to help companies choose the right options and manage their business effectively. AI solutions can analyze a large amount of data from logistics, warehouses, suppliers, and transportation systems to determine the demand-supply requirements, and balance the eco-system. An AI-driven system has many benefits, starting at inception and moving through order processing, procurement, inventory, logistics, and delivery to end-users.

Machine Learning Use Cases in Supply Chain Management

Supply chain management is complex and heavily depends on multiple types of data. Data is collected at every stage of the supply chain and processed. AI applications like the developed by AltaML are now playing an important part in the supply chain industry.

Machine learning is used for processing large amounts of input data to train the ML models. The ML model is able to predict more accurately and can also train itself over a longer time span.

Also read: What are Supply Chain Strategy and 6 Best Strategies?

These are some examples of ML in the supply chain.

  • Warehouse Management and Inventory: Inventory management and warehouse management are two key areas for ML implementation. To balance the supply and demand cycles, inventory planning must be efficient. The data can be analyzed using ML algorithms. It can use data from historical data, seasonal demand, market movements (up or down), promotions, and other sources. The result can also be used to increase the efficiency of inventory storage. Different ML models can also be used to automate warehouse processes.
  • Logistics Management: Machine Learning is used to track goods’ locations from pick-up until delivery. ML can also be used to predict the best route for transportation. It can also determine the fastest, most efficient, and lowest greenhouse gas (GHG), emissions for each model.
  • Quality and Production Management: With ML, quality can be checked and matched to the specifications. The production line is well managed and maintained. The use of computer vision to improve quality control practices can be made possible for all products, including food and automotive parts.
  • Predictive analytics: Predictive analytics is essential for demand-supply management. ML can be used to forecast the demand ahead of time. The inventory can be optimized and balanced. Based on signals from the demand sensor, investments can be proactively redeployed within a network.
  • Security and Prevention Of Fraud: Machine Learning models can analyze large amounts of data and alert you to fraudulent activities. Duplicate payments to vendors can be identified and possible fraudulent charges reduced. ML can be used for security and anti-fraud processes.
  • Customer service and delivery tracking: ML can also be used to track goods delivered at each stage. To reduce lead time prediction errors, external data sources are possible. This method has been shown to improve accuracy by as much as 85% when packages are shipped from overseas. The customer is kept informed about the status of their package. This increases customer satisfaction and controls end-to-end delivery.


Gartner predicts that 50% of global companies will use AI/ML by 2023. This means that supply chain efficiency must increase dramatically in the next few days. Artificially-driven supply chain management is the solution the industry needs.

A well-designed supply chain is essential for business success in today’s highly competitive marketplace. It is important to use disruptive technologies such as AI and ML to improve it every day.

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