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Powering Supply Chains with Machine Learning

The role of machine learning in the supply chain sector is poised for growth, transforming supply chains into not only smarter and more efficient entities but also enhancing their resilience in the face of ongoing changes

December 20, 2023 12 mins read

As a supply chain manager, have you ever thought about predicting changes in consumer demand with greater precision and accuracy? Or identifying unnecessary and redundant expenditures within supply chain processes? Or finding a way to streamline transportation and warehousing?

If your answer to any of these questions is yes, you might be looking for strategies to optimize your supply chain operations. Indeed, according to a 2023 KPMG report, 47% of supply chain organizations need to prepare themselves for the disruptions and challenges that arise.

A large number of supply chain businesses still rely heavily on legacy processes that served quite well in the past. But over time, it becomes increasingly difficult for these businesses to keep pace with the evolving business environment. The fierce acceleration of technological progress over the last several years has prompted supply chain businesses to change their approach. Relying on traditional methods is no longer enough to meet market demands. Utilizing machine learning in the supply chain for processing huge amounts of data, identifying patterns, and providing actionable insights is an agile solution that helps businesses proactively prepare for the future.

Powering Supply Chains with Machine Learning

Traditional vs modern supply chain practices

Traditionally, supply chain decisions were based on a manager’s intuition and expertise. Having worked in the same business for many years, it was easy for managers to notice seasonal increases in consumer demand for certain products. Yet, over the last several years, it has become challenging to manually process huge amounts of data. An increase in the volume of transactions and growing consumer demand has caused supply chains to become convoluted. Therefore, the traditional approaches to predicting consumer demand have become inaccurate, inefficient, and not agile.

Supply chain businesses have realized the limitations of traditional practices and shifted to a data-driven and technology-adept approach. According to a 2023 KPMG supply chain trends survey, six out of 10 supply chain organizations plan to invest in digital technology to improve supply chain processes and data analysis. More and more organizations recognize the power of data to improve supply chain operations and processes.

Complementing this trend, machine learning adoption by supply chain businesses is expected to grow fivefold, from 15% in 2022 to 73% in 2027, according to an MHI and Deloitte survey. Indeed, machine learning integration offers supply chain organizations a plethora of benefits, such as precise demand forecasting, optimization of logistics and transportation processes, and accurate stockkeeping. Moreover, it helps to automate complex and mundane data analysis processes, streamlining operations and eliminating potential errors. Supply chain organizations leverage machine learning to analyze historical data, learn from it, and become more effective and resilient to outsmart the competition.

Why is machine learning important for supply chain management?

As supply chain businesses implement machine learning for supply chain data, they have started witnessing the powerful transformation of supply chain operations. Let’s dive into the key ways in which machine learning is making a big difference for supply chain companies:

Enabling predictive intelligence

Even though supply chain organizations often possess large amounts of data, they find it challenging to extract meaningful insights from it to forecast trends and prepare for the future. Machine learning is a useful tool for anticipating future market and customer dynamics. In particular, machine learning can model various market scenarios to forecast behavior with a high degree of accuracy, detecting patterns and interconnections that aren’t apparent at first glance. For instance, a machine learning–powered model that predicts consumer demand outperformed a previous model by more than 150% as indicated in a Gallup study.

Use cases of predictive intelligence provided by machine learning include accurate demand forecasting, proactive alerts on supply chain disruptions, and prediction of transportation delays.

Automating supply chain decision-making

A lot of decisions supply chain managers make involve high stakes and bear significant consequences for cost-effectiveness and overall business performance. These decisions often involve trade-offs, such as maintaining high inventory levels to make sure all products are available versus optimizing storage space. Or delivering goods fast, which entails higher transportation costs, or choosing more thoroughly planned logistics, which may result in longer delivery times.

Machine learning can assist in these decision-making burdens by automating mundane processes such as order fulfillment, inventory renewal, and supplier selection. It can also aid in making complex decisions, forecasting demand or determining opportunities for better resource allocation. Machine learning algorithms can evaluate consumer behavior and competitor prices to suggest optimal pricing strategies so that businesses can improve their revenue and profitability. The growing reliance on machine learning for strategic decision-making is reflected in a Gartner report that estimates 50% of supply chain companies will use machine learning to improve their decision-making by 2026.

Creating adaptive systems

Warehouse automation can be made possible thanks to machine learning for supply chains

Traditional supply chains use static models and predefined strategies that can’t fully embrace the constantly changing global trade environment. For instance, they can’t promptly incorporate sudden changes in customer demand, which may lead to stockouts or overstocking. Machine learning reshapes this issue with adaptable systems that absorb all available information, such as market trends and customer preferences, to create powerful action plans that make supply chain companies more effective and resilient.

Examples of adaptive systems are end-to-end autonomous supply chain planning systems that aid in decreasing obsolete inventory by up to 20% and reducing supply chain expenditures by 10%, as reported by McKinsey. In particular, such adaptive systems can aid with forecasting changes in product demand and automatically adjust all processes and decisions along the supply chain accordingly. As a result, supply chains function more efficiently in volatile circumstances, needing less human oversight and decision-making.

Integrating IoT and machine learning

McKinsey estimates that the biggest value IoT provides is managing operations, representing up to 39% of the entire economic value created by IoT in factories and equating to an estimated savings of up to $1.3 trillion by 2030. This is evidence of the decisive role IoT plays in improving operational effectiveness. The convergence of IoT and machine learning takes it all one step further. Machine learning provides up-to-the-minute visibility of different points in the supply chain, enabling proactive identification of equipment failures, stockouts, or transportation delays. One example of this is a predictive maintenance solution developed by Intellias that integrates IoT with machine learning to monitor oil and gas leaks.

IoT sensors can detect inventory on shelves, and machine learning algorithms can determine whether the identified stock levels are the same as those documented. Even more, machine learning algorithms are capable of forecasting required stock levels and automatically reordering goods when needed.

Building ethical and sustainable supply chains

Machine learning aids with identifying environmental issues as they arise, such as excessive water use, pollution, or growing greenhouse gas emissions. Machine learning algorithms use data from different sources, such as water flow sensors and historical water usage patterns, to determine anomalies in water consumption that may indicate leaks or overuse. In addition, machine learning algorithms can track and analyze the carbon footprint of supply chain operations, determining possible sources of concern or atypical emission levels. An example of how big data transforms the supply chain is an equipment monitoring platform Intellias has developed for a wireless sensor vendor in the Baltic states. This platform is used across a network of 125 stores, enabling efficient monitoring of refrigeration equipment and saving millions of dollars in potential food spoilage while reducing energy consumption by about 20%.

Simplifying regulatory compliance

Machine learning algorithms assist with the automatic processing, categorization, and validation of supply chain documentation, such as invoices, shipping documents, bills of lading, and customs declarations. Such automation shortens the time needed to process documents, as it’s not necessary to review each of them manually. This leads to faster clearance times and speeds up the whole process. For instance, Klearnow, a startup that automates document processing with machine learning, has helped a manufacturing company exporting products from Asia to Europe to decrease its customs clearance time by 50% with machine learning algorithms that automate document processing.

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Key machine learning use cases in the supply chain

Estimating customer demand and optimizing inventory levels

The most important use case for machine learning in the supply chain is forecasting customer demand and correcting inventory levels accordingly. As per the 2022 MHI annual industry report, 46% of supply chain businesses find forecasting customer demand challenging. Machine learning can estimate customer demand with impressive accuracy by analyzing seasonal trends, historical sales, and customer behavior. Thanks to machine learning estimates, supply chain organizations can maintain just the right quantity of goods and avoid stockouts or overstocking.

For instance, if a supermarket chain lacks a sophisticated inventory management system that relies on machine learning, managers have to track goods and their movement manually, relying on their feelings about how many goods they need. Machine learning can provide exact estimates of how much stock the supermarket actually needs. Walmart leverages machine learning in its Element system to analyze data on specific items sold at each store location.

Improving route and transportation planning

Machine learning can be used to improve transportation and logistics

As transportation is one of the major cost drivers for the supply chain, focusing on reducing transportation expenditures significantly decreases overall expenses and raises business profitability. According to a report by the American Transportation Research Institute, the cost of trucking in 2021 soared to the highest level in 15 years, reaching $1.85 per mile. Machine learning can help to lower cost by reducing delivery times by up to 20%, especially in highly congested urban areas. What’s more, machine learning can optimize delivery routes and dynamically adjust delivery schedules so that supply chain organizations can deliver products faster and more efficiently.

This use case is highly relevant for manufacturing companies that dispatch a huge fleet of trucks daily all over the country. Machine learning is like having a team that constantly analyzes traffic patterns, calculates optimal routes in real time, and adjusts driver schedules to avoid delays and traffic congestion. For instance, the startup Valerann has developed a machine learning algorithm that monitors road conditions and predicts congestion using wireless sensors.

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Elevating warehouse management

Machine learning can automate mundane warehouse processes, such as picking and packing, and can suggest optimal product placement across the warehouse, maximizing spacing and stock layout. More than that, machine learning algorithms can optimize routing operations in the warehouse so that machines minimize travel time for picking orders. Robotsw can take warehouse automation to the next level, taking care of repetitive and uninteresting tasks. For instance, Alibaba, a global eCommerce giant, has opened a robot warehouse that has over 700 robots working in it.

According to an Ernst & Young survey, by 2035, 45% of supply chains are anticipated to be mostly autonomous, using automated guided vehicles (AGVs), robots, and driverless forklifts. AGVs greatly improve productivity and save costs in materials handling, providing 50% better efficiency compared to manual labor. AGVs can work around the clock, including at night and on holidays, with a single machine performing the work of nine human workers according to a Cyngn report. This results in considerable cost savings, decreasing labor costs by up to 50%.

Predicting and managing supply chain risks

Supply chain disruptions cost large companies more than $184 million a year according to the Interos Annual Global Supply Chain Report. The high cost of disruptions prompts supply chain organizations to embrace business resilience. Machine learning aids in determining potential supply chain risks by monitoring supply chain activities, historical data, and external factors such as geopolitical tensions, natural disasters, and economic shifts. In particular, supply chain managers can set up different types of alerts to get foresight about issues that might arise so they can react promptly and continue being operational and prepared for adverse circumstances. For instance, supply chain managers can set up supplier risk alerts, market demand fluctuation alerts, geopolitical risk alerts, natural disaster risk alerts, and other types of alerts to be well-informed and ready to act.

Smartly selecting suppliers

Selecting the best suppliers is a very important factor in supply chain success. Businesses use machine learning to assess supplier performance based on different criteria such as supplier history, product cost, quality lapses (if any), and delivery time. This assessment process can be automated, saving time and streamlining the supplier selection process. Walmart uses machine learning to improve its supplier selection process.

Detecting fraud in supply chains

Beyond choosing suppliers, there’s the ongoing issue of safeguarding supply chain integrity. As per an IBM report, 87% of chief supply chain officers find it challenging to anticipate and effectively manage fraud. Machine learning algorithms can tackle this problem by analyzing transactions, supplier data, and shipping patterns to identify anomalies and detect potential fraud. As a result, businesses are equipped with information about potentially fraudulent activities and can protect their assets. For instance, PayPal leverages machine learning to evaluate credit card transactions and determine potential fraud.

Enhancing the customer experience

The end goal of any supply chain is satisfied customers. Machine learning personalizes customer interactions and enhances customer service by assessing customer feedback, purchase history, and support queries (if any). This helps businesses provide forward-looking customer service with tailored product recommendations and tune their products to meet evolving customer needs.

Examples of machine learning use cases that enhance the customer experience are chatbots that handle mundane customer queries, support ticket categorization and prioritization, and a self-service knowledge base that provides tailored how-to articles according to a customer’s needs. Salesforce reports that companies using chatbots see a 27% increase in agent productivity, allowing them to handle a larger volume of customer queries at once.

Challenges of machine learning adoption for supply chains

Though machine learning promises huge improvements in predicting customer demand, estimating exact inventory levels, and enhancing transportation planning, the road to successful machine learning implementation is full of challenges.

Data availability, consistency, and quality

Accurate, complete, consistent data lies at the heart of effective machine learning implementation. Machine learning algorithms are as useful as the data they learn from. Quite often, data is siloed and dispersed across systems and departments, and integrating it all into one system is costly, time-consuming, and requires specific expertise.

Black box challenges

Machine learning algorithms, especially those built on deep learning, often resemble black boxes, as it’s unclear how they arrive at certain assumptions. This is because the algorithms use a considerable number of parameters and layers, making it difficult to track the exact path of data processing.

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Integration with existing systems

Often, existing systems have a software architecture, data format, and functionality that aren’t compatible with the latest machine learning models. Smooth integration of machine learning algorithms into current systems necessitates middleware, such as Apache Kafka or RabbitMQ, custom APIs, or considerable upgrades to existing systems, which are expensive and require thorough planning and professional help.

Expenditures and ROI

The cost of implementing machine learning algorithms includes not only investment in the technology itself but also expenditures on data collection, cleaning, validation, and uniformity. Additionally, companies might require investment in new infrastructure, such as cloud platforms or data lakes. Furthermore, machine learning algorithms have to be retrained and refined regularly, as data changes over time.

Custom supply chain solutions

Project-based supply chain solutions developed by experienced technology partners like Intellias help businesses tackle their most stringent supply chain challenges. Whether you need to predict customer demand, smartly select suppliers, improve transportation processes, or meet any other need, a tailored supply chain solution will precisely address this challenge. The Intellias team can conduct a comprehensive assessment of your supply chain operations, processes, and challenges your organization faces. Based on this, we will develop a system that is closely tailored to your business needs. Intellias will ensure that your custom supply chain solution integrates seamlessly with your existing systems and does not disrupt your operations. In addition, we build our solutions with scalability in mind, so your system will be able to scale up or down based on demand.

Intellias has a proven track record of developing custom supply chain solutions that precisely address our clients’ needs. Examples of custom supply chain solutions we have developed include:

  • Custom transportation software for a German supply chain and logistics technology provider. This platform helps the client plan truck routes using real-time data.
  • Big data analytics and equipment monitoring platform for a wireless sensor vendor in the Baltic states. This platform is used across a network of 125 stores, enabling efficient monitoring of refrigeration equipment and saving millions in potential food spoilage while reducing energy consumption by about 20%.
  • IoT-based predictive maintenance solution that uses machine learning algorithms for real-time monitoring, minimizing repairs and system damage.
  • Digital logistics platform for a multinational freight forwarding and logistics company that consolidates shipments globally.
  • Telematics solution that enables fleet managers to monitor daily operations and optimize vehicle performance, offering advantages like cost reduction, CO2 emission reduction, and fraud prevention.

If you are looking for a technology partner to develop a custom supply chain solution for your business, please contact us today.

The road ahead

Integrating machine learning into the supply chain is not just a touch-up for moving towards a more intelligent, effective, and adaptive system. Nowadays, the ability to predict customer demand, optimize inventory levels, smartly plan routes, and efficiently manage warehouses becomes essential to stay competitive. Machine learning leads this transformation by creating adaptive systems that enable predictive intelligence, automating the most mundane tasks and freeing up space for strategic planning. This is evidence of the supply chain’s progression to an agile, open, and data-driven industry.

Looking forward, the role of machine learning for supply chain companies will grow, making supply chains not just smarter and more effective but more resilient.


How do you integrate machine learning solutions with existing supply chain and ERP systems?
Integrating machine learning into current enterprise resource planning (ERP) systems may be challenging. However, with diligent planning, thorough collaboration, and meticulous implementation, the task of integrating machine learning into existing systems can substantially improve supply chain processes. Here are the key steps of integration:

1. Determining suitable use cases for machine learning integration. Not all supply chain processes can be optimized with machine learning.

2. Preparing and integrating data. Data for machine learning algorithms has to be high-quality, clean, and consistent, and it should be derived from a variety of sources to enrich the results produced by machine learning algorithms.

3. Selecting an algorithm and model. It is important to select an algorithm that tackles specific needs and ensure it performs well on available data.

4. Integrating APIs. APIs provide a unified interface for exchanging data between the machine learning algorithm and ERP systems by setting up real-time data exchange and synchronization.

5. Testing and optimizing the machine learning model. Machine learning algorithms have to be tested using functional and performance testing to make sure they operate as expected.

6. Future-proofing the solution. The goal behind creating a machine learning model is not just meeting current needs but also being ready to scale based on business needs and being able to adapt to new technologies.
What tools does Intellias use to design and implement custom supply chain solutions?
We use a range of advanced tools to design and implement tailored supply chain solutions, such as:

• Software development frameworks: Node.js, React, and .NET
• SQL and NoSQL databases
• ETL tools: Apache Kafka, Talend Open Studio, and others
• Cloud platforms: AWS, Azure, Google Cloud
• Machine learning libraries: PyTorch, TensorFlow, sci-kit-learn
• Data warehousing platforms: Amazon Redshift, Google BigQuery
• Data analytics and business intelligence tools like Tableau, Grafana, and Power BI
Which methodologies does Intellias use to design and implement custom supply chain solutions?
The methodology for a supply chain solution can vary depending on the project’s goals. We typically use the following methodologies for custom development:

• Agile software development practices: Scrum
• DevOps practices, continuous integration and deployment practices
• Software development languages for customization: Python, Java, and others
• User-centered design
• Risk management solutions: vulnerability scanning, access control, business continuity planning
• Security and data privacy practices: VPNs, antivirus software, MFA, data encryption
How will Intellias ensure that project deliverables meet our expectations and align with our business goals?
As a global technology partner with 20 years of experience, we understand the importance of the project output to meet our client’s expectations. We achieve this through an all-encompassing approach:

• In-depth requirements gathering and analysis
• Precise definition of the project scope, deliverables, and milestones
• Iterative software development processes using the Scrum methodology
• Productive project planning and communication
• Rigorous testing and quality assurance
• Regular demos and feedback implementation
• Post-deployment support and maintenance, bug fixes, and performance improvements
How do you ensure the scalability and flexibility of your supply chain solutions?
The key strategies we use to ensure that machine learning solutions for supply chain management are flexible and scalable are the following:

Modular architecture. We develop machine learning components in modules that are separated from the core supply chain software. This allows independent software engineering of machine learning algorithms.

Containerization. We use Docker and Kubernetes to make isolated and consistent environments that can be smoothly deployed across different infrastructures and sustain their integrity.

Cloud-based infrastructure. We leverage AWS, Azure, and Google Cloud to handle multiple workloads and huge data volumes.

Model versioning. We use model versioning to track all iterations, changes, refinements, and adjustments. It is also easy to roll back to the previous system version if there are problems with the current version.

Algorithm retraining. We periodically refresh machine learning models with new data to ensure they produce accurate results.

Continuous monitoring. We continuously analyze machine learning model behavior and health to be sure issues are identified early on.
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