Machine learning in supply chain: 8 use cases that will impress you

How to ensure efficient supply chain management? This is an open question for many suppliers, distributors, manufacturers, and retailers. Amid shifting supply chain market dynamics, changing ways of working, and increasingly volatile demand, businesses are wondering how to make their supply chain less vulnerable to disruption. Machine Learning holds the answer to many well-known and emerging supply chain challenges.

There are numerous use cases of Machine Learning in supply chain. The benefits of Machine Learning and AI can be traced in every part of the supply chain, including procurement, manufacturing, inventory management, warehousing, logistics, and customer service. Let’s dive deeper into the advantages of Machine Learning in supply chain management and Machine Learning use cases in supply chain.

Key challenges in the supply chain

According to the survey by Supply Chain Dive, the average cost of a supply chain disruption is $1.5M per day. So, many businesses seek to improve their supply chain management using Machine Learning to make it more resilient to disruptions.

In addition to growing customer expectations, lack of visibility, and operational complexity, companies face challenges like ensuring data security and compliance, integrating new technologies with legacy systems, managing supply chain disruptions, and staying competitive. Implementing machine Learning in supply chain can address those challenges.

In recent years, we have all witnessed the transformation of the traditional linear supply chain into digital supply networks (DSNs). With the help of technologies such as IoT, Artificial Intelligence, and Machine Learning, it is possible to transform traditional linear supply chains into connected, intelligent, scalable, customizable digital supply networks.

Benefits of Machine Learning in supply chain

Machine Learning use cases in supply chain help retailers, suppliers, and distributors drive needed transformational changes. Machine Learning in supply chain delivers unprecedented value, from cost savings through reduced operational overhead and risk mitigation to enhanced supply chain forecasting, speedy deliveries, and improved customer service, to name a few. McKinsey forecasts that the most significant benefits of Machine Learning will be providing supply chain professionals with more insights into how supply chain performance can be enhanced, anticipating anomalies in logistics costs and performance before they occur. Machine Learning also provides insights into where automation can deliver the most scale advantages. Let’s take a closer look at the complete list of benefits Machine Learning in supply chain has to offer:

Machine Learning use cases in supply chain

Machine Learning applications in supply chain are revolutionizing how retailers and suppliers work. As a branch of Artificial Intelligence, Machine Learning in supply chain uses data to train a computer model adjust to conditions without being programmed to do so. This way, the machine can teach itself over time, improving the accuracy of its algorithms. There are many Machine Learning methodologies used in the supply chain. IDC predicts that by 2026, 55% of G2000 OEMs will redesign their service supply chains using AI. This means that over half of these major manufacturers will leverage Artificial Intelligence to transform their service operations.

Read more about Generative AI in supply chain

AI-driven solutions for Machine Learning in supply chain will enable organizations to address supply chain challenges and reduce the risk of disruptions.

There are eight main Machine Learning use cases in supply chain. So, let’s take a closer look at them:

1. Inventory management

Storing and maintaining inventory in good condition is costly. So, supply chain professionals should thoroughly approach inventory planning as it directly impacts a company's cash flow and profit margins. Inventory management is one of the most typical Machine Learning use cases in supply chain. Machine Learning can help solve the problem of under- or over-stocking. With ML, you can predict demand growth based on data sourced from many areas like the marketplace environment, seasonal trends, promotions, sales, and historical analysis. You can prepare to fill your stores in advance and prevent excesses of goods or important parts for manufacturing.

For the forecast to be accurate, you need to have a wide range of data. When the number of data sets is insufficient for effective analysis, Machine Learning in supply chain offers several methods of how to solve the problem:

• Data augmentation allows you to significantly increase the diversity of data available for training models without actually collecting new data. The augmentation techniques used in Deep Learning applications depend on the data type. Techniques such as SMOTE or SMOTE-NC are popular for augmenting plain numerical data. For unstructured data such as images and text, the augmentation techniques vary from simple transformations to neural network-generated data, depending on the complexity of the application.
• Incremental learning is a method of Machine Learning that does not require a large amount of data to train a model. Instead, learning starts with a very simple model, typically predicting the average value with some degree of deviation. When a data scientist enters new data examples, the model is trained to predict more accurate results.
• Reinforcement learning (RL) is one of three basic Machine Learning techniques, alongside supervised and unsupervised learning. It uses rewards and punishment as signals for positive and negative behavior. In robotics and industrial automation, RL enables the robot to create an efficient adaptive control system that learns from its own experience and behavior.

When it comes to data, the question arises of which data storage solution to choose: data warehouse or data lake. Data lakes are often used in Machine Learning or advanced analytics solutions. They are often used in ML projects as they collect data from multiple sources in real time and store it in its original format. A data lake is ideal for those who want an in-depth analysis of broad-spectrum data gathered over a more extended period, while a data warehouse is perfect for operational processes and day-to-day activities. However, many companies are now using both storage options, especially when a data warehouse is built upon a data lake and it uses the data from a DL that has been cleaned and structured.

Another example of the ML application in the supply chain is the case of computer vision (CV) in inventory management. It is used extensively in a number of ways. First, it is applied to count and classify items that arrive. CV also helps detect visual damage to the package. With the help of computer vision, the software is also able to classify objects it “sees.” For example, robots equipped with cameras will inspect your storage and automatically build a real-time picture of your inventory. CV is one of the areas where all sorts of Machine Learning techniques—supervised, unsupervised, and reinforcement learning—can be applied.

2. Warehouse management

Machine Learning in supply chain is used in warehouses to automate manual work, predict possible issues, and reduce paperwork for warehouse staff. For example, computer vision makes it possible to control the work of the conveyor belt and predict when it is going to get blocked. NLP and optical character recognition (OCR) allow warehouse specialists to automatically detect the arrival of packages and change their delivery statuses. Cameras scan barcodes and labels on the package, and all the necessary information goes directly into the system.

Also, Machine Learning helps program autonomous vehicles and robots, which are widely used in warehouses. With the help of guides built into the system, autonomous vehicles and robots help receive, pack/unpack, transport, and upload/unload boxes. Computer vision, in this case, helps find a free place for a box, control whether it is placed correctly, and prevent collisions between robots and vehicles in warehouses.

One of our clients, a German-based Fortune 100 multinational engineering and technology company, needed to streamline the management of more than 400 warehouses around the globe. They partnered with the N-iX specialists to modernize and build a scalable logistics platform. N-iX works on a computer vision solution for warehouse cameras based on industrial optic sensors, lenses, and Nvidia Jetson devices. This solution will allow the client to automatically detect arriving packages, scan barcodes, and change the delivery statuses of the boxes. Our team is also responsible for developing the multiplatform CV mobile app. This product will help the client with object detection, package damage detection, OCR, and NLP for document processing. The modernized and scalable logistics platform will significantly improve the efficiency of warehouses in over 60 countries, reducing operational overhead and warehouse downtime.

3. Logistics and transportation

ML helps understand where a package is in the entire logistics cycle. It allows supply chain professionals to track the location of goods during transportation and provides visibility into the conditions under which the package is being transported. With the help of sensors, retailers can monitor parameters such as humidity, vibration, temperature, etc.

Besides, ML helps with real-time route optimization. It tracks weather and road conditions and recommends optimizing the route and reducing driving time. This way, trucks can be diverted at any time on their way when a more cost-effective route is possible.

4. Production

With ML, it is possible to identify quality issues in line production at the early stages. For instance, with the help of computer vision, manufacturers can check if the final look of the products corresponds to the required quality level. If the products have defects, they can be detected before they reach the customers.

One of the other widespread use cases of Machine Learning in the supply chain is predictive equipment maintenance. ML ensures reactive and preventative equipment maintenance based on real-time asset data rather than a predefined calendar. By improving asset maintenance, supply chain professionals can significantly decrease maintenance costs.

Also, ML helps to reduce the number of no-fault-found (NFF) cases. NFF is a unit that is removed from service following a complaint of the perceived fault of the equipment. If there is no anomaly detected, the unit is returned to service with no repair performed. The lower the number of such incidents is, the more efficient the manufacturing process gets.

5. Chatbots

Intellectually independent chatbots based on Machine Learning technology are trained to understand specific keywords and phrases that trigger a bot’s reply. They are widely used in supplier relationship management, sales, and procurement management, allowing staff to focus on value-added tasks instead of getting frustrated answering simple queries. With time, they train themselves to understand more and more questions. They learn and train from experience.

For example, you could write to a chatbot, “I have a problem with shipping the package.” The bot would understand the words “problem,” “shipping,” and “package” and provide a predefined answer based on these phrases.

6. Customer service

Consumers expect up-to-date information on their delivery status. Thanks to ML, it is possible to predict the delivery of the parcel, taking into account all the changing conditions. As a result, consumers receive a better customer experience with more accurate delivery date predictions. With Machine Learning, retailers can:

• Identify parcels with the risk of an issue and suggest mitigation measures;
• Automate notification flow depending on previous consumer interactions;
• Determine when to communicate with consumers for maximum engagement.

Also, Machine Learning techniques allow the company to offer an exceptional customer experience. ML does this by enabling the company to gain insights into the correlation between product recommendations and subsequent website visits by customers.

7. Security

Machine Learning algorithms can analyze vast amounts of data and draw patterns for every business to protect it from fraud. For instance, in the supply chain, ML helps identify fraudulent transactions, prevent credential abuse, accelerate fraud investigations, and automate anti-fraud processes. Moreover, with ML, supply chain professionals can automate the process of monitoring whether all parts and finished products meet quality or safety standards.

8. Strategic decision-making

From a business perspective, Machine Learning provides valuable insights that simplify and accelerate decision-making. It enables senior executives to evaluate the best and worst possible scenarios quickly. Machine Learning uses complex algorithms to suggest optimal solutions to business leaders so that they can make well-informed decisions.

For instance, stock level analysis can identify when products are declining in popularity and are reaching the end of their life in the retail marketplace. Price analysis can be compared to costs in the supply chain and retail profit margins to establish the best combination of pricing and customer demand.

How to make ML work for supply chain management

There are three significant steps you should take to adopt Machine Learning in supply chain management. They are:

1. Understand your supply chain’s structure

Before implementing Machine Learning into your supply chain, you should evaluate your entire supply chain’s structure:

• Determine the critical components of your operations;
• Conduct a detailed analysis of the supplier network, including Tier 1 suppliers and sub-tier suppliers;
• Identify hidden relationships and nodes of interconnectivity;
• Quantitatively diagnose the relative fragility of the supply chain;
• Identify bottlenecks and risk factors in the supply chain;
• Draw meaningful comparisons with peers and industry benchmarks;
• Assess the security of the supply chain;
• Evaluate your functional maturity against the process, people, and technology.

2. Establishing transparent business KPIs and calculating ROI

To understand under what circumstances Machine Learning use cases in your supply chain would benefit your business, you need to conduct a Discovery Phase and calculate ROI. You need to estimate TCO and the profitability you will gain in the short term and in the long run.

Also, it is essential to prepare a detailed plan defining your goals and the requirements needed to reach them. To eliminate inconsistencies, aligning Machine Learning KPIs with business KPIs is obligatory. In other words, you should define the business problem in ML terms.

3. Ensuring an effective ML engineering process

The success of Machine Learning use cases in the supply chain heavily depends on the following:

• Setting up a multifunctional team of professionals with expertise in data science, DevOps, Python, Java, QA, business analysis, etc.;
• Starting with a business problem statement;
• Establishing the right success metrics;
• Choosing the right tech stack;
• Considering your data readiness: focus on data quality and quantity;
• Developing, training, testing, and optimizing models;
• Deploying and retraining models;
• Monitoring model performance.

Wrap-up

Use cases of Machine Learning in supply chain management are versatile. Here, we have listed the ones that bring the most value to supply chain professionals. If you have to manage a vast network of suppliers, warehouses, and logistics service partners, supply chain management can become daunting. However, technologies such as Machine Learning and AI can help you at all stages of supply chain management. ML algorithms will correctly forecast demand, improve logistics management, help you reduce paperwork, and automate manual processes. As a result, you will get end-to-end visibility into your supply chain while ensuring it works more efficiently, requires fewer operational costs, and is less vulnerable to disruptions.

Why implement Machine Learning solutions with N-iX?

Proven expertise: With over 60 successful data science and AI projects delivered, our experience speaks volumes about our capability to handle diverse and complex AI and ML requirements.

Certified professionals: Our team includes 200 data and 400 cloud experts who bring the latest knowledge and skills to your projects, ensuring high-quality, cutting-edge solutions.

Skilled workforce: We boast a strong team of over 200 experts specializing in data, AI, and ML, ready to tackle any challenge and drive your projects to success.

Industry recognition: N-iX has been recognized as a rising star in data engineering by ISG, highlighting our excellence and innovative approach in this domain.

Long-standing experience: With 21 years of experience in the industry, we have honed our expertise and methodologies to deliver exceptional results consistently.

Extensive talent pool: Our company is powered by over 2,200 software engineers and IT experts, enabling us to scale quickly and meet the demands of any project, big or small.