A supply chain is a huge ecosystem of delivering products or services to the end customers step-by-step. It involves managing many processes - from supplying materials, communication with vendors to actually delivering the goods to consumers. 

Obviously, there are many pain points in the supply chain management sphere. Here are just a few most common ones:

  • continuously changing customer expectations,
  • poor planning,
  • constant delays and breaks,
  • complicated processes structure,
  • lack of transparency,
  • too much manual proceedings,
  • high costs and risks.

A lot of challenges in the supply chain can be addressed by automation and the utilization of intelligent systems. This approach can ease the process of making decisions in such a complex sphere based on clear data reports and smart data analysis.

As research says, the companies that have already introduced artificial intelligence into their supply chain processes have benefitted from 61% of cost reduction and 53% of revenue increase. It all thanks to predictive analysis and forecasting of market demand and sales opportunities accompanied by logistics optimization.

Supply Chain Management Process

To clearly understand the influence that intelligent systems can have in this industry, let's first dive into the major aspects that the supply chain consists of.

It includes several groups of intercorrelated activities:

  • Planning enhanced by AI helps to forecast the product demand and resources for business operations.
  • Procurement is a stage where businesses evaluate potential suppliers, agree on cooperation terms, and review their performance. The more complex business, the bigger amounts of data that should be processed, therefore, proper automatization can not be underestimated.
  • Manufacturing is the actual production stage where intelligent systems can highly increase the potential outcomes. Among strong optimization, the other opportunities are monitoring performance, efficient maintenance, cost control, and quality management.
  • Inventory management allows to control the stock conditions and manage warehouse and sales operations. All these proceedings can be optimized with AI and data analytics. Besides that, intelligent systems allow evaluating each sales channel’s performance, plan the stock refilling and reduce losses.
  • Logistics & transportation are responsible for order management and the whole goods delivery process. This is probably the part that has so much to improve and automate including optimization of fuel utilization, route directions, shipment tracking, vehicle maintenance, returns management, delays control, carriers choice, and much more. 

All these parts of the supply chain have numerous hidden factors. Intelligent systems can uncover potential risks, make operations transparent, find dependencies between each step, and forecast potential outcomes. It all makes the decision-making process proactive which leads to fewer risks and bigger opportunities.

From Insights to Action

The automation of supply chain processes helps to perform many operations faster. Data analytics allocates interesting insights about each step of the operations. Artificial intelligence allows us to convert insights into actions. 

It forms so-called cognitive automation that helps to find certain patterns in the system or people’s behavior based on processing big amounts of data and react similarly to the way a human would. Cognitive automation simulates human decisions and actions. At the same time, it eliminates human errors.

Cognitive automation can analyze data faster and deeper compared to people. It forms a tendency to switching from manual labor to computer algorithms that can scan the ecosystem quickly and find the weak points along with growth opportunities.

Overall, data analytics and artificial intelligence can optimize and enhance supply chain performance.

Use Cases of Data Analytics and AI in Supply Chain

Data analytics and artificial intelligence can present numerous solutions for each part of the supply chain management - from planning to logistics. 

Let's see what exactly this combination of innovative technologies can improve and which outcomes certain companies have already received after investing in intelligent systems.

Demand Forecasting

AI uses real data received from data analytics and allows processing forecasting faster and more accurately compared to more traditional methods. 

With an accurate demand prediction of manpower, resources, stock availability, and many other things, organizations receive better outcomes in operational costs reduction, streamlined processes, and higher customer satisfaction.

A good example of a demand forecasting system is an Alloy ML platform that monitors data from various points of sales, analyses the tendency using multiple forecast models, and provides predictions for future demand.

Supply Planning

Upon data analysis and AI application, businesses can not only predict demand but use this data for planning their supply chain flow. Real-time data help to make dynamic planning and reduce waste.

For example, if we consider a chain of pharmacy stores, we completely realize that each point of sale sells a different percent of the same pills. Instead of ordering a new supply of medicine, managers can simply analyze the sales metrics and transfer the leftovers from one pharmacy to another and thereby save money on supplies.

Intelligent Warehouse

Warehouse operation can also be improved by data analytics and artificial intelligence. As there are many obstacles that create pressure in the department, various intelligent algorithms can influence warehouse processes optimization. It includes demand prediction, inventory optimization, efficiency in material flow, and many other things to handle various situations like order returns, seasonal demand changes, etc.

For instance, the EPG warehouse control system provides material flow monitoring and transferring of materials from the warehouse to the production stage using warehouse robots.

Supply Chain Visibility

The innovative technologies help to track each step of the supply chain with accuracy and total transparency. The history of each product can be monitored from the planning stage to production and even at the last step of its shipment to the buyer. It enhances the results of the whole delivery process, the product authenticity, and the successful shipment outcome.

It does not only help managers to perform their work and ordinary operations, but the company stakeholders receive comprehensive information about the supply chain state faster and more conveniently.

For example, the FoodServiceCo company has reported receiving the whole supply chain visibility using the IoT solution and data analytics. It has improved communication between drivers and managers influencing the positive turnover of operations.

Predictive Analytics

Data analysis allows to spot certain scenarios of behavior, allocate anomalies, and predict future attitudes of equipment, systems, people, transport, etc. Companies can use predictive analysis to detect changing conditions and deal with the potential issues even before they happen. It minimizes losses in time, costs, and other risks.

Additionally, predictive analysis balances the supply chain system between the indicators of demand and supply. It reduces wastage and enhances delivery results.

Another important aspect here is fraud prevention. The supply chain requires completing the orders made by the customers. Occasionally, it happens that certain clients may behave as fraudsters while ordering a product, finding a way to replace it with a damaged copy, ask for the order cancellation, and the refund. 

Of course, there are many other fraud activities that certain individuals may perform within each business niche. It may concern not only the supply chain but other industries as well. 

For instance, social networks recently became another strong sales channel for many businesses this way acting as a part of the supply chain. The major reason for that is a huge amount of users which causes a big amount of fraudsters. In this situation, predictive analytics is a real helping hand that allows tracking certain behavioral patterns and allocates anomalies. 

One famous startup has already used a combination of machine learning, predictive analysis, and deep neural networks to detect such cases which have revealed more than 80% of fraudsters even before they commit a crime. These actions have extremely reduced the number of fraudulent activities and saved a huge amount of money.

Autonomous Things

Autonomous things work without interaction with humans, supported by the power of artificial intelligence. They include autonomous vehicles, self-driving cars, robots, drones. It drives the major transformations in the automotive industry. Many car brands adopt the notion of autonomous and connected vehicles.

For instance, Renault has initiated several programs connected with artificial intelligence implementation. A dedicated team works hard to improve their supply chain via prediction accuracy, customer lead time, supplier risk awareness, etc. 


Use cases of data analytics and AI in the supply chain are diverse. The specified ones are just a few of the most noticeable and already giving profits to those who have implemented them in their business operations.

The bigger the supply chain volume and corresponding operations your business needs to perform, the more profits you will gain by implementing digital innovations with big data and AI technologies in your proceedings. Besides, it heavily influences the potential for your business growth. 

These technologies can help improve each stage of the supply chain management by correctly forecasting demand, improving logistics management, reducing paperwork, and automating manual operations.

As a result, you can get a stable supply chain with total visibility, efficient workflow, reduced operational costs, resistance to interruptions, and increased revenues.

Dmytro Braginets
Development team lead at Uinno
Dmytro is an experienced software engineer, works as development team lead at Uinno - a digital product agency. He believes that architecture or at least a balanced approach should be everywhere. Dmytro supports the fact that the developer is primarily a Software Engineer, and languages and frameworks are just tools.

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Data Analytics and Artificial Intelligence in Action in the Supply Chain