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Fierce competition among businesses makes companies of all scales jump higher. They must earn more, spend less, sell quicker, innovate faster, and provide exclusive customer experience at the same time. While it might be challenging for employees to handle all the analytics processes, prevent fraudulent transactions, ensure on-time delivery, and identify quality problems in the line production, machines can easily do this. According to Gartner in the nearest future, 95% of Supply Chain Planning will rely on supervised and unsupervised Machine Learning. Thus, it doesn’t mean robotized employees everywhere: only the symphony of human and machine work is a solution to most of the supply chain challenges.

Machine Learning in Supply Chain


Machine Learning as a branch of Artificial Intelligence takes roots from its immersive ability to handle huge inputs of information, and even analyze the hidden patterns to make predictions and decisions where humans cannot. While the supply chain industry faces a lot of challenges to cope with information asymmetry, financial vulnerability, and organizational, transportation, and logistics inconveniences, Machine Learning algorithms used in supply chain management can easily solve these problems.


Use Cases of Machine Learning in Supply Chain


# Predictive analytics


Predictive analytics enables companies to control inventory levels to meet the customers’ demand while minimizing stock. It is similar to predicting the future, just instead of reading a palm, a mechanism prepares a mathematical model that represents the issue you’re interested in. Large amounts of data and piles of forecasting models are being tested until the one - the most reliable is established. With the help of such an analysis, organizations can forecast exchange rates and sales demand, adjust prices dynamically, and determine flawless shipping.

According to the 2021 MHI Annual Industry Report, 49% of supply chain leaders have accelerated the integration into digital technologies, in particular, predictive analytics.


Amazon, for instance, collects data from users while they are navigating the site. Through ML it analyzes the time each user spends on a particular page, the items they search for, wish lists, demographic details, etc. Hypothetically, if residents of New York usually order a lot of gloves in December, a local Amazon fulfillment center will receive a lot of them in advance and keep the gloves until the order is placed. This helps to cut delivery time just to one day, or even less depending on the distance between customer and Amazon distribution center. Actually, it’s the predictive analytics that helped Amazon stock their warehouses with a large number of facial masks when the pandemic had started.


# Warehouse Management


Being integrated into warehouse systems, Machine Learning automates manual work, is able to predict possible challenges in manufacturing, storage, or package processes, and reduces processing times. Every day new products arrive at warehouses, and they need to be stored and organized. Machine Learning analyzes the current location of items inside warehouses, and when new shipments arrive, they are just matched to the correct location. Computer vision also is able to control the working process of the conveyor belt and anticipate the moment when it gets blocked, reducing inefficiencies and delays. Robots are also programmed with the help of ML. Usage of robotics in warehouse management to a large extent simplifies and speeds up receiving, packing, transporting, and unloading operations.


Alibaba has one of the most up-to-date warehouses in terms of using technology. In their warehouses, up to 70% of work is done by robots. These robots are able to pick and carry up to 500 kilograms of goods at once. They are also equipped with special sensors so they don’t collide with each other. In case a robot needs to recharge, it goes to the nearest charging station.

# Customer Experience


There are two major touchpoints between customers and logistics companies: when a customer checks out with an online retailer and when they receive a parcel or want to return it. Machine Learning streamlines the latter, being able to forecast exact delivery time based on traffic, weather conditions, and actually, all the factors that can have an influence on it. Furthermore, through ML algorithms it’s possible to identify the items a customer is likely to order and store them in fulfillment centers beforehand, determine the best time to communicate with the customer, and automate notification flow. Virtual assistants are one more example of a versatile ML application.


Uber uses COTA (Uber’s Customer Obsession Ticket Assistant) to empower their service agents to come up with the most efficient solution to the thousands of tickets daily. When a customer replies in the app to a few questions, COTA through natural language processing becomes able to understand the problem and route it to the proper team. Machine Learning algorithms pick up the top three solutions, and the service agent chooses one of them to suggest to a customer.

# Logistics and Transportation


According to data, last-mile delivery takes a lion’s share of all supply chain delivery costs, 28%. To avoid extra costs and bringing inconveniences to the customers, companies have started actively using Machine Learning to simplify and boost transportation processes. ML enables workers to track the location of goods while transporting them, forecast possible delays, and choose the best roots, having analyzed the weather conditions, real-time traffic, etc.


An Israeli-British startup Valerann has developed a smart road system. With the help of embedded sensors, algorithms can track road conditions and predict the situation on highways.


# Production


At the early stages, Machine Learning is able to recognize quality issues in the assembly line. With the help of computer vision, items are checked if their final look corresponds to the required one without a need for manual quality inspections. Through automated analysis of damages and defects via image recognition, the chances of delivering a defective parcel to the customer become minimal. 


Another machine learning supply chain example is IBM Watson Visual Recognition. With the help of this tool, organizations can greatly automate the process of package inspection. IBM Watson Visual Recognition can identify different types of items on the shelves, immediately spot defects on-site without any need for manual intervention. The technology has gone much further and nowadays is even capable of analyяing MRI images for any defects.

A Final Note


Artificial Intelligence and Machine Learning excel at routine and debilitating tasks, improve human decision-making processes, and analyze enormous piles of data in a limited time. While volatile demand presents a dire risk for the supply chain industry, Machine Learning gives a helping hand to incorporate the end-to-end processes and turn them into profit.

If you are a company with Amazon-like innovation ambitions, don’t hesitate to contact us. A small step once makes a big future later.

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