A step-by-step guide: How to implement AI in your inventory management

The logistics industry has undergone a profound transformation in recent years, driven by new technologies. AI, as
one of the most revolutionary factors, has changed the way supply chains are managed. Looking ahead, projections
show that by 2025, AI & ML will be the most widely implemented. In this context, we present a short, step-by-step
guide on how to implement AI in inventory management effectively.

AI in Logistics

AI plays a key role in the field of logistics. This technology mainly focuses on improving the processes related to
● Order processing
● Inventory management
● Supply chain
● Distribution

Its potential manifests itself in the automation of routine tasks. This allows companies to reduce costs, increase
operational efficiency, and provide a more satisfying customer experience. Additionally, AI enables real-time shipment
tracking, which significantly improves the overall customer experience. What’s more, through rapid data analysis,
technology enables companies to make smart decisions and optimize operations.

How to implement AI in inventory management — 7 steps


It is important to define the goals of AI implementation precisely. The goal may be to minimize inventory shortages.
That is, to ensure that products are available in the right quantities at the time of demand. Alternatively, the goal may
be to reduce excess inventory to reduce costs associated with storing products. Another aspect is to improve inventory
turnover, i.e., to turn goods faster.


Once you’ve determined your implementation goal, you need data to provide to the AI tool. With the right datasets, you
will train a model that will help you manage your inventory. What data can you provide?
● Sales data (frequency of orders, revenue, units sold)
● Inventory data (stockouts, stock quantities, replenishment rates)
● Supplier data (delivery cost, delivery time)
● Data returns (data on product returns)


To get the best results from AI tools, make sure the collected information is consistent, comprehensive, and accurate. In
inventory management, it is essential that the data be representative of the various scenarios to be handled by AI.
Therefore, data verification through quality checks is a key step.

The data cleaning process involves eliminating inconsistencies, anomalies, errors, and duplicates. This results in uniform,
clear information. Once the data is cleaned, it is important to format it in a way that facilitates processing.

In addition, it is necessary to divide the data into appropriate segments, such as training, validation, and test sets. It
makes it easier for AI to manage them and assess the accuracy of the results.


Choosing the right AI model is a key step that determines the effectiveness of the solution in inventory management.
The data we collect and the business requirements determine the model we choose. There is a variety of models that
you can choose from, e.g., forecasting, regression, classification, or deep learning. When selecting a model, it is worth
● The type of data that is available
● The complexity of the data processing function


Model training involves providing data to the AI tool. You should adjust the model parameters and repeat the learning
cycle until you get the desired results. To train the model, you can use various learning techniques, such as:
● Supervised learning, which uses labeled data
● Unsupervised learning, which operates on unlabeled data
● Reinforcement learning, where the model receives feedback through trial and error
● Transfer learning, which allows knowledge to be transferred from one model to another
● Online learning, where the model is incrementally trained as new data becomes available

The next step is the evaluation of the model’s performance. You can use various techniques here, such as splitting the
data into a training set and a test set. You should monitor key performance indicators (KPIs), like profit margins and
storage costs. This helps you make sure the tool meets business expectations.

Before fully deploying the tool, it is advisable to test it on multiple data sets.


Once the training and assessment are complete, you proceed to deploy the model. The first step is to choose a cloud
platform, such as Azure or AWS. Then, you can proceed with registration, configuration, and local deployment before
moving the model to the cloud.

It is important to regularly monitor and maintain the model after deployment. This process includes checking
compliance with KPI targets, updating data, periodically training the model, and setting up alerts in case of errors.


If you want to make the most of AI in inventory management, concentrate on those essential steps:
● Defining the goal
● Collecting data
● Data preparation
● Model selection
● Training and evaluation
● Deployment

A key element, though, is regular monitoring and maintenance of the model after implementation. Also, keep in mind
that specialized knowledge offered by an AI consulting company is crucial when training an AI model for your needs.

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