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The Basics of Implementing And Training AI For Inventory Management

Craig Schoolkate April 23, 2023

The Big Supply Chain Migration: From China to Mexico

In ecommerce, inventory management is the linchpin operation every other operation revolves around. The effectiveness of your inventory management directly impacts revenue, profits, sales, and brand reputation—essential metrics that determine the success of your business.

This is why one of the most popular use cases for AI in manufacturing is inventory management, with 44% adoption.

You could be a business owner looking for ways to optimize your business’ performance or take away some of the manual labor off your plate. Or you may be a business buyer who would like to replace the previous owner with AI. Either way, implementing an AI model into your business could be a good solution. But as a business owner, you don’t want to have to become a data science expert to get access to this helpful advantage.

That’s why we wrote this article, to help everyday business owners understand the basics of how AI models are trained and implemented into business processes to help with inventory management.

The first step is to define your objective for implementing AI. For inventory management, your objective could be to minimize stockouts, reduce overstocking, improve inventory turnover, or optimize replenishment. Once you know what goal you want the AI to achieve for your business you can start training your new tech employee.

And like any good employee, AI needs information to work with.

Data Collection

The data you provide to the AI tool should give it all of the information it needs to carry out its programmed tasks. When training AI to help with inventory management, you should feed it the following data sets.

Sales Data

With sales data, AI can identify sales trends including seasonality to optimize inventory levels to accurately meet demand.

Sales data examples include:

  • Units sold
  • Revenue
  • Order frequency

Inventory Data

Inventory data allows AI to understand the stock situation in your business.

Inventory data examples include:

  • Stock quantities
  • Replenishment rates
  • Stockouts

Supplier Data

Knowing supplier lead times enables AI to factor in supply chain delays and variability to avoid stockouts or overstocking.

If you want AI to identify cost-saving opportunities, you can also feed it data on delivery performance and costs.

Returns Data

Knowing product return rates is also important for AI to be able to order the right quantities of products.

Once you’ve collected your data for AI, it’s good practice to clean up the data so it’s easy for the machine to read.

Data Preparation

AI can only work with the information it’s fed. If you want to get the best output from your AI tools, take some time to make your information coherent.

You want to make sure the data is comprehensive, accurate, and representative of the inventory management scenarios you want the AI tool to handle. Validating your data through quality checks ensures your AI is fed the best data possible for it to effectively carry out its tasks.

Data cleaning consists of removing inconsistencies, anomalies, errors, and duplicates.

After you’ve cleaned out the data muck, you may want to format your data to make organized for easy processing.

A common type of data formatting is normalization through which the data is scaled to a consistent range or format.

It’s also helpful to AI if you split your data. This is where the data is segmented into datasets so the AI knows what to do with it and so you can easily assess the accuracy of the AI outputs. Segments commonly used by entrepreneurs are training, validation, and testing.

The next step in customizing your AI tool is feature engineering.

Feature Engineering

This is where you identify the relevant features or variables from your data to be fed to the tool.

Use your expertise to choose variables that will affect how AI conducts inventory management. Variables such as sales volume, order frequency, lead time, and seasonality can all impact inventory ordering. The more variables AI can consider, the better it will be able to accurately maintain stock levels and minimize costs.

When you’re feeding the tool, make sure the historic data includes statistics you want the AI tool to consider, which could include calculations of rolling averages, moving sums, or exponential smoothing of past sales or inventory data to capture seasonality or demand trends.

You can also calculate statistical measures such as mean, median, standard deviation, or variance of relevant variables to capture distributional characteristics or variability. These statistical features can provide insights into the variability and stability of inventory management variables.

Now you have all of your data fully organized and ready to be processed, you can decide on which AI model is best suited to the data and your inventory management goals.

Model Selection

The nature of your data will ultimately determine which AI model you need as AI tools process data in different ways.

To know which model to choose, first, consider your business demands of the tool. For example, large businesses with complex supply chains need very different models than small businesses with simple supply chains. Even the level of accuracy you require will determine which model you want; an expensive, highly accurate tool might not be necessary for your business if it’s not worth the cost to have such accurate reporting and management.

The main types of AI models you can choose from are

  • Forecasting
  • Regression
  • Classification
  • Deep learning

One factor to consider is the type of data you’re using. For example, if you have historical time series data and want to forecast future inventory demand, time series forecasting models like ARIMA, SARIMA, or LSTM may be appropriate. If you have labeled data and want to classify inventory items into different categories, a classification model like logistic regression or decision tree may be the most suitable.

Another factor is the complexity of the data-processing function you want the model to carry out. If you just want basic reporting in an easy-to-digest format, then go for a simple model; if you want detailed and highly-accurate reporting and data optimization, then choose a model that can handle more complex data processing.

The important thing to keep in mind is that simpler models tend to be less accurate and can only work with data less optimized than advanced models.

Once you’ve chosen your ideal model, you can get into training.

Model Training

This process involves feeding data into the tool, adjusting model parameters, and iterating the training process until the model is producing what you need.

By adjusting the model parameters as you’re training the model, you iteratively minimize discrepancies in the tool’s outputs. The process is not dissimilar to when you’re cooking a sauce and adding ingredients and doing regular taste tests until you’re happy with its flavor.

Some model training techniques are as follows.

Supervised learning

This is where you train the AI using labeled data. The input data is paired with corresponding output labels. The model learns to make predictions on the labeled data and you assess the accuracy of the predictions.

Unsupervised Learning

This method is similar to supervised learning, except the input data is unlabeled. The tool learns to assess the data to identify trends and patterns without any guidance.

Reinforcement Learning

Through this method, the tool learns through trial and error by receiving failure and reward feedback. This method is best for training AI for situations where the best outcomes aren’t known and need to be discovered through exploration.

Transfer Learning

If you have other models that have already been trained on large datasets, you can transfer the AI’s knowledge to another model instead of training the new model from scratch.

Online Learning

This is kind of a live training technique whereby the model is incrementally trained as new data becomes available. Online learning training is ideal for training models for situations where the data it’ll be receiving will constantly be changing.

The training method you choose should be based on what the AI model is going to be producing and how it’s going to be receiving and processing data.

As you’re training your AI, you want to be evaluating its performance so you can make adjustments and get it deployment-ready.

Model Evaluation

Evaluating the output of your AI tool ensures it performs its tasks effectively to help keep your business running.

There are various evaluation techniques you can use to evaluate your AI, including hold-out evaluation whereby the data are split into two sets: training data and testing data. The model is trained on the training data and tested on the testing data.

The best way to evaluate your AI model’s output is setting some key performance indicators (KPIs) that let you know if the machine is doing what you need it to do. Some example KPIs for inventory management include profit margins, stockout rates, and storage costs.

Cross-referencing your target KPIs with the performance of the AI output allows you to see how it’s doing and make adjustments to parameters if necessary.

It’s usually a good idea to test the tool with some pre-collected data sets before integrating it into your business operations with live data. 

Model Deployment

Once you’ve optimized your tool through training and evaluation, you can deploy your model.

The first step is to choose a cloud to deploy your model to; examples include

  1. Algorithmia
  2. PythonAnywhere
  3. Heroku
  4. Google Cloud Platform
  5. AWS Sage Maker
  6. Microsoft Azure
  7. FloydHub

Then you go ahead with deployment on the chosen platform.

For example, to deploy through Azure, register the model and prepare an entry script, prepare an inference configuration and deploy the model locally to make sure everything works, then choose a compute target and deploy the model to the cloud. After deployment, you can test the resulting web service.

Once you’ve deployed your AI model, be sure to monitor and maintain it regularly. This process involves checking that it’s meeting KPI targets, keeping it up-to-date with the latest data, periodically retraining the model when data changes or if it’s failing to meet KPI targets, and setting up automated error alerts to notify you of issues.

Training an AI model for inventory management takes a considerable amount of expertise, so having a basic understanding of how it works enables you to hire the right people for the job. But having AI in your business makes it easier to run and more valuable when it comes time to sell.

Planning On Buying Or Selling A Product-Based Online Business?

Using the guidance above can help you optimize your business for a high-profit sale or give you an effective acquisition strategy that will help ensure your success when buying a business.

Regardless of how you want to use AI to help you succeed, you don’t need it to successfully acquire or sell a business.

For that, you need human help. That’s why we offer free, no-obligation exit planning for business owners to help them prepare and sell their businesses. It’s also why we offer free, no-obligation buyer consultations to help acquirers find the best business for them.

Everything you need to sell or buy a business is accessible on our platform. Create a free Empire Flippers account to get your site valued in minutes with our free valuation tool, or search through our curated marketplace using our 20+ search filters to narrow in on your ideal business in minutes.

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