Inventory optimisation using AI made simple

Inventory optimisation is a classic use case for AI. Machine learning techniques can analyse your existing sales data to forecast demand on a SKU by SKU basis. This forecast information can then help you deploy inventory optimisation techniques to reduce stock outs whilst minimising working capital.

Step 1: Gather Your Data Start by consolidating your sales and inventory data. Most SMEs maintain this in spreadsheets or basic accounting software. Export this data into a CSV file. This file should ideally have columns like ‘Product Name’, ‘Date Sold’, ‘Quantity Sold’, and ‘Stock Remaining’.

Step 2: Choose an Easy-to-use AI Tool such as Google’s AutoML Tables – see below for instructions. It allows you to upload CSV files and automatically builds machine learning models for you.

Step 3: Upload Your CSV File Go to the chosen platform (e.g., AutoML Tables) and follow the prompts to upload your CSV file. Typically, this involves:

  • Creating a new project.
  • Clicking on an ‘Upload’ button.
  • Selecting your CSV file.

Step 4: Generate Insights Once uploaded, the platform will guide you through analyzing the data:

  • Forecasting: The tool can predict future sales based on past trends. For instance, if ‘Nature’s Delight’ tea sales spike every December, it will forecast a similar spike this coming December.
  • Anomaly Detection: It identifies unusual patterns. If there’s a sudden drop in sales of a particular product, it will highlight it, prompting you to investigate further.

Step 5: Interpret Results Most of these platforms provide easy-to-understand graphical results:

  • Charts & Graphs: Visual representations of sales trends, stock levels, and predictions.
  • Recommendations: Some tools also offer actionable recommendations. For instance, if a particular tea flavor’s stock is predicted to run out faster than usual, the tool might suggest an earlier reorder.

Step 6: Act on Insights Based on the insights:

  • Adjust your inventory orders.
  • Consider promotions or discounts on overstocked items.
  • Investigate any anomalies in sales patterns.

Step 7: Regular Updates To keep insights fresh and relevant:

  • Update your CSV file monthly or quarterly.
  • Re-upload to the AI tool for new predictions and recommendations.

Using AutoML Tables for inventory optimisation: A Step-by-Step Guide

1. Setup & Access:

  • Google Cloud Account: Before you can use AutoML Tables, you’ll need a Google Cloud account. If you don’t have one, you can sign up and often get free credits to explore their services.
  • Access AutoML Tables: Once logged in to the Google Cloud Console, navigate to the Navigation Menu (hamburger icon in the top-left corner) > Artificial Intelligence > Tables.

2. Create a New Dataset:

  • Click on the + NEW DATASET button.
  • Name your dataset, something relevant to your data, like “Tea Sales Forecast.”

3. Import Your Data:

  • You’ll be prompted to import data. Here, you can upload your CSV file. This is the file that has your sales and inventory data.
  • Once uploaded, AutoML Tables will automatically detect column types (numeric, categorical, date/time).

4. Identify the Target Column:

  • After importing, choose the column you want the model to predict. For inventory forecasting, this might be something like ‘Quantity Sold’ for the next month.

5. Train the Model:

  • Once the target is set, click on the TRAIN button. This will instruct AutoML Tables to start analyzing your data and build a model.
  • Training can take a few hours, depending on the size of your dataset.

6. Evaluate the Model:

  • After training is complete, you’ll be taken to the “Evaluate” tab.
  • Here, you’ll see various metrics that indicate how well the model performed. For non-tech users, focus on the “Mean Absolute Error” (lower is better). It gives an average of how much the model’s predictions deviate from actual values.

7. Deploy & Predict:

  • Once satisfied with the model’s performance, navigate to the “Deploy” tab and deploy the model.
  • After deploying, go to the “Predict” tab. Here, you can input new data (in the form of a CSV) to get future predictions.

8. Act on Insights:

  • Based on the predictions and insights from the model, make informed decisions regarding inventory ordering, promotions, etc.

Tips for Simplifying the Process:

  • Data Cleanliness: Ensure your CSV file is clean. Remove any blank rows, ensure consistent data formats, and avoid special characters in column names.
  • Regularly Re-train: As you gather more data over months, periodically re-train your model. This ensures it stays accurate and relevant.
  • Use Google’s Resources: Google provides tutorials, documentation, and support to help you navigate AutoML Tables.

While this is a simplified guide, remember that machine learning and prediction are complex fields. The initial setup and understanding might take some time, but once you get a hang of it, the process becomes more straightforward. With tools like AutoML Tables, the goal is to make advanced analytics more accessible to businesses of all sizes.