What does NLU mean? I was reviewing VoiceFlow recently and, whilst following an introductory video, I was initially confused by a dropdown option list labelled NLU. There is so much new jargon in the AI field that it’s hard to keep up. What is the meaning of NLU?
NLU stands for Natural Language Understanding and it’s a critical element of AI systems. I regard NLU as having a similar relationship to AI services as browsers do to the internet. Browsers standardised access to websites and ultimately became the standard interface through which people interact with them on both mobiles and desktops. The simplicity, flexibility and standardisation of HTML as a means of displaying and formatting content meant that any human interface could be designed using a set of standard protocols and anyone with a browser could then view the interface without loading and running an obscure bespoke software program.
NLUs allow for something similar with conversational interfaces. They let people ask questions of systems in natural language format which is, afterall, what users are most comfortable and familiar with. AI systems such as LLMs can then parse these questions to work out what the user wants to know in more precise terms so that appropriate answers can be given, or relevant instructions followed.
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.
