Predict future product demand using SPSS Modeler

As mentioned in our previous article, our goal is to build an intelligent inventory and distribution strategy using AI. In this part of the solution, we will work on predicting future demand of our products to help us determine our optimal inventory level using SPSS Modeler for Watson Studio on IBM Cloud. SPSS Modeler is a data mining and analytics software that is used to build predictive models. Using SPSS Modeler for Watson Studio enables data scientists and data analysts to work in a collaborative, cloud-based environment and quickly share their models and results with their stakeholders.

Learning objectives

After completing this tutorial, the user will learn:

How to upload data to IBM Watson Studio
How to create an SPSS Modeler flow
How to use the SPSS tool to profile and analyze data
How to modify, filter, and prepare data for AI Model creation
How to train a machine learning model with SPSS and evaluate the results

Prerequisites

This tutorial assumes you have an IBM Cloud account. Go to the link below to sign up for a no-charge trial account - no credit card required.

IBM Cloud account

Estimated Time

Completing this tutorial should take about 30 minutes.

Steps

Create Watson Studio service on IBM Cloud
Create a project in Watson Studio and upload the data
Add a Modeler Flow to your Watson Studio Project
Import the data
Explore the data
Data preparation
Train the ML model
Evaluate the results

1. Create Watson Studio service on IBM Cloud

The first step in this tutorial is to set up your IBM Cloud Watson Studio service. See the video below to help you create your free IBM Watson Studio service.

After logging into IBM Cloud, click Proceed to show that you have read your data rights.
Click on IBM Cloud in the top left corner to ensure you are on the home page.
Within your IBM Cloud account, click on the top search bar to search for cloud services and offerings. Type in Watson Studio and then click on Watson Studio under Catalog Results.
This takes you to the Watson Studio service page. There you can name the service as you wish. For example, one may name it Watson-Studio-trial. You can also choose which data center to create your instance in. The gif above shows mine as being created in Dallas.
For this guide, you choose the Lite service, which is no-charge. This has limited compute; it is enough to understand the main functionality of the service.
Once you are satisfied with your service name, and location, and plan, click on create in the bottom-right corner. This creates your Watson Studio instance.

2. Create a project in Watson Studio and upload the data

To launch your Watson Studio service, go back to the home page by clicking on IBM Cloud in the top-left corner. There you see your services, and under there you should see your service name. This might take a minute or two to update.
Once you see your service that you just created, click on your service name, and this takes you to your Watson Studio instance page, which says Welcome to Watson Studio. Let's get started!. Click on the Get Started button.
This takes you to the Watson Studio (aka Cloud Pak for Data as a Service) tooling. Click on Projects.

There you see a heading that says Start by creating a project or New Project. and a button that says Create Project. Click on Create a Project or New project. Next click on Create an Empty project.

On the create a new project page, name your project. One may name the project - insurance-demo. You also need to associate an IBM Cloud Object store instance, so that you store the data set.
Under Select Storage service click on the Add button. This takes you to the IBM Cloud Object Store service page. Leave the service on the Lite tier and then click the Create button at the bottom of the page. You are prompted to name the service and choose the resource group. Once you select a name, click the resource group Confirm button.
Once you've confirmed your IBM Cloud Object Store instance, you are taken back to the project page. Click on refresh and you should see your newly created Cloud Object Store instance under Storage. That's it! Now you can click Create at the bottom right of the page to create your first IBM Watson Studio project :)

Run the following command to clone the project repo. You'll need to use files from this repo in subsequent steps:

git clone https://github.com/IBM/optimize-procurement-and-inventory-with-ai

Once you have created your Watson Studio Project, you see a blue Add to Project button on the top-right corner of your screen.

Select Data.
On the right-hand side bar, click on browse and then go to where you cloned the repository, and select TM1 Import.csv from the optimize-procurement-and-inventory-with-ai/tutorials/spss-tutorial/data directory.

Once the upload has finished, you should see Upload Successful as shown below.

3. Add a Modeler Flow to your Watson Studio Project

Click on your newly created project.
Once you have created your Watson Studio Project, you see a blue Add to Project button on the top-right corner of your screen.

Click on Add to Project and then select Modeler Flow.

Next, select From File and browse to where you cloned this repository. If you haven't done so, run the following command to clone the repo:

git clone https://github.com/IBM/optimize-procurement-and-inventory-with-ai/

Select the Demand Forecast.str file. Next, click Create.

4. Import the data

Next, you should see your flow, as shown below.

On the leftmost side of the flow, under Data Import, hover over the TM1 Import node. You should see a three-dot icon show up. Click it.

Next, click on Open.

This will open up the right-hand side panel with the TM1 Import node details. Click on Change data asset.

Next, from the left hand side click on Data assets and then click TM1 Import.csv. Next, click OK.

Next, click Save on the right-hand side panel.

Once the flow is updated, you will be ready to explore the data.

5. Explore the data

Before we do any analysis and predictions on the data, it's a good idea to explore your data set.

From the Data Import section, hover over the green node that says "6 Fields" and click on the three-dot symbol to the right of the node. Next, click on Run.

Once the node runs, click on the Clock icon. Next, under the Outputs section should be a section which says Data Audit of [6 Fields]. Double-click on that section.

Here you you can get a visual representation of your data set, and see some sample graphs of your columns. You can see some statistical measures such as max, mean, standard deviation and how many fields are valid. You can see that the max for our Actuals field (which is the demand for our product) is 331, but the mean is much lower, at 44. Lastly, you can see how many valid records there are for each column.

Once you are done exploring the data set, you can return back to the flow by clicking on the flow name in the top-left breadcrumb.

6. Data preparation

Next, we will focus on the data prep nodes. These are the nodes which will modify our data set to predict just the values we want. A great way to understand what each node is doing is to Preview the node.

To preview a node, such as the TM1 Import node, hover over the node, and click on the three-dot symbol.

Next, click on Preview.

This is a quick and easy way to see what the data looks like at the current node in the flow. Below is what you should see once you've previewed the node.

Hover over the Date node, click on the Three-dot symbol, and then on Preview.
Once you preview the node, you should see that the Date node adds another column to the data set, called Date and derives that value from the Period column.

The Filter node, which comes right after the Date node, takes out the Statistical Base Forecast column, and leaves the other columns as is.
Next, the Select node is very important. It's going to take a subset of the data, and it will use an expression to do so. You can see in the expression, we have only taken the US markets, and the Date when it is before Jan 2019. This will enable use to make more specific predictions, only for the US market, since that is where our retail store manager that we want to focus on is located. If you preview this node, you will see that the Markets column does not feature any value other than US.

The last of the data prep nodes is the Type node. This enables us to make specific operations on the fields within our data set. In this node, we split the Products column into individual values, so that we can later predict demand for each of our products within that column.

Next, the restructure node creates new columns for the dataset. It takes the values from the Products column, and adds the Actual demand value for that product, in a separate column, so that demand for a specific product is easily distinguishable.
Hover over the Restructure node, click on the Three-dot symbol, and then Preview to see the new columns that are added to our data set.

Also, feel free to Open the node as well, which will show you how we generated multiple fields from merging the Products and Actuals columns.

Hover over the Aggregate and then click Open. There, you will see that we are taking the largest value from the fields such as Products_mask_Actuals that we created above. This is because we want and ensure we have enough in inventory to satisfy the maximum demand.

Similarly, hover over the Aggregate node, click on the Three-dot symbol, and then Preview to see the new columns with the _Max suffix added to them.

Hover over the Type and then click Open. There, you will see we've designated the new fields that we created in the previous steps as the target variables, since we want to predict the max demand for each of the products within the Products column, within the US market.

7. Train the ML model

Hover over the 29 fields golden nugget and then click Run. This will run the time-forecasting model with the data we have prepared in the previous steps.

Once the model has finished running, you will see the Outputs tab has been updated.

8. Evaluate the results

Double-click on Analysis of [29 fields] in the Outputs tab. Here you can see an assessment of the mean error, maximum error and other values which compare the $TS-Products_Actuals_Max i.e. the maximum demand forecasted by the model for a particular product, versus Products_Actuals_Max which is the max which has been recorded for the training period i.e. 2016-2018.

Double-click on Analysis of [10 fields] in the Outputs tab. Here you will see a visual representation of a few of the interesting products which have shown to have spiked in demand over the past three months of training data. In October, November, and December of 2018, these five particular products, masks, hand sanitizer, and spray cleaner have all spiked in demand, much higher than previously recorded, such that the forecasted values for 2019, the year we are predicting, are all much higher due to these three months of training data. We can see this by the slope of the line increasing drastically in the $TS-Products_Actuals_Max charts vs. the Products_Actuals_Max charts.

Summary

This tutorial demonstrates a small example of creating a predictive machine learning model on IBM SPSS Modeler on Watson Studio and IBM SPSS Modeler software. The tutorial goes over on importing the data into the project and the modeler flow, and preparing the data for modeling. The tutorial then goes over the running a time series algorithm for the data and training a prediction model. The last step of the tutorial is about how to visualize and evaluate the results of the trained model.

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