Predictive analytics is a powerful tool to help use supply chain data to make more informed decisions about the future. This might involve analyzing data about inventory, order fulfillment, delivery times, manufacturing equipment and processes, suppliers, customers, and other factors that impact your supply chain. Predictive analytics can help you deal with some of your supply chain challenges more effectively, including demand volatility, supply shortages, manufacturing downtime, and high warehouse labor costs.

Six Steps to Inform Predictive Analytics

Knowing what’s going to happen in the future can help you transform your supply chain, but you’ll need to first understand how to leverage your supply chain data to inform predictive analytics. Here are some foundational steps to help you get started:

1. Collect Data

Predictive analytics relies on historical data to predict future events. How much data you’ll need depends on the type of problem you’re trying to solve, model complexity, data accuracy, and many other things. The types of data required depend on what you are trying to forecast. For instance, to forecast demand, you would need to gather data on past sales, customer orders, market research, planned promotions, and more.

2. Clean and Pre-Process Data

Data quality is key for predictive analytics to make accurate forecasts. Your data collection process needs to ensure that data is accurate, complete, unique, valid, consistent, and from the right time period.

3. Select a Predictive Analytics Technique

Machine learning uses algorithms and statistical models to identify patterns in data and make predictions. You need to select the appropriate machine-learning technique based on your data and the nature of your use case. Here are the major ones to choose from:

• Regression Analysis: Finds a relationship between one or more independent variables and a dependent variable.
• Decision Tree: Type of machine learning used to make predictions based on how a previous set of questions were answered.
• Neural Networks: Simulates the functioning of the human brain to analyze complex data sets. It creates an adaptive system that computers use to learn from their mistakes and improve continuously.
• Time-Series Analysis: Analyzes time-based data to predict future values.
• Classification: Prediction technique that uses machine learning to calculate the probability that an item belongs to a particular category.
• Clustering: Uses machine learning to group objects into categories based on their similarities, thereby splitting a large dataset into smaller subsets.

4. Train the Model

Training a machine learning model is a process in which a machine learning algorithm is fed with data from which it can learn.

5. Validate the Model

After training, you need to validate the model to ensure that it can accurately predict the future. This involves comparing the model’s predictions with actual data from a test period.

6. Use the Model to Forecast the Future

Once you have validated your model, you are ready to start using it to forecast data for future periods.

You’ll also need the right machine learning platform to execute these six predictive analytics steps successfully. Our blog “What Makes a Great Machine Learning Platform” helps you to discover how to evaluate a solution and learn about the Actian Data Platform’s capabilities.

Try our Actian Data Platform Free Trial to see for yourself how it can help you simplify predictive analytics deployment.

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