How to Prepare Data for Machine Learning

AI is the buzzword of the year in tech, and it’s making waves across the supply chain ecosystem. However, machine learning (ML) is the unsung headline hero. So, what’s the difference between AI and ML?

AI vs. Machine Learning

AI is the field of computer science and engineering that focuses on creating intelligent machines to perform tasks that typically require human intelligence. At the same time, ML is a subset of AI, which involves training algorithms to make predictions or decisions based on data. These algorithms use statistical models to identify patterns and relationships within data and use that information to make predictions or decisions without being explicitly programmed.

How Is AI Used In Supply Chain Management?

Machine learning models are transforming supply chain management in a variety of ways. Some of the areas being improved are demand forecasting, inventory optimization, and predictive maintenance. For example, by using ML algorithms to analyze historical data, such as sales patterns, future demand for products can be predicted more efficiently and more accurately. This can help businesses optimize inventory levels, reduce waste, and improve customer satisfaction.

How to Prepare Data for Machine Learning

Ok, all of this sounds great, but is your business ready to take advantage of ML? Whether you’ve got an internal tech team or are considering hiring a technical partner, first you’ll need to get your data in order. Your results will only be as good as the data you feed to these tools, so here are seven steps (in sequential order) to put your company in a position to benefit from Machine Learning:

7 Steps to Success with Machine Learning

1. Identify the business problem:

First, a team of leaders across departments must be formed to identify the problems that must be solved. Then, determine a system to prioritize these needs. Some examples of pain points across the supply chain ecosystem are inefficient delivery routes, high transportation costs, and poor inventory management.

2. Collect relevant data:

Once your business problems have been identified, start gathering relevant data about these problems. This data can come from various sources, such as internal systems, third-party providers, or public sources, and it should be in a structured format.

3. Clean and preprocess the data:

This is the step where errors and duplicates are removed and/or missing values are added. The data should also be transformed into a format compatible with ML algorithms, such as numerical or categorical data.

4. Label the data:

You’ll likely want to start with supervised learning tasks rather than unsupervised learning tasks. These two approaches have many differences, but the main one is that supervised learning is less complex and more accurate. So, you’ll need to label your data to help the ML algorithm learn. For instance, if a logistics company wants to predict delivery times, they must label the data with the actual delivery times.

5. Split the data into training and testing sets:

The data should be split into training and testing sets to evaluate the performance of the ML model. The training set is used to train the model, while the testing set is used to assess the performance of the model. The training set should be larger than the testing set by a 3:1 ratio.

6. Normalize or standardize the data:

The data should be normalized or standardized to measure all features on the same scale. This helps the ML model to learn from the data more effectively.

7. Select appropriate ML algorithms:

Finally, you’re ready to build your ML algorithms. You can begin determining which algorithms will be best for your business based on the problems you’re trying to solve and your available data.

Need Help Getting Started with Machine Learning?

While some businesses might be prepared to tackle all of these steps themselves, other companies might want the assistance of an ML expert. Book a meeting with LookFar Labs to discuss your ML needs.