How to Enhancing Supply Chain Efficiency with AutoML

In the world of machine learning, automation is key to maximizing efficiency and productivity. Our AutoML platform is designed to streamline the process of developing machine-learning models, making it easier to solve complex problems. Today, we’re diving into an exciting real-world use case which is monitoring temperature-sensitive environments using Polar devices equipped with DS18B20 sensors. These devices are deployed in both cold rooms and refrigerated trucks (refer trucks), ensuring that temperature-sensitive products are stored under optimal conditions.

Maintaining the right temperature is crucial for storing and transporting temperature-sensitive products. Polar devices, equipped with DS18B20 sensors, are designed to monitor temperature fluctuations in real-time within cold rooms and refer trucks. However, raw temperature data alone isn’t enough. We need to analyze this data to detect events such as door openings or defrosting, which can compromise the storage conditions.

Cold Room

How AutoML Enhances Temperature Monitoring

The AutoML platform is ideal for addressing the complexities of this use case. Let’s explore how each step of AutoML can be applied to develop a robust solution for monitoring temperature in cold rooms and refer trucks.

1. Project Initiation

The first step is to initiate the project by uploading the temperature datasets collected from Polar devices. These datasets will typically include temperature readings over time, as well as known door opening and defrosting events, which will be used to train the model.

Steps:

• Connect Dataset: Import the temperature data from DS18B20 sensors in Polar devices, collected from both cold rooms and refer trucks.
• Define Problem Type: Select the problem type, such as time series classification, to detect door openings and defrosting events.

2. Preprocessing

Preprocessing is critical to ensure the data is ready for analysis. This involves handling missing values and categorizing the data correctly.

Steps:

• Data Visualization: Visualize the cleaned and processed data to identify any potential issues and gain insights into temperature trends and anomalies.
• Column Type Selection: Define data types for temperature readings, timestamps, and any other relevant features.
• Handling Missing Values: Address gaps in the data to ensure it is clean and complete for model training.

3. Data Transformation

In the Data Transformation step, feature engineering is key to extracting meaningful insights from the temperature data.

Steps:

• Feature Selection: Identify features that can help distinguish between normal temperature variations and those caused by door openings or defrosting events.
• Train-Test Split: Divide the dataset into training and testing sets to validate the model’s performance.

4. Model Tuning

Model tuning is where AutoML’s power truly shines. It allows users to select the most appropriate algorithm and fine-tune hyperparameters to achieve the best results.

Steps:

• Algorithm Selection: Choose algorithms suitable for time series analysis such as timeseries forest classifier, xgboost etc.
• Hyperparameter Tuning: Fine-tune parameters to improve the model’s accuracy in detecting temperature anomalies, door openings, and defrosting events.

5. Model Saving and Evaluation

The final step involves saving the trained model and evaluating its performance using data from Polar devices.

Steps:

• Model Saving: Store the trained model for deployment in continuous monitoring of cold rooms and refer trucks.
• Performance Report: Analyze the model’s effectiveness in detecting door openings and defrosting events using metrics like accuracy, precision, recall, and F1 score.
• Integration with Polar: AutoML is integrated with the Polar application where all the polar devices are connected, enabling real-time inference and report generation based on live temperature data.
• Report Generation: AutoML continuously generates reports on temperature anomalies, door openings, and defrosting events, providing actionable insights for maintaining optimal storage conditions.
• Alert system: The AutoML platform tracks temperature anomalies, door openings, and defrosting events in real-time, autonomously detecting these key stages and delivering instant alerts to maintain ideal storage conditions.

By leveraging the AutoML platform to analyze temperature data from Polar devices, businesses can significantly improve the management of temperature-sensitive products in both cold rooms and refer trucks. The insights provided by the model enable better control over environmental conditions, reducing the risk of spoilage and ensuring product quality.