Optimizing data analytics for supply chains: preparing data for modeling

In logistics and supply chain management, data analytics is critical for streamlining operations, minimizing costs, and improving decision-making. Tools like anyLogistix enable businesses to manage the power of data analytics for supply chains. However, the effectiveness of these tools heavily depends on data preparation. Without well-prepared data, even the most advanced models may fail to deliver actionable insights.

Data preparation is the foundation of data analytics for supply chain operations. It involves collecting, cleaning, integrating, and structuring information from diverse sources, such as ERP systems, databases, and spreadsheets. This ensures that supply chain models accurately represent real-world systems and can generate meaningful outputs.

Contents:

1. Importance of data preparation
2. Steps in data preparation for supply chain modeling
3. anyLogistix and Kedro framework
4. Data organization for anyLogistix
5. Conclusion

Why is data preparation crucial?

Data quality directly impacts model accuracy. Inaccurate, incomplete, or inconsistent data can lead to faulty supply chain models and result in wrong decisions. Proper preparation ensures the data is reliable and improves the credibility and performance of models.

Integration across systems. Supply chain data often comes from various sources: inventory systems, transportation logs, supplier databases, and customer records. Data preparation harmonizes these datasets, allowing them to work together seamlessly in a model.

Reduces complexity in analytics. Data analytics for supply chains involve numerous variables, from demand forecasts to transportation schedules. Proper data preparation reduces complexity by organizing these variables systematically. It makes them easier to analyze and optimize.

Steps in data preparation for supply chain modeling

1. Data Collection

• Gather data from internal systems (ERP, CRM, WMS) and external sources (supplier databases, market trends).
• Ensure that the data includes all critical parameters, such as lead times, costs, stock levels, and transportation routes.

2. Data Cleaning

• Remove duplicates, fill in missing values, and standardize formats to eliminate inconsistencies.
• Address errors such as incorrect product codes, outdated inventory levels, or mismatched supplier information.

3. Data Integration

• Combine datasets from different sources into a unified framework.
• Streamline this integration process and create structured pipelines through data preparation tools.

4. Data Transformation

• Convert raw data into the formats and tables required by tools like anyLogistix.
• Apply transformations to calculate key metrics (e.g., safety stock levels, transportation costs) or create demand forecasts.

5. Data Validation

• Cross-check the prepared data against known benchmarks or business rules to ensure its accuracy and reliability.
• Use data visualization tools to trace how data is transformed at each stage.

Steps in data preparation for supply chain modeling

Enhancing data analytics for supply chains in anylogistix with kedro framework

Kedro is a powerful tool that simplifies and standardizes preparation and data analytics for supply chains. Its framework ensures a smooth transition from raw data to ready-to-use inputs for anyLogistix.

The framework is structured around several key components that help manage and organize data transformations:

• Data catalog: Provides a clear definition of how data is stored and analyzed. It ensures consistency across all sources.
• Nodes and pipelines: The tool organizes data transformations into logical steps for a smooth flow from raw data to final model-ready outputs.
• Kedro-Viz: Offers transparency into how data is processed, making it easier to validate and debug each transformation step.

For example, data from multiple suppliers can be cleaned, combined, and transformed into a unified performance table. This table, once validated, can be used in anyLogistix to model optimal supplier selection strategies.

Data organization for anyLogistix

The figure shows how nodes and pipelines in the Kedro framework help to organize the data for anyLogistix. Nodes represent each transformation applied to the data, while pipelines connect these transformations in the correct sequence.

Data organization in Kedro for anyLogistix (click to enlarge)

An internal data visualization tool, Kedro-Viz, enables users to break down the data processing into five layers:

1. Raw Data Section: The unprocessed data from ERP systems or Excel sheets.

2. Loaded Data: This data is structured and loaded into the system.

3. Primary Analysis: The cleaned data is analyzed and prepared for modeling.

4. anyLogistix Tables: The data is transformed into tables that anyLogistix can work with.

5. Final anyLogistix Model: The data is fully processed, allowing anyLogistix to create simulations and optimizations.

>Data processing visualization in Kedro-Viz showing the five data layers (click to enlarge)

You can watch the full report on using Kedro in combination with anyLogistix in data analytics for supply chain optimization. The case was presented by Nicholas Geary from Simwell at the anyLogistix Conference 2024. Slides are available as a PDF.

Better data, better models

Data preparation is a crucial step for maximizing the value of data analytics for supply chains. By investing in data preparation processes and leveraging tools like Kedro and anyLogistix, businesses can unlock the full potential of their data. This results in better decision-making, optimized operations, and improved supply chain planning.

To learn more about international businesses using anyLogistix and data analytics for supply chain optimization, check out our case studies section.