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Unleashing the Power of Data Analysis in a Network Optimization Project

In the realm of supply chain network optimization projects, data is the foundation on which success is built. It’s the gateway to uncovering hidden insights, identifying bottlenecks, and making informed decisions. Before you can optimize your supply chain network, you need a thorough understanding of it. There are different data analysis strategies and tools that can be used for efficient data analysis, all of which can be critical to the success of precision network optimization project.


When dealing with vast amounts of data, it’s essential to have a clear analysis strategy. Here are some key steps to consider:

  1. Data Categorization: One of the first documents sent to stakeholders in a network optimization project is a data request document. This document provides a broad overview of the data that required for the effort.  It segments the data into different categories, such as master files, transaction files, and shipment files. We will not focus on this data request in detail here, as it could be the subject of another entire blog post, but for our purposes a clear analysis strategy starts with appropriate data categorization.
  2. Documentation: Create a clear and organized documentation system, or as we like to call it, “Data Collection Status (DCS).” A well-structured data tracking system will help you and your team understand the data that has been received and the data that is still needed.
  3. Version Management: Implement version control to track changes and updates to the data you are receiving. For example, you can create a different version of a data collection status, such as XProject_DCS_v1, XProject_DCS_v2, and so on. This ensures that everyone is working with the most current data.
  4. Data Verification: Implement data verification protocols to identify discrepancies, anomalies, or missing values in your data. Start by cleaning and transforming your data, using the tools discussed later in this blog post. Data verification is an essential part of staying organized.
  5. Collaboration: Encourage collaboration among team members and stakeholders by holding regular check-ins to share valuable insights from previous steps. This phase ensures adherence to the project timeline and rectifies data gaps.


The choice of data analysis software depends on the complexity and scale of the data at hand. Some of the most commonly used options, though this list is by no means exhaustive, include Microsoft Excel, Microsoft Access, and SQL (Structured Query Language).

Microsoft Excel: Excel is a versatile spreadsheet application suitable for straightforward data cleansing tasks. It offers a user-friendly interface and a variety of functions and formulas for data validation and transformation. However, it may not be the ideal choice for handling large and complex datasets.

Microsoft Access: Access is a relational database management system (RDBMS) with enhanced data cleansing capabilities. It allows you to create structured databases, apply validation rules, and maintain data consistency. Access is a good choice for medium-sized datasets, but it may have limitations with extremely large datasets.

SQL: SQL is the preferred tool for complex and large datasets. It excels in managing, manipulating, and querying data in databases. SQL is the go-to choice for intricate operations, vast data volumes, and process automation.

When choosing between Access and SQL, consider the complexity and scale of your data. Access is a good choice for smaller datasets and a user-friendly interface, while SQL is your go-to for complex data cleansing with massive datasets. Personally, I always go for SQL regardless of the size of the project.

In conclusion, data analysis is the cornerstone of an effective supply chain network optimization project. By using the right strategy and employing the right tool for the job, you can unlock the potential hidden within your data. The key is to understand your project’s unique needs and select the right software to decode the data puzzle, ultimately driving efficiency and success in your project.
—Odi Niyomugabo, St. Onge Company

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