Descriptive Analysis of Supply Chain Data: Patterns, Relationships, and Strategic Insights

Abstract

Purpose: The study's purpose is to conduct a descriptive analysis of supply chain data, with the goal of unveiling patterns and relationships that can inform strategic decision-making. Design/Methodology/Approach: A dataset encompassing 200 observations across 17 columns-11 categorical and six numerical variables-was meticulously analyzed. The analysis included variables representing customer identifiers, sale dates, transaction values, discounts, currency, and geographical details. Data preprocessing ensured no missing values or duplicates were present, providing the robustness of subsequent analyses. Various statistical tools and visualization techniques, including histograms and correlation matrices, were employed to elucidate the data's characteristics. Findings: Key findings from the dataset revealed a robust linear relationship between the net and gross values of transactions. At the same time, the quantities ordered displayed a non-linear relationship with the total value. High concentration levels were noted geographically and in customer activity, with most transactions occurring within specific locations and a limited number of customers. The data also exhibited many unique product identifiers and description values, indicating a diverse range of items within the supply chain. Practical Implications:The study provides actionable insights for supply chain optimization. Recognizing patterns in transaction values and customer geography can guide strategic decisions in logistics, inventory management, and targeted marketing. Additionally, understanding product diversity and sales concentration can inform supplier negotiations and risk management. Originality/Value: The research contributes to the field of supply chain management by applying a comprehensive descriptive analysis to uncover inherent data patterns. It uniquely combines various analytical techniques to draw meaningful insights with direct practical applications, particularly in enhancing the efficiency of supply chain operations and customer segmentation strategies.