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

Author

Listed:
  • Grzegorz Bartnik
  • Tomasz Sidor
  • Winczyslaw Jastrzebski
  • Jacek Piwkowski
  • Ewelina Jurczak

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.

Suggested Citation

  • Grzegorz Bartnik & Tomasz Sidor & Winczyslaw Jastrzebski & Jacek Piwkowski & Ewelina Jurczak, 2024. "Descriptive Analysis of Supply Chain Data: Patterns, Relationships, and Strategic Insights," European Research Studies Journal, European Research Studies Journal, vol. 0(Special A), pages 114-125.
    • Handle: RePEc:ers:journl:v:xxvii:y:2024:i:speciala:p:114-125
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    References listed on IDEAS

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    1. repec:ers:journl:v:xxiv:y:2021:i:special2:p:627-636 is not listed on IDEAS
    2. Pawel Rymarczyk & Arkadiusz Malek & Ryszard Nowak & Jacek Dziwulski, 2021. "Optimization of Logistics Processes of the Supply Chain Using RFID Technology," European Research Studies Journal, European Research Studies Journal, vol. 0(Special 1), pages 637-647.
    3. repec:ers:journl:v:xxiv:y:2021:i:special2:p:637-647 is not listed on IDEAS
    4. Vicky Zampeta & Gregory Chondrokoukis, 2023. "Maritime Transportation Accidents: A Bibliometric Analysis," International Journal of Business and Economic Sciences Applied Research (IJBESAR), Democritus University of Thrace (DUTH), Kavala Campus, Greece, vol. 16(1), pages 19-26, October.
    5. Vicky Zampeta & Gregory Chondrokoukis, 2023. "A Comprehensive Approach through Robust Regression and Gaussian/Mixed-Markov Graphical Models on the Example of Maritime Transportation Accidents: Evidence from a Listed-in-NYSE Shipping Company," JRFM, MDPI, vol. 16(3), pages 1-30, March.
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    More about this item

    Keywords

    Supply chain; descriptive analysis; data analytics.;
    All these keywords.

    JEL classification:

    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • L23 - Industrial Organization - - Firm Objectives, Organization, and Behavior - - - Organization of Production
    • L91 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Transportation: General
    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
    • M11 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - Production Management

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