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Correlation analysis between different parameters to predict cement logistics

  • S.i. : Intelligence for Systems and Software Engineering
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Abstract

Globalization and altering dynamics of product life cycles greatly affect the manufacturing industry making supply chain networks important. Logistics plays a crucial role in supply chain management, which oversees the flow of goods and services. It is concerned with the delivery of goods to customers in an efficient and cost-efficient manner. Across various industrial sectors, the cement industry is regarded as the foremost industry with its per capita cement consumption being 235 kg. Logistics is an inextricable division of the cement industry that plays a crucial role in material flow and product distribution as the average lead covered by a cement bag is 300 km before its consumption and logistics account for almost 30 percent of the cost of cement. Thus, to improve cement logistics, it is important to identify the factors that can assist in achieving better supply. Therefore, this paper highlights and evaluates the correlation of various factors of cement logistics using heatmaps and correlation plots, thus depicting their bivariate relationships in categories of high, medium, and low relations. Data associated with the supply in specific districts from multiple sources of a cement organization have been gathered and analyzed. Invoice-based sales data are analyzed using Pearson correlation to understand the key parameters affecting the logistics efficiency for further fine-tuning of logistics in the organization.

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Data availability

The data used to support the findings of this study are available from the corresponding author upon request.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Punjabi University, Patiala, Punjab, 147002, India

    Gagandeep Kaur & Harpreet Kaur

  2. Department of Electronics and Communication Engineering, Punjabi University, Patiala, Punjab, 147002, India

    Sonia Goyal

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  1. Gagandeep Kaur
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  2. Harpreet Kaur
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  3. Sonia Goyal
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Correspondence to Gagandeep Kaur.

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Kaur, G., Kaur, H. & Goyal, S. Correlation analysis between different parameters to predict cement logistics. Innovations Syst Softw Eng 19, 117–127 (2023). https://doi.org/10.1007/s11334-022-00505-y

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