The Inventory Leanness and Credit Ratings Dataset: Insights from Pakistan's Manufacturing Sector
Description
Efficient inventory management is pivotal for operational streamlining, waste reduction, and the avoidance of misuse. Inventory mismanagement can severely impact a company's performance, even jeopardizing its credit rating. This study presents the Inventory Leanness and Credit Ratings Dataset, covering data from thirty-eight publicly listed firms on the Pakistan Stock Exchange, all evaluated by PACRA from 2008 to 2018. Data sources include company websites, PACRA reports, and financial statements. Credit ratings were categorized from AAA to C, excluding C and D as they denote impending default. An Empirical Leanness Indicator was devised to gauge inventory efficiency. Control variables like firm size, leverage, capital intensity ratio, and dummy variables for financial losses and subordinate debt were included. This dataset offers researchers the means to test or develop theories regarding the interplay between inventory management and credit ratings in contemporary business operations.
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Steps to reproduce
please follow these steps to reproduce Acquire the Dataset: Begin by obtaining access to the Inventory Leanness and Credit Ratings Dataset, which encompasses data from thirty-eight publicly listed firms on the Pakistan Stock Exchange, collected from 2008 to 2018. Ensure that the data sources used, including company websites, PACRA reports, and financial statements, are available and accessible. Data Preprocessing: Clean and preprocess the data to ensure its quality and consistency. This includes addressing missing values, standardizing formats, and conducting any necessary data transformations. Credit Rating Categorization: Categorize the credit ratings from AAA to C, excluding C and D, as these latter ratings signal impending default. Empirical Leanness Indicator: Calculate the Empirical Leanness Indicator, a proxy for inventory leanness, using the provided formula or methodology as described in the study. Control Variables: Incorporate the control variables, including firm size, leverage, capital intensity ratio, and dummy variables for financial losses and subordinate debt, into your analysis.