ASSESSMENT OF PERFORMANCE DRIVERS IN INVENTORY MANAGEMENT IN A MANUFACTURING COMPANY – A STUDY IN BANGALORE CITY

Inventory management plays a pivotal role in the operational efficiency and financial performance of manufacturing companies. Effective inventory control ensures that production processes run smoothly, minimizes costs, and improves customer satisfaction. This research investigates the key performance drivers influencing inventory management in manufacturing organizations. The study examines factors such as demand forecasting accuracy, inventory turnover, supplier performance, technology adoption, and sustainability practices. A mixed-methods approach is employed, combining quantitative data from industry reports and company case studies with qualitative insights from surveys and interviews with inventory managers and production staff. The findings highlight the significant impact of demand forecasting, inventory turnover rates, and technological advancements on improving inventory accuracy and reducing costs. Furthermore, supplier relationships and sustainability initiatives are identified as critical drivers in enhancing inventory efficiency. Based on these insights, the research offers recommendations for optimizing inventory management practices, including the integration of advanced forecasting tools, lean inventory strategies, and the adoption of automated systems. The study provides valuable insights for manufacturing managers and supply chain professionals seeking to improve inventory performance and align with industry best practices.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-07-15
DOI
https://doi.org/10.5281/zenodo.16743918
Primary Topic
Forecasting Techniques and Applications
Type
article
Field-Weighted Citation Impact
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article

ASSESSMENT OF PERFORMANCE DRIVERS IN INVENTORY MANAGEMENT IN A MANUFACTURING COMPANY – A STUDY IN BANGALORE CITY

Zenodo (CERN European Organization for Nuclear Research)
Forecasting Techniques and Applications
article

ASSESSMENT OF PERFORMANCE DRIVERS IN INVENTORY MANAGEMENT IN A MANUFACTURING COMPANY – A STUDY IN BANGALORE CITY

article en

Abstract

Inventory management plays a pivotal role in the operational efficiency and financial performance of manufacturing companies. Effective inventory control ensures that production processes run smoothly, minimizes costs, and improves customer satisfaction. This research investigates the key performance drivers influencing inventory management in manufacturing organizations. The study examines factors such as demand forecasting accuracy, inventory turnover, supplier performance, technology adoption, and sustainability practices. A mixed-methods approach is employed, combining quantitative data from industry reports and company case studies with qualitative insights from surveys and interviews with inventory managers and production staff. The findings highlight the significant impact of demand forecasting, inventory turnover rates, and technological advancements on improving inventory accuracy and reducing costs. Furthermore, supplier relationships and sustainability initiatives are identified as critical drivers in enhancing inventory efficiency. Based on these insights, the research offers recommendations for optimizing inventory management practices, including the integration of advanced forecasting tools, lean inventory strategies, and the adoption of automated systems. The study provides valuable insights for manufacturing managers and supply chain professionals seeking to improve inventory performance and align with industry best practices.

Zenodo (CERN European Organization for Nuclear Research)
Indian Institute of Management Bangalore (IN), Bangalore University (IN)
Industry, innovation and infrastructure
Openalex Percentile: Top 45%
Forecasting Techniques and Applications
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ASSESSMENT OF PERFORMANCE DRIVERS IN INVENTORY MANAGEMENT IN A MANUFACTURING COMPANY – A STUDY IN BANGALORE CITY · Zenodo (CERN European Organization for Nuclear Research) (2025) | TGRS Research Map | TGRS