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2021

Development of Integrated Inventory Management Models for Manufacturing Companies

International Journal of Emerging Trends in Science and Technology, 2021, 2 August 2021 , Page 15-38
https://doi.org/10.18535/ijetst/v8i7.02

Abstract

Efficient inventory management is pivotal for manufacturing companies striving to maintain operational excellence, minimize costs, and meet dynamic market demands. Traditional inventory management systems, while functional, often fall short in addressing complex supply chain challenges such as real-time visibility, demand variability, and integration with modern technologies. This study explores the development of an integrated inventory management model designed to optimize inventory control, enhance decision-making, and reduce operational inefficiencies. By leveraging advanced technologies, including IoT, AI, and ERP systems, the proposed model integrates demand forecasting, inventory optimization, and supplier collaboration into a cohesive framework. Data-driven insights and predictive analytics form the cornerstone of this approach, enabling manufacturing companies to adapt to shifting market conditions seamlessly. The research evaluates the model's performance through simulations and case studies, highlighting significant improvements in inventory turnover, cost reduction, and operational agility. This study provides a pathway for manufacturing companies to transition to scalable, efficient, and technologically advanced inventory management systems.

How to Cite

Tulli, S. krishna C. (2021). Development of Integrated Inventory Management Models for Manufacturing Companies. International Journal of Emerging Trends in Science and Technology, 8(07), 15–38. https://doi.org/10.18535/ijetst/v8i7.02

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References

  • Kumar, K., & Van Hillegersberg, J. (2000). ERP experiences and evolution. Communications of the ACM, 43(4), 22-22.
  • Vaidya, S., Ambad, P., & Bhosle, S. (2018). Industry 4.0–a glimpse. Procedia manufacturing, 20, 233-238.
  • Min, H., & Zhou, G. (2002). Supply chain modeling: past, present and future. Computers & industrial engineering, 43(1-2), 231-249.
  • Morgan, J., & Liker, J. K. (2020). The Toyota product development system: integrating people, process, and technology. Productivity press.
  • Ilgin, M. A., & Gupta, S. M. (2010). Environmentally conscious manufacturing and product recovery (ECMPRO): A review of the state of the art. Journal of environmental management, 91(3), 563-591.
  • Sorescu, A., Frambach, R. T., Singh, J., Rangaswamy, A., & Bridges, C. (2011). Innovations in retail business models. Journal of retailing, 87, S3-S16.
  • Tao, F., Qi, Q., Liu, A., & Kusiak, A. (2018). Data-driven smart manufacturing. Journal of Manufacturing Systems, 48, 157-169.
  • Power, D. (2005). Supply chain management integration and implementation: a literature review. Supply chain management: an International journal, 10(4), 252-263.
  • Scheer, A. W. (2012). Business process engineering: reference models for industrial enterprises. Springer Science & Business Media.
  • Jacobs, F. R., & Chase, R. B. (2018). Operations and supply chain management. McGraw-Hill.
  • JOSHI, D., SAYED, F., BERI, J., & PAL, R. (2021). An efficient supervised machine learning model approach for forecasting of renewable energy to tackle climate change. Int J Comp Sci Eng Inform Technol Res, 11, 25-32.
  • Mahmud, U., Alam, K., Mostakim, M. A., & Khan, M. S. I. (2018). AI-driven micro solar power grid systems for remote communities: Enhancing renewable energy efficiency and reducing carbon emissions. Distributed Learning and Broad Applications in Scientific Research, 4.
  • Joshi, D., Sayed, F., Saraf, A., Sutaria, A., & Karamchandani, S. (2021). Elements of Nature Optimized into Smart Energy Grids using Machine Learning. Design Engineering, 1886-1892.
  • Alam, K., Mostakim, M. A., & Khan, M. S. I. (2017). Design and Optimization of MicroSolar Grid for Off-Grid Rural Communities. Distributed Learning and Broad Applications in Scientific Research, 3.
  • Integrating solar cells into building materials (Building-Integrated Photovoltaics-BIPV) to turn buildings into self-sustaining energy sources. Journal of Artificial Intelligence Research and Applications, 2(2).
  • Manoharan, A., & Nagar, G. MAXIMIZING LEARNING TRAJECTORIES: AN INVESTIGATION INTO AI-DRIVEN NATURAL LANGUAGE PROCESSING INTEGRATION IN ONLINE EDUCATIONAL PLATFORMS.
  • Joshi, D., Parikh, A., Mangla, R., Sayed, F., & Karamchandani, S. H. (2021). AI Based Nose for Trace of Churn in Assessment of Captive Customers. Turkish Online Journal of Qualitative Inquiry, 12(6).
  • Khambati, A. (2021). Innovative Smart Water Management System Using Artificial Intelligence. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 12(3), 4726-4734.
  • JALA, S., ADHIA, N., KOTHARI, M., JOSHI, D., & PAL, R. SUPPLY CHAIN DEMAND FORECASTING USING APPLIED MACHINE LEARNING AND FEATURE ENGINEERING.
  • Joshi, D., Sayed, F., Jain, H., Beri, J., Bandi, Y., & Karamchandani, S. A Cloud Native Machine Learning based Approach for Detection and Impact of Cyclone and Hurricanes on Coastal Areas of Pacific and Atlantic Ocean.
  • Agarwal, A. V., & Kumar, S. (2017, November). Unsupervised data responsive based monitoring of fields. In 2017 International Conference on Inventive Computing and Informatics (ICICI) (pp. 184-188). IEEE.
  • Agarwal, A. V., Verma, N., Saha, S., & Kumar, S. (2018). Dynamic Detection and Prevention of Denial of Service and Peer Attacks with IPAddress Processing. Recent Findings in Intelligent Computing Techniques: Proceedings of the 5th ICACNI 2017, Volume 1, 707, 139.
  • Mishra, M. (2017). Reliability-based Life Cycle Management of Corroding Pipelines via Optimization under Uncertainty (Doctoral dissertation).
  • Agarwal, A. V., Verma, N., & Kumar, S. (2018). Intelligent Decision Making Real-Time Automated System for Toll Payments. In Proceedings of International Conference on Recent Advancement on Computer and Communication: ICRAC 2017 (pp. 223-232). Springer Singapore.
  • Agarwal, A. V., & Kumar, S. (2017, October). Intelligent multi-level mechanism of secure data handling of vehicular information for post-accident protocols. In 2017 2nd International Conference on Communication and Electronics Systems (ICCES) (pp. 902-906). IEEE.
  • Doddipatla, L., Ramadugu, R., Yerram, R. R., & Sharma, T. (2021). Exploring The Role of Biometric Authentication in Modern Payment Solutions. International Journal of Digital Innovation, 2(1).
  • Han, J., Yu, M., Bai, Y., Yu, J., Jin, F., Li, C., ... & Li, L. (2020). Elevated CXorf67 expression in PFA ependymomas suppresses DNA repair and sensitizes to PARP inhibitors. Cancer Cell, 38(6), 844-856.
  • Singu, S. K. (2021). Real-Time Data Integration: Tools, Techniques, and Best Practices. ESP Journal of Engineering & Technology Advancements, 1(1), 158-172.
  • Singu, S. K. (2021). Designing Scalable Data Engineering Pipelines Using Azure and Databricks. ESP Journal of Engineering & Technology Advancements, 1(2), 176-187.
  • Malhotra, I., Gopinath, S., Janga, K. C., Greenberg, S., Sharma, S. K., & Tarkovsky, R. (2014). Unpredictable nature of tolvaptan in treatment of hypervolemic hyponatremia: case review on role of vaptans. Case reports in endocrinology, 2014(1), 807054.
  • Shakibaie-M, B. (2013). Comparison of the effectiveness of two different bone substitute materials for socket preservation after tooth extraction: a controlled clinical study. International Journal of Periodontics & Restorative Dentistry, 33(2).
  • Roh, Y. S., Khanna, R., Patel, S. P., Gopinath, S., Williams, K. A., Khanna, R., ... & Kwatra, S. G. (2021). Circulating blood eosinophils as a biomarker for variable clinical presentation and therapeutic response in patients with chronic pruritus of unknown origin. The Journal of Allergy and Clinical Immunology: In Practice, 9(6), 2513-2516.
  • Gopinath, S., Janga, K. C., Greenberg, S., & Sharma, S. K. (2013). Tolvaptan in the treatment of acute hyponatremia associated with acute kidney injury. Case reports in nephrology, 2013(1), 801575.
  • Shilpa, Lalitha, Prakash, A., & Rao, S. (2009). BFHI in a tertiary care hospital: Does being Baby friendly affect lactation success?. The Indian Journal of Pediatrics, 76, 655-657.
  • Singh, V. K., Mishra, A., Gupta, K. K., Misra, R., & Patel, M. L. (2015). Reduction of microalbuminuria in type-2 diabetes mellitus with angiotensin-converting enzyme inhibitor alone and with cilnidipine. Indian Journal of Nephrology, 25(6), 334-339.
  • Gopinath, S., Giambarberi, L., Patil, S., & Chamberlain, R. S. (2016). Characteristics and survival of patients with eccrine carcinoma: a cohort study. Journal of the American Academy of Dermatology, 75(1), 215-217.
  • Swarnagowri, B. N., & Gopinath, S. (2013). Ambiguity in diagnosing esthesioneuroblastoma--a case report. Journal of Evolution of Medical and Dental Sciences, 2(43), 8251-8255.
  • Swarnagowri, B. N., & Gopinath, S. (2013). Pelvic Actinomycosis Mimicking Malignancy: A Case Report. tuberculosis, 14, 15.
  • Maddireddy, B. R., & Maddireddy, B. R. (2020). Proactive Cyber Defense: Utilizing AI for Early Threat Detection and Risk Assessment. International Journal of Advanced Engineering Technologies and Innovations, 1(2), 64-83.
  • Maddireddy, B. R., & Maddireddy, B. R. (2020). AI and Big Data: Synergizing to Create Robust Cybersecurity Ecosystems for Future Networks. International Journal of Advanced Engineering Technologies and Innovations, 1(2), 40-63.
  • Maddireddy, B. R., & Maddireddy, B. R. (2021). Evolutionary Algorithms in AI-Driven Cybersecurity Solutions for Adaptive Threat Mitigation. International Journal of Advanced Engineering Technologies and Innovations, 1(2), 17-43.
  • Maddireddy, B. R., & Maddireddy, B. R. (2021). Cyber security Threat Landscape: Predictive Modelling Using Advanced AI Algorithms. Revista Espanola de Documentacion Cientifica, 15(4), 126-153.
  • Maddireddy, B. R., & Maddireddy, B. R. (2021). Enhancing Endpoint Security through Machine Learning and Artificial Intelligence Applications. Revista Espanola de Documentacion Cientifica, 15(4), 154-164.
  • Damaraju, A. (2021). Mobile Cybersecurity Threats and Countermeasures: A Modern Approach. International Journal of Advanced Engineering Technologies and Innovations, 1(3), 17-34.
  • Damaraju, A. (2021). Securing Critical Infrastructure: Advanced Strategies for Resilience and Threat Mitigation in the Digital Age. Revista de Inteligencia Artificial en Medicina, 12(1), 76-111.
  • Damaraju, A. (2020). Social Media as a Cyber Threat Vector: Trends and Preventive Measures. Revista Espanola de Documentacion Cientifica, 14(1), 95-112.
  • Chirra, B. R. (2021). AI-Driven Security Audits: Enhancing Continuous Compliance through Machine Learning. International Journal of Machine Learning Research in Cybersecurity and Artificial Intelligence, 12(1), 410-433.
  • Chirra, B. R. (2021). Enhancing Cyber Incident Investigations with AI-Driven Forensic Tools. International Journal of Advanced Engineering Technologies and Innovations, 1(2), 157-177.
  • Chirra, B. R. (2021). Intelligent Phishing Mitigation: Leveraging AI for Enhanced Email Security in Corporate Environments. International Journal of Advanced Engineering Technologies and Innovations, 1(2), 178-200.
  • Chirra, B. R. (2021). Leveraging Blockchain for Secure Digital Identity Management: Mitigating Cybersecurity Vulnerabilities. Revista de Inteligencia Artificial en Medicina, 12(1), 462-482.
  • Chirra, B. R. (2020). Enhancing Cybersecurity Resilience: Federated Learning-Driven Threat Intelligence for Adaptive Defense. International Journal of Machine Learning Research in Cybersecurity and Artificial Intelligence, 11(1), 260-280.
  • Chirra, B. R. (2020). Securing Operational Technology: AI-Driven Strategies for Overcoming Cybersecurity Challenges. International Journal of Machine Learning Research in Cybersecurity and Artificial Intelligence, 11(1), 281-302.
  • Chirra, B. R. (2020). Advanced Encryption Techniques for Enhancing Security in Smart Grid Communication Systems. International Journal of Advanced Engineering Technologies and Innovations, 1(2), 208-229.
  • Chirra, B. R. (2020). AI-Driven Fraud Detection: Safeguarding Financial Data in Real-Time. Revista de Inteligencia Artificial en Medicina, 11(1), 328-347.
  • Gadde, H. (2019). Integrating AI with Graph Databases for Complex Relationship Analysis. International
  • Gadde, H. (2019). AI-Driven Schema Evolution and Management in Heterogeneous Databases. International Journal of Machine Learning Research in Cybersecurity and Artificial Intelligence, 10(1), 332-356.
  • Gadde, H. (2021). AI-Driven Predictive Maintenance in Relational Database Systems. International Journal of Machine Learning Research in Cybersecurity and Artificial Intelligence, 12(1), 386-409.
  • Gadde, H. (2019). Exploring AI-Based Methods for Efficient Database Index Compression. Revista de Inteligencia Artificial en Medicina, 10(1), 397-432.
  • Goriparthi, R. G. (2020). AI-Driven Automation of Software Testing and Debugging in Agile Development. Revista de Inteligencia Artificial en Medicina, 11(1), 402-421.
  • Goriparthi, R. G. (2021). Optimizing Supply Chain Logistics Using AI and Machine Learning Algorithms. International Journal of Advanced Engineering Technologies and Innovations, 1(2), 279-298.
  • Goriparthi, R. G. (2021). AI and Machine Learning Approaches to Autonomous Vehicle Route Optimization. International Journal of Machine Learning Research in Cybersecurity and Artificial Intelligence, 12(1), 455-479.
  • Goriparthi, R. G. (2020). Neural Network-Based Predictive Models for Climate Change Impact Assessment. International Journal of Machine Learning Research in Cybersecurity and Artificial Intelligence, 11(1), 421-421.
  • Reddy, V. M., & Nalla, L. N. (2020). The Impact of Big Data on Supply Chain Optimization in Ecommerce. International Journal of Advanced Engineering Technologies and Innovations, 1(2), 1-20.
  • Nalla, L. N., & Reddy, V. M. (2020). Comparative Analysis of Modern Database Technologies in Ecommerce Applications. International Journal of Advanced Engineering Technologies and Innovations, 1(2), 21-39.
  • Nalla, L. N., & Reddy, V. M. (2021). Scalable Data Storage Solutions for High-Volume E-commerce Transactions. International Journal of Advanced Engineering Technologies and Innovations, 1(4), 1-16.
  • Reddy, V. M. (2021). Blockchain Technology in E-commerce: A New Paradigm for Data Integrity and Security. Revista Espanola de Documentacion Cientifica, 15(4), 88-107.
  • Reddy, V. M., & Nalla, L. N. (2021). Harnessing Big Data for Personalization in E-commerce Marketing Strategies. Revista Espanola de Documentacion Cientifica, 15(4), 108-125.
  • Nalla, L. N., & Reddy, V. M. Machine Learning and Predictive Analytics in E-commerce: A Data-driven Approach.
  • Reddy, V. M., & Nalla, L. N. Implementing Graph Databases to Improve Recommendation Systems in E-commerce.
  • Krishnan, S., Shah, K., Dhillon, G., & Presberg, K. (2016). 1995: FATAL PURPURA FULMINANS AND FULMINANT PSEUDOMONAL SEPSIS. Critical Care Medicine, 44(12), 574.
  • Krishnan, S. K., Khaira, H., & Ganipisetti, V. M. (2014, April). Cannabinoid hyperemesis syndrome-truly an oxymoron!. In JOURNAL OF GENERAL INTERNAL MEDICINE (Vol. 29, pp. S328-S328). 233 SPRING ST, NEW YORK, NY 10013 USA: SPRINGER.
  • Krishnan, S., & Selvarajan, D. (2014). D104 CASE REPORTS: INTERSTITIAL LUNG DISEASE AND PLEURAL DISEASE: Stones Everywhere!. American Journal of Respiratory and Critical Care Medicine, 189, 1
  • Rahman, A., Debnath, P., Ahmed, A., Dalim, H. M., Karmakar, M., Sumon, M. F. I., & Khan, M. A. (2024). Machine learning and network analysis for financial crime detection: Mapping and identifying illicit transaction patterns in global black money transactions. Gulf Journal of Advance Business Research, 2(6), 250-272.
  • Chowdhury, M. S. R., Islam, M. S., Al Montaser, M. A., Rasel, M. A. B., Barua, A., Chouksey, A., & Chowdhury, B. R. (2024). PREDICTIVE MODELING OF HOUSEHOLD ENERGY CONSUMPTION IN THE USA: THE ROLE OF MACHINE LEARNING AND SOCIOECONOMIC FACTORS. The American Journal of Engineering and Technology, 6(12), 99-118.
  • Sumsuzoha, M., Rana, M. S., Islam, M. S., Rahman, M. K., Karmakar, M., Hossain, M. S., & Shawon, R. E. R. (2024). LEVERAGING MACHINE LEARNING FOR RESOURCE OPTIMIZATION IN USA DATA CENTERS: A FOCUS ON INCOMPLETE DATA AND BUSINESS DEVELOPMENT. The American Journal of Engineering and Technology, 6(12), 119-140.
  • Sumon, M. F. I., Rahman, A., Debnath, P., Mohaimin, M. R., Karmakar, M., Khan, M. A., & Dalim, H. M. (2024). Predictive Modeling of Water Quality and Sewage Systems: A Comparative Analysis and Economic Impact Assessment Using Machine Learning. in Library, 1(3), 1-18.
  • Al Montaser, M. A., Ghosh, B. P., Barua, A., Karim, F., Das, B. C., Shawon, R. E. R., & Chowdhury, M. S. R. (2025). Sentiment analysis of social media data: Business insights and consumer behavior trends in the USA. Edelweiss Applied Science and Technology, 9(1), 545-565.
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