Developing Accurate Predictive Model Using Computational Intelligence for Optimal Inventory Management

Siek, Michael; Guswanto, Kevin

doi:10.1109/icts52701.2021.9608492

Cited by 3 publications

(2 citation statements)

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“…The findings reveal that computational intelligence can significantly reduce inventory costs and waste by enabling precise demand forecasting and inventory optimization. This research underscores the instrumental role of technology in leveraging data for improved decision-making and operational efficiency in inventory management (Siek & Guswanto, 2021). The synthesis of these studies elucidates the indispensable role of data quality and technology in enhancing the accuracy of financial modeling and inventory management systems.…”

Section: Role Of Data Quality and Technology In Modeling Accuracymentioning

confidence: 86%

“…The research illustrates the transformative potential of technology in resolving data quality issues, which is equally applicable to the domain of inventory management, where accurate and reliable data are crucial for optimizing inventory levels and reducing costs (Cheng et al, 2022). Siek and Guswanto (2021) focus on the development of accurate predictive models using computational intelligence for optimal inventory management. Their study employs various machine learning algorithms to build predictive models from time series data of restaurant inventory.…”

Section: Role Of Data Quality and Technology In Modeling Accuracymentioning

confidence: 99%

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Advanced financial modeling techniques for reducing inventory costs: A review of strategies and their effectiveness in manufacturing

Ayodeji Enoch Adegbola,

Mayokun Daniel Adegbola,

Prisca Amajuoyi

et al. 2024

Financ. account. res. j.

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This study investigates the effectiveness of advanced financial modeling techniques in reducing inventory costs within the manufacturing sector, with a focus on the integration of predictive analytics, artificial intelligence (AI), and machine learning. Employing a systematic literature review and content analysis, the research scrutinizes academic journals, conference proceedings, and industry reports published between 2015 and 2024. The methodology hinges on predefined inclusion and exclusion criteria to ensure the relevance and quality of the selected literature, followed by a thematic analysis to distill key insights. Key findings reveal that advanced financial modeling significantly enhances demand forecasting accuracy, thereby optimizing inventory levels and reducing associated costs. The integration of AI and machine learning technologies not only streamlines inventory management processes but also enables manufacturers to adapt swiftly to market fluctuations, thus minimizing waste and improving operational efficiency. Despite the evident benefits, challenges such as data quality, technological integration, and ethical considerations in AI implementation were identified. The study recommends that manufacturers prioritize the adoption of these advanced models, invest in relevant technologies, and foster a culture of continuous learning and adaptation. Future research directions include exploring the scalability of these models for SMEs, assessing the long-term sustainability of cost reductions, and investigating the potential of emerging technologies like blockchain and IoT in inventory management. Finally, the strategic implementation of advanced financial modeling techniques offers a pathway for manufacturers to enhance competitiveness, achieve cost efficiencies, and navigate the complexities of the digital era in inventory management. Keywords: Advanced Financial Modeling, Inventory Cost Reduction, Predictive Analytics, Manufacturing Sector.

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Section: Role Of Data Quality and Technology In Modeling Accuracymentioning

confidence: 86%

Section: Role Of Data Quality and Technology In Modeling Accuracymentioning

confidence: 99%