An Automation Framework for Supply Chain Inventory Management using Predictive Business Analytics

Rautaray, Siddharth Swarup; Pandey, Manjusha; Das, Indrashis; Sharma, Bharat; Mishra, Sarita

doi:10.1109/ocit56763.2022.00075

Cited by 2 publications

(5 citation statements)

References 15 publications

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“…High-quality, relevant, and timely data are crucial for developing accurate predictive models. Rautaray et al (2022) emphasize the importance of managing inventory through predictive business analytics, highlighting how the algorithm behind predictive production merges with inventory precision to generate profit. This approach underscores the significance of data quality in ensuring the reliability of predictive outcomes, enabling firms to maintain optimal inventory levels and maximize profitability.…”

Section: Discussionmentioning

confidence: 99%

“…The study identifies key factors influencing supply chain efficiency, such as demand forecasting, inventory management, and transportation planning, highlighting the critical role of predictive analytics in improving the overall performance of manufacturing supply chains. Rautaray et al (2022) discuss an automation framework for supply chain inventory management using predictive business analytics. This framework employs predictive analytics to forecast inventory needs, thereby ensuring optimal stock levels and minimizing losses.…”

Section: Classification Of Predictive Analytics Techniques In Supply ...mentioning

confidence: 99%

“…This shift towards big data analytics in supply chains represents a pivotal moment in the historical evolution of predictive analytics, emphasizing the transition from intuition-based to data-driven decisionmaking. Rautaray et al (2022) discuss the automation of supply chain inventory management using predictive business analytics, illustrating the practical application of predictive models in optimizing inventory levels and minimizing losses. This advancement in predictive analytics underscores the ongoing evolution towards more automated and intelligent supply chain systems, where predictive models play a crucial role in enhancing operational efficiency and profitability.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

The Role of Predictive Analytics in Optimizing Supply Chain Resilience: A Review of Techniques and Case Studies

Adebunmi Okechukwu Adewusi,

Abiola Moshood Komolafe,

Emuesiri Ejairu

et al. 2024

Int. j. manag. entrep. res

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This study investigates the transformative impact of predictive analytics on enhancing supply chain resilience (SCR). Employing a systematic literature review and content analysis, the research aims to explore the integration, challenges, and strategic implications of predictive analytics within the supply chain ecosystem. Focusing on literature from 2014 to 2023, the study synthesizes insights from peer-reviewed articles and conference papers, adhering to stringent inclusion and exclusion criteria to ensure relevance and recency. The findings reveal that predictive analytics significantly contributes to supply chain agility, flexibility, and responsiveness, thereby bolstering SCR against disruptions. Key challenges identified include data privacy concerns, the need for skilled personnel, and the integration of predictive analytics into existing supply chain frameworks. Despite these challenges, the future outlook for predictive analytics in SCM is promising, with potential for unprecedented efficiency, sustainability, and competitive advantage. Strategic recommendations for practitioners emphasize the importance of developing predictive analytics capabilities, prioritizing data governance, and continuous staff training. For policymakers, the study suggests the need for standards and regulations that encourage innovation while ensuring the ethical use of predictive analytics. Finally, predictive analytics is pivotal in revolutionizing SCM, offering pathways for innovation, efficiency, and enhanced resilience. Future research should focus on the integration of emerging technologies, ethical data use frameworks, and overcoming adoption barriers, particularly in SMEs and developing economies. This study underscores the critical role of predictive analytics in the future of SCM, driving forward the agenda for research and practice in this evolving field. Keywords: Predictive Analytics, Supply Chain Resilience, Systematic Literature Review, Strategic Implications.

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Section: Discussionmentioning

confidence: 99%

Section: Classification Of Predictive Analytics Techniques In Supply ...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

The Role of Predictive Analytics in Optimizing Supply Chain Resilience: A Review of Techniques and Case Studies

Adebunmi Okechukwu Adewusi,

Abiola Moshood Komolafe,

Emuesiri Ejairu

et al. 2024

Int. j. manag. entrep. res

View full text Add to dashboard Cite

show abstract

“…This section explores the future trends in inventory management, focusing on the integration of predictive analytics, artificial intelligence (AI), and the Internet of Things (IoT), and their potential to transform inventory cost reduction strategies in manufacturing. Rautaray et al (2022) present an automation framework for supply chain inventory management that leverages predictive business analytics. Their study underscores the significance of predictive analytics in optimizing inventory levels, ensuring that firms maintain an optimal stock to meet demand without incurring excess costs or losses.…”

Section: Future Trends: Predictive Analytics and Automation In Invent...mentioning

confidence: 99%

“…Their study underscores the significance of predictive analytics in optimizing inventory levels, ensuring that firms maintain an optimal stock to meet demand without incurring excess costs or losses. By employing algorithms that analyze time series data to catch trends and seasonality, the framework aids in making informed decisions about inventory levels, thereby enhancing profitability (Rautaray et al, 2022). Ibrahim, Xue, & Gueye (2021) delves into the improvement of demand forecasting models through artificial neural networks, highlighting the critical role of forecasting in minimizing inventory costs while maximizing productivity and profits.…”

Section: Future Trends: Predictive Analytics and Automation In Invent...mentioning

confidence: 99%

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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An Automation Framework for Supply Chain Inventory Management using Predictive Business Analytics

Cited by 2 publications

References 15 publications

The Role of Predictive Analytics in Optimizing Supply Chain Resilience: A Review of Techniques and Case Studies

The Role of Predictive Analytics in Optimizing Supply Chain Resilience: A Review of Techniques and Case Studies

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

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