An optimised resource allocation scheme based on NOMA-SWIPT in cooperative network

Potluri, Sreeram; Swarnalatha, S.

doi:10.1080/00207217.2023.2248567

Cited by 1 publication

(5 citation statements)

References 32 publications

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“…In addressing the challenges posed by supply chain uncertainty, the strategic implementation of demand forecasting emerges as a pivotal solution. Within supply chain management (SCM), demand forecasting employs various statistical analytic methods, such as time-series analysis and regression analysis, as highlighted by [3][5]. The supply chain generates vast and highdimensional data at multiple points, encompassing factors like items, supplier capacity, orders, shipments, and interactions with consumers and retailers.…”

Section: Issn: 2550-794xmentioning

confidence: 99%

“…To achieve this adaptability, big data analytics play a pivotal role by uncovering links between demand data across supply chain networks and deriving forecasting rules from these interactions. The implementation of such approaches involves the utilization of sophisticated machineprogrammed algorithms, although these come at a computational cost [3]. Despite their complexity, these advanced analytics empower supply chain professionals to navigate the intricacies of demand forecasting in a more intelligent and responsive manner.…”

Section: Issn: 2550-794xmentioning

confidence: 99%

“…In the domain of supply chain management (SCM), there is a growing emphasis on the integration of big data analytics (BDA) for demand forecasting. Metaresearch studies have delved into the applications of BDA in SCM, shedding light on the advantages, challenges, and gaps in demand forecasting within supply chains [3]. The convergence of big data, advanced analytics, and cloud services has streamlined the handling of vast datasets, opening new avenues for data-driven demand forecasting and planning [13].…”

Section: Issn: 2550-794xmentioning

confidence: 99%

“…Additionally, teams that want to reach decision-makers must compete with a lot of noise from the data, and marketing and sales teams want to measure their success. This is because there are many features in the data that present the curse of dimensionality and inefficient forecasting given the amount of data available in the databases [3]. The answer is to use dimensionality reduction techniques to minimize the amount of features in the dataset while retaining (or even enhancing) the effectiveness of any prediction model [27].…”

Section: Issn: 2550-794xmentioning

confidence: 99%

“…While capacity, demand, and cost are recognized as standard variables in typical SCM challenges, uncertainties arise from shifts in client demand, supply chain dynamics, organizational risks, and lead times. Notably, demand uncertainty significantly influences SC performance, impacting production scheduling, inventory management, and transportation arrangements [3]. To maintain competitiveness and enhance profit margins, organizations are increasingly embracing precision marketing strategies.…”

Section: Introductionmentioning

confidence: 99%

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Enhancing Sales Demand Prediction for Supply Chain Management: A Dimensionality Reduction Approach at Mukwano Company Limited, Uganda

Mercy,

Paul,

Eze

2024

IDOSR-JSR

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In the dynamic landscape of today's business environment, the accurate prediction of sales demand plays a pivotal role for companies striving to maintain competitiveness. This study focuses on refining demand sales prediction for supply chain management at Mukwano Industries Limited through the application of dimensionality reduction techniques. The research involved a systematic iterative process encompassing problem identification, data preparation, modeling, evaluation, validation, optimization, and documentation. To ensure confidentiality, secondary data from the Mukwano IT department underwent meticulous merging and anonymization. Four dimensionality reduction algorithms, namely PCA, SVD, MDS, and t-SNE, were employed and evaluated using RMSE metrics. The results reveal that MDS and t-SNE exhibited exceptional performance, achieving accuracies of 89% and 88.8%, respectively. PCA and SVD also demonstrated commendable performance with an accuracy of 82.4%. The study underscores the crucial role of dimensionality reduction in enhancing predictive accuracy and optimizing inventory management. Recommendations include the incorporation of feature selection and regularization techniques to address the challenges associated with the curse of dimensionality. In conclusion, this research contributes valuable insights into the effectiveness of diverse dimensionality reduction techniques for demand prediction and inventory management. Additionally, the study highlights the need to address the curse of dimensionality and suggests exploring further research avenues in other aspects of supply chain management. These recommendations are essential for guiding future research efforts in this evolving field. Keywords: Demand sales prediction, Supply chain management, Feature selection, Inventory management, Predictive accuracy

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Section: Issn: 2550-794xmentioning

confidence: 99%

Section: Issn: 2550-794xmentioning

confidence: 99%

Section: Issn: 2550-794xmentioning

confidence: 99%

Section: Issn: 2550-794xmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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