Big Data Application in Supply Chain Management: Scopus Based Literature Review

Mohsen, Baha M.

doi:10.2139/ssrn.3613716

Cited by 2 publications

(3 citation statements)

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“…A critical facet in the evolution of demand forecasting models is the exploration and development of an optimal model through data dimensionality reduction techniques. Dimensionality reduction aids in curtailing the number of features, extracting vital characteristics, and amalgamating similar features to amplify model performance [15]. For example, in the printed circuit board industry, the successful implementation of K-means clustering has proven effective in eliminating noise from input data and enhancing sales forecasting precision [16].…”

Section: Issn: 2550-794xmentioning

confidence: 99%

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%

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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“…Advancements in information technology and communication, transportation and material handling, and high volume data processing and transmission are revolutionizing logistics control systems. The use of big data tools in logistics and supply chain management gives great advantages as it provides better decision making, improved efficiency, cost reduction, better risk management, and better visibility and competition [3]. Communication technology enables better, faster, and reliable supply chains, communications take place between any firm, suppliers, customers, and other members involved in the chain.…”

Section: Logisticsmentioning

confidence: 99%

“…(1) utilizing existing strengths (2) restructuring profit in contractual agreements (3) introducing the new business model (4) new incentive schemes ( 5) introducing new skills and ( 6) expressing major significance and special interest of environmental advantage.…”

Section: Sustainable Supply Chain and Logisticmentioning

confidence: 99%

Principles of Sustainable Logistics

Mohsen¹

2022

Logistics Engineering

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Effective logistics largely contributes to the success of business through quick deliveries in minimum time and cost. Logistics is the process of getting material, product, and service where and when they are needed. When addressing the concept of sustainable logistics, it is important to consider the three dimensions of sustainability: economic, social, and environmental. Sustainable logistics is beyond going green and being environmentally friendly, it has an influence on manufacturing processes, starting from where raw materials are obtained, processes involved, use, and potential recycling of the product or service. In analyzing the problem of evaluating logistic operation performance, sustainability will be one among other criteria for evaluation. Selecting a logistics partner who cares about sustainability will help in achieving company’s strategy. The chapter discusses the concept of sustainability applied to logistics. From the main definition of the logistics system, which includes the flow of materials and information, to the goal of sustainable logistics, which includes increasing profitability and reducing the environmental impact. Sustainable development involves coordination between environment and economics to reach social, economic, and environmental sustainability. The chapter aims to help managers, practitioners, scholars, and students to understand the key attributes of sustainable logistics and supply chain in general.

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Big Data Application in Supply Chain Management: Scopus Based Literature Review

Cited by 2 publications

References 0 publications

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

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

Principles of Sustainable Logistics

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