Supply Chain Management Optimization and Prediction Model Based on Projected Stochastic Gradient

Alkahtani, Mohammed

doi:10.3390/su14063486

Cited by 8 publications

(4 citation statements)

References 46 publications

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“…The proposed model can predict the harvesting period accurately (RMSE = 0.199; MAPE = 4.84%) so as to help achieve a balance between production and sales in the sustainable supply chain and reduce resource waste for better sustainability [1,11,34].The feature selection (variable selection) method was adopted to select the features (variables) that can effectively represent input features (variables) of the model and reduce the complexity of the model, and better prediction results were obtained (LSTMFS is significantly better than LSTM. Please refer to Tables 3 and 4 for details.)…”

Section: Discussionmentioning

confidence: 99%

A Crop Harvest Time Prediction Model for Better Sustainability, Integrating Feature Selection and Artificial Intelligence Methods

et al. 2022

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Making an accurate crop harvest time prediction is a challenge for agricultural management. Previous studies of crop harvest time prediction were mainly based on statistical methods, and the features (variables) affecting it were determined by experience, resulting in its inaccuracy. To overcome these drawbacks, the objective of this paper is to develop a novel crop harvest time prediction model integrating feature selection and artificial intelligence (long short-term memory) methods based on real production and climate-related data in order to accurately predict harvest time and reduce resource waste for better sustainability. The model integrates a hybrid search for feature selection to identify features (variables) that can effectively represent input features (variables) first. Then, a long short-term memory model taking the selected features (variables) as input is used for harvest time prediction. A practical case (a large fruit and vegetable cooperative) is used to validate the proposed method. The results show that the proposed method (root mean square error (RMSE) = 0.199, mean absolute percentage error (MAPE) = 4.84%) is better than long short-term memory (RMSE = 0.565; MAPE = 15.92%) and recurrent neural networks (RMSE = 1.327; MAPE = 28.89%). Moreover, the nearer the harvest time, the better the prediction accuracy. The RMSE values for the prediction times of one week to harvesting period, two weeks to harvesting period, three weeks to harvesting period, and four weeks to harvesting period are 0.165, 0.185, 0.205, and 0.222, respectively. Compared with other existing studies, the proposed crop harvest time prediction model, LSTMFS, proves to be an effective method.

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Section: Discussionmentioning

confidence: 99%

A Crop Harvest Time Prediction Model for Better Sustainability, Integrating Feature Selection and Artificial Intelligence Methods

et al. 2022

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“…In addition, in a recent review, Refs. [26,27] show that the economic and environmental aspects of sustainability are the main context of sustainable supply chain and logistics where the social aspect is still limited. On the contrary, Ref.…”

Section: Discussionmentioning

confidence: 99%

Multi-Objective Optimization of the Organization’s Performance for Sustainable Development

Fechete

Nedelcu

2022

Sustainability

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Sustainable development can be defined in terms of three main components: ecologically sustainable, economically sustainable, and socially sustainable. Conflicting objectives are involved in sustainable development issues, of which the economic, environmental, and social objectives are the most important. Multi-criteria decision-making provides a systematic methodology that combines these conflicting objectives and can provide a rational compromise solution to complex decision-making issues. Using multi-objective optimization, this paper integrated three major objectives of organizational performance, maximizing sales, minimizing expenses, and maximizing productivity, all of which are combined for the sustainable development of the organization. All these conflicting problems were included in an objective function that allowed for solving the three objectives simultaneously and, thus, achieving economic, environmental, and social performance, in order to sustainably develop the organization. The input data included the objectives planned by the company regarding the turnover to be achieved, the production and delivery costs, and the productivity achieved by employees. Customer orders, production capacity, delivery costs, and resource consumption were also taken into account for optimization. With the help of multi-objective linear programming, the optimal number of parts produced and delivered by the company was determined so as to reach its economic, environmental, and social performance.

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“…The optimization of Supply Chain Management (SCM) holds significant importance for businesses aiming to bolster efficiency, trim costs, elevate customer satisfaction, and gain a competitive edge. Cost reduction is achieved through streamlined processes, minimized inventory holding costs, and enhanced resource utilization (Alkahtani, 2022;Sulem & Tapiero, 1995). SCM optimization, thus, enhances operational efficiency, slashes lead times, and boosts productivity across the entire supply chain network.…”

Section: Introductionmentioning

confidence: 99%

“…across the entire supply chain network (Alkahtani,2022;Sulem & Tapiero, 1995). Efficient resource utilization, including raw materials, production capacity, transportation assets, and labor, leads to cost savings and enhanced profitability (Alkahtani, 2022;Sulem & Tapiero, 1995). In summary, SCM optimization serves as a cornerstone for bolstering overall business performance, driving growth, and ensuring sustainability in today's competitive and dynamic business landscape.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Operational Efficiency and Cash Flow through Supply Chain Optimization in the Oil and Gas Sector

Roshan

2024

IJBM

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The oil and gas industry continues to play a vital role in meeting the world's energy needs. As renewable energy sources gain traction, there is an imperative to refine operational models, prioritizing sustainability, and cost-effectiveness. This study employs a comprehensive conceptual framework integrating critical analysis of business scenarios, product positioning, and market segmentation. The study adopts supply network optimization, encompassing demand forecasting, inventory management, transportation, production scheduling, cash flow management, and data analytics. The research evaluates three supply models: Satellite Manufacturing, Hub Manufacturing, and Contract Manufacturing. It employs scenario analysis to assess the impact of supply chain strategies on operating and cash cycles. The analysis reveals that Hub- based contract manufacturing is the most effective model. The research demonstrates that optimizing supply networks directly influences the operating and cash cycles of companies in the Oil and Gas industry. By reducing inventory levels, optimizing supplier payment terms, and streamlining production processes, organizations can achieve cost savings, improve efficiency, and enhance cash flow management. The study underscores the importance of thorough analysis and strategic decision-making in selecting supply chain models to enhance cost efficiency, operational effectiveness, and cash flow management. The study is limited to midstream and downstream services and may vary based on industry contexts. Future research could explore additional supply chain strategies across different sectors. This research contributes to the sustainable consumption and value co-creation literature by offering practical insights for businesses operating in the oil and gas industry or similar contexts. It provides a framework for optimizing supply chain models to improve cost efficiency and cash flow management, thereby enhancing overall business performance.

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Supply Chain Management Optimization and Prediction Model Based on Projected Stochastic Gradient

Cited by 8 publications

References 46 publications

A Crop Harvest Time Prediction Model for Better Sustainability, Integrating Feature Selection and Artificial Intelligence Methods

A Crop Harvest Time Prediction Model for Better Sustainability, Integrating Feature Selection and Artificial Intelligence Methods

Multi-Objective Optimization of the Organization’s Performance for Sustainable Development

Enhancing Operational Efficiency and Cash Flow through Supply Chain Optimization in the Oil and Gas Sector

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