A decision support system for classifying supplier selection criteria using machine learning and random forest approach

Ali, Md. Ramjan; Nipu, Shah Md. Ashiquzzaman; Khan, Sharfuddin Ahmed

doi:10.1016/j.dajour.2023.100238

Cited by 19 publications

(6 citation statements)

References 101 publications

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“…• PRISMA selects the criteria to be used by Random Forest (RF) (Ali et al, 2023) • Simulation model generates the database using the K-Nearest Neighbors Algorithm and Logistic Regression (Cavalcante et al, 2019) • Data Envelopment Analysis (DEA) selects the providers that are stored in the database to train the Adaboost Algorithm (Cheng et al, 2017)…”

Section: Inventory Managementmentioning

confidence: 99%

Integration of machine learning in the supply chain for decision making: A systematic literature review

Polo-Triana,

Gutierrez,

Leon-Becerra

2024

JIEM

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Purpose: This study presents a systematic literature review that provides a broad and holistic view of how machine learning can be used and integrated to enhance decision-making in various areas of the supply chain, highlighting its combination with other techniques and models.Design/methodology/approach: An exhaustive literature review used three sets of keywords in the Scopus and Web of Science (WoS) databases. Through a rigorous filtering process, 70 articles were selected from an initial total of 410, focusing on those that specifically addressed the intersection of machine learning and decision-making in supply chain management.Findings: Machine learning has proven to be an essential tool in the supply chain, with applications in inventory management, logistics, and transportation, among others. Its integration with other techniques has led to significant advances in decision-making, improving efficiency in complex environments. Combining machine learning methods with traditional techniques has been particularly effective, and integration with emerging technologies has opened up new application possibilities.Originality/value: Unlike previous studies that focused on specific areas, this study offers a broad perspective on the application of machine learning in the supply chain. Additionally, combining machine learning techniques with other models is highlighted, representing added value for the scientific community and suggesting new avenues for future research.

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Section: Inventory Managementmentioning

confidence: 99%

Integration of machine learning in the supply chain for decision making: A systematic literature review

Polo-Triana,

Gutierrez,

Leon-Becerra

2024

JIEM

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“…The random index value is affected by the order of the n matrix [36]. Thus, the Consistency Ratio can be formulated in Equation (5).…”

Section: Consistency Testmentioning

confidence: 99%

“…The procurement process encompasses identifying, sourcing, and managing materials to ensure high-quality supplies for the company's operations [4]. 10.12928/ijio.v4i2.8127 Suppliers play a pivotal role in providing essential goods to the company [5]. Even a highly efficient company becomes ineffective if its suppliers fail to deliver quality materials or meet agreed-upon delivery schedules [6].…”

Section: Introductionmentioning

confidence: 99%

Supplier's selection of plate material using analytical hierarchy process and additive ratio assessment methods

Salsabila,

Ernawati

2023

Int. J. Ind. Optim.

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PT PAL Indonesia (PERSERO) is among the prominent shipyard companies in Indonesia that currently employs a simplistic supplier selection weighting system, prioritizing low prices and material specification conformity. This approach often leads to subjective assessments, making it challenging for the company to identify suitable suppliers from a large pool. Therefore, this study proposed a methodology to enhance supplier selection by incorporating additional criteria based on Dickson's criteria and company policies. The Analytical Hierarchy Process (AHP) and Additive Ratio Assessment (ARAS) methods were utilized for this purpose. The findings indicate that PT Krakatau Steel (A1) emerges as the top-ranked supplier with a Ki value of 0.19, followed by PT Diansakti Sejahtera (A5) in second place with a Ki value of 0.157, and PT Gunawan Dianjaya Steel (A3) in third place with a Ki value of 0.152. PT Jastindo Raya (A4) secures the fourth position with a Ki value of 0.151, while PT Gunung Raja Paksi (A2) takes the fifth and final spot with a Ki value of 0.15. This research helps the company effectively select the best suppliers, particularly in the procurement sector, by employing the AHP-ARAS method and considering Dickson's criteria, thereby addressing existing gaps the company encounters.

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“…Random Forest has proven successful in diverse applications, including surface water detection [9], human gait recognition [10], supplier selection criteria classification [11], glaucoma detection [12], and many others. Past studies have shown that Random Forest exhibits superior generalization performance, faster learning speed, and a more straightforward implementation process without requiring extensive and timeconsuming parameter tuning for diagnosing purposes [13].…”

Section: Introductionmentioning

confidence: 99%

Optimizing Heart Attack Diagnosis Using Random Forest With Bat Algorithm and Greedy Crossover Technique

Ardiyansa,

Maharani,

Anam

et al. 2024

BAREKENG: J. Math. & App.

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Cardiovascular disease stands as one of the primary contributors to global mortality, with the World Health Organization (WHO) reporting approximately 17.9 million deaths annually. Swift and accurate diagnosis of heart attacks is crucial to ensure timely and specialized intervention for patients afflicted by this ailment. A machine learning algorithm that can be employed for addressing such issues is the Random Forest algorithm. However, the efficacy of the model is significantly influenced by the features selected during the training phase. To mitigate this, the Binary Bat Algorithm (BBA) with greedy crossover has been utilized to enhance feature selection within the model. This approach is particularly adept at preventing convergence issues often associated with local minima. The optimal parameters for BBA with greedy crossover are determined to be , , , and . With these parameters, the proposed algorithm identifies the most relevant features, including age, gender, cp, chol, thalach, oldpeak, slope, and ca, achieving an accuracy of 94.19% on the training data and 91.8% on the test data. Furthermore, the precision and recall values for both classes range from 0.87 to 0.96, contributing to an approximate -score of 0.92. The proposed method has increased its -score by 0.05 if compared with the regular Random Forest model. These results underscore the effectiveness of the proposed algorithm in providing accurate and reliable predictions for heart disease diagnosis. As such, this model makes diagnosing heart attack more convenient and effective because it does not require too much medical features or patient data. Hopefully, the results of this research help medical practitioners make better and timely decisions in the diagnosis and treatment of heart attacks, as well as assist in planning more effective public health programs for heart attack prevention.

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A decision support system for classifying supplier selection criteria using machine learning and random forest approach

Cited by 19 publications

References 101 publications

Integration of machine learning in the supply chain for decision making: A systematic literature review

Integration of machine learning in the supply chain for decision making: A systematic literature review

Supplier's selection of plate material using analytical hierarchy process and additive ratio assessment methods

Optimizing Heart Attack Diagnosis Using Random Forest With Bat Algorithm and Greedy Crossover Technique

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