WITHDRAWN: Backorder prediction in the supply chain using machine learning

Malviya, Lokesh; Shreemali, Jitendra; Ojha, Rahul Kumar; Chakrabarti, Prąsun; Poddar, Sandeep

doi:10.1016/j.matpr.2020.11.558

Cited by 10 publications

(7 citation statements)

References 14 publications

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“…Backorder expectations within the supply chain utilizing the machine studying model are introduced by Malviya et al [31]. This paper considered backorder forecasts utilizing different machine learning calculations and gives essentially comprehensible and attainable backorder choice scenarios.…”

Section: 3mentioning

confidence: 99%

Retailing and servicing strategies for an imperfect production with variable lead time and backorder under online-to-offline environment

Choi

Dey

Sarkar

2023

JIMO

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<p style='text-indent:20px;'>Online-to-offline (O2O) retailing strategy is one of the trending topics in the business industry. The current study focuses on an advanced retailing strategy for a single item for a retailer and a manufacturer. Reminding the revolution in the internet world, a dual-channel retailing system is developed in this current study. The retailer offers their item through online and offline mode, though the producer prefers only offline mode. Besides, manufacturer provide different services to their clients through the product's life cycle to keep the company's brand image. The product's demand depends on the selling price and its service for both channels (offline and online) to their customers. To upgrade the services, some investments are implied to decrease the lead time. In expansion, to increase the demand and optimize the system benefit, the manufacturer gives their customers different services (unpaid, partially paid, full paid). The supply chain's benefit is optimized beside the ideal esteem of the decision variables. {Application of different service strategies in O2O retailing are near about <inline-formula><tex-math id="M1">\begin{document}$ 71\% $\end{document}</tex-math></inline-formula> beneficial compared to traditional supply chain. Numerical experiment proved that full-paid services are near about <inline-formula><tex-math id="M2">\begin{document}$ 79\% $\end{document}</tex-math></inline-formula> beneficial compare to partially paid service; however, partially paid services are recommended for industrial benefit.} Different numerical examples, exceptional cases, and sensitivity analyses were performed to prove the model's applicability along with graphical representations.</p>

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Section: 3mentioning

confidence: 99%

Retailing and servicing strategies for an imperfect production with variable lead time and backorder under online-to-offline environment

Choi

Dey

Sarkar

2023

JIMO

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“…Mathematical Problems in Engineering power, as well as the success of ML techniques, AI has been a resurgence. It has also led to apply the potential of AI techniques in SCRM by researchers in processes such as prediction, risk identification, assessment, and response [62][63][64][65][66][67][68][69].…”

Section: Application Of Machine Learning Algorithms In Managingmentioning

confidence: 99%

Application of Machine Learning in Supply Chain Management: A Comprehensive Overview of the Main Areas

Tirkolaee

Sadeghi

Mooseloo

et al. 2021

Mathematical Problems in Engineering

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In today’s complex and ever-changing world, concerns about the lack of enough data have been replaced by concerns about too much data for supply chain management (SCM). The volume of data generated from all parts of the supply chain has changed the nature of SCM analysis. By increasing the volume of data, the efficiency and effectiveness of the traditional methods have decreased. Limitations of these methods in analyzing and interpreting a large amount of data have led scholars to generate some methods that have high capability to analyze and interpret big data. Therefore, the main purpose of this paper is to identify the applications of machine learning (ML) in SCM as one of the most well-known artificial intelligence (AI) techniques. By developing a conceptual framework, this paper identifies the contributions of ML techniques in selecting and segmenting suppliers, predicting supply chain risks, and estimating demand and sales, production, inventory management, transportation and distribution, sustainable development (SD), and circular economy (CE). Finally, the implications of the study on the main limitations and challenges are discussed, and then managerial insights and future research directions are given.

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“…Table 1 shows the values of hyperparameters used in the machine learning algorithms. The multi-classification machine learning algorithms used in this study are modeled using library functions supported by Python's scikit-learn [9]. Table 1 shows the values of hyperparameters used in the machine learning algorithms.…”

Section: Data Collectionmentioning

confidence: 99%

“…Looking at the studies applying machine learning to the supply chain, Sang [8] explained the genetic algorithm combined with the SVM and the BP neural network to assess the credit risk of the supply chain of the finance industry considering information sharing and proposed that the BP neural network had a better classification accuracy than the SVM. Malviya et al [9] used machine learning algorithms (artificial neural network, random tree, logistic regression, c5.0, Bayesian network, support vector machine, and discriminant analysis) to solve supply chain shortages or backorder problems, and through the method the back order was predicted and a feasible backorder scenario was presented. Baryannis et al [10] presented a supply chain risk prediction framework by applying machine learning techniques to supply chain risk management.…”

Section: Introductionmentioning

confidence: 99%

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Determining the Tiers of a Supply Chain Using Machine Learning Algorithms

Park

2021

Symmetry

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Companies in the same supply chain influence each other, so sharing information enables more efficient supply chain management. An efficient supply chain must have a symmetry of information between participating entities, but in reality, the information is asymmetric, causing problems. The sustainability of the supply chain continues to be threatened because companies are reluctant to disclose information to others. If companies participating in the supply chain do not disclose accurate information, the next best way to improve the sustainability of the supply chain is to use data from the supply chain to determine each enterprise’s information. This study takes data from the supply chain and then uses machine learning algorithms to find which enterprise the data refer to when new data from unknown sources arise. The machine learning algorithms used are logistic regression, random forest, naive Bayes, decision tree, support vector machine, k-nearest neighbor, and multi-layer perceptron. Indicators for evaluating the performance of multi-class classification machine learning methods are accuracy, confusion matrix, precision, recall, and F1-score. The experimental results showed that LR and MLP accurately predicted companies (tiers), but NB, DT, RF, SVM, and K-NN did not accurately predict companies. In addition, the performance similarity of machine learning algorithms through experiments was classified into LR and MLP groups, NB and DT groups, and RF, SVM, and K-NN groups.

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WITHDRAWN: Backorder prediction in the supply chain using machine learning

Cited by 10 publications

References 14 publications

Retailing and servicing strategies for an imperfect production with variable lead time and backorder under online-to-offline environment

Retailing and servicing strategies for an imperfect production with variable lead time and backorder under online-to-offline environment

Application of Machine Learning in Supply Chain Management: A Comprehensive Overview of the Main Areas

Determining the Tiers of a Supply Chain Using Machine Learning Algorithms

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