A deep learning-based approach for performance assessment and prediction: A case study of pulp and paper industries

Jauhar, Sunil Kumar; Raj, Praveen Vijaya Raj Pushpa; Kamble, Sachin S.; Pratap, Saurabh; Gupta, Shivam; Belhadi, Amine

doi:10.1007/s10479-022-04528-3

Cited by 20 publications

(5 citation statements)

References 64 publications

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“…In general, AI-based neural network algorithms have been demonstrated to be an excellent predictive tool (Patel et al 2017), as evidenced by particle size prediction (Gautam et al 2021), equipment effectiveness prediction (Sivakumar et al 2022), and paper industry performance prediction (Jauhar et al 2022). Likewise, the robust search algorithm of Back Propagation Neural Network (BPNN) also a good technique for predicting facilities (Zhang and Qu 2021).…”

Section: Review Of Related Studiesmentioning

confidence: 99%

Prediction of production facility priorities using Back Propagation Neural Network for bus body building industries: a post pandemic research article

Sivakumar¹,

Singh

Arulkirubakaran

et al. 2022

Qual Quant

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The pandemic recession has caused enormous disturbances in many industrialized countries. The massive disruption of the supply chain of production is affecting manufacturing companies operating in and around India. Particularly the medium-sized bus body building works have been reduced, due to its compound anomalies. The integrated view of the production facility priorities is not an easy task. Since it is difficult for available labour to conduct an entire project, the completion of a production process is delayed. But still, the dilemma remains as to how production managers can correctly interpret the priorities of the facility. Indeed, this is a problem missing from the previous study. Fortunately, in the current competitive environment, it is essentially needed. This study has been used Back Propagation Neural Network (BPNN) approach for predicting production facility priorities. The experimental results confirm the suitability of the model for predicting priorities. A real-world problem is taken into account in making use of the model output. In this sense, this total solution facilitates production managers in assessing and enhancing the production facilities. The findings emphasize the priority of "equipment effectiveness, labour scheduling and communication" in order to strengthen the post-pandemic production facility.

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Section: Review Of Related Studiesmentioning

confidence: 99%

Prediction of production facility priorities using Back Propagation Neural Network for bus body building industries: a post pandemic research article

Sivakumar¹,

Singh

Arulkirubakaran

et al. 2022

Qual Quant

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“…Previous research has shown that the complementary features of DEA with ANN integration can be built into a promising integrated model (e.g. Misiunas et al, 2016;Rezaee et al, 2018;Jauhar et al, 2022;Pratap et al, 2022). Athanassopoulos and Curram (1996) were the first to exploit the corresponding properties of DEA and ANN.…”

Section: The Efficiency Of Canadian Universitiesmentioning

confidence: 99%

A DEA-ANN-based analytical framework to assess and predict the efficiency of Canadian universities in a service supply chain context

Jauhar

Zolfagharinia²,

Amin³

2022

BIJ

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PurposeThis research is about embedding service-based supply chain management (SCM) concepts in the education sector. Due to Canada's competitive education sector, the authors focus on Canadian universities.Design/methodology/approachThe authors develop a framework for evaluating and forecasting university performance using data envelopment analysis (DEA) and artificial neural networks (ANNs) to assist education policymakers. The application of the proposed framework is illustrated based on information from 16 Canadian universities and by investigating their teaching and research performance.FindingsThe major findings are (1) applying the service SCM concept to develop a performance evaluation and prediction framework, (2) demonstrating the application of DEA-ANN for computing and predicting the efficiency of service SCM in Canadian universities, and (3) generating insights to enable universities to improve their research and teaching performances considering critical inputs and outputs.Research limitations/implicationsThis paper presents a new framework for universities' performance assessment and performance prediction. DEA and ANN are integrated to aid decision-makers in evaluating the performances of universities.Practical implicationsThe findings suggest that higher education policymakers should monitor attrition rates at graduate and undergraduate levels and provide financial support to facilitate research and concentrate on Ph.D. programs. Additionally, the sensitivity analysis indicates that selecting inputs and outputs is critical in determining university rankings.Originality/valueThis research proposes a new integrated DEA and ANN framework to assess and forecast future teaching and research efficiencies applying the service supply chain concept. The findings offer policymakers insights such as paying close attention to the attrition rates of undergraduate and postgraduate programs. In addition, prioritizing internal research support and concentrating on Ph.D. programs is recommended.

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“…More publications and studies exist on e-commerce customer segmentation regarding return products and their attitudes toward purchase behavior (Ma, 2022). From this vantage point, we propose a digital transformation-based approach that assesses whether RFM segmentation analysis can enhance consumer segmentation (Pratap et al ., 2022; Jauhar et al. , 2022).…”

Section: Introductionmentioning

confidence: 99%

Digital transformation technologies to analyze product returns in the e-commerce industry

Jauhar

Chakma

Kamble

et al. 2023

JEIM

Self Cite

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PurposeAs e-commerce has expanded rapidly, online shopping platforms have become widespread in India and throughout the world. Product return, which has a negative effect on the E-Commerce Industry's economic and ecological sustainability, is one of the E-Commerce Industry's greatest challenges in light of the substantial increase in online transactions. The authors have analyzed the purchasing patterns of the customers to better comprehend their product purchase and return patterns.Design/methodology/approachThe authors utilized digital transformation techniques-based recency, frequency and monetary models to better understand and segment potential customers in order to address personalized strategies to increase sales, and the authors performed seller clustering using k-means and hierarchical clustering to determine why some sellers have the most sales and what products they offer that entice customers to purchase.FindingsThe authors discovered, through the application of digital transformation models to customer segmentation, that over 61.15% of consumers are likely to purchase, loyal customers and utilize firm service, whereas approximately 35% of customers have either stopped purchasing or have relatively low spending. To retain these consumer segments, special consideration and an enticing offer are required. As the authors dug deeper into the seller clustering, we discovered that the maximum number of clusters is six, while certain clusters indicate that prompt delivery of the goods plays a crucial role in customer feedback and high sales volume.Originality/valueThis is one of the rare study that develops a seller segmentation strategy by utilizing digital transformation-based methods in order to achieve seller group division.

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A deep learning-based approach for performance assessment and prediction: A case study of pulp and paper industries

Cited by 20 publications

References 64 publications

Prediction of production facility priorities using Back Propagation Neural Network for bus body building industries: a post pandemic research article

Prediction of production facility priorities using Back Propagation Neural Network for bus body building industries: a post pandemic research article

A DEA-ANN-based analytical framework to assess and predict the efficiency of Canadian universities in a service supply chain context

Digital transformation technologies to analyze product returns in the e-commerce industry

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