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A Role of Network Data Envelopment Analysis Approach in Manufacturing Industry: Review of Last 5 years

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Reliability Engineering for Industrial Processes

Part of the book series: Springer Series in Reliability Engineering ((RELIABILITY))

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Abstract

In recent years, the manufacturing industry has been recognized as a key driver of economic growth and development of a nation. It generates significant value-added output, creates employment opportunities, and spurs technological advancements. As a result, performance evaluation of manufacturing firms has become a crucial task for assessing their efficiency, identifying improvement areas, and sustaining growth in a complex network environment. This study aims to explore the application of Network Data Envelopment Analysis (NDEA) models in the manufacturing industry to assess the efficiency of interconnected manufacturing units. By considering the complex relationships and interdependencies among various entities within the manufacturing process, these models offer a comprehensive approach to evaluate the efficiency of the decision-making unit (DMU) that is a manufacturing firm. Finally, this study shows that NDEA models provide valuable insights to decision-makers by identifying areas for improvement and suggesting strategies to enhance efficiency of the system.

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References

  1. Jauhar SK, Raj PVRP, Kamble S, Pratap S, Gupta S, Belhadi A (2022) A deep learning-based approach for performance assessment and prediction: a case study of pulp and paper industries. Ann Oper Res. https://doi.org/10.1007/s10479-022-04528-3

    Article  Google Scholar 

  2. Susaeta A, Rossato FG (2021) Efficiency of pulp and paper industry in the production of pulp and bioelectricity in Brazil. For Policy Econ 128:102484. https://doi.org/10.1016/j.forpol.2021.102484

    Article  Google Scholar 

  3. Müller FM, de Oliveira D, Michels C (2023) Current status, gaps and challenges of rendering industries wastewater. J Water Process Eng 52:103480. https://doi.org/10.1016/j.jwpe.2022.103480

    Article  Google Scholar 

  4. Panwar A, Olfati M, Pant M, Snasel V (2022) A review on the 40 years of existence of data envelopment analysis models: historic development and current trends. Arch Comput Methods Eng 29:5397–5426. https://doi.org/10.1007/s11831-022-09770-3

    Article  Google Scholar 

  5. Kao C (2014) Network data envelopment analysis: a review. Eur J Oper Res 239:1–16. https://doi.org/10.1016/j.ejor.2014.02.039

    Article  MathSciNet  Google Scholar 

  6. Chen Y, Cook WD, Li N, Zhu J (2009) Additive efficiency decomposition in two-stage DEA. Eur J Oper Res 196:1170–1176. https://doi.org/10.1016/j.ejor.2008.05.011

    Article  Google Scholar 

  7. Cook WD, Liang L, Zhu J (2010) Measuring performance of two-stage network structures by DEA: a review and future perspective. Omega 38:423–430. https://doi.org/10.1016/j.omega.2009.12.001

    Article  Google Scholar 

  8. Charnes A, Cooper WW, Rhodes E (1978) Measuring the efficiency of decision making units. Eur J Oper Res 2:429–444. https://doi.org/10.1016/0377-2217(78)90138-8

    Article  MathSciNet  Google Scholar 

  9. Charnes A, Cooper WW, Golany B, Halek R, Klopp G, Schmitz E, Thomas D (1986) Two-phase data envelopment analysis approaches to policy evaluation and management of army recruiting activities: tradeoffs between joint services and army advertising. Cent Cybern Stud Univ

    Google Scholar 

  10. Ratner SV, Shaposhnikov AM, Lychev AV (2023) Network DEA and its applications (2017–2022): a systematic literature review. Mathematics 11:2141. https://doi.org/10.3390/math11092141

    Article  Google Scholar 

  11. Khezrimotlagh D, Zhu J, Cook WD, Toloo M (2019) Data envelopment analysis and big data. Eur J Oper Res 274:1047–1054. https://doi.org/10.1016/j.ejor.2018.10.044

    Article  MathSciNet  Google Scholar 

  12. Hanoum S (2021) Manufacturing enterprise performance using network DEA: a profitability and marketability framework. Int J Bus Excell 25:277–299. https://doi.org/10.1504/IJBEX.2021.119457

    Article  Google Scholar 

  13. Yang H, Zhu X (2022) Research on green innovation performance of manufacturing industry and its improvement path in China. Sustainability 14:8000. https://doi.org/10.3390/su14138000

    Article  Google Scholar 

  14. Kremantzis MD, Beullens P, Kyrgiakos LS, Klein J (2022) Measurement and evaluation of multi-function parallel network hierarchical DEA systems. Socioecon Plann Sci 84:101428. https://doi.org/10.1016/j.seps.2022.101428

    Article  Google Scholar 

  15. He K, Zhu N (2022) Eco-efficiency evaluation of Chinese provincial industrial system: a dynamic hybrid two-stage DEA approach. PLoS ONE 17:e0272633. https://doi.org/10.1371/journal.pone.0272633

    Article  Google Scholar 

  16. Liang S, Yang J, Ding T (2022) Performance evaluation of AI driven low carbon manufacturing industry in China: an interactive network DEA approach. Comput Ind Eng 170:108248. https://doi.org/10.1016/j.cie.2022.108248

    Article  Google Scholar 

  17. Shen W, Shi J, Meng Q, Chen X, Liu Y, Cheng K, Liu W (2022) Influences of environmental regulations on industrial green technology innovation efficiency in China. Sustainability 14:4717. https://doi.org/10.3390/su14084717

    Article  Google Scholar 

  18. Zhu L, He F (2022) A multi-stage Malmquist-Luenberger index to measure environmental productivity in China’s iron and steel industry. Appl Math Model 103:162–175. https://doi.org/10.1016/j.apm.2021.10.034

    Article  MathSciNet  Google Scholar 

  19. Chen X, Liu Z, Saydaliev HB, Abu Hatab A, Fang W (2021) Measuring energy efficiency performance in China: do technological and environmental concerns matter for energy efficiency? Front Energy Res 9

    Google Scholar 

  20. Pandey U, Singh S (2021) Environmental performance evaluation of European farms by assessing polluting factors in joint production. J Clean Prod 328:129457. https://doi.org/10.1016/j.jclepro.2021.129457

    Article  Google Scholar 

  21. Zhu L, Luo J, Dong Q, Zhao Y, Wang Y, Wang Y (2021) Green technology innovation efficiency of energy-intensive industries in China from the perspective of shared resources: dynamic change and improvement path. Technol Forecast Soc Change 170:120890. https://doi.org/10.1016/j.techfore.2021.120890

    Article  Google Scholar 

  22. Wang Q, Tang J, Choi G (2021) A two-stage eco-efficiency evaluation of China’s industrial sectors: a dynamic network data envelopment analysis (DNDEA) approach. Process Saf Environ Prot 148:879–892. https://doi.org/10.1016/j.psep.2021.02.005

    Article  Google Scholar 

  23. Kapelko M, Harasym J, Orkusz A, Piwowar A (2022) Cross-national comparison of dynamic inefficiency for European dietetic food manufacturing firms. Technol Econ Dev Econ 28:893–919. https://doi.org/10.3846/tede.2022.16598

    Article  Google Scholar 

  24. Li J, Qin R, Jiang H (2022) Measurement of innovation efficiency in China’s electronics and communication equipment manufacturing industry-based on dynamic network SBM model. Sustainability 14. https://doi.org/10.3390/su14031227

  25. Roudabr N, Najafi SE, Moghaddas Z, Sobhani FM (2022) A new modeling approach for undesirable factors in efficiency evaluation of cement industry with four stages structure based on piecewise linear NDEA model. Econ Comput Econ Cybern Stud Res 56:57–74. https://doi.org/10.24818/18423264/56.1.22.04

  26. Alamuti MN, Matin RK, Khounsiavash M, Moghadas Z (2022) Performance evaluation of two-stage production systems with time-lag effects: an application in the horticulture industry. RAIRO-Oper Res 56:1571–1591. https://doi.org/10.1051/ro/2022073

    Article  MathSciNet  Google Scholar 

  27. Park S, Kim P (2021) Operational performance evaluation of Korean ship parts manufacturing industry using dynamic network SBM model. Sustainability 13. https://doi.org/10.3390/su132313127

  28. Chen S, Feng Y, Lin C, Liao Z, Mei X (2021) Research on the technology innovation efficiency of China’s listed new energy vehicle enterprises. Math Probl Eng. https://doi.org/10.1155/2021/6613602

  29. Li H, Zhu X, Chen J (2020) Total factor waste gas treatment efficiency of China’s iron and steel enterprises and its influencing factors: an empirical analysis based on the four-stage SBM-DEA model. Ecol Indic 119:106812. https://doi.org/10.1016/j.ecolind.2020.106812

    Article  Google Scholar 

  30. Fang T-Y (2020) Who is the Keyman? Integrating two-stage DEA and social network analysis to evaluate operational and environmental efficiency in the semiconductor industry. Math Probl Eng 2020:e2926357. https://doi.org/10.1155/2020/2926357

    Article  Google Scholar 

  31. Lu C-C, Dan W, Chen X, Tseng C-K, Chou K-W (2021) Evaluation of the operating performance of Taiwanese machine tool industry with the dynamic network DEA model. Enterp Inf Syst 15:87–104. https://doi.org/10.1080/17517575.2019.1709662

    Article  Google Scholar 

  32. Wang M, Chen Y, Zhou Z (2020) A novel Stochastic two-stage DEA model for evaluating industrial production and waste gas treatment systems. Sustainability 12:2316. https://doi.org/10.3390/su12062316

    Article  Google Scholar 

  33. Wu T-H, Ting PJL, Lin M-C, Chang C-C (2022) Corporate ownership and firm performance: a mediating role of innovation efficiency. Econ Innov New Technol 31:292–319. https://doi.org/10.1080/10438599.2020.1799140

    Article  Google Scholar 

  34. Deng Q, Zhou S, Peng F (2020) Measuring green innovation efficiency for China’s high-tech manufacturing industry: a network DEA approach. Math Probl Eng 2020:e8902416. https://doi.org/10.1155/2020/8902416

    Article  Google Scholar 

  35. Aparicio J, Kapelko M (2019) Accounting for slacks to measure dynamic inefficiency in data envelopment analysis. Eur J Oper Res 278:463–471. https://doi.org/10.1016/j.ejor.2018.08.045

    Article  MathSciNet  Google Scholar 

  36. Lemos SV, Salgado AP, Duarte A, de Souza MAA, de Almeida Antunes F (2019) Agroindustrial best practices that contribute to technical efficiency in Brazilian sugar and ethanol production mills. Energy 177:397–411. https://doi.org/10.1016/j.energy.2019.04.053

  37. Liu Z, Lyu J (2020) Measuring the innovation efficiency of the Chinese pharmaceutical industry based on a dynamic network DEA model. Appl Econ Lett 27:35–40. https://doi.org/10.1080/13504851.2019.1606402

    Article  Google Scholar 

  38. Lin F, Lin S-W, Lu W-M (2018) Sustainability assessment of Taiwan’s semiconductor industry: a new hybrid model using combined analytic hierarchy process and two-stage additive network data envelopment analysis. Sustainability 10:4070. https://doi.org/10.3390/su10114070

    Article  Google Scholar 

  39. Lo Storto C (2018) Efficiency, conflicting goals and trade-offs: a nonparametric analysis of the water and wastewater service industry in Italy. Sustainability 10:919. https://doi.org/10.3390/su10040919

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Applied Mathematics and Scientific Computing, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

    Atul Kumar & Millie Pant

  2. Mehta Family School for Data Science and Artificial Intelligence, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

    Millie Pant

Authors
  1. Atul Kumar
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  2. Millie Pant
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Corresponding author

Correspondence to Millie Pant .

Editor information

Editors and Affiliations

  1. Amity Center for Interdisciplinary Research, Amity University, Noida, Uttar Pradesh, India

    P. K. Kapur

  2. Department of Industrial Engineering, Rutgers University, Piscataway, NJ, USA

    Hoang Pham

  3. Amity International Business School, Amity University, Noida, Uttar Pradesh, India

    Gurinder Singh

  4. Amity International Business School, Amity University, Noida, Uttar Pradesh, India

    Vivek Kumar

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Kumar, A., Pant, M. (2024). A Role of Network Data Envelopment Analysis Approach in Manufacturing Industry: Review of Last 5 years. In: Kapur, P.K., Pham, H., Singh, G., Kumar, V. (eds) Reliability Engineering for Industrial Processes. Springer Series in Reliability Engineering. Springer, Cham. https://doi.org/10.1007/978-3-031-55048-5_4

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  • DOI: https://doi.org/10.1007/978-3-031-55048-5_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-55047-8

  • Online ISBN: 978-3-031-55048-5

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