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Sea lion with enhanced exploration phase for optimization of polynomial fitness with SEM in lean technology

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Abstract

With the establishment of lean manufacturing, myriad industries implemented the lean manufacturing principles and guidelines. To train the professionals based on certain policies to achieve continuous improvements in terms of productivity and minimizing wastages. However, the complexities such as variability, sustainability, multi-dimensional views, factory size, to name just a few negatively influences the performance of deploying lean manufacturing in industries. It is, therefore, very important for companies to recognize and understand the critical success factors for successfully implementing lean manufacturing. Hence, this paper plans to develop a model on concerning the analysis of lean manufacturing to find the most important factor regarding the technology among the industries. With this intention, this paper is analyzed through three phases. In the first phase, the prepared questionnaire is distributed to the professionals in various companies. In the questionnaire, all the mandatory questions are included. Then, the professional are recommended to fill the precise information as far as possible. In the second phase, the responses from the concerned practitioners associated with the industries are considered for analysis. Herein, the analysis is carried out based on structural equation modeling approaches with the contribution of higher order statistical analysis, which is performed using the input factors such as the lean awareness, lean technology, organizational support, organizational performance, employee involvement and management commitment among the industries via attaining better maximum likelihood values of the questionnaires. In the third phase, the Prediction of polynomial fitting of the response (i.e. the objective function) is achieved with the aid a novel optimization algorithm SLnO-EE model (Sea Lion with Enhanced Exploration phase), which is the extended version of SLnO (Sea Lion Optimization). Finally, the proposed SLnO-EE model is evaluated over traditional SLnO model in terms of certain performance evaluations to exhibit the improvement in prediction accuracy.

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Abbreviations

LA:

Lean awareness

SLnO-EE:

Sea lion with enhanced exploration

LT:

Lean technology

SLnO:

Sea lion optimization

OS:

Organizational support

EFA:

Exploratory factor analysis

IT:

Information technology

MC:

Management commitment

SEM:

Structural equation modeling

TOC:

Theory of constraints

FNR:

False negative rate

SEM:

Structural equation modeling

MCC:

Mathew’s correlation coefficnet

PLS:

Partial least squares

FPR:

False positive rate

CFA:

Confirmatory factory analysis

NPV:

Net predictive value

FDR:

False discovery rate

AMOS:

Analysis of moment structures

IT:

Information technology

TOC:

Theory of constraints

AHP:

Analytical hierarchy process

PLSF:

Factor-based PLS

ANN:

Artificial neural network

CVJ:

Constant velocity joints

SMEs:

Small and medium size enterprises

SAW:

Simple additive weighting

DF:

Degree of freedom

TOPSIS:

Technique for order of preference by similarity to ideal solution

NFI:

Normed fit index

NNFI:

Non-normed fit index

RFI:

Relative fit indices

ULS:

Un-weighted least square

SRMR:

Standardized root mean residual

IFI:

Incremental fit index

GFI:

Goodness of fit index

CFI:

Comparative fit index

TLI:

Tucker-Lewis index

RMR:

Root mean square residual

References

  1. Kock N (2019) Factor-based structural equation modeling with WarpPLS. Aust Market J (AMJ) 27(1):57–63

    Google Scholar 

  2. Rodríguez-Mantilla JM, Fernández-Díaz MJ, León V (2019) Carrascosa validation of a questionnaire to evaluate the impact of ISO 9001 standards in schools with a confirmatory factor analysis. Stud Educ Eval 62:37–48

    Article  Google Scholar 

  3. Sims T, Wan H-d (2017) Constraint identification techniques for lean manufacturing systems. Robot Comput Integr Manuf 43:50–58

    Article  Google Scholar 

  4. Jiménez-García JA, Téllez-Vázquez S, Medina-Flores JM, Rodríguez-Santoyo HH, Cuevas-Ortuño J (2014) Materials supply system analysis under simulation scenarios in a lean manufacturing environment. J Appl Res Technol 12(5):829–838

    Article  Google Scholar 

  5. Nasab HH, Aliheidaribioki T, Zare HK (2012) Finding a probabilistic approach to analyze lean manufacturing. J Clean Prod 29–30:73–81

    Article  Google Scholar 

  6. Gandhi NS, Thanki SJ, Thakkar JJ (2018) Ranking of drivers for integrated lean-green manufacturing for Indian manufacturing SMEs. J Clean Prod 171:675–689

    Article  Google Scholar 

  7. Alhuraish I, Robledo C, Kobi A (2016) Assessment of lean manufacturing and six sigma operation with decision making based on the analytic hierarchy process. IFAC-PapersOnLine 49(12):59–64

    Article  Google Scholar 

  8. Susilawati A, Tan J, Bell D, Sarwar M (2015) Fuzzy logic based method to measure degree of lean activity in manufacturing industry. J Manuf Syst 34:1–11

    Article  Google Scholar 

  9. Botti L, Mora C, Regattieri A (2017) Integrating ergonomics and lean manufacturing principles in a hybrid assembly line. Comput Ind Eng 111:481–491

    Article  Google Scholar 

  10. Thomas T, Sherman SR, Sawhney RS (2018) Application of lean manufacturing principles to improve a conceptual 238Pu supply process. J Manuf Syst 46:1–12

    Article  Google Scholar 

  11. Abu F, Gholami H, Saman MZM, Zakuan N, Streimikiene D (2019) The implementation of lean manufacturing in the furniture industry: a review and analysis on the motives, barriers, challenges, and the applications. J Clean Prod 234:660–680

    Article  Google Scholar 

  12. Ghobadian A, Talavera I, Bhattacharya A, Kumar V, ArturoGarza-Reyes J, O'Regan N (2018) Examining legitimatisation of additive manufacturing in the interplay between innovation, lean manufacturing and sustainability. Int J Prod Econ

  13. Möldner AK, Garza-Reyes JA, Kumar V (2018) Exploring lean manufacturing practices' influence on process innovation performance. J Bus Res 106:233–249

    Article  Google Scholar 

  14. Moeuf A, Tamayo S, Lamouri S, Pellerin R, Lelievre A (2016) Strengths and weaknesses of small and medium sized enterprises regarding the implementation of lean manufacturing. IFAC-Papers OnLine 49(12):71–76

    Article  Google Scholar 

  15. Fullerton RR, Kennedy FA, Widener SK (2014) Lean manufacturing and firm performance: The incremental contribution of lean management accounting practices. J Oper Manag 32(7–8):414–428

    Article  Google Scholar 

  16. Alhuraish I, Robledo C, Kobi A (2017) A comparative exploration of lean manufacturing and six sigma in terms of their critical success factors. J Clean Prod 164:325–337

    Article  Google Scholar 

  17. Helleno AL, de Moraes AJI, Simon AsTadeu (2017) Integrating sustainability indicators and lean manufacturing to assess manufacturing processes: application case studies in Brazilian industry. J Clean Prod 153:405–416

    Article  Google Scholar 

  18. Orji IJ, Liu S (2018) A dynamic perspective on the key drivers of innovation-led lean approaches to achieve sustainability in manufacturing supply chain. Int J Prod Econ

  19. Büyüközkan G, Kayakutlu G, Karakadılar İS (2015) Assessment of lean manufacturing effect on business performance using Bayesian belief networks. Exp Syst Appl 42(19):6539–6551

    Article  Google Scholar 

  20. Khanchanapong T, Prajogo D, Sohal AS, Cooper BK, Yeung ACL, Cheng TCE (2014) The unique and complementary effects of manufacturing technologies and lean practices on manufacturing operational performance. Int J Prod Econ 153:191–203

    Article  Google Scholar 

  21. Cherrafi A, Elfezazi S, Chiarini A, Mokhlis A, Benhida K (2016) The integration of lean manufacturing, Six Sigma and sustainability: a literature review and future research directions for developing a specific model. J Clean Prod 139:828–846

    Article  Google Scholar 

  22. Farias Luciano Costa Santos LMS, Gohr CF, Rocha LO (2019) An ANP-based approach for lean and green performance assessment. Resour Conserv Recycl 143:77–89

    Article  Google Scholar 

  23. Onyeocha CE, Khoury J, Geraghty J (2015) Evaluation of multi-product lean manufacturing systems with setup and erratic demand. Comput Ind Engineering 87:465–480

    Article  Google Scholar 

  24. Diana T (2014) Validating delay constructs: an application of confirmatory factor analysis. J Air Transp Manag 35:87–91

    Article  Google Scholar 

  25. Zong F, Ping Yu, Tang J, Sun X (2019) Understanding parking decisions with structural equation modeling. Phys A 523:408–417

    Article  Google Scholar 

  26. Masadeh R, Mahafzah BA, Sharieh A (2019) Sea Lion optimization algorithm. Int J Adv Comput Sci Appl (IJACSA) 10(5)

  27. Alhuraish I, Robeldo C, Kobi A, Azzabi L (2017) Analytic hierarchy process used to estimate the performance of companies that implement lean manufacturing and Six Sigma. Int J Six Sigma Compet Adv 10(3-4):179–200

    Google Scholar 

  28. Jameel A, Abdul-Karem M, Mahmood N (2017) A review of the impact of ICT on business firms. Int J Latest Eng Manag Res 2(01):15–19

    Google Scholar 

  29. Jameel AS, Ahmad AR (2019) Leadership and performance of academic staff in developing countries.

  30. Remmiya R, Abisha C (2018) Artifacts removal in EEG signal using a NARX model based CS learning algorithm. Multimed Res 1(1):1–8

    Google Scholar 

  31. Veritti D, Sarao V, Lanzetta P (2013) Bevacizumab and triamcinolone acetonide for choroidal neovascularization due to age-related macular degeneration unresponsive to antivascular endothelial growth factors. J Ocular Pharmacol Therap 29(4):437–441

    Article  Google Scholar 

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Correspondence to Jobin Vijayan.

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Vijayan, J., Radharamanant, T. & Sridharant, R. Sea lion with enhanced exploration phase for optimization of polynomial fitness with SEM in lean technology. Evol. Intel. 15, 1233–1250 (2022). https://doi.org/10.1007/s12065-020-00370-3

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