Abstract
Industry 4.0 has taken extraordinary importance in massive production strategies. This new revolution represents automation in factories and interconnectivity among devices and procedures. In a technological framework, when managing large amounts of data combined with in-depth statistical analysis as a convenient tool for decision-making, Industry 4.0 modeling constitutes an indispensable support. This chapter has the objective to present, in a comprehensive way, the role of statistical analysis in a steepest ascent innovative strategy for the Industry 4.0 modeling based on released information from a production system. The method analyzes the route that data follows from a production system to a computer software for statistical analysis. This includes empirical techniques such as designed experimentation. After this procedure, human intervention exists only for consecutive analysis and decision-making purposes. Results and conclusions are disclosed at the end of this document.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
J. Trojanowska, A. Kolinski, D. Galusik, M.L.R. Varela, J. Machado, A methodology of improvement of manufacturing productivity through increasing operational efficiency of the production process, in Advances in Manufacturing, (Springer, 2018), pp. 23–32
M. Irfan, H. Zahoor, M. Abbas, Y. Ali, Determinants of labor productivity for building projects in Pakistan. Journal of Construction Engineering, Management & Innovation 3(2), 85–100 (2020)
A. Nuvolari, Understanding successive industrial revolutions: A “development block” approach. Environmental Innovation and Societal Transitions 32 (2018)
L. Melnyk, O. Kubatko, I. Dehtyarova, O. Matsenko, O. Rozhko, The effect of industrial revolutions on the transformation of social and economic systems. Probl. Perspect. Manag. 31, 381–391 (2019)
E. Popkova, Y. Ragulina and A. Bogoviz, "Industry 4.0: Industrial revolution of the 21st century Springer,. Vols. Studies in Systems, Decision and Control, 2019
W.D. Leong, S.Y. Teng, B.S. How, S.L. Ngan, A.A. Rahman, C.P. Tan, S.G. Ponnambalam, H.L. Lam, Enhancing the adaptability: Lean and green strategy towards the industry revolution 4.0. J. Clean. Prod. 273 (2020)
D. Leech, Intelligent Machine Technology and Productivity Growth (The Social Value of New Technology, 2019), pp. 245–255
A. Khan, J. Keung, S. Hussain, M. Niazi, M. Tamimy, Understanding software process improvement in global software development. ACM SIGAPP Applied Computing Review 17(2), 5–15 (2017)
S. Shim, S. Kim, Intervention meta-analysis: Application and practice using R software. Epidemiology and Health 41 (2019)
U. Grömping, R package DoE.Base for factorial experiments. J. Stat. Softw. 85, 1–41 (2018)
M. Miranda-Ackerman, A. García-Lechuga, An Overview of the Design of Experiment Workflow: Applications in Food Production Systems (IGI Global, Baja California, 2020), p. 14
J. Jacyna, M. Kordalewska, M. Markuszewski, Design of Experiments in metabolomics-related studies: An overview. Science Direct 164, 598–606 (2019)
M. Yolmeh, S.M. Jafari, Applications of response surface methodology in the food industry processes. Food Bioprocess Technol. 10, 413–433 (2017)
S.J.S. Chelladurai, M.K.A. Pratip Ray, M. Upadhyaya, V. Narasimharaj, S. Gnanasekaran, Optimization of process parameters using response surface methodology: A review. Materials Today: Proceedings 37(2), 1301–1304 (2021)
A. Law, A tutorial on design of experiments for simulation modeling. Proceedings - Winter Simulation Conference, 550–564 (2017)
T. Allen, Software overview and methods review: Minitab, in Introduction to Engineering Statistics and Lean Six Sigma, (Springer, 2018), pp. 575–600
H.I. Okagbue, P.E. Oguntunde, E.C.M. Obasi, E.M. Akhmetshin, Trends and usage pattern of SPSS and Minitab software in scientific research. J. Phys. Conf. Ser. Conf. Ser. 1734, 012017 (2021)
S. Lesik, Applied Statistical Inference with MINITAB® (Chapman and Hall/CRC, New York, 2018)
M. Akers, Exploring, Analysing and Interpeting Data with Minitab 18: First Edition (Compass Publishing, 2018)
T. Blackburn, The analyze phase with Minitab tools, in Six Sigma, (Springer, 2022), pp. 107–201
R.S. Raman, N. Kukreja, N. Singh, Process Parameters Optimization through Taguchi and ANOVA Analysis - a Review (IOP Publishing Ltd, 2021)
R.H. Myers, D.C. Montgomery, C.M. Anderson-Cook, Response Surface Methodology: Process and Product Optimization Using Designed Experiments (Wiley, 2016)
R. Myers, A. Khuri, A new procedure for steepest ascent. Communications in Statistics – Theory and Methods, 1359–1376 (1979)
G. Miró-Quesada, E. Del-Castillo, An enhanced recursive stopping rule for steepest ascent searches in response surface methodology. Communications in Statistics Simulation and Computation, 201–228 (2007)
E.D. Castillo, Process Optimization: A Statistical Approach, vol 476 (Springer, 2007)
M. Ghobakhloo, Industry 4.0, digitization, and opportunities for sustainability. J. Clean. Prod. 252 (2020)
Januardi, E. Widodo, A review of response surface methodology approach in supply chain management, in Proceedings of the 3rd Asia Pacific Conference on Research in Industrial and Systems Engineering, (2020), pp. 322–327
P. Maia, F. Alves-de-Almeida, V.-D.-C. Paes, H.-D.-F. Gomes, A. Paulo-de-Paiva, Multivariate steepest ascent method based on latent variables. Appl. Math. Model. 90, 30–45 (2021)
D.-H. Lee, S.-H. Kim, J.-H. Byun, A method of steepest ascent for multiresponse surface optimization using a desirability function method. Qual. Reliab. Eng. Int. (2020)
Q. Zeng, W. Qiu, J. Liu, R. Xu, J. Shi, Y. Sun, A high dynamics algorithm based on steepest ascent method for GNSS receiver. Chin. J. Aeronaut. 34, 177–186 (2021)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
García-Nava, P.E., Rodríguez-Picón, L.A., Méndez-González, L.C., Pérez-Olguín, I.J.C., Romero-López, R. (2023). The Technological Role of Steepest Ascent Optimization in Industry 4.0 Modeling. In: Méndez-González, L.C., Rodríguez-Picón, L.A., Pérez Olguín, I.J.C. (eds) Innovation and Competitiveness in Industry 4.0 Based on Intelligent Systems. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-031-29775-5_7
Download citation
DOI: https://doi.org/10.1007/978-3-031-29775-5_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-29774-8
Online ISBN: 978-3-031-29775-5
eBook Packages: EngineeringEngineering (R0)