A Dynamic PROMETHEE-Based Lean Manufacturing and Industry 4.0 Implementation Roadmap for Manufacturing SMEs in Emerging Economies

Bošnjak, Igor; Stojkić, Željko; Šaravanja, Luka

doi:10.47960/3029-3200.2026.2.2.60

Research Article

DOI: 10.47960/3029-3200.2026.2.2.60

Citations (Crossref, OpenAlex): …

Igor Bošnjak*

, Željko Stojkić

and Luka Šaravanja

*CA	igor.bosnjak@fsre.sum.ba
	zeljko.stojkic@fsre.sum.ba
	luka.saravanja@fsre.sum.ba

Full text:

Vol. 2 No. 2 Article 23

This article belongs to Vol. 2 No. 2, 2026

I. Bošnjak, Ž. Stojkić, and L. Šaravanja, “A Dynamic PROMETHEE-Based Lean Manufacturing and Industry 4.0 Implementation Roadmap for Manufacturing SMEs in Emerging Economies,” International Journal of Innovative Solutions in Engineering, vol. 2, no. 2, pp. 60–72, doi: 10.47960/3029-3200.2026.2.2.60.

pages 60-72

Download a citation file:

Vol. 2 No. 2 Article 23 Download

Preview and download a citation file in BibTex format that can be imported by citation management software, including Mendeley, EndNote, ProCite, RefWorks, and Reference Manager.

This article is archived in Zenodo

Abstract

This paper presents a decision-support model for generating dynamic, company-specific implementation maps for Lean Manufacturing and Industry 4.0. While the integration of these two paradigms is widely discussed, practical tools that account for a company’s current implementation maturity and its own strategic preferences remain scarce, particularly for small and medium-sized enterprises (SMEs). The proposed model combines a multi-criteria assessment of 18 Lean and Industry 4.0 dimensions with the PROMETHEE II method and GAIA visualization to produce a prioritized implementation sequence. Seven criteria: implementation duration, cost, benefit, risk, administrative constraints, technological capability, and current implementation level, are evaluated by a panel of 18 experts. The current implementation-level criterion links the model to each company’s actual state, ensuring that the resulting map is company-specific rather than generic. A sensitivity analysis confirms the marginal stability of the rankings. The model is validated against a sample of 108 SMEs from the manufacturing sector in Bosnia and Herzegovina. Results show that the ranked sequence can be meaningfully adjusted by changing criterion weights to reflect a company’s priorities, demonstrating the model’s dynamic character. The paper also provides detailed implementation steps for each dimension, offering practitioners a ready-to-use action framework.

Keywords

Lean Manufacturing, Industry 4.0, PROMETHEE, Dynamic Implementation Map, SMEs, Multi-Criteria Decision-Making

ijise ID

23

URI

https://ijise.ba/article/23/

Publication Date

Jul. 3, 2026

References

J. P. Womack, D. T. Jones, and D. Roos, The Machine that Changed the World. New York: Rawson Associates, 1990.
H. Kagermann, W. Wahlster, and J. Helbig, “Recommendations for implementing the strategic initiative Industrie 4.0,” Acatech, Frankfurt, 2013.
M. Sony, “Industry 4.0 and lean management: a proposed integration model and research propositions,” Production & Manufacturing Research, vol. 6, no. 1, pp. 416–432, Jan. 2018, doi: 10.1080/21693277.2018.1540949.
G. L. Tortorella, R. Miorando, and D. Tlapa, “Implementation of lean supply chain: an empirical research on the effect of context,” TQM, vol. 29, no. 4, pp. 610–623, Jun. 2017, doi: 10.1108/TQM-11-2016-0102.
S. Rahman, T. Laosirihongthong, and A. S. Sohal, “Impact of lean strategy on operational performance: a study of Thai manufacturing companies,” Journal of Manufacturing Technology Management, vol. 21, no. 7, pp. 839–852, Sep. 2010, doi: 10.1108/17410381011077946.
G. Nawanir, L. K. Teong, and S. N. Othman, “Impact of lean practices on operations performance,” Int. J. Lean Six Sigma, vol. 4, no. 4, pp. 407–424, 2013.
R. R. Fullerton and W. F. Wempe, “Lean manufacturing, non‐financial performance measures, and financial performance,” International Journal of Operations & Production Management, vol. 29, no. 3, pp. 214–240, Feb. 2009, doi: 10.1108/01443570910938970.
I. Belekoukias, J. A. Garza-Reyes, and V. Kumar, “The impact of lean methods and tools on the operational performance of manufacturing organisations,” International Journal of Production Research, vol. 52, no. 18, pp. 5346–5366, Sep. 2014, doi: 10.1080/00207543.2014.903348.
M. P. Sajan, P. R. Shalij, A. Ramesh, and B. A. Biju, “Lean manufacturing practices in Indian manufacturing SMEs and their effect on sustainability performance,” J. Manuf. Technol. Manag., vol. 28, no. 6, pp. 772–793, 2017, doi: 10.1108/JMTM-12-2016-0188.
A. Anvari, N. Zulkifli, and R. M. Yusuff, “A dynamic modelling of lean implementation roadmap in automotive manufacturing,” Int. J. Adv. Manuf. Technol., vol. 64, pp. 1373–1386, 2013.
F. Alhourani, “Clustering algorithm for solving group technology problem with multiple process routings,” Computers & Industrial Engineering, vol. 66, no. 4, pp. 781–790, Dec. 2013, doi: 10.1016/j.cie.2013.09.002.
F. W. Geels, “Micro-foundations of the multi-level perspective on socio-technical transitions: Developing a multi-dimensional model of agency through crossovers between social constructivism, evolutionary economics and neo-institutional theory,” Technological Forecasting and Social Change, vol. 152, p. 119894, Mar. 2020, doi: 10.1016/j.techfore.2019.119894.
R. Shah and P. T. Ward, “Defining and developing measures of lean production,” J of Ops Management, vol. 25, no. 4, pp. 785–805, Jun. 2007, doi: 10.1016/j.jom.2007.01.019.
M. Malmbrandt and P. Åhlström, “An instrument for assessing lean service adoption,” International Journal of Operations & Production Management, vol. 33, no. 9, pp. 1131–1165, Aug. 2013, doi: 10.1108/IJOPM-05-2011-0175.
T. L. Doolen and M. E. Hacker, “A review of lean assessment in organizations: An exploratory study of lean practices by electronics manufacturers,” Journal of Manufacturing Systems, vol. 24, no. 1, pp. 55–67, Jan. 2005, doi: 10.1016/S0278-6125(05)80007-X.
R. Shah and P. T. Ward, “Lean manufacturing: context, practice bundles, and performance,” J of Ops Management, vol. 21, no. 2, pp. 129–149, Mar. 2003, doi: 10.1016/S0272-6963(02)00108-0.
K. Lichtblau et al., IMPULS Industrie 4.0-Readiness. Cologne: VDMA, 2015.
G. Schuh et al., Industrie 4.0 Maturity Index: Managing the Digital Transformation of Companies. Munich: Acatech, 2017.
PwC, Industry 4.0: Building Your Digital Enterprise. PwC Global Industry 4.0 Survey, 2016.
G. L. Tortorella and D. Fettermann, “Implementation of Industry 4.0 and lean production in Brazilian manufacturing companies,” International Journal of Production Research, vol. 56, no. 8, pp. 2975–2987, Apr. 2018, doi: 10.1080/00207543.2017.1391420.
M. Ebrahimi, A. Baboli, and E. Rother, “The evolution of world class manufacturing toward Industry 4.0: A case study in the automotive industry,” IFAC-PapersOnLine, vol. 52, no. 10, pp. 188–194, 2019, doi: 10.1016/j.ifacol.2019.10.021.
Y. Kazancoglu and Y. D. Ozkan-Ozen, “Analyzing Workforce 4.0 in the Fourth Industrial Revolution and proposing a road map from operations management perspective with fuzzy DEMATEL,” JEIM, vol. 31, no. 6, pp. 891–907, Oct. 2018, doi: 10.1108/JEIM-01-2017-0015.
F. Talib et al., “A road map for the implementation of integrated JIT-lean practices in Indian manufacturing industries using the best-worst method approach,” J. Ind. Prod. Eng., vol. 37, no. 6, pp. 275–291, 2020, doi: 10.1080/21681015.2020.1788656.
A. S. F. Alves, L. J. R. Nunes, J. C. O. Matias, P. Espadinha-Cruz, and R. Godina, “An integrated PROMETHEE II-Roadmap model: Application to the recovery of residual agroforestry biomass in Portugal,” J. Clean. Prod., vol. 445, 2024, doi: 10.1016/j.jclepro.2024.141307.
S. Ghazinoory, M. Daneshmand-Mehr, and M. R. Arasti, “Developing a model for integrating decisions in technology roadmapping by fuzzy PROMETHEE,” J. Intell. Fuzzy Syst., vol. 26, no. 2, pp. 625–645, 2014, doi: 10.3233/IFS-120755.
Z. Wu and G. Abdul-Nour, “Comparison of Multi-Criteria Group Decision-Making Methods for Urban Sewer Network Plan Selection,” CivilEng, vol. 1, no. 1, pp. 26–48, Jun. 2020, doi: 10.3390/civileng1010003.
M. Behzadian, R. B. Kazemzadeh, A. Albadvi, and M. Aghdasi, “PROMETHEE: A comprehensive literature review on methodologies and applications,” Eur. J. Oper. Res., vol. 200, no. 1, pp. 198–215, 2010, doi: 10.1016/j.ejor.2009.01.021.
J. P. Brans, “L’ingénierie de la décision: élaboration d’instruments d’aide à la décision,” in L’aide à la décision, R. Nadeau and M. Landry, Eds. Québec: Presses de l’Université Laval, 1982, pp. 183–213.
Mareschal, B., “Weight stability intervals in multicriteria decision aid,” Eur. J. Oper. Res., vol. 33, no. 1, pp. 54–64, 1988, doi: 10.1016/0377-2217(88)90254-8.
J. P. Brans and P. Vincke, “A preference ranking organisation method: the PROMETHEE method for MCDM,” Manag. Sci., vol. 31, no. 6, pp. 647–656, 1985, doi: 10.1287/mnsc.31.6.647.
C. Macharis, J. Springael, K. De Brucker, and A. Verbeke, “PROMETHEE and AHP: the design of operational synergies in multicriteria analysis,” Eur. J. Oper. Res., vol. 153, no. 2, pp. 307–317, 2004, doi: 10.1016/S0377-2217(03)00153-X.
I. Bošnjak, “Integrirani model za procjenu utjecaja vitke proizvodnje i Industrije 4.0 na performanse poduzeća i izrada dinamičke mape njihove implementacije,” Doktorski rad, Fakultet strojarstva, računarstva i elektotehnike, Sveučilište u Mostaru, Bosna i Hercegovina, 2022.