Can Different MCDM Methods Really Produce the Same Decision?

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Selecting the right plastic injection molding machine is a critical investment decision that affects manufacturing productivity, product quality, operating costs, and long-term competitiveness. Because multiple technical and economic factors must be evaluated simultaneously, engineers increasingly rely on Multi-Criteria Decision-Making (MCDM) methods to support objective equipment selection. However, with more than 200 MCDM methods available today, an important question remains: do different methods actually produce different decisions when applied to the same engineering problem?

This article addresses that question by comparing four contemporary MCDM methods—PIV, PSI, FUCA, and CURLI—using the same case study involving five commercial plastic injection molding machines evaluated against ten engineering criteria. Instead of proposing another decision-making algorithm, the study investigates whether these fundamentally different methods converge on the same result. To strengthen the analysis, the authors also compare two weighting techniques, MEAN and CRITIC, allowing readers to assess both methodological consistency and ranking stability.

The findings show remarkable agreement among all four methods, with JSW J550E-C5 consistently identified as the best alternative. These results provide valuable evidence that different MCDM approaches can produce robust and reliable decisions when supported by appropriate evaluation criteria. For researchers, the study fills an important methodological gap, while for manufacturing engineers and industrial decision-makers, it offers practical guidance for selecting analytical tools that support more confident and transparent equipment procurement. 

Bibliographic Information

Item Description
Title Comparison of MCDM Methods Effectiveness in the Selection of Plastic Injection Molding Machines
Authors Do Duc Trung, Branislav Dudić, Duong Van Duc, Nguyen Hoai Son, Aleksandar Ašonja
Journal Teknomekanik
Volume & Issue Volume 7, Issue 1
Publication Year 2024
Pages 1–19
DOI https://doi.org/10.24036/teknomekanik.v7i1.29272
Publisher Universitas Negeri Padang
License Creative Commons Attribution 4.0 International (CC BY 4.0)
Research Area Manufacturing Engineering, Multi-Criteria Decision Making (MCDM), Industrial Decision Support
Keywords MCDM, PIV, PSI, FUCA, CURLI, Plastic Injection Molding Machine

Research Background

  • Selecting industrial equipment often requires balancing multiple technical and economic criteria simultaneously.
  • More than 200 MCDM methods have been developed to support complex engineering decision-making.
  • Previous studies validated PIV, PSI, FUCA, and CURLI individually across various engineering applications.
  • However, no previous research had directly compared these four methods under identical decision conditions.
  • Plastic injection molding machine selection was chosen because it represents a high-value investment requiring objective and systematic evaluation.
  • This study fills the research gap by examining whether different MCDM methods produce consistent ranking results for the same engineering problem. 

Research Objective

This study aims to:

  • Compare the effectiveness of PIV, PSI, FUCA, and CURLI in solving the same machine-selection problem.
  • Evaluate whether different weighting techniques (MEAN and CRITIC) influence the ranking results.
  • Identify the best plastic injection molding machine among five commercial alternatives.
  • Assess the robustness and consistency of the four MCDM methods through multiple ranking scenarios. 

Why This Research Matters

  • Supports more objective and transparent equipment procurement decisions.
  • Helps manufacturers reduce investment risks when selecting production machinery.
  • Demonstrates that different MCDM methods can produce consistent engineering decisions.
  • Strengthens confidence in decision-support systems used in manufacturing and Industry 4.0 environments.
  • Provides useful guidance for researchers comparing emerging MCDM techniques and for engineers selecting analytical methods in industrial applications.

Research Methodology

This study employed a comparative MCDM framework to evaluate the effectiveness of four decision-making methods in selecting plastic injection molding machines.

  • Research Type: Comparative quantitative study using Multi-Criteria Decision-Making (MCDM) methods.
  • Case Study: Selection of the most suitable plastic injection molding machine.
  • Alternatives Evaluated: Five commercial machine models.
  • Evaluation Criteria: Ten technical and economic criteria, including mold dimensions, screw diameter, clamping force, stroke length, motor power, maximum mold opening, and purchase price.
  • MCDM Methods Compared:
    • Proximity Indexed Value (PIV)
    • Preference Selection Index (PSI)
    • Faire Un Choix Adéquat (FUCA)
    • Collaborative Unbiased Rank List Integration (CURLI)
  • Weighting Methods:
    • MEAN Weight
    • CRITIC (Criteria Importance Through Intercriteria Correlation)
  • Robustness Analysis: Multiple ranking scenarios were created by removing one alternative at a time to evaluate ranking consistency and method stability.
  • Performance Evaluation: The consistency of rankings produced by each MCDM method was compared to determine whether different computational approaches would lead to the same engineering decision.

Key Findings

1. All four MCDM methods produced highly consistent rankings

Despite using different mathematical algorithms, PIV, PSI, FUCA, and CURLI generated nearly identical ranking results, demonstrating comparable effectiveness for machine selection.

2. JSW J550E-C5 was consistently identified as the best machine

Across all weighting methods and ranking scenarios, JSW J550E-C5 achieved the highest overall ranking, making it the recommended plastic injection molding machine among the five alternatives.

3. Weighting methods had minimal influence on the final decision

Using either the MEAN or CRITIC weighting approach did not change the best-performing alternative, indicating that the ranking results were robust against different weighting strategies.

4. Ranking stability was confirmed through multiple scenarios

Additional analyses, including the removal of individual alternatives, showed that the four MCDM methods maintained stable ranking patterns, further confirming their reliability and consistency.

5. Different computational approaches can reach the same engineering decision

The study demonstrates that methods with different normalization procedures and weighting requirements can converge on the same optimal solution, increasing confidence in their application to engineering decision-making.

Scientific Contribution

This study makes several important contributions to the field of engineering decision-making:

  • Provides the first direct comparison of four emerging MCDM methods—PIV, PSI, FUCA, and CURLI—using the same engineering case study.
  • Demonstrates that different computational approaches can produce consistent and reliable ranking results despite having distinct mathematical foundations.
  • Confirms that the best alternative remains unchanged when different weighting techniques (MEAN and CRITIC) are applied, highlighting the robustness of the decision-making process.
  • Expands the empirical evidence supporting the application of modern MCDM methods in manufacturing equipment selection and industrial decision-support systems.
  • Provides a practical reference for researchers and practitioners seeking suitable MCDM methods for engineering optimization problems. 

Industrial Implications

The findings of this study have several practical implications for manufacturing industries and engineering decision-makers.

  • Supports objective equipment procurement by providing a systematic framework for selecting plastic injection molding machines based on multiple technical and economic criteria rather than subjective judgment.
  • Reduces investment risk by demonstrating that different MCDM methods consistently identify the same optimal machine, increasing confidence in high-value capital investment decisions.
  • Improves production planning by helping manufacturers choose equipment that better matches production capacity, product specifications, and operational requirements.
  • Facilitates digital decision-support systems for Industry 4.0, where MCDM algorithms can be integrated into intelligent manufacturing and procurement platforms.
  • Assists small and medium-sized enterprises (SMEs) in making transparent and evidence-based machinery investment decisions without relying solely on expert opinion.
  • Provides a practical evaluation framework that can be adapted for selecting other manufacturing equipment, including CNC machines, industrial robots, additive manufacturing systems, and automated production lines.
  • Enhances manufacturing competitiveness by supporting the selection of equipment that improves productivity, operational efficiency, and long-term return on investment.

Research Limitations

Although the study provides valuable insights, several limitations should be considered.

  • The comparison was conducted using only five plastic injection molding machine alternatives.
  • The evaluation relied on ten predefined technical and economic criteria, while other factors such as maintenance cost, energy efficiency, machine reliability, and after-sales service were not included.
  • The findings are based on a single industrial application, limiting their direct generalization to other manufacturing sectors.
  • The research focuses on deterministic MCDM methods without considering uncertainty through fuzzy, probabilistic, or interval-based approaches.
  • Experimental or industrial validation was not performed because the study primarily evaluates the consistency of mathematical decision-making methods.

Future Research Opportunities

This study opens several promising directions for future research.

  • Compare additional MCDM methods such as TOPSIS, VIKOR, MARCOS, EDAS, and CODAS using the same case study.
  • Investigate fuzzy, interval, or probabilistic MCDM techniques to address uncertainty in engineering decisions.
  • Incorporate life-cycle cost, energy consumption, sustainability, and carbon footprint as additional evaluation criteria.
  • Apply the comparative framework to other manufacturing equipment, including CNC machines, additive manufacturing systems, and industrial robots.
  • Integrate MCDM with Artificial Intelligence (AI) and Machine Learning for intelligent decision-support systems.
  • Develop Digital Twin-based decision-support platforms for equipment selection in smart factories.
  • Validate the proposed framework using real industrial procurement projects involving larger datasets and more alternatives.
  • Explore hybrid MCDM frameworks that combine objective weighting, optimization algorithms, and predictive analytics to improve decision quality.

Potential for Public Policy Citation (Overton)

Although this study focuses on engineering decision-making rather than public policy, its findings have clear relevance for evidence-based industrial planning and technology adoption.

The research could potentially support policy documents related to:

  • Industrial modernization, by providing objective methods for selecting advanced manufacturing equipment.
  • Industry 4.0 implementation, where data-driven decision-support systems are increasingly adopted in smart factories.
  • Manufacturing competitiveness, through more transparent and systematic capital equipment investment.
  • SME technology upgrading programs, helping small and medium-sized manufacturers make cost-effective equipment procurement decisions.
  • Engineering education and workforce development, by promoting modern decision-analysis tools in engineering curricula.
  • National advanced manufacturing roadmaps, where MCDM techniques can support strategic technology selection and investment planning.

Rather than influencing legislation directly, this study is more likely to be referenced in government technical reports, industrial technology roadmaps, engineering best-practice guidelines, and manufacturing innovation strategies, where objective decision-support methodologies are increasingly encouraged.

Final Thoughts

This study provides convincing evidence that different MCDM methods can lead to the same engineering decision when applied to a well-defined equipment selection problem. By comparing PIV, PSI, FUCA, and CURLI under identical conditions, the authors demonstrate that these methods produce highly consistent rankings and reliably identify JSW J550E-C5 as the best plastic injection molding machine among the evaluated alternatives.

Beyond identifying the optimal machine, the research reinforces the importance of structured, data-driven decision-making in modern manufacturing. Its findings suggest that engineers can confidently employ several contemporary MCDM techniques without compromising decision quality, provided that appropriate evaluation criteria and reliable input data are available. As manufacturing systems continue to evolve toward digitalization and intelligent production, this work offers a valuable reference for researchers and practitioners seeking robust decision-support tools for equipment selection and industrial optimization. 

Suggested Citation

Teknomekanik (UNP) Style

Trung, D.D., Dudić, B., Duc, D.V., Son, N.H., & Ašonja, A. (2024). Comparison of MCDM methods effectiveness in the selection of plastic injection molding machines. Teknomekanik, 7(1), 1–19. https://doi.org/10.24036/teknomekanik.v7i1.29272

APA (7th Edition)

Trung, D. D., Dudić, B., Duc, D. V., Son, N. H., & Ašonja, A. (2024). Comparison of MCDM methods effectiveness in the selection of plastic injection molding machines. Teknomekanik, 7(1), 1–19. https://doi.org/10.24036/teknomekanik.v7i1.29272

IEEE Style

D. D. Trung, B. Dudić, D. V. Duc, N. H. Son, and A. Ašonja, "Comparison of MCDM methods effectiveness in the selection of plastic injection molding machines," Teknomekanik, vol. 7, no. 1, pp. 1–19, Jun. 2024, doi: 10.24036/teknomekanik.v7i1.29272

Harvard Style

Trung, D.D., Dudić, B., Duc, D.V., Son, N.H. and Ašonja, A. (2024) 'Comparison of MCDM methods effectiveness in the selection of plastic injection molding machines', Teknomekanik, 7(1), pp. 1–19, doi: 10.24036/teknomekanik.v7i1.29272

Vancouver Style

Trung DD, Dudić B, Duc DV, Son NH, Ašonja A. Comparison of MCDM methods effectiveness in the selection of plastic injection molding machines. Teknomekanik. 2024;7(1):1–19. doi: 10.24036/teknomekanik.v7i1.29272

Chicago (Author–Date)

Trung, Do Duc, Branislav Dudić, Duong Van Duc, Nguyen Hoai Son, and Aleksandar Ašonja. 2024. "Comparison of MCDM Methods Effectiveness in the Selection of Plastic Injection Molding Machines." Teknomekanik 7 (1): 1–19. https://doi.org/10.24036/teknomekanik.v7i1.29272

MLA (9th Edition)

Trung, Do Duc, et al. "Comparison of MCDM Methods Effectiveness in the Selection of Plastic Injection Molding Machines." Teknomekanik, vol. 7, no. 1, 2024, pp. 1–19. https://doi.org/10.24036/teknomekanik.v7i1.29272

Editorial note: This blog post is an independent scholarly review intended for educational and scientific communication purposes. It summarizes and discusses the published article in the author's own words while providing full attribution to the original publication, consistent with the principles of the CC BY 4.0 license. 

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