Optimizing the Value Chain Using A Fuzzy Gmdh Algorithm
Lian Qian
Journal of Information & Systems Management, Vol. 16, No. 1, 2026.
DOI: https://doi.org/10.6025/jism/2026/16/1/21-30
Link: https://www.dline.info/jism/fulltext/v16n1/jismv16n1_3.pdf
Abstract:
The paper explores the application of the Group Method of Data Handling (GMDH) algorithmenhanced with
fuzzy inference to analyze and optimize the value chain in the sports industry. The value chain framework
encompasses enterprise relationships, value creation processes, and structural dynamics, all of which are
critical to sustainable industrial growth. Given the sports industry's limited historical data and inherent
uncertainty, traditional modeling approaches face challenges. The GMDH algorithm offers advantages such
as self organization, global optimization, and effectiveness with small datasets. However, its susceptibility
to overfitting and structural instability prompted the integration of fuzzy logic to handle imprecise and noisy
data better. The fuzzy GMDH model improves prediction accuracy by incorporating fuzzy reasoning into the
basic processing units, enabling more robust modeling of nonlinear and uncertain systems. The study applies
this optimized model to the sports industry in "M Province," comparing performance between standard
GMDH and fuzzy GMDH. Results indicate that the fuzzy variant provides superior fit and predictive capability,
supporting more accurate evaluation of the industry's value and development potential. Despite these
improvements, the authors acknowledge room for further refinement of the algorithm's architecture. The
research underscores the importance of advanced data mining techniques, such as fuzzy GMDH, in enabling
evidence based decision making in emerging sectors, including sports, where data scarcity and volatility
are common challenges.