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<p>In recent years, the rapidly growing quick-service restaurant (QSR) industry has been striving to balance cost efficiency, food quality, and customer satisfaction. In the highly competitive market, optimized management is essential for achieving profitability and sustainable growth. This study proposes a hybrid optimization framework combined with the fuzzy Multi-Criteria Decision-Making (MCDM) technique. Important QSR supply chain risk factors or success factors (SFs) are evaluated using fuzzy MCDM, and the most significant SFs are identified based on expert views. These factors are incorporated as decision variables in a nonlinear QSR demand and profit-optimization model. The model is analytically validated through concavity and second-order derivative analysis to ensure a global maximum. Furthermore, a fuzzy extension has been proposed using the fuzzy <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\alpha \)</EquationSource> <EquationSource Format="MATHML"><math> <mi>α</mi> </math></EquationSource> </InlineEquation>-cut approach to address demand uncertainty. The fuzzy model enables managers to estimate demand and profit intervals under uncertain conditions. The results show that improvements in food quality and service consistency enhance customer satisfaction, while optimal advertising increases demand and profitability.</p>

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Fuzzy Nonlinear Profit Optimization by Introducing Critical Success Factor Analysis in Quick Service Restaurants: A Multi-Criteria Supply Chain Approach

  • 期刊论文

  • 发表日期：

    2026年6月29日

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  • Haridas Mondal,
  • Sagnic Naskar,
  • Shariful Alam

摘要

In recent years, the rapidly growing quick-service restaurant (QSR) industry has been striving to balance cost efficiency, food quality, and customer satisfaction. In the highly competitive market, optimized management is essential for achieving profitability and sustainable growth. This study proposes a hybrid optimization framework combined with the fuzzy Multi-Criteria Decision-Making (MCDM) technique. Important QSR supply chain risk factors or success factors (SFs) are evaluated using fuzzy MCDM, and the most significant SFs are identified based on expert views. These factors are incorporated as decision variables in a nonlinear QSR demand and profit-optimization model. The model is analytically validated through concavity and second-order derivative analysis to ensure a global maximum. Furthermore, a fuzzy extension has been proposed using the fuzzy \(\alpha \) α -cut approach to address demand uncertainty. The fuzzy model enables managers to estimate demand and profit intervals under uncertain conditions. The results show that improvements in food quality and service consistency enhance customer satisfaction, while optimal advertising increases demand and profitability.