A hybrid biomass supply chain optimization approach using sequential adaptive fuzzy inference learning: Insights from a cogeneration system in Europe

• A sequential adaptive fuzzy inference learning to select the best strategies. • Performing trade-offs in the selection of entrepreneurial strategies. • Analyzing biomass industry’s supply chain and forming a network of interactions. • Market share expansion raised return on capital by 32%, outperforming partnerships. • Applying the proposed knowledge-based system to a real case. In today's dynamic business landscape, entrepreneurship has emerged as a key factor propelling innovation and change within the biomass supply chain. Moreover, businesses cannot survive without strategic planning, and players in the biomass sector are constantly seeking opportunities to increase profits and strengthen their position in the market. This paper explores the biomass supply chain's untapped potential as an entrepreneurial opportunity, analyzing its complexities from raw material sourcing to product distribution. The study utilizes a hybrid, multifaceted approach to allow for such a thorough analysis, in which the first phase is to analyze questionnaires and use the fuzzy Delphi method to determine how various components and factors impact the biomass supply chain. Afterwards, the DEMATEL technique is utilized to reveal the complex network of interconnections between the key criteria and market stimulant variables in this energy sector. The proposed approach ultimately suggests meaningful scenarios that lead to the best entrepreneurial strategy selection by taking advantage of sequential adaptive fuzzy inference learning. The contributions of this paper include utilizing sequential adaptive fuzzy inference learning to determine optimal strategies for evaluating entrepreneurship, entrepreneurial strategy trade-offs with stable, stagnant, and booming market conditions, examining the biomass industry supply chain through a network of interactions using the DEMATEL approach, and applying the proposed knowledge-based system to a real-world case in the Vienna biomass supply chain. These results indicate that scenarios characterized by active engagement and market share development within the biomass supply chain consistently surpass other strategies in both stable and dynamic market conditions.