Woven fabric selection and grading for ladies’ winter shawls using multicriteria decision-making approach

Abstract

Purpose Quality assessment of textile products is of prime concern to intimately meet consumer demands. The dilemma faced by textile producers is to figure out the stability among quality criteria and efficiently deal with target specifications. Hence, the basic devotion is to attain the optimum value product which entirely satisfies the views and perceptions of consumers. Selection of best fabric among several alternatives in the presence of contradictory measures is a disputing problem in multicriteria decision-making. Design/methodology/approach In the current study, the analytic hierarchy process (AHP) and preference ranking organization method for enrichment evaluation (PROMETHEE) are proficiently used to solve the problem in selection of branded woven shawls. AHP method verifies comparative weights of the criteria selection, while the ranking of fabric alternatives grounded on specific net-outranking flows is executed through PROMETHEE II method. Findings The collective AHP and PROMETHEE approaches are applied for the useful accomplishment of grading of branded shawls based on multicriteria weights, used for effective selection of fabric materials in the textile market. Practical implications In the apparel industry, fabric and garment manufacturers often rely on hit-and-trial methods, leading to significant wastage of valuable resources and time, in achieving the desirable fabric qualities. The implementation of the findings can assist apparel manufacturers in streamlining their fabric selection processes based on multiple criteria. By adopting this method, industry players can make informed decisions, ensuring a balance between quality standards and consumer expectations, thereby enhancing both product value and market competitiveness. Originality/value The methods of Visual PROMETHEE and AHP are assimilated to offer a complete method for the selection and grading of fabrics with reference to multiple selection criteria.