A framework for the Selection of a packaging design based on the SWARA method
DOI:
https://doi.org/10.5755/j01.ee.26.2.8820Keywords:
Step-wise Weight Assessment Ratio Analysis (SWARA), packaging selection, packaging design, MCDM, pairwise comparisonAbstract
Well-designed packaging is intended to present a product in the best possible way, which would have a significant impact on sales improvement. Creating and finding the ideal design solution for packaging is an extremely complex process. Many characteristics of a packaging design can affect the level of customer satisfaction, where the different characteristics of the packaging tend to have a different significance. To make the problem more complex, the significance of characteristics of a packaging design is not the same for all customers. Therefore, creating and finding the ideal design solution for packaging often involves an evaluation of a number of variants, typically evaluated on the basis of multiple criteria, often with different significance. To provide an efficient approach for the selection of appropriate packaging, a framework for selecting the appropriate packaging design which meets customer preferences, based on the SWARA method and group decision making, is proposed. The usability and efficiency of the proposed framework is considered in the case of the selecting the appropriate packaging design for the wine of the autochthonous grape variety called Black Tamjanika. On the basis the considered examples, it can be concluded that SWARA method can be successfully used to solve many similar problems, and that in some cases may have some advantages over similar methods, such as AHP method or Conjoint Analysis. As an advantage of the proposed procedure can be mentioned a much smaller number of comparisons in pairs, compared with the AHP method, and much more comprehensible procedure for selecting the most acceptable alternative, compared with Conjoint Analysis. The proposed framework can also be easily adjusted to solve a significant number of MCDM problems