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A Novel Integrated Fuzzy-Rough MCDM Model for Assessment of Barriers Related to Smart Logistics Applications and Demand Forecasting Method in the COVID-19 Period

Author

Listed:
  • Željko Stević

    (Faculty of Transport and Traffic Engineering, University of East Sarajevo, Doboj, Republic of Srpska Bosnia and Herzegovina)

  • Selçuk Korucuk

    (Department of International Trade and Logistics, Bulancak Kadir KarabaÅŸ Vocational School, Giresun University, Giresun, Turkey)

  • ÇaÄŸlar KaramaÅŸa

    (Department of Business Administration, Faculty of Business, Anadolu University, EskiÅŸehir, Turkey)

  • Ezgi Demir

    (Department of Management Information Systems, Gebze Technical University, Kocaeli, Turkey)

  • Edmundas Kazimieras Zavadskas

    (Institute of Sustainable Construction, Vilnius Gediminas Technical University, Sauletekio al. 11, LT-10223 Vilnius, Lithuania)

Abstract

During the pandemic period, smart logistics applications have rapidly changed the way organizations do business in order to provide competitive products and services while still remaining flexible. Smart logistics applications and demand forecasting, which have an important place in ensuring customer satisfaction and increasing competitive advantage, came to the fore even more in this period. However, smart logistics applications are often bogged down by several barriers, and then there is the need to choose the most ideal demand forecasting method despite these barriers. The main purpose of this study is to assess the barriers to the smart logistics applications in companies that receive and provide logistics services with corporate identity in Ordu Province, and to choose the most ideal demand forecasting method during the COVID-19 period. This study has the characteristic of a roadmap that helps the construction of smart logistics transformation applications by detecting barriers related to smart logistics applications and determining the most ideal demand forecasting alternative in logistics sector. Fuzzy FUCOM (FUll COnsistency Method)-based interval rough EDAS (Evaluation based on Distance from Average Solution) methodology was used to weight the barriers and to rank and choose the most ideal demand forecasting method during COVID-19 period, respectively.

Suggested Citation

  • Željko Stević & Selçuk Korucuk & ÇaÄŸlar KaramaÅŸa & Ezgi Demir & Edmundas Kazimieras Zavadskas, 2022. "A Novel Integrated Fuzzy-Rough MCDM Model for Assessment of Barriers Related to Smart Logistics Applications and Demand Forecasting Method in the COVID-19 Period," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 21(05), pages 1647-1678, September.
    • Handle: RePEc:wsi:ijitdm:v:21:y:2022:i:05:n:s0219622022500274
    DOI: 10.1142/S0219622022500274
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