A fuzzy AHP-TOPSIS framework for ranking the solutions of Knowledge Management adoption in Supply Chain to overcome its barriers

By Sachin K. Patil, R. Kant. Expert systems with applications, 2014

411 citations recorded by the source corpus.

Subfields: Management Information Systems, Management Science and Operations Research, Strategy and Management

View Paper: https://www.semanticscholar.org/paper/f7690d269081e257e9b7b59c6316690e81954156

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Management Information Systems

1. Tossapol Kiatcharoenpol
2. Jaroslaw Watrobski
3. Pornwasin Sirisawat

In Management Science and Operations Research

1. Pawel Ziemba
2. M. Zioło
3. Tossapol Kiatcharoenpol
4. Jaroslaw Watrobski
5. Artur Karczmarczyk
6. Jarosław Jankowski
7. Pornwasin Sirisawat

In Strategy and Management

1. Tossapol Kiatcharoenpol
2. Jaroslaw Watrobski
3. Pornwasin Sirisawat

Citations in the corpus

• Fuzzy AHP-TOPSIS approaches to prioritizing solutions for reverse logistics barriers (2018, methodology): “Patil and Kant (2014) applied fuzzy AHP-TOPSIS to identify and prioritize the solutions of Knowledge Management (KM) adoption in Supply Chain to overcome its barriers in which the results can help organizations to concentrate on high rank solutions and develop strategies to implement them as…”
• Generalised framework for multi-criteria method selection (2018, methodology): “New MCDA methods often use the fuzzy set theory [14] , which allows using uncertain data as trapezoidal fuzzy numbers, i.e. Ñ = (n l , n u , α F , β F ) , or triangular fuzzy numbers, i.e. Ñ = (n, α F , β F ) [53-55] .”