Implementation Of The Best-Worst Method For Supplier Selection Of Products Transportation Service In A Pharmaceutical Company

Main Article Content

Henrikus Banu Alyodya
Johan Krisnanto Runtuk
Poh Kiat Ng


Supplier selection is an important aspect that must be carried out properly to ensure that the company's supply chain can run well. PT. XZ is a pharmaceutical company that regularly require product transportation service from a dedicated supplier. Currently the supplier selection process in the company is a general process that can be applied for any supplier selection case yet does not have an adjustable criteria and weight to accommodate different evaluation standard for different case. The company prefer to simplify the selection process by neglecting the criteria selection and weight calculation. Numerous studies in the field of multi-criteria decision-making (MCDM) have delved into methods to enhance the supplier selection process and minimize errors. This research aims to assess supplier selection, identify relevant criteria, and incorporate the best-worst method to optimize the choice of the most suitable supplier for product transportation services. The best-worst method (BWM) is employed to assign weights to criteria by utilizing user preference ratings, resulting in a refined and accurate criterion weighting process. With the determined criteria, the alternatives are evaluated by individual assessment form. The evaluation score is normalized and multiplied by the weight with the respect of the specific criteria to find the final weighted score. The result is one of the logistic company’s scores is higher than the other alternatives which indicates that alternative is the best to be chosen.

(Manuscript received: 7 July 2023 | Accepted: 28 August 2023 | Published: 30 September 2023)

Article Details

How to Cite
Alyodya, H. B., Runtuk, J. K., & Ng, P. K. (2023). Implementation Of The Best-Worst Method For Supplier Selection Of Products Transportation Service In A Pharmaceutical Company. International Journal on Robotics, Automation and Sciences, 5(2), 33–42.


Bowersox, D. J., Closs, D. J., & Cooper, B. (2002). Supply Chain Logistics Management. New York: The McGraw-Hill Companies,

Chopra, S., & Meindl, P. (2013). Supply chain management: strategy, planning, and operation. New Jersey: Pearson Education, Inc.

Haldar, A., Qamaruddin, U., Raut, R., Kamble, S., Kharat, M. G., & Kamble, S. J. (2017). 3PL evaluation and selection using integrated analytical modeling. Journal of Modelling in Management, 12(2), 224-242. doi:10.1108/JM2-04-2015-0016

Kheybari, S., & Ishizaka, A. (2022, December 15). The behavioural best-worst method. Expert Systems with Applications, 209(118265). doi:10.1016/j.eswa.2022.118265

Liang, F., Brunelli, M., & Rezaei, J. (2020). Consistency issues in the best worst method: Measurements and tresholds. Omega, 96(102175), 305-483. doi:10.1016/

Modibbo, U. M., Hasan, M., Ahmed, A., & Ali, I. (2022). Multi-criteria decision analysis for pharmaceutical supplier selection problem using fuzzy TOPSIS. Management Decision, 60(3), 806-836. doi:10.1108/MD-10-2020-1335

Pitchipoo, P., Venkumar, P., & Rajakarunakaran, S. (2013). Modeling and development of a decision support system for supplier selection in the process industry. Journal of Industrial Engineering International, 9(1). doi:10.1186/2251-712X-9-23

Punniyamoorty, M., Mathiyalagan, P., & Lakshmi, G. (2012). A combined application of structural equation modeling (SEM) and analytic hierarchy process (AHP) in supplier selection. Benchmarking: An International Journal, 19(1), 70-92. doi:10.1108/14635771211218362

Rezaei, J. (2015, June). Best-Worst Multi-Criteria Decision-Making Method. Omega, 53, 49-57. doi:10.1016/

Sharma, J., & Tripathy, B. B. (2023, January). An integrated QFD and fuzzy TOPSIS approach for supplier evaluation and selection. The TQM Journal. doi:10.1108/TQM-09-2022-0295

Yadav, V., & Sharma, M. K. (2015). An application of hybrid data envelopment analytical hierarchy process approach for supplier selection. Journal of Enterprise Information Management, 218-242. doi:10.1108/JEIM-04-2014-0041