Designing an Effective Questionnaire using Orthogonal Array for Improving Inventory Accuracy at Electricity Warehouse

Abstract

The process of classical data collection through a survey is time-consuming, expensive, and may not be feasible. In practice, it is always good to make sure that the survey design works with the diversity of factors and levels that affect the products or services. Since the data quality gathered depends solely on the quality of the survey constructed and the written questions, this paper presents one of the most effective tools in designing a stated-choice survey using orthogonal array design to gather the information that improves the inventory accuracy in electricity company’s warehouses. The stated-choice survey provides an effective way to explore survey respondents’ preferences and evaluate the interchanges that survey respondents make in considering several product and service combinations. This method will help inventory engineers and workers for data collection with more information and a minimum number of questions and efforts.

Keywords

• Orthogonal Array
• Stated-Choice Survey
• Discrete Choice Experiments and Analysis
• Inventory Control
• Inventory Accuracy

References

1. Abdi, H., & Williams, L. J. (2010). Principal component analysis. Wiley Interdisciplinary Reviews: Computational Statistics, 2(4), 433-459. doi:10.1002/wics.101
2. Ambreen, N., Khan, S., & Fatima, U. (2019). A Survey on Contemporary Inventory Techniques and Problems Faced By Manufacturing Organizations in Pakistan. In: Proceedings of 2019 IEEE 10th International Conference on Mechanical and Intelligent Manufacturing Technologies (ICMIMT) (pp. 157-160). IEEE. doi:10.1109/ICMIMT.2019.8712008
3. Bagozzi, R. P. (2011). Measurement and meaning in information systems and organizational research: Methodological and philosophical foundations. MIS Quarterly, 35(2), 261-292. doi:10.2307/23044044
4. Boyes, A., Girgis, A., & Lecathelinais, C. (2009). Brief assessment of adult cancer patients' perceived needs: development and validation of the 34‐item Supportive Care Needs Survey (SCNS‐SF34). Journal of Evaluation in Clinical Practice, 15(4), 602-606. doi:10.1111/j.1365-2753.2008.01057.x
5. Brosnan, K., Grün, B., & Dolnicar, S. (2018). Identifying superfluous survey items. Journal of Retailing and Consumer Services, 43, 39-45. doi:10.1016/j.jretconser.2018.02.007
6. Cervellera, C., Wen, A., & Chen, V. C. P. (2007). Neural network and regression spline value function approximations for stochastic dynamic programming. Computers & Operations Research, 34(1), 70-90. doi:10.1016/j.cor.2005.02.043
7. Dolnicar, S. (2013). Asking good survey questions. Journal of Travel Research, 52(5), 551-574. doi:10.1177/0047287513479842
8. Fan, W., & Yan, Z. (2010). Factors affecting response rates of the web survey: A systematic review. Computers in Human Behavior, 26(2), 132-139. doi:10.1016/j.chb.2009.10.015

9. Garcia-Diaz, A., & Phillips, D. T. (1995). Principles of experimental design and analysis. Springer.
10. Gopalakrishnan, K., & Stinson, D. R. (2006). Applications of orthogonal arrays to computer science. In: Proc. of ICDM (pp. 149-164).
11. Ho, W., & Ma, X. (2018). The state-of-the-art integrations and applications of the analytic hierarchy process. European Journal of Operational Research, 267(2), 399-414. doi:10.1016/j.ejor.2017.09.007
12. Hoerger, M. (2010). Participant dropout as a function of survey length in Internet-mediated university studies: Implications for study design and voluntary participation in psychological research. Cyberpsychology, Behavior, and Social Networking, 13(6), 697-700. doi:10.1089/cyber.2009.0445
13. Kolenikov, S. (2010). Resampling variance estimation for complex survey data. The Stata Journal, 10(2), 165-199. doi:10.1177/1536867X1001000201
14. Liao, H., Xu, Z., & Zeng, X. J. (2015). Hesitant fuzzy linguistic VIKOR method and its application in qualitative multiple criteria decision making. IEEE Transactions on Fuzzy Systems, 23(5), 1343-1355. doi:10.1109/TFUZZ.2014.2360556
15. Meimand, S. E., Khalifah, Z., Zavadskas, E. K., Mardani, A., Najafipour, A. A., & Ahmad, U. N. U. (2017). Residents’ attitude toward tourism development: A sociocultural perspective. Sustainability, 9(7), 1170. doi:10.3390/su9071170
16. Mi, X., Li, J., Liao, H., Kazimieras Zavadskas, E., Al-Barakati, A., Barnawi, A., & Herrera-Viedma, E. (2019). Hospitality brand management by a score-based q-rung ortho pair fuzzy VIKOR method integrated with the best worst method. Economic Research, 32(1), 3266-3295. doi:10.1080/1331677X.2019.1658533
17. Otchere, A. F., Adzimah, E. D., & Aikens, I. (2016). Assessing the inventory management practices in a selected company in Ghana. International Journal of Development and Sustainability, 5(3), 105-19, ISDS Article ID: IJDS14122701
18. Sloane, N. J. A. (2007) "A Library of Orthogonal Arrays" http://neilsloane.com/oadir/
19. Verma, R., & Thompson, G. M. (1996). Basing service management on customer determinants: the importance of hot pizza. The Cornell Hotel and Restaurant Administration Quarterly, 37(3), 18-23. doi:10.1016/0010-8804(96)86809-1
20. Vetschera, R. (2017). Deriving rankings from incomplete preference information: A comparison of different approaches. European Journal of Operational Research, 258(1), 244-253. doi:10.1016/j.ejor.2016.08.031
21. Yager, R. R. (2017). Generalized orthopair fuzzy sets. IEEE Transactions on Fuzzy Systems, 25(5), 1222-1230. doi:10.1109/TFUZZ.2016.2604005