Evaluation of Augmented Reality Tools Performance in Digital Supply Chain Management: A Group Decision Making Method

Year 2021, Issue: 23, 149 - 162, 30.04.2021

Abdullah Yıldızbası , Babek Erdebilli (b.d.rouyendegh) , Barış Özen Yavuz Selim Özdemir

https://doi.org/10.31590/ejosat.829921

Cited By: 2

Abstract

The supply chain plays a key role for companies that want to gain an advantage in the competitive market. Most of the companies are want to make their supply chain processes more reliable and sustainable with technological developments. With Industry 4.0, augmented reality is one of the most important technological developments in daily lives and companies' supply chain processes. In this study, the effects of augmented reality tools, which companies will increasingly recognize, are discussed on digital supply chain processes. The purpose of this study is to integrate the increasingly augmented reality tools in the supply chain in the most appropriate way and determine the most suitable one for the supply chain processes among the hardware augmented reality tools. Three augmented reality tools were evaluated with 4 main criteria and 12 sub-criteria. The Fuzzy Analytic Hierarchy Process (AHP) and Fuzzy Technique for Ordering Preference by Similarity to Ideal Solution (TOPSIS) methods are among the most important multi-criteria decision-making methods, are used in the decision-making process. As a result, the obtained results were evaluated and managerial implications were presented.

Keywords

Augmented Reality, Performance Evaluation, Digital Supply Chain Management, Fuzzy Group Decision Making

Dijital Tedarik Zinciri Yönetiminde Artırılmış Gerçeklik Araçlarının Performans Değerlendirmesi: Bir Grup Karar Verme Yöntemi

Abstract

Tedarik zinciri, rekabetçi pazarda avantaj elde etmek isteyen şirketler için kilit bir rol oynamaktadır. Şirketlerin çoğu, teknolojik gelişmelerle tedarik zinciri süreçlerini daha güvenilir ve sürdürülebilir hale getirmek istemektedir. Endüstri 4.0 ile artırılmış gerçeklik, günlük yaşamda ve şirketlerin tedarik zinciri süreçlerinde en önemli teknolojik gelişmelerden biri haline gelmiştir. Bu çalışmada, şirketlerin giderek daha fazla kullanacağı artırılmış gerçeklik araçlarının dijital tedarik zinciri süreçlerine etkileri tartışılmaktadır. Bu çalışmanın amacı, tedarik zincirinde giderek artan artırılmış gerçeklik araçlarını en uygun şekilde entegre etmek için Artırılmış Gerçeklik (AR) araçları arasından tedarik zinciri süreçlerine en uygun olanı belirlemektir. Üç artırılmış gerçeklik aracı 4 ana kriter ve 12 alt kriter altında değerlendirilmiştir. Karar verme sürecinde en önemli çok kriterli karar verme yöntemleri arasında yer alan Bulanık Analitik Hiyerarşi Süreci (AHP) ve İdeal Çözüme Benzerliğe Göre Tercih Sıralaması İçin Bulanık Teknik (TOPSIS) yöntemleri kullanılmaktadır. Sonuç olarak, elde edilen sonuçlar değerlendirilerek yönetimsel çıkarımlar sunulmaktadır.

Keywords

Artırılmış Gerçeklik, Performans Değerlendirme, Dijital Tedarik Zinciri Yönetimi, Bulanık Grup Karar Verme

References

• Azuma, R. T. (1997). A Survey of Augmented Reality. Presence Teleoperators Virtual Environ., 6(4), 355–385.
• Beroule, B., Grunder, O., Barakat, O. and Aujoulat, O. (2017). Order Picking Problem in a Warehouse Hospital Pharmacy. IFAC-PapersOnLine, 50(1), 5017–5022.
• Bechtsis, D., Tsolakis, N., Vlachos, D., and Srai, J. S. (2018). Intelligent autonomous vehicles in digital supply chains: a framework for integrating innovations towards sustainable value networks. J. of Clean. Prod., 181, 60-71.
• Bottani, E. and Rizzi, A. (2006). A fuzzy TOPSIS methodology to support outsourcing of logistics services. Supply Chain Manag., 11(4), 294–308.
• Büyüközkan, G. and Göçer, F. (2018). Digital Supply Chain: Literature review and a proposed framework for future research. Computers in Industry, 97, 157-177.
• Büyüközkan, G. and Göçer, F. (2018). An extension of ARAS methodology under Interval Valued Intuitionistic Fuzzy environment for Digital Supply Chain. Appl. Soft Comput. J., 69, 634–654.
• Çalık, A. (2018). Otomotiv Tedarik Zincirinde Risk Değerlendirmesi için Bulanık AHP ve TOPSIS ile Bütünleşik Bir Yaklaşım. İşletme Araştırmaları Dergisi 10(4), 868-886.
• Calık, A., Paksoy, T., Yıldızbaşı, A., Pehlivan, N.Y. (2017). A Decentralized Model for Allied Closed-Loop Supply Chains : Comparative Analysis of Interactive Fuzzy Programming Approaches. Int. J. Fuzzy Syst., 19, 367–382.
• Çalık, A. (2020). A Comparative Perspective in Sustainable Supplier Selection by Integrated MCDM Techniques. Sigma J Eng & Nat Sci. 38(2), 835-852.
• Caricato, P., Colizzi, L., Gnoni, M. G., Grieco, A., Guerrieri, A. and Lanzilotto, A. (2014). Augmented reality applications in manufacturing : a multi-criteria decision model for performance analysis. IFAC Proc. Vol., 47(3), 754–759.
• Chen, C. T., Lin, C. T. and Huang, S. F. (2006). A fuzzy approach for supplier evaluation and selection in supply chain management. Int. J. Prod. Econ., 102(2), 289–301.
• Cirulis, A. and Ginters, E. (2013). Augmented reality in Logistics. Proc. Comput. Sci. 26, 14-20.
• Eissa, M., Rashed, E. (2020). Application of statistical process optimization tools in inventory management of goods quality: suppliers evaluation in healthcare facility. Jour. Tur. Ops. Mane., 388 – 408.
• Formaneck, S. (2018). A study of sustainable facilities management from a green supply chaın perspective in the united arab emirates. Jour. Tur. Ops. Mane., 314-323.
• Jetter, J., Eimecke, J. and Rese, A. (2018). Augmented reality tools for industrial applications: What are potential key performance indicators and who benefits? Comput. Human Behav., 87, 18–33.
• Kahraman, C., Cebeci, U. and Ruan, D. (2004). Multi-attribute comparison of catering service companies using fuzzy AHP: The case of Turkey. Int. J. Prod. Econ., 87(2), 171–184.
• Kesim, M. and Özarslan, Y. (2012). Augmented Reality in Education: Current Technologies and the Potential for Education. Procedia - Soc. Behav. Sci., 47(222), 297–302.
• Klein, R. G. (1975). The relevance of Old World archeology to the first entry of man into the New World. Quat. Res., 5(3), 391–394.
• Koçak, M. and Çalık, A. (2020). Banka Seçim Tercihlerinin Bulanık Kümelere Dayalı Yeni Bir Karar Verme Çerçevesi İle Değerlendirilmesi. İstanbul Ticaret Üniversitesi Sosyal Bilimler Dergisi, 73-94.
• Korpela, K., Hallikas, J., and Dahlberg, T. (2017). Digital supply chain transformation toward blockchain integration. In proceedings of the 50th Hawaii international conference on system sciences.
• Lima Junior, F. R., Osiro, L. and Carpinetti, L. C. R. (2014). A comparison between Fuzzy AHP and Fuzzy TOPSIS methods to supplier selection. Appl. Soft Comput. J., 21, 194–209.
• Merlino, M. and Sproge, I. (2017). The Augmented Supply Chain. Procedia Eng., 178, 308–318.
• Nasiri, M., Ukko, J., Saunila, M., and Rantala, T. (2020). Managing the digital supply chain: The role of smart Technologies. Technovation, 96-97, 102121.
• Nee, A. Y. C. and Ong, S. K. (2013). Virtual and augmented reality applications in manufacturing. IFAC Proc. Vol., 46(9), 15–26.
• Öztürk, C., and Yildizbaşi, A. (2020). Barriers to implementation of blockchain into supply chain management using an integrated multi-criteria decision-making method: a numerical example. Soft Comput., 1-19.
• Persaud, A. and Azhar, I. (2012). Innovative mobile marketing via smartphones: Are consumers ready?. Mark. Intell. Plan., 30(4), 418–443.
• Rouyendegh,B.D., Yildizbasi, A. and Yilmaz, I. (2020). Evaluation of retail ındustry performance ability through ıntegrated ıntuitionistic fuzzy TOPSIS and data envelopment analysis approach. Soft Comput., 24, 12255–12266.
• Rouyendegh, B.D. and Can, G.F. (2012). Selection of working area for industrial engineering students. Procedia-Social and Behavioral Sciences, 31, 15-19.
• Schrauf, S. and Berttram, P. (2016). Industry 4.0: How digitization makes the supply chain more efficient, agile, and customer-focused. Strategy&. Recuperado de https://www. strategyand. pwc.com/media/file/Industry4. 0. pdf.
• Tabucanon, M. T., Batanov, D. N. and Verma, D. K. (1994). Decision support system for multicriteria machine selection for flexible manufacturing systems. Comput. Ind., 25(2), 131–143.
• Yıldızbaşı, A., Öztürk, C., Efendioğlu, D., and Bulkan, S. (2021). Assessing the social sustainable supply chain indicators using an integrated fuzzy multi-criteria decision-making methods: a case study of Turkey. Environ. Dev. Sustain. 23, 4285–4320.
• Wang, X. (2009). Augmented Reality in Architecture and Design : Potentials and Challenges for Application. Int. J. of Architectural Comput., 7(2), 309-326.
• Zadeh, L.A. (1965). Fuzzy Sets. Information and Control. 2, 338-353.
• Zyoud, S. H., Kaufmann, L. G., Shaheen, H. S., and Fuchs-Hanusch, D. (2016). A framework for water loss management in developing countries under fuzzy environment: Integration of Fuzzy AHP with Fuzzy TOPSIS. Expert Syst. Appl., 61, 86–105.