Otomat Makinası Ürün Dağıtımında Risk Değerlendirmesi

Year 2022, Volume: 9 Issue: 4, 1215 - 1227, 31.12.2022

Aslıhan Yıldız Coşkun Özkan Selçuk Alp Ertuğrul Ayyıldız

https://doi.org/10.31202/ecjse.1132087

Abstract

Müşteri taleplerindeki değişim, sosyal algı, bilgiye erişim kolaylığı, teknolojideki ilerlemeler, artan ihtiyaçlar, değişen çevresel şartlar gibi pek çok faktörle beraber karmaşık hale gelen tedarik zincirinin koordineli olarak yönetilmesi işletmelere büyük kolaylık sağlamaktadır. Ürün ve hizmetlerin son tüketiciye kadar dağıtımında kilit rol oynayan perakendeciler için tedarik zincirinin ve bu zincirdeki tüm operasyonların etkin bir şekilde yönetimi büyük önem taşımaktadır. Satıcı yönetimli bir sistemde perakendecilerin müşterisi olarak tabir edilen otomat makinaları ürün veya hizmetlerin son tüketiciye ulaştırılmasında yaygın kullanılan dağıtım kanallarındandır. Çalışmada otomat makinalarına ürün dağıtımına yönelik risk değerlendirme analizi yapılması amaçlanmaktadır. Bu amaçla tedarik risklerinin belirlenmesi ve bu risklerin değerlendirilmesi için Çok Kriterli Karar Verme yöntemlerinden biri olan Best Worst yöntemine başvurulmuştur. Çalışma için belirlenen dokuz risk kriterinin yönteme göre değerlendirilmesi sonucunda, öncelikli olarak dikkat edilmesi gereken riskler “Talep takibindeki hatalar”, “Rakiplere göre nitel ve nicel yetersizlikler”, “Yetersiz araç bölmesi ve kapasitesi” olarak belirlenmiştir.

Keywords

best worst, ürün dağıtımı, otomat makinası, çok kriterli karar verme

Best-Worst Yöntemi ile Otomat Makinası Ürün Dağıtımındaki Risklerin Değerlendirilmesi

Abstract

Successfully managing the supply chain, which has become complex with many factors such as changes in customer demands, social perception, ease of access to information, advances in technology, increasing needs, and changing environmental conditions, provides great convenience to businesses. Effective supply chain and all operations management in this chain has great importance for retailers, which play a key role in the distribution of products and services to the end consumer. Vending machines, which are called the customers of retailers in a vendor-managed system, are among the distribution channels that are widely used in delivering products or services to the end consumer. The study, it is aimed to make a risk assessment for product distribution to vending machines. For this purpose, the Best Worst method, which is one of the Multi-Criteria Decision Making methods, is used to determine and evaluate supply risks. As a result of the evaluation of the nine risk criteria determined for the study according to the method, the risks that should be considered primarily are determined as "Errors in demand tracking", "Qualitative and quantitative inadequacies compared to competitors", "Insufficient vehicle compartment and capacity".

Keywords

Risk assessment, best worst, product distribution, vending machine, multi criteria decision making

References

• [1] A. Yildiz, “An integrated interval-valued intuitionistic fuzzy AHP-TOPSIS methodology to determine the safest route for cash in transit operations : a real case in Istanbul,” Neural Comput. Appl., vol. 0123456789, 2022, doi: 10.1007/s00521-022-07236-y.
• [2] A. Prashar and S. Aggarwal, “Modeling enablers of supply chain quality risk management: a grey-DEMATEL approach,” TQM J., vol. 32, no. 5, pp. 1059–1076, 2020, doi: 10.1108/TQM-05-2019-0132.
• [3] E. Ayyildiz and A. Taskin Gumus, “Pythagorean fuzzy AHP based risk assessment methodology for hazardous material transportation: an application in Istanbul,” Environ. Sci. Pollut. Res., pp. 1–13, Mar. 2021, doi: 10.1007/s11356-021-13223-y.
• [4] Z. Chen, H. Li, H. Ren, Q. Xu, and J. Hong, “A total environmental risk assessment model for international hub airports,” Int. J. Proj. Manag., vol. 29, no. 7, pp. 856–866, Oct. 2011, doi: 10.1016/J.IJPROMAN.2011.03.004.
• [5] M. Xu et al., “Supply chain sustainability risk and assessment,” J. Clean. Prod., vol. 225, pp. 857–867, Jul. 2019, doi: 10.1016/J.JCLEPRO.2019.03.307.
• [6] R. Rostamzadeh, M. K. Ghorabaee, K. Govindan, A. Esmaeili, and H. B. K. Nobar, “Evaluation of sustainable supply chain risk management using an integrated fuzzy TOPSIS- CRITIC approach,” J. Clean. Prod., vol. 175, pp. 651–669, Feb. 2018, doi: 10.1016/J.JCLEPRO.2017.12.071.
• [7] A. T. Gumus, “Evaluation of hazardous waste transportation firms by using a two step fuzzy-AHP and TOPSIS methodology,” Expert Syst. Appl., vol. 36, no. 2 PART 2, pp. 4067–4074, 2009, doi: 10.1016/j.eswa.2008.03.013.
• [8] Y. Fan and M. Stevenson, “A review of supply chain risk management: definition, theory, and research agenda,” Int. J. Phys. Distrib. Logist. Manag., vol. 48, no. 3, pp. 205–230, 2018, doi: 10.1108/IJPDLM-01-2017-0043.
• [9] B. Zlaugotne, L. Zihare, L. Balode, A. Kalnbalkite, A. Khabdullin, and D. Blumberga, “Multi-Criteria Decision Analysis Methods Comparison,” Environ. Clim. Technol., vol. 24, no. 1, pp. 454–471, 2020, doi: 10.2478/rtuect-2020-0028.
• [10] S. Khan, M. I. Khan, A. Haleem, and A. R. Jami, “Prioritising the risks in Halal food supply chain: an MCDM approach,” J. Islam. Mark., vol. 13, no. 1, pp. 45–65, 2022, doi: 10.1108/JIMA-10-2018-0206.
• [11] I. Mzougui, S. Carpitella, A. Certa, Z. El Felsoufi, and J. Izquierdo, “Assessing Supply Chain Risks in the Automotive Industry through a Modified MCDM-Based FMECA,” doi: 10.3390/pr8050579.
• [12] S. DuHadway, S. Carnovale, and B. Hazen, “Understanding risk management for intentional supply chain disruptions: risk detection, risk mitigation, and risk recovery,” Ann. Oper. Res., vol. 283, no. 1–2, pp. 179–198, 2019, doi: 10.1007/s10479-017-2452-0.
• [13] Y. Ali, M. Asees Awan, M. Bilal, J. Khan, A. Petrillo, and A. Ali Khan, “Risk assessment of China-Pakistan Fiber Optic Project (CPFOP) in the light of Multi-Criteria Decision Making (MCDM),” Adv. Eng. Informatics, vol. 40, pp. 36–45, Apr. 2019, doi: 10.1016/J.AEI.2019.03.005.
• [14] M. Abdel-Basset, M. Gunasekaran, M. Mohamed, and N. Chilamkurti, “A framework for risk assessment, management and evaluation: Economic tool for quantifying risks in supply chain,” Futur. Gener. Comput. Syst., vol. 90, pp. 489–502, 2019, doi: 10.1016/j.future.2018.08.035.
• [15] U. S. K. De Silva, A. Paul, K. W. Hasan, S. K. Paul, S. M. Ali, and R. K. Chakrabortty, “Examining risks and strategies for the spice processing supply chain in the context of an emerging economy,” Int. J. Emerg. Mark., 2021, doi: 10.1108/IJOEM-07-2020-0776.
• [16] S. A. Kharisma and R. Ardi, “Supply chain risk assessment of generic medicine in Indonesia Using DEMATEL-Based ANP (DANP),” IEEE Int. Conf. Ind. Eng. Eng. Manag., vol. 2020-Decem, pp. 716–720, 2020, doi: 10.1109/IEEM45057.2020.9309793.
• [17] J. Rezaei, “Best-worst multi-criteria decision-making method,” Omega, vol. 53, pp. 49–57, Jun. 2015, doi: 10.1016/J.OMEGA.2014.11.009.
• [18] A. Sarwar, A. Zafar, and A. Qadir, “Discover Sustainability Analysis and prioritization of risk factors in the management of Halal supply chain management,” vol. 2, p. 30, 2021, doi: 10.1007/s43621-021-00039-6.
• [19] E. Çakir, “Best- Worst Yöntemine Dayalı ARAS Yöntemi ile Dış Kaynak Kullanım Tercihinin Belirlenmesi : Turizm Sektöründe Bir Uygulama Determination of Outsource Usage Preference by ARAS Method based on Best – Worst Method : An Application in The Tourism Sector,” vol. 23, pp. 1273–1300, 2019.
• [20] M. Deveci, V. Simic, and A. E. Torkayesh, “Remanufacturing facility location for automotive Lithium-ion batteries: An integrated neutrosophic decision-making model,” J. Clean. Prod., vol. 317, p. 128438, Oct. 2021, doi: 10.1016/J.JCLEPRO.2021.128438.
• [21] H. Abdali, H. Sahebi, and M. Pishvaee, “The water-energy-food-land nexus at the sugarcane-to-bioenergy supply chain: A sustainable network design model,” Comput. Chem. Eng., vol. 145, 2021, doi: 10.1016/j.compchemeng.2020.107199.
• [22] E. Ayyildiz, “Interval valued intuitionistic fuzzy analytic hierarchy process-based green supply chain resilience evaluation methodology in post COVID-19 era,” Environ. Sci. Pollut. Res., no. 0123456789, 2021, doi: 10.1007/s11356-021-16972-y.
• [23] F. Omidvari, M. Jahangiri, R. Mehryar, M. Alimohammadlou, and M. Kamalinia, “Fire Risk Assessment in Healthcare Settings: Application of FMEA Combined with Multi-Criteria Decision Making Methods,” Math. Probl. Eng., vol. 2020, 2020, doi: 10.1155/2020/8913497.
• [24] M. Yazdani et al., “A fuzzy group decision-making model to measure resiliency in a food supply chain: A case study in Spain,” Socioecon. Plann. Sci., p. 101257, Feb. 2022, doi: 10.1016/J.SEPS.2022.101257.
• [25] M. Abdel-Basset, R. Mohamed, A. E. N. H. Zaied, A. Gamal, and F. Smarandache, “Solving the supply chain problem using the best-worst method based on a novel Plithogenic model,” Optim. Theory Based Neutrosophic Plithogenic Sets, pp. 1–19, Jan. 2020, doi: 10.1016/B978-0-12-819670-0.00001-9.
• [26] P. Pakdeenarong and T. Hengsadeekul, “Supply chain risk management of organic rice in Thailand,” Uncertain Supply Chain Manag., vol. 8, no. 1, pp. 165–174, 2020, doi: 10.5267/j.uscm.2019.7.007.