Evaluation of Logistics 4.0 Potentials Based On Transportation Safety and Security for a Logistics Company
Abstract
Transportation sector faces growing pressure to handle the global trends while meeting the customer demands. Fourth Industrial Revolution (Industry 4.0) is one of these challenges and gained attraction by the researchers and practitioners of all sectors in recent years and expected to continue its challenging effect in the next decades. This revolution is named as Logistics 4.0 in the context of Industry 4.0 implications in logistics. This era prompts the logistics companies to transit to smarter facilities. In this paper, the problem is denoted as selection of the optimum logistics service provider (LSP) for a logistics firm regarding transportation safety, security, quality and cost criteria based on fourth industrial revolution. The LSP selection problem is to choose the appropriate LSP to meet the increasing demands and provide the good or service in the logistics chain. Analytic Hierarchy Process (AHP), which is one of the most widely used multi-criteria decision making methods (MCDM) is proposed to identify and rank the LSPs, and Fuzzy approach is also developed to obtain most important criteria and best LSPs. Safety and security criteria is obtained as two important criteria in the fuzzy approach with the percentage for selection of best LSP is % 83.7 while security is the most important criteria in the AHP with 0,568 score. Computational results are promising for the decision makers in terms of both solution simplicity and usefulness for logistics sector.
References
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- Göçmen E, Erol R, 2018. The transition to industry 4.0 in one of the Turkish logistics company. International Journal of 3d Printing Technologies and Digital Industry 2(1): 76-85.
- Gürcan ÖF, Yazıcı İ, Beyca ÖF, Arslan ÇY, Eldemir, F, 2016. Third party logistics (3PL) provider selection with AHP application. Procedia-Social and Behavioral Sciences 235: 226-234.
- Hasan MM, Jiang D, Ullah AS, Noor-E-Alam M, 2020. Resilient supplier selection in logistics 4.0 with heterogeneous information. Expert Systems with Applications 139: 112799.
- Jain V, Sangaiah AK, Sakhuja S, Thoduka N, Aggarwal R, 2018. Supplier selection using fuzzy AHP and TOPSIS: a case study in the Indian automotive industry. Neural Computing and Applications 29(7): 555-564.
- Kadam A, Karnewar AS, Umrikar B, Sankhua RN, 2019. Hydrological response-based watershed prioritization in semiarid, basaltic region of western India using frequency ratio, fuzzy logic and AHP method. Environment, Development and Sustainability 21(4): 1809-1833.
- Kauf S, 2016. City logistics-a strategic element of sustainable urban development. Transportation Research Procedia 16: 158-164.
- Li YL, Ying CS, Chin KS, Yang HT, Xu J, 2018. Third-party reverse logistics provider selection approach based on hybrid-information MCDM and cumulative prospect theory. Journal of Cleaner Production 195: 573-584.
- Luthra S, Mangla SK, 2018. Evaluating challenges to Industry 4.0 initiatives for supply chain sustainability in emerging economies. Process Safety and Environmental Protection 117:168-179.
- Memari A, Dargi A, Jokar MRA, Ahmad R, Rahim ARA, 2019. Sustainable supplier selection: A multi-criteria intuitionistic fuzzy TOPSIS method. Journal of Manufacturing Systems 50: 9-24.
- Pamucar D, Chatterjee K, Zavadskas EK, 2019. Assessment of third-party logistics provider using multi-criteria decision-making approach based on interval rough numbers. Computers & Industrial Engineering 127: 383-407.
- Prakash C, Barua MK, 2015. Integration of AHP-TOPSIS method for prioritizing the solutions of reverse logistics adoption to overcome its barriers under fuzzy environment. Journal of Manufacturing Systems 37: 599-615.
- Simon J, Trojanova M, Zbihlej J, Sarosi J, 2018. Mass customization model in food industry using industry 4.0 standard with fuzzy-based multi-criteria decision making methodology. Advances in Mechanical Engineering 10(3): 1687814018766776.
- Stević Ž, Pamučar D, Puška A, Chatterjee P. 2020. Sustainable supplier selection in healthcare industries using a new MCDM method: Measurement of alternatives and ranking according to COmpromise solution (MARCOS). Computers & Industrial Engineering 140: 106231.
- Tadić S, Zečević S, Krstić M, 2014. A novel hybrid MCDM model based on fuzzy DEMATEL, fuzzy ANP and fuzzy VIKOR for city logistics concept selection. Expert Systems with Applications 41(18): 8112-8128.
- Wind Y, Saaty TL, 1980. Marketing applications of the analytic hierarchy process. Management science 26(7): 641-658.
Bir Lojistik Şirketi İçin Taşımacılık Emniyeti ve Güvenliği Kapsamında Lojistik 4.0 Potansiyellerinin Değerlendirilmesi
Abstract
Ulaştırma sektörü, müşteri taleplerini karşılarken küresel eğilimleri ele almak konusunda artan bir baskı ile karşı karşıyadır. Dördüncü Sanayi Devrimi (Endüstri 4.0) bu zorluklardan biridir ve son yıllarda tüm sektörlerin araştırmacıları ve uygulayıcıları tarafından ilgi kazanmıştır ve önümüzdeki yıllarda da etkisini sürdürmesi beklenmektedir. Bu devrim, lojistikte Endüstri 4.0 sonuçları bağlamında Lojistik 4.0 olarak adlandırılmaktadır. Bu dönem lojistik şirketlerini daha akıllı tesislere geçmeye teşvik etmektedir. Bu makalede problem, bir lojistik firması için dördüncü sanayi devrimine dayanan nakliye emniyeti, güvenlik, kalite ve maliyet kriterleri ile ilgili optimum lojistik hizmet sağlayıcısının (LHS) seçimi olarak belirtilmiştir. LHS seçim problemi, lojistik zincirinde hizmet veya ürün sağlamak ve artan talepleri karşılamak için en uygun LHS’yi seçmektir. En yaygın kullanılan çok kriterli karar verme yöntemlerinden (ÇKKV) biri olan Analitik Hiyerarşi Süreci (AHP), LHS’ leri tanımlamak ve sıralamak için önerilmiştir ve en önemli kriterleri ve en iyi LHS'leri elde etmek için Bulanık yaklaşım da geliştirilmiştir. Emniyet, AHP’de 0,568 değerle en önemli kriter olurken güvenlik ve emniyet kriteri, bulanık yaklaşımda en iyi LHS seçmek için % 83.7 ile en önemli iki kriter olmaktadır. Hesaplama sonuçları, karar vericiler açısından lojistik sektörü için çözüm basitliği ve kullanışlılığı açısından umut vericidir.
References
- Abdulhasan MJ, Hanafiah MM, Satchet MS, Abdulaali HS, Toriman ME, Al-Raad AA, 2019. Combining gis, fuzzy logic, and ahp models for solid waste disposal site selection in Nasiriyah, Iraq. Applied Ecology and Environmental Research 17(3): 6701-6722.
- Barreto L, Amaral A, Pereira T, 2017. Industry 4.0 implications in logistics: an overview. Procedia Manufacturing 13: 1245-1252.
- Erdogan M, Ozkan B, Karasan A, Kaya I, 2018. Selecting the best strategy for industry 4.0 applications with a case study. In Industrial Engineering in the Industry 4.0 Era (pp. 109-119). Springer, Cham.
- Göçmen E, Erol R, 2018. The transition to industry 4.0 in one of the Turkish logistics company. International Journal of 3d Printing Technologies and Digital Industry 2(1): 76-85.
- Gürcan ÖF, Yazıcı İ, Beyca ÖF, Arslan ÇY, Eldemir, F, 2016. Third party logistics (3PL) provider selection with AHP application. Procedia-Social and Behavioral Sciences 235: 226-234.
- Hasan MM, Jiang D, Ullah AS, Noor-E-Alam M, 2020. Resilient supplier selection in logistics 4.0 with heterogeneous information. Expert Systems with Applications 139: 112799.
- Jain V, Sangaiah AK, Sakhuja S, Thoduka N, Aggarwal R, 2018. Supplier selection using fuzzy AHP and TOPSIS: a case study in the Indian automotive industry. Neural Computing and Applications 29(7): 555-564.
- Kadam A, Karnewar AS, Umrikar B, Sankhua RN, 2019. Hydrological response-based watershed prioritization in semiarid, basaltic region of western India using frequency ratio, fuzzy logic and AHP method. Environment, Development and Sustainability 21(4): 1809-1833.
- Kauf S, 2016. City logistics-a strategic element of sustainable urban development. Transportation Research Procedia 16: 158-164.
- Li YL, Ying CS, Chin KS, Yang HT, Xu J, 2018. Third-party reverse logistics provider selection approach based on hybrid-information MCDM and cumulative prospect theory. Journal of Cleaner Production 195: 573-584.
- Luthra S, Mangla SK, 2018. Evaluating challenges to Industry 4.0 initiatives for supply chain sustainability in emerging economies. Process Safety and Environmental Protection 117:168-179.
- Memari A, Dargi A, Jokar MRA, Ahmad R, Rahim ARA, 2019. Sustainable supplier selection: A multi-criteria intuitionistic fuzzy TOPSIS method. Journal of Manufacturing Systems 50: 9-24.
- Pamucar D, Chatterjee K, Zavadskas EK, 2019. Assessment of third-party logistics provider using multi-criteria decision-making approach based on interval rough numbers. Computers & Industrial Engineering 127: 383-407.
- Prakash C, Barua MK, 2015. Integration of AHP-TOPSIS method for prioritizing the solutions of reverse logistics adoption to overcome its barriers under fuzzy environment. Journal of Manufacturing Systems 37: 599-615.
- Simon J, Trojanova M, Zbihlej J, Sarosi J, 2018. Mass customization model in food industry using industry 4.0 standard with fuzzy-based multi-criteria decision making methodology. Advances in Mechanical Engineering 10(3): 1687814018766776.
- Stević Ž, Pamučar D, Puška A, Chatterjee P. 2020. Sustainable supplier selection in healthcare industries using a new MCDM method: Measurement of alternatives and ranking according to COmpromise solution (MARCOS). Computers & Industrial Engineering 140: 106231.
- Tadić S, Zečević S, Krstić M, 2014. A novel hybrid MCDM model based on fuzzy DEMATEL, fuzzy ANP and fuzzy VIKOR for city logistics concept selection. Expert Systems with Applications 41(18): 8112-8128.
- Wind Y, Saaty TL, 1980. Marketing applications of the analytic hierarchy process. Management science 26(7): 641-658.
Details
| Primary Language | English |
|---|---|
| Subjects | Engineering |
| Journal Section | Endüstri Mühendisliği / Industrial Engineering |
| Authors | |
| Publication Date | December 15, 2020 |
| Submission Date | March 5, 2020 |
| Acceptance Date | July 14, 2020 |
| Published in Issue | Year 2020 Volume: 10 Issue: 4 |