Evaluation and prioritisation of manufacturing flexibility alternatives using integrated AHP and TOPSIS method

Abstract

Purpose The purpose of this paper is to propose a novel integrated approach using analytical hierarchy process (AHP) and technique for order of preference by similarity to ideal solution (TOPSIS) methods for evaluation and prioritization of appropriate manufacturing flexibility type required in the face of multiple environmental uncertainties. Design/methodology/approach Using a case study of an Indian fashion apparel firm, the study demonstrates the application of the proposed integrated framework for evaluation and prioritization of manufacturing flexibility. The study uses AHP method to determine importance weight of environmental uncertainty criteria and subcriteria and then employs TOPSIS method to determine the final ranking of manufacturing flexibility types required to cope up with these uncertainties. Findings The findings of the case suggest that the proposed integrated approach is feasible and practically implementable for manufacturing flexibility assessment. Research limitations/implications AHP has been extensively studied and used, but the major limitation of this proposed approach is the involvement of large number of pairwise comparisons leading to difficulty in maintaining consistency in pairwise comparisons. Practical implications The proposed approach can work as a benchmarking tool to practitioners in evaluating and prioritizing manufacturing flexibility alternatives and to suggest strategic allocation of resource by prioritizing different manufacturing flexibilities types. Originality/value Unlike conventional approaches, the study provides meaningful knowledge to decision makers by demonstrating a simple, flexible, and efficient method to evaluate and rank the appropriate manufacturing flexibility types.

A hybrid PCA-AHP-Multi-grade Fuzzy approach to assess marketing-based flexibility

“…Despite the widespread importance of marketing-based flexibility, majority of the studies is limited to manufacturing-based flexibility (Chuu, 2005) and there are few studies that assess how firms develop marketing-based flexibility to compete in the marketplace. Although, a variety of approaches, such as EFA (Koste et al , 2004), G theory (Malhotra and Sharma, 2008), analytical hierarchical process (AHP) (Mishra et al , 2017), fuzzy set theory (Chuu, 2005; Das and Caprihan, 2008) have been used to quantify and assess single or multiple dimensions of flexibility, studies have not been done to assess marketing-based flexibility in terms of its source factors. Since marketing-based flexibility is determined by the flexibility of its source factors, it is important to assess marketing-based flexibility in terms of its source factors.…”

“…This study proposes a hybrid approach to assess marketing-based flexibility and demonstrate the application of the approach in Indian apparel firms. Indian firms are selected mainly due to following reasons: flexibility implementation practices are considerably low in India (Mishra et al , 2017); compared to developed nations, Indian firms give highest priority to quality and least priority to flexibility (Dangayach and Deshmukh, 2005); adoption of advanced manufacturing technologies (AMTs) at shop-floor level is really low and Indian firms give more emphasis on simple, standalone and less capital intensive AMTs compared to AMTs, which require considerable investments and demand integration among different technologies (Thakur and Jain, 2008).…”

Havaalanlarinda Hi̇zmet Kali̇tesi̇ni̇n Entropi̇ Ve Topsis Yöntemleri̇ İle Değerlendi̇ri̇lmesi̇: Avrupa’nin en Yoğun Havaalanlari Üzeri̇ne Bi̇r Uygulama

“…Entropi Yönteminin Kullanıldığı Bazı ÇalıĢmalar Üretim Verilerinin Değerlendirilmesi (Song, Zhu, Peng, & Gonzalez, 2017) Kurumsal Sürdürülebilirlik Değerlendirmesi (Aras, Tezcan, Furtuna, & Kazak, 2017) Havayolu Performans Değerlendirmesi (Ömürbek & Balcı, 2017) Tedarikçi Seçimi (Shemshadi, Shirazi, Toreihi, & Tarokh, 2011;Liu & Zhang, 2011) Coğrafi Pazar Seçimi (Yavuz, 2016) Risk Değerlendirmesi (Liu, Zhao, Weng, & Liua, 2017) Pazar Bölümlemesi (D"Urso, De Giovanni, Disegna, & Massari, 2013) Finansal Etkinliğin Değerlendirilmesi (Çatı, Eş, & Özevin, 2017) Havaalanı Ramp Emniyetinin Değerlendirilmesi (Gonçalves & Correia, 2016) Müşteri Tatminin Değerlendirilmesi (Li, Liu, & Li, 2014) Kurumsal Performans Ölçümü (Öztel, Köse, & Aytekin, 2012;Tunca, Ömürbek, Cömert, & Aksoy, 2016; Materyal Seçimi (Hafezalkotob & Hafezalkotob, 2016) TOPSIS Yönteminin Kullanıldığı Bazı ÇalıĢmalar Havayolu Birleşme Faktörlerinin Değerlendirilmesi (Shyr & Kuo, 2008) Havayolu Uçak Seçimi (Čokorilo, Gvozdenović, Mirosavljević, & Vasov, 2010) Performans Değerlendirmesi (Demireli & Tükenmez, 2012;Barros & Wanke, 2015;Orçun & Eren, 2017) Materyal Seçimi (Mousavi-Nasab & Sotoudeh-Anvari, 2017) Üretim Seçeneklerinin Değerlendirilmesi (Mishra, Pundir, & Ganapathy, 2017) Finansal Performans Değerlendirme (Burcu, 2012;Ömürbek & Kınay, 2013;Temizel & Bayçelebi, 2017;Gümüş, Özdağoğlu, Gümüş, & Özdağoğlu, 2017) Şehirlerin Yenilik Potansiyellerinin Değerlendirilmesi (Luty, Kożuch, Makutėnas, & Butvilaitė, 2015) İşgücü Performansı Değerlendirmesi (Karakaş, Kıngır, & Öztel, 2016) Tedarikçi Seçimi (Bhutia & Phipon, 2012;Çalışkan, Kurşuncu, Kurbanoğlu, & Güven, 2012) Emniyet Değerlendirmesi (Li, ve diğerleri, 2011) Lokasyon Seçimi…”

Decision-Making System Based on a Fuzzy Hierarchical Analysis Process and an Artificial Neural Network for Flow Shop Machine Scheduling Model Under Uncertainty

“…In the same way, several approaches of multi-criteria assessment methods have been identified to address decision-making processes, such as: Analytic Hierarchy Process (AHP), Preference Ranking Organization Methods for Enrichment Evaluations (PROMETHEE), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) [4], [5], [6]; such work is based on the knowledge of experts. The AHP and its fuzzy AHPD variant stand out in the last decade [7], [8], [9], its main applications are oriented to the choice of manufacturing strategies, flexibility in production processes and action plans in various circumstances [10], [11], [12], [13], [14], [15], [16]. Additionally, the works of [17], [18], [19], [20] have been identified where uncertainty is used as a tool that strengthens the solution convergence of solving methods of machine scheduling models, and not as a determining variable in the final scheduling.…”