CNC router selection for SMEs in woodwork manufacturing using hesitant fuzzy AHP method

Abstract

Purpose Despite being a low-tech industry, woodwork manufacturing industry that includes furniture and cabinet making, witnessed technological leaps in production technologies due to technical developments in computer numerical control (CNC) machining processes. The managers of this industry have attached high importance to the selection of efficient machines as their decisions directly affect the quality and performance of products produced by the firms. Improper selection process can result in a significant decrease in productivity and flexibility. Therefore, a systematic decision-making procedure is needed to prevent inaccurate investments on machines. The purpose of this paper is to purpose a hesitant fuzzy analytic hierarchy process (HFAHP) based multi-criteria decision making (MCDM) system for CNC router selection in small- and medium-sized enterprises (SMEs) in woodwork manufacturing. Design/methodology/approach The study proposes a hierarchical model consisting of 4 main criteria and 11sub-criteria for woodwork manufacturing. Technical, personnel, economic and vendor aspects constitute the main criteria. Because of the hierarchical structure of the model, HFAHP is utilized to define the importance weights of the criteria, and to select the most appropriate CNC alternative for a manufacturing company under focus. In a selection procedure, the judgments of decision makers may have vagueness to specify the importance of criteria affecting the decision process. In the literature, the fuzzy set theory has been utilized to deal with such uncertainties. However, when the ideas of the managers have high potential to fall into contradiction in pairwise comparisons, a novel approach is needed to overcome the obstacles. HFAHP allows the membership degree having a set of possible values. It is specifically useful in compromised decisions where experts cannot agree on a single value and prefer to come up with an interval of linguistic variables. Findings It is revealed that for SMEs in woodwork manufacturing, the most important criterion in selecting the CNC routers is the technical aspects. It may seem counter intuitive that they do not refrain finding the technical criteria superior to the economic aspects, even though they have limited budgets compared to large-scale firms. This demonstrates that in current competitive environment, SMEs understand the need for high-quality production strategy. The weights of the remaining two criteria (personnel and vendor aspects) are relatively low because they expect that they can easily overcome the problem of adapting the workers by training, and all vendors have quality standard qualifications so they can offer a satisfactory service and supplementary systems. Practical implications The ready-to-use model proposed is specialized for SMEs in woodwork manufacturing. However, to make it an easily adaptable model for every company in the woodwork industry regardless of its size, the calculation process of the priority weights is illustrated in detail with a numerical example. Any company can follow the process using their own preferences to end up with a specific model that will perfectly reflect their own specific priorities. For demonstrating the application of the model, a case study is conducted in a woodwork manufacturing SME to select the best CNC router among three alternatives. Originality/value The originality and value of the paper is twofold. First, to the best of our knowledge, this is the first study that proposes a woodworking-specific CNC router selection for SMEs. Second, to handle the high uncertainty in the judgements, and to facilitate consensus among the experts during face to face meetings to develop compromised matrices, a very recently developed method, HFAHP is used.

A comparison of TOPSIS, grey relational analysis and COPRAS methods for machine selection problem in the food industry of Turkey

“…When these studies are examined, it is possible to see that machine selection problems in different sectors are addressed. On the one hand, earlier studies (Özgen et al, 2011;Kumru and Kumru 2015;Özceylan et al, 2016;Kabak and Dağdeviren, 2017;Camcı et al, 2018) confirm that companies operating in the manufacturing sector have benefited from MCDM methods for various machine selection problems. On the other hand, some other studies (Clarke et al, 1990;Samanta et al, 2002, Alpay andIhpar, 2018;Štirbanović et al, 2019) also provided evidence that MCDM techniques are applied in the selection of machines used in the mining industry.…”

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“…In the literature, this method has been used by many researchers. For instance, Camci et al 72 73 Zhu et al 74 have extended HFAHP by introducing two indices to compute the consistency and considering the possibilities in the prioritization process. This method is titled hesitant preference analysis.…”

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