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Top1. Introduction
Decision making at early stage of product development is an important task that usually captures the interest of manufacturers. It provides an insight on the selection of optimal design in order to have a share of the competitive market that is flooded with various designs having multifarious attributes (Aikhuele, 2017; Olabanji & Mpofu, 2019a). In view of this, manufacturers are faced with the challenge of selecting the optimal design that will gain customers attention and yield profit. Basically, selection of optimal design from a set of alternatives is usually done considering several design attributes that are pertinent to the performance of the design (Olabanji & Mpofu, 2019b; Yeo et al., 2004). However, considering the intentions of manufacturers to have a design with extended life cycle and profit maximization, the basic selection process cannot provide an improved decision process (Olabanji, 2018; Renzi et al., 2017). In order to address the challenge of decision on optimal design, task allotted to the design team goes beyond conceptualizing and drafting a design or model that will be used for the fabrication process. The design team will need to harness relevant information about customer needs, design requirements and manufacturing constraints and other limitations. These information are refined into design features and related sub features are grouped under these design features (Olabanji & Mpofu, 2014; Renzi & Leali, 2016). In essence, there exist several design features that can be fragmented into various sub features and as such, a Multi Criteria Decision-making Model (MCDM) is needed to provide a robust decision-making process (Olabanji & Mpofu, 2020a).
Multicriteria decision making analysis can be broadly classified into two models namely; Multi-Objective Decision-making Models (MODM) and Multi-Attribute Decision-making Models (MADM). The MADM are tools that provides reliable solution to decision problems involving various criteria that can be attributed to the available alternatives (Mardani et al., 2015; Renzi et al., 2017). Examples of the MADM tools are Analytic Hierarchy Process (AHP), Elimination and Choice Translating Reality (ELECTRE), Weighted Sum Model (WSM), Weighted Decision Matrix (WDM), VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) and lots more (Okudan & Tauhid, 2008; Olabanji, 2020). In situations where the criteria of evaluation are of dissimilar dimensions with different units and several sub-criteria, these models are fuzzified and used as fuzzified MADM in order to provide a robust decision-making process (Chakraborty et al., 2017). MODM are employed in situations where a discrete set of explicitly defined alternatives does not exist. It is a useful tool for generating explicit set of alternatives for more detailed evaluation. Renzi et. al, (2017) affirmed that it is employed in the implementing higher level strategic decisions at a more detailed operational level. MODM technique includes Goal Programming (GP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS)(Olabanji & Mpofu, 2020b).