A data-driven multi-criteria decision-making approach for assessing new product conceptual designs

Abstract

The surge in competition among companies to acquire a more significant portion of the market as well as respecting customer preferences in high quality and diverse products result in a reduction of product life cycles. Accordingly, companies are under enormous pressure to introduce new high quality and diverse products on time. Assessing new product designs at the primary phases of new product development (NPD) is a necessary and complex activity that can considerably reduce the time and cost of introducing new products to the market. The current methods of evaluating new product conceptual designs, including employing decision-making methods based on subjective opinions of experts, utilizing simulation packages, and following trial-and-error approaches in prototyping, may be inefficient, very time-consuming, and costly. To overcome this issue, this paper develops a quantitative data-driven Multi-Criteria Decision-Making (MCDM) approach founded on the combination of an Artificial Neural Network (ANN) method and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), to assess the new conceptual designs. So that the ANN method is utilized to predict the performance characteristics of new designs based on the related existed data of similar products, and TOPSIS is employed to score and rank different proposed alternatives designs. Finally, a case study of evaluating new product conceptual designs in an automotive research and development company is considered to demonstrate the performance and applicability of the proposed approach.

Digital Engineering Methods in Practical Use during Mechatronic Design Processes

“…Other applications support the concept phase by providing suggestions for later manufacturing strategies [30], manufacturing capabilities [31,32], and assembly [33]. Additionally, product configurations [34,35] and product family management [36] have been explored as use-cases as well as concept analyses [37][38][39][40]. There are also attempts to influence product reliability during system design using test and field data [41].…”

Evaluation of product conceptual design based on Pythagorean fuzzy set under big data environment

“…Step 2 The matrix A = (a ik ) m×m gives the difference matrix D = (d ik ) m×m by Eqs. (11) and (12).…”

“…Extensive research on decision making for conceptual design has found that Jing and others 6 and others have summarised MCDM methods for conceptual solutions into three types, one is to build pairwise comparison matrices to obtain the weights of evaluation criteria by calculation, for example, BWM (Best Worst Method) 7 and AHP (Analytic Hierarchy Process) 8 can deal with the extent to which different assessment criteria influence each other, but are susceptible to the subjective preferences of decision makers. The alternate approach is to combine the assessment figures across various criteria to generate a summed assessment value for each assessment option, and to calculate the combined indicator values to derive the option ranking results, like VIKOR (Vlsekriterijumska Optimizacija I Kompromisno Resenje) 9 , 10 and TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) 11 , this type of method does not capture the impact of each evaluation criterion on the overall design. The third type is characterised by an order of preference, which identifies the strengths and weaknesses of different solutions to arrive at the best solution, such as ELECTRE (Elimination Et Choix Traduisant La Realité) 12 and PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluation) 13 , 14 , but they cannot deal directly with uncertainties and have limitations in solving realistic decision problems.…”

Automatic e-content sequencing system for personalised learning environments by using fuzzy AHP based on multiple intelligences

“…MCDM methods are employed for various reasons to provide complex problems with an answer considering the userdefined preferences and criteria [40][41][42][43]. AHP, the most well-known MCDM method, is a simple-to-use, flexible and efficient one due to the fact that it has basic mathematical expressions [44] and is able to execute both quantitative and qualitative criteria by changing comparison matrix values of priorities to obtain more precise results.…”