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

2021 ◽  
pp. 095440542199141
Author(s):  
Hamidreza Arbabi ◽  
Behdin Vahedi-Nouri ◽  
Seyedhossein Iranmanesh ◽  
Reza Tavakkoli-Moghaddam
Keyword(s):  
Decision Making ◽  
New Product Development ◽  
New Product ◽  
Life Cycles ◽  
Data Driven ◽  
Multi Criteria Decision Making ◽  
Complex Activity ◽  
High Quality ◽  
Conceptual Designs ◽  
Artificial Neural Network Ann

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.

Application of Statistical Analysis Tools and Concepts to Big Data and Predictive Analytics to New Product Development

2020 ◽  
Vol 3 (1) ◽  
pp. 17-35
Author(s):  
Brian J. Galli
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Data Analysis ◽  
Product Development ◽  
Competitive Advantage ◽  
New Product Development ◽  
New Product ◽  
Big Data Analysis ◽  
Life Cycles ◽  
The Impact

In today's fiercely competitive environment, most companies face the pressure of shorter product life cycles. Therefore, if companies want to maintain a competitive advantage in the market, they need to keep innovating and developing new products. If not, then they will face difficulties in developing and expanding markets and may go out of business. New product development is the key content of enterprise research and development, and it is also one of the strategic cores for enterprise survival and development. The success of new product development plays a decisive role both in the development of the company and in maintaining a competitive advantage in the industry. Since the beginning of the 21st century, with the continuous innovation and development of Internet technology, the era of big data has arrived. In the era of big data, enterprises' decision-making for new product development no longer solely relies on the experience of decision-makers; it is based on the results of big data analysis for more accurate and effective decisions. In this thesis, the case analysis is mainly carried out with Company A as an example. Also, it mainly introduces the decision made by Company A in the actual operation of new product development, which is based on the results of big data analysis from decision-making to decision-making innovation. The choice of decision-making is described in detail. Through the introduction of the case, the impact of big data on the decision-making process for new product development was explored. In the era of big data, it provides a new theoretical approach to new product development decision-making.

scholarly journals A DATA-DRIVEN APPROACH TO USER-EXPERIENCE-FOCUSED MODEL-BASED ROADMAPPING FOR NEW PRODUCT PLANNING

2021 ◽  
Vol 1 ◽  
pp. 61-70
Author(s):  
Ilia Iuskevich ◽  
Andreas-Makoto Hein ◽  
Kahina Amokrane-Ferka ◽  
Abdelkrim Doufene ◽  
Marija Jankovic
Keyword(s):  
New Product Development ◽  
User Experience ◽  
Planning Process ◽  
New Product ◽  
Data Driven ◽  
User Needs ◽  
User Testing ◽  
Model Based ◽  
Data Driven Approach ◽  
To Receive

AbstractUser experience (UX) focused business needs to survive and plan its new product development (NPD) activities in a highly turbulent environment. The latter is a function of volatile UX and technology trends, competition, unpredictable events, and user needs uncertainty. To address this problem, the concept of design roadmapping has been proposed in the literature. It was argued that tools built on the idea of design roadmapping have to be very flexible and data-driven (i.e., be able to receive feedback from users in an iterative manner). At the same time, a model-based approach to roadmapping has emerged, promising to achieve such flexibility. In this work, we propose to incorporate design roadmapping to model-based roadmapping and integrate it with various user testing approaches into a single tool to support a flexible data-driven NPD planning process.

A new fuzzy decision-making approach for selecting new product development project

2016 ◽  
Vol 24 (3) ◽  
pp. 240-250 ◽  
Author(s):  
Chiu-Chi Wei ◽  
Agus Andria ◽  
Houn-Wen Xiao ◽  
Chiou-Shuei Wei ◽  
Ting-Chang Lai
Keyword(s):  
Decision Making ◽  
Product Development ◽  
New Product Development ◽  
New Product ◽  
Development Project ◽  
Fuzzy Decision Making ◽  
Fuzzy Decision ◽  
Product Development Project

The framework of data-driven and multi-criteria decision-making for detecting unbalanced bidding

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Huimin Li ◽  
Limin Su ◽  
Jian Zuo ◽  
Xiaowei An ◽  
Guanghua Dong ◽  
...  
Keyword(s):  
Decision Making ◽  
Distance Measure ◽  
Public Procurement ◽  
Unit Price ◽  
Data Driven ◽  
Relative Distance ◽  
Multi Criteria Decision Making ◽  
Ideal Solution ◽  
Content Type ◽  
The Government

PurposeUnbalanced bidding can seriously imposed the government from obtaining the best value for the taxpayers' money in public procurement since it increases the owner's cost and decreases the fairness of the competitive bidding process. How to detect an unbalanced bid is a challenging task faced by theoretical researchers and practical actors. This study aims to develop an identification method of unbalanced bidding in the construction industry.Design/methodology/approachThe identification of unbalanced bidding is considered as a multi-criteria decision-making (MCDM) problem. A data-driven unit price database from the historical bidding document is built to present the reference unit prices as benchmarks. According to the proposed extended TOPSIS method, the data-driven unit price is chosen as the positive ideal solution, and the unit price that has the furthest absolute distance measure as the negative ideal solution. The concept of relative distance is introduced to measure the distances between positive and negative ideal solutions and each bidding unit price. The unbalanced bidding degree is ranked by means of relative distance.FindingsThe proposed model can be used for the quantitative evaluation of unbalanced bidding from a decision-making perspective. The identification process is developed according to the decision-making process. The finding shows that the model will support owners to efficiently and effectively identify unbalanced bidding in the bid evaluation stage.Originality/valueThe data-driven reference unit prices improve the accuracy of the benchmark to evaluate the unbalanced bidding. The extended TOPSIS model is applied to identify unbalanced bidding; the owners can undertake objective decision-making to identify and prevent unbalanced bidding at the stage of procurement.

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