A Space of Apt Product Designs Based on Market Information
Source abstract
Abstract
This paper describes procedures that can be adopted during product development, so that there is a higher chance that new products are properly designed before being placed on the market. To implement the suggested procedures, market data are first obtained, then processed in a specified way for the new products to reflect relevant market information. This means putting into use suitable data-processing algorithms and information technologies, based on the type of collected information. The text deals with two information scenarios in this regard, the first assuming that not much is known about customers’ desires and the second working with larger amounts of more specific customer data. While the former requires a more sophisticated approach to product design, theoretically speaking, the latter is simpler to handle from the theoretical point of view, but potentially less so from a practical perspective, given the amounts of data. These theoretical and practical problems are discussed in what follows, and their possible solutions are presented.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Consumer Market Behavior and Pricing
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Quality Management, VSB-Technical University of Ostrava, 708 00 Ostrava, Czech Republic
Funding
VSB-Technical University of Ostrava
References (21)
Show source-provided references
1. Saaty, T.L. (1980). The Analytical Hierarchy Process, McGraw Hill.10.21236/ADA214804 ↗
2. Ishizaka, A., and Nemery, P. (2013). Multi-Criteria Decision Analysis, John Wiley & Sons.10.1002/9781118644898 ↗
3. Davies, T.M. (2016). The Book of R, No Starch Press.
4. Ibaceta, P., and Hand, P. (2018). Qlik Sense, Packt Publishing.
5. Yahav, Y. (2014). SAP BusinessObjects Reporting Cookbook, Packt Publishing.
6. Pupilati, S. (2022). Data Storytelling with Google Looker Studio, Packt Publishing.
7. Saaty, T.L. (2005). Theory and Applications of the Analytic Network Process: Decision Making with Benefits, Opportunities, Costs, and Risks, RWS Publications.
8. Undecidability and hardness in mixed-integer nonlinear programming10.1051/ro/2018036 ↗
9. Bazaraa, M.S., Sherali, H.D., and Shetty, C.M. (2006). Nonlinear Programming, John Wiley & Sons.10.1002/0471787779 ↗
10. Jitesh, J., and Thakkar, J.J. (2021). Multi-Criteria Decision Making, Springer.10.1007/978-981-33-4745-8 ↗
11. Belton, V., and Steward, J. (2002). Multiple Criteria Decision Analysis—An Integrated Approach, Kluwer Academic Publishers.10.1007/978-1-4615-1495-4 ↗
12. Analysis of the Advantages and Disadvantages of Four Comprehensive Evaluation Methods10.54097/fbem.v9i3.9578 ↗
13. Notes on Dependent Attributes in TOPSIS10.1016/j.procs.2014.05.273 ↗
14. A Componential Segmentation Model with Optimal Design Features10.1111/j.1540-5915.1989.tb01874.x ↗
15. Genetic Algorithms for Product Design10.1287/mnsc.42.8.1105 ↗
16. Models And Heuristics For Product Line Selection10.1287/mksc.4.1.1 ↗
17. Heuristics For Pricing And Positioning A Product Line Using Conjoint And Cost Data10.1287/mnsc.39.2.160 ↗
18. A Heuristic Approach To Product Design10.1287/mnsc.33.12.1523 ↗
19. Heuristics For Product-Line Design Using Conjoint Analysis10.1287/mnsc.36.12.1464 ↗
20. Bonnans, J.F., Gilbert, J.C., Lemaréchal, C., and Sagastizábal, C. (2003). Numerical Optimization: Theoretical and Practical Aspects, Springer.10.1007/978-3-662-05078-1 ↗
21. Martin, R.K. (1999). Large Scale Linear and Integer Optimization: A Unified Approach, Kluwer Academic Publishers.10.1007/978-1-4615-4975-8 ↗