Validation issues of a performance management system for design: three case studies. Issue 4 (3rd June 2020)

• Record Type: Journal Article
• Authors: Radici Fraga, Paula Görgen; Bernardes, Maurício Moreira e Silva; van der Linden, Julio Carlos de Souza; Vieira, Darli Rodrigues; Chain, Milena Chang
• Is Part Of: International journal of productivity and performance management. Volume 70:Issue 4(2021)
• Page Start: 916
• Page End: 940
• Publication Date: 2020-06-03
• Subjects: Validation -- Focus group -- Indicators -- Design management; Industrial productivity -- Periodicals; Performance -- Management -- Periodicals; Performance -- Measurement -- Periodicals; Time study -- Periodicals; Motion study -- Periodicals; 658.3125
• DOI: 10.1108/IJPPM-02-2019-0063
• Languages: English
• ISSNs: 1741-0401

Abstract

Abstract : Purpose: This study aimed to discuss issues related to the process for validating a performance management system for design (PMSD) in three product development companies. Design/methodology/approach: The use of multifunctional groups becomes important because it favors viewing the organization as a whole, thereby reducing existing gaps between segments of the company. To support this study, focus group research was used. Findings: Viewing design as a resource that contributes to increased competitiveness offers companies benefits, such as improved performance measurement. This measurement is based on indicators and, to be useful, an indicator system should stimulate the company's interest. In addition, the present study made it possible to conclude that the validation process is essential in preimplementation stages because validation allows the PMSD to be adapted to bring it closer to the reality of companies, thus increasing the chances of success during the implementation stage. Originality/value: Validation of the metrics from the perspective of senior management enabled critical analyses of the applicability of the PMSD, as well as its suitability and approximation to the reality of businesses, by selecting the most relevant data.