Monitoring, measurement and simulation of individual and teamwork performance in collaborative product development and design Mario Štorga, Stanko Škec, Marija Majda Perišić, Tomislav Martinec University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture Abstract: Current management methods for intangible aspects of development projects are diversified across different research fields with lot of open questions. This paper presents a practical approach for monitoring, measuring and simulation of individual and teamwork performance within the collaborative engineering development and design projects. Keywords: individual and teamwork performance, actionable framework, work sampling, organizational meta-matrix, agent-based simulation 1. Introduction The execution of complex development project could be seen as a socio-technical conduct, “defined by history, context, individual values and wider structural frameworks” – actuality of the project [1]. However, an open question remains in a literature about how to deal with the aspects whose nature is intangible and non-financial. Measurement of intangible aspects intrigued many researchers, which provided and recommended various frameworks and methods related to the performance indicators for intangible elements in organizations. However, current measurement methods focusing on individuals’ and teams’ performance are scarce and scattered across different research fields. Extending project management indicators towards intangible aspects of collaborative development project could provide new insights and allow wider perspective on project execution [2], [3], [4], [5]. To supplement existing management tools, the objective of this paper is to propose a practical approach for monitoring, measuring and simulation of individual and teamwork performance within the collaborative engineering development projects. 2. Performance indicators for engineering systems development projects Performance indicators are operative part of any performance measurement system, which provides information about the accomplishment of given objective. Takim and Akintoye [6] stated: “Performance indicators specify the measurable evidence necessary that a planned effort has achieved the desired result”. As such, measuring performance can provide feedback about process or organization efficiency and effectiveness and increase odds of project and organization success [7], [8]. After analysis of project performance indicators implemented in different public and private organisations, Parmenter [9] concluded several facts about their nature and characteristics:  Monetary measures are lagging indicators; therefore, non-monetary ones could provide more input-oriented perspective to the performance of the observed phenomenon.  Frequent measurement can provide real-time information promptly.  Appropriately formed performance indicators should indicate what type of action is necessary to improve the performance.  Measures cannot just be used at the organizational level but also at lower levels (individuals and teams) to improve the management process in an operative way.  Good performance indicators should influence more than just one aspect of the organization. The performance indicators for intangible elements of collaborative engineering projects that can be found in the literature are usually abstract measures without proper description or metric. To enable measurement and monitoring of those aspects, there is a necessity to propose indicators that will focus on individual and team level and consequently provide socio-technical perspective [10]. In comparison with existing retrospective and lagging indicators, usage of leading indicators would provide a more accurate snapshot of the current situation and would enable monitoring of project performance from a different perspective. The approach presented in the paper is built on state of the art principles for intellectual capital performance measurement for collaborative development projects in different sectors (such as aerospace, automotive, energy, transportation, and healthcare) [11]. Indicators extracted from the literature were subject to the screening process with an aim to select ones that are relevant for the development context. Identified indicators were classified into four categories that have been set as a focus of the approach:  Competencies and knowledge development;  Communication and information exchange;  Innovativeness and ideation capability; and  Motivation and satisfaction. Refinement phase resulted in the candidate list of 140 performance indicators that was sent to industrial partners for validation (2 companies working on collaborative development projects in automotive and energy sectors). To each indicator, data gathering method was assigned to define measurement procedure and specify requirements for the implementation