Abstract
Due to the situational and contextual individuality of engineering work, the in-progress monitoring and assessment of those factors that contribute to the success and performance in a given scenario poses a distinct and unresolved challenge, with heavy reliance on managerial skill and interpretation. Termed engineering project health management (EPHM), this paper presents a novel approach and framework for monitoring of engineering work through data-driven and computational analytics that in turn support the managerial interpretation and generation of higher level, context-specific understanding. EPHM is formed through the first adaptation of integrated vehicle health management (IVHM) to the field of engineering management; an approach that has been used to-date for the machine monitoring and predictive maintenance. The approach is applied to four industrial cases, which demonstrates the generation of project-specific information. The approach thereby acts to increase understanding of an engineering activity and a work state, and is complementary to existing managerial toolsets and approaches. A key tenet of the adaption of IVHM is to place the manager in a central role, supporting their professional judgment while reducing investigative effort.
Highlights
I N CONCERT with the globalization and driven by the development age, the modern engineering management faces challenges in scale [1], complexity [2], [3], risk [4], [5], and geographic distribution [6], [7]
Adapting the approach of integrated vehicle health management (IVHM) [22]–[25], this paper presents a framework for the automatic and context-specific generation of analyses of the engineering activity, and the processes by which such information may be used to affect informed and context-applicable decision making
Engineering management is subject to a multitude of critical success factors (CSFs), see examples given in Table I, with varying levels of importance and context-specificity on a per-case basis
Summary
I N CONCERT with the globalization and driven by the development age, the modern engineering management faces challenges in scale [1], complexity [2], [3], risk [4], [5], and geographic distribution [6], [7]. Whereas the literature presents a synthesized generalization of factors for the success, it is challenging to determine the importance and impact of each for every unique scenario To support such management challenges, this paper proposes an approach to the engineering activity monitoring and managerial decision-making rooted in low-level data-driven analysis. Adapting the approach of integrated vehicle health management (IVHM) [22]–[25], this paper presents a framework for the automatic and context-specific generation of analyses of the engineering activity, and the processes by which such information may be used to affect informed and context-applicable decision making. In adaptation to the engineering management scenario, this framework promotes the active monitoring of in-progress activity through large-scale and broad-spectrum data analyses, to generate a data-driven and context-specific understanding of work state and, through managerial interpretation, promote understanding of the project health.
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