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Nabil Raad

Nabil Raad

Nabil Raad

Director of Product Development Analytics at Ford Motor Company

Nabil Raad is Director of Product Development Analytics at Ford Motor Company. In this capacity, Nabil leads the development of analytical solutions that support the design and engineering of Ford and Lincoln vehicles. This includes diverse applications ranging from virtual testing to the analysis of collaborative innovation networks. Previously, Nabil was the Director of Enterprise Risk. Other Ford roles include Director of Ford Credit’s Global Business Center in India, Regional Risk Director for Asia-Pacific and Africa based in Australia, Pricing Analytics Manager, and Portfolio Manager for North America. Prior to joining Ford, Nabil held various positions in the Financial Services industry and consulting that include Chief Information Officer, Head of Quality, and Vice President of Business and Technology Innovation. Nabil has extensive experience in the field of Complex Adaptive Systems, Systems Thinking, and System Dynamics where he has largely focused on developing transformative strategies that shape and shift the behavior of social-technical systems across many disciplines. Nabil holds BS in Computer Science, a MBA, and PhD in Industrial and Systems Engineering. Nabil has extensive teaching and speaking experience in areas related to simulation, Computer Science, and leadership in Analytics. He serves as a global mentor across several organizations.

Track: Analytics Process

Tuesday, April 16, 10:30–11:20am

Understanding The Impact Of Virtual Mirroring-based Learning On Collaboration In Data And Analytics Function: A Resilience Perspective
Large multinational organizations are struggling to adapt and innovate in the face of increasing turbulence, uncertainty, and complexity. The lack of adaptive capacity is one of the major risks facing such organizations as the rapid change in technology, urbanization, socio-economic trends, and regulations continues to accelerate and outpace their ability to adapt. This is a resilience problem that organizations are addressing by investing in Data and Analytics to improve their innovation and competitive capabilities. However, Data and Analytics projects are more likely to fail than to succeed. Competing on Data and Analytics is not only a technical challenge but also a challenge in promoting collaborative innovation networks that are based on two key characteristics of resilient systems. One characteristic is the ability to learn while the second is the ability to foster diversity. In this study, we examine how a newly-established Data and Analytics function has evolved over a one-year period. First, we conduct a baseline survey with two sections. The first section captures the structure of Innovation, Expertise, and Projects networks using network science techniques. In the second section we extract four resilience-based workstyles that provide a behavioral representation of each phase of the Adaptive Cycle Theory. Following the survey, we conduct a controlled experiment where the Data and Analytics population is divided into four groups. One group acts as control mechanism while the remaining three groups are exposed to three different Virtual Mirroring-Based Learning (VMBL) interventions using simulation techniques. A virtual-mirror, which is a visualization of an employee’s own social network that provides a self-reflection as a learning process. The premise is that exposure to self-insights leads to a change in collaborative behavior. After a period of nine months, the baseline survey is repeated and then the effects of the interventions are analyzed. The findings provided original insights into the evolution of the Data and Analytics function, the characteristics of an effective VMBL design, and the relationship between resilience-based work styles and brokerage roles in social networks. The applied and theoretical contributions of this research provide a template for practitioners in Data and Analytics functions while advancing the theory and measurement of resilience.