Teamwork phenomena : exploring path dependency and learning in teams during architectural design of sustainable maritime shipping systems

• Main Author: Pelegrin Alvarez, Lorena
• Other Authors: Bryan R. Moser.
• Format: Others
• Language: English
• Published: Massachusetts Institute of Technology 2018
• Subjects: Engineering and Management Program.; Integrated Design and Management Program.
• Online Access: http://hdl.handle.net/1721.1/118531

Thesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, System Design and Management Program, 2018. === Cataloged from PDF version of thesis. === Includes bibliographical references (pages 133-134). === The systems that we are attempting to build today are becoming increasingly complex, as we ask for more function, more performance, more robustness, more flexibility, and more interconnectedness. At the same time, design and implementation of these systems is becoming a highly collaborative process across countries, cultures and goals, driving an increase of interfaces, interaction, and concurrency of work, ultimately adding complexity to the way teams work. In the face of both increased product complexity and organizational complexity, project teams need to be equipped with processes and tools that enhance their individuals' and collective cognitive abilities. Recent developments in social science research about teamwork indicate that individual intelligence, personality, or skill, matter less than the pattern of idea flow in a team: the characteristics of higher performing groups are a large number of ideas, dense interactions, and diversity of ideas. Also, this body of research argues that the number of opportunities for social learning is often the largest single factor in company productivity. Social learning is learning happens when people learn from one another. How is this body of research relevant to engineering design teams? Can we think about social learning happening in multi-stakeholder, design workshops? What are the signals of social learning in such settings? Can we detect those signals and find patterns? This thesis project has initiated the development of a new class of teamwork experiments concerned with exploring the dynamics of engineering teams during the early stages of architecture selection in design of complex systems. In contrast to much of the teamwork research available, this class of teamwork research is model-based: teams engage in a design activity supported by a system of systems model of the problem, and product subject of design. Moreover, these series of experiments implement novel software user interfaces that include interactive visualization and passive collection of socio-metric data regarding design and experience. This research has been developed on a case study from the Japanese commercial maritime shipping industry in response to the new revision of IMO MARPOL Annex VI requirements setting limits on sulphur oxides and nitrous oxides emissions from ship exhausts. According to Japanese authorities, it is expected that the transition from the currently predominant use of Heavy Fuel Oil (HFO) to Liquified Natural Gas (LNG) will proceed, and LNG-fueled ships have already launched in part of North America and Europe where emissions control is advanced. In this transition, different stakeholders (incl. shipping operators, ship building companies, cargo owners, port operators, fuel suppliers, regulatory bodies and classification societies) might follow different strategies in order to fulfill these new regulatory requirements, and the associated choices will be in trade-off with other technology and business requirements. The design problem that teams face in this series of teamwork experiments consists in modifying a reference crude oil shipping system involving a tankers' fleet composed of Very Large Crude Carriers (VLCCs), currently fueled with HFO and transporting crude oil from a supply port in the Persian Gulf to a delivery port in Japan. The design goal is to reduce SOx emissions and NOx emissions, while fulfilling shipping contracts, at the lowest possible cost. In the teamwork design challenge proposed, individuals representing various stakeholders and teams consider, enumerate, and evaluate feasible system architectures according to pre-defined system goals and performance metrics in a tradespace, whereby the Pareto frontier of non-dominated architectures is sought, and a set of preferred architectures is selected. During the design process, data is collected about key teamwork phenomena, such as attention allocation, decision, and learning. This series of experiments has been developed and piloted in collaboration with University of Tokyo and a committee of Japanese maritime shipping professionals over four workshop sessions between October 2017 and March 2018 at University of Tokyo in Kashiwa-no-ha (Japan), and Massachusetts Institute of Technology (USA). The pilot experimentation tested and rehearsed, between others, the viability of different versions of the design case, and the feasibility of proposed sensors for capturing teamwork phenomena. The pilot experimentation phase also served for prototyping the computer simulator that implements the system of systems model and the interactive visualization software user interface. The main experiment took place at Massachusetts Institute of Technology (USA). For the specific domain problem and solution set explored in these experiments, the results support the claim that higher-performing teams explore more options, analyze options from more viewpoints, and learn more, than lower performing teams. The experimental results also suggest that those teams with clear goals, learn more. This thesis project has also demonstrated that it is possible to sense and visualize learning cycles, including surprises (events that trigger reflection and reframing), as well as path-dependent sequences (course of action or moves) that lead a team to decision in the selection of a best option. Furthermore, it has been observed that social learning in diverse teams can be facilitated with computerized interactive visualization tools. These results suggest an enormous potential for "engineering" high-performance design teams at the meso-scale with collaborative machine-human systems. More pilots in industry cases could provide more data to support/ refute this proposition, and gradually transition into a more social and productive engineering experience for teams. === by Lorena Pelegrin Alvarez. === S.M. in Engineering and Management