Published in Systems Engineering

A new open access research article authored by Alkim Avsar and Paul Grogan appears in Systems Engineering today.

Effects of Differential Risk Attitudes in Collaborative Systems Design

Collaboration enables multiple actors with different objectives to work together and achieve a goal beyond individual capabilities. However, strategic uncertainty from partners’ actions introduces a potential for losses under failed collaboration relative to pursuing an independent system. The fundamental tradeoff between high-value but uncertain outcomes from collaborative systems and lower-value but more certain outcomes for independent systems induces a bistability strategic dynamic. Actors exhibit different risk attitudes that impact decisions under uncertainty which complicate shared understanding of collaborative dynamics. This paper investigates how risk attitudes affect design and strategy decisions in collaborative systems through the lens of game theory. First, an analytical model studies the effect of differential risk attitudes in a two-actor problem with stag-hunting strategic dynamics formulated as single- and bi-level games. Next, a simulation model pairs actors with different risk attitudes in a 29-game tournament based on a prior behavioral experiment. Results show that outcomes collaborative design problems change based on the risk attitudes of both actors. Results also emphasize that considering conservative lower-level design options facilitates collaboration by providing risk-averse actors with a safer solution. By accepting that decision-making actors are not all risk-neutral, future work seeks to develop new design methods to strengthen the adoption of efficient collaborative┬ásolutions.

Published in IEEE Open Journal of Systems Engineering

A new open access research article authored by Rodrigo Caporali de Andrade, Paul Grogan, and Somayeh Moazeni appears in IEEE Open Journal of Systems Engineering today as Early Access / Open Access.

Simulation Assessment of Data-Driven Channel Allocation and Contact Routing in Customer Support Systems

Data-driven operations management methods can transform company operations, respond rapidly to customer demands, and enable new business models. However, companies face the challenge of measuring and evaluating how new technology will impact operational processes. This paper takes a systems engineering approach to assess the tradeoffs of adopting data-driven mechanisms to improve operational processes in a multichannel customer support system. We investigate potential cost savings from two technology applications: classification methods to direct customers to efficient self-service communication channels and routing methods to match customers with agents based on the query type and available skill set. Discrete event simulation evaluates how new technology adoption affects system-level performance. What-if scenarios combine distinct configurations of customer classification mechanisms and available communication channels to evaluate the reduction in the total number of agents required to meet a target service quality level. Discussion includes practical examples of how operational managers could use experimental information to make strategic operational decisions when adopting data-driven technologies.