A methodology to measure coordination of business processes

Coordination of business processes is the management of dependencies where dependencies constrain how the tasks are performed. It has been traditionally done in an intuitive fashion, without paying much attention to the coordination load. Coordination load is being defined as the ratio between the time spent on coordination activities and the total task time. Previous efforts to understand and analyze coordination have resulted in mostly qualitative approaches to categorize and recommend coordination strategies. This research seeks to answer two questions: (1) How can we analyze process coordination problems to improve overall performance? (2) What guidance can we provide to reduce the coordination load of the process and consequently improve the organization's performance? Thus, this effort developed a quantitative measure for coordination load of business processes and a methodology to apply such measure. ^ This effort used a management simulation game to have a controlled laboratory environment enabling the manipulation of the task factors variability, analyzability, and interdependence to measure their impact on coordination load. The hypothesis was that the more variable, non-analyzable, and interdependent a process, the higher the coordination load, and that a higher coordination load would have a negative impact on performance. Coordination load was measured via the surrogate coordination time, and performance via profit. ^ A 22 x 31 full factorial design, with two replicates, was run to observe the impact on the variables coordination time and profit. Properly validated spreadsheets and questionnaires were used as data collection instruments for each scenario. The experimental results indicate that lower task analyzability (ρ=0.036) and higher task interdependence (ρ=0.000) lead to higher coordination load, and higher levels of task variability (ρ=0.049) lead to lower performance. However, contrary to the hypotheses postulated by this work, coordination load did not prove to be strong predictor of performance (correlation of -0.086). ^ These findings from the laboratory experiment and other lessons learned were incorporated to develop a quantitative measure, a tool (survey) to use to gather data for the variables in the measures, and a methodology to quantify coordination load of production business processes. The practicality of the methodology is demonstrated with an example.^

An agent-based model of team coordination and performance

This research is based on the premises that teams can be designed to optimize its performance, and appropriate team coordination is a significant factor to team outcome performance. Contingency theory argues that the effectiveness of a team depends on the right fit of the team design factors to the particular job at hand. Therefore, organizations need computational tools capable of predict the performance of different configurations of teams. This research created an agent-based model of teams called the Team Coordination Model (TCM). The TCM estimates the coordination load and performance of a team, based on its composition, coordination mechanisms, and job’s structural characteristics. The TCM can be used to determine the team’s design characteristics that most likely lead the team to achieve optimal performance. The TCM is implemented as an agent-based discrete-event simulation application built using JAVA and Cybele Pro agent architecture. The model implements the effect of individual team design factors on team processes, but the resulting performance emerges from the behavior of the agents. These team member agents use decision making, and explicit and implicit mechanisms to coordinate the job. The model validation included the comparison of the TCM’s results with statistics from a real team and with the results predicted by the team performance literature. An illustrative 26-1 fractional factorial experimental design demonstrates the application of the simulation model to the design of a team. The results from the ANOVA analysis have been used to recommend the combination of levels of the experimental factors that optimize the completion time for a team that runs sailboats races. This research main contribution to the team modeling literature is a model capable of simulating teams working on complex job environments. The TCM implements a stochastic job structure model capable of capturing some of the complexity not capture by current models. In a stochastic job structure, the tasks required to complete the job change during the team execution of the job. This research proposed three new types of dependencies between tasks required to model a job as a stochastic structure. These dependencies are conditional sequential, single-conditional sequential, and the merge dependencies.