Abstract Manager

Background and Key Issues

Transportation control centres in many industries are busy complex environments. Optimal workload balance is crucial for productivity and worker well-being. Excessive workload can lead to stress and burnout, while insufficient workload may result in boredom and underutilization. There exists an increased potential for human error for both high and low workload scenarios which may lead to safety critical consequences.

Objectives

Measuring and managing workload is essential to ensuring safety, quality and workforce integrity. Based on ISO 10075, Ergonomics Principles Related to Mental Workload [1], this paper will describe an integrated data analysis methodology to assess Control Operator workload.

Methodology

This paper describes a comprehensive approach to Control Room Operator workload assessment using several data analysis techniques and triangulation of data to ensure data quality. The approach includes qualitative and quantitative data gathering and analytics techniques:

• Quantitative analysis of the back-end data from control room software systems which record both Operator inputs (commands) and triggers that require Operator response (alarms and events).
• Quantitative workload scoring by the Operators using a subjective scale based on the Instantaneous Self Assessment tool for workload measurement [2].
• Qualitative observational data from an independent party during various shifts.

Using multiple data sources allows comparison of system data and Operator perception of workload which both describe the cognitive load.

Conclusions

Through the use of data analysis described in this study, organizations can make informed staffing decisions and promote appropriate allocation of roles, responsibilities and relief management. Data analytics for the measurement of workload can allow companies to strive to maintain optimal balance: promoting productivity and employee morale as well as reducing accident potential.

References

1. ISO 10075-1, Ergonomics Principles Related to Mental Workload. 2017
2. Instantaneous Self-Assessment of Workload Technique (ISA), Jordan and Brennen. 1992