- Introduction This study is intended to improve the techniques available to safety assessors and provide tools for decision making in safety management. This is done by fostering a new paradigm for safety management, which forms the basis for the performance measurement and process mapping/monitoring (PMPM) method.
The research examines safety management philosophies and compares methods. This examination is intended to provide a broad understanding of the fundamental safety and risk concepts.
The FRAM was adopted for Arctic ship navigation: where three captains were interviewed to form the basis for a functional map of the way ship navigation work can be performed. Also, variations in the ways ship navigation work is performed was recorded from the captains to help understand some of the ways captains may adjust their work to the dynamic conditions they face.
Figure 1 – FRAM model for ship navigation with input from ship navigators
Two additions to the FRAM are presented in this work: 1) functional signatures and 2) system performance measurements. Functional signatures provide a method for assessors to animate the FRAM and visualize the functional dynamics over time. (figure 2 ) System performance measurement provides a way to bring an element of quantification to the FRAM. Quantification can then be used to help compare different scenarios and support decisions. These additions to the FRAM have been demonstrated using data from an ice management ship simulator experiment. The demonstration can be used as a basis to continue future analysis of using this method in the maritime domain or transfer this approach to other domains.
Figure 2 – A functional signature for a given time (t)
- Safety Management In this paper, three approaches to safety are examined: fault trees (FT), Bayesian networks (BN), and the Functional Resonance Analysis Method (FRAM). A case study of a propane feed control system is used to apply these methods. In order to make safety improvements to industrial workplaces high understanding of the systems is required. It is shown that consideration of the chance of failure of the system components, as in the FT and BN approaches, may not provide enough understanding to fully inform safety assessments. FT and BN methods are top-down approaches that are formed from the perspective of management in workplaces. The FRAM methodology uses a bottom-up approach from the operational perspective to improve the understanding of the industrial workplace. The FRAM approach can provide added insight to the human factor and context and increase the rate at which we learn by considering successes as well as failures.
- Ship Navigation A methodology is presented on how to apply the FRAM to a domain, with a focus on ship navigation. The method draws on ship navigators to inform the building of the model and to learn about practical variations that must be managed to effectively navigate a ship. The Exxon Valdez case is used to illustrate the model’s utility and provide some context to the information gathered by this investigation. The functional signature of the work processes of the Exxon Valdez on the night of the grounding is presented. This shows the functional dynamics of that particular ship navigation case, and serves to illustrate how the FRAM approach can provide another perspective on the safety of complex operations.
- Resilience The concepts of resilience, such as robustness and rapidity, can be used to inform safety management decisions. A methodology is presented that uses quantitative techniques of system performance measurement and qualitative understanding of functional execution from the Functional Resonance Analysis Method (FRAM) to gain an understanding of these resilience concepts. Examples of robustness and rapidity using this methodology are illustrated, and how they can help operators manage their operation is discussed.
- Operational Dynamics In this paper, a method is presented for visualizing and understanding the operational dynamics of a shipping operation. The method uses system performance measurement and functional signatures. System performance measurement allows assessors to understand the level of performance that is being achieved by the operation. The functional signatures then provide insight into the functional dynamics that occur for each level of performance. By combining system performance measurement with functional signatures, there is a framework to help understand what levels of performance are being achieved and why certain levels of performance are being achieved. The insight gained from this approach can be helpful in managing shipping operations. Data from an ice management ship simulator is used to demonstrate this method and compare different operational approaches.
The Simulator Experiments
An experiment was done using a ship simulator configured for an ice management operation.
Figure 3 – Sketch of the Ice Management Simulator setup
Thirty-three participants used the simulator to execute an operation that consisted of clearing pack ice from a lifeboat launch site at an offshore petroleum installation. The Own-ship (the vessel in which the simulation takes place) is modelled on an Anchor Handling Tug Supply (AHTS) vessel. An array of five computers collected data during the simulations. This included a time history of ice concentration within a specified zone, as well as position, speed, and heading. A video “Replay” file was also recorded during each simulation, which upon playback showed the entire simulation from start to finish. Figure 4 shows a screenshot example from such a Replay video.
Figure 4 – Snapshot of a replay file
The data analysis of this experiment consisted of assessing the overall performance of each participant and determining the functional signatures for each participant, as per the methodology section. The metric used to define the performance of each participant is the percentage of time that the lifeboat launch zone was free of ice. Each participant performed ice management for 30 mins, so the best performing participants were deemed to have kept the area under the lifeboat launch zone ice free for the longest amount of time within the 30-minute simulation. The lifeboat launch zone was defined as a circular area of radius 8 m located 8 m off the port quarter where the lifeboat davits are located. An image processing script was then used to determine if ice was present in the lifeboat launch zone.
In order to determine when decisions and actions were made by the navigator, the functional signature was approximated. It is not known exactly when the participant was trying to make a course change (speed or heading), but it can be approximated by examining the peaks and troughs in the speed trace. A trough implies that a speed change was made to increase speed and a peak implies a speed change was made to decrease speed.
The output for observing ice conditions was also approximated. It was assumed that the navigator checked the ice conditions in the lifeboat zone at least once every 30 s. This was the resolution of the data for the presence of ice in the lifeboat zone.
Times when the speed of 3 knots was exceeded and very high ice loads occurred were flagged. This can help understand when the highest ice loads were on the vessel, and particularly, the relationship between the highest ice loads and speeds above the regulatory maximum as imposed by the POLARIS system.
Based on these criteria, a case file was generated for each participant. The case file contained time stamped events, such as speed and heading changes, ice observations, speed limit violations, and very high ice loads.
After the functional signatures were approximated and the performance quantified for each participant, the functional signatures were compared. This can be a basis for understanding why one person performed better than another, and also for identifying practices that are common to high or low performance types. The functional signatures contain information pertaining to the function execution for each participant, including the outputs of tasks, the relationships between them, and the times at which the tasks occur.
Figure 5 – Snapshot of functional signature for V42 at 0 seconds
The first step is to bin the performance measurements from Figure 5.7 to “group” the data. The bins can be setup to the desired levels of granularity that the assessor wishes to investigate. In this assessment, the bins were chosen to be 0-25%, 25-50%, and 50-75% to represent poor performance, medium performance, and high performance, respectively, (see Figure 5.15). The groups are then examined using a boxplot.
The groups were then examined to understand the functional activity of each group. This measure can provide insight into the level of functional activity that occurs in each group. Figure 5.16 shows the functional activity for the 3 groups in this assessment. For each group there is a wide variation in functional activity, with the 0.25-0.5 group having the least variability.
The temporal distribution of the functional signatures can be examined as well. Figure 5.17 shows the time distribution of active functions. It shows that the high-performance group is more functionally active in the earlier part of the simulation than the other 2 groups. Similarly, the time distributions for each specific function can be examined this way.
The variability for the functional outputs can also be monitored, which can be used to help understand the nature of the output variability for certain functions. For instance, the vessel speed is an output of the “monitor vessel parameters” function. This output is displayed in the functional signature every time the “monitor vessel parameters” function is active. Figure 5.18 shows the distribution of vessel speed for the participants’ speed changes
The functional signatures promote the monitoring of many system parameters by way of functional outputs. This allows certain system parameters, such as regulations, to be examined. In many systems, regulations are created to improve safety, but rarely are the effects of the regulation checked to see if they are as intended. Also, the possibility that a regulation could have unintended effects on the system can be examined.
Figure 6 – Components of PMPMM method for safety management
- Conclusions Operational practices influence performance of shipping operations. It is not always obvious which practices will produce certain outcomes because of the dynamic conditions in which ships operate. This paper presents a method to help visualize the way certain practices influence the performance of an operation. The method is demonstrated through the application of an ice management simulator experiment. A metric is used to measure the performance of each participant. This helps understand the level of performance that is being achieved, but does not help understand why certain levels of performance are being achieved. In order to provide more insight into why participants are achieving low or high performance, functional signatures are used to monitor the system functionality. This paper demonstrates some of the ways a comparison may be made to examine the performance data. In this example, enough insight was obtained to understand some qualities of high and low performance and suggest an approach for improving future performance. These are valuable insights for system management.
Publication
Smith, D. (2019) A NEW SYSTEMS APPROACH TO SAFETY MANAGEMENT WITH APPLICATIONS TO ARCTIC SHIP NAVIGATION, A Thesis submitted to the School of Graduate Studies in partial fulfilment of the requirements for the degree of Doctor of Philosophy Faculty of Engineering and Applied Science Memorial University of Newfoundland.