Controller Workload

8/12/2019 Controller Workload

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Controller Workload: What are they doing and why are they doing so

much of it?

By David A. Strobhar

Beville Engineering, Inc.

201 West Franklin St., Suite D

Dayton, Ohio 45459

Abstract

The ability to measure a phenomena is critical to being able to control and/or

modify it. The workload imposed on pipeline controllers has until recently been measured

only by subjective assessments from pipeline personnel. However, Beville Engineering has

undertaken recent studies to quantify controller workload. The studies relied ontechniques developed for measuring the workload of oil refinery operators. Use of job

sampling for steady state workload analysis will be explained. The job sampling

methodology will be described along with the type of data obtained from the pipeline

studies. The relationship between time spent on tasks and the number of tasks that need to

be performed will be discussed. The impact of type of pipeline and SCADA configuration

on steady state workload will be highlighted. Methods to lower controller workload will

be discussed relative to the objective data.

Changing anything requires some understanding of that which you are attempting

to change. This holds true for pipeline controllers, where an understanding of the position

is needed in order to change the jobs to make them more efficient, higher performing,and/or different in nature (e.g., commodity based versus geography based). Until recently,

most of the understanding came from people who had worked the job previously.

However, unless one person had worked all the positions, comparisons across positions

became difficult. Subjective assessments of the job were utilized, but they usually lacked a

common frame of reference for what was difficult or easy.

Objective measures of job characteristics are needed in order to assess how a job

can be altered and/or how it should be changed in light of new technology. Beville

Engineering has been developing such measures and applying them to oil refinery

personnel for the past ten years. Recently, the same techniques have been applied to

pipeline controllers with considerable success.

The technique applied is referred to as job sampling. It is a variation of traditional

time and motion studies. In time-and-motion study, the objective is to determine a

standard time for a repeated task. However, because of the continuous nature of process

operations, repeated activities occur only over a long period. The objective of job

sampling is to capture a snapshot of the controllers shift when the controller is most likely


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to be heavily loaded. Taking a long sample of the controllers activity at the beginning and

ending of the shifts captures those repeated activities the controller is likely to experience

day-to-day.

Questions typically occur on the ability to use only a few samples of a controllers

activity as representative of the entire job, Isnt every day different? Certainly everydayis slightly different, but not as much as might be suspected. While the object of the activity

(a specific pipeline) may vary from day-to-day, the types of activities tend to repeat or

have a constancy (starting/stopping a line). Sample/Re-sample tests on the reliability of the

technique show an unaccounted variance of less than 6%.

Two four-hour samples were collected for each pipeline position. The sample start

times were adjusted to each particular location to capture the first four hours of the

controllers day shift. The second sample was taken in the late afternoon and/or early

evening in order to catch a quieter, yet not dead, time period. The two samples

provide indication of peak workload, the difference from peak to normal, and an

average workload.

All job related activities are recorded during the sample period. The start and stop

time for each task is logged, and notations made as to the task itself. The task data is later

categorized. In the case of the pipeline controllers, the data collected were categorized

into one of three areas: (1) communication interactions, including with whom they

interacted, (2) SCADA interactions, including instrument inspections, control moves,

alarms, CRT and display usage, and (3) administrative activities, including logging and line

balances.

One measure obtained from job samples is the actual amount of time the controller

spends on job related tasks, which Beville refers to as direct time. A higher direct timeindicates a greater portion of time being spent on job related activities. Direct time is

represented as a percentage, calculated by dividing recorded task times (in minutes) by the

sample duration (in minutes).

Figure 1 shows the average breakdown of percentage direct time for both

controllers and refinery board operators. The breakdown of the time is different due to the

different nature of the two jobs.

Figure 1. Controller & Refinery Board Operator Direct Time Averages

Controller Refinery Board Operator

Task % Time Task % Time

Communication 16.8 Administrative 17.4SCADA Interaction 21.4 Operational 21.3

Admin/Logging 23.2 Maintenance 1.9

Inspections 9.9

Laboratory 0.8

Total 61.4 Total 51.3


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Despite the difference in the task categories, some general comparisons can be

made between pipeline controllers and refinery board operators. The controllers are

typically spending more time, about 10%, on job related tasks that are board operators.

This difference in utilization may stem from board operator jobs matching processing

units, and therefore not easily dividable for balancing workload. Pipeline controllers also

spend more time on logging, which is one sub-task of the board operators administrativetask.

While direct time is important, it captures only half the story. In addition to how

much time is spent on job related tasks, the number of tasks that must be performed

influences workload. Spending 40 minutes out of an hour on four tasks will be perceived

to be far less busy than spending 40 minutes out of an hour on ten tasks. Combining the

direct time loading with the number of tasks provides an indication of busyness.

Busyness is simply the combination of two measures of steady state loading, (1)

direct time and (2) the number of tasks, to form a third derived measure of workload,

mean time between task (MTBT). Mean time between task is calculated as follows

A chart showing the relationship between the direct time and number of tasks is

shown in Figure 2. The lines on the chart are equivalency curves, where different levels of

direct time and number of tasks have the same degree of busyness, or mean time between

tasks. Movement up or right on the chart indicates increasing busyness, decreasing mean

time between tasks.

Three equivalency curves are shown on the chart. A MTBT 2.0

(lower left of chart) is characteristic of an under-loaded job.

The controller workload samples shown in the figure represents different types of

controller workload:

Position A (Crude Pipeline) Large swing in workload is seen between the AM

and PM samples. The controller was handling predominately crude pipelines,

which are characterized by a significant increase in workload for the early morning

period. The workload for the AM sample is less than .5 minutes mean timebetween tasks, which is more typical of an upset situation. This is in contrast to

the PM sample, which was in the under-loaded range.

Position B (LPG Pipeline) The busyness for this controller stayed constant over

the two sample periods, with a shift in number of tasks to direct time from the AM

to PM period. This type of response is typical for stable positions such as product

pipeline control.

[ ]Sampl eLength Mi nutes Dir ectTime Mi nutesNumber of Task s

( ) ( )


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confused with how often each CRT was looked at. Since eye movements were not

recorded, it is impossible to say precisely which CRTs were looked at. However, despite alarge number of screens, the controllers typically only actively interacted with two. This is

consistent with similar findings for refinery board operators. While the other screens may

be providing value, most the control actions will be on two.

The job sample data provided insights and information on ways to alter controller

jobs to enhance performance. Understanding the current workload allows a determination

to be made as to whether the controller can handle additional tasks or systems without any

associated job changes. If the position is under loaded, additional tasks or responsibilities

can be added without other changes to the system. If the job is overloaded, or nearly

overloaded, then changes can be made to the job to reduce the workload, either to

improve performance or enable the controller to take on additional tasks/responsibilities.

The sample data can indicate opportunities to alter controller workload. If one task

is taking up a disproportionate amount of the controllers time, as indicated by the direct

time breakdown (such as logging), then it is a candidate for automation or possible system

re-design. Busyness indicates opportunities to reduce operator workload. Reducing

frequent, short duration tasks, such as responding to alarms or communication with the

field, can dramatically reduce the controllers level of busyness. Large swings in busyness,

Figure 3 - Control Display Usage

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similar to that for the crude pipeline controller, are candidates for either differential

staffing (provide AM help) or dynamic task reallocation (where another controller could

help on some of the tasks). If a large amount of display paging is occurring, display

redesign can reduce the paging demands.

Workstation design should reflect the reliance on the number of CRTs used tomake most of the control changes. The data should not be interpreted as saying that only

two CRTs should be provided, but it does say eight CRTs for one controller is overkill. In

addition, the display system structure should reflect the number of CRTs that are typically

used for most operational adjustments by the controller.

Job sampling to identify controller workload has been successfully used for oil

refinery operators and has now been successfully applied to pipeline controllers. The data

provide objective measures of current controller characteristics that provide insight into

how the job can or should be altered in the future. By establishing where the controllers

are now, it is easier to understand how to get them to where you want them to be in

the future.

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Controller Workload