CHAPTER 12
Work sampling is a method of finding the percentage occurrence of a certain
activity by statistical sampling and random observations.
 Work sampling is the process of making sufficient random observations of an
operator’s activities to determine the relative amount of time the operator
spends on the various activities associated with the job.
The major goal of work sampling is to determine how long, or how much of the
work day, is spent on specific types of work.
 Work sampling may identify the fact that certain operators spend a large
portion of their time waiting for work, or performing paperwork tasks, or even
performing activities that are not included in their job descriptions.
 One of the basic foundations of statistical sampling theory is the concept that
the larger the sample size, the results will be more accurate.
 In work sampling, a sufficient number of observations must be made to be
sure that the results accurately summarize the work performed. There are
statistical formulas to help determine how many observations should be made.
CONDUCTING A STUDY
 It is recommended that a uniform procedure should be
followed to perform a work sampling study is to
1. Establish the Purpose
 First, the objective of the study should be established. Work
sampling can be used to determine an overall perspective on
the work done.
2. Identify the Subjects
 Second, the people performing the task must be identified, i.e.
general office work is being studied with the objective of
determining overall productivity.
3. Identify the Measure of Output
 The third step in making the study is the identification of the
measure of the output produced or the types of activities
performed on the jobs being studied. This step is especially
important if the objective of the study is to measure productivity
with the intent of setting a standard.
4. Establish a Time Period
 Fourth, the time period during which the study will be conducted
must be established. Starting and stopping points for the study must
be defined as well.
5. Define the Activities
 This step involves defining the activities that are performed by the
people under study. For example, the definition used in a machine
utilization study, including only the categories of working, idle, and
idle-mechanical breakdown.
6. Determine the Number of Observations Needed
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After the work elements are defined, the number of observations for
the desired accuracy at the desired confidence level must be
determined. If a reasonable guess cannot be made, then a trial study
of perhaps 20 to 40 observations should be made to get an estimate.
7. Schedule the Observations
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Once the number of required observations has been determined,
either from appropriate statistical calculations or from tables, and
the actual observations must be scheduled. Typically, the analyst will
assign an equal number of observations each day during the course
of the study. For example, if 800 observations are required and 20
work days are established as an appropriate observation time, 40
observations should be recorded each day.
A random number table can be used to establish the random times
for each observation.
8. Inform the Personnel Involved
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Before the study is actually performed, the personnel
involved should be informed about the objective of the
study and the methodology that will be employed.
9. Record the Raw Data
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The next is the actual recording of the raw data. Although
this recording can be performed by anyone, it is desirable
that a trained analyst be employed.
It is also very important that the observations be made at
exactly the same location every time.
10. Summarize the Data
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After the data have been collected, they must be
summarized.
A few words about sampling
 Sampling is mainly based on probability. Probability has been defined as
“the degree to which an event is likely to occur”.
 A simple and often-mentioned example that illustrates the point is that of
tossing a coin.
 The law of probability says that we are likely to have 50 heads and 50 tails
in every 100 tosses of the coin. The greater the number of tosses, the more
chance we have of arriving at a ratio of 50 heads to 50 tails.
 The size of the sample is therefore important, and we can express our
confidence in whether or not the sample is representative by using a
certain confidence level.
Establishing confidence levels
 Let us go back to our previous example and toss five coins at a time, and
then record the number of times we have heads and the number of times
we have tails for each toss of these five coins. Let us then repeat this
operation 100 times.
 To make things easier, it is more convenient to speak of a 95 per cent confidence level than of a
95.45 per cent confidence level.
 To achieve this we can change our calculations and obtain:
95 per cent confidence level or 95 per cent of the area under the curve = 1.96 σp
 99 per cent confidence level or 99 per cent of the area under the curve = 2.58 σp
 99.9 per cent confidence level or 99.9 per cent of the area under the curve = 3.3 σp
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 In this case we can say that if we take a large sample at random we can be confident that in 95 per
cent of the cases our observations will fall within ± 1.96 σp
Determination of sample size
 As well as defining the confidence level for our observations we have to decide on
the margin of error that we can allow for these observations.
 Let us look at our example about the productive time and the idle time of the
machines in a factory. There are two methods of determining the sample size that
would be appropriate for this example:
 the statistical method and the nomogram method.
Statistical method. The formula used in this method is:
 Let us assume that some 100 observations were carried out as a preliminary study
and at random, and that these showed the machine to be idle in 25 per cent of
the cases (p = 25) and to be working 75 per cent of the time (q = 75).
 We thus have approximate values for p and q; in order now to determine the
value of n.
 Let us choose a confidence level of 95 per cent with a 10 per cent margin of error
(that is, we are confident that in 95 per cent of the cases our estimates will be ±
10 per cent of the real value).
Nomogram
method
An easier way to
determine sample
size is to read off
the number of
observations
needed directly
from a nomogram
such as the one
reproduced
in figure 91.
Making random observations
 To ensure that our observations are in fact made at random, we can use a random
table such as the one in table 12.
 Various types of random table exist, and these can be used in different ways. In
our case let us assume that we shall carry out our observations during a day shift
of eight hours, from 7 a.m. to 3 p.m. An eight-hour day has 480 minutes. These
may be divided into 48 ten-minute periods.
 We can start by choosing any number at random from our table, for example by
closing our eyes and placing a pencil point somewhere on the table. Let us assume
that in this case we pick, by simple chance, the number 11 which is in the second
block, fourth column, fourth row (table 12).
 We now choose any number between 1 and 10. Assume that we choose the
number 2; we now go down the column picking out every second reading and
noting it down, as shown below (if we had chosen the number 3, we should pick
out every third figure, and so on).
 11 38 45 87 68 20 11 26 49 05
 Looking at these numbers, we find that we have to discard 87, 68 and 49 because they are
too high (since we have only 48 ten-minute periods, any number above 48 has to be
discarded).
 Similarly, the second 11 will also have to be discarded since it is a number that has already
been picked out. We therefore have to continue with our readings to replace the four
numbers we have discarded. Using the same method, that is choosing every second number
after the last one (05), we now have 14 15 47 22
 These four numbers are within the desired range and have not appeared before. Our final
selection may now be arranged numerically and the times of observation throughout the
eight-hour day worked out. Thus our smallest number (05) represents the fifth ten-minute
period after the work began at 7 a.m. Thus our first observation will be at 7.50 a.m., and so
on (table 13).
Example: Conducting the study
 Determining the scope of the study. Before making our actual observations, it is
important that we decide on the objective of our work sampling.
 The simplest objective is that of determining whether a given machine is idle or
working. In such a case, our observations aim at detecting one of two possibilities only:
 We can, however, extend this simple model to try to find out the cause of the stoppage
of the machine:
Making the observations
 So far we have taken the first five logical steps in conducting a work
sampling study.
 selecting the job to be studied and determining the objectives of
the study;
 making a preliminary observation to determine the approximate
values of p (idle) and q (working);
 in terms of a chosen confidence level and accuracy range,
determining n (the number of observations needed) determining
the frequency of observations, using random tables;
 designing record sheets to meet the objectives of the study.
 There is one more step to take: that of making and recording the
observations and analyzing the results.
Work Sampling Observation Form
Advantages of Work Sampling
 Can be used to measure activities that are impractical to
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measure by direct observation
Multiple subjects can be included
Requires less time and lower cost than continuous direct
observation
Training requirements less than DTS or PMTS
Less tiresome and monotonous on observer than continuous
observation
Being a subject in work sampling is less demanding than being
watched continuously for a long time
Disadvantages and Limitations
 Not as accurate for setting time standards as other work
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measurement techniques
Usually not practical to study a single subject
Work sampling provides less detailed information about work
elements than DTS or PMTS
Since work sampling deals with multiple subjects, individual
differences will be missed
Workers may be suspicious because they do not understand the
statistical basis of work sampling