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Time Study

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Time Study / Work Sampling “Lab” Report

Objective 1. To perform a time and motion data analysis of an assembly task and compute the mean and standard deviation of the times recorded. 2. To discuss and analyze the set of data from (1), analyze current method, and figure out possible approaches to simplify the job and improve overall productivity. 3. To analyze the activity-time study for the secretary and compute % of time spent on each activity using different methods. 4. To develop learning curve for a jigsaw puzzle activity.

Introduction
This lab focuses on the study of time study, work sampling, and the learning curve. The three studies can be used to analyze the performance of a production system, thus giving adequate background to improve the system, make planning and scheduling to minimize cost, and increase safety.
Time Study
In this lab, Time Study data was obtained by measuring the time it takes for a pretend assembly worker to finish a task which consists of four elements. Time was taken by using a stopwatch in two different manners: running time and stop time. In running time, the process time was measured without resetting the time in between elements. In stop time, the process time was measured as individual time taken to complete one element. In this case the stop watch was stopped and reset after the completion of each element. Afterwards the data was analyzed and an evaluation on the current performance and suggestions for improvements were made.
Work Sampling
In Work Sampling section, an activity-time study for a secretary was used. From the activity-time study, the percentage spent in each activity category was computed by using three different methods: using all the data, using a sequential pattern, and using a random time for every ten minute time period. When using all the data, the total minutes spent on each activity throughout the day was measured. Sequential pattern calculation made use of a record of total minutes spent on each activity for a particular minute every ten minutes. In random time, an Excel Spreadsheet was used to generate a set of random numbers in between one to ten, and the set of random numbers was used to compute the total minutes spent on each activity for every ten minutes. The three results were then compared and further analyzed.
Learning Curve
The last part of the lab is the Learning Curve section. In this part someone made a jigsaw puzzle and the time it took to complete the puzzle was measured by using a stopwatch three times. From the time measurement, a learning curve was generated by using Excel. Generally a down curve pattern is expected because learning curve theorized that for worker gain skill and knowledge of the product process over time as they do repeat the process.
The rest of this report will elaborate on each study.

Data and Calculation
Time Study
The assembly process was divided into four elements: 1. Connecting metal parts 2. Connecting 1 to yellow cap 3. Connecting 2 to another yellow cap 4. Screwing final assembly
The assembly process was done four times. For each trial, two methods were used to obtain time measurement of the assembly process: running time and stop time. Raw data is attached to this report. The average time and standard deviation for each trial was computed by using Excel Spreadsheet. Manually, average time can be computed by taking a total of the four trials for each element and divided that total with the number of trials which are four. Standard deviation can be computed by using the formula: .
The data measurement is as follows: Stop Time | | | | | | | | | Element | Time (s) | Total | Average | Standard Deviation | | | 1 | 2 | 3 | 4 | | | | 1 | Connecting metal part | 9.1 | 5.3 | 15.4 | 3.6 | 33.4 | 8.35 | 5.23 | 2 | Connecting 1 to yellow cap | 19.2 | 6.6 | 19.2 | 16.2 | 61.2 | 15.3 | 5.97 | 3 | Connecting 2 to second yellow cap | 10.5 | 13.6 | 9.9 | 10.6 | 44.6 | 11.15 | 1.66 | 4 | Screwing final assembly | 20.6 | 39.2 | 17.6 | 21.3 | 98.7 | 24.675 | 9.82 | | | | | | | | | | Running Time | | | | | | | | | | Element | Time (s) | Total | Average | Standard Deviation | | | 1 | 2 | 3 | 4 | | | | 1 | Connecting metal part | 9.1 | 5.3 | 15.4 | 3.6 | 33.4 | 8.35 | 5.23 | 2 | Connecting 1 to yellow cap | 28.3 | 11.9 | 34.6 | 19.8 | 94.6 | 23.65 | 9.91 | 3 | Connecting 2 to second yellow cap | 38.8 | 25.5 | 44.5 | 30.4 | 139.2 | 34.8 | 8.48 | 4 | Screwing final assembly | 59.4 | 64.7 | 62.1 | 51.7 | 237.9 | 59.475 | 5.62 |

Discussion
It can be seen that the data have large standard deviation relative to the data values. This is because of the difficulties the time keeper had in distinguishing when each element start and finish. Furthermore, the stopwatch has to be stopped and reset when measuring the stop time although the assembly process was kept going. Because of those human errors the data measurements are not so accurate and they contain large variations.
From the data pattern obtained, it appeared that the assembly process was not very efficient as shown by the time variation and standard deviation. For example, in the third trial it took 15.4s to connect the metal parts while it only took 3.6 s to do the same step in trial 4. Due to some problems during assembly it may take longer to do certain steps (15.4s), but the data shows that the assembly worker had the potential to connect the metal part in only 3.6s.
Some problems that can make an assembly process takes longer include lack of training, defects in raw materials, inefficient set up, difficulty in locating tools e.g. screw driver, and many others. Certain improvements should be taken in order to make the process more efficient. Simplifying the job can be done by separating the assembly into different workstations, thus allowing the assembly worker to only do one step and let another worker do the second step, and so on. By doing one particular process over time the worker will obtain a learning curve and can do the job more efficiently in less time. Other methods to improve productivity include quality control. In factory life, when a worker finds defects in raw materials, the worker will spend time on analyzing the defects and spend more time to fix the defects or find someone to fix the defect. This made the process inefficient. By doing quality control before the assembly process, defects in materials can be reduced and efficiency improved. If the workspace is not properly arranged and set up, it will be rather difficult for the assembly worker to do the job. For example, it will take time for the worker to locate the screw driver when he/she wants to do the fourth step, screwing the final assembly. By organizing the workspace and making the workstation smaller, the assembly process will be more efficient. It appeared that the elements in the task performed in this lab can be done by automated assembly, for example the screwing part. Workers can be replaced by more reliable machines and robots to increase efficiency because automation will provide relatively lower and fixed process time.

Work Sampling
The activity time study for the secretary is attached to this report. Three methods are used to obtain the time taken for each activity: all data, sequential pattern, and random time. For the all data, minutes of day for each activity have been provided in the activity time study. For the sequential pattern, the activities at tenth period and twentieth period were measured. For the random time, random numbers between one to ten were generated by using Excel and then used to compute the time taken for each activity at that period. Percentage of each activity is calculated by using Excel spreadsheet. The data is as follows: All Data | | | Acticity | Time (minutes) | Percentage | Typing | 96.2 | 20% | Filing | 40.2 | 8% | General Work | 186.6 | 39% | Telephone | 59.2 | 12% | Reception | 36.4 | 8% | Personal | 61.4 | 13% | Total | 480 | | | | | Sequential pattern for minutes 10 and 20 | Acticity | Time (minutes) | Percentage | Typing | 8.66 | 18% | Filing | 4 | 8% | General Work | 19.33 | 40% | Telephone | 6 | 13% | Reception | 5 | 10% | Personal | 5 | 10% | Total | 48 | | | | | Random time pattern | | Acticity | Time (minutes) | Percentage | Typing | 8 | 17% | Filing | 5 | 10% | General Work | 19 | 40% | Telephone | 7 | 15% | Reception | 4 | 8% | Personal | 5 | 10% | Total | 48 | | Random numbers | | | Random numbers | | 4 | 1 | | 1 | 25 | 8 | 2 | | 3 | 26 | 7 | 3 | | 9 | 27 | 1 | 4 | | 9 | 28 | 3 | 5 | | 10 | 29 | 6 | 6 | | 9 | 30 | 8 | 7 | | 6 | 31 | 3 | 8 | | 7 | 32 | 8 | 9 | | 4 | 33 | 3 | 10 | | 7 | 34 | 9 | 11 | | 8 | 35 | 1 | 12 | | 8 | 36 | 8 | 13 | | 9 | 37 | 9 | 14 | | 8 | 38 | 2 | 15 | | 1 | 39 | 7 | 16 | | 4 | 40 | 8 | 17 | | 7 | 41 | 6 | 18 | | 2 | 42 | 6 | 19 | | 8 | 43 | 8 | 20 | | 9 | 44 | 9 | 21 | | 2 | 45 | 9 | 22 | | 6 | 46 | 9 | 23 | | 1 | 47 | 10 | 24 | | 3 | 48 |

Comparison | | | | | | | Method | Activity | | Typing | Filing | General Work | Telephone | Reception | Personal | All data | 20% | 8% | 39% | 12% | 8% | 13% | Sequential Pattern | 18% | 8% | 40% | 13% | 10% | 10% | Random Pattern | 17% | 10% | 40% | 15% | 8% | 10% |

Discussion
The comparison table shows different percentages of each activity for each data method, however the difference is not much and all three methods indicate the same pattern. From the table it can be concluded that Reception task and Filing took the least time, followed by Personal, Telephone, Typing, and General Work took the longest time. As shown by the similar heights of the bar graphs, it is known that the patterns generated by the tree methods are similar.
It is most accurate to do the percentage computation when using All Data because more data lead to subsequent increase in accuracy. This data number difference is pretty significant because the total data in the All Data method is 480 while the total data in the other two methods are only 48. However, measuring all the data in the activity time-study will take much time and lot of works (fortunately the data values are given in this lab, but in real life the data has to be calculated). If accuracy is required then the All Data method would be the best option but depending on the situation, if only the patterns or trends of the activities are required, then the sequential and random pattern methods would be the better options. Also, the accuracy of these two methods can be increased by taking samples from larger data.

Learning Curve
The time it takes to complete a jigsaw puzzle was measured (Tn). Three trials (n) were done in the lab. The ln n and ln Tn were calculated using calculator, and a graph was generated using Excel. The data is as follows: n | Time Tn (s) | Ln n | ln Tn | 1 | 296.6 | 0 | 5.597 | 2 | 269.8 | 0.6931 | 5.5976 | 3 | 208.6 | 1.0986 | 5.34 |

Calculation
Tn = T1 * n^b ln(Tn) = ln(T1) * b ln(n) b = b = -0.208 (From the slope of the linear trend line on graph)
-0.208 = ln(learning%) ln(2)
-0.1442 = ln(learning%) e-0.1442 = learning% learning% = 0.866 = 86.6 %

Discussion
The learning% obtained from the learning curve is 86.6% which means that the time will reduced by 13.4% as the number of unit produced is increased. The data obtained are not so reliable because only three trials are made and during the three trials the learning curve pattern was not yet apparent. This is shown by the second trial time that is approximately the same with the first trial, meaning that the puzzle solvers were still confused on how to solve the puzzle. On the third trial there was a very significant decrease in time consumed which means that finally the puzzle solvers became skilled at solving the puzzle. If only the fourth and fifth trials were made, then the learning curve would surely be much more significant. To be more efficient in solving the puzzle, it is suggested for the solvers to see the picture of the finished puzzle because workers will be more skillful if they have the knowledge of the product.
Basically, it is expected for the time it takes to complete the puzzle to decrease. This is because workers will find it easier to do a particular job if he/she has done it lots of times before. They will also improve their own performance by developing more efficient ways to do the job as they grasp the knowledge of the product process.

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