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ISSN : 1598-7248 (Print)
ISSN : 2234-6473 (Online)
Industrial Engineering & Management Systems Vol.18 No.4 pp.685-691
DOI : https://doi.org/10.7232/iems.2019.18.4.685

# Workload Analysis in a University Maintenance Division

Ratna Sari Dewi*, Arief Rahman, Retno Dwi Astuti
Industrial Engineering Department, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia
Corresponding Author, E-mail: ratna.sari.dewi80@gmail.com
May 7, 2019 September 17, 2019 October 18, 2019

## ABSTRACT

Similar to its industrial counterpart, a maintenance division of a university has an important role to guarantee the sustainability of all teaching, research, and administrative activities. Institut Teknologi Sepuluh Nopember (ITS) is one of largest public universities in Indonesia with more than 17 thousand student bodies and 32 departments (academic units). To support the university daily activities, a maintenance division was established. The Maintenance Division of the university consists of two sections, i.e. Equipment Maintenance Section and Facility Maintenance Section. The Equipment Maintenance Section is responsible on maintaining education and research facilities, while the Facility Maintenance Section has more diverse jobs, such as asset and equipment registration, room/vehicle allocations and scheduling, also general administration in the Maintenance Division. For upholding the productivity of the Maintenance Division, the workload of each technician and staff should be carefully managed by avoiding both overload and underload conditions. This study aimed to analyze current technician and staff workload in both sections of the Maintenance Division by employing work sampling method. This study showed that the workload of the ITS Maintenance Division was considered as low to suggest for reducing the number of employees, designing more efficient work procedures, and conducting job enlargement. These results will be beneficial for improving the current working system at ITS Maintenance Division as well as demonstrating the workload analysis at a university which is still less frequently conducted compared to in industries.

## 1. INTRODUCTION

Similar to its industrial counterpart, a maintenance division of a university has an important role to guarantee the sustainability of all teaching, research, and administrative activities. Institut Teknologi Sepuluh Nopember (ITS) is one of largest public universities in Indonesia with more than 17 thousand student bodies and 32 departments (academic units). To support the university daily activities, a maintenance division is established. The Maintenance Division of ITS consists of two sections, i.e. Equipment Maintenance Section and Facility Maintenance Section. The Equipment Maintenance Section is responsible for maintaining the education and research facilities. The staffs of this section are mainly technicians who have a qualification in repairing classroom/room appliances such as air conditioners and sound systems. On the other side, the Facility Maintenance Section has more diverse jobs, such as asset and equipment registration, room/vehicle allocations and scheduling, also the general administration of the Maintenance Division.

Based on the preliminary interview conducted to the Head of Maintenance Division of ITS, there were several issues related to the workload on both sections. In the Equipment Maintenance Section, some of the technicians were idle in a long period of time, and some others were performing maintenance jobs which were not suitable with their main skills or technical specializations. On the other side, in the Facility Maintenance Section, there were administrative staffs who had excessive workload resulting in the pending and accumulating of many documents they need to process. These issues lead to the need for better workload management in the Maintenance Division of ITS.

The workload is defined as a mental construct reflecting the interaction of mental/physical demands imposed by the operators by the task they attend to (Cain, 2007). The workload measurement is important to characterize the performance of a task relative to the operator capability (Gopher and Braune, 1984). Particularly for the maintenance division of ITS, the proper workload management will mitigate adverse impacts such as inhibition of activity in units related to equipment and the utilization of less efficient technicians.

There are several approaches to evaluating workload. O’Donnell and Eggemeir (1986) proposed several criteria for selecting workload measurement methods such as the method should be reliably sensitive to changes in task difficulty or resource demand and discriminate between significant variations in workload. Work sampling (Goto et al., 2014;Hajikazemi et al., 2017) is one of the most common methods employed to measure the workload. Besides the globally renowned approaches, for Indonesia, the Ministry of Administrative Indonesia (Kementerian Pandayagunaan Aparatur Negara Indonesia, 2004) issued a regulation on guidelines for calculating the number of employees/civil servants needed by a government institution. The regulation, Kep/75/M.PAN/2004 issued in 2004, also considered employees’ workload on the calculation procedure which employs Full-Time Equivalent (FTE) approach (OrientPoint, 2016).

Thus, this study aimed to analyse the workload of technicians and staffs of the ITS Maintenance Division. Work sampling method was chosen in this study since most of the tasks in the Maintenance Division do not have repetitive work cycles. Furthermore, the jobs consist of various non–routine tasks rather than a single repetitive task. However, since ITS is a public university where almost all of their officers are civil servants, it is also required that the workload analysis is conducted following the regulation (i.e., Kep/75/M.PAN/2004). Therefore, the result of the work sampling analysis will be verified by the calculation of civil servant numbers regulated at Kep/75/M.PAN/2004). The results of this study are expected to provide recommendations on the ideal number of required employees with appropriate workloads in the Maintenance Division of ITS.

## 2. METHODOLOGY

### 2.1 Object of the Study

The study was conducted on both sections of ITS Maintenance Division covered five administrative jobs and four technicians. The nine job titles were general administration staff, asset administration staff, warehouse administration staff, financial administration staff, education facility administration staff, air conditioner technician, electrical technician, construction technician, overhead projector and sound system technician.

### 2.2 Data Collection

Work sampling for each job title was conducted at five working days (Monday to Friday) from May 15th to 19th 2017. The work sampling was performed by observing the staff activities of each job title for 7.5 hours starting from 7.30 until 16.00 with the lunch break at 11.30 up to 12.30. Work sampling observations were made randomly within the time frame in order to capture the diversity of activities performed in each of the job titles. Table 1 presents an example of the work sampling forms used in this study which listed productive and non-productive activities of a general administration staff. List of the activities was collected during preliminary observation. While observing the staff in performing his job, the researcher checked the specific activity performed by the observed staff on a particular random time as specified in the first column on Table 1.

The first steps in analysing the workload based on Kep/75/M.PAN/2004 are calculating effective working days in a year and effective working hours for each day. After subtracted by national holidays, weekends, annual leaves, and communal leaves there were 227 effective days in 2017. Effective working hours for each day was calculated by multiplying the working hours each day (i.e., 7.5 hours) and allowance (i.e., 10%) which equal to 6.75 hours or 405 minutes. In the next step, the workload analysis forms were distributed to several workers as representatives for each job position. Every worker was required to fill in the form with work elements, deliverables, actual completion time, and monthly occupancy pattern in according to his/her job descriptions. Based on the collected workload forms and performance rating judgments from direct supervisors which was then verified by the Head of Maintenance Division of ITS, the normal time and workload index for each position was calculated.

## 3. RESULTS

### 3.1 Workload Analysis based Work Sampling

Table 2 presents the proportion of productive activities for every job title extracted from the work sampling forms. To verify whether the number of observations was enough, the sampling adequacy test was performed. The sample adequacy test was performed by comparing the number of work sampling observations had been conducted (N) to the required sample size (N’). The required sample size was calculated with formula (1) (Wignjosoebroto, 2008). The k, s, and p represent standardized z value for the specified confidence level, margin of error, and proportion of productive activities respectively. For this current study, the confidence level was set to 85% and the margin of error was set to 15%. Table 3 shows the comparison of the number of work sampling observations had been conducted (N) to the required sample size (N’) for every job title. Comparing both values for every job title showed that the number of work sampling observations which had been conducted was sufficient. Table 4 presents the workload index for every job title in the Maintenance Division of ITS. The workload was calculated by multiplying the proportion of productive activities and the number of current employees.

$N ′ = k 2 p ( 1 − p ) ( s p ) 2$
(1)

### 3.2 Workload Analysis based Kep/75/M.PAN/2004

Figure 1 shows an example of the workload analysis form for general administration staff while the performance rating for all job titles listed in Table 5. The performance rating covered skill, effort, condition and consistency factors. The normal time which is stated as the average ability standard in the regulation was calculated as the multiplication of actual time and (1 + performance rating). As an example, the workload index calculation for General Administration staff is presented at Table 6. The data of actual times and deliverables are extracted from the workload analysis form (Figure 1). While daily completion time is the multiplication of normal time and number of daily deliverables. As presented in Table 6, the workload index of the General Administrative Staff is 0.812 which is calculated from the total completion time for each day divided by effective working time. The summary of the workload index based on Kep/75/M.PAN/2004 for each job title is presented in Table 7.

## 4. ANALYSIS

As presented in Figure 2, the workload indices based on work sampling and the regulation published by the Ministry of Administrative (Kep/75/M.PAN/2004) were similar to each other. Employing paired t-test on both values with α=0.05 and df=8 shows that both workload analysis methods were not significantly different, and they verified to each other. However, it is important to consider the applicability of each method for assessing the workload for staffs and technicians. For staffs, since by nature, their tasks do not require them to move around too much, both work sampling method and the workload analysis method based on the Ministry of Administrative are possible to be implemented efficiently. However, for the technicians who need to move around the campus to fix or install facilities/equipment, work sampling is less efficient compared to the procedure proposed by the regulation. It is because, in work sampling, an observer needs to follow the workers around their workplaces which also mean it is more difficult to make sure that the workers will work in a natural manner considering there are people who obviously following and observing them.

On the next stage of the analysis, the workload index for each job title was compared to its number of workers. In general, the workload index for each job title was smaller compared to its number of workers. Particularly for the electrical technician, the workload index calculation was less than 1 (i.e., 0.84 based on work sampling and 0.74 based on Kep/75/M.PAN/2004) while the number of electrical technicians was 2 (see Table 4 and Table 7). Considering the significant difference, this study recommended reducing the number of electrical technicians become 1 worker only. Another similar case was for air conditioner technicians. The existing number of technicians was 3 while the workload index for this job title was 2.06. Based on the author's observation, one of the inefficiencies was the preparation of equipment and spare parts. Technicians needed a lot of time to pick up tools and buy spare parts that they need for completing their job. Thus, it is expected that the efficiency of this working method can reduce the number of AC technicians to 2 people and the excess load of 0.06 can be solved with more efficient working methods. For other 7 job titles, this study recommended the Maintenance Division of ITS to redesign the job by conducting job enlargements (Möller et al., 2004;Gichuki and Munjuri, 2018). For example, on technicians, in this current situation, they were only responsible to do the maintenance jobs for several buildings in ITS, such as Rectorate and Research Center. Thus, job enlargement could be conducted by also assigning them to do the maintenance jobs for the several other departments in the university.

Finally, one limitation of this study is the moderate values of the confidence level and margin of error. The values were set due to the time limitation for the data collection period. For future research, it is suggested to set a higher confidence level and lower margin of error.

## 5. CONCLUSIONS

Two workload analysis methods were implemented in analysing the workload analysis of staffs and technicians in the Maintenance Division of Institut Teknologi Sepuluh Nopember Surabaya Indonesia. The analysis showed that the both methods provided similar results and verified to each other. However, it is also important to consider the applicability for each method. The workload analysis indices from this study provided the basis for better workload management, including reducing the number of workers and job enlargements.

### 5.1 Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

## Figure

Comparison between workload analysis result based on work sampling and Ministry of Administrative Indonesia (2004).

## Table

An example of a work sampling form for a general administration staff

Proportion of Productive Activities

Calculation of workload index for every job title based on work sampling

Performance ratings for each job title

Workload index for every job title based on Kep/75/M.PAN/2004

## REFERENCES

1. Cain, B. (2007), A review of the mental workload literature, Defence Research and Development, Toronto, Canada.
2. Gichuki, M. M. and Munjuri, M. G. (2018), Influence of job enlargement on employee performance in the railway industry In Kenya, Archives of Business Research, 6 (5), 244-259.
3. Gopher, D. and Braune, R. (1984), On the psychophysics of workload: Why bother with subjective measures? Human Factors, 26(5), 519-532.
4. Goto, R. , Arai, K. , Kitada, H. , Ogoshi, K. , and Hamashima, C. (2014), Labor resource use for endoscopic gastric cancer screening in Japanese primary care settings: A work sampling study, PloS One, 9(2), e88113.
5. Hajikazemi, S. , Andersen, B. , and Langlo, J. A. (2017), Analyzing electrical installation labor productivity through work sampling, International Journal of Productivity and Performance Management, 66(4), 539-553.
6. Kementerian Pandayagunaan Aparatur Negara Indonesia (2004), Pedoman Perhitungan Kebutuhan Pegawai Berdasarkan Beban Kerja dalam Rangka Penyusunan Formasi Pegawai Negeri Sipil (Kep. Men. PAN Nomor: KEP/75/M.PAN/7/2004), Jakarta (ID): Kementerian Pendayagunaan Aparatur Negara.
7. Möller, T. , Mathiassen, S. , Franzon, H. , and Kihlberg, S. (2004), Job enlargement and mechanical exposure variability in cyclic assembly work, Ergonomics, 47(1), 19-40.
8. OrientPoint (2016), FTE Analysis and Models, Available from: http://www.orientpoint.com/FTE.htm.
9. O’Donnell, R. D. and Eggemeier, F. T. (1986), Workload assessment methodology. In K. R. Boff, L. Kaufman, and J. P. Thomas (Eds.), Handbook of perception and human performance Vol. 2 Cognitive processes and performance, Oxford, England: John Wiley & Sons, 1-49.
10. Wignjosoebroto, S. (2008), Ergonomi: Studi Gerak dan Waktu (Ergonomics: Time and Motion Study), Guna Widya, Surabaya, Indonesia.