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Lean is a philosophy that is seen as a solution to resolve the problem of efficiency in various industries. It can be used to eliminate all forms of waste in the workplace. The implementation of lean is not only applied in manufac- turing but is very important to be applied in other fields, such as in Higher Education Institution. Studies on the topic of lean in the workplace have been carried out, but most of the study has been conducted within a manu- facturing context. This study aims to determine the type of waste that is most important to be eliminated first by using the Waste Assessment Model and find the root of the waste problem. This study developed the relationship between waste and find out the effect of waste on each other in Higher Education Institution that focused on teaching and learning process. The steps of this study consist of three-step, such as waste identification, waste assessment, and root cause analysis. From data collection show that there are 46 forms of waste in the teaching and learning process. The results of the Waste Relationship Matrix showed three types of waste must be removed first, namely overproduction, defects, and non-utilized talents. 5-Why's is used to find out the root causes of waste which is the most important to be eliminated first in the teaching and learning process. Key words: lean, waste, waste assessment model, 5-why’s, higher education institution INTRODUCTION Lean principles are increasingly being seen as a solution to Lean principles and practices have been discussed in pub- resolve the problem of efficiency in various industries. lic and private organizations over the past decades. It was This can be seen from the many studies that use lean to introduced by Toyota as known as “Toyota Way” to reduce waste as their research topics. LM implementation achieve operational excellence. Lean is a philosophy to began in the automotive industry. The Lean concept de- make continuous improvement in a workplace to make veloped in Japan after World War II. Moreover, imple- the best use of resources . The goal of lean is to reduce mentation of LM spread out other industries, including all forms of activities that do not add value to the final textiles, construction, food, medical, electricity and elec- product according to customer desires . So, it can be tronics, ceramic industry, plywood, furniture, slippers, said that lean is a philosophy that is used to eliminate all shell, and the service industry [5, 6, 7, 8, 9]. forms of waste that have an impact on the final product Currently, there are seven types of waste, namely over- that the customer wants by using the best possible re- production, waiting, transportation, excess processing, in- source. ventories, motion, and defects  However, following Lean and waste are two related terms. Waste is all activi- the times and the development of needs, the seven ties that do not add value to a process and carrying out wastes are developed into eight . Eight wastes have these activities requires time and money . In the lean now been widely applied in various sectors, including principle, waste must be eliminated . So, it can be said Higher Education Institution (HEI). The eight wastes are that all activities that do not provide added value (waste) defects and rework, over-production, waiting, non-uti- must be eliminated because doing these activities re- lized talents, transportation, inventory, motion, and extra quires time and money. processing . Lean can be implemented to identify and © 2022 Author(s). This is an open access article licensed under the Creative Commons BY 4.0 (https://creativecommons.org/licenses/by/4.0/) I. J. MULYANA et al. – Lean Waste Identification in Higher Education… 201 eliminate waste for continuously improving process qual- waste. These questions constitute a condition activity or ity in HEI. Limited study of the lean in HEI has been carried behavior that can produce a certain waste. out such as the study by Douglas et al. , Kazancoglu et al. , Zighan et al. , Narayanamurthy et al. . METHODOLOGY OF RESEARCH Even though those studies focused on lean in HEI, none of This study was conducted at two faculties of an HEI in Su- them have done assessments based on the relation rabaya, East Java, Indonesia, focused on teaching and among the waste. This study addresses this gap by devel- learning process and initially divided into three steps as oping the relationship between waste and find out the ef- summarized in Table 1. fect of waste on each other. Table 1 Steps in this study WASTE IN HIGHER EDUCATION INSTITUTION The process at HEI is complex that crosses functional and Step Techniques departmental boundaries, this has the consequence that Random observation, Interviews, the process becomes longer and the stages become more Waste Identification Gemba, Literature Review, numerous . Lean and Higher Education sector are Questionnaire. close to each other which known as Lean Higher Educa- Waste Relationship Matrix (WRM), tion (LHE). The lean implementation brings benefit to HEI, Waste Assessment Waste Assessment Questionnaire. such as improve operational processes, maintain compet- itiveness, obtain customer satisfaction, achieve best per- Root Cause Waste 5-Why’s formance [12, 13, 16, 17]. Waste in the HEI has a different understanding from manufacturing or other service sec- First, waste identification is conducted by random obser- tors. Several articles explained the waste in higher educa- vation, interviews, Gemba, and literature review. Then tion institutions such as [12, 13, 15, 16, 18]. Douglas et al. design waste identification questionnaires and distribute In  waste was divided into eight categories such as ex- them to respondents. Waste WRM is used to determine cess motion, excess transportation, underutilized people, the relationship among the waste. Root cause analysis is inventory, defects, overproduction, waiting, over-pro- conducted to determine the root cause of the most waste cessing. Narayanamurthy et al.  modified seven be removed first because it can generate other types of wastes in the manufacturing sector becoming waste in waste. To find out the critical waste to be eliminated first educational institutions, those are rework, motion, wait- can be seen in the WRM in the total score section which ing, over-processing, over-production, and defect. In  has the largest percentage value. Root cause was con- waste category in higher education was divided to eight ducted by using 5-why's. wastes in the manufacturing sector: overproduction, over-processing, waiting, motion, transportation, inven- RESULT AND DISCUSSION tory, defects, and talent Waste Identification Waste identification was conducted by several techniques WASTE ASSESSMENT MODEL such as random observation, interviews, Gemba, a litera- Waste Assessment Model (WAM) was developed by ture review that has done by previous researchers, such Rawabdeh  to identify critical waste in order to create as Robinson & Yorkstone , Douglas et al. , Höfer & solution in eliminating waste. WAM consists of Waste Re- Naeve , and Kazancoglu et al. . Tabel 2 shows sev- lation Matrix (WRM) and Waste Assessment Question- eral wastes in HEI. Then the result of the waste identifica- naire (WAQ). All types of waste are interdependent, and tion process was used to design the questionnaire. each type has influence on the other and simultaneously The questionnaire consists of eight types of waste that in influenced by the others, and relationship each cate- break into 46 Questions (Table 3). gory of waste are not equal weights . The waste rela- A waste identification questionnaire was distributed to all tionships are assessed using questionnaire and the weight members of faculties to find out the major waste and col- of answer ranging from zero to four . WAQ consists of lected 31 faculty members (Table 4). several different questions for the purpose of allocating 202 Management Systems in Production Engineering 2022, Volume 30, Issue 3 Table 2 Waste in Higher Education Institution Category No. Form of Waste in Higher Education Institution of Waste The lecturer failed to find the document. Going to the wrong classroom. The lecturer did not inform the absence/canceling of class on the due class schedule. Lecturers change the lecture schedule. Lecturers make mistakes when inputting grades in the academic information system. The lecturer re-examines students. 1 Defects The lecturer encountered inaccessible documents. The lecturer has encountered teaching material media that cannot be opened. Human error in typing Lecturers have made mistakes in typing learning preparation and teaching materials. The lecturer found the projector connecting cable unusable. Lecturers have experienced a shortage of exam scripts. Lecturers print documents/exam questions/journals/handouts in excess. The teaching load every semester is excessive. Lecturers add lecture hours outside the predetermined schedule. 2 Overproduction There is excessive dissemination of information/announcements. There are too many lecturers in the department. Lecturers do administrative tasks outside of working hours Lecturers reply to messages/questions from students for quite a long time. The delay of the lecturer in collecting reports from a predetermined time. Lecturer delay in attending meetings. Lecturers wait for class when the class changes. Repair of campus facilities has taken a long time. 3 Waiting The lecturer is waiting for the meeting to determine the results of the teaching task. Lecturers wait for students to attend lectures. The lecturer waits for students to collect answers to the exam. Students are late in submitting assignments. Lecturers get jobs/assignments that are not in accordance with their scientific field. Lecturers do not conduct research every semester. 4 Non-Utilized Talent Lecturers do not do community service every semester. Lecturers make mistakes in sending documents/files between work units. 5 Transportation The lecturer keeps the email on the draft. 6 Inventory The lecturer keeps the previous year's exam questions. Lecturers keep a large number of documents (for example: teaching materials/handouts/exam ques- tions/journals). 6 Inventory Lecturers keep large amounts of Office Stationery. Class facilities that are owned are not used during operating hours. The distance between the classroom and the office/work space is quite far. 7 Motion The lecturer workspace is always in an untidy condition. Lecturers look for documents/files/journals for a long time. Lecturers input student scores more than once in different systems. Receiving information through more than one information channel (WhatsApp, email, hard copy, etc.). 8 Extra Processing The posting of the same information/announcement repeatedly. The lecturer checks/corrects the same files (exam answers, theses, correspondence, etc.) repeatedly. The lecturer checks the teaching material repeatedly. The lecturer teaches the same material over and over. Lecturers attend/make meetings repeatedly with the same discussion. I. J. MULYANA et al. – Lean Waste Identification in Higher Education… 203 Table 3 Validity Test Number of Question The validity test needs to be done after distributing the No Type of waste Number of question waste identification questionnaire. The validity test is a 1 Defects 12 test to determine the accuracy between the data ob- 2 Overproduction 6 tained and what the study reported . The results of 3 Waiting 9 validity test showed that all aspects of the question have 4 Non-Utilized Talent 3 exceeded the r table, it means valid. 5 Transportation 1 6 Inventory 5 Reliability Test 7 Motion 2 The second test is the reliability test. In contrast to the va- 8 Extra Processing 8 lidity test, the reliability test is a test to determine the level of consistency of the questionnaire answers an- Table 4 swered by a respondent . The reliability test was car- Respondent’s Answer ried out using SPSS software. The SPSS results showed No of Respondent Category Question that the Cronbach Alpha obtained is 0.926 where the Very Frequ- of Waste No Rarely Never Frequently ently value is more than 0.6. So it can be said that the results 1 0 4 20 7 are reliable. 2 0 0 12 19 Below Table 5 states the rank of waste that affects the 3 0 0 16 15 higher education institute. The majority of respondents 4 0 1 25 5 stated that three waste such as defect, waiting and extra 5 0 0 12 19 processing are the major waste that occurred in the 6 0 0 13 18 Defect higher education institute. The overproduction, inven- 7 0 4 21 6 tory, non-utilized talent, motion, and transportation 8 0 3 18 10 waste are ranked as 4, 5, 6, 7, and 8 respectively. 9 0 0 21 10 10 0 3 22 6 Table 5 11 0 6 16 9 Rank of Waste 12 0 3 13 15 Category of Waste Total Score Rank 13 0 4 14 13 14 0 0 11 20 Defect 629 1 Overproduc- 15 0 4 16 11 Overproduction 330 4 tion 16 3 8 7 13 Waiting 565 2 17 0 2 2 27 18 2 15 13 1 Non-Utilized Talent 171 6 19 0 3 20 8 Transportation 51 8 20 0 1 22 8 Inventory 319 5 21 0 8 16 7 Motion 123 7 22 0 4 17 10 Waiting 23 0 7 18 6 Extra processing 515 3 24 0 4 16 11 25 1 7 21 2 Waste Relationship Matrix (WRM) 26 0 9 21 1 It is very important to find out the major waste that has 27 0 15 16 0 the highest influence on the overall process in HEI. The 28 2 4 17 8 waste Assessment Model (WAM) is used to find out the Non-Utilized 29 0 4 15 12 Talent effect of waste on each other. All types of waste influence 30 1 2 17 11 the others and simultaneously is influenced by the others Transportation 31 1 4 9 17 and the relationship among wastes in complex because 32 0 4 16 11 the influence of each category can appear directly or indi- 33 6 14 6 5 rectly . Based on the study of Rawabdeh  and Inventory 34 1 4 16 10 brainstorming among the head of Faculties and Depart- 35 0 5 17 9 ment then the model of the relationship among category 36 1 5 21 4 was developed based on the category of waste shown in 37 0 7 13 11 Motion 38 1 8 15 7 Figure 1. 39 0 6 22 3 40 1 5 11 14 41 6 15 8 2 Extra 42 1 4 17 9 processing 43 3 6 17 5 44 0 11 17 3 45 0 3 18 10 46 0 0 24 7 204 Management Systems in Production Engineering 2022, Volume 30, Issue 3 Table 7 Waste Relationship Matrix F/T O I D M T P W Ta O A O I I O X O X I X A X X X X X X D I X A O I X I X M X I X A X O O X T X I O O A X U U P O I X X X A O O W X X O X X X A X Ta I X I X O I X A The next thing to do when you have obtained a matrix containing letters is to convert these letters into numbers. The letter A will be converted to the number 10, the letter Fig. 1 The Relationships of Eight Wastes in Teaching and Learn- ing Process E to the number 8, the letter I to the number 6, the letter O to the number 4, the letter U to the number 2, and the Data collection for waste assessment was carried out us- letter X to the number 0 . ing a questionnaire to determine the effect of one type of Table 8 explains the type of waste in the form of overpro- waste on other types of waste. It was distributed to the duction is the type of waste that is most capable of caus- head of Faculties and Department and feedback received ing the emergence of other types of waste. This is because from them was used for analysis. Each question in the the "from" score of overproduction waste reaches a value questionnaire has a score that will be used to calculate the of 16.83% where this value is the greatest value when total score for each waste type relationship. Table 6 below compared to the "from" score of other types of waste. For is the result of the average score for each relationship other types of waste, waste with defect types and non- among the waste. After distributing the questionnaire, utilized talents can affect the appearance of other types the following is done in calculating the total score for each of waste by 15.84%, waste with transportation and extra respondent. processing types can affect the appearance of other waste by 13.86%, waste with motion types can affect the ap- Table 6 pearance of other waste was 11.88%, the waiting type of Average Score Calculation of WRM waste could affect the appearance of other types of waste Average Average by 6.93%, and the waste with the inventory type could af- No Relationship No Relationship Score Score fect the appearance of other types of waste by 4.95%. It 1 O_I 8 14 T_I 10 can be seen that over production, defects, and non-uti- 2 O_D 9 15 T_D 6 lized talents are the three critical waste. 3 O_M 11 16 T_M 5 4 O_T 9 17 T_W 4 Table 8 Waste Matrix Value 5 O_W 9 18 T-Ta 4 Score 6 D_O 10 19 P_O 6 F/T O I D M T P W Ta Score (%) 7 D_M 8 20 P_I 8 O 10 4 6 6 4 0 4 0 34 16,83 8 D_T 10 21 P_W 7 I 0 10 0 0 0 0 0 0 10 4,95 9 D_W 9 22 P_Ta 8 D 6 0 10 4 6 0 6 0 32 15,84 10 W_D 7 23 Ta_O 10 M 0 6 0 10 0 4 4 0 24 11,88 11 M_I 11 24 Ta_D 11 T 0 6 4 4 10 0 2 2 28 13,86 12 M_P 7 25 Ta_T 6 P 4 6 0 0 0 10 4 4 28 13,86 W 0 0 4 0 0 0 10 0 14 6,93 13 M_W 8 26 Ta_P 9 Ta 6 0 6 0 4 6 0 10 32 15,84 Score 26 32 30 24 24 20 30 16 202 100 From Table 6 above, there are several notations such as O Score which indicates overproduction, I which indicates inven- 12,87 15,84 14,85 11,88 11,88 9,90 14,85 7,92 100 (%) tory, D which indicates defect, M which indicates motion, T which indicates transportation, P which indicates extra Root Cause Waste Analysis processing, W which indicates waiting, and Ta which indi- Root cause waste analysis is conducted to determine the cates non-utilized talents. root cause of waste which is most capable of generating The measurement criterion analysis was organized in other types of waste. To find out the root causes of the Waste Relationship Matrix (WRM). Table 7 shows the most important waste, an analysis was carried out using WRM. the 5 Why's method based on brainstorming with the head of faculty and department. Table 9 below is a 5- why's table that has been formed from the results of the author's brainstorm. I. J. MULYANA et al. – Lean Waste Identification in Higher Education… 205 Table 9 5-Why’s No Critical Waste Why 1 Why 2 Why 3 Why 4 Why 5 Because Because many lecturers Because the lecturer the lecturer hopes Because lecturers Because lecturers often do administrative wants 1 Overproduction that the assign- want not to interfere want to serve tasks outside to focus ment can be com- with lecture activities students well of working hours. on giving lectures pleted. 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Jaka Mulyana Industrial Engineering Departement Widya Mandala Surabaya Catholic University Dinoyo 42-44, Surabaya, East Java, 60236, Indonesia e-mail: email@example.com Lusia Permata Sari Hartanti (corresponding author) Industrial Engineering Departement Widya Mandala Surabaya Catholic University Dinoyo 42-44, Surabaya, East Java, 60236, Indonesia e-mail: firstname.lastname@example.org Vincentius Aditya Herdianto Industrial Engineering Departement Widya Mandala Surabaya Catholic University Dinoyo 42-44, Surabaya, East Java, 60236, Indonesia e-mail: email@example.com Ivan Gunawan Industrial Engineering Deaprtement Widya Mandala Surabaya Catholic University Dinoyo 42-44, Surabaya, East Java, 60236, Indonesia e-mail: firstname.lastname@example.org Herwinarso Herwinarso Physics Department Widya Mandala Surabaya Catholic University Dinoyo 42-44, Surabaya, East Java, 60236, Indonesia e-mail: email@example.com
Management Systems in Production Engineering – de Gruyter
Published: Sep 1, 2022
Keywords: lean; waste; waste assessment model; 5-why’s; higher education institution
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