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Productivity Improvement Through Time Study Approach: A Case Study from an Apparel Manufacturing Industry of Pakistan
Available online at www.sciencedirect.com
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Available online at www.sciencedirect.com
ScienceDirect
Procedia Manufacturing 00 (2019) 000–000
www.elsevier.com/locate/procedia
Procedia Manufacturing 39 (2019) 1447–1454
25th International Conference on Production Research Manufacturing Innovation: Cyber Physical Manufacturing August 9-14, 2019 | Chicago, Illinois (USA)
Productivity Improvement Through Time Study Approach: A Case Study from an Apparel Manufacturing Industry of Pakistan Ateeq ur Rehmana, Muhammad Babar Ramzana*, Muhammad Shafiqb, Abher Rasheeda, Muhammad Salman Naeema, Matteo Mario Savinoc Department of Garment Manufacturing, National Textile University, Faisalabad-37610, Pakistan Department of Industrial Engineering, University of Engineering and Technology, Taxila, Pakistan C Department of Engineering, University of Sannio, 82100 Benevento, Italy
a b
Abstract Standard processing timings play an important role in efficient production management and are measured using work measurement techniques. Time study is a widely used work measurement technique in repetitive manufacturing processes. In present study, time study technique was used to enhance productivity of a garment production line in a work wear manufacturing factory. The factory consisted of 350 sewing machines and was producing work wears including trousers, vests, jackets and coveralls. Stop watch time study – analysis of existing set up – explored the existing time losses in production line caused by unevenness of work, excessive material transportation and higher work-in-process. Improvements were made through: (i) sequence of activities, (ii) tasks reassignment to the work stations and (iii) line balancing at predetermined cycle time. Hourly production charts were maintained at each work station to monitor outputs. The results of machine productivity were compared before and after employing changes. From results, 36% increase in average machine productivity was observed. Hence, it can be concluded that time study is an effective tool for increasing productivity in apparel manufacturing. The added advantages including optimization of material flow, balanced production line and reduced work in process were also achieved. The results of this study are useful for sewing factories making other woven, knitted and home textile products. In addition, football, shoe and gloves manufacturing industry may reef benefits from findings of this study. © 2019 2019 The The Authors. © Authors. Published Published by by Elsevier Elsevier Ltd. B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of the ICPR25 International Scientific & Advisory and Organizing committee members
* Corresponding author. Tel.: +92-301-504-2823; fax: +92-(0)41-923-0098. E-mail address: [email protected] 2351-9789 © 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Selection and peer review under the responsibility of ICPR25 International Scientific & Advisory and Organizing committee members 2351-9789 © 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of the ICPR25 International Scientific & Advisory and Organizing committee members 10.1016/j.promfg.2020.01.306
2
Ateeq ur Rehman / Procedia Manufacturing 00 (2019) 000–000
Selection and peer review under the responsibility of ICPR25 International Scientific & Advisory and Organizing committee 1448 Ateeq ur Rehman et al. / Procedia Manufacturing 39 (2019) 1447–1454 members Keywords: Time study; Garment manufacturing; Productivity improvement; Pakistan
1. Introduction Work measurement is concerned with determining standard time for a job, using a specified method [1]. Standard time is the time taken by a qualified and trained operator to complete certain job while working at efficient, yet sustainable rate, using specific method, tools, equipment, arrangement of workplace and working conditions [2]. One of the important application of work measurement techniques is to balance manufacturing / production lines which refers to dividing equal amount of work among machine operator/ workers [1] which in turn promises for higher productivity [3]. Commonly used techniques for developing standard times include stop watch time study, historical times, predetermined data and work sampling [2]. Historical times and predetermined data belongs to past and can only provide time estimation. Activity sampling is preferred for estimating machine allowance. Stop watch time study is preferred for sewing operations because it is live and extended method of time. Time study is the determination of rate at which a specific job is being done in a repetitive job process [4]. The concept of stop watch time study was introduced by Frederick Winslow Taylor. Although it faced a lot of criticism, yet it gained acceptance over the time and it is now a widely used technique for work measurement. William Charles introduced performance rating and elaborated procedure of work study [5]. Merrick suggested that fatigue and other allowances should be added to the measured times of job [6]. Bedaux established relaxation allowance in work measurement [7], which was further expanded to personal and fatigue allowances [8], [9]. Wiberg attached operator performance with job time reduction over repetitions [10]. International Labour Organization (ILO) proposed work measurement as a tool for increasing productivity by investigation and reduction of ineffective time contents of job [11]. It provides basis for improvement processes [12] and helpd in increasing organizational productivity [13]. It has proven success in manufacturing [14], [15], construction industry [16], maintenance industry [17], retail [18] hospital, data processing and service organizations [2], [19], [20]. Various factors have been studied that impact the accuracy of time study including, number of observations taken, amount and type of allowances to include and rating of operator [21]. GPS systems to track worker movements [22] and digital video based approach in time study was proved helpful in generation of rapid time standards for 21st century changing needs [23]. Although, apparel manufacturing sector is reefing benefits of time study applications from decades, yet it is hard to find relevant published research. In present study, time study technique was used to enhance productivity of a garment production line in a work wear manufacturing factory in Pakistan. The factory consisted of 350 sewing machines and was producing work wears including trousers, vests, jackets and coveralls. 2. Materials and Methods During initial visit to the manufacturing floor we found some of the machine operators were waiting for work while the others were having huge pile up of semi stitched goods, waiting to be processed. It was enough of evidence to understand that production floor lacked work leveling. Further investigation revealed that a lay man approach to work division, without paying attention to the activity or time required to carry out a certain sewing operation, was sole responsible of the situation. In addition, cross and backward material movement, irregular sequence of activities, improper machine layout was responsible for creating further mess of material on shop floor. In a nutshell, sewing floor was incurring certain lean wastes like excessive transportation, higher inventory at some workstations while keeping the others in wait. In order to get rid of these wastes operation breakdown was made and work was divided among work stations based on the cycle time. Sewing lines were arranged in sequence and respective jobs were allocated to the machine operators, as shown in Table 2. It was decided to make necessary changes at the production floor to increase effectiveness and initially a single production line was selected. The line consisted of about 65 sewing machines and was producing a protective coverall, shown in Figure 1(a). In order to realize the benefits of improvement process, machine productivity was chosen as Key Performance Indicator (KPI). Machine Productivity (P), for the selected production line, was calculated using Equation 1 [2].
Ateeq ur Rehman et al. / Procedia Manufacturing 39 (2019) 1447–1454 Ateeq ur Rehman / Procedia Manufacturing 00 (2019) 000–000
P
Output pcs Machinehours
1449 3
(1)
The line output for recent fifteen consecutive working days was obtained and it was calculated that average machine productivity was 0.51 garments per machine hour, shown in Figure 1. (a)
(b)
Machine Productivity Before Changes 0.58 0.56 0.54 0.52 0.5 0.48 0.46 0.44 0.42
1
2
3
4
5
6
7
Daily Machine Productivity Figure 1
8
9
10 11 12 13 14 15
Average Machine Productivity
a. Coverall ; b. Machine Productivity in Existing Setup
Operation breakdown was developed for coverall, explaining sequence of operations. In order to determine standard times for individual operations following steps were considered. Determining number of cycles to be observed Recording observations and calculating observed time (OT) Calculation of normal time (NT) Calculation of standard time (ST) Number of cycles to be observed were calculated using Equation 2 [2].
zs n e
2
(2)
Where z = number of standard deviations for desired confidence interval s = sample standard deviation e = acceptable time error Keeping in view average cycle time of 1 minute (60 sec), z-value of 2.58 for desired confidence of 99% and taking acceptable time error of ±2 seconds, the number of cycles to be observed were 15. Since, the average cycle time of operations was observed to be around 60 seconds, therefore same number of observations were taken for all individual operations. Digital stop watch was used to take 15 cycle times of each operation and average single cycle time (ASCT) was calculated by using Equation 3, which was further converted into observed time (OT) by Equation 4 [2]. Thus,
Ateeq ur Rehman et al. / Procedia Manufacturing 39 (2019) 1447–1454 Ateeq ur Rehman / Procedia Manufacturing 00 (2019) 000–000
1450 4
ASCT OT
Where
x
i
x
i
(3)
n
ASCT 60
(4)
= sum of recorded cycle times
n = number of cycles observed
In order to adjust the pace of operator pace rating was used, each operator was allocated a rating factor ranging from 0.70, for lowest pace, to 1.30, for highest pace of work. The standard rating factor was considered 1 which was allocated to efficient yet sustainable rate of work. Normal time was calculated by multiplying observed time (OT) and rating factor (RF) [2]. That is,
NT OT RF
(5)
Machine allowance (MA), personal and fatigue allowance (P&F) and bundle handling allowances (BHA) were added to the normal time to obtain standard time (ST) [24].
ST NT MA P & F BHA
(6)
Machine allowances were different according to the machine used. For example, a lower machine allowance is expected to be added for single needle lock stitch, as compared to two needle lock stitch machine. Table 1 includes detailed machine allowances, to be added, as a percentage of normal time (NT) [24]. Table 1 Sewing Machine Allowance Percentages Type of Machine Single needle lock stitch machine (SNLS) Double needle lock stitch machine (DNLS) Three thread overlock machine (3TH O/L) Five thread overlock machine (5TH O/L)
Allowance(%) 12.5 14.0 12.0 18.0
Type of Machine Bar tack machine (Bartack) Single needle chain stitch (SNCS) Multi needle chain stitch (FOA)
Allowance(%) 12.0 14.0 16.0
A flat 7.5 percent of collective personal and fatigue allowance was added for all the operators. Similarly, bundle handling allowance of 0.03 minute was added for each operation. In order to balance the line, cycle time was calculated at desired line output rate of 350 pcs per day at 80% target production efficiency of the line. The cycle time of 1.08 minute was obtained by dividing shift time over desired output. The line was balanced theoretically at obtained cycle time “Operations Bulletin” (OB) was prepared by adding standard times of respective operations to operations breakdown sheet and further calculating targets and number of machines required. Table 2 provides detailed operations bulletin of studied garment under balanced line conditions. Once theoretical balancing of line was done, keeping all technical limitations in view, it was proceeded for ground actions. Machines were arranged in sequence mentioned in operations breakdown and work was assigned to the operators. In order to have control over changes made, hourly production monitoring charts were maintained at each work station. Production data was collected on hourly basis from these charts for 20 days.
Ateeq ur Rehman et al. / Procedia Manufacturing 39 (2019) 1447–1454 Ateeq ur Rehman / Procedia Manufacturing 00 (2019) 000–000
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Table 2 Operations Bulletin
Sr # 1 2 3 4 5 8 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 Subtotal 24 25 26 27 28 29 30 31 32 Subtotal 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 Subtotal 49
Order# Style# Total SAM Buyer Operation Small Parts Collar Making Collar Turn & Top R/Tape to Chest Pkt Flap (2) Valcro Pkt Flap (2+2) Chest Pkt Flap Make (4 Cornor) (2) Side Pkt Flap Make (2) Flap Turn n Top Flap Turn n Top R/Tape to Chest Pkt Chest Pkt Hem Side Pkt Pleat Making (2+2) Side Pkt Box Pleat Making (2) Side Pkt Hem Valcro to Chest n Side Pkt (2+2) Side Pkt Strip Attch (2) Collar Patch Fold and Top Labels to Patch Pkt Bag Hem Pocket Bag Close R/Tape to Sleeves (2+2) Pkt Bag Turn n Top Pkt Fly Tack R/Tape to Knee Panel + LBL Knee Panel Hem Back Section R/Tape to Back Panel Back Pleat Make Back Pleat Top Tack R/Tape to Back Leg Panels (2*2) Back Rise Back Upper and Lower Join Back Upper and Lower Join Top W/B Elastic Tack W/B Attch
Operational Bulletin 100% Target Per Day Coverall 80% Target Per Day 54.34 100% Target Per Hour 80% Target Per Hour Machine Type Standard Time Machines Requried 0.3 SNLS 0.60 0.55 SNLS 0.50 0.46 SNLS 0.60 0.55 SNLS 1.00 0.92 SNLS 0.80 0.74 SNLS 0.80 0.74 DNLS (Split) 0.70 0.64 DNLS (Split) 0.45 0.41 SNLS 1.40 1.29 SNLS 0.40 0.37 SNLS 0.70 0.64 SNLS 1.00 0.92 SNLS 0.60 0.55 SNLS 1.00 0.92 SNLS 1.80 1.66 SNLS 0.40 0.37 SNLS 0.50 0.46 SNLS 0.60 0.55 SNLS 0.40 0.37 SNLS 2.20 2.02 SNLS 0.40 0.37 SNLS 0.40 0.37 SNLS 1.40 1.29 SNLS 0.50 0.46 19.55 17.99
442 353 55 44 Roundup 1 3
1 1 1 1 1 1 1 2 1 1 2 1 1 1 20
SNLS SNLS SNLS SNLS FOA 5 Thread O/L SNLS SNLS SNLS
1.56 0.37 0.55 1.84 0.23 0.41 0.37 0.37 0.83 6.53
2
Front section R/Tape to Front Upper Chest Pkt Attch Chest Flap Attch Pkt Setting Hang Sewing Secure Top and Side with Facing Knee Panel Attch + Side Secure R/Tape to Front Leg Attch Front Upper and Lower Attch Front W/Band Front Neck Cornor Tack Front Rise O/L Left Zipper Attch Rt Zipper Attch Brand Tape Attch to Rt Zipper Run Join Crotch
1.70 0.40 0.60 2.00 0.25 0.45 0.40 0.40 0.90 7.10
SNLS DNLS (Split) DNLS (Fix) SNLS SNLS SNLS SNLS SNLS 5 Thread O/L Waist Belt SNLS 3 Thread O/L DNLS (Fix) SNLS DNLS (Fix) SNLS
2.94 1.93 0.55 0.46 0.41 0.64 1.75 0.92 0.53 0.46 0.37 0.30 1.10 1.07 0.64 0.37 14.46
3 2 1 1
Assembly Section Shoulder Join
3.20 2.10 0.60 0.50 0.45 0.70 1.90 1.00 0.58 0.50 0.40 0.33 1.19 1.16 0.70 0.40 15.71
5 Thread O/L
0.45
0.41
1
1 2 1 1 1 1 9
1 2 1 1 1 1 1 1 1 1 1 19
Ateeq ur Rehman et al. / Procedia Manufacturing 39 (2019) 1447–1454 Ateeq ur Rehman / Procedia Manufacturing 00 (2019) 000–000
1452 6
50 51 52 53 54 55 56 57 58 59 60 Subtotal Total
Armhole Join Shoulder Top Side Seam Feedo Collar Attch Collar Close and Edge Stitch Knee Pkt Attch Knee Flap Attch Bartacks Inseam Feedo Slv Hem Bottom Hem
5 Thread O/L FOA FOA SNLS SNLS SNLS SNLS Bartack FOA SNLS SNLS
0.85 0.40 2.50 0.93 1.13 1.70 0.90 1.27 0.85 0.50 0.50 11.98 54.34
0.78 0.37 2.30 0.86 1.04 1.56 0.83 1.16 0.78 0.46 0.46 11.02 50.00
3 1 1 2 1 1 1 1 1 13 61
3. Results and Discussion After making necessary changes, line output data was continuously monitored. Because of changes in job content and other changes made on machine layout, the production line had to achieve its learning curve. The line outputs, therefore, decreased initially. The collected data of line output was examined carefully to exclude the time spent by production line in achieving learning curve. This was because of the reassignment of operations that each operator had either reduced, increased or completely changed job. With the passage of time speed and handling of operators achieved optimization which is classical property of the learning curves. Once the line gained a stable output rate, output data was collected for 15 working days and new line productivity was calculated, shown in Figure 2.
Machine Productivity After Changes 0.74 0.73 0.72 0.71 0.7 0.69 0.68 0.67 0.66
1
2
3
4
5
6
Daily Machine Productivity
7
8
9
10
11
12
13
14
15
Average Machine Productivity
Figure 2 Machine Productivity After Changes
Although the techniques employed in present study are well known, yet there is no published data to compare in the apparel manufacturing industry. Therefore, the results obtained have been compared to the existing line productivity, to realize the magnitude of improvement. It is apparent from Figure 2 that new average machine productivity was about 0.70 pcs per machine hour which was obviously higher than the earlier 0.51 pcs per machine hour. For further comparison of machine productivity of both the phases, before and after changes were made, combined data has been presented in Figure 3.
Ateeq ur Rehman et al. / Procedia Manufacturing 39 (2019) 1447–1454 Ateeq ur Rehman / Procedia Manufacturing 00 (2019) 000–000
1453 7
Machine Productivity Comparison
0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 Daily Machine Productivity
Average Machine Productivity
Figure 3 Machine Productivity Comparison Before and After Time Study Applications
The graph represents that initially line was performing at a stable output and machine productivity. Once the line was rearranged for sequence of machine and work was allocated to workstations according to the cycle, machine productivity declined suddenly to 0.30 pcs per machine hour, on day sixteen. For next six days, machine productivity exhibited a vertically upward graph which is typical of learning curves. On day 21 line acquired a new normal and the average productivity for next 15 days was higher. 4. Conclusions It was concluded that time study is an effective tool for efficient apparel manufacturing floor management. Standard time calculation is vital for task assignment and equalizing workload among machine operators. It is further important to balance the lines at required production rate and cycle time. The sequential arrangement of sewing machines reduced excessive material movement and improve material flow. Further, balanced workload allowed the reduction of work in process and visibility of the process also improved. The consequent result was a 36% increase the machine productivity of production line. The study findings are useful for sewing factories making other woven, knitted and home textile products. In addition, it is equally important football, shoe and gloves manufacturing industry. Modern industry 4.0 technologies can revolutionize the time study in near future. Artificial intelligence and big data analytics may help in determination of bottle neck operations for time study and also can rate the machine operator more precisely. Internet of things provides the opportunity of conducting time study without physical presence. The possibility of eliminating the subjective evaluations coupled with real-time data sharing shall make the developed time standards more accurate. References [1] [2] [3] [4] [5] [6] [7] [8] [9]
G. Kanawaty, Introduction to Work Study, 4th ed. Geneva: International Labour Office, 1992. W. J. Stevenson, Operations Management, 12th ed. McGraw-Hill Education, 2015. S. Lawrence, Work Measurement and Methods Improvement. Wiley, 2000. R. E. Glock and G. I. Kunz, Apparel Manufacturing : Sewn Product Analysis. Pearson/Prentice Hall, 2005. W. Charles, Theory and Practice of Time Study. Century, 1966. D. V Merrick, Time Studies as a Basis for Rate Setting. 1919. L. C. Morrow, “The Beduax Principle of Human Power Measurement,” The American Machinist, 1922. R. M. Barnes, Motion and time study : design and measurement of work. Wiley, 1980. A. Mosso, “Les lois de la fatigue étudiées dans les muscles de l ’ homme,” Am. J. Psychol., vol. 3, no. 3, pp. 379–381, 1890.
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[10] M. Wiberg, Work-time distribution, time study analysis of performance differences., [2d ed.]. Chicago: McClure Hadden & Ortman, 1951. [11] International Labour Organization, Introduction to work study. Universal Book Corporation, 1979. [12] J. Greene, “Observe and report: these case studies reveal how work measurement is the basis of any process improvement,” Ind. Eng., vol. 42, no. 7, pp. 47–53, Jul. 2010. [13] R. Jain, S. Gupta, M. L. Meena, and G. S. Dangayach, “Optimisation of labour productivity using work measurement techniques,” Int. J. Product. Qual. Manag., vol. 19, no. 4, p. 485, 2016. [14] J. Mancha, M. Puente, F. F. Chen, H.-D. Wan, and G. Kuriger, “Work Measurement and Standard Work Instruction Improvement: A Molding Plant.” [15] I. Bhiradi and B. K. Singh, “Work Measurement Approach for Productivity Improvement in a Heavy Machine Shop,” 5 th Int. 26th All India Manuf. Technol., no. Aimtdr, pp. 1–6, 2014. [16] T. Moyo, E. Mangore, B. Chigara, W. Engineering, and U. Design, “Work Measurement Techniques Utilized by The Building Industry in The Midlands Province Of Zimbabwe,” Int. J. Sustain. Constr. Eng. Technol., vol. 5, no. 1, pp. 42–52, 2014. [17] S. O. Duffuaa and A. Raouf, “Maintenance Work Measurement,” in Planning and Control of Maintenance Systems, Cham: Springer International Publishing, 2015, pp. 95–114. [18] B. Tolo, “21st-century stopwatch: powerful work measurement in retail applications,” Ind. Eng., vol. 37, no. 7, pp. 34–38, Jul. 2005. [19] N. Yusoff, A. Jaffar, N. M. Abbas, and N. H. Saad, “Work measurement for process improvement in the car seat polyurethane injection manufacturing line,” Procedia Eng., vol. 41, no. Iris, pp. 1800–1805, 2012. [20] C. Duran, A. Cetindere, and Y. E. Aksu, “Productivity improvement by work and time study technique for earth energy-glass manufacturing company,” Procedia Econ. Financ., vol. 26, pp. 109–113, 2015. [21] H. Gershoni and N. Rudy, “An analysis of the total variation of work measurement techniques,” Int. J. Prod. Res., vol. 19, no. 3, pp. 303– 316, 1981. [22] A. Freivalds, “Work Measurement in the 21 st Century,” Ind. Syst. Eng. Res. Conf. A., pp. 4130–4134, 2013. [23] M. Elnekave and I. Gilad, “Rapid video-based analysis system for advanced work measurement,” Int. J. Prod. Res., vol. 44, no. 2, pp. 271– 290, 2006. [24] V. Ramesh Babu, Industrial Engineering in Apparel Production. Woodhead Publishing India Pvt. Ltd., 2012.
ScienceDirect
Available online at www.sciencedirect.com
ScienceDirect
Procedia Manufacturing 00 (2019) 000–000
www.elsevier.com/locate/procedia
Procedia Manufacturing 39 (2019) 1447–1454
25th International Conference on Production Research Manufacturing Innovation: Cyber Physical Manufacturing August 9-14, 2019 | Chicago, Illinois (USA)
Productivity Improvement Through Time Study Approach: A Case Study from an Apparel Manufacturing Industry of Pakistan Ateeq ur Rehmana, Muhammad Babar Ramzana*, Muhammad Shafiqb, Abher Rasheeda, Muhammad Salman Naeema, Matteo Mario Savinoc Department of Garment Manufacturing, National Textile University, Faisalabad-37610, Pakistan Department of Industrial Engineering, University of Engineering and Technology, Taxila, Pakistan C Department of Engineering, University of Sannio, 82100 Benevento, Italy
a b
Abstract Standard processing timings play an important role in efficient production management and are measured using work measurement techniques. Time study is a widely used work measurement technique in repetitive manufacturing processes. In present study, time study technique was used to enhance productivity of a garment production line in a work wear manufacturing factory. The factory consisted of 350 sewing machines and was producing work wears including trousers, vests, jackets and coveralls. Stop watch time study – analysis of existing set up – explored the existing time losses in production line caused by unevenness of work, excessive material transportation and higher work-in-process. Improvements were made through: (i) sequence of activities, (ii) tasks reassignment to the work stations and (iii) line balancing at predetermined cycle time. Hourly production charts were maintained at each work station to monitor outputs. The results of machine productivity were compared before and after employing changes. From results, 36% increase in average machine productivity was observed. Hence, it can be concluded that time study is an effective tool for increasing productivity in apparel manufacturing. The added advantages including optimization of material flow, balanced production line and reduced work in process were also achieved. The results of this study are useful for sewing factories making other woven, knitted and home textile products. In addition, football, shoe and gloves manufacturing industry may reef benefits from findings of this study. © 2019 2019 The The Authors. © Authors. Published Published by by Elsevier Elsevier Ltd. B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of the ICPR25 International Scientific & Advisory and Organizing committee members
* Corresponding author. Tel.: +92-301-504-2823; fax: +92-(0)41-923-0098. E-mail address: [email protected] 2351-9789 © 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Selection and peer review under the responsibility of ICPR25 International Scientific & Advisory and Organizing committee members 2351-9789 © 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of the ICPR25 International Scientific & Advisory and Organizing committee members 10.1016/j.promfg.2020.01.306
2
Ateeq ur Rehman / Procedia Manufacturing 00 (2019) 000–000
Selection and peer review under the responsibility of ICPR25 International Scientific & Advisory and Organizing committee 1448 Ateeq ur Rehman et al. / Procedia Manufacturing 39 (2019) 1447–1454 members Keywords: Time study; Garment manufacturing; Productivity improvement; Pakistan
1. Introduction Work measurement is concerned with determining standard time for a job, using a specified method [1]. Standard time is the time taken by a qualified and trained operator to complete certain job while working at efficient, yet sustainable rate, using specific method, tools, equipment, arrangement of workplace and working conditions [2]. One of the important application of work measurement techniques is to balance manufacturing / production lines which refers to dividing equal amount of work among machine operator/ workers [1] which in turn promises for higher productivity [3]. Commonly used techniques for developing standard times include stop watch time study, historical times, predetermined data and work sampling [2]. Historical times and predetermined data belongs to past and can only provide time estimation. Activity sampling is preferred for estimating machine allowance. Stop watch time study is preferred for sewing operations because it is live and extended method of time. Time study is the determination of rate at which a specific job is being done in a repetitive job process [4]. The concept of stop watch time study was introduced by Frederick Winslow Taylor. Although it faced a lot of criticism, yet it gained acceptance over the time and it is now a widely used technique for work measurement. William Charles introduced performance rating and elaborated procedure of work study [5]. Merrick suggested that fatigue and other allowances should be added to the measured times of job [6]. Bedaux established relaxation allowance in work measurement [7], which was further expanded to personal and fatigue allowances [8], [9]. Wiberg attached operator performance with job time reduction over repetitions [10]. International Labour Organization (ILO) proposed work measurement as a tool for increasing productivity by investigation and reduction of ineffective time contents of job [11]. It provides basis for improvement processes [12] and helpd in increasing organizational productivity [13]. It has proven success in manufacturing [14], [15], construction industry [16], maintenance industry [17], retail [18] hospital, data processing and service organizations [2], [19], [20]. Various factors have been studied that impact the accuracy of time study including, number of observations taken, amount and type of allowances to include and rating of operator [21]. GPS systems to track worker movements [22] and digital video based approach in time study was proved helpful in generation of rapid time standards for 21st century changing needs [23]. Although, apparel manufacturing sector is reefing benefits of time study applications from decades, yet it is hard to find relevant published research. In present study, time study technique was used to enhance productivity of a garment production line in a work wear manufacturing factory in Pakistan. The factory consisted of 350 sewing machines and was producing work wears including trousers, vests, jackets and coveralls. 2. Materials and Methods During initial visit to the manufacturing floor we found some of the machine operators were waiting for work while the others were having huge pile up of semi stitched goods, waiting to be processed. It was enough of evidence to understand that production floor lacked work leveling. Further investigation revealed that a lay man approach to work division, without paying attention to the activity or time required to carry out a certain sewing operation, was sole responsible of the situation. In addition, cross and backward material movement, irregular sequence of activities, improper machine layout was responsible for creating further mess of material on shop floor. In a nutshell, sewing floor was incurring certain lean wastes like excessive transportation, higher inventory at some workstations while keeping the others in wait. In order to get rid of these wastes operation breakdown was made and work was divided among work stations based on the cycle time. Sewing lines were arranged in sequence and respective jobs were allocated to the machine operators, as shown in Table 2. It was decided to make necessary changes at the production floor to increase effectiveness and initially a single production line was selected. The line consisted of about 65 sewing machines and was producing a protective coverall, shown in Figure 1(a). In order to realize the benefits of improvement process, machine productivity was chosen as Key Performance Indicator (KPI). Machine Productivity (P), for the selected production line, was calculated using Equation 1 [2].
Ateeq ur Rehman et al. / Procedia Manufacturing 39 (2019) 1447–1454 Ateeq ur Rehman / Procedia Manufacturing 00 (2019) 000–000
P
Output pcs Machinehours
1449 3
(1)
The line output for recent fifteen consecutive working days was obtained and it was calculated that average machine productivity was 0.51 garments per machine hour, shown in Figure 1. (a)
(b)
Machine Productivity Before Changes 0.58 0.56 0.54 0.52 0.5 0.48 0.46 0.44 0.42
1
2
3
4
5
6
7
Daily Machine Productivity Figure 1
8
9
10 11 12 13 14 15
Average Machine Productivity
a. Coverall ; b. Machine Productivity in Existing Setup
Operation breakdown was developed for coverall, explaining sequence of operations. In order to determine standard times for individual operations following steps were considered. Determining number of cycles to be observed Recording observations and calculating observed time (OT) Calculation of normal time (NT) Calculation of standard time (ST) Number of cycles to be observed were calculated using Equation 2 [2].
zs n e
2
(2)
Where z = number of standard deviations for desired confidence interval s = sample standard deviation e = acceptable time error Keeping in view average cycle time of 1 minute (60 sec), z-value of 2.58 for desired confidence of 99% and taking acceptable time error of ±2 seconds, the number of cycles to be observed were 15. Since, the average cycle time of operations was observed to be around 60 seconds, therefore same number of observations were taken for all individual operations. Digital stop watch was used to take 15 cycle times of each operation and average single cycle time (ASCT) was calculated by using Equation 3, which was further converted into observed time (OT) by Equation 4 [2]. Thus,
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ASCT OT
Where
x
i
x
i
(3)
n
ASCT 60
(4)
= sum of recorded cycle times
n = number of cycles observed
In order to adjust the pace of operator pace rating was used, each operator was allocated a rating factor ranging from 0.70, for lowest pace, to 1.30, for highest pace of work. The standard rating factor was considered 1 which was allocated to efficient yet sustainable rate of work. Normal time was calculated by multiplying observed time (OT) and rating factor (RF) [2]. That is,
NT OT RF
(5)
Machine allowance (MA), personal and fatigue allowance (P&F) and bundle handling allowances (BHA) were added to the normal time to obtain standard time (ST) [24].
ST NT MA P & F BHA
(6)
Machine allowances were different according to the machine used. For example, a lower machine allowance is expected to be added for single needle lock stitch, as compared to two needle lock stitch machine. Table 1 includes detailed machine allowances, to be added, as a percentage of normal time (NT) [24]. Table 1 Sewing Machine Allowance Percentages Type of Machine Single needle lock stitch machine (SNLS) Double needle lock stitch machine (DNLS) Three thread overlock machine (3TH O/L) Five thread overlock machine (5TH O/L)
Allowance(%) 12.5 14.0 12.0 18.0
Type of Machine Bar tack machine (Bartack) Single needle chain stitch (SNCS) Multi needle chain stitch (FOA)
Allowance(%) 12.0 14.0 16.0
A flat 7.5 percent of collective personal and fatigue allowance was added for all the operators. Similarly, bundle handling allowance of 0.03 minute was added for each operation. In order to balance the line, cycle time was calculated at desired line output rate of 350 pcs per day at 80% target production efficiency of the line. The cycle time of 1.08 minute was obtained by dividing shift time over desired output. The line was balanced theoretically at obtained cycle time “Operations Bulletin” (OB) was prepared by adding standard times of respective operations to operations breakdown sheet and further calculating targets and number of machines required. Table 2 provides detailed operations bulletin of studied garment under balanced line conditions. Once theoretical balancing of line was done, keeping all technical limitations in view, it was proceeded for ground actions. Machines were arranged in sequence mentioned in operations breakdown and work was assigned to the operators. In order to have control over changes made, hourly production monitoring charts were maintained at each work station. Production data was collected on hourly basis from these charts for 20 days.
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Table 2 Operations Bulletin
Sr # 1 2 3 4 5 8 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 Subtotal 24 25 26 27 28 29 30 31 32 Subtotal 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 Subtotal 49
Order# Style# Total SAM Buyer Operation Small Parts Collar Making Collar Turn & Top R/Tape to Chest Pkt Flap (2) Valcro Pkt Flap (2+2) Chest Pkt Flap Make (4 Cornor) (2) Side Pkt Flap Make (2) Flap Turn n Top Flap Turn n Top R/Tape to Chest Pkt Chest Pkt Hem Side Pkt Pleat Making (2+2) Side Pkt Box Pleat Making (2) Side Pkt Hem Valcro to Chest n Side Pkt (2+2) Side Pkt Strip Attch (2) Collar Patch Fold and Top Labels to Patch Pkt Bag Hem Pocket Bag Close R/Tape to Sleeves (2+2) Pkt Bag Turn n Top Pkt Fly Tack R/Tape to Knee Panel + LBL Knee Panel Hem Back Section R/Tape to Back Panel Back Pleat Make Back Pleat Top Tack R/Tape to Back Leg Panels (2*2) Back Rise Back Upper and Lower Join Back Upper and Lower Join Top W/B Elastic Tack W/B Attch
Operational Bulletin 100% Target Per Day Coverall 80% Target Per Day 54.34 100% Target Per Hour 80% Target Per Hour Machine Type Standard Time Machines Requried 0.3 SNLS 0.60 0.55 SNLS 0.50 0.46 SNLS 0.60 0.55 SNLS 1.00 0.92 SNLS 0.80 0.74 SNLS 0.80 0.74 DNLS (Split) 0.70 0.64 DNLS (Split) 0.45 0.41 SNLS 1.40 1.29 SNLS 0.40 0.37 SNLS 0.70 0.64 SNLS 1.00 0.92 SNLS 0.60 0.55 SNLS 1.00 0.92 SNLS 1.80 1.66 SNLS 0.40 0.37 SNLS 0.50 0.46 SNLS 0.60 0.55 SNLS 0.40 0.37 SNLS 2.20 2.02 SNLS 0.40 0.37 SNLS 0.40 0.37 SNLS 1.40 1.29 SNLS 0.50 0.46 19.55 17.99
442 353 55 44 Roundup 1 3
1 1 1 1 1 1 1 2 1 1 2 1 1 1 20
SNLS SNLS SNLS SNLS FOA 5 Thread O/L SNLS SNLS SNLS
1.56 0.37 0.55 1.84 0.23 0.41 0.37 0.37 0.83 6.53
2
Front section R/Tape to Front Upper Chest Pkt Attch Chest Flap Attch Pkt Setting Hang Sewing Secure Top and Side with Facing Knee Panel Attch + Side Secure R/Tape to Front Leg Attch Front Upper and Lower Attch Front W/Band Front Neck Cornor Tack Front Rise O/L Left Zipper Attch Rt Zipper Attch Brand Tape Attch to Rt Zipper Run Join Crotch
1.70 0.40 0.60 2.00 0.25 0.45 0.40 0.40 0.90 7.10
SNLS DNLS (Split) DNLS (Fix) SNLS SNLS SNLS SNLS SNLS 5 Thread O/L Waist Belt SNLS 3 Thread O/L DNLS (Fix) SNLS DNLS (Fix) SNLS
2.94 1.93 0.55 0.46 0.41 0.64 1.75 0.92 0.53 0.46 0.37 0.30 1.10 1.07 0.64 0.37 14.46
3 2 1 1
Assembly Section Shoulder Join
3.20 2.10 0.60 0.50 0.45 0.70 1.90 1.00 0.58 0.50 0.40 0.33 1.19 1.16 0.70 0.40 15.71
5 Thread O/L
0.45
0.41
1
1 2 1 1 1 1 9
1 2 1 1 1 1 1 1 1 1 1 19
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50 51 52 53 54 55 56 57 58 59 60 Subtotal Total
Armhole Join Shoulder Top Side Seam Feedo Collar Attch Collar Close and Edge Stitch Knee Pkt Attch Knee Flap Attch Bartacks Inseam Feedo Slv Hem Bottom Hem
5 Thread O/L FOA FOA SNLS SNLS SNLS SNLS Bartack FOA SNLS SNLS
0.85 0.40 2.50 0.93 1.13 1.70 0.90 1.27 0.85 0.50 0.50 11.98 54.34
0.78 0.37 2.30 0.86 1.04 1.56 0.83 1.16 0.78 0.46 0.46 11.02 50.00
3 1 1 2 1 1 1 1 1 13 61
3. Results and Discussion After making necessary changes, line output data was continuously monitored. Because of changes in job content and other changes made on machine layout, the production line had to achieve its learning curve. The line outputs, therefore, decreased initially. The collected data of line output was examined carefully to exclude the time spent by production line in achieving learning curve. This was because of the reassignment of operations that each operator had either reduced, increased or completely changed job. With the passage of time speed and handling of operators achieved optimization which is classical property of the learning curves. Once the line gained a stable output rate, output data was collected for 15 working days and new line productivity was calculated, shown in Figure 2.
Machine Productivity After Changes 0.74 0.73 0.72 0.71 0.7 0.69 0.68 0.67 0.66
1
2
3
4
5
6
Daily Machine Productivity
7
8
9
10
11
12
13
14
15
Average Machine Productivity
Figure 2 Machine Productivity After Changes
Although the techniques employed in present study are well known, yet there is no published data to compare in the apparel manufacturing industry. Therefore, the results obtained have been compared to the existing line productivity, to realize the magnitude of improvement. It is apparent from Figure 2 that new average machine productivity was about 0.70 pcs per machine hour which was obviously higher than the earlier 0.51 pcs per machine hour. For further comparison of machine productivity of both the phases, before and after changes were made, combined data has been presented in Figure 3.
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Machine Productivity Comparison
0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 Daily Machine Productivity
Average Machine Productivity
Figure 3 Machine Productivity Comparison Before and After Time Study Applications
The graph represents that initially line was performing at a stable output and machine productivity. Once the line was rearranged for sequence of machine and work was allocated to workstations according to the cycle, machine productivity declined suddenly to 0.30 pcs per machine hour, on day sixteen. For next six days, machine productivity exhibited a vertically upward graph which is typical of learning curves. On day 21 line acquired a new normal and the average productivity for next 15 days was higher. 4. Conclusions It was concluded that time study is an effective tool for efficient apparel manufacturing floor management. Standard time calculation is vital for task assignment and equalizing workload among machine operators. It is further important to balance the lines at required production rate and cycle time. The sequential arrangement of sewing machines reduced excessive material movement and improve material flow. Further, balanced workload allowed the reduction of work in process and visibility of the process also improved. The consequent result was a 36% increase the machine productivity of production line. The study findings are useful for sewing factories making other woven, knitted and home textile products. In addition, it is equally important football, shoe and gloves manufacturing industry. Modern industry 4.0 technologies can revolutionize the time study in near future. Artificial intelligence and big data analytics may help in determination of bottle neck operations for time study and also can rate the machine operator more precisely. Internet of things provides the opportunity of conducting time study without physical presence. The possibility of eliminating the subjective evaluations coupled with real-time data sharing shall make the developed time standards more accurate. References [1] [2] [3] [4] [5] [6] [7] [8] [9]
G. Kanawaty, Introduction to Work Study, 4th ed. Geneva: International Labour Office, 1992. W. J. Stevenson, Operations Management, 12th ed. McGraw-Hill Education, 2015. S. Lawrence, Work Measurement and Methods Improvement. Wiley, 2000. R. E. Glock and G. I. Kunz, Apparel Manufacturing : Sewn Product Analysis. Pearson/Prentice Hall, 2005. W. Charles, Theory and Practice of Time Study. Century, 1966. D. V Merrick, Time Studies as a Basis for Rate Setting. 1919. L. C. Morrow, “The Beduax Principle of Human Power Measurement,” The American Machinist, 1922. R. M. Barnes, Motion and time study : design and measurement of work. Wiley, 1980. A. Mosso, “Les lois de la fatigue étudiées dans les muscles de l ’ homme,” Am. J. Psychol., vol. 3, no. 3, pp. 379–381, 1890.
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[10] M. Wiberg, Work-time distribution, time study analysis of performance differences., [2d ed.]. Chicago: McClure Hadden & Ortman, 1951. [11] International Labour Organization, Introduction to work study. Universal Book Corporation, 1979. [12] J. Greene, “Observe and report: these case studies reveal how work measurement is the basis of any process improvement,” Ind. Eng., vol. 42, no. 7, pp. 47–53, Jul. 2010. [13] R. Jain, S. Gupta, M. L. Meena, and G. S. Dangayach, “Optimisation of labour productivity using work measurement techniques,” Int. J. Product. Qual. Manag., vol. 19, no. 4, p. 485, 2016. [14] J. Mancha, M. Puente, F. F. Chen, H.-D. Wan, and G. Kuriger, “Work Measurement and Standard Work Instruction Improvement: A Molding Plant.” [15] I. Bhiradi and B. K. Singh, “Work Measurement Approach for Productivity Improvement in a Heavy Machine Shop,” 5 th Int. 26th All India Manuf. Technol., no. Aimtdr, pp. 1–6, 2014. [16] T. Moyo, E. Mangore, B. Chigara, W. Engineering, and U. Design, “Work Measurement Techniques Utilized by The Building Industry in The Midlands Province Of Zimbabwe,” Int. J. Sustain. Constr. Eng. Technol., vol. 5, no. 1, pp. 42–52, 2014. [17] S. O. Duffuaa and A. Raouf, “Maintenance Work Measurement,” in Planning and Control of Maintenance Systems, Cham: Springer International Publishing, 2015, pp. 95–114. [18] B. Tolo, “21st-century stopwatch: powerful work measurement in retail applications,” Ind. Eng., vol. 37, no. 7, pp. 34–38, Jul. 2005. [19] N. Yusoff, A. Jaffar, N. M. Abbas, and N. H. Saad, “Work measurement for process improvement in the car seat polyurethane injection manufacturing line,” Procedia Eng., vol. 41, no. Iris, pp. 1800–1805, 2012. [20] C. Duran, A. Cetindere, and Y. E. Aksu, “Productivity improvement by work and time study technique for earth energy-glass manufacturing company,” Procedia Econ. Financ., vol. 26, pp. 109–113, 2015. [21] H. Gershoni and N. Rudy, “An analysis of the total variation of work measurement techniques,” Int. J. Prod. Res., vol. 19, no. 3, pp. 303– 316, 1981. [22] A. Freivalds, “Work Measurement in the 21 st Century,” Ind. Syst. Eng. Res. Conf. A., pp. 4130–4134, 2013. [23] M. Elnekave and I. Gilad, “Rapid video-based analysis system for advanced work measurement,” Int. J. Prod. Res., vol. 44, no. 2, pp. 271– 290, 2006. [24] V. Ramesh Babu, Industrial Engineering in Apparel Production. Woodhead Publishing India Pvt. Ltd., 2012.