Enhancing operation efficiency of leather manufacturing industry through hybrid of value stream mapping and discrete event simulation

Figures & data

Table 1. Overview of LM applications in literature in the context of manufacturing industry.

Author/s	Method/Lean tool	Considered Manufacturing Industry							Results
		Automotive	Textile and apparel	Leather and leather products	Basic and Fabricated metals	Food and beverage	Plastics	Others
(Papetti et al., 2019)	Resources value mapping							X	Non-value-added consumption have been identified
(Alaya, 2016; Busert & Fay, 2021; Dhingra et al., 2019; Jasti & Sharma, 2014; Kumar et al., 2018a, 2018b; Lacerda et al., 2016; Sangwa & Sangwan, 2023; Singh & Singh, 2013)	VSM	X							Reduction in cycle time, TAKT time, work in-process inventory, production lead time, non-value-added activities, costs, and man power and improved product quality, line efficiency, productivity, and extended VSM
(Akçagün et al., 2012; Kumar et al., 2020; Saad et al., 2021; Suhardi et al., 2020; Yildiz & Güner, 2013)	VSM		X						Reduced production lead time, work-in-process inventory, and improved line efficiency
(Reda & Dvivedi, 2022a, 2022b)	VSM			X					Reduced production lead time and cycle time
(Gebeyehu et al., 2022; Grewal, 2008; Salwin et al., 2021; Saravanan et al., 2018; Sudhakara et al., 2020; Vinodh et al., 2010)	VSM				X				Reduction of lead time, total cycle time, cost, work in progress, non-value adding time, total distance, defects, and improved productivity
(Correia et al., 2018; Ramani & Lingan, 2022; Seth et al., 2017)	VSM							X	Reduced design costs, waste, cycle time and improved productivity
(Cabrera et al., 2020; Caldas-Miguel et al., 2020; Hmidach et al., 2020; Kohata-Tirado et al., 2023; Liu et al., 2020; Myint & Smutkupt, 2022)	VSM					X			Reduced total cycle time, work-in-process inventory, costs, delivery time, losses and improved service quality and plant efficiency
(Issa, 2023; Khadijah & Pratama, 2023; Sophian et al., 2022)	VSM						X		Reduced non-value- added time, cycle time and improved performance
(Kumar et al., 2023)	Expert systems	X							Sequencing of VSM tools
(Dhiravidamani et al., 2018)	VSM and lean audit system	X							Reduced lead time, non-value-added time, set-up time and labor force
(Prasad et al., 2020)	Meaning stream mapping		X						Lean implementation framework
(Wen et al., 2021)	Energy value mapping				X				Energy value mapping
(Dom et al., 2023)	Ergo-VSM				X				Reduced psychosocial risk factors and improved productivity
(Ali et al., 2024; Toki et al., 2023)	Single minute exchange of die		X						Reduced changeover time, waste, and cost
(Gavriluţă et al., 2021)	An algorithm-based approach						X		Decreased production area, cycle time and number of operators
(Andrade et al., 2016; Katsigiannis et al., 2024; Stadnicka & Litwin, 2019)	VSM and simulation	X							Proposed extended VSM and improvement in lead time, throughput, and the work-in- progress inventory
(Reda & Dvivedi, 2023)	VSM and simulation			X					Improved output and line efficiency
(Baumer-Cardoso et al., 2020; Helleno et al., 2015; Heravi & Firoozi, 2017; Mullisa & Abdul-Kader, 2023)	VSM and simulation				X				Reduced production lead time, cost and the risks and enhanced process efficiency
(Atieh et al., 2015; Schmidtke et al., 2014; Zahraee et al., 2020)	VSM and simulation							X	Reduced production lead time, value-added time, and Takt time
(Pérez et al., 2022)	Lean-green maintenance						X		Proposed lean-green maintenance management procedure
(Utama & Abirfatin, 2023)	Sustainable VSM					X			Improved the manufacturing sustainability Index

Table 2. Parameters/metrics/for current state analysis of classical VSM.

Name of Measurement	Parameters (Metrics)	Author/s	Percentage
Production (operational) efficiency	WIP	(Oliveira et al., 2017; Patil et al., 2021; Santosa & Sugarindra, 2018; Singh & Sharma, 2009; Suhardi et al., 2020; Venkataraman et al., 2014)	30.43%
	Changeover time	(Mohanraj et al., 2015; Oliveira et al., 2017; Patil et al., 2021; Salwin et al., 2021; Venkataraman et al., 2014; Yuvamitra et al., 2017)	30.43%
	Cycle time	(Lacerda et al., 2016; Mohanraj et al., 2015; Oliveira et al., 2017; Patil et al., 2021; Salwin et al., 2021; Santosa & Sugarindra, 2018; Singh & Sharma, 2009; Venkataraman et al., 2014; Yuvamitra et al., 2017)	43.5%
	Lead time	(Lacerda et al., 2016; Mohanraj et al., 2015; Oliveira et al., 2017; Patil et al., 2021; Salwin et al., 2021; Santosa & Sugarindra, 2018; Singh & Sharma, 2009; Suhardi et al., 2020; Venkataraman et al., 2014; Yuvamitra et al., 2017)	47.83%
	Waiting time	(Lacerda et al., 2016)	4.35%
	Non-value-added time	(Salwin et al., 2021)	4.35%
	Manpower	(Lacerda et al., 2016; Mohanraj et al., 2015; Salwin et al., 2021)	17.4%
	Processing time	(Lacerda et al., 2016; Patil et al., 2021; Salwin et al., 2021; Yuvamitra et al., 2017)	17.4%
	Transportation	(Oliveira et al., 2017)	4.35%
	Value-added time	(Lacerda et al., 2016; Salwin et al., 2021; Singh & Sharma, 2009; Suhardi et al., 2020; Venkataraman et al., 2014)	21.74%
	Uptime	(Patil et al., 2021; Singh & Sharma, 2009; Venkataraman et al., 2014)	13.04%
Availability	Overall equipment effectiveness (OEE)	(Oliveira et al., 2017)	4.35%
	Available working time	(Mohanraj et al., 2015)	4.35%
	Shift	(Singh & Sharma, 2009)	4.35%
Cost efficiency	Cost of the activity execution	(Salwin et al., 2021)	4.35%
Quality	Takt time	(Salwin et al., 2021)	4.35%

Table 3. Parameters/metrics/for current state analysis of extended VSM.

Name of Measurement	Parameters (Metrics)	Author/s	Percentage
Production (operational) efficiency	Manpower	(Agyapong-Kodua et al., 2009; Bait et al., 2020; Dinis-Carvalho et al., 2014; Gurumurthy & Kodali, 2011; Seth et al., 2017; Stadnicka & Litwin, 2019)	26.1%
	Value added ratio (process ratio)	(Gurumurthy & Kodali, 2011)	4.35%
	Cycle time	(Gurumurthy & Kodali, 2011; Seth et al., 2017; Seyedhosseini et al., 2013; Stadnicka & Litwin, 2019; Andrade et al., 2016; Atieh et al., 2015; McDonald et al., 2002; Liu & Yang, 2020)	34.8%
	WIP	(Andrade et al., 2016; Dinis-Carvalho et al., 2014; Gurumurthy & Kodali, 2011)	13.04%
	Lead time	(Agyapong-Kodua et al., 2009; Alvandi et al., 2016; Andrade et al., 2016; Atieh et al., 2015; Bait et al., 2020; Gurumurthy & Kodali, 2011; Seyedhosseini et al., 2013; Stadnicka & Litwin, 2019; McDonald et al., 2002;Liu & Yang, 2020)	43.48%
	Changeover time	(Andrade et al., 2016; Atieh et al., 2015; Dinis-Carvalho et al., 2014; Gurumurthy & Kodali, 2011; Seyedhosseini et al., 2013; Stadnicka & Litwin, 2019; McDonald et al., 2002; Liu & Yang, 2020)	37.8%
	Value added time	(Alvandi et al., 2016; Andrade et al., 2016; Atieh et al., 2015; Dinis-Carvalho et al., 2014; Gurumurthy & Kodali, 2011; Liu & Yang, 2020; McDonald et al., 2002; Seth et al., 2017; Seyedhosseini et al., 2013)	39.13%
	Uptime	(Atieh et al., 2015; Liu & Yang, 2020; Seyedhosseini et al., 2013)	13.04%
	Processing time	(Bait et al., 2020; Seth et al., 2017; Stadnicka & Litwin, 2019)	13.04%
	Output rate	(Agyapong-Kodua et al., 2009; Dinis-Carvalho et al., 2014)	8.69%
	Resource Utilization	(Agyapong-Kodua et al., 2009; Alvandi et al., 2016; Dinis-Carvalho et al., 2014)	13.04%
	Non-value-added time	(Alvandi et al., 2016; Dinis-Carvalho et al., 2014; Seth et al., 2017)	13.04%
	Transportation	(Dinis-Carvalho et al., 2014)	4.35%
	Throughput time	(Alvandi et al., 2016; Dinis-Carvalho et al., 2014)	8.7%
	Motion	(Dinis-Carvalho et al., 2014)	4.35%
	Waiting time	(Agyapong-Kodua et al., 2009; Bait et al., 2020; Liu & Yang, 2020)	13.04%
Availability	Available working time	(Gurumurthy & Kodali, 2011; Stadnicka & Litwin, 2019)	8.69%
	Shift	(Dinis-Carvalho et al., 2014; Liu & Yang, 2020; McDonald et al., 2002; Seyedhosseini et al., 2013)	17.39%
	MTTR (Mean Time To Repair)	(Alvandi et al., 2016; Stadnicka & Litwin, 2019)	8.69%
	MTBF (Mean Time Between Failures)	(Alvandi et al., 2016; Stadnicka & Litwin, 2019)	8.69%
Cost efficiency	Cost of the activity execution	(Agyapong-Kodua et al., 2009; Bait et al., 2020)	8.69%
Energy	Energy consumption	(Alvandi et al., 2016; Bait et al., 2020; Seth et al., 2017)	13.04%
Quality	Scrap	(Alvandi et al., 2016; Bait et al., 2020; Stadnicka & Litwin, 2019)	13.04%
	Good quality output	(Bait et al., 2020; Stadnicka & Litwin, 2019)	8.69%
	On time delivery	(Stadnicka & Litwin, 2019)	8.69%
	TAKT time	(Gurumurthy & Kodali, 2011)	4.35%

Figure 1. Assembly process chart.

Figure 2. Conceptual integration model for VSM and DES.

Table 4. Product matrix.

Products	Processes																															Product Family
	A1	A2	A3	A4	A5	A6	A7	A8	A9	A10	A11	A12	A13	A14	A15	A16	A17	A18	A19	A20	A21	A22	A23	A24	A25	A26	A27	A 28	A29	A30	A31
Document case	х	х				х					х			х						х			х	х		х					х	1
Briefcase	х	х			х				х						х	х				х			х	х		х					х
Ladies’ hand bag	х	х			х										х	х					х			х		х					х
Luggage	х	х			х										х	х					х		х	х		х					х
School Bag	х	х	х		х					х					х	х							х	х		х		х			х
Leather jacket	х	х	х		х			х							х			х	х	х		х			х		х		х	х	х	2
Leather articles (Belt)	х	х		х			х					х																			x	3
Leather articles (wallet)	х	х				х					х			х																	х
Soccer Ball	х	х		х									х				х														x	4

Figure 3. Summary of enhanced current state VSM.

Figure 4. As-Is simulation model.

Table 5. As- Is simulation experiment results for waiting time and WIP inventory.

S.N	Queue station name	Waiting time (Sec.)	WIP inventory (pcs)
1	RM inspection queue	5180.2	25 (6*25 = 150 sq.ft)
2	Cutting station queue	21.5	1 (6*1 = 6 sq.ft.)
3	Dispatching station queue	7.6	1
4	Preparation station queue	3548.3	20
5	Shoulder strap station queue	326.1	1
6	Back body station queue	5463.5	40
7	Front body station queue	10.6	0
8	Gusset station queue	28.7	0
9	Sub-assembly station queue	5420.8	20
10	Assembly station queue	4973.1	25
11	Trimming station queue	1.8	0
12	Final inspection queue	0	0
Total		24,982.2	133
Total run time:302,544.00 s

Table 6. As-Is simulation experiment summary.

S.N	Parameters	Result
1	Total waiting time (Sec.)	24,982.2
2	Average Utilization (%)	50.94
3	Total input (pcs)	1454 (8724 sq.ft)
4	Total WIP (pcs)	133
5	Total Defective RM (pcs)	39 (39*6 = 234 sq.ft.)
6	Total Throughput (pcs)	1315
7	Total defective products (pcs)	54
8	Total good product output (pcs)	1261
Total run time (Sec)		302,544

Figure 6. WIP waiting for the process.

Figure 7. Over produce WIP in various stations and finished goods.

Figure 8. Overview of transport distance in shop floor value streams.

Figure 5. WIP inventories in various station queue.

Figure 9. Cutting operation in standing position.

Table 7. Equivalent time value of identified wastes.

S.N	Name of identified wastes	Equivalent time value (sec.)	Percentage value (%)	Cumulative percentage value (%)
1	Waiting	24,982.2	78.24	78.24
2	Inventory	2782.12	8.72	86.96
3	Overproduction	1380.5	4.32	91.28
4	Transportation	1010.5	3.16	94.44
5	Motion	960.5	3.01	97.45
6	Defect	815.81	2.55	100
Total		31,931.63	100

Figure 10. Pareto diagram for identified wastes.

Figure 11. Sample experiment results for 133 WIP level.

Table 8. Impact of varying WIP level.

S.N	Total WIP (pcs)	Total waiting time (seconds)	TPLT (days)	TPLT (days)	Throughput (pcs)
1	98	14,244.6	13.19	13.19	1282
2	107	20,824.3	13.2	13.2	1260
3	133	24,982.2	13.221	13.221	1261
4	136	26,797.5	13.223	13.223	1251
5	149	28,495.6	13.233	13.233	1254
	Run time: 302,544 seconds

Table 9. Summarized results of simulation experiment for proposed scenarios.

S.N	Parameters	Scenario One	Scenario Two	Scenario Three	Scenario Four	Scenario Five
1	Total waiting time (Sec.)	8297.1	7174.2	8296.9	7389.9	7164.8
2	Average Utilization (%)	51.33	56.13	58.8	65.5	62.94
3	Total input (pcs)	1454 = (1454*6 = 8724 sq.ft.)	1454	1436 = (6*1436 = 8616 sq.ft.)	1454	1439
4	Total WIP (pcs)	50	40	35	31	35
5	Total Defective RM (pcs)	39 = (39*6 = 234 sq.ft.)	39	39	39	39
6	Total Throughput (pcs)	1409	1422	1405	1425	1411
7	Total defective products (pcs)	60	60	60	60	60
8	Total good product output (pcs)	1349	1362	1345	1365	1351
Total run time (Sec)		302,544	302,544	302,544	302,544.00	302,544.00
VA		2029.474 s	1978.514 s	2000.644 s	1942.021 s	2000.644 s
NVA		13.43 d	13.342 d	13.283 da	13.272 d	13.3 d
TPLT		13.503 d	13.413 d	13.356 d	13.342 d	13.37 d

Figure 12. Developed To-Be simulation model.

Figure 13. Dynamic behavior of sample value stream in the To-Be model.

Figure 14. Developed future state VSM.

Table 10. Result comparison.

S.N	Parameters (Metrics)	ECSVSM	FSVSM
1	Total WIP (pcs)	184	31
2	Takt time (sec.)	230.1	214.05
3	VA (sec.)	2198.244	1942.021
4	NVA (days)	14.59	8.33
5	TPLT (days)	14.67	8.4
6	VA ratio (%)	0.54	0.84
7	Total throughput (pcs)	1315	1425
8	Total good product output (pcs)	1261	1365
9	Total waiting time (Sec.)	24,982.2	7389.9
10	Travel distance (m)	36.25	19.3
11	No. of operators	58	66
12	Average utilization (%)	50.94	65.5
13	Operational (production) efficiency (%)	86.72	93.8

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Data availability statement

The data that support the findings of this study are available from the corresponding author, upon reasonable request.