Lean waste prioritisation and reduction in the apparel industry: application of waste assessment model and value stream mapping

Figures & data

Table 1. Description of the types of wastes in manufacturing processes (Obeidat et al., 2012; Ohno, 1988; Rahmanasari et al., 2021; Wu, 2003).

Type of waste	Description
Overproduction	It is manufacturing parts and products that are beyond the demanded amount by the subsequent process and the end customer, respectively. It results in an increased number of employees creating more wastes and quality problems.
Waiting	It is the time waste that arises from employees looking for upstream processes or activities to finish their work or until machines perform some activities or get ready
Transportation	It is unnecessary transport of materials between facilities because of improper layout
Over-processing	It is the waste created by poor tools, bad product design, unclear customer requirement, and defective products demanding longer time and effort than normal
Motion	Results from unnecessary movement of employees looking for parts, tools, and help in awkward postures resulting stress and related compensation expenses
Unnecessary inventory	Excessive inventory created by overproduction ties up money and creates cluttered workstation
Defects	Created by not producing the right products the first time and looking for corrective actions

Table 2. Purpose and category of some lean tools implemented in manufacturing industries.

Category	Purpose	Lean tools	Sources
Diagnostic tools	To identify wastes and areas of improvement	VSM, affinity diagram, Gemba, failure mode and effect analysis	Abdulmalek and Rajgopal (2007), Helleno et al. (2015), Jana and Tiwari (2021b), Rother and Shook (2003), Zahraee et al. (2021), Zahraee et al. (2020)
Prioritisation tools	To rank the wastes and identify the priority areas of improvement	Waste assessment model, VALSATs, Pareto chart	Hines and Rich (1997), Jana and Tiwari (2021b), Kosasih et al. (2019), Rawabdeh (2005)
Improvement tools	To improve the workplace and make visualisation easy	Kaizen, 5S, single minute exchange of die, just in time, line balancing, layout adjustment	Barzola-Cisneros et al. (2021), Ferdousi and Ahmed (2009), Hodge et al. (2011), Jana and Tiwari (2021b), Karunarathne and Dhananjana (2022), Makhija et al. (2021), Ratnayake and Dinosha (2018), Rother and Shook (2003), Silva (2012), Silva et al. (2011)
Monitoring tools	To follow up and sustain the improvement	First in first out, Kanban, dashboard, Gantt chart	Jana and Tiwari (2021a), S. Prasad & Panghal (2022), Reda and Dvivedi (2021), Yu and Kuan (2011)
Performance metrics	To measure the improvements	Lead time, customer takt time, cycle time, throughput time	Behnam et al. (2018), Jana and Tiwari (2021b), Reda and Dvivedi (2021), Rother and Shook (2003)

Table 3. Review of applications of VSM in the textile and apparel industry.

Authors	Country	Method	Lean tools	Section	Findings
Hodge et al. (2011)	USA	Qualitative interview, observation, and case studies	VSM (two case studies) 5S (three case studies)	Spinning, weaving, and knitting	A five-stage hierarchical approach to lean implementation was proposed VSM can be used for all levels of the lean implementation
Marudhamuthu and Pillai (2011)	India	Case study	VSM and single minute exchange of die (SMED)	Manufacturing (cutting and sewing)	The time to change the setup from one style to another was reduced from 28 min to around 8 min 58 min and 48 s of non-value-adding time was saved per shift
Akçagün et al. (2012)	Turkey	Case study	VSM	From raw materials to finished goods warehouse	The time to accomplish all processes of outwear products was reduced from 27–33 weeks to 9–14 weeks
B. S. Kumar & Sampath (2012)	India	Case study	VSM, cellular layout	Sewing	A reduction in WIP of a T-shirt manufacturing increased the profit by 20–25 % from the existing level
Obeidat et al. (2012)	Jordan	Case study	VSM, line balancing, layout design	Sewing	Results showed a reduction of 96% in production wastes and 43% in production lead time
Silva (2012)	Sri Lanka	Case study	VSM, 5S, one-piece flow, cellular manufacturing	Processes from supplier to customer	VSM can be applied to the apparel industry as the initial step of waste identification It helped the managers to visualise the different types of waste generated in the organisation and future possibilities of eliminating or reducing them
Sultana and Islam (2013)	Bangladesh	Quantitative (13 garments industries)	VSM	From spreading to sewing and finishing	Value addition increased from 15.71 % to 69.55 % on average The benefits of VSM arise from the interaction of the people making the maps with the process and observing on the shop floor
Yildiz and Güner (2013)	Turkey	Case study	VSM	Processes from supplier to customer	A 9.782 days production lead time of an order for jeans products was reduced to 3.4 days
S. Kumar and Thavaraj (2015)	India	Case study	VSM, cellular layout, single-piece flow, and Kaizen	Sewing section	Improved process ratio from 3.73 to 12.71% About 36.25% reduction in standard minutes value (SMV) Grouped operations of knitted men’s wear from 16 to 8 Reduced WIP inventory by 72.97%
Behnam et al. (2018)	–	Case study	VSM, analytical hierarchy process	From fabric cutting to garment packing	Mudas in the production line of clothing factory were identified and prioritised according to seven categories of Mudas using an analytical hierarchy process Defects such as failure in enzyme process and cutting wastes, and waiting wastes such as enzyme shop waiting, delay in receiving fabric from supplier, and delay in fabric cutting were the top five wastes Suggested a future state value stream map to improve the priority wastes
Phuong and Guidat (2018)	–	Case study	Sustainable VSM, radio-frequency identification (RFID)	From cutting to quality control and packing	RFID gives managers the chance for a distant monitoring Due to the RFID, the SSVM can run in real-time and the figures can be tracked at any time Presents a methodology for SSVM and implementation of industry 4.0
Tapia-Leon et al. (2019)	Peru		VSM, line balancing, 5S, Kaizen, layout improvement		The lead time decreased by three days The cycle time decreased by 9 min The downtimes decreased by 25% Order fulfilment increased by 15%
Carvalho et al. (2019)	Brazil	Exploratory case study	VSM, Kanban	From fabric spreading to sewing	The main wastes were found to be inventory and waiting The efficiency of the cycle (dividing VA time by lead time) has been improved from 27.67% to 40.1% after the implementation of Kanban
Suhardi et al. (2020)	Indonesia	Waste waiting questionnaire Time study	VSM, WAM	Cutting, sewing, packing	Motion with a 21.15% contribution was found to be the main waste After the implementation of the future VSM, the total time for the selected Bra production process was 3 min faster than before the improvement The average value of the selected Bra model line production efficiency increased by 6.17%
Cuellar-Valer et al. (2020)	Peru	Simulation	VSM, SMED, TQM, JIT	Cutting and sewing	The VSM and SMED diagnostic tools were first used to standardise the analysed process (cut and sew) The joint implementation of the lean tools improved significantly the service level, quality, and non-compliant products
M. M. Prasad et al. (2020)	India	Case study	VSM, 5S, Kanban, Kaizen, Poka-yoke, Visual controls	Fabric manufacturing	Reduced warp and weft breaks Improved organisation of working environment Motivated workers Improved efficiency of weaving machines
Barzola-Cisneros et al. (2021)	Peru	Case study	SIPOC, VSM, 5S	SME: T-shirt manufacturing	Improved productivity by 5% in the first two months 35% improvement in the level of compliance: timely compliance Satisfaction of final customer increased by 5%
Hartini et al. (2021)	Indonesia	Case study	Sustainable – VSM, 5S	Natural dyeing batik production	Reduced setup time and transportation time Improved working environment Reduced material consumption and increased recovered waste Reduction in production costs
Akter et al. (2022)	Bangladesh	Multiple-case study	VSM, material stream mapping	Textile and apparel manufacturing	Identifies, categorises, stream maps and determines the value addition of materials waste Describes the potentials for empirical research extensions and materials waste management interventions in the textile and apparel industry
Hussain and Figueiredo (2023)	Pakistan	Case study	VSM	Preparatory stage of fabric manufacturing	Improvements in terms of available capacity utilisation, balancing work flow in the preparatory stages, visibility of the process by measuring its capability and flexibility for the planning function
Santos et al. (2023)	–	Case study	VSM, Gemba, 5S, Kaizen, Kanban, Poka-Yoke	Upholstery industry of vehicle seats	Process waste minimised by about 47% Process lead time decreased by about 26% Production increased by 33%

Figure 1. Research framework.

Table 4. Groups of waste assessment questions by type of waste.

S. No	Type of question	Number of questions (Ni)
1	From overproduction	3
2	From inventory	6
3	From defects	8
4	From motion	11
5	From transportation	4
6	From process	7
7	From waiting	8
8	To defects	4
9	To motion	9
10	To transportation	3
11	To waiting	5
Total number of questions		68

Table 5. An algorithm for computing the ranks of the seven wastes (Rawabdeh, 2005).

Steps	Description	Formula
1	Count the number of questions with ‘From’ and ‘To’ notes for each type of waste	Ni (see Table 2)
2	Obtain the weights from the waste relationship matrix, and expand it to the 68 questions following their notes (‘From’ or ‘To’)	See Table 4
3	Divide the values in the table created in step 2 by Ni	$W_{m,k}$
4	Sum the values obtained from step 2 for each waste	$S_m={\sum }_{k=1}^{68}\frac{W_{m,k}}{N_i},$ m stands for each lean waste and k stands for each question
5	Count the frequency of the cells with non-zero weights for each type of waste	Fm
6	Multiply each value obtained in step 3 by the corresponding answers to each question Xk
7	Sum the values obtained in step 6	$s_m={\sum }_{k=1}^{68}{X}_k\frac{W_{m,k}}{N_i}$
8	Count the frequency of the cells in step 6 with non-zero weights for each type of waste	fm
9	Compute the initial indicator Ym for each type of waste	$Y_m=\left(\frac{s_m}{S_m}\right)\left(\frac{f_m}{F_m}\right)$
10	Calculate Pm by multiplying the percentages for the ‘From’ and ‘To’ for each waste type obtained from the waste relationship matrix
11	Calculate the final waste factor Ymfinal	$Y_{\mathit{\text{mfinal}}}=Y_m{P}_m$
12	Rank the seven wastes based on Ymfinal

Table 6. Waste relationship matrix.

To From	O	M	I	T	W	P	D	Score	%
O	A (10)	U (2)	A (10)	I (6)	O (4)	X (0)	I (6)	38	13.7
M	X (0)	A (10)	E (8)	U (4)	A (10)	I (6)	E (8)	46	16.6
I	I (6)	I (6)	A (10)	X (0)	O (4)	X (0)	I (6)	32	11.5
T	E (8)	E (8)	I (6)	A (10)	O (4)	X (0)	E (8)	44	15.9
W	E (8)	X (0)	A (10)	X (0)	A (10)	E (8)	X (0)	36	12.9
P	I (6)	E (8)	E (8)	X (0)	U (4)	A (10)	I (6)	42	15.1
D	U (2)	A (10)	E (8)	I (6)	U (2)	U (2)	A (10)	40	14.4
Score	40	44	60	26	38	26	44	278
%	14.4	15.8	21.6	9.4	13.4	9.4	15.8		100

Table 7. Sample analysis of the responses to the waste assessment questionnaire.

Question (Q.) type	Ni	k	AR	Wo,k	Wm,k	Wi,k	Wt,k	Ww,k	Wp,k	Wd.k
Man (7 Q.s)
To motion	9	1	0.366	0.08	0.41	0.25	0.41	0	0.33	0.41
From motion	11	2	0.167	0	0.15	0.15	0	0.15	0.12	0.12
From defects	9	3	0.567*	0	0.71	0.57	0.71	0.14	0	0.71
From motion	11	4	0.133	0	0.12	0.12	0	0.12	0.09	0.09
Material (24 Q.s)		⋮
To waiting	5	8	0.67	0.53	1.34	0	0.53	1.34	1.07	0.27
From waiting	8	9	0.57	0.57	0	0.57	0	0.71	0	0.14
From transportation	3	10	0.22	0.44	0.55	0.44	0.55	0.22	0	0.44
From inventory	6	11	0.85	1.42	0.85	1.42	0	0	0	1.13
From inventory	6	12	0.92	1.54	0.92	1.54	0	0	0	1.22
From defects	9	13	0.32	0	0.41	0.32	0.41	0.08	0	0.41
From inventory	6	14	0.9	1.5	0.9	1.5	0	0	0	1.19
From waiting	8	15	0.63	0.63	0	0.63	0	0.79	0	0.16
To defects	2	16	0.8	1.6	1.6	1.6	1.6	0.4	1.2	2
From defects	9	17	0.56	0	0.7	0.56	0.7	0.14	0	0.7
Machine (12 Q.s)		⋮
From process	7	32	0.3	0.17	0.34	0.43	0	0.34	0.43	0.26
To waiting	5	33	0.67	0.54	1.34	0	0.54	1.34	1.07	0.27
From process	7	34	0.2	0.11	0.23	0.29	0	0.23	0.29	0.17
From transportation	3	35	0.23	0.47	0.59	0.47	0.59	0.23	0	0.47
Method (25 Q.s)		⋮
To transportation	3	44	0.62	1.24	0	0	2.06	0	0	2.06
From motion	11	45	0.2	0	0.18	0.18	0	0.18	0.14	0.14
From waiting	8	46	0.7	0.7	0	0.7	0	0.875	0	0.175
		⋮
From motion	11	65	0.133	0	0.12	0.12	0	0.12	0	0.09
Form overproduction	5	66	0.52	1.7	0.35	1.39	1.04	0.69	0	1.39
From process	7	67	0.23	0.13	0.26	0.33	0	0.26	0.33	0.19
From defects	8	68	0.56	0	0.7	0.56	0.7	0.14	0	0.7
Score (sm)				37.5	36.2	38.6	31.5	23.2	17.9	43.7
Frequency (fm)				50	57	60	37	50	33	68

Note: W_o,k: weight of overproduction waste according to k^th question (k = 1, 2, 3, … 68); AR: the average of the 30 responses for each question, k = 1, 2, 3, …, 68, *highlighted values show the questions (wastes) with higher scores (near to 1 which is a complete agreement of its existence).

Table 8. Summary of lean waste assessment and the rank of the seven lean wastes.

	O	M	I	T	W	P	D
Sm	60.9	74	75.8	58.9	49.6	38	82
Fm	50	57	60	37	50	33	68
sm	37.5	36.2	38.6	31.5	23.2	17.9	43.7
fm	50	57	60	37	50	33	68
Ym	0.615	0.489	0.509	0.53	0.467	0.47	0.53
Pm	197.28	262.28	248.4	149.4	172.86	141.94	227.52
Ym final	121.32	128.25	126.43	79.18	80.69	66.7	120.58
Final result (%)	16.78	17.7	17.5	10.95	11.2	9.2	16.67
Rank	3	1	2	6	5	7	4

Figure 2. Current state value stream map of a shirt manufacturing process.

Figure 3. Future state value stream map of a shirt manufacturing process.

Figure 4. WIP inventory of shirt manufacturing processes.

Figure 5. Value-added time and production lead time of shirt manufacturing.

Figure 6. SMV of shirt manufacturing.

Table

No	Question	Choices	Weight
1	Does n produce m?	Always Sometimes Rarely	4 2 0
2	What is the type of relationship between n and m?	As n increases, m increases As n increases, m reaches a constant level Random (depends on conditions)	2 1 0
3	The effect of m due to n:	Appears directly and clearly Needs time to appear Not often appears	4 2 0
4	Eliminating the effect of n on m is achieved by:	Engineering methods Simple and direct methods Instructional solution	2 1 0
5	The effect of m due to n, mainly influences:	Quality of products Productivity of resources Lead time Quality and productivity Productivity and lead time Quality and lead time Quality, productivity, and lead time	1 1 1 2 2 2 4
6	To which degree does the effect of n on m increase manufacturing lead time?	High degree Medium degree Low degree	4 2 0

Note: n stands for any type of waste which affects the other type of waste m.

Appendix B. Waste assessment questionnaire customised from Rawabdeh (2005) (Y = yes = 1, M = medium = 0.5, and N = no = 0)

SN	Category	Type	List of questions	Y	M	N
1	Man	To motion	Management transfer operators to various jobs and machines so that operations can be performed by all individuals
2		From motion	Supervisors frequently check operators but they don’t empower them
3		From defects	Hourly workers are not adequately supervised during overtime
4		From motion	Positive steps are not taken to raise hourly workers’ morale and work interest
5		From motion	Training for new employees is usually provided on the job
6		From defects	Workers don’t instil a sense of responsibility towards their jobs
7		From process	Workers’ safety protection tools are not used in the work area
8	Material	To waiting	Supplier’s lead time is not usually made available to production schedulers
9		From waiting	Schedules are not usually checked for material availability before being released to production
10		From transportation	Components are not received in unitised loads/appropriate bundle sizes
11		From inventory	Production planning doesn’t give sufficient advanced notice of required items to warehouse personnel
12		From inventory	Warehouse personnel is not notified in advance about planned inventory changes
13		From defects	There is an excessive accumulation of materials awaiting to be repaired, reworked, or returned to suppliers
14		From inventory	Materials appear to be standing around piled up unnecessarily on the receiving platforms of different sections
15		From waiting	Production workers usually stand around waiting for materials to arrive
16		To defects	Materials are moved more often than necessary
17		From defects	Small components are frequently damaged, mixed, or lost in transportation activities
18		From transportation	Items in temporary storage areas are mixed with the materials being used or the finished products which need to be transferred to the next process
19		To motion	Loading and unloading of raw materials or components are not handled in an appropriate manner
20		From waiting	Adequate containers are not used before the packaging process to simplify the counting process and make the transfer of goods easier
21		From motion	Similar goods or raw materials are not stored in one area to facilitate and reduce the time required for searching
22		From transportation	There are repeated transfers of goods or raw materials with small containers
23		From defects	Incoming materials are not properly checked to determine the suitability of their standards for the quality and quantity of goods
24		From motion	Material or goods are not labelled for the convenience of identification
25		From inventory	Workers keep goods that are in process (WIP) in the production process areas
26		From inventory	Standard orders/ materials are stored in warehouse supplies, although not needed immediately
27		To waiting	There is free time for items that are still inside process (WIP) before further processing
28		From defects	There is a rework process for size/weight/shape/product color mismatch
29		From waiting	Raw materials don’t usually arrive on time when needed
30		From overproduction	There is an accumulation of raw materials in the warehouse when there are no customers scheduled
31		To motion	Raw materials and equipment are not properly stored
32	Machine	From process	Tests on the efficiency of the machines, compared to the manufacturer’s specifications, are not periodically conducted
33		To waiting	The workload for each machine is not predictable in sufficient detail
34		From process	Once a machine is installed, there is no sufficient follow-up to see if it performs according to specifications
35		From transportation	The capacity of the material handling equipment /transportation can is not enough to carry the heaviest items/bundles
36		To motion	When using moving tools for goods or raw materials, the amount of material being carried is not full
37		From overproduction	There is a management policy to produce more than necessary to maximise capacity and machine usage
38		From waiting	The machines stop frequently due to mechanical disturbances
39		From waiting	The necessary tools are not always available and sufficient for the production process of each product
40		To defects	The material handling equipment is at risk of damaging materials or products
41		From waiting	Long setup times may cause delays to the operating flow
42		To motion	There are tools that are damaged or unused in the work area
43		From process	The considerations for reducing machine setup time by customising scheduling and design are rarely practiced
44	Method	To transportation	The storage area is insufficient to avoid the blockage of warehouse aisles
45		From motion	There is no labelling for materials to make it easier to store, refer and retrieve raw materials or products
46		From waiting	The storage space is not used effectively for storage with the aid of shelves and trolleys
47		To motion	There is no division of warehouse area, activity areas for orders most frequent, and reserve areas for other orders
48		To waiting	The production time is not adjusted to the number of needs and customer orders
49		To defects	The production schedule is not well-communicated to all sections, and its contents are not widely understood
50		From motion	The standard operating procedure to use machines in the production process is rarely adhered
51		From defect	There is no quality control system for each department/section to guarantee quality
52		From motion	There is no preset standard time for each operation
53		To waiting	When there is a delay, it is not well-communicated to all sections
54		From process	There is no schedule arrangement to avoid repeated machine settings/arrangements to reproduce similar products
55		From process	The possibility to combine steps so that processing becomes simpler is rarely practiced
56		To defects	There is no procedure for process inspection or inspection against returned products
57		From inventory	The inventory records are not used to make purchases of raw materials and production schedules
58		To transportation	The aisles are not well cleaned and tidied up
59		To motion	The storage areas are not marked and labelled in certain sections
60		To transportation	The alley area is not sufficient for transportation movement with freedom
61		To motion	There is improper storage of materials stored in the warehouse area
62		To motion	There is no regular schedule for cleaning the production area
63		From motion	The production doesn’t flow in one direction
64		From motion	There is no team that handles designs, sample development, and standardisation of processing time
65		From motion	Work standards don’t have clear and specific goals
66		From overproduction	Work imbalances cannot be forecasted in time to adjust them
67		From process	The working procedures are not capable of screening or eliminating redundant or unnecessary works
68		From defects	The result of quality control, product testing, and evaluation are not passed on to engineering for improvement

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