R EDUCING FABRIC CONSUMPTION

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EDUCING FABRIC

CONSUMPTION

–

BY IMPROVING MARKER EFFICIENCY

Thesis number 2020.7.06 Thesis for Two year Master, 30 ECTS

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Acknowledgements

Our yesterday, today and tomorrow… Every moment of our life is very precious. At the same time, our resources are invaluable and not unlimited. Sustainable use of resources is required for a better quality of life. In the textile world, this issue is one of the most crucial. In this study, we tried to contribute to a more sustainable world.

First and foremost, we are deeply thankful to our supervisor, Niina Hernández, who has been very supportive and helpful throughout the thesis writing. We are very grateful to her for all the help she has kindly given to us. Without her extensive knowledge and unlimited support, this thesis would not have been possible. Secondly, we would like to thank Vijay Kumar, who has given us valuable feedback and new ideas as well as our opponents who took some of their precious time to contribute with their knowledge on our topic. We would also like to thank all participants for their help in data collection and conducting the interviews.

Lastly, we would like to give our special thanks to our families for their constant support and motivating words during our studying process.

Sincerely,

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Title: Reducing fabric consumption – by improving marker efficiency Publication year: 2020

Authors: WVL Kumara and Serkan Kizilirmak Supervisor: Niina Hernández

Abstract Background

Resource degradation is a significant problem in the world, which is directly related to the textile and fashion industry. Efficient use of the material has been identified as an essential aspect to be addressed seriously. It is a critical topic that has attracted the attention of people and companies in recent years and has become a fundamental issue of sustainability. This research study was based on UN sustainable development goals number 12 and 8, which focuses on resource efficiency. The research is designed in considering fabric consumption, which has a significant impact on the textile and clothing industry to contribute to a brighter future and a more sustainable life.

Purpose

The purpose of this study is to reduce the fabric consumption through improving marker efficiency. The research focuses on investigating the behaviour of marker efficiency concerning usable fabric widths, markers with different sizes and marker with style combinations to reduce fabric consumption. The improvements of the existing markers lead to reduce fabric wastage during the cutting process while improving resource efficiency in consumption and production.

Methodology

In this study, the explanatory sequential design of mixed research method is employed with carrying out experiments to collect and analyze quantitative data, explained and elaborated with qualitative findings through expert interviews to get insights into the quantitative findings in a deductive approach.

Results and findings

The marker efficiency significantly varies according to the combination of sizes and style and usable fabric width. The improvements of the marker efficiency, reduce the fabric consumption per garment and increase resource efficiency while preventing waste generation. A saving of 1% of a material which consumed millions of tons per year, significantly affect on reducing resource depletion and environmental pollution.

Research limitations and implications

This study is limited to five usable fabric widths, four size marker combinations and two style combinations. Moreover, it is focused on material efficiency, and cost efficiency is not considered. There are possibilities for clothing manufactures’ to improve resource efficiency by improving marker efficiency while planning the demand, considering size and multi-style markers. They can concern usable fabric widths, which provide higher marker efficiencies during material purchasing.

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List of Tables and Figures

Table 1: Summary of 4415 experiments conducted ………...…..….14

Table 2: Summary of experiments trouser style 3a between-subjects factors ...……...………16

Table 3: Descriptive Statistics of trouser style 3a ...………..………17

Table 4: ANOVA Tests of Between-Subjects Effects-trouser style 3a ………....………19

Table 5: Post Hoc Test Multiple Comparisons for usable fabric widths ……….………...20

Table 6: Post Hoc Test Multiple Comparisons for marker combinations.…………....……….21

Table 7: Summary of data, short style 3b between-subjects factors …...…………....…….…..22

Table 8: Descriptive Statistics of short style 3b ……….………23

Table 9: ANOVA Tests of Between-Subjects Effects………..…….25

Table 10: Post Hoc Test Multiple Comparisons for usable fabric widths……….26

Table 11: Post Hoc Test Multiple Comparisons for marker combinations………27

Table 12: Summary of experiments style combination between-subjects factors……….28

Table 13: Descriptive Statistics of style combinations……….…….29

Table 14: ANOVA Tests of Between-Subjects Effects………...………...31

Table 15: Post Hoc Test Multiple Comparisons for usable fabric widths……….……….31

Table 16: Summary of data, dungaree style 3c between-subjects factors ……….……….33

Table 17: Descriptive Statistics of dungaree style 3c….………...……….34

Table 18: ANOVA Tests of Between-Subjects Effects……….……….36

Table 19: Post Hoc Test Multiple Comparisons for usable fabric widths…………..………….36

Table 21: Summary of data, jacket style 3d between-subjects factors ….……….39

Table 22: Descriptive Statistics of jacket style 3d ……….40

Table 23: ANOVA Tests of Between-Subjects Effects……….42

Table 25: Summary of experiments style combination between-subjects factors……….43

Table 26: Descriptive Statistics of style combinations………...….……..44

Table 27: ANOVA Tests of Between-Subjects Effects……….….46

Table 28: Post Hoc Test Multiple Comparisons for usable fabric widths……….46

Table 29: Results of the qualitative study for the management aspects………49

Figure 1: A marker placement on fabric……….6

Figure 2a: Manual marker making……….….6

Figure 2b: Marker making by using CAD System ……….6

Figure 3a: Company A trouser style ……….10

Figure 3b: Company A short style ……….10

Figure 3c: Company B dungaree style……….….10

Figure 3d: Company B jacket style …….……….….10

Figure 4: Automatic processing of marker nesting………....11

Figure 5: Simplified experiment design……….13

Figure 6: Behaviour of marker efficiency means relation to usable fabric width style 3a..…….18

Figure 7: Behaviour of marker efficiency means relation to marker combinations style 3a…...19

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Figure 8b: Behaviour of marker efficiency means relation to usable fabric width

without marker combination 1………..………...………...….…24 Figure 9: Behaviour of marker efficiency means relation to marker combinations 4.…….….25 Figure 10: Behaviour of marker efficiency means relation to usable fabric width

for style combinations………..……….30 Figure 11: Behaviour of marker efficiency means relation to style combinations…………....30 Figure 12: Behaviour of marker efficiency means relating to the usable fabric

width of 3c……...………...………..35 Figure 13: Behaviour of marker efficiency means relating to the marker

combinations of 3c...………..………...35 Figure 14: Behaviour of marker efficiency means relating to the usable fabric

width of 3d ...………...………41 Figure 15: Behaviour of marker efficiency means relation to marker combinations of 3d ………….………..41 Figure 16: Behaviour of marker efficiency means relation to usable fabric width

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1 Introduction

Under this section, the importance of studying about improving fabric utilization and how improving fabric utilization effects on the sustainability aspects will be discussed. Furthermore, in this section, why it has become a problem, and the impact of fabric utilization on resource consumption are discussed under the heading background. Moreover, this section explains about the identification of the problem and the scope of the study. Apart from that, it tells the purpose of the research stating limitations as well.

1.1 Background

Resources becoming more and more scares with the increasing population in the world, while increasing life expectancy, quality of life and wealth, resulting in more resources required to fulfil the needs and wants of the increasing population (Bocken, Miller, Weissbrod, Holgado & Evans 2017). Apart from that the "Earths Overshooting day", the day that ecological resource demand, surpassed the regeneration capacity of the Earth, is declining significantly since the 1970s, from 1st_{of January to 29}th_{July in 2019 (Global Footprint Network 2019). Resource} depletion has become an essential topic of sustainability. Sustainable development ensure that resources will be available for the next generations while consuming those efficiently and effectively. UN Sustainable Development Goals (SDGs) for 2030, goal number 12 is about ensuring sustainable consumption and production patterns. Under this goal-achieving sustainable management and efficient use of natural resources, substantially reduce waste generation and strengthening scientific and technological capacity for shifting towards more sustainable production and consumption patterns are some of the key targets. Moreover, under goal number 8, progressively improving resource efficiency in consumption and production is set as a key target as well.

According to the Lenzing AG report (Kerkhof 2018), global apparel fibre consumption was about 106 million tons in 2018. From that, 70 – 75% of the fabric produce goes into the apparel sector. Apparel industry utilizes a vast number of natural resources to produce 1 kg of fabric, approximately natural textile needs 350- 1500 grams of chemicals, 700 litres of water for finishing (Muthu 2014) processes only. The resource depletion caused by fabrics in the apparel industry can be reduced by efficient use of material, contributing to a more sustainable production environment. Furthermore, fabrics are the primary raw material in this sector, approximately accounting for 60% of the total cost of a garment (Kayar, Dal & Mistik 2015) of clothing. The cutting room plays an essential role in cutting the fabrics into required shapes to form a garment (Cooklin 1991). Minimizing the fabric consumption per garment, while reducing the wastages during the cutting operation, cut the fabric requirement, improve both economic and environmental sustainability as a whole.

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Furthermore, garments are producing in many colours and many sizes as per the demand. To achieve the most economical, accurate and adequate volume for the sewing room, planning of cutting room operations are crucial. To accomplish economic cut order quantities, marker planning, lay planning, and cut order planning processes are essential. For the cut order plan, cutting table dimensions and cutting equipment are constraints. For lay planning and marker planning additionally fabric properties and characteristics such as fabric thickness, elasticity, symmetry or asymmetry of fabric, strips and check designs are constraints.

Moreover, optimizing fabric usage or minimizing the consumption per garment, in the short term as well as long term effects both the economy and environment. Eventually, it reduces the cost per garment while lowering the total fabric requirement for the specific style. Apart from that efficient use of materials, reduce the impact on the environment reducing the demand for resource depletion.

Fabric utilization optimization or fabric consumption reduction can be made by altering different factors, which affect it. Cutting marker utilization or marker utilization directly affect the fabric consumption, besides, ply length, marker length, cutting losses, and fabric length in the cutting lay affects fabric consumption. Moreover, marker utilization is affected by many factors such as type and shape of the style, garment size, fabric type, fabric width and marker type. (Geršak 2013b).

Furthermore, there are four types of markers, namely whole marker, half marker, single-size and multi-size markers. The whole garment is a type of marker planning used only for open width fabrics that include all garment pieces like left and right sides. However, a half marker is a type of marker plan suitable for folded, tubular-shaped or face-to-face spread fabrics, and only half of the garment pieces (e.g. right side) are used. The single-size marker contains all pieces of the garment for a single size (Enam 2020b). On the other hand, multi-size marker, which includes different sizes of the same style, is an option to improve the marker utilisation while reducing the fabric consumption (Fister, Mernik & Filipič 2008). Therefore, improving marker utilisation, improve fabric utilization, in other terms, reduce fabric consumption. Improving utilisation or the efficiency of a marker improve fabric utilization or improve the efficient use of material or reduce consumption. According to Khan and Islam (2015), marker efficiency is affected by fabric width and the combination of different sizes in a marker.

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On the other hand, previous studies were mainly focused on the economic aspects of marker efficiency improvements. (Dumishllari & Guxho 2015; Kayar, Dal & Mistik 2015; Manik & Jahan 2016; Pamuk & Yildiz 2016). This study focuses more on improving resource efficiency and adopting new production patterns to improve the efficient use of natural resources in a more sustainable manner. Furthermore, combining two styles of the same fabric, which the industry is not practising at present is such a new pattern. However, the purpose of this study neither related to achieving zero waste in the garment industry nor optimizing cost. This study was formed with two willing companies. Empirical data were obtained through experiments conducted using information such as existing product patterns, order history and material consumption.

1.2 Problem Identification

Resource depletion has been identified as a significant issue within the textile and fashion industry. Resources such as water and energy consumption in the supply chain of the mentioned industry have been a focal point in the sustainability discussions. Furthermore, being the second largest polluting industry, it contributes immensely to environmental pollution, resource depletion, energy wastage and climate changes (Bocken et al. 2017). Moreover, resource efficiency is becoming an essential aspect of future competitiveness and resilience in the textile and fashion industry (Preston 2012).

Under UN sustainable development goals, responsible consumption and production have been addressed as goal number 12. It includes targets related to this study such as;

12.2 by 2020, to achieve sustainable management and efficient use of natural resources. 12.4, by 2030 to reach chemical control and all waste, reducing their release significantly to air, soil and water.

12.5, by 2030 to achieve a reduction of waste generation substantially by preventing, reduction recycling and reuse.

Furthermore, goal number 8 decent work and economic growth, include targets related to this study such as;

8.2 achieve an increased level of productivity from technological upgrading, innovation, diversification.

8.4, progressively improve global resource efficiency in consumption, while insisting on decoupling environmental degradation with economic growth, accordance with goal number 12.

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4 Problem statement

Resource degradation is a significant problem in the world, which is directly related to the textile and fashion industry. Efficient use of the material has been identified as an essential aspect to be addressed seriously.

1.3 Purpose

The purpose of this study is to reduce the fabric consumption through improving marker efficiency. The research focuses on investigating the variation of marker efficiency concerning usable fabric widths, markers with different garment sizes and marker with style combinations to reduce fabric consumption/improve fabric utilisation.

● Research question: How marker efficiency varies along with the fabric width, combination of different garment sizes and combination of styles?

1.3.1 Limitations of the study

The volunteered companies provided the information required for the experiments such as graded pattern files, order history and material consumption. Apart from that cutting constraint, such as cutting table dimension, cutting equipment and lay height and marker planning constraints such as grainline, orientation, space between patterns and marker lengths were based on the volunteered company practices/standards.

Concerning the time and resources limitation, the study was developed for five basic fabric usable widths, three combinations of sizes of the same style and two combinations of styles. The selection of fabric width was based on previous studies, where companies used fabrics of 140cm to 163cm (Kayar, Dal & Mistik 2015). However, in this study 128, 138, 148, 158 and 168 cm usable fabric widths were selected to extend the previous findings. The choice of two, threes and four size markers, inclusive of absolutely different sizes of the same style was based on the time limitation and the conventional industry practice. The choice of two style combinations with one garment size from each style and two garment styles from each style was based on the time factor as well as to reduce the style-wise impact to marker efficiency. Moreover, due to the time limitation of the thesis work, only two styles from each company were selected. For the style combinations, those two selected styles were used.

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2 Literature study

This chapter provides insights to technical terms, techniques and theories related to marker efficiency. Moreover, it provides management aspects related to marker efficiency.

2.1 Background of the literature

Sustainability has been one of the most critical issues addressed by companies in the last thirty years, due to the limited world resources and awareness of people (Esfahbodi, Zhang & Watson 2016; Sarkis, Gonzalez-Torre & Adenso-Diaz 2010; Shumon, Halim, Rahman & Ahsan 2019). Textile and garment manufacturing industries are considered top polluting sectors among the most polluting industries in the world. The textile and garment industry stands out in global discussions on climate change, water scarcity, environmental pollution and human rights. (Boström & Micheletti 2016). Therefore, various researches are carried out in the textile and garment production sector to reduce material use. Research on marker planning-efficiency can be beneficial for sustainable production because most of the fabric losses in the garment production sector occur during marker making (Bilgiç & Baykal 2017). The process of marker making is the process of deciding to have a most efficient formation of pattern pieces for a style, size distribution and fabric (Glock 2005).

2.2 Marker planning

Marker planning is a critical process performed in the cutting room, and a marker is a process of arranging patterns precisely and accurately on a large white paper for a particular style of garments (Enam 2020a; Wong & Leung 2009). This arrangement can be made either lengthwise or area-wise. First, more significant pieces are placed, and then smaller pieces are placed in small spaces. Even if this rule is adhered to, sometimes the width of the fabric is not suitable, or the size diversity distribution negatively affects the fabric usage efficiency. (Kayar, Dal & Mistik 2015).

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Figure 1: A marker placement on fabric.

Another parameter may be the size distribution of assortments in the purchase order. According to Kayar, Dal and Mistik (2015), when the size distribution of assortments decreases, the possibility of placing the patterns on the marker plan decreases, resulting in higher fabric wastage. In contrast, when there are larger pattern pieces, the fabric waste rate increases due to inappropriate fabric width. Besides, while the dimensions of the cutting table determine the limits of marker planning, the height of the cutting machine limits the number of fabric plies.

2.3 Marker making

The primary purpose in the marker making is to create a picture in which the fabrics are used at maximum efficiency. Marker making can be done either manually or using CAD (Computer-Aided Design) system depending on the marker making method used in the production. Figure 2a shows the manual marker making process, and Figure 2b shows a CAD-based marking making process.

Figure 2a: Manual marker making (Enam 2020a), 2b: Marker making by using CAD System

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and grading. Correspondingly, it has been further developed for fashion and clothing design (Burke & Sinclair 2014). CAD is a useful and powerful tool in the hands of people in new product design, the emergence of innovations and research on the product (McCartney, Hinds, Seow & Gong 2000). CAD software not only improves the quality and efficiency of the design but also allows the user to improvise (Jhanji 2018).

With the widespread use of the CAD system in the clothing industry since the 90s, it has had a significant impact on marker planning. This system reduces the processing time in marker planning, making marker making almost as efficient as an operator. CAD software creates markers automatically or manually, with information that is entered by the user. At the same time, in addition to the markers obtained from the computer, user tries to do an efficient marker planning by entering detail information such as cutting table length, fabric width, cutting direction of fabric into the system. CAD systems are accurate, efficient and useful for the marker making process. Markers obtained as a result of the information entered by CAD users are printed on the plotter paper and processed in the cutting room (Kiron 2020).

On the other hand, in the manual marker making process, the marker is made with hand. It is a prolonged system. Once the marker is made, it is challenging to increase its efficiency. Manual marker making is a time-consuming process. It does not allow reuse or saving for future usage.

There are different kinds of marker making software (CAD) created for manual and automatic placement of pieces. Nowadays, CAD products such as Modaris (Lectra), AccuMark (Gerber), Optitex, CAD. Assist (Human Solution Assyst AVM), TUKAcad (Tucatech) are used for marker planning in the garment industry.

2.4 Marker efficiency

The optimization of markers is a vital preparation step, determining the size of the garments to be laid and cut together, and is in a very crucial position in the garment industry. (Fister, Mernik & Filipič 2008). The marker efficiency ratio is the number obtained by dividing the total area of all patterns on a marker and that marker area (Rahman, Rashid & Zulfikar Hasan 2017). The formula below is used to find marker efficiency manually. With the developing technology, marker efficiency is also generated automatically with marker making software.

Marker efficiency = Area of all patterns on the marker

Area of marker 𝑥 100%

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Companies carry out projects on behalf of sustainability, which is one of the most important issues of recent years. In the area of product sustainability, marker efficiency has a significant impact. Because the higher the marker efficiency, the more efficient the fabric is used, when marker efficiency is low, more material will be consumed, so there will be more chemical, dyes and materials utilized and generate more waste. Therefore, marker efficiency has a direct effect on the whole supply chain in the textile and garment industry (Khan & Islam 2015).

Apart from environmental and economic impacts, as mentioned earlier, marker making is part of the cut order planning, and hence the better marker efficiency to be achieved will affect the cut order planning. The cut order planning process usually starts with a retail order that includes the sizes, colours and quantity information of garments (UKEssays 2018). This process is in constant interaction with many dynamic areas that are continually changing, such as stock/material management, purchasing, sales and demand management (Zhezhova, Demboski & Panov 2013).

2.5 Management aspects relevant to marker efficiency

Material management, procurement and demand forecasting are management aspects in the garment industry, which are in constant interaction with each other in a continually changing and developing environment. Another example of the management aspect in the field of the garment industry can be stated as waste management. In an environment where resource consumption is excessive, waste management is a critical issue. According to Haque (2016), many different techniques, such as cut order plan or efficient marker making, can be used to minimize the fabric waste to have a more sustainable production environment. On the other hand, the cut order planning process, which is affected by marker efficiency, is a dynamic process that responds to the constantly changing situation of many factors such as raw materials, sales, inventory levels, availability of labour and equipment (Jacobs-Blecha, Ammons, Schutte & Smith 1998). For this reason, the response to customer demands may be affected by the change and development of the cut order plan. However, the results to be obtained in the cutting room may affect fabric consumption, thus causing changes in inventory management.

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3 Methodology

This chapter presents the methodology employed for the research, explaining the materials and equipment utilized, selection of independent variables, data collection, data analysis and data interpretation methods and techniques.

3.1 Research Approach

This research is classified as applied research, which focuses on expanding the knowledge and understanding a specific problem in a particular area of a specific industry (Saunders, Lewis & Thornhill 2009). This research focuses on understanding how marker efficiency, which derives the material efficiency of garment manufacturing, behave depending on the usable fabric width, combinations of garment sizes and mix of styles.

There are three main approaches to research studies, according to Bryman and Bell (2015), inductive, deductive and abductive. Inductive reasoning entails with the collection and analyzing data on a problem and trying to interpret how it is caused and possibilities of solving it. From data to theory/concept. Deductive reasoning entails with selecting an accepted theory and verifying or testing or carrying out experiments to find evidence in the research problem, from theory to data. Abductive reasoning involves within the search of explaining a problem or a puzzle, through analyzing and interpreting data by selecting the best explanation by going back and forth with theories and concept to find the best match. In this study, a deductive approach is applied, where experiments were carried out to find out how marker efficiency varies along with the selected factors, besides the theory, which explains that marker efficiency varies according to many factors and the factors considered in this study are three out of many of them.

Furthermore, when marker efficiency increases the fabric consumption decreases. Moreover, many factors are affecting the marker efficiency, as explained in the background section. However, in this study fabric usable width, the combination of different sizes in a marker and combination of two styles in a marker, are the factors selected for analyzing, how marker efficiency varies accordingly.

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3.2 Materials and equipment

Two volunteered companies in the workwear industry, Company A and B have provided existing product styles of their choice along with the order history and consumption details of those styles for this study. Company A provides styles namely a trouser and a short as their samples along with the graded pattern files in MDL format, order history and material consumption details. Company B has provided existing product styles of an overall, two trousers, a jacket and a dungaree along with graded pattern files in MDL format, order history and material consumption details. To increase the variability of the data 4 different styles, which two styles from Company A, the trouser (3a) and the short (3b) and two styles of company B, the dungaree (3c) and the jacket (3d) were selected for the research study shown in Figure 3. Apart from that, the styles of company A and B consist of the same main fabric. Moreover, company A, both styles utilize 60% cotton 40% polyester ripstop woven main fabric with 250 g/m2, and both styles of company B uses 65% cotton 35% polyester woven main fabric with 245 g/m2.

Figure 3a: Company A trouser style Figure 3b: Company A short style

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The sizes were selected based on the company order history representing 80% from order quantities. Then, markers were prepared to contain a single size, two sizes, three sizes, four sizes and combined styles. A total of 4415 markers were developed, processed and nested using an automatic marker making software. The possible combinations of sizes, both single style and two style combinations were generated using the online calculator tool available in PlanetCalc website (Timur 2014). In this study, the Modaris (Lectra) Marker Manager V6R2, Marker Making V6R2 and ModarisV8R1 application software were employed. Those are utilised to occupy information from graded pattern files in MDL file format, making markers with the required combination of sizes and styles and nesting those markers to find marker efficiencies.

First, the template for the marker is prepared, providing material constraints in orientation, direction and properties. The grainline was kept parallel to the length, and the main fabric does not contain designs or textured surface. Fabric width was set based on the experiment; the type of fabric was set as 1 representing the main fabric, and 1 mm space was kept between pattern pieces. The primary fabric constraint was selected as two way since the pattern pieces were allowed either direction parallel to the grain line. Rotation and flipping of individual pattern pieces within a garment were not allowed. Then different sizes based on factorial combinations were selected from the MDL file and prepare marker files of PLX format. After that, the developed markers were processed and nested using automatic processing, keeping the processing time of 3 minutes for each marker. The process of automatically arranging pattern pieces of selected sizes was handled by the marker manager software, as shown in Figure 4 below. Within the given process time, it provides the maximum marker efficiency by arranging pattern pieces closer to each other as much as possible while reducing the length of the marker as well.

Figure 4: Automatic processing of marker nesting

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3.3 Experiments and interview

The sub-chapter presents the details of the quantitative study, which experiments were carried out and the qualitative research derived from the experiment results. Information of both studies was included under each heading.

The purpose of this research study is to investigate how selected independent variables affect the selected dependent variable. An experiment should consist of a dependent variable, independent variable, unit of measurement, control unit and the relationship between cause and the effect (Hernández 2018). In this study, three independent variables were selected;

a) Fabric usable width of 128, 138, 148, 158 and 168 measured in centimetres

b) Combination of sizes - number of different garment sizes of the same style, two sizes, three sizes and four sizes in a single marker

c) Combination of styles- number of different garment sizes of two styles, two sizes and four sizes in a marker, including one each of two styles and two each of two styles respectively.

The dependent variable is the marker efficiency as a percentage. The research is design in a way that only one variable is tested at once keeping the other two in a control state. For both independent variables b & c mentioned above, possible combinations of selected sizes are calculated using factorial combination calculation formula; Where "n" represents the number of things to choose from and choose "r" out of them, without repetition and order.

C(n, r) = n! r! (n − r)!

In this study, we select garment sizes to prepare markers inclusive of two sizes, three sizes and four sizes. For an example, if selected four garment sizes, n=4, and if we need to find possible combinations of markers contain two sizes out of selected four sizes, where r=2, using this formula we can see how many combinations without repetition is possible. Answers 6, as per the calculation shown below.

= 4! 2! (4 − 2)!= 4x3x2x1 2x1x2x1 = 24 4 = 6

The possible combinations were generated, as explained in section 3.2. Where it gives those six combinations of two sizes, for example, if size 44, 46, 48, and 50 were selected, the factorial combinations of two sizes are 44 &46, 44&48, 44&50, 46&48, 46&50, and 48&50. The reason for choosing factorial combinations without repetition is that when the same size of a garment is in a marker, it does not represent as a multi-size marker. Moreover, the purpose of this study is to find out how the combination of different sizes in a marker, namely 2, 3 and 4 garment sizes change the marker efficiency.

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To be able to find out qualitative insights into the quantitative findings, a questionnaire was developed based on material management aspects. Further, this study being a collaboration with two companies, their expert/practical knowledge related to the findings were crucial for the validity and implications of this study. The logic behind providing questionnaires for companies and an interview from an academic expert is that in a company, several individuals are responsible for different areas. Then those individuals can provide answers specific to their expertise covering diverse management aspects. Further, the academic can provide more technical yet relevant aspects linking quantitative findings with management. The quantitative results were presented to all the experts before handing over the questionnaire and conducting the interview.

Figure 5: Simplified experiment design

Sampling method

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group of individuals is based on a specific purpose of answering the questions formulated from quantitative findings. Qualitative insights to the quantitative outcomes gathered from the experts representing company A and B. In addition to that, the expert from academia was selected considering his previous industrial exposure and being a lecturer in the programme. The main objective of the qualitative study is to improve the validity of quantitative findings and to deliver plausible explanations from the management perspective.

Data collection

Data have two forms, primary data and secondary data. Secondary data are the data available from either previous studies or any sort of collection of data. It can be data from Eurostat or UN statistical data, form previous academic and non-academic publications, different government and non-government sources and so on. Since there are few articles and publications directly related to the purpose of this study, primary data collections were done through experiments. Moreover, this study follows an explanatory sequential design, quantitative research followed by a qualitative insight. An open-ended questionnaire was formulated and presented to the relevant personnel of the companies.

Moreover, to provide a clear understanding of the questionnaire questions, initial findings were presented beforehand. A similar questionnaire with more academic insights was formulated for the interview with the lecturer of the Swedish School of Textiles. The purpose of these questionnaires is to bridge the gap between technical findings and the management aspects of those findings.

The data of the dependent variable, marker efficiency percentage of each factorial combination was recorded as the primary data from the experiments based on 4415 nested markers referred to Table 1 shown below. Answers for the two questionnaires presented to both companies were collected through email after relevant personnel of the company answer them. Data from the semi-structured interview with the academic were collected and transcribed for thematic analysis.

Table 1: Summary of 4415 experiments conducted

Styles and combinations Fabric usable widths Selected combinations of sizes

Selected sizes No. of

experiments

Trouser 3a

128, 138, 148, 158 & 168

1 size 2, 3, & 4 sizes in a marker 44, 46, 48, 50, 52, 54, 56 & 58 810 Short 3b 46, 48, 50, 52 & 54 150 Trouser 3a + Short 3b

1 garment size from each style & 2 garment sizes from each style of selected sizes

Above mentioned sizes combined

1600

Dungaree 3c Same as above 46, 48, 50, 52, 54, 56, 58 490

Jacket 3d 46, 50, 54, 58, 62 140

Dungaree 3c + Jacket 3d

Same as above Above mentioned sizes combined

1225

Data analysis

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and the marker efficiency % as the dependent variable were recorded as tables in excel. Then those data were statistically analyzed using IBM SPSS Statistics version 25 software. Information was gathered using descriptive statistics to understand the deviations of data for further analysis using ANOVA. Because there were two factors, namely fabric width and combination of sizes and styles, univariate analysis was conducted to analyze the variance of means.

The hypothesis which ANOVA tests;

H0: The marker efficiency do not vary with the usable fabric width, marker combination and there is no interaction effect

H1: The marker efficiency vary with usable fabric width, marker combination, and there is interaction effect.

Single size markers, two sizes, three sizes and four sizes markers were considered as category 1, 2, 3, and 4, respectively. Furthermore, in the style combination, the combination of one size of each style and two sizes from each style categorized as 5 and 6, respectively.

Furthermore, to understand which category of the independent variables show a significant variance of their means, posthoc tests were conducted. Fisher's Least Significant Difference (LSD) test, Hochberg's GT2 and Bonferroni post hoc tests were conducted. Because the sample sizes were different in size and style combinations, but fabric width-wise samples are equal. Moreover, the Bonferroni test was conducted since LSD does not control type 1 error inflation. Furthermore, estimated marginal means were plotted fabric width-wise and combination wise to understand the behaviour graphically. In this data, estimated marginal means are the actual means calculated from data. Apart from that, the level of significance for the analysis was 0.05.

The data collected from the interview and the questionnaire were analyzed using thematic analysis.The data received from the qualitative study, from experts in the two volunteered companies and the academia, analyzed based on material efficiency, order quantity, ability to store a variety of fabric widths, ability to store and manage higher volumes, effectiveness in replenishment orders and sustainability perspective.

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4 Results and analysis

This chapter presents the results of the quantitative study, along with the developed qualitative research and its findings. Furthermore, the analysis of the quantitative and qualitative studies are presented under this chapter. The chapter is separated into a sequence of quantitative research study followed by the qualitative insights.

4.1 Quantitative study

In this subchapter, the quantitative research study is explained, dividing is based on the two volunteered companies and the provided styles. The results and the analysis is presented based on the style.

4.1.1 Company A results and analysis

The experiments were carried out using Lectra Diamino MarkerManager V6R2 and MarkerMaking V6R2. The patterns were provided by company A as MDL file extensions. According to the research design, the selected fabric widths 128cm, 138 cm, 148 cm, 158 cm and 168 cm were experimented using combinations of two sizes, three sizes and four sizes in a marker. Apart from that, selected styles were combined, where the same garment size was used for the experiments. Furthermore, one garment size from each style and two garment size from each style were combined, resulting in two size marker and four size markers of style combinations.

For the trouser style 3a, a total of 810 experiments were carried out, and the marker efficiencies of all the combinations and single sizes were recorded as primary data. From the selected sizes of the short style 3b, a total of 150 trials were carried out. Furthermore, for style combination, two size markers with one garment size from each style result in 200 experiments, while four size markers with two garment sizes from each style, result in 1400 tests. For company A, a total of 2560 tests were conducted, and marker efficiencies were recorded as primary data for the analysis.

Results of the trouser style 3a

The results of the experiments are presented as descriptive statistics constructed based on Table 2 summary of experiments for trouser style 3a, considering usable fabric width and the marker combination as elements. Moreover, it also provides the number of data points represented by "N" between the subject and the aspect. Here marker efficiency % as the subjects and five usable fabric widths and four marker combinations were the elements.

Table 2: Summary of experiments trouser style 3a between-subjects factors

Elements N= number of data points

Usable fabric widths (cm) 128 162

138 162

148 162

158 162

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Marker combinations 1 40

2 140

3 280

4 350

According to Table 2 above, the number of data points or experiments conducted based on the usable fabric width factor provides an equal number of results. Though, marker combination wise number of trials varies due to the factorial combinations, the possibilities of choosing "r" out of "n" number of selected sizes. Here, number 1 represents single size markers having 40 data points from five usable fabric widths and eight chosen sizes. Number 2 represents markers with two different garment sizes of the same style, 140 experiments were conducted 28 combinations into five usable fabric widths. Moreover, number 3 and 4 represent three garment sizes, and four garment sizes of the same style nested in a marker with 56 combinations and 70 combinations into five fabric widths result in 280 and 350 data points respectively.

Table 3 below shows the descriptive statistics of the 810 experiments conducted for the style 3a. "N" represents the number of data points or trials under each independent variable element. Number 1, 2, 3, & 4 represent the number of garment sizes nested in a marker as explained above. Actual means of the dependent variable; marker efficiency %, along with the standard deviation of marker combinations were presented based on, usable fabric widths in the table below.

Table 3: Descriptive Statistics of trouser style 3a

Dependent Variable: Marker Efficiency %

Usable fabric widths (cm) Marker combinations Mean Std. Deviation N

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18 3 89.01 0.33 56 4 89.16 0.29 70 Total 88.88 0.62 162 Total 1 87.68 0.96 40 2 88.88 0.53 140 3 89.24 0.43 280 4 89.36 0.34 350 Total 89.15 0.59 810

According to Table 3, the standard deviations of each marker combination category for five fabric widths are below 1. It indicates individual garment size in a combination of different sizes, does not affect significantly to the marker efficiency. Even though the effect of particular garment size, in the selected marker combinations, is irrelevant to the research scope, the lower value of standard deviation positively justifies analysis of variance through categorizing maker combinations as single, two sizes, three sizes and four sizes. Furthermore, it enhances the construct validity of the study by ensuring the method of analysis matches the construct of intended findings.

According to the results, 138 cm usable width fabric provides the lowest deviation from the mean marker efficiency showing 0.35 from the total. Furthermore, when compared with other marker combinations, marker combination 4, a marker with four different sizes of the same style results in the lowest values of deviation and highest marker efficiency. Moreover, marker combination 1, single size markers obtained the highest difference from means for all the fabric widths.

The behaviour of means based on usable fabric widths and marker combinations are presented in the graphs below.

Figure 6: Behaviour of marker efficiency means relation to usable fabric width style 3a

According to Figure 6, for the trouser style 3a, the highest mean value of marker efficiencies for all the marker combinations are obtained from 138 cm usable fabric width. Furthermore, marker combination 4, indicates higher mean marker efficiency values, compared to the rest, except under 138 cm width. Moreover, single size markers mean efficiencies show the lowest figures for all the fabric widths.

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Figure 7: Behaviour of marker efficiency means relation to marker combinations style 3a

According to Figure 7, it is clear that marker combination 4, markers with four different sizes, provides higher marker efficiencies. Moreover, it also indicates 138 cm usable fabric offers better results for the trouser style.

Analysis of results; trouser style 3a

The results of the trouser style 3a, were analyses using analysis of variants (ANOVA) method using IBM SPSS. The hypotheses tested is that there is no significant variance of means between the two independent variables and their interaction. The following Table 4 shows the ANOVA test results for the trouser style 3a.

Table 4: ANOVA Tests of Between-Subjects Effects-trouser style 3a

Source Type III Sum of Squares df Mean Square F Sig. Corrected Model 195.353a ₁₉ _10.282 _94.312 _0.000

Intercept 3270311.833 1 3270311.833 29997867.888 0.000

Usable Fabric widths 69.948 4 17.487 160.405 0.000

Marker combinations 114.007 3 38.002 348.586 0.000

Usable fabric widths * Marker combinations

16.836 12 1.403 12.870 0.000

Error 86.124 790 0.109

Total 6438341.450 810

Corrected Total 281.477 809 a. R Squared = .694 (Adjusted R Squared = .687)

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0.05 of significance. The P-value is less than 0.05, rejecting the null hypothesis (H0) and accepting the alternative hypothesis (H1). Furthermore, usable fabric widths and marker combinations also show significance having P-value less than 0.05. Moreover, the analysis indicates that the usable fabric width and marker combination variations and interaction between usable fabric width and marker combinations significantly affect the marker efficiency %. Apart from that, the R2 value for the relationship between independent variables, its interaction and the dependent variable is 69.4%, a reasonably good relationship.

To identify which, elements of the factors causes the significant variation of means, Post Hoc test was conducted. The following Table 5: indicates the results of the three post Hoc tests conducted via SPSS for individual factor or independent variables.

Table 5: Post Hoc Test Multiple Comparisons for usable fabric widths

(I) Usable fabric widths Mean Difference (I-J) Std. Error Sig.

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21 168 .1376* _0.03669 _0.002 _0.0346 _0.2406 138 128 .5378* _0.03669 _0.000 _0.4348 _0.6408 148 .1225* _0.03669 _0.009 _0.0195 _0.2255 158 .6577* _0.03669 _0.000 _0.5547 _0.7607 168 .6754* _0.03669 _0.000 _0.5724 _0.7784 148 128 .4152* _0.03669 _0.000 _0.3123 _0.5182 138 -.1225* _0.03669 _0.009 _-0.2255 _-0.0195 158 .5352* _0.03669 _0.000 _0.4322 _0.6382 168 .5528* _0.03669 _0.000 _0.4499 _0.6558 158 128 -.1199* _0.03669 _0.011 _-0.2229 _-0.0170 138 -.6577* _0.03669 _0.000 _-0.7607 _-0.5547 148 -.5352* _0.03669 _0.000 _-0.6382 _-0.4322 168 0.0177 0.03669 1.000 -0.0853 0.1206 168 128 -.1376* _0.03669 _0.002 _-0.2406 _-0.0346 138 -.6754* _0.03669 _0.000 _-0.7784 _-0.5724 148 -.5528* _0.03669 _0.000 _-0.6558 _-0.4499 158 -0.0177 0.03669 1.000 -0.1206 0.0853

Based on observed means.

The error term is Mean Square(Error) = .109. *. The mean difference is significant at the .05 level.

According to the multiple post hoc test results shown above, for the independent variable; usable fabric width, mean difference of each element (I-J) was tested for significance. Furthermore, the difference between the means of marker efficiency for different usable fabric widths compared to identify whether there is a significant difference between them or not. Except for the mean difference between 158 cm and 168 cm usable fabric widths, all other mean differences are substantial. All three post hoc tests show P-values greater than 0.05 for the mean differences between 158 and 168 cm widths. From this analysis, we can argue that, for the style 3a, the marker efficiency varies significantly up to 158 cm usable width and from 158 cm to 168 cm, the behaviour is insignificant. The following Table 6 shows the results of the post hoc test for the marker combination factor.

Table 6: Post Hoc Test Multiple Comparisons for marker combinations

(I) Marker combinations

Mean Difference

(I-J) Std. Error Sig.

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22 Hochberg 1 2 -1.1951* _0.05920 _0.000 _-1.3512 _-1.0390 3 -1.5610* _0.05581 _0.000 _-1.7082 _-1.4138 4 -1.6747* _0.05511 _0.000 _-1.8201 _-1.5294 2 1 1.1951* _0.05920 _0.000 _1.0390 _1.3512 3 -.3659* _0.03418 _0.000 _-0.4560 _-0.2758 4 -.4797* _0.03302 _0.000 _-0.5668 _-0.3926 3 1 1.5610* _0.05581 _0.000 _1.4138 _1.7082 2 .3659* _0.03418 _0.000 _0.2758 _0.4560 4 -.1138* _0.02647 _0.000 _-0.1836 _-0.0440 4 1 1.6747* _0.05511 _0.000 _1.5294 _1.8201 2 .4797* _0.03302 _0.000 _0.3926 _0.5668 3 .1138* _0.02647 _0.000 _0.0440 _0.1836

According to the results shown in Table 6, the mean difference of elements for marker combinations is significant, having P-value less than 0.05. Furthermore, all three tests confirm that mean differences between single, two, three and four size markers indicate a considerable difference between their means. From this test, we can argue that marker efficiency of the style 3a varies significantly with the combination of different sizes of the same style in a marker.

Results of the short style 3b

The results were presented as descriptive statistics created based on Table 7 summary of experiments for short style 3b, concerning usable fabric width and the marker combination as elements. Moreover, it also provides the number of data points represented by "N" between the subject and the element. Here marker efficiency % as the subjects and five usable fabric widths and four marker combinations were the elements.

Table 7: Summary of data, short style 3b between-subjects factors

Elements N= number of data points

Usable fabric widths (cm) 128 30

138 30 148 30 158 30 168 30 Marker combinations 1 25 2 50 3 50 4 25

According to Table 7 above, the number of tests conducted based on the usable fabric width factor provides an equal number of results. Though, marker combination wise number of trials varies due to the factorial combinations, the possibilities of choosing "r" out of "n" number of selected sizes. Similar to the trouser style number 1, 2, 3 and 4 represent single, two, three and four size markers, inclusive of five garment sizes of the same style, into five usable fabric widths. Further, two and three size markers contain ten factorial combinations, and four size marker has five factorial combinations providing 150 total data points.

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Number 1, 2, 3, & 4 represent the number of garment sizes nested in a marker as explained above. Actual means of the dependent variable; marker efficiency %, along with the standard deviation of marker combinations were presented based on, usable fabric widths in the table below.

Table 8: Descriptive Statistics of short style 3b

Usable fabric widths Marker combinations Mean Std. Deviation N

128 1 82.5720 0.32538 5 2 86.5740 0.35806 10 3 87.1420 0.31080 10 4 87.1160 0.31342 5 Total 86.1867 1.69293 30 138 1 82.5720 0.32538 5 2 88.6020 0.46380 10 3 88.7660 0.36888 10 4 88.7300 0.15732 5 Total 87.6730 2.34849 30 148 1 76.9940 0.30394 5 2 87.1510 0.34343 10 3 87.6630 0.36148 10 4 87.3620 0.51616 5 Total 85.6640 3.96539 30 158 1 71.9920 0.28402 5 2 86.7730 0.59735 10 3 87.1600 0.35251 10 4 86.9840 0.21755 5 Total 84.4737 5.69435 30 168 1 67.7080 0.26612 5 2 86.7870 0.42335 10 3 86.9460 0.22112 10 4 87.0580 0.31148 5 Total 83.7053 7.28363 30 Total 1 76.3676 5.98402 25 2 87.1774 0.85807 50 3 87.5354 0.73632 50 4 87.4500 0.72966 25 Total 85.5405 4.82060 150 According to Table 8 above, the standard deviations of each marker combination category for all fabric widths are below 1. Similar to the trouser style 3a, short style 3b, correspondingly indicates individual garment size in a combination of different sizes, does not affect significantly to the marker efficiency. Even though the effect of particular garment size, in the selected marker combinations, is irrelevant to the research scop, the lower value of standard deviation positively justifies the selected analysis method. Furthermore, it enhances the construct validity of the study by ensuring the process of analysis matches the construct of intended findings.

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The behaviour of means based on usable fabric widths and marker combinations are presented in the graphs below.

Figure 8a: Behaviour of marker efficiency means relating to the usable fabric width of style 3b

Figure 8b: Behaviour of marker efficiency means relation to usable fabric width without marker combination 1

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one-piece waistband, even though the factor that shape of the pattern pieces affects the marker efficiency is irrelevant to this study, it is clear that it affects the short style 3b’s marker efficiency. To be able to focus more on the marker combination 2, 3 and 4, marker combination 1 is excluded for the graph to be more detailed and comparable.

Figure 9: Behaviour of marker efficiency means relation to marker combinations 3b

According to Figure 9, it is clear that marker combination three and four do not show a significant increase. Similarly to trouser style 3a, 138 cm usable fabric indicates better efficiencies for the short style 3b as well.

Analysis of results; short style 3b

The results of the short style 3b, were analyses using analysis of variants (ANOVA) method using IBM SPSS. The hypotheses tested is that there is no significant variance of means between the two independent variables and their interaction. The following Table 9 shows the ANOVA test results for the trouser style 3b.

Table 9: ANOVA Tests of Between-Subjects Effects

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Error 17.762 130 0.137

Total 1101039.911 150

Corrected Total 3462.485 149 a. R Squared = .995 (Adjusted R Squared = .994)

According to the ANOVA test, as shown above, the variance of means of the dependent variable, marker efficiency varies with the independent variables and their interaction. Usable fabric width and Marker combination interaction is significant with an F value of 385.765 under 0.05 of significance. The P-value is less than 0.05, rejecting the null hypothesis (H0) and accepting the alternative hypothesis (H1). Furthermore, usable fabric widths and marker combinations also show significance having P-value less than 0.05. Moreover, the analysis indicates that the usable fabric width and marker combination variations and its interaction significantly affect the marker efficiency %. Apart from that, the R2 value for the relationship between independent variables, its interaction and the dependent variable is 99.5%, indicating a significant relationship.

Furthermore, to identify which, elements of the factors causes the significant variation of means, Post Hoc test was conducted. The following Table 10 indicates the results of the three post Hoc tests conducted via SPSS for individual independent variables.

Table 10: Post Hoc Test Multiple Comparisons for usable fabric widths

(I) Usable fabric widths Mean Difference (I-J)

Std.

Error Sig.

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27 158 1.1903* _0.09544 _0.000 _0.9178 _1.4629 168 1.9587* _0.09544 _0.000 _1.6861 _2.2312 158 128 -1.7130* _0.09544 _0.000 _-1.9855 _-1.4405 138 -3.1993* _0.09544 _0.000 _-3.4719 _-2.9268 148 -1.1903* _0.09544 _0.000 _-1.4629 _-0.9178 168 .7683* _0.09544 _0.000 _0.4958 _1.0409 168 128 -2.4813* _0.09544 _0.000 _-2.7539 _-2.2088 138 -3.9677* _0.09544 _0.000 _-4.2402 _-3.6951 148 -1.9587* _0.09544 _0.000 _-2.2312 _-1.6861 158 -.7683* _0.09544 _0.000 _-1.0409 _-0.4958 Hochberg 128 138 -1.4863* _0.09544 _0.000 _-1.7579 _-1.2148 148 .5227* _0.09544 _0.000 _0.2511 _0.7942 158 1.7130* _0.09544 _0.000 _1.4414 _1.9846 168 2.4813* _0.09544 _0.000 _2.2098 _2.7529 138 128 1.4863* _0.09544 _0.000 _1.2148 _1.7579 148 2.0090* _0.09544 _0.000 _1.7374 _2.2806 158 3.1993* _0.09544 _0.000 _2.9278 _3.4709 168 3.9677* _0.09544 _0.000 _3.6961 _4.2392 148 128 -.5227* _0.09544 _0.000 _-0.7942 _-0.2511 138 -2.0090* _0.09544 _0.000 _-2.2806 _-1.7374 158 1.1903* _0.09544 _0.000 _0.9188 _1.4619 168 1.9587* _0.09544 _0.000 _1.6871 _2.2302 158 128 -1.7130* _0.09544 _0.000 _-1.9846 _-1.4414 138 -3.1993* _0.09544 _0.000 _-3.4709 _-2.9278 148 -1.1903* _0.09544 _0.000 _-1.4619 _-0.9188 168 .7683* _0.09544 _0.000 _0.4968 _1.0399 168 128 -2.4813* _0.09544 _0.000 _-2.7529 _-2.2098 138 -3.9677* _0.09544 _0.000 _-4.2392 _-3.6961 148 -1.9587* _0.09544 _0.000 _-2.2302 _-1.6871 158 -.7683* _0.09544 _0.000 _-1.0399 _-0.4968

According to the multiple post hoc test results shown above, for the independent variable; usable fabric width, mean difference of each element (I-J) was tested for significance. Furthermore, the difference between the means of marker efficiency for different usable fabric widths compared to identify whether there is a significant difference between them or not. The results indicate that all mean differences are substantial. All three post hoc tests show P-values of less than 0.05. From this analysis, we can argue that, for the style 3b, the marker efficiency varies significantly along with the usable fabric width. The following Table 11 shows the results of the post hoc test for the marker combination factor.

Table 11: Post Hoc Test Multiple Comparisons for marker combinations

(I) Marker combinations Mean Difference (I-J) Std. Error Sig.

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28 4 -11.0824* _0.10455 _0.000 _-11.3625 _-10.8023 2 1 10.8098* _0.09054 _0.000 _10.5672 _11.0524 3 -.3580* _0.07393 _0.000 _-0.5561 _-0.1599 4 -.2726* _0.09054 _0.019 _-0.5152 _-0.0300 3 1 11.1678* _0.09054 _0.000 _10.9252 _11.4104 2 .3580* _0.07393 _0.000 _0.1599 _0.5561 4 0.0854 0.09054 1.000 -0.1572 0.3280 4 1 11.0824* _0.10455 _0.000 _10.8023 _11.3625 2 .2726* _0.09054 _0.019 _0.0300 _0.5152 3 -0.0854 0.09054 1.000 -0.3280 0.1572 Hochberg 1 2 -10.8098* _0.09054 _0.000 _-11.0515 _-10.5681 3 -11.1678* _0.09054 _0.000 _-11.4095 _-10.9261 4 -11.0824* _0.10455 _0.000 _-11.3615 _-10.8033 2 1 10.8098* _0.09054 _0.000 _10.5681 _11.0515 3 -.3580* _0.07393 _0.000 _-0.5554 _-0.1606 4 -.2726* _0.09054 _0.019 _-0.5143 _-0.0309 3 1 11.1678* _0.09054 _0.000 _10.9261 _11.4095 2 .3580* _0.07393 _0.000 _0.1606 _0.5554 4 0.0854 0.09054 0.920 -0.1563 0.3271 4 1 11.0824* _0.10455 _0.000 _10.8033 _11.3615 2 .2726* _0.09054 _0.019 _0.0309 _0.5143 3 -0.0854 0.09054 0.920 -0.3271 0.1563

According to the results shown in Table 11, the mean difference of elements for marker combinations three and four indicates insignificance having P-value higher than 0.05. Furthermore, all three tests confirm that mean differences between single, two, three and four size markers indicate a considerable difference between their means, except between the three size and four size combinations. From this test, we can argue that marker efficiency of the style 3b varies significantly with the combination of different sizes of the same style in a marker, except the variation between three and four sizes nested markers.

Results of style combination; trouser style 3a and short style 3b

The results of the style combination are presented in descriptive statistics based on Table 12 summary of experiments conducted for the style combinations of trouser and short styles, considering usable fabric width and the marker combination factors. Moreover, it also provides the number of data points represented by "N" between the subject and the element. Here marker efficiency % as the subjects and five usable fabric widths and four marker combinations were the elements.

Table 12: Summary of experiments style combination between-subjects factors

Elements N= number of data points Usable fabric widths 128 320

138 320 148 320 158 320 168 320 Style combinations 5 200 6 1400

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selected sizes. Furthermore, number 5 and 6 represent one garment size from each style and two garment sizes from each style, respectively. Moreover, from trouser eight selected sizes and short five selected sizes, generate a total of 200 tests for style combination 5. Further, for style combination 6, 1400 experiments were conducted.

Table 13 below shows the descriptive statistics of the 1600 experiments conducted for the style combinations. "N" represents the number of data points or trials under each independent variable element. Number 5, & 6 represent the number of garment sizes nested in a marker as explained above. Actual means of the dependent variable; marker efficiency %, along with the standard deviation of marker combinations were presented based on, usable fabric widths in the table below.

Table 13: Descriptive Statistics of style combinations

Usable fabric width Style combinations Mean Std. Deviation N

128 5 87.7665 0.36879 40 6 88.0805 0.35519 280 Total 88.0412 0.37119 320 138 5 87.6668 0.43755 40 6 88.1024 0.34534 280 Total 88.0480 0.38541 320 148 5 87.6283 0.41872 40 6 88.1145 0.36523 280 Total 88.0537 0.40502 320 158 5 87.4723 0.45619 40 6 88.0952 0.39097 280 Total 88.0173 0.44912 320 168 5 87.4303 0.47527 40 6 88.1315 0.37002 280 Total 88.0438 0.44869 320 Total 5 87.5928 0.44636 200 6 88.1048 0.36556 1400 Total 88.0408 0.41281 1600

According to Table 13 above, the standard deviations of each marker combination category for all fabric widths are below 1. Similar to the trouser style 3a and short style 3b, correspondingly style combinations also indicates, individual garment size in a combination of different sizes, does not affect significantly to the marker efficiency. Even though the effect of particular garment size, in the selected marker combinations, is irrelevant to the research scop, the lower value of standard deviation positively justifies the selected analysis method. Furthermore, it enhances the construct validity of the study by ensuring the process of analysis matches the construct of intended findings.