SWOT Framework Based on Fuzzy Logic, AHP, and Fuzzy TOPSIS for Sustainable Retail Second-hand Clothing in Liberia
Article Category: Research Article
Pubblicato online: 30 dic 2022
Pagine: 27 - 44
DOI: https://doi.org/10.2478/ftee-2022-0050
Parole chiave
© 2022 Peter Davis Sumo et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.
There has been a significant increase in output volume following technological breakthroughs and the Industrial Revolution, particularly for the textile industry. The rise of fast fashion (FF) since the early 2000s as a fast-paced manufacturing and consumption pattern for profit maximization is hastening the growth of the volume and value of discarded clothing. According to [1], in the previous two decades, worldwide textile consumption has climbed from 7 to 13 kg per person, totaling 100 million tonnes, of which two-thirds end up in landfills. The FF supply chain is characterized by erroneous projections, leading to excess production against demand and a shorter life cycle. Globally, 85% of two-week-old garments end up as unfashionable or worn-out items that must be discarded as waste, disposed of for recycling, or donated to charities [2, 3].
A recent survey by THREDUP [4] estimates that, in the US alone, 36 billion clothing items are annually trashed, of which 95% are reusable. According to the same survey, over 94,000 tons of waste of single-used garments were produced in 2019 alone. A similar study from the UK highlights that consumers across the country currently have an estimated $64.7 billion worth of unworn clothes in their closets [5].
With this colossal increase in textile waste, resource efficiency is one of the biggest challenges facing the fashion industry, which now calls for a swift implementation of new sustainable business and consumption models to extend garments life cycles [6]. This demand for sustainable consumption encourages consumers to reuse, recycle and resell while addressing textile waste as a renewable resource [7]. The resell campaign known as second-hand clothing (SHC) has immensely flourished with nearly 11% of annual growth, extensively reshaping the fashion industry [8].
SHC retailing is a growing market worldwide, with over 70% of the world's population using SHC. The figure is poised to increase yearly, with current global forecasts predicting a 185% increase over the next ten years, compared to FF, which will expand by just 20%. This growth proliferation is primarily due to price affordability in underdeveloped nations and consumers’ consciousness of sustainability in the developed world [9–10].
Most SHCs are processed and resold to developing markets [11], essential in getting clothing and associated commodities to consumers in poor African countries [12]. Over 80% of Africa's population is estimated to wear SHCs imported mainly from the US., Europe, India, Pakistan, and China [13]. These clothing items have ready markets across the continent since they are less expensive, easier to wear, more durable, and more trendy than those locally manufactured [14]. The African SHC trade has far-reaching consumers benefits due to the limited purchasing power of consumers and the shift in taste to western-style clothing from traditional African style clothing [15]. The trade now holds significant market shares in Ghana, Nigeria, Ivory Coast, Tanzania, Benin, Uganda, and Kenya [11]. Its demand is creating employment avenues such as trading, distribution, repairs, laundry services, and upcycling, which sustain hundreds of thousands of livelihoods on the continent [15–16].
Studies on Africa's SHC trade lack significant official datasets [17]. As such, the majority of the few pieces of works of literature [12, 14, 15], [18,19,20,21,22,23,24] draw analysis and conclusions using qualitative and secondary data sources.
To close some of these gaps, we empirically contribute to the literature by assessing the significance of SHC trade in Liberia. In part one of our study on the SHC trade in Africa, we extend this assessment by developing a SWOT framework to identify criteria and sub-criteria prioritizing SHC retailing in Liberia and Africa. We achieve this by conducting multi-criteria decision-making (MCDM) research among 100 retailers from the four largest markets in Liberia (Red-Light, Waterside, Duala, and Omega markets). SWOT has wide usage in the textile industry for evaluating and improving strategies. Since SWOT and its subsequent strategies do not determine the importance of factors nor provide implementation details, we, therefore, transform the framework into a hierarchical structure for further assessment of each factor. We use the Analytical Hierarchical Process (AHP) developed by Saaty [25] to compare multiple factors and sub-factors for their SWOT priorities. We apply fuzzy logic to resolve the resulting impression in human judgments arising from discrete decision-making during our survey. To consolidate the results derived from the two-hybrid approaches (AHP and Fuzzy AHP), we employ the Ensemble method, which finds the average of the two weights. Finally, we introduce the TOPSIS method for ranking attributes most important to SHC retailing, livelihoods, sustainability, and poverty alleviation from a developing country's perspective.
This paper is organized as follows: in section two, we provide a literature review of SHC in Africa, next section three details our methodology, study points, and methods data collection and analysis. Section four analyzes our findings and generates results, while section five presents the conclusion and implications.
The fast-paced manufacturing and consumption patterns are hastening the growth of the volume of discarded clothing [26, 27]. A significant number of fast-fashion products are often discarded just after two weeks of use, with an estimated 95% of these clothing items recyclable [4, 28]. Many studies have been conducted to devise sustainable clothing consumption models for minimizing environmental impacts. For example, [29,30,31], among many others, have identified the important consumer drivers of sustainable consumption patterns, which now calls for a swift implementation of new sustainable business models to extend garments life cycles [6]. This sustainable consumption encourages consumers to reuse, recycle and resell while addressing textile waste as a renewable resource [7]. Thus, the resell campaign known as second-hand clothing (SHC) has immensely flourished with nearly 11% of annual growth, extensively reshaping the fashion industry [8].
Africa is the biggest consumer of SHCs. Used clothes were shipped to Africa as donations during colonial times and were transformed to trade during the economic liberalization in the 1980s. The SHC trade is reshaping African textile consumption. For instance, SHC is a striving business and a crucial role player for Ethiopia's textile consumption and retail segment and key sustenance of livelihoods [32]. Findings from [33] share similar concerns—the research conducted across Angola, Malawi, and Mozambique on 3485 respondents highlight that participants instead used second-hand items to replace worn-out garments due to their low purchasing power. In particular, affordability and availability place Malawians as one of the principal consumers of SHC with the need for an organizational structure and interconnection between sales and distribution channels that will lead to the growth of small businesses in the country's SHC formal sector [12].
With the growing sense of sustainability, the circular economy model applied to clothing items provides new knowledge about how old clothes, widely available in markets around Africa, can be creatively rebranded into new clothes and accessories [22]. More people are turning to fashion re-consumption [34]. This sense of consciousness is advocated as replacing the more resource-intensive production systems. In addition, SHC rebranding has been seen as a new way of adding African styles and values. As Thompson & Peter [34] find among retailers in Port Harcourt, Nigeria, formal education on value addition and innovation techniques is an excellent step in improving used clothing with a sense of Africanness. Findings from James & Kent's [22] workshop position SHC as reviving the African textile industry since applying the circular economy model provides new knowledge for creatively extracting and rebranding used clothes into new ones allowing for African designs and innovation.
However, Frazer [35] and several East African leaders argue that there is a negative relationship between domestic textile production and SHC import. [35] draws his argument on a regression analysis of panel data of selected Sub-Sahara African (SSA) countries obtained from UNComtrade between 1981 and 2000. He highlights a 40% decline in output and a 50% decline in employment on the continent due to SHC import.
However, Brooks & Simons [17] disagree, claiming that studies on Africa's SHC trade lack official and frequently updated datasets. In addition, Africa's textile manufacturing capability is dwarfed by infrastructural limitations intensified by obsolete technology, low productivity, the high unemployment rate for textile graduates and the intense competition in its value chain, mainly from cheap imports from China and other Asian nations [36].
Many of the papers surveyed so far derived conclusions based on secondary data sources (see for instance [12, 15, 17, 23, 24, 35]). As such, linking Africa's textile industrial decline to the continent's importation of used clothes in the absence of official data sets on SHC's import (either legal or illegal) is entirely difficult [17].
The extant literature has identified issues relating to the growth of used clothing commercialization in Africa [15, 32, 33], [37,38,39,40]. Some efforts have been made to use MCDM tools such as AHP to prioritize livelihood measures to support effective and sustainable interventions for alleviating poverty in developing countries [41]. However, no work in the extant literature has combined SWOT, AHP, fuzzy logic, and TOPSIS to classify attributes most important to SHC retailing, livelihoods, sustainability, and poverty alleviation from a developing country's perspective. In addition, there is no research on Liberia's SHC retail sector. Examining the SHC retail sector in Liberia is essential to this study and the entire SHC literature.
In the following sub-sections, we provide brief literary overviews of each method used.
SWOT (Strengths, Weaknesses, Opportunities, and Threats) is a simple analytical tool vital for strategic planning. It follows a structured approach for assessing an organization's strategic position in planning, identifying an institution's strengths and weaknesses, and comparing them to opportunities and threats in their inner and outer environments [42–43]. SWOT has received immense attention in the extant literature, especially in the textile industry. For instance, [44] used SWOT to evaluate the educational system of a textile university to devise ways for improving the syllabus for students pursuing careers in the textile industry in Turkey. A similar approach by [45] uses SWOT to analyze the challenges facing Pakistan's textile industry.
Strength relates to attributes distinguishing an institution and its competitors; weaknesses are inherent features that need improvement. Opportunities are openings or chances for growth, while threats refer to environmental characteristics that cause derailment for an organization. Employing SWOT analysis allows for decision-making about where and how an improvement can be made [46].
The Analytical Hierarchy Process (AHP), developed by Professor Thomas L. Saaty in the 1970s, is an MCDM tool [47]. Hierarchical decision-making criteria or objectives characterize MCDM problems to find an ultimate goal. They are usually based on some criteria or sub-criteria that influence the decision-making process with some degree of importance. Many MCDM problems are psychological, and selecting multiple criteria without carefully vetting the alternatives is detrimental to the project management. Applying an AHP approach in an MCDM problem transforms psychological attributes to mathematical reasoning with relative importance using hierarchal structures [46]. It uses pairwise comparisons to design a quantitative framework that guides the criteria and alternatives for the required objective. In this way, each criterion's relative importance or weightage is evaluated and scored for comparison at each level.
The mathematical nature, methodological convenience, and the flexibility of obtaining input data in a hierarchical structure make AHP a versatile MCDM tool with widespread adoption and use in various research fields [48], including the textile and apparel (TA) industry. [49] applied AHP and TOPSIS methods in a complex decision-making process that required optimal distribution of work to subcontractors in a garment factory in Turkey. Their model enabled decision-makers to select subcontractors, providing companies with the benefits of quality products and on-time delivery. The strength of AHP has been tested well beyond just a pairwise comparison of criteria. [50] combined AHP, DEA, and TOPSIS to develop an integrated framework incorporating suppliers’ selection and comprehensive evaluation of their overall performance. A UK-based retailer has implemented their framework with over 7000 supermarkets and 460,000 employees. [51] used the technique combined with TOPSIS to develop a decision support system (DSS) software for classifying and selecting cotton fibers under all aspects of yarn quality. AHP was employed to infer the relative weights of the cotton fiber properties. Using a similar approach, [52] compared fabrics from three different fibers (regenerated bamboo, polyester, and cotton) for mechanical, thermal comfort, and moisture properties. Computation of AHP pairwise comparison revealed 100% cotton fabric as mostly preferred for summer consumption. Extending this idea, [53] combined fuzzy logic and AHP to develop a conceptual framework for functional, expressive, and aesthetic considerations in designing raincoats for children aged 7 and 8. Their model accounts for three significant levels—development, requirement, and design solutions for creating children raincoats. In addition to selection and allocation problems, AHP has assisted in gaining an understanding of fashion consumers. [54] used AHP and VIKOR techniques to identify factors influencing consumers’ preferences for online fashion retailers. Forty factors divided into seven categories were most preferred among online fashion retailers in India. A similar consumer study by [55] used AHP to examine the effect of the perceived value of Omni-channel service attributes on users’ decision-making processes about omnichannel use in Japan and Korea.
Fuzzy Technique for Order Preference by Similarity to Ideal Solution (Fuzzy TOPSIS) is an MCDM methodology used to evaluate alternative options involving subjective assessments [56]. The method uses a similar pairwise comparison technique found in traditional AHP to aid decision-makers in making comparative judgments while using a linguistic evaluation approach to deliver final assessments [57]. TOPSIS has received widespread attention in the MCDM literature mainly due to its simple calculation and simultaneous evaluation of the shortest distance from the positive ideal solution (PIS) and the farthest from the negative ideal solution (NIS) [58]. However, TOPSIS cannot cope with the ambiguities and uncertainties that decision-makers face. Decision-makers, therefore, utilize fuzzy set theory to include unquantifiable, incomplete, and non-available information into the decision model [59]. The inclusion of fuzzy set theory enables fuzzy values in the decision-making since it takes a more realistic approach by relying on linguistic judgments rather than numerical values [60].
Due to these flexibilities, fuzzy TOPSIS is now widely used for handling difficulties associated with ranking in MCDM, especially in the textile industry. For example [61] use fuzzy TOPSIS combined with AHP to diagnose critical areas for implementing Lean tools in a textile sector. [62] extend the power of TOPSIS by combining TOPSIS and Pythagorean fuzzy sets (PFSs) to select the best cotton fabric comfortability. Their results highlight a significantly high correlation coefficient with other distance measures and aggregation operators. [63] used the FTOPSIS model to prioritize 19 major sustainable supply chain management (SSCM) practices, particularly for India's automobile, textile, and food industries. Their result ranks ISO 14000 and 14001 certification, value stream mapping, and corporate social responsibility as the topmost priorities when making decisions to ensure perfect sustainability in supply chains.
The combination of several MCDM methods to form a hybrid technique has proven very robust in solving complex decision problems. [64] study proposes a new hybrid methodology based on FAHP and FTOPSIS for helping textile industry workers select the optimal waste-water treatment process. Similarly, a recent study by [65] proposes a hybrid MCDM model that combines fuzzy AHP and fuzzy TOPSIS to aid the garment industry's supplier selection process in a fuzzy decision-making environment in Vietnam. The authors used FAHP to analyze the performance and weightage of each criterion, while TOPSIS was used for ranking the possible suppliers.
Interviews and data collected for this study were taken from four (4) large markets—Red Light market district, Waterside market, Duala market, and Omega market—in Monrovia, Liberia. These markets are the most prominent shopping points in Liberia. They host diverse marketeers, including SHC retailers. The Red Light market is the largest and busiest market in Liberia, with a massive population of various vendors exceeding 15,000. Duala market has a population of more than 7,000 marketeers. In contrast, the Waterside market is the most significant commercial area in central Monrovia, offering almost everything for sale, including colorful textiles, shoes, leather goods, and ceramics, with a population of over 10,000 marketers [66]. On the other hand, the Omega market is a new development for relocating marketeers of the old Gobachop and Red Light markets, hosting over 3000 marketers [67].
The Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis is an effective and efficient tool for strategic planning. It, therefore, has been used in this study to assess the direction of development strategies for SHC trade. We use [43, 68] to explain the framework of Strengths relating to the company's characteristics or projects seen as weaknesses compared to others. Weaknesses as characteristics that classify one company or project as a loss compared to another; Opportunities as elements in the outer environment that a company or project can use to its advantage and Threats as elements in the external environment that tend to derail the company or project.
This paper aims to identify factors for a sustainable enrichment of the SHC trade in Africa, particularly Liberia. We conduct the SWOT analysis in three stages. First, we obtain the SWOT factors from a survey of SHC retailers in the four markets. Second, we group the factors to develop the SWOT framework. Third, we conduct pairwise comparisons of 21 factors in total (five from the strengths group, six factors of weaknesses, four factors of opportunities, and five from the threats group) see table 1.
SWOT factors for SHC retailing
| S1 | There is a strong presence of SHC country-wide with a great variety of products and increased choosability options | W1 | Lack of storage/warehouse facilities and pressure to provide lower prices in the competitive environment |
| S2 | SHC are quality products, durable and affordable for the local majority | W2 | Prices from wholesalers are high because of the high importation and other related costs |
| S3 | Retailers and suppliers can independently operate businesses and grow financially faster | W3 | Obtaining financial assistance to startups is difficult for young entrepreneurs |
| S4 | The serviceability range is more comprehensive for consumers such that it extends garment's life-cycle and reduces fast fashion demand | W4 | Poor supply chain delivery systems. There is no good logistics infrastructure to transport goods around |
| S5 | It's easier shopping second-hand; this gives space to boost sales and market growth improvements | W5 | Clothes are less hygienic and sometimes require alteration for which there are existing skillset-gaps |
| W6 | Low level of corporation between members of the supply chain | ||
| O1 | Selling SHC creates employment avenues that lead to poverty alleviation | T1 | Increased competition from new entrants |
| O2 | Favorable conditions for investment that support women empowerment | T2 | Fast-changing consumers’ choices in the clothing segment |
| O3 | Loan opportunities may lead to growth in demand and promote new market expansion | T3 | Unstable tax schemes and import duties |
| O4 | Quantity discount from wholesalers to retailers has the potential to allow the low capital requirement for beginners | T4 | Competition from the Fast Fashion market that changes the taste of consumers |
| T5 | High likelihood of banning SHC to promote the local textile industry | ||
| T6 | Unstable prices and exchange rates due to dual currency and frequent government demolition of market structures | ||
The first stage of this process was a survey to assess SHC import, economic performance, contribution to society and trade, and the challenges its retailers face in Red Light, Waterside, Duala, and Omega markets. Questionnaires for the survey were gathered from a thorough study of existing literature. The total sample size from the four study points includes 100 respondents of SHC retailers, see Table 2. The 100 respondents gave us access to basic primary data on retail activities and challenges from various stages of the SHC supply chain between June 25, 2021, and August 25, 2021. Using this time frame enabled us to capture the July 26 (Independence Day) sales since there were more buyers and sellers during this period. Inclusion criteria for our respondents were straight but brief: respondents must be the owner of the business, must have not less than two years of SHC retail experience, and that the business should constitute more than 70% of SHC.
Respondents
| Gender | Male | 4 | 10 | 9 | 30 | 3 | 15 | 1 | 10 |
| Female | 36 | 90 | 21 | 70 | 17 | 85 | 11 | 90 | |
| Age | 10–19 | 2 | 5 | 3 | 10 | 1 | 5 | 1 | 10 |
| 20–29 | 18 | 45 | 9 | 30 | 5 | 25 | 2 | 20 | |
| 30–39 | 5 | 12.5 | 9 | 30 | 6 | 30 | 3 | 30 | |
| 40–49 | 6 | 15 | 5 | 16.7 | 5 | 25 | 2 | 20 | |
| 50–59 | 4 | 10 | 2 | 6.7 | 2 | 10 | 1 | 10 | |
| 60 & above | 5 | 12.5 | 2 | 6.7 | 1 | 5 | 1 | 10 | |
The second stage of data processing included refining and categorizing the data collected from the survey into each SWOT group. All outside of this paper, six experts were asked to categorize factors into each SWOT group (see table 1).
SWOT and its subsequent strategies do not determine the importance of factors nor provide implementation details. Therefore, we transform the framework into a hierarchical structure by integrating the analysis with AHP, whose calculations are based on eigenvalues [42]. Using AHP prioritizes attributes by comparing them in pairwise matrices to devise strategic actions for SWOT. Following experts’ identification of the 21 factors, a panel of 100 SHC retailers, all with over five years of selling experience, were asked to prioritize them based on AHP, completing the third phase. Compared to previous literature, a consistency check is performed after analyzing the strategy, generating proposals [68].
To reduce errors or overcome the ambiguity of human thoughts, [69] introduced the fuzzy set theory. It is based on the rationality of uncertainty that arises from the imprecision of human judgment. Weights obtained from the AHP analysis were fuzzified for a more refined grading of factors. This MCDM method analyzes the importance of each factor, thus allowing the incorporation of strategies that are vital indicators for the sustainable development of SHC trade.
For fuzzy and AHP methods, we use Saaty's comparison scale in table A2 to evaluate the alternative strategies from the most preferred to the least preferred.
Finally, we introduce the TOPSIS method to rank the most suitable strategies for the sustainable enhancement of SHC trade in Liberia. The factors for the sustainable development of SHC are considered and compared to determine the essential improvement factors.
We begin the decision-making analysis by a goal, a set of criteria, and sub-criteria, which we fed to the AHP model to extract important alternatives relevant to the SHC supply chain. We generated pairwise comparisons based on judgments of 100 retailers by following the steps below:
The elements of the weighted matrix show the relative weights between the criteria being compared. A higher value indicates a higher preference for each criterion.
A comparison between the computed
Their pairwise comparisons were revised for responses with higher consistency values exceeding the 0.10 (10%) threshold.
Radom index values
| Random index (RI) | 0 | 0 | 0.58 | 0.9 | 1.12 | 1.24 | 1.32 | 1.41 | 1.45 | 1.49 | 1.51 | 1.58 |
The fuzzy set theory differs from classical set theory in that it uses triangular or trapezoidal fuzzy arguments to reflect the uncertainty of parameters association. However, this paper uses triangular fuzzy numbers (see figure 3) to express the fuzzy relative importance of factors and sub-factors of SHC SWOT analysis.
The scale level for each indicator and the weighting of the criteria are obtained from the matrix
Thus,
We define an aggregated alternative and criteria weightage of fuzzy decision matrix for SO1, SO2, SO3, WO1, WO2, WO3, ST1, ST2, ST3, WT1, WT2, and WT3 using the fuzzy-TOPSIS method to rank the strategies.
The alternative with the highest closeness coefficient is the ideal solution. Thus, the alternative that defines the ideal solution is the alternative with the shortest distance from the positive ideal solution (FPIS) and the shortest distance from the negative ideal solution (FNIS).
The primary aim of this paper was to assess the significance of SHC trade in Liberia. We extend this assessment by developing a SWOT framework to identify criteria and sub-criteria prioritizing SHC retailing in Liberia and Africa. We applied fuzzy logic and AHP to develop matrices from pairwise comparisons of the SWOT group and sub-factors groups using a comparison scale of 1–9 [25]. We first determined the weights of both factors and sub-factors and then obtained the global weight by multiplying the factor and sub-factor weights. The results are presented in tables 4 & 5. Ensemble values in table 4 show that SHC, with a Strength of 28.8%, is a potential contributor to revenue generation, employment avenues, and improvement of livelihoods. However, it faces enormous Threats (37%) mainly due to poor import and market regulations [15].
Group and ensemble weights of SWOT group from AHP & FAHP computations
| 0.255 | 0.321 | 0.288 | |
| 0.111 | 0.197 | 0.154 | |
| 0.193 | 0.185 | 0.189 | |
| 0.442 | 0.298 | 0.370 | |
Local, global, and ensemble weights of all sub-factors from AHP & FAHP computations
| 0.086 | 0.056 | 0.022 | 0.018 | 0.020 | |||
| 0.280 | 0.316 | 0.071 | 0.102 | 0.086 | |||
| 0.255 | 0.279 | 0.065 | 0.089 | 0.077 | |||
| 0.136 | 0.112 | 0.035 | 0.036 | 0.035 | |||
| 0.243 | 0.237 | 0.062 | 0.076 | 0.069 | |||
| 0.082 | 0.065 | 0.009 | 0.013 | 0.011 | |||
| 0.321 | 0.353 | 0.036 | 0.070 | 0.053 | |||
| 0.324 | 0.360 | 0.036 | 0.071 | 0.053 | |||
| 0.150 | 0.141 | 0.017 | 0.028 | 0.022 | |||
| 0.059 | 0.037 | 0.007 | 0.007 | 0.007 | |||
| 0.064 | 0.043 | 0.007 | 0.008 | 0.008 | |||
| 0.474 | 0.581 | 0.091 | 0.107 | 0.099 | |||
| 0.247 | 0.230 | 0.048 | 0.043 | 0.045 | |||
| 0.097 | 0.053 | 0.019 | 0.010 | 0.014 | |||
| 0.182 | 0.136 | 0.035 | 0.025 | 0.030 | |||
| 0.060 | 0.040 | 0.027 | 0.012 | 0.019 | |||
| 0.043 | 0.028 | 0.019 | 0.008 | 0.014 | |||
| 0.230 | 0.252 | 0.102 | 0.075 | 0.088 | |||
| 0.060 | 0.047 | 0.027 | 0.014 | 0.020 | |||
| 0.420 | 0.469 | 0.186 | 0.140 | 0.163 | |||
| 0.186 | 0.164 | 0.082 | 0.049 | 0.065 | |||
We further conducted pairwise comparisons of each alternative of the SWOT matrix within each SWOT group. To obtain global weights, we multiplied the local weights of each sub-factors by the weights obtained in the parent/category pairwise comparison matrix. We achieve the ensemble values by averaging global weights from fuzzy logic and AHP.
From Ensemble weights in table 5, sub-factor S2 (8.6%) for the Strengths strategy has the highest weightage—
Results from our survey indicate a high level of entrepreneurial intentions among SHC retailers. But, as shown in the Weaknesses sub-factor of Table 5, W3 (5.3%)—
Over 1 billion people in Africa wear SHC. Its demand is creating employment avenues (O1, 9.9% from table 5 of the Opportunities sub-factors strategy) such as trading, distribution, repairs, laundry services, and upcycling, which sustain hundreds of thousands of livelihoods on the continent [15]. These employment opportunities are vital enablers of women's empowerment in Africa (O2, 4.5%). The two Opportunities sub-factors, in particular, are significant prioritizations that seek investments to attain economic growth and transfer of technology.
[35] argues that the trade of SHC in Africa is the major contributing factor to the demise of textile industries. Predicated upon this, some governments, particularly East Africa, have instituted bans on SHC to “promote the local textile industry” (T5, 16.3% table 5 of the Threats sub-factors strategy). However, it is unclear whether local textile production and employment would recover even without SHC. Local African markets are now flooded with low-cost textiles imports from Asia [15, 17]. Even though the trade has enormous Opportunities such as O1 9.9%, it is impeded by
Fig. 1
SWOT group matrix analysis
Fig. 2
The SWOT AHP hierarchical model
Fig. 3
A Triangular fuzzy number (TFN)
Fig. 4
Local Weights of both AHP & FAHP
Fig. 5
Global weights of both AHP & FAHP
We introduce the TOPSIS method to rank strategies that need immediate improvement. The most critical aspects of SHC's sustainable development are considered and contrasted to find the essential criteria for advancement. Both FAHP and TOPSIS methods thus extend the SWOT strategy by identifying key issues to improve. The weighting results calculated in Fuzzy AHP were used to derive the TOPSIS aggregated fuzzy weights. Table 6 shows the weighted performance score of the alternatives and their closeness coefficient (
TOPSIS
| SO1 | 3.660 | 3.536 | 0.491 | 4 |
| SO2 | 5.705 | 1.481 | 0.206 | 6 |
| SO3 | 4.808 | 2.387 | 0.332 | 5 |
| WO1 | 5.865 | 1.307 | 0.182 | 7 |
| WO2 | 6.669 | 0.488 | 0.068 | 11 |
| WO3 | 6.661 | 0.498 | 0.070 | 10 |
| ST1 | 6.352 | 0.795 | 0.111 | 8 |
| ST2 | 2.306 | 4.886 | 0.679 | 2 |
| ST3 | 2.027 | 5.138 | 0.717 | 1 |
| WT1 | 2.535 | 4.651 | 0.647 | 3 |
| WT2 | 6.585 | 0.595 | 0.083 | 9 |
| WT3 | 7.041 | 0.107 | 0.015 | 12 |
ST3 with a
In this research, we used a mixture of SWOT, AHP, fuzzy TOPSIS, and the Ensemble methods in an MCDM preference analysis to evaluate essential factors in implementing a sustainable SHC supply chain to recommend strategic directions towards their successful implementation. Our results highlight that SHC has received more preferences over local and imported clothing due to its low cost and availability. In our survey, we also learned from wholesalers, retailers, and consumers that there is a strong positive relationship between SHC and revenue generation, creation of employment avenues, women empowerment, and the improvement of livelihoods (more on these matrices in our forthcoming paper). This is indicated by the SWOT group analysis score of 28.8% for Strengths. However, results show that SHC faces enormous Threats (37%) mainly due to poor import and market regulations. The trade is constrained from threats of governments’ plans to ban its importation completely. However, as respondents of our survey highlighted, many smaller countries such as Liberia, Sierra Leone, Gambia, Guinea Bissau, etc., lack the financial resources to establish textile industries that will withstand the competition from China and other Asian countries while offering low costs with broad coverage for the local majority.
Both W3 and W2 provide important implications of this study for potential policy interventions. They emphasize the need for governments and regulatory bodies to stimulate employment opportunities by promoting strategies that will provide better financial access to young entrepreneurs.
Additionally, many of our respondents expressed satisfaction with the fashion remanufacturing, rebranding, and upcycling initiatives already begun in many parts of Africa. Remanufacturing, rebranding, and upcycling are adding African styles and values more cost-effectively. Governments investing in more such initiatives would open more employment opportunities that will lead to women's empowerment.
Lastly, our hybrid model can handle ambiguity, inaccuracy, and complexity of choices from several varied and competing criteria. Future researchers could continue developing the model by adding new criteria that might be utilized to undertake a more complete study of the possibilities provided and integrate more information about the study point. Our method could also be applied to other countries to assist policymakers in mapping the critical factors in their SHC supply chains.