Neha Yadav, Post Graduate Academic Scholar, Department of Fashion Management Studies, National Institute of Fashion Technology, Ministry of Textiles, Govt of India, Daman Campus.
Dr. Rahul Kushwaha, Assistant Professor, Department of Fashion Management Studies, National Institute of Fashion Technology, Ministry of Textiles, Govt of India, Daman Campus.
Abstract
This paper deals with the various challenges and solutions pertaining to inventory management in the fast-changing fashion-making industry, particularly in the case of Paarth Clothing. Through firsthand experience gained during internship activities, there are many issues such as surplus inventory, cancellation of purchase orders, and defective stock build-up-all referring to an industry with fluctuating demand and constantly changing trends. To overcome the problems in inventory management, the project proposes that Myntra’s Seller Just In Time (SJIT) model be adopted as a strategic solution. A customer survey of sample size 100 shows strong consumer preferences towards quality and price, high influence of social media and online platforms. By ANOVA test, there exist statistically significant differences of consumer characteristics (like gender, occupation, shopping frequency) with respect to perceptions of quality, comfort, and brand reputation. Research highlights that consumers may be price-sensitive, but will spend for value-based perceptions. This means that the implementation of SJIT systems would catalyze reduction in stock movement, warehousing costs, and consequently efficacious adaptation to consumer behaviour changes. The study makes a data and technology enabled solution for streamlining inventory practices for fashion manufacturers who want to achieve the balance of operational efficiency with customer demands.
Key words: Stock Movement, Inventory Management, Trend Changes, Quality and Comfort, Social Media Influence.
Introduction
Inventory management plays a pivotal role in garment manufacture, especially in the fast and furious fashion world of demand and the rapidly changing trends. Poor forecasting of demand along with ever-changing customer preferences, and overproduction, usually result in overstocking, obsolete inventory, and generation of dotted POs-all tying up cash flow and wasting space. Managing excess and defective stock efficiently has been one of the major concerns concerning Paarth Clothing. The project proposes adopting the Seller Just-In-Time model for Myntra. The idea is quite simple; suppliers would have inventory at their place and ship them only upon an order. It significantly reduces the cost of storage, minimizes dead stocks, and gives better agility.
It’s tech-driven research that aims to study solutions for better control of the inventories, waste reduction, and smart handling of PO cancellations, all with the flavor of lessons learned from the learning of Myntra’s SJIT system. The project also aims to develop a scalable and sustainable inventory strategy that will enhance efficiency and profitability.
Objectives
The objective is to develop a comprehensive plan to manage excess inventory by identifying its root causes, streamlining the process to list and sell remaining stock on Myntra’s SJIT platform, and optimizing pricing strategies for clearance. Additionally, the goal is to establish a process for handling defective items and improving supply chain coordination to prevent similar issues in the future, ultimately enhancing inventory management efficiency.
Literature Review
Management of the inventory in the fashion industry is very complicated owing to extremely dynamic features as seasonal cycles and consumer behaviour become increasingly complex to predict. Most of the literature stresses that the traditional inventory approaches hardly work but let surplus stocks lying without allowing them to be converted by the real-time demand. According to Bruce and Daly (2006), one of the causes of overproduction, leading to financial and storage costs, is forecast inaccuracies and inflexible supply chains.
Fisher (1997), fashion is one of the categories of products termed as ‘highly unpredictable.’ He champions responsive systems that tie production much closer to demand signals. Therefore, this strongly advocates for the idea of the adoption of Just-In-Time (JIT) systems that are meant to bring down inventory holding costs and promote agile supply chains. (Tokatli, 2008) When applied to apparel, just-in-time models allow smaller, more frequent shipments based on actual sales rather than projected demand. Mukherjee & Roy, (2022) such are the principles that the SJIT model by Myntra has always followed. The company allows suppliers to hold inventory in their own storage facilities and deploy it only after customers order it, reducing thus warehousing costs and the risk of having to deal with unsold stock. This model empowers sellers while satisfying those dynamics of fast- fashion in terms of order fulfillment efficiency.
Choi (2013) states that because of the trend of consumers driven by their perceptions of quality, price sensitivity, and popularity in social media, inventory decisions must be closely tied to it. (Sudha & Sheena, 2017) It is through system technologies such as SJIT that real-time visibility and responsiveness have become increasingly necessary with social media and digital influence commensurate with changes in consumer behaviour.
Singh & Garg (2020), minor quality issues cause capital to get blocked and wastage in addition to an increase in costs. An SJIT model of a technology-integrated inventory fosters traceability along with fast pathways for resolution, lessening the chances of accumulation of such items without sale. Defective stock piling is also one of the very important reasons leading to mismanagement of inventory that usually does not come in view.
Methodology
This research both Primary and Secondary data with applied research objectives to solve a few selected practical problems or improve improvements in the current process. The research deals with major goals which show understanding of theories, problems, and prediction in scientific research. It has a cross-sectional design in which data are collected from different sources. The primary source of data is that derived from a Google survey, while the secondary of data is supported by literature and articles.
Quantitative and qualitative data have to be included in order to study the entire picture. An attempt is being made to collate 100 survey responses from women between the ages of 18 and 45 years, taking into consideration the city of Mumbai and focused on customer preference. An ANOVA test will be conducted on the data for identification of significant differences among groups.
Data Analysis & Interpretation
The information shows that a huge majority of 78.2% of respondents have sometimes paid extra for superior quality, pointing towards quality being important but only in moderation. While on the other hand, 10.9% always pay extra for higher quality, reflecting on a loyal band that is committed to value. The other 10.9% always choose the cheap one irrespective of quality. This points out that although price sensitivity is present, consumers are willing to pay more when they perceive improved value or longevity.
Things seem to be quite good, while customers are willing to compromise in terms of the quality of items for a lower price; the greatest acceptance for a trade-off would be limited design choices. Delayed delivery is worth mixed reviews’ value, which means time still matters. The least acceptable of all trade-offs seems to be no free return/change, as a majority of respondents would
not agree to it. Here comes the novelty of flexibility and convenience that goes hand in hand with affordability in the smart consumer’s expectations today.
There statistically, 86.4% of them are influenced by online influencers and social media in purchasing decisions: 49.1% to some extent and 37.3% to a great extent, while very few remained unimpressed (8%) or indifferent (5%). This reinforces that the digital space is growing in influence, especially in beauty, fashion, and lifestyle, where trends and imagery are relevant to consumer behaviour. Therefore, brands can leverage these influencers to drive engagement and sales.
ANOVA-1
- Hypothesis
H0: There is no difference between gender and quality in clothing in respect of Unique design and appearance
H1: There is difference between gender and quality in clothing in respect of Unique design and appearance
ANOVA How do you define quality in clothing? [Unique design and appearance]
Inference
The P value is 0.036 which is less than 0.05 therefore H0 is rejected, establishing that there is difference between gender and quality in clothing in respect of Unique design and appearance.
ANOVA-2
What do you usually sacrifice when shopping for a lower price? [Brand reputation] Hypothesis-2
H0: There is no difference between Occupation and Brand reputation
H1: There is difference between Occupation and Brand reputation
Inference
The P value is less than 0.001 which is less than 0.05 therefore H0 is rejected, establishing that there is difference between Occupation and Brand reputation.
ANOVA -3
What do you usually sacrifice when shopping for a lower price (Comfort)
H0: There is no difference between Occupation and Comfort H1: There is difference between Occupation and Comfort
Inference
The P value is less than 0.001 which is less than 0.05 therefore H0 is rejected, establishing that there is difference between Occupation and Comfort.
ANOVA-4
What do you usually sacrifice when shopping for a lower price- Fabric quality
H0: There is no difference between Shopping frequency and Fabric quality
H1: There is difference between Shopping frequency and Fabric quality
Inference
The P value is less than 0.023 which is less than 0.05 therefore H0 is rejected, establishing that there is difference between Shopping frequency and Fabric quality.
Findings & Suggestions
The survey findings, based on 110 responses, reveal that the majority of respondents (73.6%) prefer online shopping, with the 18-25 age group dominating the sample. Key insights include the prioritization of product quality (82.7%), willingness to compromise on quality for affordability (79.1%), and sacrifice for better products (78.2%). The study suggests that trust in brands and return policies significantly influence purchasing decisions. The findings support the implementation of the Seller-Just-In-Time (SJIT) inventory system, leveraging real-time consumer behavior and return feedback to inform stock replenishment decisions, reduce surplus stock, and create a more adaptable and customer-oriented inventory approach. All of this results in an inventory approach that is more adaptable, easier to track, and customer- oriented.
Suggestions
The suggested model synchronizes production with actual consumer demand through a quick-response approach, leveraging behavioral data and social trends for consumer-oriented inventory planning. It’s ideal for fast-track actions where trend responsiveness is crucial. Key strategies include incorporating reverse logistics to promote circular retail, reduce waste, and minimize inventory losses. Additionally, AI-driven forecasting can enhance the model’s effectiveness, enabling data-driven decisions and optimized inventory management.
Conclusion
The study points to the urgent need for smarter inventory management strategies in the rapidly changing fashion industry, especially companies similar to Paarth Clothing. Analyzing consumer behavior tends to show that there is price consciousness tempered by the willingness to pay for quality, coupled with high social media influence; therefore, the traditional techniques of inventory do not suffice. Consider the Seller-Just-In-Time (SJIT) model implemented by Myntra: It is a neat, very responsive, and cost-efficient method, requiring no warehouses while preventing overstock and matching production to actual demand in real time. Also, being technology-oriented, the new age production process supports sustainability through better production planning, reverse logistics, and waste reduction. Models such as this will keep fashion alive and competitive in the market today; it will also ensure focus on customers and agility of operations.
References
Bruce, M., & Daly, L. (2006). Buyer behaviour for fast fashion. Journal of Fashion Marketing and Management, 10(3), 329–344. https://doi.org/10.1108/13612020610679303
Choi, T.-M. (2013). Local sourcing and fashion quick response system: The impacts of carbon footprint tax. Transportation Research Part E: Logistics and Transportation Review, 55, 43–54. https://doi.org/10.1016/j.tre.2013.04.001
Fisher, M. L. (1997). What is the right supply chain for your product? Harvard Business Review, 75(2), 105–116. https://hbr.org/1997/03/what-is-the-right-supply-chain-for-your- product
Mukherjee, S., & Roy, S. (2022). Digital transformation and inventory management in e- commerce: The case of Myntra’s SJIT model. Indian Journal of Supply Chain Management, 9(2), 115–122.
Singh, P., & Garg, S. K. (2020). Inventory management strategies for defectives in fashion retail: A lean approach. International Journal of Productivity and Performance Management, 69(3), 521–535. https://doi.org/10.1108/IJPPM-10-2018-0373
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