Industry
- Retail
Omnichannel Experience & Intelligent – The Future of Retail
Solution
- Consulting
TCS Consulting Services to Achieve Success in the Business 4.0 Era
Highlights
- The conventional linear approach to supply chain management offers very little flexibility to respond to a volatile market environment and, therefore, is not optimal for fast fashion.
- A bimodal supply chain strategy is more suited to navigate the disruptions and complexities in fashion retail.
- It helps retailers take on new-age competitors with speed and agility and improve operational efficiencies.
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Fast fashion’s problem with overproduction
By 2030, the global apparel consumption is projected to rise by 63%.
At 62 million tons today, it is estimated to grow to 102 million tons—equivalent to more than 500 billion additional T-shirts—by the end of this decade. Reports also suggest that more than 50% of fast fashion clothes are discarded within a year of purchase; 30% of global clothing is never sold; and another 30% is only sold at a discount. This has resulted in mounting piles of unsold stock, with several large retailers carrying unsold inventory worth billions of dollars.
All this adversely impacts the retailer, denting revenue and margins besides impacting customer experience and sustainability goals (Figure 1). In addition, there is a huge impact on the environment. The fashion industry accounts for up to 10% of global carbon emissions; it is also the second-largest consumer of water and generates about 20% of the world’s wastewater.
Figure 1: Impact due to overproduction and dead inventory
These problems are a result of the linear supply chain, which is not all that agile and responsive to changing market conditions. To navigate the disruptions and complexities in fast fashion, a bimodal approach to supply chain is recommended.
Linear supply chains are out of fashion
The conventional linear approach to supply chain management is not the best fit for fast fashion.
Traditional fashion retailers operate largely on a push-based model characterized by long product lead times and huge production quantities planned and produced season on season.
The conventional approach to sourcing has been to design assortments and leverage a trusted supplier network for production, typically suppliers in the Far East to take advantage of lower costs. This one-size-fits-all conventional approach to fashion supply chain planning, where make, buy, and move take place in a lean and operationally efficient way, has a flip side.
The average product lead time for traditional fashion retailers is 36-40 weeks. Due to the long production cycles and shipping and logistics lead times, fast fashion retailers typically order large quantities to avoid being out of stock, which negatively impacts their revenue. It also offers very little flexibility to respond to a volatile market environment characterized by fast-changing consumer trends, rising supplier costs, shrinking product life cycles, and disruptions such as geopolitical instability and climate change.
Future proof your supply chain with a bimodal strategy
A bimodal supply chain strategy attempts to strike a balance between efficiency and agility.
Fast-fashion retailers can take a bimodal approach as follows:
Linear supply chains for traditional assortments (mode 1)
Agile, responsive supply chains for trendy fashion assortments (mode 2)
While mode 1 offers operational efficiency and cost savings, mode 2 is characterized by shorter time to market, demand volatility, shorter life span, and low to medium order quantities, ideal for assortments that require innovation and high responsiveness (Figure 2).
Figure 2: A bimodal supply chain approach for fashion retail
Retailers will need to evaluate their supply chain to determine where to apply mode 1 and mode 2, and transform the way they plan, produce, and distribute assortments by driving digital to the core of their business and harnessing AI in supply chain at scale.
Transforming to a bimodal supply chain
Leveraging AI helps build more efficient, transparent, and optimized supply chains.
Since demand is predictable for traditional assortments (mode 1), retailers should streamline the end-to-end sourcing process and adopt AI at scale to optimize product development, production, and logistics operations and reduce lead time (Figure 3). The availability of rich, year-on-year historical data makes it feasible to develop AI models for data-driven buy, make, and move decisions. AI can be leveraged to:
Eliminate sourcing delays with end-to-end real-time visibility.
Enable dynamic sourcing and supplier capacity planning based on the assortment needs.
Fast track buying with optimization engines that provide insights for decision-making.
Identify assortments with predictable demand by segregating products and assortments based on factors such as sales patterns, anticipated demand, and product lifespan.
Optimize material planning through economies of scale and visibility into upcoming assortments; this improves cost efficiency significantly and reduces risk against material price volatility.
Optimize supplier capacity by avoiding over and underutilization.
Improve supplier negotiations by predicting product costs and lead time and ensuring best deals in price, quality, and other parameters such as lead time, on-time delivery, and sustainability performance.
Collaborative production planning by working closely with suppliers on production processes and real-time visibility on order status.
Optimize logistics planning with network resilency and speed.
Mode 2 focuses on business agility and innovation to cater to fast-evolving consumer trends. It, therefore, requires close collaboration between retailers and suppliers. Retailers should identify suppliers across geographies who can fulfill small order sizes as well as complex new products. AI helps them to:
- Inspire design through early and continuous detection of trends based on social signals.
- Enable in-season assortment refresh by moving away from traditional seasonal planning.
- Lower the lead time for product development by recommending countries and suppliers for specific products and by automating order execution.
- Lower shipping and logistics lead time by near-shoring suppliers and evaluate new modes of transport for suppliers in distant locations.
- Help meet sustainability goals with lower production lead time, wastage, and water consumption enabled through automated, smart factories.
- Maximize sales by increasing space allocation for trending items based on sales and demand patterns.
Figure 3: AI-powered bimodal supply chains
Key considerations while adopting a bimodal supply chain
Retailers need to revisit their business fundamentals to maximize supply chain efficiencies and gain competitive advantage.
Shift in mindset: Pivoting to a bimodal strategy requires enterprise-wide change—change in organization culture and an agile approach to adopting new ways of working with people, processes, and technology.
Robust data foundation: Build a high-performance data and AI-ML organization to make more informed business decisions and deliver best-in-class customer experiences.
Right pricing strategy: A bimodal supply chain will increase the price points due to higher operating costs, and retailers need to get their pricing strategies right so that margins are not negatively impacted.
Strong supplier collaboration: A more integrated and tighter collaboration with suppliers is required to ensure that the bimodal strategy is a success. Retailers need to build a strong ecosystem partner hub driven by well-established processes and systems.
Supply chain transparency: Supply chain control towers or digital twins offer end-to-end, real-time visibility across the entire network, and help mitigate disruptions and risks before they escalate.
Get the best of both the worlds
By combining the best of traditional and agile supply chains, the bimodal strategy helps navigate the disruptions and complexities in fashion retail.
Taking proven supply chain concepts and driving innovations into the process with flexible solutions and powerful technologies such as AI will help your company meet the growing demands of consumers before the competition does. For example, a leading fast fashion retailer has successfully leveraged several mode 2 strategies to establish a competitive advantage. By leveraging data and AI across the fashion value chain and by adopting innovative sourcing strategies, the retailer accelerated go-to-market by reducing the production cycle to less than a week. By leveraging real-time order and customer interest tracking tools, the retailer is able to automatically reorder more quantities of products that consumers like, resulting in high sell-through rates while minimizing waste. Further, a close supplier collaboration network enables the retailer to churn out more than 2,500 new styles on an average each week. A bimodal supply chain strategy will help you take on new-age competitors with speed and agility and improve operational efficiencies.
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