How technology breaks down the silos of the fashion value chain

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This article first appeared in State of fashion: technologya detailed report published by BoF and McKinsey & Company.

Rapid changes in consumer demand and constant interruptions in the supply chain are just some of the factors that contribute to the complexity of the functioning of the fashion brand today.

The industry needs a new digitized value chain model that integrates multiple internal processes and data sources, from demand forecasting to pricing. Indeed, when it comes to digitalization, 61 percent of fashion executives believe that end-to-end process management is among the most important investment areas for their organizations between 2021 and 2025. The result will be stronger shock-resistant companies capable of navigating today unstable business environment.

Many fashion companies are improving individual value chain processes with digital technologies. But fully integrated background systems and workflows are still a long way off.

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One reason is that relatively few ready-made applications are designed to optimize the fashion value chain from end to end. While companies such as Nextail, Logility, and O9 offer solutions that address specific activities such as purchasing, first product allocation, replenishment, and store transfer, no solution covers the entire value chain. Brands therefore need to identify solutions that address their sore points or custom applications, which requires a lot of resources. At the same time, development costs remain high and companies face gaps in their technology groups and talents.

Five critical workflows in the fashion value chain enable end-to-end integration: product performance, category performance, supply chain optimization, inventory management, and purchasing and demand forecasting. Integrating key parts of the journey through the value chain could accelerate market entry by up to 50 percent, full-price sales by up to 8 percent higher and production by up to 20 percent cheaper.

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Product performance, or an assessment of which products are selling well – shows the impact of end-to-end integration in practice. The Separate Pricing and Promotions app can use AI and machine learning to determine a product’s promotional price by analyzing current stock, seasonal prices, seasonal times, and expected resilience. In contrast, investing in end-to-end integration would expand the scope of the application to take into account similar products that are already in trade or will arrive soon, as well as the expected return or range of competition. Each of these data points affects the expected sales, and thus the corresponding promotional price, which can ultimately increase gross margins.

At Levi’s, company-wide machine learning combined with a cloud data repository containing internal and external sales and inventory information provides multiple processes with resources to make better decisions about everything from pricing to consumer marketing, according to core strategy and AI officers, Katia Walsh. Data-based knowledge sharing also helps Levi’s determine the best locations to deliver its products, identifying the store or distribution center closest to the delivery address, helping it control logistics costs and seamlessly manage store inventory.

Integrating key parts of the journey through the value chain could accelerate market entry by up to 50 percent.

Shein this goes even further. Not only has the ultra-fast fashion player integrated its internal processes, but it has also linked those internal processes to the processes of its suppliers. This allows for a fast and efficient ordering and replenishment path. Shein uses AI modeling to estimate millions of social media posts on various platforms to determine which products to produce, while advanced analytics helps its design teams review the performance of design attributes down to details such as zippers and fabrics. With its vertically integrated supply chain using Singbad software, Shein’s designs could reach customers within about three weeks of being first designed.

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Certainly, the operational models of fashion players will continue to require a finely tuned balance of art and science so as not to lose sight of the creative aspects of experience-focused decision-making that are critical in fashion. Managers should be prepared to address potential resistance to work in a more connected way, the way data and knowledge flow through processes seamlessly. Embracing deep digital integration will require a focus on change management. Teams will need to be qualified or retrained, and tools will need to be designed with a user-centered mindset to ensure their adoption. For example, this may mean adopting an “explanatory AI” where people can easily understand and manage the predictions and results of artificial intelligence, as opposed to the “black box” model, which is difficult to interpret and therefore believable.

Finally, fashion companies – from the mass market to luxury – will benefit from optimizing their time to market, flexibility and product availability at a time when many companies are struggling to maintain margins. Value chain integration will prove to be a critical point of competitive differentiation.

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