How technology is breaking down the silos of the fashion value chain

This article first appeared in The state of fashion: technologyan in-depth report co-published by BoF and McKinsey & Company.

Rapidly changing consumer demand and persistent supply chain disruptions are just some of the factors that add to the complexity of running a fashion brand today.

The industry needs a new digitized value chain model that brings together multiple internal processes and data sources, from forecasting demand to pricing. In fact, when it comes to digitization, 61% of fashion executives believe end-to-end process management is among the most important investment areas for their organizations between 2021 and 2025. The result will be companies more fortified and impact resistant, able to navigate today’s volatile business landscape.

Many fashion companies have improved individual value chain processes with digital technologies. But back-end systems and fully integrated workflows are still a long way off.

One reason is that relatively few standard applications are designed to optimize the fashion value chain from end to end. While companies like Nextail, Logility, and O9 offer solutions that address certain tasks such as purchasing, first product allocation, replenishment, and store relocations, no one-size-fits-all solution covers the entire value chain. Brands must therefore identify solutions that address their weaknesses or customized applications, which require a lot of resources. At the same time, development costs remain high and companies face gaps in their technology stacks and talent pools.

Five critical workflow “journeys” in the fashion value chain lend themselves to end-to-end integration: product performance, category performance, supply chain optimization, inventory management, and procurement and demand forecasting . Integrating key parts of a value chain path could speed up to market by up to 50% faster, full selling price up to 8% and manufacturing up to 20% cheaper.

State of Fashion Technology Report Chart 11.

Product performance, or the assessment of which products are selling well, shows the impact of end-to-end integration in practice. A siled pricing and promotion application could use artificial intelligence and machine learning to determine a product’s promotional price by analyzing current stock, seasonal price, seasonal period, and predicted elasticity. Conversely, investing in end-to-end integration would broaden the scope of the application to also consider similar products already in store or on the way, as well as expected returns or a range of competition. Each of these data has an impact on the expected sell-through and therefore on the appropriate promotional price, which can ultimately increase gross margins.

At Levi’s, enterprise-wide machine learning, combined with a cloud-based data repository containing internal and external sales and inventory information, provides more processes with resources to make better decisions on everything from pricing to consumer marketing. , according to Katia, head of strategy and AI Walsh. Data-driven knowledge sharing also helps Levi’s determine the best locations to ship its products from, identifying the store or distribution center closest to the shipping address, helping them control landed costs and manage their inventory. shop smoothly.

Integrating key parts of a path into the value chain could increase speed to market by up to 50%.

Shein takes him even further. The ultra-fast fashion player has not only integrated its internal processes, but has also linked those internal processes to those of its suppliers. This allows for a quick and efficient ordering and replenishment journey. Shein uses AI modeling to evaluate millions of social media posts across all platforms to determine which products to produce, while advanced analytics helps her design teams review the performance of design attributes down to details like hinge and tissue. With its vertically integrated supply chain using Singbada’s software, Shein’s projects could reach customers within approximately three weeks of their first conception.

State of fashion technology report chart 10.

To be sure, the operating models of fashion practitioners will continue to demand a finely tuned balance between art and science in order not to lose sight of the creative and experience-centric decision-making aspects that are fundamental in fashion. Executives should be prepared to face the potential resistance to working in a more connected way, where data and knowledge flow seamlessly through processes. Embracing deep digital integration will require focusing on managing change. Teams will need to be retrained or retrained, and tools will need to be designed with a user-centered mindset to ensure adoption. For example, this could mean adopting “explainable AI” whereby AI predictions and results can be easily understood and managed by humans, unlike “black box” models that are difficult to interpret and therefore reliable.

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

The banner of the state of fashion technology