Unravelling the Future of Fashion: The Dawn of AI-Driven PLM Systems

BY CHRIS JONES | DECEMBER 13, 2023 | 4 MINUTE READ

In the dynamic and intricate world of fashion, Product Lifecycle Management (PLM) systems are not just beneficial; they are essential. The fashion supply chain, known for its complexity, rapid evolution, and fragmentation, is on the brink of a transformative era. The integration of cutting-edge technologies like advanced data integration solutions and generative AI is set to revolutionise this domain, offering more comprehensive, efficient, and interconnected management tools.

Imagining the Future of Fashion PLM

What does the future hold for fashion PLM?

Picture a scenario where a merchandiser inputs parameters for a seasonal range – categories, budgets, target revenue, target margins, price points, and SKUs for newness and carryover. Here, Generative AI becomes a game-changer, sifting through extensive data models with precision and insight.

This AI initially delves into the licensed trends data of choice, and using a model that the design team has trained with the brand’s design language, the GenAI model defines candidate products. It then utilises another trained model to apply a rich tapestry of fashion models, celebrities, athletes, locations, and poses to create photo-realistic images.

For every product option, the GenAI tool delves deep, interrogating a myriad of critical data points. These include product blocks, specifications, sales history, demand forecasting, and insights from suppliers, manufacturers, and other partners across the supply chain. It assesses products, prices, resources, capacity, logistics, timelines, supply chain disruption risks, environmental impact, and estimated profitability.

The GenAI tool then crafts an assortment of product, colourway, and sourcing options, complete with profitability, timelines, and impact assessments for individual products and rolled up for the entire range. Its user-friendly interface fosters seamless real-time collaboration, boosts creativity, enhances internal decision-making, and streamlines external partnerships. It swiftly adapts to consumer behaviour shifts, suggesting order updates and managing open-to-buy to optimise inventory.

This scenario promises to revolutionise digital product creation, adoption, sourcing, development, and supply chain operations, significantly shortening the time to market. The inherent transparency and traceability of the data in the solution can resolve many industry challenges. The benefits are clear: enhanced speed-to-market, more sustainable products, greater full-price sales, increased revenue, and improved margins.

So, let’s do it! How is this scenario achieved?

Barriers to Realisation

However, realising this vision is not without its challenges. The cornerstone of Generative AI is accurate data – and lots of it. Two significant barriers need addressing:

Accurate Data Collection Across the Supply Chain

Brands cannot accomplish this in isolation. It necessitates specialist companies to manage data collection. There is also potential for consolidation across the manufacturing base. This approach could create value-added manufacturing packages accompanied by comprehensive product datasets for competitive advantage to the manufacturer. For example, integrating primary process data from raw material suppliers, fabric mills, dye houses, processing partners, logistics partners, and manufacturers could significantly enhance the accuracy of sustainability metrics.

Large manufacturing groups may be best placed to leverage relationships and invest in collaborations with specialist providers to capture primary data from the broad range of supply chain partners.

Integration Technology

With over 60 different types of solutions used across the supply chain, no single software vendor can cater to every need. Therefore, vendors must form broad partnerships, enabling customers to integrate best-of-breed packages tailored to their specific requirements. This integration must be seamless and user-friendly, avoiding the complexities of multi-platform management.

Collaboration and Business Expertise

Another critical element for successful implementation is process knowledge. Although abundant in the industry, this knowledge is often fragmented and distributed, mirroring the complexity of the fashion industry's supply chain. Successful execution relies on effectively facilitating collaborative efforts to gather, map, and re-engineer best practices. This collaboration will enable the design of methods to capture and consolidate primary data at each process stage efficiently.

The Reality of the Future

Contrary to assumptions, this future vision is not a distant dream. Much of the necessary technology already exists, with some vendors actively developing Fashion PLM solutions grounded in generative AI. Elements of our envisioned scenario are already operational, such as tools enabling merchandisers to create photo-realistic and comprehensive design briefs. These tools are equipped with necessary security and legal protections, including 'guide rails' from trained and licensed models and real-time collaboration features.

Conclusion: The Imminent Future

The future of Fashion PLM, powered by generative AI, is tantalisingly close. The technology to streamline creative processes, boost productivity, and accelerate market responsiveness is already here. The emergence of the next-generation AI-based Fashion PLM is just around the corner, poised to weave together the threads of process, software, integration, and data collection to revolutionise the fashion industry. This evolution is set to enhance creativity and efficiency and redefine the fabric of fashion business operations, steering it towards a more sustainable, responsive, and consumer-centric future.

Author Chris Jones

Chris has helped global brands, retailers, and manufacturers align people, processes, and technology for over three decades, driving transformation projects to maximise business impact.

This article is also published on LinkedIn: Unravelling the Future of Fashion: The Dawn of AI-Driven PLM Systems (www.linkedin.com)