New style design decisions are creative decisions but often gut-based with little means to rely on real-time facts making season planning unpredictable. Fishing out cues from past season successes to decide next season designs is a time-consuming process, with few chances of success. In addition, which attribute—fabric, texture or color in swatches—contributes to a winning style is also difficult to gather as sample swatches lie in files not easily retrievable. And with no existing ERP transactions effectively capturing this information, line reviews were usually not backed with facts, making the entire line planning process rather unpredictable.
A combination of style performance analytics and social media analytics is helping the industry foresee dynamic changes and consumer impulses. Several retailers and fashion designers have mined and analyzed social intelligence for real-time feedback on fashion trends to gauge consumer response to their launched or upcoming designs. They monitor social conversations to test reactions to new fashions and even build activities such as apps and games to engage consumers on social media.
The fashion industry is going through a technology revolution with players becoming more open to effective data analysis for business direction; and more data-driven and flexible to take into account real-time opportunities.
PLM tools helped usher a new wave of monitoring product development lifecycles and made the entire process transparent while shaving off cost and time. The library feature stores the entire design elements of the brand which can then be used to design a new style. The level of detail goes much further and helps view a style as more than just an article code and further into design elements.
A Style Performance Analytics solution combines the attribute level information of PLM with data from ERP and uses business intelligence tools to discover winning styles and attributes. Adding fashion elements into a new design need not be gut-based but can now rely on facts to validate if similar inspirations were successful with certain customer segments or geographies. Insights can now predict 'what will sell' and 'what will not sell' and will give contextual feedback on what customers like. Cues can thus be taken to reconfigure designs and the line plan, tweaking assortments to incorporate more styles which fly-off the shelves rather than warm the shelves.
Industry benefits gained
In the move from uncertain, gut-based decisions to fact-based predictable decisions, the industry can thrive from improved revenue; and lower working capital and inventory levels. The customer is now an important part of the manufacturing and product line planning process by testing styles and providing valuable insights into preferences and patterns-which then can translate into products they would actually purchase to benefit the fashion industry.
There’s no doubt that the RFA industry will become more complex and keep evolving at a rapid pace. And with more educated and demanding customers and new customer engagement channels, the industry will need to adapt quickly to satisfy customer needs. Customers demand personalization and involvement in the design process. This is possible only when the fashion industry actually listens to what customers are talking about—and then includes them in the product life cycle.
L .N. Balaji is President of ITC Infotech Inc. and heads the company’s operations in North America.