3 Ways PLM with Machine Learning Increases Margins for Retailers



Shrinking trend cycles. Complex global supply chains. How do you maximize margins in the hypercompetitive retail market while developing on-trend products that consumers want to buy? Product lifecycle management (PLM) solutions help simplify the design and development process, but there are still difficult decisions that must be made.

How fast should you bring your product to market? Which vendors will you source the materials from and why? Can you guarantee how the product will perform in-store? These are some of the questions that product developers guesstimate answers for each season. Getting it right isn’t easy, and getting it wrong can be costly and even damage brand reputation.

However, thanks to advances in artificial intelligence, retailers using PLM with embedded machine learning capabilities know exactly which levers to pull to bring the most profitable on-trend products to market. Here are three ways that PLM with machine learning can help retailers increase their operating margins.

Reduce time to market

Speed to market is now the number one concern for fast fashion retailers. Why? Because they only have a little over a month to identify a trend, create a product aligned with that trend, and bring it to market. When retailers can’t react with agility, they find it impossible to have the right product in the right place at the right time. They face late deliveries, which leads to poor customer service and market “misses” that drain revenues and erode market penetration.

Being late to react to trends usually leads to higher overall product costs due to rushed orders, higher material pricing, and more expensive air shipping. Consumers begin to lose faith in the brand’s ability to provide trend-right merchandise. Negative feedback can spread like wildfire on social media, damaging brand reputation.

Retailers need to know which products are at risk of being delayed across complex global supply chains. They need to know the status of new collections currently in various phases of design and product development. PLM with embedded machine learning capabilities provides this actionable intelligence.

Direct business to better performing suppliers

If supply chain issues are causing product development delays, a PLM solution that uses machine learning can quickly recommend alternative suppliers at competing costs, in a geographical location that won’t increase costs or cause further delays.

In addition, these automated recommendations can factor in historical supplier performance and reputation to ensure that quality and compliance standards are met and the best performing suppliers are chosen. For brands whose goal is to source from ethical or sustainable vendors, machine learning eliminates the need for excessive research by filtering out suppliers who don’t fit certain criteria.

Drive product performance

A critical step in the product design process is learning from the past: which styles sold well, which ones had low margins, what specific feedback did you get from customers? The problem is that designers seldom have easy access to historical sales data and unfiltered customer feedback to help them answer these questions. PLM solutions with machine learning capabilities enable designers to quickly analyze sales performance and unfiltered customer comments about their products in order to improve the next year’s product designs.

By leveraging a PLM solution with embedded machine learning capabilities, retailers can determine which of the millions of possible combinations of product attributes will likely result in higher gross margins. They can identify which combinations of circumstances increase the risk of a product order being delayed, and direct business to better performing suppliers as a result.

To learn more, watch this on-demand webinar featuring Quach Hai, VP of Retail Solutions at PTC, and Matt Priest, President of FDRA.