Utilising PLM to fuel growth and fulfil demand
2 August 2016 | Features & Interviews | Source: Lee Adendorff
Faster time-to-market is perhaps one of the clearest advantages of product lifecycle management (PLM) software, as greater supply chain transparency enables more efficiencies to be made, from the designer's pen to the store shelf. But connecting to other business processes through PLM, such as retail metrics, supplier inventory as well predictive analytics to forecast consumer choices, is opening up an array of new business possibilities.
The evolution of PLM as a means to not only manage the supply chain but to also fuel growth has been gradual. Many analysts agree that companies are a far cry from employing the full capabilities the software has to offer.
"Most are still very much tied to the way they've always worked in the past – and often using PLM as little more than a PDM (product data management) system," says Robert McKee, global fashion industry strategy director, at US-based international software provider Infor.
"The old fashion mantra was 'seduce the consumer and control the supply chain.' Now, we have a new industry mantra: 'listen to the consumer and collaborate with across-value networks.'
"The ability to loosely couple customer relationship management, demand management, and supply networks becomes critical to completing the full lifecycle of any fashion product. PLM can't only be about product development – the lifecycle has to be about the past, present and future of fulfilling consumer demand."
The enhanced connectivity of PLM reflects the way new products are being developed, according to Andrew Brown, managing director at UK-based specialist Fast React Systems.
A good example of PLM that integrates this increased connectivity is the Bamboo Rose platform, incorporating the rebranded and revamped PLM formerly known as TradeStone. The new system offers a slick interface with appeal for the perpetually connected, who can share design and trend ideas, especially through mobile apps, source suppliers and access common libraries in real time, as well as access more traditional PLM functions such as a bill of materials, landed costs and distribution.
The software, being employed by 400 brands, according to the US-based company, is being promoted as a platform for the 'new retail economy,' where a large part of trend spotting and even production decision-making is made on the fly through mobile apps.
Internet of things
Such power could be especially useful as the 'internet of things' (IoT) continues to prove a disruptive force in how companies conceive of product development. Many PLM software vendors have already predisposed their products to cater for product development based on data harvested from the IoT.
US-based PTC, for example, released the latest version of its retail PLM solution in April this year, PTC FlexPLM 11. The software makes use of ThingWorx technology that allows PLM to incorporate data from physical objects (such as tags and labels), other web or retail databases and legacy software systems in real time, to inform production decisions.
American market researcher Gartner has forecast that 25bn devices will be connected to the IoT by 2020. Meanwhile, UK-based software provider Evrythng has announced a major collaboration with Avery Dennison Retail Branding and Information Solutions (part of US-based smart label vendor Avery Dennison Corporation) in April this year to connect 10 billion items of apparel and footwear over the next three years to the IoT.
This large-scale deployment of unique identifiers for clothing and footwear is a significant extension of 'radio frequency identification' (RFID) tagging and 'near field communication' technology across product categories and price points, which in turn will allow a significantly larger pool of data to be collected about choices customers are making in-store while shopping or browsing online and what informs their selection behaviour.
Still, the leap to being able to faithfully predict what consumers will buy next season is a quantum one, and still relies on human intervention. Automation of these predictions is, however, well underway.
The BlueCherry Suite from US-based CGS, for instance, uses computer-generated predictive analytics to optimise assortments, inventory and production operations. According to the company, it can predict changes in consumer behaviour and demand based on season, location, promotions and other factors, intersecting this data with inventory and supplier data in real time to fulfil these predictions.
Data that informs predictive analytics is also being fed in from outside the traditional supply chain.
The acquisition of health and nutrition apps MyFitnessPal and Endomondo by US-based Under Armour last year saw one of the world's most successful activewear companies gain access to a massive amount of data about the demographics, size, lifestyle habits and fitness aspirations of the apps' 100 million registered users.
Reading and understanding the consumer's expectations to predict future demand is the way of the future, according to McKee.
"Social is the voice – the source for the data that will tell us what the consumer is thinking. Leading companies are already tracking consumer behaviour through the use of social trending and allowing that to lead line planning, assortment planning, collection planning, which are all PLM inputs. Today much of this is being done in a very manual way. Automation of this effort is the future," he says.
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