Fashion’s business model will never return to the way it operated pre-pandemic. In recent years our industry has been moving further ahead, both upstream and downstream, and what we’ve experienced in the last year has only accelerated change across our entire value-chain. New software, modules and process changes, mean there’s no going back to the pre-pandemic days.
Fashion businesses both large and small, must continue to examine their supply chain models, continuing to build integrations into their PLM backbone operations. And businesses should now consider integrating e-Commerce into planning, design and development – and beyond to factories and their partners – in order to streamline end-to-end processes. And they should also use a single set of harmonised data. As we move beyond 2021, retailers and brands should carefully consider their value-chain partners and how, together, they can work to streamline the integrations between software solutions – solutions that share a common set of data, driving the inputs and outputs that in turn, will deliver greater speed, efficiencies, insights and near-time transparency.
Complexity & Customer Expectations
The global pandemic has further accelerated what was already a surge in online shopping; without COVID, it would no doubt have taken 5 or even 10 years to evolve to where we are today when it comes to Internet buying. With what feels like a total shift to e-Commerce, there has been further competition between retailers and brands of all sizes, driven by retail survival. That shift is also making history with China last week hitting 52.1% of the country’s retail sales coming from e-Commerce, up from 44.8% a year ago. This means that for the very first time anywhere in the world, the majority of retail sales for an entire country have transacted online. And by the year 2040, it’s estimated that 95% of all purchases with be through e-Commerce.
Whether these businesses were already established online, or are fairly new to the online world, this ever-increasing online competition will of course result in greater choice for the consumer. But on the other hand, it will create greater complexity in the number of product options and SKUs for retailers and their value-chain partners to turn around – in less time!
To help manage this disruption, we need to turn the working model from a traditional supply-chain to a new value-chain model. You might be asking what the difference is between a supply-chain and a value-chain; put simply, the supply-chain operates on a mostly disconnected basis, whereas the new value-chain model refers to a digitally connected, end-to-end chain, that can be integrated to help support the on-demand, ‘Just-In-Time’, design, development and manufacturing processes.
To achieve this goal the retail and manufacturing sectors will need to share data coming directly from e-Commence. This data can come in multiple forms: structured data, which is basically regular data that includes names, addresses, gender type, a person’s age; preferences of customers visiting a particular site; previous sales history; and returns data. Naturally, companies are able to collect this data without much effort, especially if they operate their own platforms and obtain data analytics from partner platforms. There is also unstructured data that can be gathered from the internet, competition sites, social media including Instagram, Facebook, TikTok, and YouTube, as well as scrapping data including peoples likes, dislikes, retweets and views. It is much harder to collect this unstructured data, and harder to obtain insights, but nonetheless important. 74% of consumers reportedly rely on their social networks to make purchasing decisions, and 75% of Instagram users have taken an action, such as visiting a website, after looking at an Instagram ad. In fact, according to eMarketer, worldwide spending on Facebook and Instagram combines will reach nearly $95 billion annually this year.
It’s these predictive datasets that will allow companies to deliver just what their customers need and will be expecting from brands and retailers. In order to get closer to the bullseye, companies must become smarter at predicting what their customers want. Today, e-Commerce is not just based on individual retailers’ marketing abilities but also on their abilities to use analytics to predict what their customers are likely to purchase.
The same predictive analytics can also help companies to forecast extreme weather events that may require a business to change umbrellas to bathing suits overnight!
In turn the structured or unstructured data can be gathered and converted to practical insights feeding trends, be that silhouettes, materials, colours, plains & prints, or price points that will be converted to new style options. Once the design process is completed and approved, the design outputs will be pushed automatically into the development, sampling, testing and costing processes. Unlike the traditional supply-chain model, suppliers will be able to view the continuous end-to-end process and will use the same data inputs/outputs to model their factories, to support a new, demand-driven eco-system.
The Value-Chain
Brands, retailers and e-Commerce companies have to manage their supply-chains efficiently, using the same datasets that are now shaping the next trends, designs and product developments. They work with a vast number of supply-chain partners, made up of factories, mills, components & trims, and packaging suppliers. Using datasets, our industry is gearing up to turn the traditional supply chain into what is starting to emerge as the new, digitally connected value-chain. Businesses will not be able to survive in the long run unless they change their models to data-driven ones. The smartest companies are increasingly turning to data-driven models in order to re-shape and re-invent their supply chain, into value-chain processes.
These growing value-chain advancements will force companies to urgently innovative and create end-to-end value chains that will see e-commerce feeding both ERP and PLM simultaneously, placing contracts and orders, as well as new designs, developments and manufacturing. So far, we’ve seen the online world focusing on the downstream, right down to the last mile; we’ve even seen high-street retailers turning their stores into click-and-collect and returns facilities.
The PLM backbone will also go beyond the downstream factories and into distribution centres, enabling warehouse staff to inspect products using the most up-to-date (real-time) product specifications and supporting visuals.
The New Downstream Technology Stack
When it comes to PLM, businesses are experiencing various levels of success through implementing automated solutions across the value-chain. The tech-savvy leaders have been sharing the use of their own PLM solutions, connecting them deeper into the downstream processes. Some have changed the model to operate on a co-design basis, with the main brief coming from the design teams, but options, sampling and testing being completed by the suppliers. Today, we are also seeing greater integration of 3D when it comes to the suppliers. We are talking about 3D design and development (styling, fitting, etc.), leaving the virtual design and photography to the retail and marketing teams. The main objective is to improve visibility and transparency across the entire value-chain through near-time information sharing and big data management.
Reducing Supplier Risk
2020 was an incredibly challenging time for suppliers around the globe. I think many would even describe it as a ‘car crash’ of a year; with many companies not able to trade, retailers and brands stopped orders overnight, and manufacturers had no choice but to layoff millions of workers. Suppliers should not have to carry the burden of risk, in a future demand-driven value-chain. Parties should be able to work together to share the risks and the rewards, no longer working on ‘guestimates’ of what might sell, but rather working together on what was sold – or at the very least using AI (Artificial Intelligence) and ML (Machine Learning) to predict more accurately what is likely to be sold in the right style, colour and size options.
The industry needs to develop a pro-active, partner-driven approach to sharing retail and manufacturing solution eco-systems which, operating together, will not only streamline end-to-end processes, but they will lead to reduced risk, and indeed create greater value. Neither parties can afford to operate on what is already an outdated supply-chain model, and it’s critical that all parties (both downstream and upstream) should work together to re-model the supply-chain, with the aim of turning it into tomorrow’s digitally connected value-chain.
The day is nearing when we will move away from what is ‘best guessing’ of what was sold last season and what will sell next season, and shift toward near-time predictive analytics and intelligence. Retail contract and purchase order systems that are based on bulk will quickly become a thing of the past, and continuous, data-driven value-chain management must become retail’s new model. The model should closely mirror other industries like automotive, which has been working on the ‘Just-In-Time’ model since the 1960s and, today, only manufactures what it sells. I expect that many readers will be saying that we are not selling cars, but garments that can be designed and sourced in a matter of weeks, but even so, we can make strides to turn this approach into a less risky model than that we operate today.
Moving to a new digital value-chain will require strategic transformation. We will need to review our end-to-end businesses, both internally and externally. The end goal should be to collaborate on projects with all value-chain partners – not only with factories but with all tiers, mills, component and trim suppliers, testing companies, and so on. Success will come from deep integrations and relationships based on mutually shared eco-systems, processes, and common data models. IoT instruments will help by providing the required transparency and visibility that builds trust, driving the required ongoing transformation.
