In mathematical optimisation, dynamic programming proposes simplifying a complicated problem by breaking it down into simpler sub-problems. Then optimising these one by one, starting with the most downstream problem, and working backwards towards the first problem in the chain.
When looking to improve the fashion supply chain, it is very tempting to jump in at the start: the beginning of a product's development, pricing, or manufacture. Although these areas require a lot of focus, it is, from an optimisation perspective, preferable to start at the end. By optimising Markdown, Outlet and Liquidation processes, you build the foundations on which to create a much more efficient system as a whole.
Over the past 12 months, the PreWarp team have run markdown processes on behalf of our brand partners that total in excess of $2bn in total inventory. During that time we have learnt what makes a successful markdown, and what stands in the way of realising their full potential.
Here are 5 points on running an optimal markdown process:
1. Trust and Communication, above all else
Communicate and be intentional about implementing a change management process.
Machine learning models and optimisation algorithms can do marvellous things, but this is still, ultimately, a human business and the actuators of any change are still people.
Merchandisers with a wealth of experience, and a deep understanding of their current metrics and how they impact their business, can show a healthy scepticism towards anything presented as an opaque “black box” solution.
The best way to counteract this is through collaboration; co-formulation of objectives and intentional change management. This may appear to be the simplest advice but is often the most overlooked.
At PreWarp we find the best results come when we actively engage with teams, taking time to explain the technical aspects of our product, reflect their internal workflows and learn from our brand partners. Embodying the endless collaboration between the Art and Science of successful Merchandising.
2. What you measure matters
Align teams around mutually agreed metrics that match up to broader business aims.
Issues arise when the aims of your optimisation, business and users differ.
For example, CFOs tend to favour revenue and margin metrics by channel, Merchandisers favour inventory liquidation and sell-through, while the business’s ultimate goal is profit maximisation across the product life cycle.
Setting targets that correlate with both the business’s aims of a Markdown and the metrics that core users are tracking each day is a crucial step for aligning teams around a successful Markdown.
3. Less is more: information overload
By using an analytics platform for the heavy lifting on data analysis, you can free your teams to focus their expertise where it counts.
Traditionally during a markdown process merchandisers and planners are individually analysing the sales performance of 1000s of products in very short periods of time.
Given the time and resource constraints on these teams, one major benefit of using a price optimisation platform like PreWarp’s is that it focuses attention on the c.10% of the collection that would benefit most from human input.
Although our modelling and optimisations work very well for the majority of use cases, by freeing merchandisers to focus on the exceptions - where experience and broader context is most important - we get a far better outcome than human or machine working in isolation.
4. Garbage in garbage out
Your analytics are only as good as the data that goes into them.
You put garbage in, you get garbage out. This engineering adage is another lesson that is critical to the success of any analytics platform.
Given the quick turnaround between data being available (weekly trading reports) and decisions needing to be made (<24hrs) for a successful Markdown, a data strategy is even more pertinent here.
The following approach to data has put us and our brand partners in good stead:
- • Be specific and plan ahead around data requirements.
- • Implement backups for integrations: they will fail at some point, so be prepared.
- • Adapt to incomplete data: nobody has perfect data and flexibility and contingency is vital.
- • Active communication of blind spots and delays on both sides.
These processes have enabled us to work with brand partners on implementing profitable change management while meeting them where they are on their digital transformation journey.
5. The value of Qualitative input
When running a markdown, do not underestimate the value of bringing in the diverse perspectives of your team around the world into your decision making.
By actively listening and including their input, we have seen a marked improvement in the outcomes for our brand partners.
One of the most highly valued parts of our platform is the ability of regional teams and store managers to provide qualitative insights alongside quantitative analysis, to give a more complete picture of what is happening on the ground in local markets and stores.
The first step towards a sustainable product lifecycle
Although starting at the end might feel counterintuitive, it is the essential first step towards building the foundations of a much more efficient product lifecycle.
We hope that these learnings that our team have gathered over the last 12 months are useful as you embark on your journey towards a Markdown transformation.
If you have any questions or are interested in our approach, please reach out on firstname.lastname@example.org.