Predictive Optimization For Fashion

Lokad’s predictive optimization technology tackles every single decision through the entire life-cycle for every product from the brand. Our technology is a mix of machine learning - used to extract patterns or predictions from the data - and of numerical optimization - used to generate optimized decisions.

The preparation of the new collection starts with the delivery of an assortment optimization - also referred to as a range plan. The Lokad technology lets you compose the right range of sizes, colors and shapes on top of the initial designs. This range plan is intended to fit as closely as possible to the wishes and expectations of your customer base. Lokad can help you generate the business case that supports the range plan, and offers the possibility to planners to refine the plan with their high-level insights - as opposed to micro-managing every single product variant.

As soon as the range plan is finalized, we deliver a purchasing optimization, suggesting exactly how many units should be produced or purchased for every single product variant - and when to order as well. Naturally, most products have never been sold before. This task is typically complicated by multiple MOQ (Minimal Order Quantities) constraints, price breaks, as well as varying lead times (e.g. Chinese New Year slowing down production in Asia). Lokad can also optimize the transportation mix (e.g. sea vs. air freight) and the source mix (e.g. overseas suppliers vs local suppliers).

As an example, our MOQ solver can tackle multiple overlapping MOQ constraints: there might be a MOQ at the product level (e.g. a minimum of 100 units per product for every purchase order), another MOQ at the fabric level (e.g. a minimum of 3000 meters of fabric per color), and a final MOQ at the supplier level (e.g. a minimum of $50,000 worth of merchandise purchased per order). Addressing all of these MOQs while keeping stock levels under control is a major hassle when MOQs are processed manually. Lokad streamlines the process entirely through numerical solvers that allow to identify the most profitable purchase order “enveloppe” that satisfies all given constraints.

We also take into account discounts originating from loyalty programs - uniformly applied over many products - which may replace and sometimes supplement discounts at the product level.

Assortment optimization, purchasing optimization, stock allocation optimization, identification of best-sellers and slow-movers, pricing optimization: Lokad can be used to optimize all these decisions - and more - with a consistent end-to-end perspective on the products’ full lifecycle. However it’s also possible to start small with a more narrow scope. For any inquiries, drop us an email at contact@lokad.com

Once again, assortment matters, and the uplift of demand depends on the overall promotional intensity. It’s unwise to expect the same uplift if the product is the only one being promoted in the store, compared to a promotion applied to the whole store. Forecasts don’t just stop with the demand; returns and lead times must be forecasted as well.

Every source of uncertainty requires proper statistical forecasts, and while future demand is central, there are needs beyond demand forecasting and we have engineered our technology accordingly. Those forecasts are also usually probabilistic and tend to be combined with the demand forecasts themselves - e.g. forecasting the demand over the lead time. The latest generation of our forecasting technology relies on differentiable programming. This descendent of deep learning is particularly suitable to deal with sparse and intermittent demand as commonly observed in the fashion industry.

What about weather and social media?

Our experience indicates that most companies are dramatically underusing their own “basic” historical data. Most demand forecasts are not even leveraging the customer loyalty data, and most purchase orders are made without a formal lead time forecast. Thus, while we do not disregard taking clues from external data sources like social media or weather data, we firmly believe that companies should start by making the most of the data they already have.

Instead, our forecasting engine relies exclusively on advanced machine learning algorithms to automatically detect the similarities that may exist between products, and to identify, by itself, which specific products are relevant for forecasting a new product in the collection.

This automatic detection of similarities relies on the numerous product attributes that are typically present in fashion: product type, product family, size, color, fabric, style, price point, brand, etc. While one might be worried about the amount of data required, our experience at Lokad indicates that catalog data, as it exists for operating the facets of an e-commerce front-end for example, is usually enough for achieving good results.

Traditional forecasting solutions that are based on manual pairing between products are too time-consuming to be effective - there are too many pairs to consider - since it is precisely the pairing that happens to be the main ingredient of the forecasts.

Due to the ineffectiveness of this method, companies tend to revert back to their spreadsheets as the manual pairing forecasting solution fails to deliver the necessary value. Lokad tackles the challenge head-on, focusing on the core difficulty of the challenge, instead of passing the burden on to the users.

However, at Lokad, while we can refer to this forecasting process as product pairing, we do not assume that there is a 1-to-1 mapping between products from an old and a new collection. For example, one product may be split into multiple variants, which may generate cannibalizations. Then, another product might be truly “new”, with no close-matching past products. In such case, the forecasting engine falls back on broader considerations, such as the product category, family, brand or price point.

The first purpose of those economic drivers is to support the numerical optimization process that generates the decisions (e.g. purchase order quantities or product discounts). There is no optimization without measurement: the economic drivers are literally what makes the optimization process work.

Lokad is not a substitute for deep strategic insights (those come from your team’s expertise), our technology is merely designed to make it possible to deploy at scale those strategic insights - as reflected through the economic drivers - in every single decision taken by the company.

The second purpose of those drivers is the “whiteboxing” process. The purpose of the whiteboxing process is to deliver to your teams a high degree of transparency for every single decision generated by Lokad. Your team needs to understand why this decision is suggested by Lokad. In order to achieve that, every decision generated by Lokad comes with its own set of metrics - measured in euros or dollars - which explain the business intent of the decision. Those metrics are the different reflections of the economic drivers. Our experience indicates that this approach is superior to trying to shed light on the algorithmic fine-print of the calculation, that is not only incredibly tedious, but also largely nonsensical except to machine learning experts or numerical optimization experts.

From a management perspective, economic drivers are a powerful mechanism to steer the company in the right direction. These drivers are not seen as “immutable” but on the contrary are actually always susceptible to change, in order to reflect the evolution of the market. Lokad’s technology is designed to provide a high degree of agility: with limited efforts, it is possible to elaborate complex what-if scenarios portraying various alternative strategies.