At Zalando, we are constantly looking into ways to widen our assortment, in depth and width. This is to make sure that all fashion items are available anywhere and at anytime for our customers. Our Partner Program helps to bring this vision to life. Through the Partner Program, brands and retailers can integrate their own e-commerce stock into the Zalando Fashion Store and ship their products directly from their own warehouse to Zalando customers.
Following this, we not only want to offer the best and freshest assortment to customers, but a frictionless shopping experience throughout the whole process – including delivery and returns. We are constantly improving our service proposition and also want our partners to fulfill the high standards that our customers are used to – standards that some partners often struggle with due to limited logistics capabilities for certain markets.
With Zalando Fulfillment Solutions (ZFS), we’re now able to help our partners in the Partner Program with these challenges and offer up our logistics expertise, taking over all logistics processes from inbound to pick, pack, shipping plus returns. But better availability of products is regarded as extremely important – not only for Zalando to offer the best assortment, but also for our partners to further grow their business. With Zalando Fulfillment Solutions we are able to provide our current and future brand partners with highly customized and reliable solutions, enabling them to sell their merchandise through our platform and without having to worry about logistics concerns.
Zalando Fulfillment Solutions addresses different target groups - smaller brands and retailers, as well as bigger partners, by using synergies and the one parcel principle: More than half of the orders of an item from our Partner Program also contain an article from Zalando Wholesale. With all items from Partner Program and Wholesale in our Zalando warehouse, we can simplify the process for all parties involved, meaning customers no longer receive two different parcels, but one combined package, with shipping costs being shared with our partner. This is not only more efficient but also more profitable overall. However, bigger partners still sell their products via different channels and prefer full flexibility for their inventory. This introduces the idea of replenishment, with Zalando wanting to enable its partners to replenish the right amount of fashion items to reduce:
- Lost sales; due to insufficient inventory
- Inventory holding costs; due to too much inventory
To deliver on this we have developed the FAST Replenishment Algorithm, which serves ZFS partners with recommendations on what fashion items need to be replenished and in what quantity. In the following post, we address the challenges in the proposition, key product features, and possible improvements for future iterations.
Challenges and opportunities
In short, we face two main challenges in the project: The forecasting of demand and the delivery of operational excellence with our FAST supply.
Supply comes in two flavours: The ZFS partner’s replenishment and returns from customers. Both are by far not deterministic with regards to:
- The quantities our partner actually replenishes: In some cases, partners can have insufficient inventory units to follow the recommended quantity.
- The lead-time between when the partner has received the replenishment recommendation and when their replenished inventory units are available for sale.
- The quantities and lead-time of customer return.
Demand forecasting can be seen as even more challenging, for reasons such as:
- Fashion is seasonal, meaning a fashion article’s life cycle is short (< 180 days) and continues to get shorter (with fast fashion having a 28 day cycle).
- Demand steering with promotions (advertisements) while inventory management works on SKU level (named “article sample”, size, or EAN).
- Demand forecasting of fashion-type products is described as being a problem of high uncertainty, high volatility and impulsive buying behavior. Several authors advise against forecast demand for these products, but instead build an agile supply chain that can satisfy demand as soon as it occurs.
Replenishment planning is always integer planing and thus presents another challenge. It’s impossible to replenish the fraction of fashion item demand required for your intended days of coverage. Therefore it’s crucial to verify, for each demand pattern, the impact of rounding up, rounding down, or proportionally rolling the dice.
Key solution concepts
A FAST supply chain gives us a powerful strategic advantage. FAST is a reference to the speed of replenishment, which can be broken down into the following steps:
- Zalando calculates a replenishment recommendation
- Our partner coordinates their inventory availability and replenishment shipping schedules
- Zalando receives the replenishment
A high replenishment process speed is equivalent to shorter replenishment lead-time, and therefore equivalent to the lower inventory quantity levels needed to fulfill customer demand.
Currently, FAST is implemented as a weekly inventory review. Zalando, together with its ZFS partner BESTSELLER, is able to execute replenishment with a one week cycle-time. Other ZFS partners aim to increase their cycle-time as well.
The key contributions here are clear wins for both sides. Lower out-of-stock notifications mean higher sales, while the partnership yields lower inventory costs and thus higher margins.
How agile product development helped the process
Agile product development is a perfect fit for data-driven product development, especially when the product is a replenishment algorithm.
In order to start quick and learn fast, our Logistics Algorithms team focused on continuous interactions between the customer, our ZFS Partner, and Zalando, organised into weekly build, measure, and learn cycles.
The Logistics Algorithms team was able to successfully contribute real business value within one week by radically focusing on the problem and reducing the scope in order to build an MVP.
This was done with a script that created a CSV file with the ZFS partner’s SKUs and a “recommended” replenishment quantity. The minimal quality on “recommendation” leads to the question of how to assess the quality of any “ZFS Partner Replenishment Algorithm” and therefore what to measure. The Logistics Algorithms team started with some standard standard inventory control KPIs as a basis for this.
In order to build quick, the Logistics Algorithms team used Anaconda as their data science platform.
From the open data science pillar, Python and Jupyter Notebooks were used to collaborate and share results, including data science models and visualizations, as well as to reproduce results and govern the ZFS replenishment algorithm product as a whole.
On the data front, the team used standard ODBC connectivity to extract, transform and load sales, on top of inventory and article data from Zalando’s EXASOL. Postgres is our standard for data storage.
Any type of replenishment is based on forecasting the demand of items. The quality of the demand forecast is defined as the forecast accuracy, which depends on the level of detail and the time horizon. Our FAST replenishment algorithm requires SKU-level demand forecast for a time horizon of about one to two weeks. One great method to assess the demand forecast quality is benchmarking your performance within the industry. The Institute of Business Forecasting and Planning serves those benchmarks for the short-term, meaning they delivered a one month outlook of:
- Aggregate forecasts that had an average error rate between 10.4% and 15%
- SKU level forecasts had ranged between a staggering 27% to 37.7%.
Forecast errors on high volumes can cause greater issues for a business than slower moving SKU. If the stock-out is caused by low forecast accuracy on a fast mover, it makes a huge impact on sales volume and profitability. In the case that low forecast accuracy has caused overstocking, it holds too much working capital on inventory and leads to extra warehousing costs.
To forecast the demand, our Logistics Algorithms team applied standard quantitative methods such as a naive moving average with several lookback times (7 days, 14 days, 28 days, 42 days), as well as simple exponential smoothing based on historic sales data on an SKU-level. The demand forecasts for new articles perform best on a higher aggregation level with article configuration, brand or category. The team also applied the principle of combining severable reasonable forecasting methods which yielded more accuracy overall.
Key product features
For ZFS partners, features are configurable and include sales channels, replenishment cycle-time, as well as inventory cycle-time at the service level. We also provide automatic inventory detection for partners via their current inventory on hand in order to detect stock-outs. Historic sales data is also taken into account.
Stock-up recommendations on the SKU level are based on demand pattern segmentation and best-in-class forecast methods respectively when it comes to forecast accuracy.
How do we further improve the service?
To speed up the supply chain even more, our ZFS FAST Replenishment Algorithm must incorporate check-point events along the supply chain. This could look like the following:
- When our ZFS partner acknowledges replenishments
- When Zalando accepts replenishments inbound
- When ZFS partners ship replenishments
- When Zalando receives replenishments
- When Zalando stores replenishments
When the supply chain is controlled, Zalando and its ZFS partner are enabled to move from a weekly periodic review to a continuous review while processing multiple replenishment cycles in parallel.
The Zalando platform is an operating system for the fashion world, with multiple ways of integrating all sorts of fashion contributors and stakeholders. Our logistics services enable the platform, and ZFS is merely one example of how we cater to specific stakeholder needs. We see ZFS as supporting the growth of our Partner Program by meeting high delivery standards and supporting one of our core values: To make the fashion experience as frictionless as possible.
Currently, Zalando supports ZFS from only one dedicated warehouse. In the future, ZFS will be rolled out to multiple warehouses, which means the FAST Replenishment Algorithm must consider multi-warehouse allocation for ZFS inventory.
We expect an increase in the level of organisational and technology maturity as the next iteration of this service: From manual execution and supervision (build, measure, learn) to an even more automated approach. In the end, we aim to enable partners to further build up their business, becoming the go-to digital strategy for their growth. We see further partners and further countries being added to increase scope and scale our solution.