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Introducing Smart Initial Allocation - Let predictive algorithms do the work!

"The current development step is very important for us, as we not only want to improve the overall in-season management with replenishment or alternative products but also make the entire business more sustainable" Ben Vermin, CEO & Founder

Sustainability is something on almost everyone's mind lately. Which is a good thing, because we only have one world, and we need to take care of it. But is sustainability possible in the fashion industry?

Imagine having to manually analyze in which part of the country consumers buy items and in which part they don’t. Imagine taking that a step further and analyzing the same item but then based on color too. Then imagine taking that even further and analyzing not only the item and color but also the size. You will be spending hours and hours analyzing this, not even to mention the chances of human error. No thank you, I'll pass on that!

So, what do you do then? Count your losses and throw anything that isn’t sold on time into the outlet ignoring the current trend in sustainability awareness? Or do you ask for help when you are not able to do it yourself anymore?

At Chainbalance, sustainability and battling waste are some of the top incentives. Therefore, each time a new feature is launched this is based on actual figures and trends active in the market. It is a big challenge and requires experience with product groups, product characteristics, and POS (point-of-sale). What kind of customer buys at that specific store and what do they buy? Gathering all this information based on actual sales data makes it possible to optimize the value chain even further.

Introducing Smart Initial Allocation - Let predictive algorithms do the work!

The main question asked when launching new items in a store is: “what product groups, styles, colors, and size keys and in what quantity needs to be available in-store to meet the initial consumer demand?” You will probably answer this question based on experiences you had with similar items in the past, right? Then why not have this done automatically!

The analysis of sales from the ‘mirror season’, a previous season with similar items, is used as the foundation for smart decision making, avoiding overstocking and running out too early. These analyses are extremely extensive and therefore very time-consuming. It is necessary to figure out what curation is needed for the specific POS based on the historical performance of the POS.

How does it work?

The A-B-C clusters are often used in predicting sales and roughly indicating the size of the POS and assortment. With the A-B-C cluster method, you mark your big size assortment with the letter A, middle size with the letter B, and small size with the letter C.

This method has been used throughout the years and has always shown the big differences between stores located in the north and the south of a country. But what happens when you zoom in on these locations and analyze differences in demand and trends on a city level?

What you really need to know is what the differences are between two stores in the same city. Yes, they both have their own customer journey, sell different products, and appeal to different customers with various shopping behaviors, but why are they so fundamentally different?

So, to create a dynamic and individuell cluster for each POS these differences need to be identified and change dynamically along with the products, time, and existing market conditions.

What you see is what you get, right?

When introducing new items onto the market the time of the season will play an important role. A store that only sells big, warm fluffy coats will probably be more successful in the winter. However, this is no longer a default rule. The buying behavior of consumers is becoming more and more dynamic. It is no longer what you see is what you get, but more a what you want is what you get the mindset. If you want that fluffy coat in August, then nothing is stopping you from buying it. Meaning that a static protocol doesn’t fully fit the industry anymore.

The textile and fashion industry has a solution for this. To optimally meet the dynamic demand throughout the year, the industry offers several collections and capsules, also known as Ready to Wear. Thereby 12-24 collections are not uncommon any more. This is a much better way to meet demand, but it also increases the efforts of the industry and retailers. The analysis effort increases immeasurably when you consider that 12 collections per year, with many SKUs, need to be managed according to dynamic clusters.

How does Chainbalance step in?

Through 12 years of experience in replenishment, learned business rules, and the application of dynamic and intelligent algorithms, automated decisions are made for the optimal initial allocation.

This is based, among other things, on dynamic clusters for POS performance and the performance of specific items in relation to the current market situation. For an initial calculation of the first allocation, sales information from just 4-13 weeks is needed.

From this, a forecast for the optimal quantity allocation per size, color, and style is created and made available to you.

"The current development step is very important for us, as we not only want to improve the overall in-season management with replenishment or alternative products but also make the entire business more sustainable. To do this, we must go into pre-season optimization together with our current customers. Which products are needed, especially how many and when a product needs to be reproduce or not, are crucial questions we want to answer."
Ben Vermin CEO & Founder

Start today and make your business more sustainable!

A sustainable business comes from understanding your customers better and using data to gain valuable insights. The current over-production and resulting leftover goods are hurting the environment and the business.

Chainbalance will help! After a successful start and test, we are now happy to provide other brands with this solution to make the entire business even more sustainable. You want to know about our solution for the initial allocation? Let's get in contact!

Graaf Engelbertlaan 75
4837 DS Breda
The Netherlands

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