A Glimpse Into the Future - Intelligent Fashion Forecasting
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Intelligent Fashion Forecasting

In this post, we look at why intelligent forecasting is key in putting your best foot forward in the fashion industry.

The modern fashion consumer is dynamic, fickle and unpredictable, and despite the best efforts of the industry, demand for fashion products is incredible difficult to forecast. The fashion industry is complex with a high degree of chaos, and in recognition of this dynamic, many companies have moved their focus away from forecasting and instead, created strategies which enable products to be designed, manufactured and delivered based on real-time demand.  In fact many fast fashion companies, such as H&M and Zara, have focused efforts into their supply chains and drastically reduced lead times by way of a reward.    

Of course forecasting remains a crucial element of fashion, but must be executed in the right way, using technology which is designed to support the level of complexity involved in fashion. Platforms such as SAP HANA harness the multi-layered dynamics involved in the fashion industry to optimize forecasting.     

Fashion facts

The fashion industry is subject to the following dynamics, an in-depth understanding of which is key to forecasting accurately. 

  1. Short life‐cycles: the product is often ephemeral, designed to capture the mood of the moment; consequently, the period in which it will be saleable is likely to be very short and seasonal, measured in months or even weeks.
  2. Short selling season: today’s fashion market place is highly competitive and the constant need to ‘refresh’ product ranges means that there is an inevitable move by many retailers to extend the number of ‘seasons’ in the year. The implications of this trend for supply chain management are clearly profound.
  3. Long replenishment lead times.
  4. High impulse purchasing: many buying decisions by consumers for these products are made at the point of purchase.
  5. High volatility: demand for these products is rarely stable or linear. It may be influenced by the vagaries of weather, films, or even by pop stars and footballers. There are numerous sources of uncertainty in a fashion supply pipeline, starting with demand through to the reliability on the part of suppliers and shippers.
  6. Low predictability: because of the volatility of demand it is extremely difficult to forecast with any accuracy even total demand within a period, let alone week‐by‐week or item‐by‐item demand.
  7. Tremendous product variety: demand is now more fragmented and the consumer more discerning about quality and choice.
  8. Large variance in demand and high number of stock keeping units: as a result, the volume of sales per SKU is very low, and demand for SKUs within the same product line can vary significantly.

Forecasting on different levels

The fashion industry needs forecasting capabilities at two main levels of data aggregation:

  • The “family level” is composed of items of same category, i.e. t-shirts, trousers, handbags, and this enables companies to plan and to schedule purchase, production and supply in the mid-term. For this aggregation level, historical data usually exists.
  • The “SKU level” which is required to replenish and to allocate inventory in stores at a shorter horizon. At this level, references (SKU) are ephemeral since they are created for only one season. Thus, historical data is not available, even if many items more or less similar have usually been sold in previous seasons.

The role of historical data  

Most fashion items are sold during only one season and companies have to estimate the sales without any historical data. The forecasting system should be then designed for new product sales. New product forecasting is hugely challenging as no precedent exists.  Indeed, standard forecasting methods are not suitable. In this context, a two-step methodology is applied:

1. To cluster and to classify new products to forecast their sales profile (mid-term forecast).

2. To adapt and to readjust this profile according to the first weeks of sales (short term forecast).

If no historical data exists for the considered item, but similar products have already been sold in previous seasons, this can be used. Indeed, new products usually replace old ones with almost the same style and/or functionality, i.e. a skirt or jacket. It is therefore possible to use historical data of similar products to estimate the sales profile of the new products.

Thus, to forecast the sales profiles of new products such as garments with clustering and classification techniques, descriptive attributes (price, life span, sales period, style) of historical products and new products should be taken into account. The aim is to model the relationship between historical data, i.e. between sales and descriptive criteria of related items, and then to use these relationships to forecast future sales from descriptive criteria of new items. These relationships are often complex and non-linear. For this kind of problem, machine learning methods have demonstrated high degrees of efficiency for building simple and interpretable pattern classification models

The intelligent forecast within SAP HANA is a statistical forecast tool with built-in models incorporating trends and seasonal factors. It includes 2 forecasting methods. SAP Business One automatically selects the best algorithm, TESM or LRDTSA.   

TESM stands for Triple Exponential Smoothing. TESM is used to handle time series data containing seasonal components. It works by incorporating a stationary component, trends, and seasonal factors. Both the trend and seasonal factors can be additive or multiplicative in nature.

LRDTSA stands for Linear Regression with Damped Trend and Seasonal Adjust. It is chosen for forecasting when times series data presents a trend. A damped smoothing parameter is used to smooth forecasted values and prevent over-casting. This method also detects seasonality in your data in order to adjust your forecasting results.

Modern tools

There’s no doubt that fashion forecasting is challenging to say the least, but with the right tools, such as those highlighted here, fashion businesses can apply all relevant insights and algorithms to allow them the best view into the future possible. With market volatility at an all-time high, those companies who ignore the capabilities afforded by modern solutions will no doubt pay the price as their competitors forge ahead.

For more information on how Argentis can help your business to improve its forecasting, contact us.

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