The global fashion industry is a sophisticated, enormous machine that produces billions of items of clothes and accessories and sells them to millions of customers throughout the world. This market generated around 1.53 trillion dollars in sales in 2022. The Consumer Market Outlook predicts that by 2027, this amount would have nearly doubled to $2 trillion USD.

While sophisticated supply chain is a common theme for any industry these days, the fashion industry is tricky in particular due to short product cycles, wide array of products, segmentation of customers and often protracted lead times. In this blog, we discuss how AI powered Demand prediction and Inventory optimization in real time helps fashion brands overcome these challenges. 

Short sales cycles resulting in excess unsold inventory 

Conventionally, fashion trends lasted for nearly 10 - 20 years. But today, the advent of innovative technologies and social media has drastically condensed the life cycle of fashion trends. Fast changing fashion trends and seasonal launches also lead to very short sales cycles by introducing new styles in a relatively lesser duration. These practices have motivated customers to keep up with all fleeting trends while swiftly ditching the outdated ones. Eventually fashion companies struggle to gauge demand and plan supply accurately leading to unsold inventory at the end of a season or once the trend becomes outdated. They also lack liquidity for further crucial activities as their working capital gets tied up with the excess inventory. Even if they manage to sell this stock later, they still end up incurring a loss. 

Cyclicity and Excess Inventory Alerts

Demand prediction and Inventory optimizations become easier in such cases with the help of technologies like AI and ML. Kronoscope, AI powered Demand prediction solution simplifies this process even for SKUs which have short sales cycles. This is done with the help of Demand Sensing approach. It predicts the cyclicity of a particular SKU i.e sales patterns for each SKUs for specific/limited duration. This is also done across channels to help gauge the demand accurately.. These may be the first day of the week effect, the last day of the month, or workdays Vs. weekends. Thus training the predictive engine to factor demand patterns for short sales cycles in future predictions.

Further, the solution also has an inventory control tower feature that continuously monitors inventory and gives proactive alerts when there is excess inventory that is tying up working capital and increasing storage expense. This helps to quickly identify and liquidate this excess inventory. 

Inaccurate demand forecasting due to erratic demand peaks 

Demand trends for fashion and apparel products are usually erratic due to surge in customer demand during the cleansing phase. It is a common practice to clear out leftover inventory at the end of the season through clearance sales. There are also certain fad fashion trends that lead to an increase in demand. These peaks in demand make demand forecasting difficult. Oftentimes, brands end up predicting demand inaccurately resulting in understocking or overstocking.

Outliers Correction & Promotional uplift in demand 

Kronoscope helps overcome such industry specific challenges  by continuously sensing demand signals and refreshing them. It identifies any anomalies in the Historical data to reduce the error percentage while predicting demand.This way, any uplift in demand that doesn't have to be considered for future forecast will be detected. This makes sure that the demand prediction is accurate even when the trends are erratic.

In addition to this, some of these clearance/end of season sales could be recurring and it is crucial to capture their impact on demand to simplify the promotion planning process in future. The Events and Promotions feature helps record any promotions/marketing events to capture their impact on demand. This is then used by the system to accurately predict the promotional uplift in demand for similar promotions in the future.

Frequent new collection/store launches

As mentioned earlier, the fashion industry is characterized by frequent new styles and trends being launched every now and then. This is also a mechanism to sustain the competition in the market. It is challenging to predict demand accurately for such new product or store launches as there’s no historical data available. 

Similar collection/store attribute demand sensing

Kronoscope simplifies this by intelligently associating new products or new stores with existing ones that have similar attributes. Eventually, the system learns continuously and trains itself to predict demand and plan inventory for new products/stores independently.

In some cases when this similar attribute mapping is not possible, then the system    analyzes product entry for the different stores based on first weeks’ sales (early sales). This data is then used to train the system for further predictions. 

Aggressive Competition

With the growing popularity of online shopping and social media competition in the fashion industry is only getting more aggressive by the day. When a particular dress is not available or affordably priced by one brand, it is often replaced by another brand that has a slightly lower price or isn’t out of stock. Brand switching happens in a matter of seconds here. Hence it’s crucial to ensure availability of products and set the right prices to retain existing customers or to gain new ones.

Price Optimization, Out of Stock 

The Price Optimization feature allows users to experiment with different price points for various SKUs. This is done by establishing a price elasticity curve by capturing the price points and historical sales at each of these points. This essentially helps to identify the impact of price changes on demand in real time. Businesses can ensure that their revenue targets are met even while offering their products at a much affordable price.

Kronoscope also helps ensure that you don’t lose any sales due to product unavailability. It gives proactive alerts on out of stock losses and enables real time purchase plans for SKUs that are at risk of running out of stock.

Long delivery times

Ecommerce fashion brands usually struggle with long lead times and delivery times eventually affecting their prompt order fulfillments. These deliveries take anywhere between 5-7 days to get shipped which get stretched to nearly 14 days when there are supply chain discrepancies. Even for companies that have a sound logistics support, delivery time becomes longer owing to supplier uncertainty. This leads to poor customer experience and sometimes even losing a sale. 

Real time inventory optimization and dynamic safety stock adjustments

Kronoscope makes it possible to deliver orders at the right time and maintain availability levels across SKUs even when the supply is uncertain. With its sophisticated AI algorithms, Kronoscope adjusts inventory in real time as demand changes. When there are multiple suppliers for a certain SKU, it also helps in choosing the ideal supplier to fulfill various business needs by evaluating and scoring suppliers based on crucial factors like lead time, fill rate and price. 

On top of this, Kronoscope calculates dynamic safety stock based on the Theory of Constraints that helps to maintain the right quantity of safety stock. This becomes useful to meet any unexpected surge in demand. Essentially, Kronoscope equips you with the ability to right size your inventory at all times to fulfill your customer demand promptly.