Probabilistic forecasting: how Artificial Intelligence and Machine Learning make Fashion Retail more sustainable

Have you ever got lost in thousands of spreadsheets while chasing the best forecast for your products demand? Have you ever heard of the many benefits of data-driven probabilistic forecasting solutions?

Let’s first look at what “forecasting” means and the difference between human-instinct driven approach and data-driven advanced solutions. 

For markets and industries, the term “forecasting” means “prediction of a possible outcome or future state of something”. The traditional retail statistical approach implies lots of manual work and relies mainly on human instinct. Planners that deal with wide collections and huge number of stores are forced to simplify reality, looking at clusters, averages, or medians, that often leads to over stock slow selling products and big stores and/or understock best sellers and small but high-performance stores. A planner supported by a data-driven advanced platform, can instead take decisions at the most granular level while being democratic among the stores network, saving time and focusing on value-added tasks.

Artificial Intelligence and Machine Learning have turned Retail Planning and Merchandising from a stressful and high risky guessing process into an effective and agile method to increase productivity and sustainability, especially when applied to an unstable and volatile market demand. 

Artificial Intelligence is essentially the simulation of the human intelligence process by computer systems and machines. It includes the constant adjustment to new inputs to perform tasks and activities way better than a human being would. On the other hand, Machine Learning is a branch of Artificial Intelligence and computer science that imitates the human learning process through the continuous analysis of big data volume and algorithms. 

Digital innovation to Retail Planning becomes extremely more effective for the entire supply chain when AI and ML together with deep learning, data analysis and predictive modeling are applied to market demand forecasting. Why? Because it gives organizations a chance to prevent and predict changes instead of reacting once an event has already occurred, hence deriving new business strategies and maximizing performance. 

Why probabilistic analysis of market demand is essential in the Fashion industry 

To fully understand why data-driven forecasting of market demand plays a pivotal role for companies operating in the Fashion industry, let’s highlight the context in which they operate and the challenges they currently face. 

Ethical responsibility of “doing more with less”, is the key business strategy that considers the current scarcity of natural resources. The past couple of years – and the overall shock caused by the COVID-19 pandemic – re-prioritized corporate values and have sped up the profound transformation in the fashion industry, leading to an increase in demand for sustainable products and production approaches. “Zero waste” must be a result and no longer an impalpable goal set somewhere along the line; sustainability has stopped being an aleatory concept, becoming an active part of day-to-day business strategies. 

In such a complex and challenging context, advanced technologies such as Artificial Intelligence and Machine Learning are helping fashion retailers and brands to integrate sustainability within their supply chain. Such technologies not only automate manual, tedious or repetitive processes while reducing human errors and inefficiencies but also allow companies to make more informed, specific and data-driven purchasing and allocation decisions. In fact, when used efficiently, AI and ML can decrease the inventory by 25%-30%, all while covering the same or even bigger amount of sales. 

This is how probabilistic demand forecasting concretely helps retailers: by providing all the data necessary to evaluate possible future horizons and define which one is more likely to occur. For example, the new systems based on data science can now calculate the chance that each SKU code (SKU codes are essential for inventory control and management) will be sold across different channels and locations, considering business constraints such as visual merchandise, capacity, delivery times and operating costs. 

The probabilistic analysis provides a profound change in business approach, as it embraces uncertainty and accepts that virtually anything can happen. By doing so, companies can become more resilient and reactive to changes and sudden evolutions in the market demands and, in practical terms, lose fewer sales than before while purchasing less. 

By allowing such a change in mentality and operational process, Artificial Intelligence and Machine Learning can effectively and quickly help fashion retailers to reach the post-pandemic operational stability that is mandatory to stay competitive, profitable and to reach a high level in consumer loyalty.

Nextail: The smart platform for retail merchandising created by retailers for retailers 

To guide fashion clients in their path toward a successful Digital Transformation journey, Syscons Interactive has entered a strategic partnership with Nextail, the leading smart platform for retail merchandising. This cutting-edge solution is developed by retail experts, delivering agile and data-driven decisions to support omnichannel business growth in the currently volatile, uncertain, complex, and ambiguous (VUCA) market. 

Fashion companies that invest in listening to their data and in automating decisions can better avoid overuse of resources. This approach will allow them to adopt more sustainable practices such as in-season buying and localized allocation, helping them to achieve higher sell-outs and margins.

Nextail is a cloud-based modular platform that empowers retailers and brands to sell more with less stock, through hyper-local demand forecasting and agile process automation. The Nextail demand forecast and optimization engines overcome the limitations of traditional systems, anticipating a robust demand at the lowest level of granularity (SKU-Store/Channel) to maximize the probability of sales of each product while minimizing the inventory level and stockouts.

Nextail achieves these results by leveraging state of the art deep-learning models to predict demand, to decide where each and every product should be located across a retail network at any given time for a higher likelihood of sale, and by identifying similar products. What is more, as the time goes by, and the more data that Nextail algorithms can “train” with, the smarter they get.

The Nextail platform computes millions of scenarios for every single SKU, in milliseconds, and considers three different types of data for a unique 360 view of demand: a retailer’s own data for thousands of products, stores and channels; Nextail-generated data (e.g. product attributes, size curves, comparables etc.); and external data (e.g. seasonality patterns, weather forecasts etc.).

In conclusion, the same technology that makes retailers more agile, responsive, and cost-efficient in the face of demand shifts, are the same ones that help them advance fashion sustainability by avoiding detrimental overproduction and waste.

Syscons Interactive is part of the leading brand Syscons Group and specializes in Omnichannel & Digital Supply Chain Solutions. Get in touch with our consultants to increase the chance of success of your next big project.