AI is a game changer: Transforming demand forecasting

AI is making a powerful impact across industries, and it is here to stay. But can it truly revolutionize the fashion industry? The answer is a resounding yes! One of the most transformative changes lies in demand forecasting. As fashion leaders adopt AI, they are gaining unprecedented precision and insight, reshaping how they anticipate fashion trends and respond to consumer demands.

According to Just Style’s Buyers Guide, "Zara is among the retailers leveraging AI to improve in-store customer service and support the BOPIS model." A case study by AI Expert Network highlights that Zara has, “integrated AI into various aspects of its operations, not only boosting efficiency and customer engagement, but also extending these improvements across its supply chain and inventory management.” Other retailers, such as Walmart and Amazon, are also using AI to refine demand forecasting to predict trends, manage inventory, automate warehousing, and monitor competitor pricing.

To appreciate the transformation happening in the industry, let us explore how AI is surpassing traditional methods and driving innovation forward.

Traditional vs AI-Driven Demand Forecasting

• Effective forecasting requires accuracy. Traditional processes rely on historical data and rigid models. This, combined with infrequent updates can lead to inaccuracies and obsolete forecasts as soon as market conditions change. Unpredictable consumer behavior results in volatile demand, make it difficult to detect and respond to short-term changes. In contrast, AI forecasting algorithms can improve accuracy by continuously updating forecasts based on current information from larger data sets, enabling brands to sense and respond to short-term changes in demand.
• Ability to plan different scenarios and manage risk. Traditional forecasting methods, which rely on simpler statistical techniques and historical information, offer only basic scenario planning and risk assessment. If that plan doesn’t react to changes promptly or adapt to different scenarios, it can lead to inefficient demand management and increased costs. However, AI-based forecasting can simulate various scenarios and assess their impact on demand, helping businesses to quickly evaluate alternatives and prepare for the different outcomes. By analyzing patterns and identifying potential anomalies in the data, AI can better identify potential risks and uncertainties.
• The need for efficiency and automation. The long-established forecasting methods often require substantial effort for data analysis, model adjustments, and forecast updates, making them slower, less dynamic and less efficient. In comparison, AI-based forecasting provides a significantly higher level of automation and efficiency compared to traditional methods. AI-based forecasting automates many aspects of demand forecasting, including data collection, model training, and forecast generation. This automation increases efficiency and continuously improves forecasts through ongoing learning and optimization.

Adopting AI for Future Growth

Apparel and footwear companies worldwide are increasingly adopting AI for their demand forecasting needs. This shift is driven by the need to manage complex supply chains, respond to dynamic consumer preferences, and optimize inventory levels. Overall, AI is transforming demand forecasting by enhancing accuracy, agility, and efficiency, enabling businesses to make informed decisions and stay competitive in dynamic markets. By leveraging AI technologies, apparel companies are improving their demand forecasting capabilities, leading to better decision-making, more efficient operations, and improved customer satisfaction.

CloudSuite Fashion

Built on Infor’s OS platform our solutions come pre-wired for AI and pre-defined machine learning (ML) templates which can typically be deployed in under 90 days. Find out more about our solutions, or, read our latest brochure on how to enhance your decision-making with AI and machine learning.