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How Can Demand Planning Help the Fashion Industry to Overcome Short Product Cycles?

demand planning and forecasting


When buying a luxury dress or a purse, you probably do not pause to consider the complexities in the supply chain and analytics for ensuring that the fashion items are in stock. Sophisticated supply chain planning is a part of any industry with a complex demand but the planning for the fashion industry in particular is difficult to manage as it caters to a huge variety and segmentation and often protracted supply lead times.

The fashion industry operates in a fast-paced environment with ever-changing trends and short product cycles. In such a dynamic landscape, demand planning and forecasting play a crucial role in helping fashion businesses navigate the challenges and capitalize on opportunities. Demand forecasting for retail and fashion products also has complex characteristics, with hard-to-predict, sporadic demand, high volatility and impulsive purchases.

Understanding Short Product Cycles in Fashion

Short product cycles are a defining characteristic of the fashion industry. Consumers’ preferences rapidly shift, influenced by factors like celebrity endorsements, fashion shows, social media trends, and fast fashion brands. As a result, fashion companies face intense pressure to deliver new collections and keep up with the latest styles. However, this fast-paced environment poses significant challenges, including inventory management issues, production constraints, and increased costs.

Demand Planning for the Fashion Industry

Demand planning and forecasting involve forecasting and managing the demand for products. In the fashion industry, accurate demand planning is crucial to anticipate customer preferences, optimize production schedules, and align inventory levels with expected sales. By leveraging historical data, market trends, and consumer insights, fashion businesses can develop robust demand plans that minimize the risks associated with short product cycles.

Fashion retailers possess the tools and data necessary to forecast and shape consumer demand by analyzing metrics such as sell-in, sell-out, sell-through, conversion rates, campaign ROI, stock turnover, markdowns, and store performance. Their close proximity to customers enables them to swiftly and efficiently anticipate and respond to trends, as well as influence collection development during the season, considering distribution and production aspects based on supply chain limitations.

Necessities for Demand Planning for the Fashion Sector

There is no hard and fast rule for demand planning and forecasting for the fashion industry in all phases of the life cycle but there are ways to get better results.

In the fashion industry, a distinction can be made between “continuous products,” which span multiple collections, and “new products,” specific to a particular collection. Demand estimation for continuous products relies on traditional time series analysis algorithms, whereas for new products without sales data, forecasts are generated by associating the item with similar ones based on attributes like style, fabric, color, and size. This approach can be further enhanced through the utilization of machine learning for new product forecasting.

To effectively plan demand in the fashion industry, it is essential to consider different levels of granularity such as line, family, model, item, and item/warehouse. This approach enables the analysis of sales history, the establishment of baselines, and the exploration of various scenarios through what-if analysis. By incorporating target and/or budget values, including commercial overrides, fashion businesses can make informed decisions and optimize their demand planning and forecasting process.

To achieve accurate demand forecasting in the fashion industry, it is crucial to possess the following essential demand planning and forecasting capabilities:

  • In-season Short-Term Demand Forecasting: Forecast demand changes during the season to guide replenishment strategies at points of sale effectively.
  • Outlier Filtering: Identify and filter erratic demand peaks or outliers during the cleansing phase of data analysis to ensure accurate forecasting.
  • Cross-Functional Collaboration: Facilitate “top-down and bottom-up” collaborative processes across various teams through workflow rules, promoting effective communication and alignment.
  • Comparable Product Launch Profiles: Utilize launch profiles of comparable products, considering variables such as communication level, events, viral campaigns, etc., to model aggressive forecasts and optimize product introductions.
  • Early Sales Analysis: Analyze the first weeks’ sales data to make informed decisions regarding product entry and allocation across different stores, enabling efficient inventory management.
  • Optimal Stock Mix: Automatically determine the best combination of service level and fixed stock by item/class, supporting the achievement of optimal inventory levels.
  • Dynamic Stock Allocation: Effectively allocate stock throughout the supply chain in an omnichannel environment, considering factors such as demand patterns, customer preferences, and logistical constraints.

To succeed and flourish, fashion businesses must adopt a robust and innovative supply chain planning approach supported by advanced tools. These measures are essential for navigating the industry’s challenges, seizing opportunities, and staying ahead in an ever-changing landscape.

Benefits of Effective Demand Planning

Improved Inventory Management: Demand planning and forecasting helps fashion companies strike a balance between overstocking and understocking. Accurate demand forecasts enable businesses to optimize inventory levels, reducing the risk of excess stock or shortages. This, in turn, lowers holding costs and minimizes the need for markdowns.

Enhanced Production Efficiency: By aligning production schedules with anticipated demand, fashion companies can optimize their manufacturing processes. This leads to improved efficiency, reduced lead times, and better utilization of resources, ultimately mitigating the challenges imposed by short product cycles.

Minimized Costs and Increased Profitability: Effective demand planning and forecasting enables fashion businesses to allocate resources strategically. By avoiding overproduction or last-minute rush orders, they can reduce production costs and enhance profitability.

Enhanced Customer Satisfaction: Understanding customer demand patterns and preferences allows fashion companies to provide the right products at the right time. By meeting customer expectations promptly, brands can build loyalty and foster positive customer experiences.

Leveraging Technology for Demand Planning

Technology plays a pivotal role in optimizing demand planning processes in the fashion industry. Advanced analytics, machine learning algorithms, and AI-powered demand planning software enable businesses to analyze vast amounts of data, identify trends, and make accurate predictions. By leveraging these technologies, fashion companies can gain a competitive edge, adapt to short product cycles more efficiently, and make informed decisions.

About the Company

Fountain9 offers Kronoscope, an advanced demand planning software that leverages artificial intelligence (AI) to deliver precise inventory forecasting, mitigating stockouts and wastage risks. Utilizing a state-of-the-art demand sensing and pricing engine, Kronoscope incorporates historical sales patterns, seasonal variations, holidays, markdown events, and pricing fluctuations to provide accurate predictions for future inventory needs and optimize pricing strategies. By aligning predicted demand with supply-side data, the software identifies optimal suppliers and replenishment quantities, minimizing the chances of stockouts and unnecessary inventory waste.

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