viernes, 16 de octubre de 2015

The apparel industry uniquely combines characteristics of make-to-stock manufacturing with consumer-driven fashion volatility. While an apparel maker may have a base demand that is amenable to cost-saving lean manufacturing, its fashion-driven business requires enhanced agility to achieve needed service levels in the face of: 

Short lifecycles. Fashion products are saleable for a very short period or season, measured in months or even weeks. 
High volatility. Demand is rarely stable or linear, and may be influenced by factors such as the weather, popular culture, or celebrity tastes. 
Low predictability. The volatility of demand makes it extremely difficult to accurately forecast overall demand within a period, or item-by-item. 
High impulse purchasing. Consumers decide to buy these products at the point of purchase. A consumer cannot be stimulated to buy unless the product is present.

Setting up the right solutions are crucial for your business to maintain customer satisfaction, maximize sales and reduce inventory carrying costs.
SAP Business One can do the heavy lifting of demand planning, such as calculating when to replenish inventory, while still enabling retailers to give approval or manually make inventory adjustments when necessary. These are some tips you can follow: 

1. First, identify your seasonal inventory. 

To improve your demand planning and forecasts in this area, you need to balance historical sales data with external factors that could affect your sales. Sales forecasts cannot be planned at SKU level long before the season starts, as this planning is more related to style-color and the style profile (gender, type region, etc.)

2. Secondly, identify your inventory bread-and-butter items that consistently sell all year.
With clothing retailers, identifying bread-and-butter items is more complex because what sells depends on what’s currently in style. This is especially true with the female clothing line. As a clothing retailer, you may know that you need to order more tank tops during the summer season, but the trick is identifying which style tank tops. That’s going to be based on the latest trends and maybe can be planned at SKU level.

Problems to forecast at size level

The complexity in the apparel industry is determining how much inventory to stock for different clothing sizes. Proportional profiles (also called distributions or “size curves”) that parse an item’s demand forecast by attributes such as color, size, and more, are crucial to creating accurate demand plans at the SKU level.

For example, you could set up a default allocation of women’s clothing sizes for 20 percent X-small, 50 percent Small, 20 percent Medium and 10 percent large.

This is especially important for apparel supply chains, where life cycles are short, uncertainty is high, and stock-outs and obsolescence are simultaneous threats. Even apparel manufacturers who adopt a lean manufacturing approach upstream must have a responsive, agile strategy downstream, in order to serve the unpredictable, fashion-driven segments of the marketplace.

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