Today’s product portfolios c
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an contain hundreds of thousands SKUs. In some cases a company must manage several million. This is not to say a company will have this many product families. In fact, many of these SKUs are based on product configurations within a product family. For example a style of shoe can have a SKU for each gender, color, size and width. A family of sofas can have individual SKUs for fabric, color, pillows, and legs. A USB memory stick can have variations for memory size, color and indicator light.
If “Plan High, Source Low” was a bumper sticker, it would mean something to supply chain professionals who need to plan demand at both the aggregate levels as well as detail levels for each important product attribute (e.g. gender, style, color, size, gender, finish, region, speed, power, material type, trim level, configuration, and many more). Accurate detailed forecasts drive better supply planning, smarter sourcing decisions at an earlier stage, and more insightful production schedules.
So why aren’t you doing it? Because your product portfolio is too large? Because you don’t have enough bandwidth to create 3- or 4- or 5-tiers-deep attribute profiles covering every style, region, life cycle stage, etc.? Because you are using a “one-size-fits-all” statistical model for all your products, even though you know it isn’t the best fit? The reality is high-level demand planning is more predictable, and matches up well with business requirements at a high-level, but doesn’t translate into an accurate forecast for every product variant is complex.
An advanced tool like Voyager Proportional Profile Planning™ bridges the gap between demand planning and supply, sourcing, and production planning. It offers a practical way to generate attribute curves, or proportional profiles, from the demand histories of existing products with similar characteristics. Using existing sales data to build multi-tiered proportional profile templates early in the product development cycle enables you to make supply planning decisions even before marketing plans are final. And employing multiple, time-phased profiles tuned to the different stages of a product life cycle can mean the difference between profitability and filling the discount bins.