High accuracy forecasting for configurable products, and products with variants

Most products today fit into one of two categories: configurable products or products with a fixed bill of materials (BOM). Both can cause daunting product complexity challenges.

Fixed BOM vs. configurable productsWith configurable products, customers mix and match features to customize the product they want to buy. This results in a very large number of configurations that can change year over year – as many as 30% to 50% of the configurations produced in a year may never have been ordered before.

Fixed BOM products have an established number of configurations (SKUs) from which customers can select. With fixed BOMs, growing demand for feature choices inevitably leads to model and product proliferation (more SKUs).

Customers don’t configure a fixed BOM product, but in trying to stimulate customer demand manufacturers offer so many configurations that the product behaves like a configurable product. It is not uncommon for a fixed BOM product to have thousands of SKUs, including many low-volume configurations taking up the lion’s share of a product portfolio.

Cell phones are a good example of this phenomenon. While cell phones aren’t configurable, phone manufacturers produce and offer a huge selection of models and features. Customers choose whichever model offers the features they want. The number of features and services has grown so much that cell phones now have more product complexity than computers! Motorola and many others have had to wage numerous wars on complexity due to product and parts proliferation and sourcing challenges1.

Customers don’t configure a fixed BOM product, but in trying to stimulate customer demand manufacturers offer so many configurations that the product behaves like a configurable product.

1The Incredible Payback, Dave Nelson, Patricia E. Moody, Jonathan Stegner, pp. 66-67.

Limitations of current forecasting toolsCurrent forecasting tools are designed for products with a fixed BOM (or SKU) with a limited set of configurations; each SKU is considered a unique entity. When the number of SKUs is limited, this forecasting method works. However, as the number of configurations grows, forecasting accuracy drops dramatically. This is because core assumptions made by these forecasting tools are violated.

The “no demand migration between SKUs” assumptionAs the number of configurations grows, customers pick and choose among them based on the features that match their needs. Now a fixed BOM product is acting like a configurable product. Demand migrates between SKUs based on feature sets; the old assumption that a single SKU has captive demand is not true any more. Under this assumption, forecasting tools can’t forecast demand for new SKUs or for customer migration between SKUs with similar feature sets. The result is high-error forecasting. One lighting manufacturer that offers thousands of SKUs gave up forecasting because each year new SKUs made up at least 40% of the total product offering. The company’s forecasting tools didn’t work in this situation.

The phantom configuration assumptionSince current forecasting tools were designed for fixed BOM products, a common workaround for configurable products is to create “phantom” configurations that mimic a fixed BOM product. For example, a truck manufacturer may choose 15 or 20 phantom configurations for planning and forecasting, but this tactic is doomed – manmade assumptions can’t closely approximate the thousands of configurations that are typically ordered and built. The rate of forecasting error is very high, and suppliers and other business partners who rely on planning results to manage inventory pay a very heavy price in added costs and reduced efficiency.

The feature choice independence assumptionCurrent forecasting tools also assume that customers select one feature at a time and that selections are unrelated to previous decisions. This overly simplified calculation assumes that if 10% of customers bought a DVD player and 30% bought a GPS with a particular car model, then 3% of customers will order both.

However, feature selection is not independent. Customers select groups of features together based on their needs and price point. This creates dependencies between features that should be leveraged to improve the product offering and forecasting. Feature dependencies are dynamic, changing as the product changes – as new features are introduced and as prices fluctuate. Straight-line forecasting systems fail to account for these patterns. The result: a poor product mix and erroneous forecasts.

Demand migrates between SKUs based on feature sets; the old assumption that a single SKU has captive demand is not true anymore.

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Where EmcienMix succeedsEmcienMix’s unparalleled ability to model configurable products overcomes the limitations of traditional planning tools, which can’t represent thousands or millions of customer-orderable combinations.

Configurable products need to be modeled as a large number of dynamic configurations because customers buy features and feature groups, not SKUs.

The fabled long tail is in fact made up of configurations that share many common features. These common features represent what customers value in a product and can be leveraged to improve forecasting and the product offering. Feature sets that customers value are like building blocks that make up configurations. True demand management requires the ability to auto-detect these feature building blocks for forecasting and product mix planning.

Complexity increases as customization and the number of configurations increase. How well does your forecasting tool fit your products?

EmcienMix forecasts configurable products and products with many SKUs based on the principle that customers seek and buy combinations of features that meet their needs. So while the configurations are dynamic, customers configure products in distinct patterns, e.g., small-business customers buy desktop computers with Microsoft Office, Quick Books, a low-end graphics card and a backup drive. Buyers in niche markets show distinct feature choices that can be leveraged to serve them better.

EmcienMix automatically detects the feature clusters and applies these dependencies to forecasting. The clusters are groups of features, which may sometimes be as large as 30 features in a highly configurable product. The clusters convey that these features are bought together. When the clusters are exploded into parts, it conveys the correct mix of parts for high service level and product availability.

Buyers in niche markets show distinct feature choices that can be leveraged to serve them better. EmcienMix™ automatically computes and applies these logical feature clusters.

The long tail is made up of configurations that share many common features.

About Emcien, Inc.Emcien is an Atlanta, GA-based software firm that solves complexity problems for discrete manufacturing companies. EmcienMix is a unique software solution that optimizes product mix to maximize profits, all while aligning closely with customer demand. Customers include Fortune 500 companies and manufacturers in the electronics, automotive and industrial sectors. Emcien was named a 2008 Cool Vendor in SCM and ERP 2008 by Gartner, Inc. To learn more, call 404-961-6360 or visit www.emcien.com.

EmcienMix’s forecasting engine leverages the clusters to forecast full configurations. The full configurations are dissolved into features/options, and then into parts requirements. The full configuration forecast ensures that the forecast maintains the cluster integrity observed in the demand. This forecast delivers a highly accurate parts mix, to ensure that your supply chain is stocked with the right parts, in the right ratios, so that you can build and deliver the orders in a timely manner.

If you offer a configurable product and you do not utilize a forecasting method that leverages feature dependencies, the supply chain will often have the wrong mix of parts. The symptom of this is having too much parts inventory, and running into parts shortages. The financial impact of having the wrong mix of parts is increased inventory cost, increased freighting cost and reduced product availability. The significant financial and operations impact is driving an urgent need for improved forecasting for configurable products.

Emcien forecasting tools for configurable products offers the dynamic ability to:

• Auto-detect and leverage feature dependencies to improve mix and forecasting

• Keep up with ever-changing configurations and improve product mix planning

• Produce high-accuracy full configuration forecasts that improve product mix visibility and customer service

ConclusionProduct complexity continues to grow as customers demand more feature choices in more market segments at lower cost with higher service level. A company’s survival in this competitive environment depends on the ability of the supply chain to forecast the product mix to ensure the highest level of service in the most efficient manner.

Visibility is dramatically improved with EmcienMix, enabling companies to align their resources to meet demand most efficiently. The effects are systemic and continuous, adding value and clearing out costly inefficiency throughout the enterprise.

The significant financial and operations impact is driving an urgent need for improved forecasting for configurable products.