Product complexity is a challenge for manufacturers, with many companies looking to scale their product lifecycle management (PLM) systems to accommodate the influx of product configuration data. This strategy assumes that product configuration complexity is solely an engineering concern.
However, adding an “intelligence layer” using configuration lifecycle management (CLM) to your PLM system can make it easier to manage the intricacies in defining, handling, and analyzing product configuration rules at scale.
Features and Modeling
CLM is about ensuring alignment between the various functional areas within an organization when it comes to available product variants and configuration. The product model captures rules from all areas in a single model, providing a single source of truth with respect to configuration.
Features is the common “language” used to express product variants. Features are managed in a global feature library so that they align across the enterprise, and they can be grouped — into sales, marketing, or technical features, for instance. The result of a configuration is a feature string.
Configuration models are used to express the valid variants in terms of features for a given configurable product. Engineering provides the technical rules in the configuration model and they define how the feature string is mapped to a bill of materials (BOM). Sales defines marketing rules in the model to manage the feature offering across numerous markets. The price is also typically derived from the feature string (feature-based pricing).
A CLM system sits between sales (CRM/CPQ) and engineering (PLM) using the “language” of the feature string to align between them.
Why PLM Isn’t Enough
You might be wondering, “Why do we need another system? Why can’t PLM do it all?” That question has frequently been raised in engineering departments where they do all the work in PLM. They cannot understand why any other system would be necessary, because most of the modern PLM systems have basic capabilities of defining features and rules.
However, most PLM providers leave sales out of the equation. And CLM captures rules from all domains, not just from engineering. It focuses on best-in-class tools for managing product models with advanced authoring analysis capabilities, and it has channel-specific views on single-source-of-truth.
Rules should be easy to understand for those who maintain them. With an approach like this, complexity is managed at the feature level. A single source of truth and access to solution spaces allow users to optimize based on features.
Taking a configuration lifecycle management approach makes it possible to move from engineered-to-order (ETO) to configured-to-order (CTO). Instead of manipulating BOMs directly for each order, the configuration model defines allowable options by combining rules from engineering combined with commercial rules.
The engineering rules may be defined within the CAD environment (e.g., to define geometrical constraints), and imported into the configuration model, where they are combined with the marketing rules. This provides a single model to be used in the sales process (selecting an allowed feature combination) which then can be applied to the CAD to provide an order-specific CAD model and drawings. This enables the full power of virtual tabulation technology.
There may be cases where the customer wants to “break” the rules to get special options. The configuration solution may allow this and trigger a traditional ETO process where engineering needs to review and approve such cases.
When accepted, the configuration model should be updated to reflect the new options and in this way evolves the process from ETO towards a more efficient CTO process. Furthermore, switching to this type of model helps engineers to eliminate errors, bring products to market faster, reuse modeling data, and optimize the offered combinations.
Toward More Intelligent Lifecycle Management
By adding an “intelligence layer” using configuration lifecycle management to your PLM system, you’ll reduce the amount of time spent manipulating BOMs, manage more products with the same resources, and facilitate the transition from ETO to CTO, all with fewer manual processes and no mistakes.
