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Manufacturers have always managed variation. Customers want different features. Regions may require some different rules. Applications call for different engineering choices. What has changed is the speed and volume of those variations, according to a recent Industry 4.0 Club fireside chat with Johan Salenstedt, CEO of Configit.
The old model still works in some cases: build a standard product, then handle exceptions one order at a time. But it breaks down when the product includes software, electronics, service commitments, greater regional requirements, and years of aftermarket responsibility.
At that point, configuration is no longer just an engineering issue. It affects what sales can quote, what operations can build, what service can support, and what the business has promised to the customer.
A Configuration Lifecycle Management (CLM) approach gives manufacturers a way to manage product flexibility without letting complexity spread unchecked across the business.
For years, manufacturers improved performance by optimizing individual functions. Engineering adopted better PLM systems. Manufacturing improved efficiency through lean practices and ERP. Sales teams implemented CRM and CPQ tools. Service organizations built their own systems to support customers in the field.
Those investments made sense. The problem is that a configurable product does not stay inside one function. It starts as an engineered possibility. It becomes a sellable option. It turns into a manufacturable order. Later, it becomes an installed asset that may need service, upgrades, parts, and eventual retirement.
When configuration data is scattered, each team works from a partial view that can result in rework, longer lead times, quoting delays, avoidable exceptions, and risk that no single department fully owns.
CLM addresses that gap by connecting configuration logic across the lifecycle. The principle is straightforward: engineer what can be sold, sell what can be built, and support what has actually been delivered.
Many manufacturers still rely on spreadsheets to manage product rules. In small parts of the business, that can seem reasonable. Spreadsheets are familiar, flexible, and easy to change.
They become fragile when product complexity grows.
Experienced teams can often make manual methods work with enough review cycles and tribal knowledge. The larger issues are time and accuracy. Manual configuration slows quoting, creates dependency on a few experts, and makes it harder to respond when suppliers, regulations, or customer requirements change.
Speed matters because manufacturers now compete on responsiveness. A customer asking for a valid quote cannot wait while several departments confirm whether a requested configuration is possible. If a supplier constraint appears, teams need to know which products, orders, and service commitments are affected.
That requires connected data and repeatable logic. A spreadsheet can document rules. It cannot reliably govern them acrossengineering, sales, production, and service.
Artificial intelligence is now part of nearly every manufacturing technology conversation. Used well, it can help teams work faster, model products more efficiently, search knowledge, generate documentation, and improve how users interact with complex systems. It may also make a CLM approach easier to implement and use.
But AI does not remove the need for deterministic outcomes.
In manufacturing, a configuration is not just a suggestion. It becomes material, labor, cost, compliance, delivery, and customer commitment. A product that is almost correct can still be wrong enough to cause scrap, delays, warranty issues, or safety problems.
The future is AI working with reliable configuration logic. Probabilistic tools can help people move faster, but the final configuration still has to be valid, repeatable, and explainable.
The value of a CLM approach goes beyond product configuration at the point of sale. Its role becomes more important as manufacturers grow service-based revenue, support long-lived assets, and manage products that stay in the field for decades.
A strong CLM approach connects the configuration rules and product logic used across PLM, ERP, CRM, CPQ, MES, and service systems. It gives them a common way to understand what options are valid, what combinations are allowed, and what configuration was actually delivered.
That matters after the sale. Service teams need to know what is installed. Operations needs to know what can be built. Sales needs to know what can be promised. Engineering needs to understand how product rules are being used outside the design environment.
Without that shared logic, each function keeps solving the same product complexity problem in its own system.
The technical case for CLM is strong, but the hardest partis often organizational. Configuration complexity touches engineering, sales, manufacturing, supply chain, service, finance, IT, and leadership. No single function can solve it alone.
Senior leaders need to align teams around shared data, shared rules, and shared accountability. They also need to reduce the business’s dependence on knowledge trapped inside departments or held by a few experts.
When product knowledge stays in silos, the company becomes slower. It also becomes more fragile. Decisions take longer, exceptions multiply, and risk moves downstream to the teams least prepared to absorb it.
A practical first step is to identify where configuration pain is already visible. That may be slow quoting, frequent engineering exceptions, manufacturing rework, inconsistent product options, or service teams struggling to identify what is installed in the field.
The first CLM initiative should be narrow enough to deliver value, but not so narrow that it creates another isolated tool. It should solvea real problem while establishing configuration logic that can scale across the lifecycle.
As customization grows, manufacturers need a better way to manage complexity without slowing the business down. A CLM approach connects the rules behind what can be sold, built, delivered, and serviced, helping companies successfully move from mass production to mass customization and improve competitiveness.
Want to learn more? Watch the full webinar, “Mastering Complexity and Customization at Scale.”
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