Case studies from Sofon and Hive CPQ projects

Every machine was fully engineered from scratch, regardless of exactly what had been sold. Every order went from sales engineering to engineering, meaning a large part of lead time was lost re-working machines that were, at their core, very similar.

First, a detailed analysis was made of all previously quoted configurations, to work out which combinations of choices covered most real-world cases. Based on that, the machine was rebuilt as a modular structure: around 80% of all quotes can now be assembled from predefined standard modules. The remaining 20% — genuinely unique configurations — deliberately stays custom work and goes to engineering.

The standardisation didn't stop at the Sofon model itself, but ran through the entire workflow: from quote, through drawings, to engineering and ultimately production. Every department was involved in the part that concerned them, with weekly alignment, until all the information came together in a single specification and model.

For the 80% of quotes now running through the standardised model, engineering time for a complex machine has dropped from around 8 weeks to under 4 hours. That lets the business push significantly more orders through the process in the same amount of time, with higher revenue and margin as a result — without abandoning the special 20%.

A telecom company called with an urgent problem: the knowledge needed to manage its existing Sofon model was no longer present within the organisation. As a result, quotes were being generated incorrectly and prices no longer added up — with direct commercial risk.

Because the underlying model structure was already familiar, management of it could be picked up quickly, without a long ramp-up. No big project — just getting straight to work restoring the model and making it manageable again.

The client can once again add new products and process price changes independently, without this leading to incorrect quotes.

At a machine builder, Sofon knowledge had completely disappeared from the organisation. The models hadn't been maintained in several years. As a workaround, staff manually noted which changes were needed after configuring in order to arrive at the right output — an error-prone and time-consuming extra step on every quote.

Together with a team, the models were thoroughly worked through. For each department, it was mapped out which information was coming out of the system incorrectly and what it should have been; these issues were resolved at a fast pace.

Alongside this overdue maintenance, a group of employees was trained, so that knowledge of their own models sat with the company again. Finally, a few entirely new machines were added to the system.

The company now handles day-to-day changes independently again, without the earlier manual, error-prone extra step. For adding an entirely new machine to the model structure, Dissolvi is still brought in for targeted help.

A prospect doubted whether a configurator could handle the complexity of their product. Removing that doubt called for concrete proof — not a theoretical pitch, but a genuinely working configurator for one of their own products.

Working with the prospect, a suitable product was chosen together, and the team jointly worked out how that product was put together both technically and commercially. Based on that, a fully working Hive CPQ configurator was built for that specific product — within the maximum 5-day limit set for a Seeing Is Believing project.

Within 5 days there was a working configurator that let the prospect see and experience that their product could indeed be configured — exactly the proof needed to make the decision.

A canopy manufacturer had been working with Hive CPQ for some time. Management set a hard deadline: a specific, non-standardised product line — where almost everything was calculated — had to be live in Hive before a planned ERP implementation kicked off. That created considerable time pressure.

Dissolvi was brought in to build this product line in Hive CPQ together with the company's own engineer, so that knowledge was embedded internally right from the project itself.

Within the available time, the product line was 90% built out in Hive CPQ. The remaining part was then completed by the company itself together with another party — not because of any dissatisfaction with the work, but because the next phase called for a dedicated, full-time on-site presence. Given the travel distance, that didn't fit how Dissolvi works, and that was discussed openly.