The $4 Million Retrofit
That Never Had to Be Built
A soap manufacturer was consolidating several plants into one. Over 300 products, new equipment, a clean sheet. One of the machine configurations under consideration would have worked on paper and then, quietly, produced a quality problem expensive enough to need a $4 million retrofit. The model found it before the concrete was poured.
The decision
The company needed to collapse manufacturing from multiple sites into a single optimised facility. The existing sites between them made over 300 different products, and the consolidated plant had to absorb all of it.
That is not an equipment-sizing problem. Whether the plan works depends on things that only exist over time:
- Variable demand and seasonality
- Product mix
- Breakdowns and equipment variability
- Crewing patterns
- Product run sequencing
The logistics manager chose simulation for a reason the study records plainly: it was the one tool that captured real-world variability and the interaction between subsystems. A spreadsheet can size a machine. It cannot tell you what happens when a seasonal peak, a changeover sequence and an unreliable upstream stage line up in the same week.
What the model changed
Two things came out of it. The first was ordinary and valuable: the model characterised the balance of the line and predicted the optimal buffer size between processing and packaging — the number that decides whether the two halves of a plant can run independently.
The second was the one that paid for the study. For a given set of products, the envisioned machine assignments produced excess time in system. Material sat longer than it should, and for this product that is not an efficiency problem, it is a quality problem. Left in place it would have surfaced months into production, in a plant already built around those assignments.
One configuration predicted severe long-term quality problems that would have required a $4 million retrofit. Alternative product routings and machine assignments were tested in the model instead, and the problem was avoided completely.
Note what kind of finding that is. Nothing was broken. Every machine met its specification. The failure was in how the parts combined over time, which is exactly the class of problem that survives every static review and then shows up in production.
What happened afterwards
The most telling outcome is not the $4 million. It is that the company changed its process: it now requires a simulation study before committing to any major capital expenditure. The logistics manager on this project went on to extend the same approach to supply and distribution policy across the division.
The method
The model was built by the team using the discrete-rate technology for high-volume processes that ReliaSim now packages, along with an embedded database driving the dynamic factors and multi-stage sequential schedule definition. This work predates ReliaSim as a product; the approach is the same one, and it is the approach being described here rather than the current software.
The client is not named in the source material and is not named here.
Related: How big should a buffer be? · The Hidden Bottleneck · All case studies