The filling line operates with a bottleneck OEE of 60.6%. Shift reports show fragmented causes: mechanical failures, material waits, recalibrations. Shift-to-shift variability is high and cannot be traced to a single root cause.

Management must make two decisions in parallel: reorganise the production layout and launch an OEE improvement plan. The complexity is not mathematical but methodological: the two levers influence each other and, without a separation method, results cannot be attributed reliably.

Without a method to isolate effects, any output improvement can be attributed to layout, OEE, or both. The resulting ROI is not attributable to a single lever and the recommendation is not defensible.

An unstructured approach exposes the organisation to four concrete risks:

The cost of a layout error is not purely financial: it includes ramp-up time, operational rigidity and loss of internal credibility around the decision.

The model is structured in two separate, sequential steps.

The structural comparison of layouts is performed at the reference volume of 1,900,000 units/year. The result changes the decision perspective: this is information that does not emerge from an empirical comparison or an unstructured spreadsheet.

Starting from the selected layout, the model quantifies the OEE effect as a separate lever and isolates a third operational benefit that is often not accounted for: reduction in throughput time.

The model operates in Profit mode (variable capacity): the objective function is the maximisation of incremental profit generated by increased sellable capacity, net of variable costs. Fixed costs are excluded from the profit calculation because they are not cashable in the scenario analysed.

The total accounting value is €102,884/year. Applying cashability and risk factor, the defensible value becomes €90,605/year.

Following implementation, operational data recorded over 60 shifts allows a direct comparison between model estimates and actual values.

The quality of a decision model is measured by the minimal deviation between estimate and reality, and by its ability to withstand empirical verification without the original decision needing to be revisited.

The case illustrates three methodological contributions that cannot be obtained through unstructured analysis.

The primary contribution is not the identification of the lowest-cost layout. It is the quantification of the cost of inertia: maintaining the AS-IS layout has a precise, measurable cost of EUR 152,290/year.

The objective is not process optimisation. It is the reduction of risk associated with an initially incorrect structural decision.

ORVEN does not handle implementation or training. It produces explicit alternatives, quantified trade-offs and a structured recommendation, validated against the client’s real operational data.

The machines handling this production have a departmental layout and operate 40 days a year, with two months of seasonal production on a product with a defined market window. Lost shifts are not recoverable and the time margin is tight.

Management has indications of layout inefficiency but has never quantified the annual cost of that configuration. With such a short season, the prevailing perception is that a re-layout requires time and resources incompatible with the production calendar.

Without a precise quantification, the decision tends to be deferred season after season. The model produces the missing number: the annual structural cost of the current layout.

The model analyses both levers: layout and OEE. For the bottleneck machine, the current OEE is 77.4% with an achievable target of 86.6% (+9.3pp). Three root causes were identified:

The interventions are technically feasible. However, the model returns a clear conclusion: on a 40-day-per-year production run, the additional OEE saving does not justify the cost and implementation time of SMED, TPM, feeder reconfiguration and tools that require months of ramp-up to produce stable effects.

The next bottleneck after filling is the packaging machine. If the season lengthens or volumes grow, OEE on filling and balancing with packaging would be the next lever to analyse.

The model is configured in LAYOUT_ONLY mode with a cost-minimisation objective function. OEE is held constant across all scenarios: the comparison is structurally clean by construction.

The analysis produces a result in which U-Layout becomes optimal from 34,000 units. Below this threshold the AS-IS layout is better.

Methodological note. The model distinguishes between gross operating saving (variable cost and handling reduction) and net structural saving (including fixed cost delta). NPV is calculated on the risk-adjusted economic flow applying an 85% risk factor on cost savings and a 6% discount rate. This makes the business case conservative and defensible.

Following the re-layout (November 2025), production data recorded over 16 complete shifts allows a direct comparison between model estimates and actual values.

OEE holds at 77.7%, consistent with expectations, in the absence of any machine intervention. This confirms that the saving is entirely structural: it does not depend on the operator or machine performance, it depends only on the layout.

The case addresses three common beliefs in seasonal production and provides, for each, a quantitative data-based answer.

The primary contribution is not the identification of the lowest-cost layout. It is the quantification of the structural cost of each season in which the layout is not optimised.

The objective is not process optimisation. It is the reduction of risk associated with an initially incorrect structural decision.

ORVEN does not handle implementation or training. It produces explicit alternatives, quantified trade-offs and a structured recommendation, validated against the client’s real operational data.