The Challenge
A mid-sized pickles and condiments manufacturer was operating continuous 24/7 production to meet demand. Management suspected there was significant hidden capacity, but lacked the tools to prove it. Key questions remained unanswered: Could they consolidate to 5-day operations without missing orders? How would changeovers and tank utilisation be affected? What was the true cost of their current product mix?
Spreadsheet-based planning couldn’t model the interdependencies between their bulk storage tanks, batch sauce and syrup cooking processes, and multiple filling and packing lines. They needed a way to test scenarios before committing to operational changes.
The Solution
Wolfwyse built a complete digital twin of the production facility, modelling every constraint from raw material intake through to finished goods dispatch:
Bulk storage — Vinegar, brine, and ingredient tanks with capacity limits, fill rates, and cleaning cycles
Batch processing — Sauce and syrup cooking vessels with recipe-specific cycle times, temperatures, and sequencing rules
Intermediate storage — Buffer tanks between cooking and filling, tracking hold times and quality constraints
Filling lines — Multiple bottle sizes and formats with changeover matrices and line speeds
Packing and palletising — Case packing with labour allocation and shift patterns
Optimisation Approach
The model simulated multiple scenarios, testing the impact of different shift patterns, production sequences, and product portfolios. Unlike theoretical optimisation tools, every scenario was validated against real-world constraints: tank capacities, minimum batch sizes, changeover requirements, and crew capabilities.
Wolfwyse’s constraint-based scheduling engine identified production sequences that were physically achievable—not just mathematically optimal.
Results
| Area | Outcome |
|---|---|
| Shift Pattern | Moved from 24/7 continuous to 24/5 operations, eliminating weekend shifts |
| Labour | Reduced crewing levels by sharing trained crews across filling lines |
| Changeovers | Resequenced production to cut total changeover time by 30% |
| WIP Storage | Reduced intermediate tank requirements by 40% through better scheduling |
| Product Range | Identified and removed low-margin SKUs consuming disproportionate capacity |
| Energy | 15% reduction through consolidated production runs and reduced equipment idle time |
“By modelling the entire production chain, we identified that 12% of SKUs contributed just 3% of margin but consumed 25% of changeover time. Rationalising the product range freed capacity for higher-value production.”
Implementation
Model build: 6 weeks including data gathering and validation
Scenario testing: 2 weeks of iterative simulation with operations team
Transition period: 8-week phased rollout from 24/7 to 24/5 operations
Why It Worked
Spreadsheets told management they should be able to run 5 days. Wolwyse showed them exactly how—which products to sequence together, which crews to share across lines, and which SKUs to discontinue.
The difference between theoretical capacity and achievable output was the difference between a plan that fails on the factory floor and one that delivers results.