Capacity Without Capital
What to Change Before You Buy
When a line can’t keep up, the first proposal is usually equipment: a faster machine, bigger storage, a second line. Sometimes that’s the right call. But most lines have capacity the plant already paid for and isn’t getting, and the cheap changes are often worth more than the expensive ones. The hard part is knowing which is which before anything is spent.
Where the capacity you’ve paid for goes
Output gets lost in four places that don’t take capital to fix:
- The wrong failure mode gets the attention. Maintenance hours go to the biggest bar on the downtime report, which isn’t always the one that gives the most back.
- Storage sits in the wrong place. The same amount of buffer can be worth a lot or very little, depending on where it is.
- The schedule fights the plant. Run lengths and sequences that don’t match what comes next lock up storage the plant already has.
- Habits on the floor. Small, frequent stops that everyone has learned to live with.
None of these shows up on a capacity spreadsheet. A spreadsheet works with averages, and these losses come from timing: when stops happen, how long they last and what’s in storage at the time. A line model plays that timing out. Here’s what each change is worth on our demo models and in plants we’ve modeled.
1. Fix the failure mode that gives the most back
Maintenance and operating practice cost hours, not capital. The question is which hours. Ranking problems by downtime is the usual approach, and it’s often wrong, because some stops hurt the whole line and others are mostly absorbed.
Demo bottling line · no buffers · 90 days, ten runs each
| Problem removed | Hours down | Line efficiency |
|---|---|---|
| None (the line as it is) | – | 54.3% |
| Capper: upper plow area, the biggest problem on the constraint | 198 h | 61.5% |
| Labeler: misalignment | 143 h | 59.2% |
| Filler: micro stops | 148 h | 62.1% |
The Filler’s micro stops cost fewer hours than the Capper’s plow-area stops, and removing them gives back more. The published gain/loss for the same line makes the same point from the other side. The Labeler’s misalignment costs 6.79% on paper and gives back 5.0 points; the Filler’s micro stops cost 6.72% and give back 8.1. See micro stops vs breakdowns and which loss to fix first.
2. Put storage where it protects the constraint
Accumulation isn’t free to move, but it costs far less than a new machine, and where it sits matters as much as how much there is.
Same line · the same twelve slots of buffer (600 in all) · 90 days, ten runs each
| Where the buffer goes | Pallets | Line efficiency |
|---|---|---|
| No buffer | 70,432 | 54.3% |
| Worst placement: 2 slots at Buffer 1, 10 at Buffer 2 | 81,255 | 62.7% |
| Best placement: 9 slots at Buffer 1, 3 at Buffer 3 | 88,227 | 68.1% |
That’s the same amount of storage, with 8.6% more pallets from where it goes. Pair the Filler fix with its own best placement and the line reaches 77.2%. How big should a buffer be? runs the full sweep.
3. Fix the schedule before the plant
In a make-store-pack plant, the bins between making and packing only help if the right product is in them when packing needs it. The Sandbox’s demo vegetable plant has a packing schedule timed to 24-hour making runs. Cut the making runs to 12 hours and making moves on to the next vegetable before packing is ready for it. Its bins fill, making blocks, and packing waits for product nobody is making. That’s bin lock.
Demo vegetable plant · same packing schedule in every run
| Plan | Cases packed | Minutes the plant is stuck |
|---|---|---|
| Making runs cut to 12 hours | 27,916 | 654 |
| The capital answer: 12-hour runs, bins twice as big | 31,455 | 571 |
| The schedule answer: making runs timed to packing | 36,832 | 0 |
Doubling the bins wins back about 40% of the lost cases, and the plant still locks up. Fixing the schedule costs nothing and wins it all back. You can try it yourself in the Sandbox’s bin lock question.
Real plants show the same thing. A specialty olive packer tests scheduling rules on a model of the whole plant, including a Theory of Constraints-based daily schedule, and gets 15% more throughput with no new equipment (case study). A cereal plant runs its model before every shift so the planners can change the plan before trouble reaches the floor.
4. Change habits on the floor
Some of the cheapest gains come from the people running the line. At a salad dressing plant, a model built from the plant’s own downtime records finds that at least two failure modes on the labeler have high leverage, and that closer attention from the operators can address them. No single machine turns out to be the answer. Many small improvements add up to “many points of %OEE,” and operators who see on screen how their own stops move the line become more vigilant (case study). A flight simulator for your plant covers training on the model.
5. Don’t fund the project that wouldn’t have worked
The cheapest capital is the capital you don’t spend on the wrong thing. A major food manufacturer saves an estimated $5 million across 40 models, mostly by stopping bad projects (case study). A soap maker consolidating plants finds that its planned machine assignments would cause a quality problem needing a $4 million retrofit, and tests other routings in the model that avoid it (case study).
When capital is the answer, it’s a smaller answer. The salad dressing model also flags two machines worth doubling up. Running the no-capital changes first doesn’t rule out spending. It means the request that’s left is smaller and aimed at the right machine.
The order to work in
- Build the line from its own stop records, one failure mode at a time, and check it against what the line actually measured.
- Find the ceiling. Run the line with no stops to see the most it can make, then see where the gap goes.
- Rank the failure modes by what comes back, not by hours lost.
- Sweep buffer placement before buffer size.
- Test the schedule: run lengths, sequences, changeovers.
- Then price capital against whatever gap is left.
Try changes 1 and 2 yourself: Beat the Bottleneck. Fix one problem on the demo bottling line, place twelve slots of buffer, and ReliaSim runs the line for 90 days, ten times over. The biggest problem on the constraint is the obvious fix. See whether it wins. Play Beat the Bottleneck →
Frequently asked questions
How do you increase production capacity without capital investment?
Fix the failure modes that give the most throughput back, put existing storage where it protects the constraint, match run lengths and sequences to what the next stage needs, and work on operating habits. A model of the line shows what each change is worth before you make it.
Will bigger storage fix a line that keeps locking up?
Not always. If the schedule fills storage with product the next stage doesn’t need yet, bigger storage only delays the lock. On our demo vegetable plant, doubling the bins still leaves the plant stuck 571 minutes; fixing the schedule takes it to zero.
How do I know which downtime to fix first?
Remove each failure mode in a model of the line, run it, and rank by how much output comes back. That ranking often differs from the downtime Pareto.
When is new equipment the right answer?
When the gap that’s left after the no-capital changes is still bigger than the target. The model then shows which machine to spend on and how much that buys before the next constraint takes over.
Find the capacity you already own
Bring one question about your line. We model it on your own data and send back a two-page readout in about five business days.
Apply for a free line assessment → Try bin lock in the Sandbox