More Salad Dressing Without Another Line
A Plant Model That Matches Its OEE to Within 1%

Demand for a national brand of salad dressing is rising, and the plant needs more output. New lines are expensive and slow to arrive, so the team looks for the capacity in the equipment it already has. They build a simulation from the plant’s own downtime records and check it against a year of real performance. Then they use it to decide which fixes are worth making. The model finds many small improvements that together add many points of OEE, and it changes how the operators work.

The business

A household-products company’s salad dressing plant · several products, process and packaging lines

The question

How much more can we make without adding a line?

The model

20+ unit operations, up to 20 failure modes each

Checked against

A year of the plant’s actual OEE, within 1%

Outcome

Many small fixes, many points of OEE; two machines flagged for redundancy

What the plant is up against

Demand is climbing, and a sudden surge pushes it higher still. Some of the plant’s lines run below industry benchmark OEE. A new line means a large capital bill and a long lead time.

The company’s own engineering team and consultant Tom Lange set out to find how much more the existing equipment can make, and which changes will get it there. The work is published as a Winter Simulation Conference paper.

How the model is built

The plant already records every stop and start on its lines. From those records the team works out how long each machine runs before each kind of failure, and how long each failure takes to clear. They do this for every product category. That comes to up to twenty failure modes on each of more than twenty unit operations, about four hundred in all.

They fit a distribution to each failure mode in JMP. Then they build a discrete rate and reliability model of the plant in ExtendSim, using the real tank volumes, conveyor speeds and buffer sizes. Plant operators help map the model to the equipment and help test it.

The detail matters because each machine’s failure modes sit inside that machine, and the buffers between machines decide how a stop on one spreads to the rest. That’s why multiplying machine availabilities understates what a buffered line produces (more in reliability block diagram vs simulation). The paper puts it this way:

Checking it against the plant

Before anyone uses the model for a decision, a one-year simulation has to land within 1% of the plant’s actual OEE for each product category, and it does.

The check also compares observed and simulated availability for each failure mode, not only the total. A single overall number can be right for the wrong reasons, with one mode overstated and another understated. Checking every mode separately rules that out.

What the model shows

For every machine and failure mode, the team compares two numbers. One is the availability it costs on paper. The other is what the whole plant gains in the simulation when that loss is removed. Where the whole-plant gain is bigger than the loss, that fix is worth doing first.

• Two machines are worth doubling up. The Case Erector and the Palletizer affect the whole plant enough to justify looking at a second machine for each.
• Some fixes need no capital. On the Labeler, at least two failure modes have high leverage, and closer attention from the operators can address them.
• Buffer can beat reliability. More conveyor space at one point shields the rest of the plant from the less reliable section beside it. Where there’s floor space, more buffer can cost less than making equipment more reliable.
• Faster is better, up to a point. Running faster makes machines fail more often, but throughput still rises until it reaches an optimum speed.
• Short stops aren’t harmless. Frequent, brief stoppages look minor, but the linked line magnifies each one.

No single machine is the answer. The gain comes from many small improvements, which together add “many points of %OEE,” in the paper’s words. The model also lets the team weigh staffing, capital and material changes against each other.

What changes on the floor

The operators can see on screen how their own actions move the whole line. The paper reports that this makes them more vigilant and gives them more ownership of the improvements. It also builds enthusiasm and commitment for the improvement work.

The same model in ReliaSim®

The published model is rebuilt in ReliaSim and independently validated by Tom Lange. It comes in within 1% of both the plant’s measured OEE and the original ExtendSim model. On the same laptop, over the same one-year run, the ReliaSim model runs roughly 1200× faster. See ReliaSim vs ExtendSim: the same validated model in both engines.

Speed changes how a study like this one runs. If a one-year run takes all night, you test a few scenarios picked in advance. When it takes seconds, you can test every failure mode and every buffer size. The buffer sweep behind How big should a buffer be? is 7,770 runs.

About the source

This case study is built on a published, peer-reviewed paper written by the plant’s engineering team and Tom Lange, at his own consulting practice. The paper is two pages and open access, so you can read it for yourself. It’s also the featured case study on ExtendSim’s own reliability block diagram page.

Both tools use discrete rate engines from the same inventor. ReliaSim is the third generation, rebuilt from scratch (lineage and comparison).

ExtendSim® is a registered trademark of Andritz Inc. It is referenced here for identification and historical purposes only; no affiliation or endorsement is implied.