Why Simulation
Belongs in the Capital Plan

A capital request for a production line comes down to a payback estimate. The payback depends on what the line will produce once the change is in, and that’s the hardest number to know ahead of time. Your teams are smart, and even smart teams need a tool that can play a whole line forward through a year of stops before anyone signs.

You get one shot at a capital decision

A new crewing pattern or changeover sequence can be tried on the floor and rolled back. A second case packer can’t. Once the equipment is ordered and installed, the money is spent, whether the line responds the way the business case said or not.

Most of the evidence behind those requests comes from the plant’s own reports. They’re accurate about what already happened. A capital request is a claim about a line that doesn’t exist yet, and nothing in last year’s numbers describes it.

It’s the stops that drive your output

Totals hide what matters about a stop. A timing belt that fails once a week for two hours logs 120 minutes. Bottle jams that stop a machine 120 times a week for a minute each also log 120 minutes, and the loss report gives the two the same priority. Micro stops versus breakdowns works through why they aren’t the same.

The line treats them very differently. A buffer can ride through one long stop, because it has stock when the stop begins. Against a hundred short stops it never refills, so the losses spread downstream instead of being absorbed.

ReliaSim® is the first simulation software built expressly for high-speed, high-volume lines and the stop data they produce. It models rates and flow, the way product actually moves on a fast line, rather than tracking pieces and parts one at a time. Each machine carries its own stops by cause, with how often each one comes around and how long it lasts. Blocking and starving come out of the run instead of being assumed. It’s a new way to think about throughput, where the question moves from which machine lost the most to which change gives the most back.

What a loss tree can’t tell you

A loss tree adds up downtime by machine and by cause. Anyone in the room can audit it, and it’s usually right about the total. Where it goes wrong is the ranking, because it treats each loss as if it stood alone.

Losses don’t stand alone, because stops ripple. When one machine stops, the machine after it runs out of product and the machine before it has nowhere to send any. Those neighbors log starved or blocked time under their own names, and the machine that caused it looks moderate on the report.

In our bottling line case study, Labeler Misalignment cost 6.79% and Filler Micro Stop cost 6.72%. The Pareto ranked them together, and most teams would fix the Labeler first because it’s the bigger single event. The model removed each one in turn and re-ran the whole line. The Labeler fix returned 5.0 points and the Filler fix returned 8.1, which is 62% more.

The Labeler fix meant $400K of alignment tooling. The Filler fix was a $50K intervention, automated jam clearing. On a $50M/year line, following the loss tree instead of the simulation leaves $1.5M on the table.

Why experience and spreadsheets run out

Your people know which machine jams, which changeover runs long and who clears a fault fastest. What nobody can do is hold a whole line in their head through a year of random stops, with dozens of failure modes firing on their own rhythms and buffers filling and draining between them.

A spreadsheet can’t either, because it has no clock. It shows how many minutes were lost but not the order they were lost in, and the order decides whether a stop costs anything. Arithmetic on averages also overstates what a buffered line will produce, and the error always runs the same way. A forecast built from the loss tree comes out optimistic however careful the people building it are.

Risk only shows up over time. If nothing changed, there’d be no risk, and everything on a line changes. The formula is where you start. The model is where you find out.

Simulate before you spend

A model of the line is a playing field, the scope of the system you play scenarios out on. It lets your team ask what would happen if. What if we add a buffer before the capper, or fix the micro stops before the misalignment? The model changes one thing, runs the whole line forward and compares the result against the line you have.

Speed decides how many of those questions get asked. On the five-machine bottling line, one simulated year takes 0.154 seconds on a laptop, so a thousand one-year runs finish in about two and a half minutes. When a run takes all night, a team tests a few scenarios picked in advance. When it takes seconds, they can test every failure mode and every buffer size.

Each option runs a thousand times, repeatably, so anyone can check the answer and see the spread a single run hides. You can try this on a real bottling line in the ReliaSim Sandbox, where every number on screen comes from the engine.

A validated model gives your team a defensible answer

We’ve perfected the art of creating playing fields that get the math right, and validation is how you know. The model has to reproduce a year you’ve already measured, failure mode by failure mode, since one overall number can be right for the wrong reasons. A published plant model built this way came in within 1% of measured OEE, and so did its rebuild in ReliaSim.

Matching a year you can check is what lets the model speak for a configuration nobody has built. When someone in the capital review asks why this fix and not that one, your team can show the topology, the assumptions and the sensitivity check behind the answer.

What plants found when they simulated first

A soap manufacturer consolidating several plants into one facility, with over 300 products, modeled the whole plant before moving. The planned machine assignments would have held some products in the system too long, causing a quality problem that needed a $4 million retrofit. Other routings, tested in the model, avoided it. The company now requires a simulation study before any major capital expenditure.

At General Mills, Malcolm Beaverstock, its manager of advanced control and simulation, estimated $5 million saved from 40 models. Of that, $3 million came from scrapping projects that would have been counterproductive. “Cost avoidance is a concept accountants hate, but it’s real,” he told Food Engineering.

Bell-Carter Foods tested scheduling rules in a model of its olive plant and found 15% more throughput without new equipment. The team behind ReliaSim did these projects with the same rate-based method, before ReliaSim was a product.

From a single machine to the whole supply chain

The playing field scales to the size of the decision. It can hold one machine’s failure modes, a full line with its buffers or a whole plant. It can also hold a supply chain, for questions like where to hold stock across a network. There, perfect delivery has a price nobody wants to pay, so you agree the service target first, then find the cheapest way to hit it.

The Sandbox has four tiers: Flow · Discrete Rate, Stats · ReliaStats®, Lines · ReliaSim® and Networks · Supply chain.