The Faster I Go the Behinder I Get – back when I was a project engineer at Kraft Foods (as it was known then), our objective for every packaging line was to increase output. And the obvious way to do that was to run the line faster. Unfortunately, at some point, as you increase operating speed, production actually decreases, because you lose more product to problems than you gain from the increased speed.

I agree that this case exists and when a line performing very poorly, slowing things down to get control of the situation is the first and easiest thing you can do. There is, however, a reason management wants to increase production: It’s incredibly profitable to do so and worth the time (and money) to figure out how. Slowing down is not the only way to increase throughput on a consistent basis.

if the new filler you bought can run 300 bottles/min, but the labeler can only consistently run 280, you will encounter problems.

Jack’s slowdown approach would have us run the filler at a consistent rate of 280/min to match the labeler. Let’s assume that “running consistently” equates to an efficiency of 98% and this can be achieved at both the filler and the labeler. At a line speed of 280/min, the overall throughput of our simple line is 0.98 * 0.98 * 280 = 268.9 products per minute (ppm).

Higher efficiency doesn't always mean more throughput

If you raise the line to the rated filler speed (300/min) and raise the labeler to 320/min, you’re going to take a hit in efficiency on the labeler, right? If we add a buffer with a capacity of 900 products between the filler and labeler, we can withstand efficiencies on the labeler as low as 84% before dropping the line speed below 268.9ppm. For reference ow how bad 84% is, that is equal to one hour, sixteen minutes, and forty-eight seconds of downtime in an eight hour shift. If we can consistently hold an efficiency of 92% (38 minutes of downtime per shift) at the higher speed, our line will run at 294ppm. This is a 9.3% increase over slowing the line down.

Slow Down:
0.98 * 0.98 * 280 = 268.9ppm

Buffered:
0.98 * 300 = 294ppm
0.92 * 320 = 294.4ppm

294 – 268.9 = 25.1
25.1/268.9 = 0.093 = 9.3%

Jack ends with three steps:

• Slow the line incrementally until your long-term (daily? weekly?) throughput peaks and begins to decrease.
• Continue to slow the line for a short period to make sure that you have really maximized throughput and that the throughput is stabilized across all shifts.
• Slowly increase speed until output starts to fall off. Then, go back to the sustainable speed.

In many cases this may work to improve results, but it will not maximize throughput or get management off your back. If the line described above runs two eight hour shifts per day, five days per week, fifty weeks out of the year, and has a profit margin of $0.50 per product, a 9.3% improvement translates to $3,012,000 in yearly positive cash flow.

To maximize throughput:

• Identify the constraint
• Protect the constraint from all interruptions
• Increase the speed of the constraint if possible

More examples and formulas:
Packaging Throughput Example
How Big Should my Buffer Be?
Non-linear line analysis
Accumulation in a Puck Line

I’m a controls systems validation engineer who is doing a part-time degree in
mechatronics. My project is on exploring the replacing of the FIRO (First In / Random Out) buffers we have at work on our filling/packaging line (Bottles) for FIFO (First In / First Out) buffers and then looking at how that helps us with maintaining traceability etc.

This is a fantastic question and one I thought I understood until I started thinking about it. I also like the term FIRO, which I have to admit I never heard before. Most accumulation systems I’ve used are generally either FILO or FIFO with some randomness thrown in. A few are near perfect FIFO.

The answer to her question starts with a definition of traceability. Traceability in packaging is the power to locate when a particular product was packaged in order to isolate a problem. In the event of a recall, packagers want to minimize losses and the more traceability they have the less product gets scrapped and the more confident they are that the public is protected from sickness (or worse). Raising the precision of your traceability has significant operational and efficiency costs, so companies must select the level that makes sense for them.

What type of accumulation system you needdepends on the level of traceability you're looking for.

Here are acceptable ways to use accumulation under various levels of traceability:

Traceability by Day
If you need a product to be traceable back to a specific day, then any accumulation style will work (FIFO, FILO, FIRO, etc.). All you have to do is make sure all buffers, hoppers, and reject collectors are cleared out between shifts. Have an inspector walk the line and make sure no product has been taken off the line and set aside during production. When stacked in pallets, make sure each pallet is wrapped and marked in a way that no products can easily be removed or have their documentation tampered with. Pallets should be stored so they are not mixed with other days’ pallets.

Traceability by Hour
Keeping track of a specific hour’s production requires more rigorous procedures. All buffers should have a maximum dwell time that when added to the total production time, is no more than 30 minutes (ie. 2 buffers with 10 minutes of maximum dwell time, plus 10 minutes of total production time makes the maximum amount of time on the line 30 minutes). If the OEM cannot provide a maximum dwell time that is low enough to achieve this, then periodically you will have to artificially introduce down time to empty the buffer if it has not emptied on its own before the alloted time. This has a significant impact on efficiency.

Traceability by Case or Product
The highest level of traceability goes down to an individual product or small group (ie. case). In this scenario, no product should ever be able to pass each other on the conveyor system or in buffers, which requires tamper proof conveyors with overhead covers. Constant monitoring is required to make sure no products are removed from the packaging line unless recorded by an automatic rejector. Rejected products should be dropped into a locked box that can’t be opened in the packaging area. At that point all these extra measures begins to make RFID tracking an economical solution, which would allow you to once again use any accumulation system, even FILO ones.

Check out our selection of accumulation tables.

In automated processing and packaging lines, Mean Time to Repair is the average time it takes to repair a machine once it malfunctions. It’s used to calculate how much buffering is required upstream or downstream. If your line’s longest MTR is 3 minutes, then you need at 3 minutes of accumulation.

Wait, but doesn’t that mean I will only be able to buffer against half the downtimes? If you have an even distribution of downtime durations, then yes, you’d only buffer against half the downtimes. What we see in reality is that most packaging machinery has a high number of very short downtimes (under 3 minutes) and a small number of very long downtimes (10-60 minutes). Here’s some representative (but fabricated) sample data to illustrate this point.

The MTR on this machine is 3.76 minutes. A 3 minute buffer would cover 92% of all downtimes on this machine. What would be the machine and facilities cost of capturing that last 8%?

How do I reduce MTR?
MTR is made up of three components: Knowing, Finding, and Fixing. You must reduce one of these three to reduce MTR.

Knowing: The time it takes for anyone to realize the machine has malfunctioned and requires human intervention. The ways to reduce this component are by adding alerts (light stacks, alarms) or operators to continually watch the equipment.

Finding: The time it takes for someone to find out what the problem is. This step can be reduced by programming helpful error messages into the machine’s HMI that tell you exactly where the problem is. Modern photocopiers do this by telling you where the likely jam points are. When training operators, make sure they know what the most common malfunction reasons are so they can check those areas first.

Fixing:: The time it takes to actually fix this problem. Reducing this step is the hardest and most difficult to change once a machine is installed. Hopefully this has been thought about and discussed during the design phase of the equipment. Machines that are easy to repair must start and stop gracefully, have disconnects easily within reach, accessible doors and guarding, and easily explained repair instructions.

In many cases, the first two steps take far longer than the third so there is a lot of room for improvement. Reducing MTR helps keep your line running efficiently and allow you to tolerate more frequent hiccups in production.

Can you increase efficiency and throughput by slowing down?

I recently had a discussion with a customer and an engineering firm about increasing throughput by slowing down the rate of a labeler from 400/minute to 380/minute. They recorded an improvement in efficiency to justify the change.  I questioned the decision from a thruput standpoint and wanted to come up with a good way to determine if an increase in efficiency actually increased the thruput or not.

Is slower better?

Efficiency is calculated using the following formula:

MTBF / (MTBF + MTR)

MTR = Mean Time to Repair
MTBF = Mean Time Between Failure

A properly buffered line should have capacity to handle the longest MTR on the line. The difference in maximum rates should be such that the table can go from full to empty in less time than the MTBF. For example, if you have a line like this:

Your labeler must have a MTR of 3 minutes or less and an MTBF of 9.9 minutes or more, giving us a minimum labeler efficiency of 77%. So what if I decrease my max rate in an effort to improve efficiency?

My minimum required MTR is the same since I’m still filling the buffer at 330 bpm, but my minimum required MTBF is now 19.8 minutes. This gives us a way to measure whether the decrease in rate has affected throughput. My labeler efficiency must now remain above 87% (19.8 / (3+19.8)) to keep the filler running and maintain throughput.

As the labeler slows down, its efficiency must go up to maintain throughput for the packaging line.

The formula can even more simply be expressed like this:

e = (Fr / Lr)

Where e = the minimum efficiency needed to maintain throughput
Fr = the max rate of the constraint
Lr = the max rate of the machine in question

If I run the labeler at 400 bpm I need to maintain a labeler efficiency of 77%. If I run it at 380bpm, I must maintain an efficiency of 87%. So if slowing the max rate of the labeler resulted in improving the efficiency from under 77% to something over 87%, then yes it will have improved throughput. If they were already running above 77% prior to the rate change, then throughput will be unchanged. If efficiency is under 87% after the rate change, then throughput will decrease.

In practice you have to find the right balance between rate and efficiency. It may be tremendously more difficult to maintain 87% than 77%, due to inconsistent materials or operator error.

A puck system is used to move unstable products through a packaging line. They’re popular in the cosmetics and personal care industries and can be purchased from a number of vendors such as Advantage Puck. The pucks and the products are separated at the end of the line and the pucks must be conveyed and reintroduced back to the beginning. A puck system provides a unique challenge for our line analysis calculation because stoppages in the line affect the flow of pucks upstream and downstream simultaneously.

Let’s look at a simple example of three machines.

Three machines in a packaging line with a puck return system.

Like any normal packaging line, this line has a constraint. Machine A runs at 300 products per minute (ppm) and the line can’t go any faster than that. Machine B accepts products from Machine A and sends them to Machine C. Machine C performs some sort of operation on the product, removes the product from the puck, and returns the empty back back to Machine A. If any machine malfunctions, all other machines must also shut down. Normally we can add an accumulation to help protect the constraint from the downtimes on other machines like this:

We added an accumulation table between Machines A and B to improve throughput, but does it help?

Unfortunately, Machine A requires a steady stream of empty pucks from Machine C to run. Downtimes on Machines B and C interrupt this stream, so despite having an accumulator downstream to allow A to keep running, line production shuts down anyway.

Downtime on Machine C cuts off the flow of empty pucks to Machine A, shutting down the whole line.

The same thing happens for downtime on Machine B.

We solve this problem by placing a second buffer upstream from the constraint in the empty puck return conveyor.

A second accumulator primed with empty pucks will allow the constraint to keep running.

If we have downtime on B or C, the second accumulator that was pre-primed with empty pucks will start emptying out and the original accumulation table will start to fill up at a rate of 300ppm. If the malfunctions on B or C are corrected before these tables empty out or fill up, then no production has been lost and throughput has been increased.

If B or C malfunctions, the first accumulator starts to fill up and the second starts to empty out, but the important thing is that Machine A keeps going.

Some important notes and questions:

How big should the accumulation tables be?
Both tables should hold enough products to handle the longest average repair time of any non-constraint machine, plus the number of pucks in transit.

Do the tables have to be the same size?
If you have two tables, they must be the same size because they are emptying out and filling up at the same time.

Does the second accumulator have to be in the empty puck return section?
No, but for the majority of cases it is the best place. Otherwise you will have to remove all the empty pucks from the system at the end of a shift or allow empty pucks to pass through machines without products in them. It makes sense from throughput standpoint and an operations perspective to accumulate all the empty pucks on accumulator 2 at the end of the shift.

How many pucks should I put on the system?
Fill the accumulation table prior to Machine A with empty pucks and don’t put any more on. Adding more pucks will decrease the line’s throughput.

Remember that game show the Weakest Link?

The first step in maximizing your throughput is identifying your constraint, or bottleneck. Take the efficiency of each machine and multiply it by its maximum rate. This will give you the net rate of each machine. The machine with the lowest net rate is your constraint and your goal should to keep it running as fast and as often as possible.

Example:
A simple packaging line: (ppm = products per minute)

Some interesting notes:

• Each machine runs 92% or higher, but the line net efficiency is only 83.9%. This is because downtime on just one machine shuts down the whole line (ie. if the Casepacker jams, the Filler and the Labeler also shut down). The net efficiency of the line is calculated by multiplying the efficiencies of each machine in sequence:
  0.94 * 0.97 * 0.92 = 0.839 = 83.9%
• To get the overall line speed, multiply the net efficiency by the max rate of the slowest machine: 0.839 * 200 = 167.7 ppm
• The machine with the lowest net rate is the labeler (194 ppm), making it the constraint. 194 ppm is the upper limit of what we can achieve on this line.

So how do you achieve the upper limit of 194 products per minute? By making sure downtime on the Filler and Casepacker don’t ever cause the Labeler to shut down. By adding an accumulation table in between the Filler and the Labeler, you are segregating the line into two separate systems. This breaks the compounding effect of the machine efficiencies.

Since the line is now in two sections, we have to calculate their throughput separately. The throughput of the first section is 282ppm (0.94 * 300 ppm = 282 ppm). The throughput of the second section is 178.5 ppm (0.97 * 0.92 * 200 ppm = 178.5 ppm). We are still limited by the bottleneck of the second section, so the throughput of this line is 178.5 ppm, a 6.4% increase over our previous line.

Since we haven’t reach our upper limit of 194 ppm we should add another buffer between the Labeler and the Casepacker.

Now we’ve broken the line into three sections with each machine running independently. We have no compounding of efficiencies, so all we have to do is pick the lowest net rate of the machines. In this case, the Labeler’s net rate is 194 ppm and this is throughput of the line, a 15.7% increase over our original throughput of 167.7 ppm.

What does 15.7% mean to a company’s bottom line?

If you are having trouble meeting market demand for a product it could be huge.
Let’s say the line is running:
2, 8 hour shifts per day
7 days a week
Profit is a conservative 50 cents per product

2 shifts * 8 hours * 60 minutes / hour * $0.50 cents * 26.3 extra products per minute = $12,624.00 per day in additional revenue

Over six months it generates $2,203,880.00