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What are the different sources of variability that you should be thinking

about when your trying to plan for a good capacity utilization number?

So first we can think about the sources of variability from

the internal activities and from the suppliers.

So suppliers send us raw materials and

if there are inconsistencies in the raw material and

there's variability in that, then it might take us more time than we planned.

It might take us more than the average processing time, the cycle time might be

longer based on their being inconsistencies in the raw material, and

therefore we have to account for

that when we are thinking about a planned capacity utilization number.

Similarly, product variety,

if we have a lot of products that have been produces from that same process, then

the product variety is going to impact how much capacity utilization we can plan for.

Because there will be changeovers.

Similarly, which set up times, the changeovers might actually

require different amounts of time based on the sequence of production, and

that's something that needs to be thought about

in terms of it will eat up some of the capacity so should be how

do we incorporate that in our planned capacity utilization number.

Different needs for changeovers,

sometimes you'll need more changeovers in a particular week than in other weeks and

some other weeks you might need more changeovers.

So when you're planning for your weekly capacity utilization number, you want to

incorporate the mix that you are going to make in that particular week, or whatever

period you're talking about, it could be a month that you might be talking about.

There might be inherent differences in the times that

different people working on that same task might take.

An experienced person might be able to do things in less time than a novice.

And that's something that will affect how that capacity is being used, and

therefore, planning for capacity utilization is going to get impacted.

And finally, any kind of errors.

Any kind of errors in the operator doing something on a machine, or

the machine itself deteriorating and not working at the same rate.

So you have a certain rate that you expect from a machine and

it may not perform at that rate all the time.

So the variability that you get from the time that the machine takes needs to

be thought about, needs to be incorporated when you're talking about planning for

a capacity utilization number.

When you're planning for how much capacity, you will have a variable

from that particular activity, that particular task, that particular process.

Next, let's look at the variability that can come from customer demand.

So different products might have different processing times.

And therefore, when you're planning for your capacity utilization,

you have to say, well, it depends on how much demand of which product I have.

If I'm making multiple products from that same process, then I will have to

incorporate the variability in processing times for different products.

Fluctuations in mix of demand in certain weeks.

I might have the same product being demanded, and

sometimes I might have a different mix.

Different transfer batch sizes between different tasks,

might have to be thought about when you're thinking about capacity utilization.

So what do we mean by that is if you have two different tasks, that are in two

different manufacturing plants, you might have a transfer batch size.

You might not be taking one unit and sending it off to the next activity,

to the next task.

You'll make a certain transfer batch size before you send it off.

And that is going to impact the capacity utilization,

that it comes in certain batches.

And that batch will affect how you do changeovers and

how you plan for your capacity utilization based on the changeovers.

And finally, different batch sizes of production, so similar to transfer batch

sizes you might have transfer of production batch sizes and

having different batch sizes for

one activity versus another might affect the utilization.

Because if there is an imbalance there in terms of the total processing time,

then one process would have to sacrifice some of it capacity and

wait for the previous process or the next process and

it'll have some impact on the planned capacity utilization.

So let's take a look at what does variability

mean in terms of utilization, and how does it impact what utilization

does in turn to the average wait time, the average flow time.

So on the X axis, you have utilization going from very low utilization,

starting from about 25% going to 100%.

On the Y axis, you have average flow time and

you can also think of that as average weight time.

How much would costumers have to wait those two would go together?

So you can think of the Y axis as being average weight time, as well.

Now, what the curve line is showing you is that when you are at very

low utilization numbers, there's going to be almost no waiting.

And as you move towards a high degree of utilization,

100% utilization, the wait time and the flow times will increase steeply.

Now this should be intuitively obvious to you if you think about this from

a day-to-day perspective of how you plan your work.

If you have a schedule for your day,

in which you try to have meetings with people, the more you pack your day,

the more utilized you try to make your day in terms of

having let's say 16 half hour meetings over an 8 hour period,

the more chances you have of there being a delay.

So high utilization, the more chances you have of there being a delay,

because you simply don't have buffers for

anything that might happen in between those meetings.

Now, as the variability in a process goes higher, so

if I'm talking about meetings in a day, if people are good about keeping their 30

minute time slots, maybe I can have a higher degree of utilization and

get away with it without having any kind of delays.

But as the variability goes high, as people get worse and worse at keeping

their 30 minute times slots, what's going to happen is that my utilization

is going to impact my flow time at a much lower utilization number.

So earlier, you saw that I could go up to maybe

about 60, 70, 80% utilization and still have a low wait time.

And after that, steeply increased.

As my variability increases, so as the variability is increasing

either in demand or in the processing times, what is happening is that,

that kink in the graph is coming earlier and earlier in terms of the utilization.

So if I'm expecting my process to have a lot of variability,

then I'd better plan for

it to have less utilization, otherwise I'm going to have a lot of waiting time.

I'm going to have a longer flow time for each of my processes.

So what are the implications of variability if you were

to put all of this together?

It means that higher levels of utilization will result in waiting.

If I try to plan for 100%, stuff will happen,

and it'll cause there to be some delays, and there will be some waiting.

As the variability increases, the sensitivity of

wait time of having flow time increased to utilization will get higher.

And what does that mean to me from a practical perspective?

As the variability increases, my wait times will start worsening at lower and

lower degrees of utilization.

So what should I do in terms of trying to plan?

For having a higher degree of utilization, the lesson is reduce variability.

And if I can reduce variability, I can plan for a higher degree of utilization,

and at the same time, not have higher flow times or higher wait times for

customers or higher wait times for products that I'm selling to customers.

If you think about the idea of variability,

this is something that you can measure in a lot of different ways if

you think about what is causing the variability in the process.

And we'll go over a few of the metrics.

Here, are metrics that you see in total quality management programs.

So first time yield of an activity, it measure the percentage of

units that are completed correctly the first time they're worked on.

So that's telling you,

I meant to make 100 units this hour, I actually made 100 units.

My first time yield was 100%, I didn't have to rework anything,

I didn't have to scrap any of the parts that gave me a higher yield.

Rolled throughput yield, if you have many different tasks in a process,

many different activities, it's simply the product of all of those activities,

taking the first time yield of all those activities and

multiplying that, gives me rolled throughput yield.

So if I have 2 activities that are working at 90%,

my rolled throughput yield is multiplying both of them 90 times 90% gives me 81%.

So that would be my rolled throughput yield.

So the better my rolled throughput yield,

the less time I need for rework kind of activities.

And the more utilization I'm getting, the more utilization I can plan for.

A compound metric of different aspects of variability

is the idea of overall equipment effectiveness.

And this metric actually takes three different metrics and

takes the product of them.

So it's the availability.

It's the performance.

The availability is calculated as actual divided by total time.

Performance is actual output divided by total output.

And yield is the defect free output divided by total output.

So the OEE, or the overall equipment effectiveness is

an indicator that's a product of all these three metrics.

Now, what you would have noticed is that although it's a product of these three

metrics, each of these metrics will impact one another.

So if your yield is low, it's going to affect the performance.

The actual output divided by potential output and

it's also going to affect the actual operating time.

If the yield is low you probably going to have to stop the process and

work on it to make some fixes to come up with the solutions and

that's going to take away from your total plan time, and so

your availability might also get reduced.

So there might be interactions that might occur between these three or

among these three that are going to make it worse when one of them gets worse.

So one more matrix that you can think of is this idea of takt time.

Now, the way takt time is calculated is it's based on customer demand.

If the customer demand rate is known to you,

that's the rate at which you want each of your processes to be performing.

Each of your activities in the process to be performing.

If you can balance each of the activities of a multi-activity process

to the rate at which the customer is consuming the end product,

that's going to give you a process that's completely balanced and you're going to

have good capacity utilization across each of those activities in the process.

So takt time is a good way of thinking about the ideal time,

the ideal cycle time that you should have for each process because that balances it

with the way the customer is actually consuming your product.

If you can reduce your variability and

you think about the impact on the three matrix that we think about when you

talk of Little's Law, it's inventory, throughput time, throughput rate.

Reducing variability allows you to work with lower levels of inventory.

Reducing your variability allows you to have,

or results in having shorter flow times.

Reducing variability also frees up your capacity so

you can make more for customers, you can make more to sell to customers.

So variability can affect these three metrics that

we talked about when we talked about Little's Law.

If you think about reducing variability and

you tie it to this notion of total quality management,

the basic underlying principle of total quality management

is the idea that variability in production processes,

variability in processes, all variability should be reduced.

So to put it in the words of Edward Deming who's known as the father of

the Total Quality Management movement,

the central problem of management is to understand the meaning of variation.

And to extract the information contained in the variation.

So his perspective was that you focus on variation and

you see how you can interpret that variation to say how much should be there,

and what is variation that you should be able to eliminate.

So that's the basic principle behind the total quality management movement and

quality management initiative such as even lean,

the total production system and six sigma.

2-2.4b Impact of Variability

Operations Management

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