Key Performance Indicators and KPI Trees

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來自 University of Pennsylvania 的課程

运营管理概论

個評分

University of Pennsylvania

运营管理概论

個評分

課程 5（共 6 門，Specialization Business Foundations）

Learn to analyze and improve business processes in services or in manufacturing by learning how to increase productivity and deliver higher quality standards. Key concepts include process analysis, bottlenecks, flows rates, and inventory levels, and more. After successfully completing this course, you can apply these skills to a real-world business challenge as part of the Wharton Business Foundations Specialization.

從本節課中

Productivity

You'll learn the key components of productivity in this module. Beginning with the academic definitions of productivity, you'll move on to the common blockers, measurements, indicators of effectiveness, improvement techniques and financial values. By the end of this module, you'll be able to identify, describe and measure the relationship between inputs and outputs and also be able to develop strategies for decreasing inputs while increasing outputs, thereby boosting productivity wherever you see the need to.

Intro to Productivity, Taylorism7:56

The Seven Sources of Waste9:38

Financial Value of Productivity6:44

Key Performance Indicators and KPI Trees 6:21

Overall Equipment Effectiveness12:34

Quartile Analysis9:11

Productivity Ratios9:59

Review of Productivity19:57

與講師見面

Christian Terwiesch
Andrew M. Heller Professor at the Wharton School, Senior Fellow Leonard Davis Institute for Health Economics Co-Director, Mack Institute of Innovation Management
The Wharton School

Last session we saw how small changes in operational variables can have

big financial rewards.

One tool that helps us understand that relationship between operational variables

and financial variables is the KPI tree, where KPI stands for

key performance indicators.

KPI trees are powerful ways to visualize.

So the relationship between the many operational variables and

the financial bottom line.

KPI creates also the starting point for

sensitivity analysis that would formally let us identify

those operational variables that yield the biggest financial improvements.

Let's go back to our restaurant example from the last session.

And see KPI trees in action.

The number that we care about in the restaurant is the daily profit.

What drives this profit?

Profit is simply

revenue minus cost.

What drives the revenue here?

Revenue is driven by the flow rate, the number of customers that we serve.

[SOUND] Times the dollars that we make per customer.

In our example there was $6 per customer.

Flow rate, in turn, is defined as a minimum between demand and capacity.

The capacity is driven by the bottleneck because there was a step

with the lowest capacity between stations one, two, and three.

Now a case.

Station two was a bottleneck, and then was in turn driven by the processing time of

station two, which consisted of the time of putting the onions on the sandwich,

lettuce, tomato, everything else to boxing the napkin.

Along with the order.

On the cost side of the business, we have fixed and variable cost.

The variable cost are driven simply by the number of

sandwiches that we make times the dollars per sandwich.

Or the dollars per order.

These in turns are driven by the ingredients in the sandwich,

including the bread.

The meat, the cheese, the vegetables and

again everything all the way up to the napkin.

So we can think of a KPI tree as basically one big mathematical equation

that combines the many variables

that showed up in our spreadsheet analysis before the data we need to drive profits.

In one visual way.

Evaluating changes in the leaves of the tree and

predicting the impact on profits is the idea of sensitivity analysis.

For example, we can ask ourselves how much more profits do we make

if we can put the onions on the sandwich faster?

How much more profits would we make if we would reduce our fixed costs?

If we would source our bread any cheaper?

Mathematically, this corresponds to a derivative.

We take the partial derivative of profits with respect to an operational variable.

In practice, however, as we saw in the previous session evaluating

the impact of change is much easier.

It doesn't require any calculus.

All we have to do is build an Excel spreadsheet and evaluate the changes.

Why was the impact of a productivity change so big on profit?

Think about our business from a financial perspective.

Let's plot revenue and cost as a function of flow rate.

Our revenues are quite simple.

They go up at a slope of $6 per every customer that we serve.

Our costs, however, are a little different.

Remember that we have to pay $250 in terms of fixed cost, and another $60 of labor.

This amount of money we have to spend even if we don't sell any sandwiches.

From then onwards this line slopes up, but

now at a much lower slope than the revenue curve.

In fact the slope is simply $1.50 per customer capturing our variable cost.

You see that here we have a point at which we stop making money.

This is typically called the break even point.

In our baseline analysis,

in the previous session, we noticed that we were just beyond that break even point.

We made a little bit of profit.

However, from then onwards having an additional customer through the system,

has a very strong marginal effect.

Every customer, that we serve, brings us $4.50 that go right into the bottom line.

Is the juice worth the squeeze?

Changing an operation is hard but

there are many operational variables that we could focus on.

The KPI tree is a powerful tool that helps us

identify those operational variables that we should focus on.

The KPI tree is also a powerful visual that helps us understand the casual system

that connects all these operational variables with the financial bottom line.

So I suggest to you that you always start any operational improvement projects

by first mapping out the process, drawing out the process flow diagram.

Then you do the process analysis calculating the operational variables

that we introduced in the first module.

And then you build a KPI tree to help you understand how these operational variables

drive financial performance.

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