Business Process Intelligence- Case-II help


A- Please read the following files:
HBR – Best Laid Plans Case
B- Please review to the following files:
APQC Process classification framework
Kaplan & Norton’s Balanced Scorecard
BPMN-Tutorial.pdfThe task: use the readings to inform your answers to the following questions about the Best Laid Plans case. Justify your answer with citations from the readings.
1. Use the first Ramias article to evaluate the approach Rain Barrel used to establish metrics
2. Choose a Rain Barrel process and use the second Ramias article to build a measures chain and identify the pitfalls that could happen at Rain Barrel.
3. Use the third Ramias article to answer the questions on page 2 and to build a management domain matrix for Rain Barrel.Notes:  1- number of pages about 4 to 5 pages or less if the answer is clear and sufficient 2- (Justify your answers with citations from the readings), answers without citation form the reading will result in declining the job.  3- I’m giving more time for this job to help you in reading the readings


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Measuring Process Performance
One of the most important – but frequently most challenging and vexing – aspects of installing
business process management in an organization is metrics. There is seldom much argument
anymore about the necessity of having metrics at the process level to enable process owners and
performing teams alike to monitor performance, diagnose variation, and make effective course
corrections. Once a business process has been created or redesigned, measurement of process
performance is critical. Measurement can be used to ensure the process is installed properly,
produces desired results, and design integrity is maintained. Ongoing measurement is the basis
for continuous improvement.
But selecting, designing, implementing, and using metrics is a complex set of activities and
loaded with pitfalls, and enabling software can either help or make it worse, depending on the
human intelligence being applied to such questions as what to measure, when to measure, who
should be watching performance, what to do with data, and how to diagnose and react to
performance issues.
This Column will be the first of three devoted to process metrics. We will start by citing some of
the pitfalls we have encountered over the years – some of the mistakes we have made ourselves
or seen others commit. We’ll describe metrics that don’t work as effective indicators of process
performance, or that end up being barriers to understanding and collaboration among those who
perform, manage, or support a process. Some of these pitfalls have to do with the design of
metrics themselves; others have to do with how they are used as management tools.
Then, in future Columns, we will describe the approach and tools we use to overcome these
obstacles to process performance measurement, the aim being to help you avoid some of the
difficulties we have experienced and to speed you forward to effective design, implementation,
and use of process metrics.
The Bolt-On Approach
The most common pitfall we have witnessed in organizations that have installed process metrics
is what we call the “bolt-on approach.” Metrics have been identified (usually after an
improvement project) and the data is being dutifully collected and looked at by someone. But
measurement and accountability for the performance of the process as a whole is lacking. A
close look at the metrics themselves reveals that they usually look a lot like the old functional
metrics. There may be some area of performance that has not been measured before – cycle
time, let’s say – but for the most part these new metrics are simply added to the existing pile. Old
metrics never really went away; there was no fundamental rethinking of what is important in the
organization and what should be measured; there is just more data being collected – making this
a bolt-on measurement system. Nobody is held accountable for process performance.
Copyright © 2010 Performance Design Lab. All Rights Reserved.
And bolt-on measurement goes hand-in-hand with bolt-on management. The forum to review
process metrics is often a separate management meeting – an extra event that those dubbed
process owner or process manager or process management team need to attend, even though
everyone knows that the real management decisions are made at the “regular” management
meetings. Very often these process management meetings are not connected to anything. The
data being reviewed are not integrated or correlated with other data regarding business
performance. And thus the decisions or corrective actions are also not integrated with the real
management decisions and, in fact, are quite often undone by them.
Metrics without Management
The next most common scenario we’ve seen in organizations is the development of process
metrics, and that’s it: Nobody asked for them, nobody is accountable, nobody is in charge of
process performance so nobody in particular wants to see these metrics or the data.
Why would this happen? Because metrics is something that most business people understand.
An organization that enters BPM territory may get confused and uncertain about all the concepts
and terminology and tools, but metrics is a familiar device, and it gets latched onto as something
that can be quickly designed and implemented, but with little understanding of how these metrics
should be used, and what transformative effect should take place as horizontal management is
integrated into the existing vertical, functionally oriented management system.
Another version of this we have seen is that the right people are not looking at the process
performance data. So, instead, only staffers are gathering and looking at the information, and
they lack the authority to act on what the data tells them. So, once again, it’s a measurement
“system” that is dangling, not connected to the existing business performance management
A Chaos of Metrics
Without some structure or logic for selecting which metrics will be useful in understanding
performance, an organization can end up with metrics for reasons that make little sense.
Measuring virtually everything – every activity, every output, and every variable. Or not
measuring the most important things. Or measuring activities simply because we can (and the
use of software makes this more and more tempting).
Sometimes we find multiple, even redundant measurement systems, where people in different
parts of the company are measuring essentially the same thing but with a slightly different slant or
by different names. Every department has its own version of the truth.
But these practices can lead to a mess: a pile of data, of reports, of indicators that don’t add up,
don’t provide a clear picture of performance, and that, ultimately, confuse the people who are
trying to cut through the noise and understand what is going on. Evidence that something like
this has happened can be found in organizations where people seem to be drowning in reports
but have difficulty explaining succinctly what’s happening in and to the business. Adding a set of
process metrics just deepens the clutter.
“Bad” Metrics
Aside from how process metrics are often used inside organizations, the metrics themselves can
be flawed in design. In addition to being bounded by functions (because the process has been
defined functionally), we frequently see the following:

Metrics not connected to customer requirements. Instead, they only focus on internal

Only defining metrics for which there are existing data (instead of figuring out how to get
the data we need).
Defining the metrics and not the rest of the management system – (who will watch this,
why, what do they do about it?)
Not distinguishing between temporary metrics (those used to ensure that implementation
happens properly) and ongoing management metrics (those needed to manage the
process post-implementation).
The funniest example of temporary metrics we observed was in a company that had everyone in
the process verifying that the data going into a system matched the data coming out. This metric
was first created as a data integrity check of newly implemented software and intended to be in
place for only a short time, but years later people were still verifying the data, even though it
always matched.
Integration into Flawed Management Systems
As we have said, very often process metrics are stand-alone devices, not integrated into the
existing management system, thereby greatly reducing their potential. But in those cases where
the process metrics have been included in existing measurement and management systems,
there can still be problems if the existing management system has some design flaws of its own.
These are some of the defects we see far too frequently:

The Balanced Scorecard approach popularized by Kaplan and Nortoni made the use of
measurements “dashboards” ubiquitous in corporations. Many executives love these
red-yellow-green indicator boards as a quick way to take a snapshot of organizational
performance. The problem is that these indicators are often shallow. They tend to either
be readings of “spot” data, instead of trends or identification of major spikes in trend data.
In either case, they trigger knee-jerk responses. In fact, they are designed to do just that.
A “yellow” or “red” creates a flurry of action, often ineffectual, rather than reasoned
analysis and careful response. And the opposite can happen too. Everyone is happy with
the green lights until one day something turns red, to everyone’s great surprise. But, in
fact, the performance had been trending to red for months yet never hit the yellow
threshold and nobody knew to look into the gradually eroding results. 

Dashboard metrics tend to be a single unit of measure (that is, they measure one
variable, such as financial). So if the metrics on the dashboard are not correlated against
each other, the diagnosis can lead to a superficial understanding of causes.

Dashboard metrics also tend to be lagging indicators. Despite a “flashing yellow” light, it
is often too late to do much about the performance. Effective linkage of the corporate
metrics to the work processes can help alleviate this lag, but if the linkage is not there, all
data is necessarily lagging.

Finally, it is sometimes unclear who has responsibility for diagnosing and acting on the 
data shown on a dashboard.   That’s why the everyone‐out‐for‐a‐pass reaction becomes 
so common.   
Effective use of process performance information requires more than the metrics, and more than
the designation of someone to “be in charge” of the process. It requires a transformation of the
business from a vertical orientation to a horizontal one, from management of functional areas as if
they were independent fiefdoms to management of business processes that require
interdependent decisions and actions.
Requirements for Effective Measurement of Process Performance
Now that we have trashed much of the well-intentioned measurement work we have seen out
there among process practitioners, it is incumbent on us to provide some requirements for good
measurement. These are the requirements we use on our own measurement design work:

Metrics should measure the right things, which are outputs and results, not activities.

Metrics should measure the relevant variables, or dimensions, of a given output or result.
The variables may be the usual ones of time, cost, and quality, or they may be special and
unique to a given output, but, in any case, you need to know what those variables are.

It is often necessary to have multiple metrics correlated to multiple variables (whatever is
important to the customer and the business)

Whatever is measured at the process, subprocess, or task level should be traceable upward
to business and customer requirements. There should be a clear line of sight from process to
total business variables.

Metrics should track trends, not single snapshot data. Overreaction and under-reaction are
both less likely when using trend data.

Metrics should be assigned at each management level so it is clear who is responsible for
tracking, reporting, diagnosing, acting, following up. (We often see cascading measurement
systems that skip whole levels of management or have gaps from, say, the business to the
job level.)

At least some metrics should be leading indicators of future performance problems. These
are singled out for special attention.
With these requirements in mind, we will talk next time about building the metrics for a given
business process.
Kaplan, R.S. and Norton, D.P., “The Balanced Scorecard: Measures that Drive Performance,”
Harvard Business Review, February 1992.
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Building Metrics for a Process
Our last column kicked off a series on process metrics. We started off by citing some of the
problems and pitfalls we have encountered in our work with clients, such as creating metrics (and
process management roles) that were unlinked to management of the business; creating
disorganized piles of metrics instead of a logical set; measuring too much, too little, or the wrong
In this column we provide some principles and a tool to remedy the most significant problems.
We will describe the guidelines we follow in creating process metrics for clients and will apply
those guidelines using a tool for identifying the right process metrics. This tool is part of a larger
toolkit that we employ when helping an organization build a comprehensive process-focused
management system; this particular tool is central to the task of producing good, useful process
To apply the following principles and tools, some assumptions are necessary: You have singled
out a business process, you have mapped it in enough detail to identify its major subprocesses or
phases, and you understand the existing management system into which these process metrics
will be inserted.
Principles for Process Metrics Design

Every process is designed to reliably produce one or more outputs, so, in deciding what
metrics to develop, we always focus at first on process outputs, not activities,. The
metrics should measure whether the process not only produces the outputs but also that
all appropriate expectations are met every time the process is executed.

Metrics should be applied to all the significant outputs of the process. If the process is
order fulfillment, for example, the output is not just the product but also the invoice, the
order documentation, and customer information that will be used again for future orders.

We always start outside the process itself and try to understand the expectations of the
receivers of the outputs. The receiver may be an actual customer, or it may be an
internal party who is also a “customer” for a given output. Regardless whether the
process has an external or internal customer, the starting point is to understand what is
important to that customer – what are the expectations that we can then translate into
what we call the “critical dimensions of performance.” Once we know the expectations
for the process, we then create and distribute metrics along the process that measure all
of the relevant critical dimensions of performance, such as timeliness, quality, economics,
volume, compliance, and so on.

In first developing metrics, we focus on what to measure, not how measurement is going
to happen. There are several reasons for this: First, the decision about whether to
create a given metric is more important at first than determining exactly how the data
Copyright © 2010 Performance Design Lab. All Rights Reserved.
might be tracked, reported, archived, and so on. While measurement can be costly and
sometimes not worth the effort, we have watched some teams talk themselves out of a
potentially valuable metric just because they weren’t exactly sure at first how to collect
the data. Usually, you have choices as to what to collect and report – for example,
maybe you focus only on exceptions, or only reporting quarterly – that can reduce the
cost. And metrics don’t have to be perfect. Collecting data on secondary indicators of
performance may be quite adequate for triggering a closer look, rather than collecting
volumes of data that sometimes obscure what is actually going on. There is also a
tendency these days to fixate only on data from systems, but visual data (go and look)
and interview data (go and ask) are also viable ways to collect data.

The most useful performance data helps one see trends in performance. So, metrics that
can be constructed to yield a trend are the most useful, and most metrics can be
formulated this way. For example, “number of defects” for a given output is not that
useful except for fixing a single product, but number of defects by product type by day,
week, and month could provide a lot of insight into where performance problems are
originating. So in the table below, once we have identified all of the metrics we want, we
turn them into trend data by adding an element that enables trend tracking (such as per
lot, per day/week/month, per location per week, and the like).

We seek to identify metrics that will be both leading and lagging indicators of
performance. Lagging indicators are the common ones: they provide data on events in
the past. But leading indicators provide insight into the future; they center on data that
act as an early warning on emerging problems or declining performance. When chosen
well, a leading indicator can signal the need for a course correction before the problem
gets out of control. The tool we describe below is a great way to identify possible leading
The Measures Chain
The Measures Chain is used to develop metrics for a given process. The concept is shown in
Figure 1. The essence of this tool is to identify and link external requirements to an internal
process. The technique is to start with customer requirements and work backwards and
downwards into more detail that would be used for each dimension of performance.
The starting point is outside the process, where the process output is received. In Figure 1, the
process is order fulfillment, the output is a product, an invoice, and order documentation, and the
receivers are customers in a given market. (However, this tool and its principles can be applied
to processes that deliver outputs to internal “customers” too.)
Once we know what the customer of the process wants, we can identify where to place
appropriate metrics inside the process to see if we are meeting the customer’s expectations. We
end up creating a “chain” of metrics (hence the name) related to some performance requirement,
such as timeliness across the process.
To build a Measures Chain for a given process, we usually create a table like the one in Figure 2.
We already know the process inputs and outputs as well as the outputs of each subprocess. We
start by asking what the customer requirements or expectations are. These become the highest
set of metrics – what we call M-1-External. In Figure 2, the M-1-E metrics for the product are in
three dimensions (economics, timeliness, and quality) because the customer has requirements in
those three categories. Specifically, the customer cares about percent of deliveries made on time
and about price, so those are metrics we place in the table. We also decide to track customer
complaints and returns because those are direct forms of feedback from the customer about
product quality.
We then ask if there are additional business requirements for this output. Business requirements
are those things a customer may not necessarily know or care about (our costs, our internal
standards, our compliance requirements, etc.), but we want to measure this output against any of
those requirements that might exist. So in Figure 2 you can see business metrics (called M-1Internal metrics) in the same dimensions of quality, economics, and timeliness, but price is now
reclassified as profit, because the business wants to measure how profitable this product is, and
timeliness is measured in process cycle time.
Then we go inside the process and place metrics on the outputs of the subprocesses (M-2 level)
in the same dimensions of quality, timeliness, and economics. We …
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