HOW TO MEASURE THE
STATE OF YOUR BUSINESS AND ACHIEVE OPTIMAL WHAT-IF SCENARIOS.
When you're driving, you only glance into the rear-view mirror because, if
you stare too long, you just might crash. Many businesses today are spending
way too much time looking in the rear-view mirror at the road they've
traveled by focusing on historical performance management methods. A few
leading companies, however, are applying many of the basic principles and
technology of historical performance management methods to forward-looking
performance management. This new trend is scenario-based planning, and it
concentration on the future rather than on what has already happened.
By generating multiple what-if scenarios using data you already have and
applying performance management as if the scenarios were actual situations,
you can take performance management to the next level. The result: You can
integrate optimal what-if scenarios with traditional budgeting and planning
systems to change faster than industry norms and increase your
organization's competitive advantages.
Unfortunately, most businesses aren't intertwining scenario-based planning
and performance management. Consider the frenzy to move to the
Internet-based business model. Companies that moved into the dot-com world
without the business intelligence to support the decision often failed in
this model. You can just as easily replace the dicks-and-mortar example with
changes like increased competition and a downturn economy.
Let's look at how most companies use performance management by and large for
financial measures only and the shortcomings of most business intelligence
solutions. I'll then explain performance management optimization (PMO),
which combines the disciplines of scenario-based planning and performance
management to provide a holistic, integrated view of the business.
MYOPIC VIEW OF FINANCIAL MEASUREMENTS
Until recently, performance management has been focused solely on history.
Companies could sec at a certain point in time how they had performed up
until that day, but looking forward wasn't an option. Combining metrics,
benchmarks, and processes, performance management analyzes financial as well
as non-financial metrics. These combinations of metrics provide the
complete-report card of an enterprise, but organizations often gauge
themselves purely by financial measurements. The result: They overlook key
indicators, such as customer satisfaction, on-time delivery, or employee
retention rates. After all, even if financial figures are neutral or
positive, other metrics can indicate a less-than-healthy enterprise.
This myopic view of financial measurements is often the result of two
factors. First, external measurement of an enterprise by investors,
analysts, and the markets are almost entirely financial measurements
(revenue, margin, and the like). Second, financial measurements are usually
readily available from existing systems, such as financial or accounting
packages, while other metrics are more commonly buried deeper in an
organization's systems and processes. Often these non-financial metrics
aren't understood or are represented without empirical backing, and, worst
of all, they aren't usually integrated with the financial metrics.
Beyond the focus on financial measurements, another problem is that most
performance management systems are disjointed from the actual enterprise
resource planning (ERP) or customer relationship management (CRM) systems
from which they're getting their data. Thus, the technical aspects of
creating a performance management system not only relate to the system
itself but to the feeding from these upstream systems.
A widely known shortcoming of any business intelligence application is the
separation of analytical and actionable processes. If you can't
automatically translate the business intelligence into a process on which
you can act, such as a completed budget or business plan, the analytics
become nothing more than fancy and complex reporting. The information is
This unnatural separation of analytical and actionable processes creates
disconnects on both the business process and the technical implementation.
Disconnects of this level lead to double work in the form of data entry and
are prone to error, just like any double entry. The separation also robs the
organization of valuable time that it could spend on what-if scenarios.
Budgeting has traditionally been one of the only forms of metric-based
planning an enterprise uses. Most organizations use a yearly budget and
attempt to track actual figures against the budget. These budgets can be
highly aggregated at a corporate level and then pushed down to divisions and
Until recently, companies barely had the time to create a budget and make
minor adjustments to it. With scenario-based planning, they usually create a
baseline budget, which is then used to generate multiple, sometimes hundreds
of, scenarios. Top-down budgeting enables splashing — the ability to
enter data at any level of aggregation (cost center, product line, division)
— and have the data apportioned based on patterns or business rules. With
splashing, enterprises can create what-if scenarios. For example, one
organization creates a top-line revenue target. The budget is then shared
with product line managers who are responsible for dozens of products. The
product line managers can then adjust their target for each product or
adjust their overall product target. With each change, the top-line numbers
are being changed. The same budget is shared with regional sales managers.
They can adjust their targets for sales representatives, and the data are
also automatically combined. The sum of these changes is saved as a
scenario, and the process is repeated. This collaborative processing among
people throughout the enterprise allows multiple scenario building.
The same method of splashing also allows multiple scenario building using
non-financial metrics in the planning process. If you understand the trends
of past metrics, such as employee attrition, on-time delivery rate, or
average lead-time for new products, you can derive forward-looking
forecasts. For example, you may use past on-time delivery percentages to
forecast next year's expected percentage. Also, you can modify certain
constraints and variables to determine a new forecast for the on-time
delivery percentage. Although some organizations are performing this type of
forecast, which is actually quite similar to forecasting financial values,
few are integrating it with their other metrics, particularly financial
PERFORMANCE MANAGEMENT OPTIMIZATION
Performance management optimization finds the optimal solution through a
1. Using the disciplines and tools of performance management to
create a static snapshot that serves as a baseline to future scenarios.
2. Using what-if scenario planning tools to create dozens of
scenarios for best and worst cases. There can be simultaneous adjustments to
multiple views of the business. For example, product managers can update
their what-if measures while the research and development managers are
updating theirs against the same model. The what-if scenarios are for
financial and non-financial metrics. A centralized group keeps versions of
these scenarios for later analysis.
At the same time, performance management is being done against the what-if
scenarios. By measuring these future states, you can determine strengths and
weaknesses before committing resources to any changes. Besides measuring
each scenario individually, you can analyze cross-scenario measurements to
determine if a hybrid of two scenarios may be optimal.
3. Selecting an optimal scenario based on both financial and non-financial
measures. The optimal solution may not always be the solution with the
highest revenue or margin (although these measures typically are weighted
higher). For example, an optimal scenario for revenue may be a dismal
scenario for projected customer churn.
OBTAINING THE DATA
The data for performance management optimization will come from internal and
external sources. Here's a look at both.
Many organizations will start with the internal focus to test the PMO
methodology and to provide early return on investment (ROI). Their own
financial and non-financial measures with existing ERF and CRM systems
provide much of the information. There are two benefits of concentrating on
your processes and data. First, the data are usually readily available,
although the quality and usability of the data may be questionable. Second,
understanding how the raw data were created should be information
This internal focus, though, won't provide a complete view. Global supply
chains, business partnerships, outsourced relationships, and changing
corporate structures have driven the need for an external as well as
internal focus for performance management optimization.
The external focus does add a new set of challenges, which may not only be
difficult to implement but difficult to understand. Generally, the processes
and data outside an organization won't follow the same structure,
documentations, and standards as internal processes. Take, for example, an
organization that outsourced the delivery of end products via a third-party
logistics (3PI.) company. While much may be understood regarding the
products being developed and produced, including common naming and product
codes, this level of data may not be available from outside systems. A
third-party company may just be able to track batches or distribution of
products as compared to internal systems that can link products to sales to
It isn't impossible to obtain data from third parties. If business partners
are truly partners, their information technology staff will communicate the
structure and content of the data. There are a number of emerging technology
standards, based on Internet protocols, to facilitate this data sharing.
In a truly networked performance management optimization environment, the
internal focus of one organization will be the external focus of another
business partner. In the outsourced logistics example, the logistics
company has certain key performance indicators (KPIs), such as on-time
delivery, average lead-time, empty container percentage, and the like. If
these measures are being managed as internal-focused PMO indicators, they
can be shared with the product manufacturer for integration as external
focus measures for their PMO system. This two-way sharing of measures will
drive further mutual benefit for true business partners.
One caveat in the networked PMO environment, though, is data security and
privacy. Existing technologies, such as segmentation of servers and
firewalls, can safeguard the data. But these technical safeguards need to be
complemented by process safeguards including policies, procedures, and
One final note on data gathering. When obtaining data from other systems,
you must build a framework and technical architecture that doesn't hard-wire
the organization to any internal or external systems. It's almost certain
that one or more of these systems will be retired, swapped, or upgraded
within a two-year period. Implementations must be flexible enough to avoid
being at the mercy of technical limitations and upgrades. Thus, the
framework must be built for business measures, not technical fields or
SUCCESS FACTORS OF PMO
Combining the disciplines of performance management and scenario-based
planning requires five critical success factors: usability, splashing,
integration of non-financial measures, collaboration, and distribution.
Without applying all five, the performance management optimisation
methodology will be difficult, if not impossible, to implement.
A successful implementation of PMO will build on the successful budgeting
systems of today. Business users need to view and manipulate data in their
technology of choice. With a vast majority of financial users budgeting and
planning in Microsoft Excel®, it's the logical platform choice. The
increased functionality of pivot tables and multidimensional views are
becoming more common, and Microsoft SQL Server provides a built-in
multidimensional engine. Other platforms also have that support — DB2 and
Oracle, for example — but not for free.
As mentioned earlier, splashing is the ability to enter data at any level of
aggregation (cost center, product line, division) and have the data
apportioned based on patterns or business rules. At the same time, the
top-line rollup is also updated. This gives the user true middle-out
planning capabilities. Much of the current technology can aggregate up when
low-level figures are entered, fewer can apportion down, and even fewer can
Splashing must have multiple methods. First, even distribution will splash
the aggregated number over all the elements below it at equal levels.
Second, percent changes can be combined with the even distribution. Thus, a
divisional number could be spread out evenly among departments but with each
individual number increased by 10%. Third, and more complex, is contour
distribution. With contouring, a number is entered at an aggregated level
but is splashed based on a second set of numbers. Thus, a department could
enter a forecast number for the revenue target but splash based on last
year's product distribution. A true splashing environment combines all three
forms in a workflow that leverages the concurrency of decentralized planning
while retaining control of centralized functions.
For example, a tire manufacturer can allow country sales managers to
forecast revenue. Using contour distribution splashing, distribution within
that country of product revenue can be automatically generated based on past
performance. Concurrently, product managers can update their cost
projections and perform percent increases based on expected increased cost
of raw materials. When completed, a central planning group can then
fine-tune the top-line numbers and splash accordingly.
Integration of Non-financial Measures
Many systems can measure financial information, such as revenue, profit, and
cost of goods. Some of these systems can also perform activity-based
costing. Generally, though, organizations need to have a separate system for
other types of performance, such as manufacturing, human resources, or
supply chain. PMO delivers its greatest return on investment when all these
measures can be integrated into one application.
Collaboration and Distribution
The optimal PMO environment will allow collaboration and distribution at all
levels and various technologies without needing custom code or third-party
add-ons. Technology has made collaboration possible for budgeting. Although
many organizations don't take advantage of this technology yet, the ability
exists to perform combined decentralized (sales field office, divisional)
and centralized (home office, consolidated financial) budgeting. During this
process, companies are using collaborative technologies such as intranets
and shared databases to facilitate information sharing in real time.
This same process needs to be applied to scenario-based planning.
Organizations can take advantage of the decentralized facilities to perform
what-if plans on a field office or product-line level. At the same time, a
centralized organization can apply enterprise-level assumptions to the same
Key to success is the planning of both financial and non-financial measures.
For example, a sales manager in a Latin American subsidiary could plan for
increased customer satisfaction by adding a local language help desk. This
would be offset by the increased overhead for the Latin American office. The
corporate office in California can be estimating the cost of rerouting all
Spanish-speaking help desk calls to the new call center and examining
economies of scale. This results in a collaboration of decentralized
scenarios with centralized sharing control.
To cost effectively distribute the results and reports of these what-if
scenarios, an intranet makes sense, and, on the executive level, the concept
of a briefing book in a concise, easy-to-read business document or
presentation is key.
STAYING AHEAD OF THE COMPETITION
Organizations can achieve significant competitive advantage through the use
of performance management optimization. The two disciplines of performance
management and scenario-based planning can be intertwined, creating an
environment where not only the actual as-is state is measured, but the
optimal what-if scenario is achieved.
by Anthony L. Politan (
Email: firstname.lastname@example.org )