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Aligned Resource Optimization
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Transcript of Aligned Resource Optimization
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WHITE PAPER
ALIGNED RESOURCE OPTIMIZATION
How to optimally allocate resources in alignment with enterprise-level objectives
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Table of Contents
Executive Overview.......................................................................... 1Aligned Resource Optimization The Resource Optimization Model ...... 3
Five steps to resource optimization.................................................... 4
The technology to support the Resource Optimization Model ................ 8
Underlying technology to support optimization ..................................11
Closing thoughts ............................................................................ 12
From SAS, the leader in business intelligence ....................................13
Examples Resource optimization across the enterprise ....................14
Optimizing retail revenue ............................................................14
Optimizing proft........................................................................ 15
Optimizing human capital ...........................................................16
Optimizing or sustainability ........................................................ 17
Optimizing marketing campaigns .................................................18
Optimizing IT perormance ..........................................................19
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ii
Becca Goren, Ed Hughes, Mary Crissey and others at SAS contributed to
this white paper.
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Executive Overview
The ast-ood ranchiser has regional distribution hubs, a eet o trucks o various
capacities (some rerigerated and some not) and hundreds o stores needing on-time
deliveries that vary rom week to week. Given carton and pallet dimensions, sell by
dates, distance, urgency, number o drivers, weather and restrictions on working
hours, what is the best way to load trucks and route these deliveries?
The catalog retailer wants to better manage its call centers, direct mail and e-mail
channels. The millions o customers in its database represent the gamut o buying
histories, buying propensities, proftability, demographics and cost to serve. Given
capacity and costs or each channel, which customers should receive which oers
through which channel? What will happen i you add a channel, trim budget or
another or initiate a new contact policy?
The manuacturing line has been underperorming on one shit due to periodic
shortages o sta and materials, and bottlenecks in product inspection. Should the
company invest in a just-in-time inventory system, add third-shit sta, reduce the
sample size o post-production testing, cut one shit but add a new production line or
outsource the more time-consuming processes?
In each case, the answer would be, It depends. The best way to allocate
resources depends on the nature o the resources and constraints at hand and the
organizations mission.
Is it a Six Sigma organization, striving to reduce process variability and increaseproduct quality? Is it a lean manuacturing outft, driving out every possible cost?
Does the organization live and breathe Total Quality Management (TQM), where
everyone is tasked to deliver ever-improving value to customers at continually
lower costs? Does the organization embrace perormance-based budgeting or
Economic Value Added (EVA) principles, which link costs to results yet recognize
some costs as investments in disguise? Or has it adopted a balanced scorecard
approach, which provides an organization-side approach to measuring and tracking
perormance against objectives?
By defnition, optimization is the design and operation o a system or process to
make it as good as possible in some defned sense. It is in the defned sense where
things get murky. What is optimal or you, with your goals and values, could very wellbe suboptimal or the next person. Every perormance management paradigm, every
mission statement, could point to a dierent defnition o success and thereore to a
dierent way to optimally allocate resources (people, money, technology).
Even within one organization, theres no one-size-fts-all proposition. I you legislate
a sole method o resource optimization across the organization, you could miss
out on the advantages o uniquely tailoring the approach to optimize the attributes
o greatest interest in each unctional area. Least cost, highest quality, greatest
innovationdierent teams could realistically have very dierent charters, all under
the umbrella o a uniying, top-level strategy.
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However, on the ip side, when every department handles perormance measures and
processes in its own way, it can be difcult to determine exactly how resources are
being applied to drive organizational strategy. Without a clear picture o resource use
across the enterprise, including interdependencies across unctional areas, managers
cannot know how to allocate resources to optimize organizationwide results.
How, then, does an organization do this well? How do you optimize resources
in what ultimately is a dynamic and oten poorly defned environment or one
that is well-defned but ineectively executed? In the past, these were daunting
challenges. Resources, constraints and market conditions continually change.
Even i you managed to get the necessary details to develop optimization models,
complex models could take days to run. Opportunities might pass beore they
were even revealed.
That was then. Technology has refned the possibilities. Now, strategic visions
are shared and managed through scorecards and strategy maps. Hidden costsare transparent, their roots understood. Analytically derived intelligence drives
perormance improvements. Models that ormerly took days to run can now deliver
insights in minutes. Reports that once required special requests to the IT department
can now be accessed on demand via sel-service, Web-based interaces. Sales
targets and perormance metrics that were once defned based on instinct and
intuition can now be mathematically validated (or invalidated). Results can be
automatically woven back into the process or continual improvements.
Organizations that have embraced these new technologies report gains o millions
o dollars and payback in just a ew months. I that sounds good, read on or a
look at the people, process and technology attributes that are the basis or alignedresource optimization.
Optimization helps you determine the best that can happen, so you can take action in
ways that will deliver signifcant perormance improvements. Advances in technology
have made this process easier and more powerul.
2
Functional leaders must recognize
that their departments are
connected to each other, and how
they are connected matters. A
ocus on unctional optimization
leads a company into the in
isolation yes, in combination no
sub-optimization trap.
Steve Beeler
Director, Special Situations,
Production Modeling Corp. (PMC)
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The Resource Optimization Model
Eective resource optimization requires a certain rigor, consistency and agreement
on process. Whether you are developing a mathematical optimization, or just trying
to drive more eective and efcient use o resources across the organization, the
resource optimization model would be based on the ollowing components:
Anobjective that is the goal o the optimization exercise; something measurable
to be achieved. Examples include maximizing proft, minimizing distance traveled
and minimizing unused raw materials.
Decision variables, the available actions or choices, which can be represented
numerically or mathematical ormulation. Examples include production levels,
price settings, and capital or human resource allocations.
Constraints speciying requirements or rules, placing limits on how the objective
can be pursued by limiting the permissible values o the decision variables.
Constraints can be fnite, available resources, such as raw materials, machine
processing capacity per hour, customer demand by sales territory or monetary
budgets. Constraints can also be sot considerations, which encourage but do
not compel compliance with the rule. For both types o constraints, consider the
greater sphere, including suppliers, customers, partners, market conditions and
regulatory requirements.
Within this ramework o objective, decision variables and constraints, the purpose o
optimization is to maximize or minimize, as appropriate, the perormance metric in the
objective by assigning values to the decision variables that satisy the constraints.
3
Results Measured/Model Updated
ResourceOptimization
Model
Objective
DecisionVariables
Constraints Recommended actions
Data Inputs
Historical or current operational dataor analytically derived information.Description of goal
to be achieved.
The optimal course to meet the objectivebalanced against constraints and decision variables.
Implementation
Execution on recommended actions.
Actions or choicesthat can realisticallybe carried out in pursuit
of the objective.
Requirements,limitations or
rules restrictingavailable decisions.
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Five steps to resource optimization
Step 1. Deine the objective, relecting organizational
mission and strategy.
As mentioned earlier, the so-called optimal way to allocate limited resources will
depend on how your organization defnes success and that may vary even within
the organization, rom one team to another. The resource optimization model must
reect not only the well-defned, oten narrow departmental objectives, but also the
objectives that are most important to the organization as a whole. There also needs
to be an understanding o how activities will support these objectives, and how
success or ailure will be measured.
Relevant relationships and interdependencies among departments must be actoredinto the optimization. I not, expect suboptimal results. Cross-unctional teams
should collaborate to identiy the elements o the model: objectives, constraints
and decision variables. An eective optimization model defnes realistic decisions/
decision variables and ties them to measured results. Scorecards and strategy
maps, supported by business intelligence and analytics, capture organizational
dynamics, along with the executives vision and mission, and help clariy the right key
perormance indicators (KPIs) to pursue, across unctional units.
Step 2. Get buy-in and oster accountability.
Will people act on the inormation provided by the resource optimization model?Who has decision-making authority, inuence and incentive and who does not?
Which decisions will actually be made as a result? Is there commitment to acting on
recommendations? Will people be accountable or expected results?
In a February 2007 study by BusinessWeek Research Services, consultants said lack
o accountability was both a primary stumbling block and primary beneft o eorts to
align resources with overall objectives. But you have to do more than plaster a slogan on
the company walls. Accountability demands measuring and aligning the results with the
organizational structure in a way that makes it clear which managers are responsible or
which results, says Steve Williams, President o DecisionPath Consulting.
Mark Graham Brown, business consultant and author o three books on balanced
scorecards (an established perormance management methodology), says creating a
culture o accountability is a matter o three simple steps that organizations rarely ollow:
1. Set clear and measurable goals and expectations or employees with little overlap
in responsibilities.
2. Develop a scorecard or all employees that provides eedback on key perormance
measures at least monthly.
3. Provide personal and powerul positive and negative consequences or good and
poor perormance via promotions, perks, compensation and perormance ratings.
4
Its not enough or executives to
agree on the goals, business rules
and constraints, and decisions that
will be made. An aligned process
will ensure the best choice
or each decision variable the
recommended actions will actually
be implemented, and that requires
accountability and commitment rom
all parties.
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Successul organizations identiy a champion an executive with power and
inuence to endorse the resource optimization project. Champions can help scope
the project, identiy obstacles and implementation issues, and ensure accountability
to drive the project to completion.
Armed with an eective scorecard and strategy map, this champion can share this
inormation with other executives, managers and employee teams, so everyone
across the organization can work together and understand how their daily activities
contribute to the companys success.
Step 3. Deine the conceptual resource optimization model.
Determine what input data is available.An optimization model is only as eective
as the data going into it. Are you collecting the right data? Do you have enough or
a meaningul model? The cleaner and more accurate the data, the better. The morehistorical depth and relevance, the better.
During this assessment, you might identiy the need to collect more data beore even
attempting a resource optimization exercise or you might choose to test a model
or two in the hopes that the model results will be useul even without all the data that
would be ideal to have. When modeling a new scenario where no historical data is
available, you have to do some guesswork and tests to refne the model.
Identiy decision variables and decisions that can realistically be made.
This may seem sel-evident, but many organizations establish metrics that have no
associated action or responsible party. Results may show a trend in the movement o
a metric, but there has been no decision made about what will happen under those
conditions, and who will do it. This inertia can be avoided i responsive tactics are
determined in advance, where possible.
Consider the ripple eect. To be eective on an organizationwide level, you must
be aware o how decisions will aect other departments, and how department-level
objectives support the organizations objective or not. What are the cause-and-
eect relationships among unctional areas, resources and metrics upstream and
downstream? How will resource decisions help or hinder departments? What are the
political and customer relations ramifcations? A myopic or department-level view will
lead to models that dont reect the actual complexities o organizational processes.
To prevent unwanted backlash rom well-intentioned resource optimization eorts,
get all aected parties and key decision makers involved in the decision making.
Conicts or weaknesses can then be identifed and addressed early.
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ALIGNED RESOURCE OPTIMIZATION
Step 4. Deine the descriptive resource optimization model.
At this point you have documented the discoveries o the previous steps; you have
defned the business problem and how important actors relate to each other in
the decision-making process. This conceptual model can later be expanded to
incorporate more data or fll in any process gaps that may be uncovered later.
Although it is tempting to stop with the conceptual model, which is no easy eat to
create, it is the descriptive model that can best support resource optimization. With
a model that leverages analytics, you gain competitive advantage because youll be
armed with quantitative metrics that guide the organization toward optimal decisions
and actions.
Step 4 is the translation o your conceptual model into a descriptive model with more
rigor and detail, by representing it in mathematical terms. In this ormulation step,
you begin to ormally code the key elements o the optimization model objective,constraints and decision variables.
Theobjective is expressed as a measurable unction o the decision variables.
Constraints are expressed as equalities or inequalities involving unctions o the
decision variables.
Decision variables represent decisions by ranges o allowable values, each
corresponding to a permissible assigned choice.
There is no single right way to use mathematical expressions to represent the
elements o a decision problem. The same business scenario can be expressed
dierently, depending on the mathematician doing the ormulation. Translating theconceptual model into mathematical terms involves both art and science. Consider
that two artists, looking at the same subject, will create unique sculptures or
paintings. Similarly, mathematical modelers will have unique approaches. Some
may preer to create simple basic models that capture essential ingredients without
attempting to capture all minute details. Others will try to quantiy every known
inuence.
In reality, every ormulation represents a compromise because no mathematical
representation can reect everydetail o a real-world scenario. Such a model would
probably be too large to solve efciently. Furthermore, its directives would be so
detailed that they would amount to micromanagement, likely to be selectively ignoredby the people tasked to implement them.
Get buy-in rom key executives and implementers. Those who have bought
in to the conceptual model may be wary o a more specifc descriptive model that
is clearly tied to decisions and outcomes. Beyond executives, it is important that
implementers agree to support the decisions that will be made. Beore investing
signifcant eort into the resource optimization exercise, confrm the descriptive
model and decision-making processes with all parties who will be involved in or
aected by the activity.
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Step 5. Implement and update the model.
Run the model: Using analytical sotware such as SAS
, build and implement
the descriptive model. Its output can provide recommendations as to the best
combination o decision variables to support the objective, given the constraints and
data available. For real-world examples, see the supplementary chapter o this paper,
Examples: Resource Optimization Across the Organization.
Test the optimization model or suitability. No sotware can determine the most
appropriate representation o a decision problem by an optimization model. Sotware
is a tool that guides you. Training and experience, oten rom an optimization
modeler, will help you to choose the best model.
This means that once youve built the model and used it to produce a solution, you
need to consider whether the mathematically derived optimal solution is suitable
or the original business problem. At this point it is not unusual to discover that somekey element o the model has been overlooked or misconstrued, making the optimal
solution (and the decisions that it represents) unsuitable.
The model might be correct, but some data used by the model might be incorrect.
Or your understanding o the original business problem might be awed. In these and
other such cases, you need to step back through the modeling process, address the
difculty and then move orward with the improved model. This iterative process is
quite common and represents another aspect o the art o optimization modeling.
Fine-tune the conceptual and descriptive models in an iterative process.
Models can and should be updated as needed, and should always be exible. You
wont always choose the best one rom the start. Dont let this deter your eorts.
Defne an initial model and refne it as you move orward and learn more.
One o the key dierentiators o SAS sotware is that users are never stuck with
black-box calculations. Ater looking at the output generated rom the analytical
model, you can go back and tweak the model by relaxing certain constraints or
adjusting the primary objective to be optimized.
This iterative, what-i process, reassessing assumptions to tweak the ormulation,
also adds valuable insights into the organization and process at hand. Early
assumptions may be overturned by the insights revealed by analytics. For example,
you may have initially assumed sta resources were fxed and then fnd that hiringextra sta yields optimal output that more than compensates or the additional cost.
Establish ormal mechanisms or learning rom past actions. You would want to
know how well the model works in the real world, and incorporate the knowledge
rom previous iterations into uture ones.
What did the implementation look like in the end? Were decisions made? I so, were
they based on acts rather than gut instinct? Were those decisions eective in driving
improvement in alignment with organizational goals? I not, why? How can the
process be improved? Were we measuring the right things?
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ALIGNED RESOURCE OPTIMIZATION
How did this decision or process aect related business units or the organization as
a whole? Did the optimization recommendation make sense? In other words, was
it workable or unworkable? Why? I the result was not what you would expect rom a
mathematical optimization model, revisit the model to determine whether objective,decisions, constraints, resources and so on were properly identifed.
The results o this assessment should be automatically incorporated back into the
system to continuously improve models, metrics and uture results.
The technology to support the Resource Optimization Model
The technology or aligned resource optimization must enable decision makers to
see, manage and improve business perormance. See how value ows throughthe organization and how resources contribute to outcomes. Manage resource
allocations or maximum advantage. Improve outcomes through mathematical
optimization and continual refnement o models and processes.
These elements are especially vital when trying to achievealignedresource
optimization across the organization. The technology is readily available to excel
in all these areas and it doesnt come rom renegade spreadsheets and siloed
inormation systems.
Technology enablers See it.
See the big picture across unctions, departments and the enterprise. Which
resources, constraints and bottlenecks are present? Who is doing what and why?
How are resources applied to support organizational goals? Which resources/
activities are misaligned with the organizations objectives or undermine another
divisions perormance?
These are tough questions or most organizations to answer, because traditionally
there has been little or no sharing o inormation and metrics across unctional areas.
That makes it hard to get a uniorm picture o resources, risks and results across
units a viewpoint that is essential or resource optimization. Todays technology
removes these limitations.
See the costs and the profts. You cant maximize proftability without seeing
all the costs, but many organizations make broad-brush averages o costs across
products, customers, channels and so on. To accurately account or resource use
and whether resource allocations support strategic goals you need a close
understanding o how costs relate to activities, not just to traditional accounting units,
such as departments, line items or product categories. With activity-based costing,
organizations can better understand which resources are consumed by an activity
and the fnancial consequences.
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Technology enablers Manage it.
Manage optimization eorts at an enterprise level. Perormance management
(PM) applications help align resource optimization eorts with the organizations
mission and objectives. With this big-picture view, you can better manage the
allocation o resources across business units and unctional areas, balancing resources
o all types (people, money or technology) against desired outcomes. You cannot
optimize in isolation. Seen or unseen, interdependencies are an inescapable reality.
I the business is measuring and tracking the right key perormance indicators
(KPIs), perormance management can help clariy how well resource allocations and
activities are supporting division and organizational goals or why certain areas
are underperorming. These insights are critical in determining where best to ocus
optimization eorts.
Manage interdependencies among metrics.Are your resource allocations drivingsuccess? A combination o analytical methods helps you zero in on meaningul
measures o success. Exploratory data analysis, combined with predictive analysis,
can reveal important relationships between variables. You can determine i the
movement o one variable simply coincided with the movement o another, or is
consistently associated with it. Using advanced modeling techniques, these causal
relationships can then be isolated and highlighted.
Once these relationships are known, organizations can more eectively bring
business units and resources into alignment and use the insights to guide ongoing
optimization eorts.
Manage or actionable results. Integrated scorecards reect what needs to
change where and by how much. The scorecards dashboard can give executives
an at-a-glance picture o organizational health and perormance. Users should be
able to see within seconds which resource allocations have the greatest impact,
where to ocus and where to drill deeper to discover the root cause o an issue.
Technology enablers Improve it.
Technology should do more than support resource allocation decisions and track the
eects; it should also proactively inuence desired outcomes.
Improve outcomes with optimization sotware. Operations research (OR)systems can apply sophisticated mathematical programming capabilities to answer
all manner o complex business questions, rom resource allocation to product
management to supply chain optimization any problem or which variables,
constraints and desired outcomes can be mathematically defned.
Analysts can choose rom a broad array o optimization, project management,
scheduling, simulation and decision analysis techniques to identiy the actions that will
produce the best results, while operating within resource limitations and other relevant
restrictions. You can build and update a unique model or each optimization initiative.
Minimum cost does not equal
maximum proits. Otherwise,
companies would have no people and
no assets. Missing in many companies
are enterprise-level analytical tools
to enable collaborative eorts to
continuously improve inancial and
operational perormance.
Steve Beeler
Director, Special Situations,
Production Modeling Corp. (PMC)
Resource optimization must support
organizational goals. Scorecards
and strategy maps help show the
interdependencies among resources
and objectives, and how resources do
or do not support organizational goals.
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Improve outcomes with multiple analytic techniques. With or without
optimization sotware, analytics have a powerul role in resource optimization. With
analytic intelligence, you can confdentlyanticipate the result o a strategy in advance,
testvarious scenarios and use optimization sotware to select the best o all possible
courses. You can explore and understand complex relationships among resources,
behavior, systems and processes; assess the impact o changes in KPI values; and
respond more quickly with act-based decisions. Then this technology helps you
learn rom past results so you can use that knowledge to realign indicators and
improve resource allocations at the next iteration.
Unlike generic business intelligence sotware reports on what has happened (orcing
you to fgure out what will happen next and what to do about it), optimization sotware
and business analytics identiy the best orward-looking course o action the best use
o limited resources to achieve strategic objectives. Balancing goals against limitations,
you can answer questions such as these:
Iswhatweretryingtoaccomplishpossible?
Howarewedoingnow?
Howcanwedobetter?
Whatsthebestwecando?
Whathappensifconditionschange?
The organization that could successully answer these questions would have a clear
advantage, yet ew have capitalized on the (readily available) means to do it.
Your chosen technology can be
implemented in low-risk stages. Start
with a pilot project, prove its worth
and expand it as the business case
warrants. This phased approach ismade possible with a technology
platorm that is aordable at the
startup level yet scalable to the
enterprise level.
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Underlying technology to support optimization
Examples in the last section o this paper highlight several varieties o optimization
toward dierent objectives, such as optimizing proftability, marketing campaigns,
IT eectiveness, sustainability and workorce management. Here are oundation
technologies that support such optimization initiatives.
Technology What it does How it supports resource optimization
Data integrationand data cleansing
Integrates data rom acrossthe organization, transormsand cleanses it in real time,and ensures accuracy andconsistency.
Creates a common oundation or deliveringtrusted inormation throughout the enterprise.
Adds value to corporate data and ensuresaccess to the best possible data or operationsand decision support.
Optimization Answers diverse businessproblems or which variables,constraints and outcomes canbe mathematically defned.
Mathematically calculates optimum resourceallocation to achieve stated objectives, givenmultiple, weighted decision variables andconstraints.
Dashboards/scorecards
Monitors and displayskey perormance indicatorsthat tie to strategy, withat-a-glance visuals.
Helps organizations ocus on perormanceand opportunities to take appropriate action,align resources and day-to-day activitieswith corporate strategy, and adapt to meetchanging conditions.
Strategy map Provides a visual macro viewo an organizations strategy.
Helps align the organization and its resourcesby articulating goals and the initiatives thatsupport those goals throughout the enterprise.
Activity-basedmanagement
Helps determine accuratecosts and cost drivers at theactivity level.
Clarifes how resources are consumed byan activity, and the fnancial consequences;identifes the cost constraints o anoptimization exercise.
Financialmanagement
Used by business-unit headsor budgeting and planning andby fnance executives also orconsolidation and reporting.
Helps synchronize fnancial and operationalstrategy across the organization, to everylevel with repeatable, sustainableprocesses or fnancial reporting, riskanalysis and achieving goals.
Department-levelperormance/resourcemanagement
Manages processes andresources at the departmentlevel to support strategic goals.
Delivers unction-specifc analysis and insightsthat can be incorporated into optimizationmodels and eedback loops.
Industry solutions Provides packaged solutionswith prebuilt models andmetrics or specifc industries.
Delivers analysis and insights that canbe incorporated into optimization modelsor specifc industries, such as banking,insurance, retail, government, educationand manuacturing.
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Closing thoughts
Fast, cheap, quality: pick any two. Good or a chuckle but hardly wise counsel or
market success. Classic resource optimization questions are a balancing act o all
three, within the resources and constraints at hand colored by the organizations
unique mission.
In the past, this was a daunting challenge. Optimization must address actors that span
unctional areas and multiple stakeholders. Without high-level sponsorship, the project
would lack the necessary data and consensus. Without big-picture perspective, the
optimization model would yield suboptimal results. And without buy-in, even the best
mathematical models could generate answers that no one actually implements.
This document outlines a conceptual model and fve-step process that address
these organizational challenges o resource optimization projects:
Step1.Denetheobjective,reectingorganizationalstrategyandobjectives.
Step2.Getbuy-inandfosteraccountability.
Step3.Denetheconceptualresourceoptimizationmodel.
Step4.Denethedescriptiveresourceoptimizationmodel.
Step5.Implementandupdatethemodel.
Organizations that have embraced this process, in one orm or another, report gains
o millions o dollars and payback in just a ew months. SAS provides the technologyoundation to make it possible.
I the possibilities o resource optimization sound intriguing, be sure to read the
supplementary section o this paper, Examples: Resource Optimization Across
the Organization,or a high-level look at optimization exercises in support o
various objectives, rom maximizing proft to improving marketing campaigns to
revealing the most productive workorce strategies and sustainability initiatives.
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13
From SAS, the leader in business intelligence
SAS provides the broadest, deepest range o oerings or resource optimization in
the context o enterprisewide perormance management. All our sotware is built on
a single enterprise intelligence platorm that seamlessly integrates data integration,
storage, business intelligence and analytic intelligence.
O particular note, SAS has the broadest range o analytical capabilities, enabling
you to identiy, quantiy and prioritize improvement opportunities, mitigate threats
and measure results. Integrated orecasting and simulation, coupled with correlation
analysis, enable you to anticipate the uture state o operations. Only SAS can
orecast and provide a confdence interval or its projections.
SAS or Perormance Management brings context and direction to business
intelligence initiatives and supports a continuous process or improvement across theenterprise. Together, SAS capabilities let you do more thanmanage the perormance
o your organization; they help you improve it.
Thats why customers at 44,000 sites use SAS to gain insights rom vast amounts
o data. Since 1976, SAS has been giving customers around the world THE POWER
TO KNOW
.
To fnd out more about SAS solutions or perormance management, visit
www.sas.com/solutions/pm.
Visit the sas.com resource center to download the companion white papers:
TheAlignedOrganization:Howperformancemanagementcanalignactivities
and resources with enterprise-level strategy and market conditions.
OptimizationwithSAS/OR
: What it is, whats new and how it adds value
PredictivePerformanceManagement:Continuallyimproveperformanceby
applying the power o analytics
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ALIGNED RESOURCE OPTIMIZATION
EXAMPLESResource optimization across the enterprise
Organizations that have implemented the types o resource optimization projects
described earlier achieve notable successes not just in isolation, but in alignment
with organizational goals. Some o the examples here relate to a specifc unctional
area, but they also reect a realistic frst step toward enterprisewide optimization.
Optimizing retail revenue
Competing in retail has always meant oering the right product to the right customer
at the right price. But just what is the right price? A mere 1 percent increase in
product price can raise operating proft by as much as 8 to 11 percent or prompt
the customer to buy rom the low-price competition. The margin or error is small.
Every day, strategies or pricing, promotions and markdowns must be based on
accurate, predictive intelligence, using reliable inormation about what customers
want now and are likely to want in the uture. Retailers must rapidly identiy and ocus
on the most value-generating activities, the ones that repeatedly maximize margin
and revenue across all products and all stores.
How SAS can help: The SAS Revenue Optimization Suite enables retailers to
manage revenue and margin through the entire merchandise lie cycle. This suite
combines advanced data management, orecasting and optimization capabilities
within an easy-to-use interace that helps retailers set and manage regular prices,
plan optimal promotions and execute the most successul markdown strategies.
Sample case: A large clothing retailer needed to liquidate clearance goods more
proftably at a aster pace. The goal was to reduce end-o-season product clutter
on the selling oor and drive higher sell-through o regularly priced merchandise.
Using SAS Markdown Optimization, the retailer achieved all these goals. In act, the
companys margin guidance or one quarter increased 10 to 20 basis points over the
previous year and 30 to 40 basis points the ollowing quarter.
Example o a price optimization model
Results Measured/Model Updated
ResourceOptimization
Model
Objective
DecisionVariables
ConstraintsRecommended actions
Data Inputs
Store level demand forecast, historical data on item purchases,and customers and price, competitive pricing, price elasticity,
projected inventory, product cost.
Maximize sell-through/items sold.
List of optimal price per product per by item by store.
Implementation
Fed into price execution and core merchandising system.
Regularly update model with new purchase data.
Determine optimalprice for eachproduct by store.
Businessperformance,
costs, demand,distribution spread,
time/season, storelocation.
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Optimizing proft
Understanding and maximizing proft used to mean little more than reporting the
bottom-line proft and loss results o legal entities and erreting out costs whereverpossible. Now it also means interpreting fnancial perormance to predict the uture
impact o business decisions and having proft-and-loss inormation or each
customer and product, calculated at the individual transaction level.
The challenge is that many organizations combine inaccurate cost inormation rom
traditional costing systems with other fnancial and operational data to generate
reports on customers and products. This approach doesnt show true proftability,
so it does not accurately reveal which customer, product or channel mix scenarios
will be optimal.
How SAS can help: SAS Financial Intelligence helps businesses improve the
fnancial perormance o the entire organization. The suite includes an enterprise
business intelligence platorm, integrated consolidation, budgeting and planning,
scorecards and strategy maps, and cost and proftability management.
Sample case:A state department o transportation sought to satisy diverse mobility
needs, address concerns or public saety and the environment, and maximize the
use o existing resources within the agencys $430 million annual budget. The agency
adopted SAS Financial Intelligence to identiy the costs associated with business
processes and to determine i activities and resource allocations were aligned with the
organizations mission. As a result, employees gained a better sense o how their work
contributed to the agencys objectives. In addition, the elimination o various high-cost,
low-beneft activities saved the agency $2 million in the frst year alone.
Example o a proft optimization model
Results Measured/Model Updated
ResourceOptimization
Model
Objective
DecisionVariables
Constraints Recommended actions
Data Inputs
Customer dimensional data, individual order line transactions,
product dimensional data, customer behavior rates.Maximize profitability by understandingindividual customer profit and loss.
Determine appropriate service levels to customersbased on profitability.
Implementation
How will customers react to different service levels?
Update model if loyal customer
response is negative.
Allocation rules toindividual transactions,trim unprofitable segmentsor segments.
Widely distributed,drillable profit and
loss reports neededper customer
reporting timeliness.
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ALIGNED RESOURCE OPTIMIZATION
Optimizing human capital
According to recent studies by McKinsey, the biggest ocus or management in the
next decade is vying or top talent in an intensively competitive global marketplace.This reality requires organizations to use human capital inormation in a ocused,
deliberate and proactive way to optimize the work orce.
At its most basic level, workorce optimization means getting the right employee
in the right position at the right time and in the right place. More specifcally, it can
mean minimizing vacancy time and cost, maximizing retention o critical workers, or
optimizing reorganization and downsizing. Unortunately, most organizations lack a
consistent and holistic view o the work orce and the needed analytics to perorm
workorce optimization.
How SAS can help: SAS Human Capital Intelligence helps customers optimize their
work orce by providing the relevant, holistic and predictive human capital inormation
that drives strategic decisions. With this insight and oresight customers can address
workorce demands at every stage o the talent lie cycle and support critical business
decisions.
Sample case: One o the oldest banks in Europe needed a way to identiy which
ofits5,000employeesweremostlikelytoresignandpreventlossofthesevaluable
and expensive intellectual assets. Using SAS Human Capital Intelligence, the bank
consolidated important employee data, perormed ad hoc, what-i analysis and
salary simulations so managers could quickly answer questions that previously had
taken days. With a SAS predictive analysis retention model, the bank now has an
accurate way to identiy employees likely to leave and has reduced employee turnoverto 3-4 percent.
Example o a workorce optimization model
Results Measured/Model Updated
ResourceOptimization
Model
Objective
DecisionVariables
Constraints Recommended actions
Data Inputs
Employee location, job titles, salary/salary grades,employee skills and profiles, # of employees needed
per site/per season/per hour.Maximize workforce distribution.
Prioritized # of employees, skills, job titlesand location combinations to pursue.
Implementation
Will we need to hire to fill the gap?Do we need to reallocate resources?
Add relocation costs when quantified.
Allocate X # of Ytype of employee to
Z location.
Cost, supply,demand, time/
season, site location.
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Optimizing or sustainability
Across industries, organizations are responding to the critical imperative to be
socially, environmentally and economically responsible. But there are constraintson how much investment can be made in pursuit o this mission. Organizations
must transorm complex inormation into eective, cost-conscious strategies, and
determine which eco-investments will optimize results.
Environmental penalties can be quantifed. So can the costs o implementing green
policies and practices. The economic benefts o being a responsible corporate citizen
can be estimated. By applying trusted analytics to these inputs on opportunities and
constraints, organizations can identiy and prioritize the most productive sustainability
practices as well as the ones that most eectively increase brand value.
How SAS can help: SAS sustainability solutions provide an analytic perormance
management ramework or measuring, analyzing and optimizing key sustainability
indicators.
Sample case: In the energy industry, the aring and venting o natural gas is a
saety mechanism to burn o excess gases and maintain sae operating pressures
during the production process. However, this process is strictly regulated, because
the emissions contribute to climate change. Gas aring activities around the world
emit some 390 million tons o carbon dioxide every year.
To better manage the production process and minimize regulatory penalties, a
large publicly owned energy company has implemented a rigorous perormance
management system, supported by robust analytics. The system combines dataon various events to help the organization target resources, manage its business
more eectively and have more immediate access to accurate inormation about
perormance on key environmental indicators to acilitate executive and operational
decision making.
Example o a sustainability optimization model
Results Measured/Model Updated
ResourceOptimization
Model
Objective
DecisionVariables
Constraints Recommended actions
Data Inputs
Project-specific inputs such as utility utilization,activity costs, projected fines, waste disposal options.Maximize return on investment
in sustainable practices.
$X initial investment with $Y monthly incremental costwill yield $Z return/avoidance of penalty in #T years.
Implementation
Apply resources to develop and execute project plans.
Update model based on actual fines/new disposal options.
Prioritize list ofsustainability projectsthat minimize risk whilemaximizing ROI.
Availability of alt.energy/fuel sources,
water, productdemand, budget,
personnel costs,technical expertise
supply chain flexibility.
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Optimizing marketing campaigns
In spite o the prolieration o marketing automation products, many organizations are
still not reaping all the return they could rom their marketing campaigns. Automationmakes campaigns aster, but it wont necessarily tell you i campaign A would
make more sense than campaign B. How do you know you have the proper mix o
customer, channel, oer and timing to maximize overall proft rom these campaigns?
When you run hundreds o campaigns a month, this question cannot be answered
with intuition or marketing savvy alone; it requires mathematical optimization.
How SAS can help: SAS Marketing Optimization provides the ability to plan and
prioritize outbound customer communications in order to maximize results, while
balancing the capacity to deliver and customers likeliness to respond.
Sample case: A direct-marketing insurance company uses marketing optimization
to manage more than 600 projects, optimizing on present value o uture proft.
The companys ormer, homegrown optimization model took three days to run and
sometimes crashed. Its SAS model assesses multiple constraints and inputs across
direct mail and telemarketing channels, and delivers optimized results in minutes.
Supporting more eective use o limited marketing resources, the sotware paid or
itsel in only two months.
A regional telecom service provider uses SAS to optimize its monthly promotional
campaigns or DSL, wireless, cable and phone service optimizing on customer
lietime value. The company reported $6 million a month proft gains during the trial
phase alone.
Example o a campaign/oer optimization model
Results Measured/Model Updated
ResourceOptimization
Model
Objective
DecisionVariables
ConstraintsRecommended actions
Data Inputs
Business requirements, priorities/service levels,capacity, existing projects.Maximize return on direct and
telemarketing campaigns.
Which combination of offer channel/customer to use.
Implementation
Determine campaign timing, prioritize bysegment purchase behavior.
Update model based on campaign response.
Add loyalty and house-holding data to customer segments.
Which customersegment should betargeted with whichoffer for which type
of campaign.
Minimum numberof customers,
budget, productavailability, privacy
requirements.
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Results Measured/Model Updated
ResourceOptimization
Model
Objective
DecisionVariables
Constraints Recommended actions
Data Inputs
Business requirements, priorities/service levels,capacity, existing projects.
Maximize IT activities and resourcesto best support business requirements.
Prioritized list of IT activities/projects and associated resourcesand service levels to support business requirements.
ImplementationBuild recommendations into documented, monitored and
automated processes/controls for example ITIL, CMMI, PRINCE2and update/document/communicate IT goals within
scorecard/performance management application.
Update model based on adjustments topriorities, capacity, resources, etc.
Match activitiesresources, servicelevels and support
capacity.
IT capacity, budget# of resources,
project length,available technology,
service level agreements.
Optimizing IT perormance
CIO responsibility extends ar beyond keeping the IT inrastructure aoat, juggling
data and applications, and delivering on service level agreements. Todays CIO isexpected to contribute strategic thinking about how to add value to corporate data,
create new insights to drive success, and optimize IT resources in alignment with the
organizations mission and goals.
In the quest to optimize IT perormance, CIOs ace a host o conicting objectives.
They are pressured to provide more processing power, servers, storage space,
redundancy, bandwidth, power and sel-service capabilities than ever. And they have
to deliver it on less than ever: ewer people, dollars and days. To succeed, CIOs
need a ull understanding o resource utilization and costs, optimized in alignment
with business requirements and service level agreements.
How SAS helps: SAS IT Intelligence is a comprehensive solution or IT that helps
you optimize resources, services and fnancial impact, all in support o strategic
business goals.
Sample case: A major European fnancial services organization, with more than
US$694billioninassetsand56,000employees,hadbeengrowingrapidlythrough
acquisitionsandattainedmorethan5millionretailbankingcustomers.ButtheIT
organization aced major challenges in integrating the acquired systems and keeping
more than 1,900 applications running smoothly especially as Internet banking grew
by 40 percent. Capacity management was a process o crisis management.
With SAS IT Intelligence, the team was able to align IT direction with the corporatebottom line, while bridging organizational gaps. According to the institutions IT
operations manager, the bank can now ensure that adequate resources are
available and unctional at the required time, and that everything perorms according
to specifcations, while correctly accounting or and allocating all costs.
Example o an IT optimization model
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