aligned resource optimization

7/29/2019 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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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.

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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.

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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.

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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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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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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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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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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



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



Model

Objective

DecisionVariables


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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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



Model

Objective

DecisionVariables


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



Model

Objective

DecisionVariables


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



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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Model

Objective

DecisionVariables


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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