In the Demand Intelligence Universe, Perspective Matters.

© 2010 LumiData • In the Demand Intelligence Universe, Perspective Matters. p. 1

In the Demand Intelligence Universe, Perspective Matters.


Humans used to think the earth was the center of the universe.

Understandable — it was a matter of perspective. With your feet firmly

planted on terra firma, it certainly looked that way.

It wasn’t until Galileo viewed the heavens through his hand-

crafted telescope that the Copernican “sun-as-the-center-

of-the-universe” theory was confirmed. Having viewed the

universe from a perspective never seen before, Galileo made

whole new realms of data and insights available.

So how do things operate within your terra firma — your CPG

enterprise? Are you constrained by looking at things from

just one perspective? Let’s hope not. I mean, you would never

focus your incremental sales growth initiatives on one retailer.

Or one SKU. Or one promotion. Right?

If you’re only looking at raw data, favoring a single data source

or making decisions based on a limited perspective — you’re

missing the bigger picture. Everyone in your organization has

a different perspective and that perspective determines ex-

actly what data they need to see and when they need to see

it. When you pull these varied perspectives together across

your enterprise, you — like Galileo — will be granted a larger,

more informed perspective, based on a 360-degree view of

your entire retail universe.

Just how do you gain a greater perspective?

You start by going vertical.

Right now, many CPG organizations follow a horizontal

data delivery path, in which warehouse data is pushed

out horizontally from business process systems using ETL

capabilities. With ETL platforms, the emphasis is primarily

on delivering raw data —simply making data available to

everyone in the enterprise no matter what their position is

within the enterprise. Little or no regard is paid to providing

the right data to the right user teams, nor providing them

with ready analytics that will help them make more informed

business decisions quickly. As a result, retail team members


(tier-two) are left to sort the wheat from the chaff, searching

for data insights most relevant to them when it comes to

managing performance. And functional C-level executives

(tier-one) are left to wonder why they appear to have

actionable information within their grasp, but always just out

of reach.

To get a 360-degree perspective you need a vertical business

intelligence (BI) enterprise stack (Figure 1) — the right BI

architecture, tiered BI demands analytics and BI applications

that suit the specific demand intelligence needs of each

member of your organization. This model ensures that you get

the right data to the right people at the right time using the

right analytics. By doing so, you facilitate effective, strategic

decision-making across the enterprise.

CategoryManagement

SalesManagement

ForecastManagement

Supply ChainManagement

S&OPManagement

BI Demand Analytics

POS Data Market Data Forecast Data Order Data Shipment Data

Tier 1: Management TeamIn-house ETL,

process, infrastructure

Tier 2: Retail TeamRetail ETL, conversion, harmonizing

BI Demand Applications

OIT Architecture

BI Demand Enterprise Stack

(Figure 1)


Which BI architecture offers greater perspectives?

One that integrates and supports varied perspectives, rather

than putting those perspectives into silos. Your BI architecture

serves as a blueprint for how your organization manages

demand intelligence and how you communicate around that

intelligence. Done right, it ensures that each member of your

organization receives the data they need and opens up vertical

and horizontal channels of communication that enhance

strategic decision-making.

In general, three BI architectures are currently in place within

major CPG organizations. The primary differentiators among

the three have to do with who controls data distribution, the

tools used to distribute data, what data individuals receive

and the resulting flow of communication and decision-making.

INSIDE-OUT (Figure 2)

The Inside-Out BI architecture favors tier-one managerial

teams and relies heavily on the use of retailer-driven, build-

to-inventory internal order data, and supply side process

operations. Decision making data is largely a by-product of

data flowing from within the company and its order process-

ing systems out to the tier-one managerial teams — thus, an

Inside-Out approach. With the Inside-Out architecture both

suppliers and retailers are potentially vulnerable, because the

supply-demand enterprise is in a perpetual state of reaction

and as such, requires a timely and accurate demand forecast.

In this architecture, data is controlled and processed by IT,

then distributed via an Extract-Transfer-Load (ETL) capability.

Data is not sent according to team needs, but is simply sent

en masse with the expectation that users will utilize the data

to develop their own unique applications. Also, ETLs do not

typically collect and integrate weekly POS data in a timely

manner. Because of this latency, tier-two teams are confined

by conflicting process schedules, and challenged to integrate

dated internal data with weekly POS data. They are also

responsible for writing custom programs to analyze internal

data and then convert, cleanse, harmonize and move POS

data into heir own unique BI application.

While this architecture meets the needs of tier-one teams,

this architecture isn’t well suited to the needs of tier-two retail

sales teams that need timely POS demand data. In most cases,

CPG enterprises with a BI solution based upon this inside-out

architecture now find themselves with an expensive, clumsy

architecture that doesn’t allow retail teams to respond to

demand as it happens nor effectively communicate across

organizational tiers.

p. 4

Operations

Marketing

Sales

Executive Team

Supplier Corporate Headquarters

Corporate DataWarehouse

ETL Data Cube Platform

Business Process Systems

Shipment DataOrder Data

Raw Retailer POS Data

General Demand Management

Analytics

(Figure 2)


OUTSIDE-IN (Figure 3)

This architecture is driven by, and serves, the tier-two sales

teams — providing them with the real-time demand data they

need. Teams obtain POS data via the retailers’ ETL systems

and integrate disparate sources of order and shipment

data depending upon the supplier’s priorities. While this

architecture can facilitate communication with the retailer,

the retail team’s ability to gather data from their retailers

is subject to each retailer’s ETL system, or lack thereof.

If a retailer does not have a user-friendly, efficient or reliable

ETL system, the team must spend crucial time downloading,

converting, verifying, cleansing and harmonizing that data.

Further, while this architecture promotes active and construc-

tive communication between the retailer and tier-two team

members, it does little for communication between tier-two

and tier-one teams because the majority of the data is POS

data, which is more relevant to tier-two teams.

OUTSIDE-IN AND THROUGHOUT (Figure 4)

This ideal BI architecture is structured around each and every

team member’s internal and POS demand data needs. It allows

both tier-one and tier-two users equal access to unified data

— each receiving exactly the data they need — and fosters

communication up, down and throughout the enterprise.

Supply Chain

Category Management

Sales

Field Sales Team

Database

Merchandising System

ETL Platform

POS Data

Retailer Supplier Field Sales O�ce

General Demand Management

Analytics

(Figure 3)

(Figure 4)

Mass Grocery/Food Drug

Member Stores Big Box Specialty

OperationsBusiness Process Systems

Marketing

Sales

Executive Team

attributes

harm

onizing

security

metrics

consolidation

cleansing

Corporate DataWarehouse

Shipment Data

Order Data

Supplier Corporate Headquarters

Uni�ed RetailPOSDatabase

Enterprise DemandAnalytic Platform


In this architecture, real-time POS data for every retailer in

every channel is gathered and integrated with internal order

and shipment data into a unified enterprise demand database.

Both tier-one and tier-two team members are in complete

control of the timing, availability, and utilization of the demand

and internal data because the data is made instantaneously

available to every team member based upon their unique

needs. People receive what data they want, when they want

it. Ultimately, that means your enterprise can make strategic

decisions based on demand as it happens, as well as access

historical data that helps your enterprise better anticipate

demand going forward.

Now that you understand how critical the right BI architecture

is in utilizing the right BI solution, let’s turn our attention to the

importance of BI analytics.

Why do CPG team members need different analytics?

Because you need to create informed perspectives that build

a 360-degree view of your enterprise, your retailers and

your consumers. Your organization, and the team members

who comprise it, cannot reach their full potential without the

right information and the right analytic tools that help them

strategize based upon a holistic 360-degree view of demand.

If you want them to proactively identify and respond to

opportunities and challenges, then you need to provide them

with the data most relevant to their responsibilities and in

sync with their unique time frame. To do so, you need tiered

business analytics that can address the time frame and job

role disparities of your various team members.

TIME FRAMES

One of the greatest obstacles to using demand intelligence

effectively is coordinating the dynamic decision-making time

frames of consumers, suppliers and retailers.

Consumers make decisions every single day. When they go to

the store, they expect their needs to be met. The result? Daily

POS consumption data is captured that reflects changes and

trends in their purchasing behaviors.

On the other hand, retailers, in cooperation with suppliers,

adjust their decisions on a weekly basis in response to the

previous week’s consumption data. Every week retailers adjust

purchasing, promotional assortments and item allocations

to better meet the consumer’s needs. In turn, these weekly

adjustments affect their overall tactical and operational

merchandising plans and business activities.

Besides adjusting to the weekly needs of retailers, suppliers

utilize historical data and current POS data to adjust their

monthly production plans in order to come up with more

accurate current and new product assortments across all

channels that best meet consumer demands.

JOB ROLES

Everyone in your enterprise has very specific responsibilities.

They all inhabit a very distinct job space — such as a C-level

marketing officer, Target retail team member, Wal-Mart cat-

egory manager, and supply chain manager. And how they

view retail data — and what specific data they need to look

at — is highly dependent on where they are and what they are

charged to do. A c-level marketing executive and a retail team

For a more in-depth discussion of BI

architectures, visitwww.lumidata.com/

whitepapers/


category manager for Target need to look at the same data at

certain times and very different data at other times.

If you’re just providing tier-one and tier-two team members

with generic or limited demand analytics, they under use

or lose their much-needed unique perspective. And your

company loses

efficiency, synergy

and time. To clearly

identify and act

upon challenges

and opportunities,

they need to

receive the right

information at the

right time. And then

communicate that

distinct perspective

across the internal demand chain, creating a 360-degree

“universal” view comprised of various perspectives.

While no one person can perfectly predict demand, get-

ting close can mean millions of dollars in increased sales.

And “getting close” means paying attention to what spe-

cific data each team member needs and making sure they

get that data in sync with their decision cycle (Figure 5).

Who needs what?

For the most part, tier-one is composed of corporate marketing,

sales, operations and finance managers responsible for

creating monthly supply and operations forecasts that drive

competitive positioning and synergistic execution across

sales, marketing, finance and supply chain channels. To do so,

they rely on a variety of internal and external data and high-

level metrics organized around their specific key performance

indicators (KPI).

Each tier-one team member requires data specific to his or

her responsibilities. For example, sales personnel may focus

on sales volume, mix and percentage growth, and gross profit

margin. Tier-one marketing personnel may rely on category

and product line profit and loss objectives, new product launch

performance and market share growth percentage. And supply

chain management’s key metrics routinely include items

such as manufacturing ROI, inventory turns, order fill-rate,

forecast accuracy,

and finished goods

inventory levels.

If tier-one members

identify a challenge

or an opportunity,

they can then ask

their tier-two team

to do a deeper

dive to facilitate

discussion and gain

consensus on the viability of exploiting the opportunity.

Tier-two retail sales team members look for weekly demand-

driven opportunities they can share with their retailer. With

direct access to retailer-specific POS data, tier-two team

members are uniquely positioned to react to daily and weekly

changes in preferences and trends. Their insights can be

shared within their tier-two team, as well as at the tier-one

level, to optimize promotions, drive volume, and coordinate

tactical and operational plans of the key functions in sales,

marketing, operations and finance.

Because tier-two teams are responsible for broader data

interrogations than tier-one teams, they require customized

analytics that extract, analyze, integrate and report POS data

for all downstream team functions. They require analytics

that can summarize POS data by product line, product family,

SKU item, channel of distribution, and division. With this in

hand, they can provide insights that improve short- and long-

term forecasting, optimize prices and promotions, examine

true incremental lift, identify supply chain challenges, and

capitalize on sales and marketing opportunities.

KEY OPERATING DECISION CYCLE (Figure 5)

customers retailers suppliers


By integrating current internal order and shipment data as

well as historical data, tier-two teams can establish baselines

against which to set up targeted alerts that identify problems

and opportunities that keep the enterprise proactive. The

Data and Analytics Requirements Table (Figure 6) presents an

instructive depiction of the critical differences that can be made

by aligning data and analytics organizationally to drive strategy,

synergy, and productivity.

Tier One

Sales POS Every Bottom-up 30 forecast Days

DATA• POS• WIP• Orders• Shipments

DECISION CYCLE Every 30 Days

KPI/ANALYTICS• Contingency forecasting plans• Current review of supply• Current demand review• Forecasting accuracy rate• Shipments and orders• Work-in-process position• Fulfillment performance• Forecasting accuracy rate• Gross margin ROI• Inventory utilization efficiency

DATA• POS• Bottom-up forecast


KPI/ANALYTICS

• New item launch analysis• Sales trend analysis• Actual vs. plan analysis• Gross margin analysis• Channel performance• Contingency demand forecasting

DATA• POS• Category• Syndicated


KPI/ANALYTICS• Marketing-mix spending impact• Product line P&L performance• Gross contribution performance• New item launch analysis• Item and category trends• Category performance• Channel performance • Brand equity• Share position

MARKETING

SALES

SUPPLY CHAIN/OPS

Tier Two

Sales POS Every Bottom-up 30 forecast Days

DATA• POS• Orders• Shipments


KPI/ANALYTICS• Out-of-stock analysis• Exception reporting• Weeks of supply• On-hand quantity analysis• Root cause analysis• Build orders• Seasonality• Product allocation• Retailer forecast accuracy• Inventory overage ratio• Internal forecasting• Distribution issues

DATA• POS• Sales orders• Retailer forecast


KPI/ANALYTICS

• Sales trends analysis• Item promotion• Pricing optimization• New item sell-in strategy• Growth drivers analysis• Seasonality• SKU ranking• Forecast analysis

DATA• POS• Category• Syndicated


KPI/ANALYTICS

• Category strategy• Category performance• Category scorecarding• Share trends• Promotional effectiveness• Item attribution • Item segmentation• New item incrementality

CATEGORY MANAGEMENT

SALES

SUPPLY CHAIN/OPS

DATA AND ANALYTICS REQUIREMENTS TABLE (Figure 6)


Aren’t all analytics the same?

No — analytics are as varied as the individuals in your

organization. Business analytics can be as simple as a single

user-defined Excel calculation or as robust as a collection of

advanced quantitative methods and statistical techniques. The

broad domain of analytics is classified into two basic types:

deterministic and predictive. Each is designed to be used on

different forms and dimensions of data, at different times, in

different parts of the business, and for different purposes.

Deterministic analytics include a wide range of user-defined

analytics, tailored to meeting the unique KPI-related business

requirements of the user. Deterministic analytics are usually

included in a BI application that allow a user to intuitively

and easily explore past performance, correlate metric trends,

and ask self-directed iterative questions – usually on the fly.

Because these analytics are user-defined, they can more

readily adapt to rapid fluctuations in consumer trends, and

provide answers to questions that speak to at-the-moment

challenges in sales trends. With the help of these analytics,

users can also review and analyze historical demand to affirm

what’s already known, validate current facts, and discover

new insights that can improve performance.

Predictive analytics, on the other hand, are static pre-written

statistical functions and quantitative data-handling methods

that routinely analyze massive amounts of data in order to

determine the best indicator(s) of future performance or de-

sired outcomes. Predictive analytics initially found an earlier

home in manufacturing and supply chain operations — em-

bedded within supply forecasting routines in order to use

multi-variables such as economic order quantities, inventory,

orders, shipments, plant capacity, conflicting and competing

line constraints, and transportation logistics to determine the

optimum production forecast.

Now, CPG organizations and major retailers use predictive

analytics on different hierarchical slices of POS data to

analyze current and past consumer purchasing behaviors

and from that, determine how likely consumers are to act in

a similar manner in the future and what to do about it. Users

can identify both risks and opportunities by examining factors

such as consumer demographics, marketing mix elements,

SKU assortment, promotions, and seasonality to see which

combination is the most effective in improving sales.

Both deterministic and predictive analytics allow users to:

• Accommodate and handle large amounts of data.

• Quickly understand and interpret informational insights.

• Generate basic reporting and tracking of current-period

results.

• Measure current position against key performance

indicators.

• Address multiple levels of data, with drill-down

interrogative and investigative capability.

• Present summary analysis in a wide variety of reporting

formats, dashboards, charts, graphs, and tables.

Which analytics give me the 360-degree view of my business?

Again, it all comes down to perspective — a person’s posi-

tion in the organization, what that person’s responsibilities

are and how that defines their unique data and analytic needs.

Each member of your organization is charged with analyzing

performance data, providing insights based on that data and

using those insights to successfully manage their own unique

KPIs.

In general, predictive analytics are most commonly used by

specific tier-one teams as an integral part of business process

systems and one-off special projects in marketing and

operations management. As an example, a tier-one corporate

marketing officer might use predictive analytics to gain a

fresher look at how national demographics such as disposable

income, age, ethnicity, and geographic regions are statistically


related to the sales performance of a single SKU, or group of

SKUs, sold over time. The outcome of such an analysis would

then be used to fine-tune the marketing mix spending model.

While predictive statistical models are rather reliable in

predicting future performance when business and market

conditions remain relatively stable, these tools are less

effective when unpredictable market forces occur, such as a

sharp downturn in the economy, spiraling interests rates, and

rapidly increasing gas prices. Thus, predictive analytics are

best used in conjunction with tier-two team members using

deterministic analytics to look at current demand data in more

responsive and tactical ways.

Tier-two teams are the most common users of deterministic

analytics because these analytics are more agile and allow the

teams to respond rapidly to the dynamic changes in

downstream retail channels. These comprehensive, flexible,

intuitive, and easy-to-use

analytics can be customized

around the unique needs of the

retailer and supplier. And they

can be used to examine the

effects of varied factors on

demand, such as statistical facts,

exogenous trends, shifting

demand, new product

introduction, and evolving

shopper insights. Deterministic analytics can also incorporate

built-in performance-driven alerts that flag potential problems

and potential opportunities.

Thus, deterministic analytics play to the needs of tier-two

sales teams that need to respond rapidly to blips in

performance by providing innovative analysis and insights as

to how the company and retailer should respond. If your

organization’s downstream sales teams aren’t armed with

deterministic analytics, you can gradually lose competitive

ground and aisle share. So in reality, your organization will

need a demand intelligence solution that provides you with

both deterministic and predictive analytics in order to respond

on a daily, weekly and monthly basis to ever-changing

consumer trends and the unique data needs of each of your

team members.

Gain perspective — start asking questions.

In the recent past, supply and order data were the center of

the CPG universe. Today, demand intelligence has widened

our perspective and presents CPG organizations with the

opportunity to make timely decisions that better meet the

needs of consumers on a daily, weekly and monthly basis.

When assessing how to either better use your current demand

intelligence solution — or whether a new demand intelligence

solution is needed — look to the BI demand enterprise stack

to help blueprint your strategy.

Every level of the stack is

critical to optimizing the use

of demand data and driving

strategic synergy and cross-

functional communication

and collaboration throughout

your enterprise. You need an

Outside-In and Throughout BI

architecture that inherently fuels

enterprise-wide collaboration

and communication. You need a mix of deterministic and

predictive BI demand analytics, capitalizing on the strengths of

each analytic to ensure you get the right data into the hands of

the right people at the right time. And then you look to specific

BI demand applications that can be further customized to the

unique needs of each member in your organization.

Start by asking relevant, practical and strategic questions.

Yes, in the greater sense, everyone at every tier needs a

unified data source. But the trick is understanding what they

specifically need from that data source and when they need it.

So ask them — and yourself — what data and analytics matter

What unique mix of demand intelligence

capabilities will best enable you to perform

at optimal levels?


most relative to achieving superior business performance

from their perspective? What unique mix of demand

intelligence capabilities will best enable you to perform at

optimal levels? And how soon — and often — do you need it?

This will help you determine which set of analytics will best

suit their needs.

True CPG champions will ask these questions. And with that

information in hand, they will build a BI demand enterprise

stack that increases the productivity, organizational synergy,

and incremental gains that help them strategically compete in

the CPG industry and — like Galileo — provide much-needed

new perspectives that open up a universe of opportunity.

ABOUT LUMIDATA

LumiData is a demand intelligence specialist providing retail demand information that assists Fortune 1000 consumer goods companies to evolve their business strategies. LumiData’s combina-tion of software dashboard applications and client services creates easy-to-visualize demand planning solutions utilized to drive sales and marketing strategies that increase revenue, improve margins, enhance retailer partnerships and strengthen market positions.

ABOUT THE AUTHOR

Ransom Stafford, MBARansom Stafford has served as the president and CEO of LumiData for five years. He has more than 25 years of corporate experience in information technology, business consulting and infra-structure change management, and has held a variety of managerial positions with IBM, Control Data and The St. Paul Companies. He is an expert at business architecture re-engineering — aligning strategic intent, technology, business processes and people to achieve maxi-mum organizational productivity. During his career, he has been instrumental in helping organizations optimize their success by providing employees with the right resources they need to maximize their contribution.

12 S. Sixth Street 710 Plymouth Building Minneapolis, MN 55402Phone: 612.767.2880

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In the Demand Intelligence Universe, Perspective Matters.

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Transcript of In the Demand Intelligence Universe, Perspective Matters.