THE POWER OF ANALYTICS

IN COST MANAGEMENTNational PDI 2016

03 June 2016

Mr. David Molinari, HQDA

Ms. Denise Oberndorf, CALIBRE

WHAT IS COST MANAGEMENT?

Purpose of Cost Management

Provide the ability to make resource informed decisions

Resource informed decisions:

• Allow organizations to identify

more optimal solutions,

• Provide an ability for proactive

management, and• Inform trade off decisions and

impacts.

WHAT IS ANALYTICS?

Analytics

The discovery, interpretation and

communication of meaningful patterns

in data

Relies on the application of statistics,

computer programming and

operations research

Includes data visualization

ANALYTICS IN COST MANAGEMENT

Understanding Value

Budget and spending information is not sufficient

Must be able to understand impact to benefits with

each change in cost (value)

Understanding Costs

Cost Driver: Unit of activity that causes change in

cost

Methods for identifying cost driver include scatter

plots and regression analysis

Understanding Performance

Performance Measurement

Identify key performance indicators and goals

Measure progress toward a desired outcome

DATA STRUCTURES MATTER

Data structures

Format for organizing and storing data

Typically specialized

Impacts transparency and efficiency of data access

Ideal Characteristics

Dynamic: Allows data to inform multiple decisions Rubik's

Integrated: Allows for data to be combined

Efficient: Allows for information to be easily accessed without overburdening the user

DATA MODELS AND APPROACH

Model Types

Forecasting Models

Process to forecast future based on past and present data

Forecast something we can’t control

Time-series methods are most common

Moving Average

Autoregressive

Trend Estimation

Examples of data for forecasting

Price of Electricity

Demand of a good or service

Structural Models

Process to develop a mathematical model to describe how two or more observable variables relate to one another

Influence and inform decisions

Cross Sectional method is common

Examples of observable variables to compare

Consumption of electricity and cost of electricity

Operational hours of a training facility and readiness

FORECAST EXAMPLE: ELECTRICITY PRICE FORECAST (1 OF 6)

First let’s collect historical price data

FORECAST EXAMPLE: ELECTRICITY PRICE FORECAST (2 OF 6)

Try Trend Analysis: $month = A + (Δ$/month) x (Month)

FORECAST EXAMPLE: ELECTRICITY PRICE FORECAST (3 OF 6)

Next let’s try autoregression (AR): $Month = A + Anchor x ($Month-1)

FORECAST EXAMPLE: ELECTRICITY PRICE FORECAST (4 OF 6)

The results of autoregression? Don’t forget out-of-sample testing.

!?

FORECAST EXAMPLE: ELECTRICITY PRICE FORECAST (5 OF 6)

AR with Trend: $Month = A + Anchor x ($Month-1) + (Δ$/month) x (Month)

FORECAST EXAMPLE: ELECTRICITY PRICE FORECAST (6 OF 6)

Comparing forecasts

STRUCTURAL EXAMPLE CASE: BASE MANAGEMENT (1 OF 2)

Drivers:

Energy Consumption – How much do we consume?

Readiness – How many hours can operate training support facilities?

Life, Health, Safety – How fast can we respond to emergencies?

Morale & Welfare – How much does it cost per person?

Trade-offs we face:

What are the priorities? Where can we take risk?

Are we willing to trade training for safety? ….for morale?

How much do we need to budget? What are the impacts if we don’t?

Can we identify best practices and spot potential problems?

STRUCTURAL EXAMPLE CASE: BASE MANAGEMENT (2 OF 2)

Integrating risk allows us to use

structural models to tell us:

How confident we can be

that our budget is sufficient to

achieve desired outcomes

What outcomes we can

expect given our budget

Simulation tools are used to

combine error distributions of

different shapes

RISK!

VISUALIZATION OF ANALYSIS

Visualization

Present data in a pictorial or graphical format

Visualize difficult concepts

Identify new patters

Decision Support

Improves response times

Provides information in a simple format

Increases transparency

Enhance collaboration

VISUALIZATION EXAMPLE Study results on optimizing

response times in Ann Arbor, Michigan

Reduce fire house locations from 5 to 3 and improve response times

Red dots are all fire locations over the last decade

Shaded area represents response times

Station 3, 6 and 4 will close (grey hats)

Station 2 not currently being used, will reopen

Source: The Ann Arbor Chronical

VISUALIZATION EXAMPLE

World coal consumption by region

Automation can help display changes over time

Source: U.S. Energy Information Administration, International Energy Statistics

Link: Rising Asian demand drives global coal consumption growth - Today in Energy - U.S. Energy Information Administration (EIA)

CONCLUSION

Power of analytics in cost management

Better decisions, faster

Predict future budgets and impacts

Informed trade off decisions

Visualize information for easy

digestion