Crescent Electric Sales Forecasting

A Summer Project by Intern Bo Anderson

www.cesco.com

How do you Forecast Sales?

• Find variables outside company related to sales– Outside company, economic factors

• Measure effect of variables on sales– So X is ↑, do sales go ↑ or ↓?

• Adjust for company– Seasonality– Size of company (branches)

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How do you find those variables?

• Find ones that make logical sense• Find ones that trend with sales

– Correlation• “the degree to which two or more attributes or measurements

on the same group of elements show a tendency to vary together”

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Correlation Types

• Positive Correlation– If one thing goes up/down, so does the other

• My weight goes up and so does my waist size.

• Negative Correlation– If one thing goes up/down, the other does the

opposite• I spend more time playing video games and my GPA goes

down

• No Correlation– If one thing goes up/down, the other doesn’t respond

• I eat more cheetos but my shoe size stays the same

Correlation Examples

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Correlation Examples

• Hypothetical Examples– Left Shoe & Right Shoe

• Correlation = 1.0

– # of Apples Person 1 & Person 2 if sharing• Correlation = -1.0

• Real Life Examples– Unemployment Rate & # of Hires

• Correlation = -0.93

– MLB 2011: Wins & Saves• Correlation = -0.03

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No Correlation

Regression

• Linear Regression:– the relation between variables in an equation that

measures how certain independent variables effect a dependent variable

• Measure the effect of variables while holding “everything else constant”

• Hand size & weight relationship, really it’s just height and weight– Must hold height constant to really see a relationship

Let’s do our own Linear Regression

What is the relationship between the Economy and Crescent’s Total Sales?

Variable Real Sales Real Sales Log

Job Openings: Total Nonfarm 0.7416 0.7444

Industrial Production Index 0.7264 0.7281

Real Private Nonresidential Fixed Investment 0.7263 0.7227

Quits: Total Nonfarm 0.7006 0.7025

Unemployment Rate -0.6947 -0.6947

All-Transactions House Price Index for California 0.6799 0.6749

S&P Case-Shiller 20-City Home Price Index 0.6774 0.6720

Manufacturers' New Orders: Nondefense Capital Goods Excluding Aircraft 0.6734 0.6724

Manufacturers' New Orders: Durable Goods 0.6708 0.6707

All Employees: Goods Producing Industries 0.6593 0.6589

Real Personal Income Excluding Current Transfer Receipts 0.6449 0.6374

Real Retail and Food Services Sales 0.6330 0.6404

Natural Gas Price: Henry Hub, LA 0.6239 0.6158

Real Private Nonresidental Investment

• Measured in billions of 2005 dollars. This means it’s adjusted for inflation

• Measures business investment purchases (buildings, inventory etc.)

• The majority of our sales count under this

Real Personal Income Excluding Current Transfer Receipts

• Measured in Billions of 2005 Dollars, meaning adjusted for inflation

• Measures individuals ability to consume

• Our customers livelyhood

# of Branches

• Measured in… well branches. Closings and openings are adjusted by the year/quarter of change so decimals are used (Close in late June then .5 branches for year)

• Help measure an increase in our available market

S&P Case-Shiller 20-City Home Price Index

• Index with January of 2000 as a base of 100

• Increase in demand for a housing, means increases in complimentary products (ex: lights, wiring, etc.)

• Type 1 Customers are effected by housing

All Employees: Goods Producing Industries

• Measured in thousands of persons

• As companies expand payrolls they’ll also be in a position to expand inventories and builidings

Our Model

• Q1 Sales =- $35,2464,272.30

+ ($46,758.22)*RPNFI

+ ($55,242.36)*SPCSHHPI

+ ($7,492.30)*AEGPI

+ ($32,962.60)*RPIECTR

+ ()*Branches

Seasonal Adjustments: Q2 + $29,606,440.82; Q3 + $36,307,769.30; Q4 + $29,550,914.42

Efficiency

Year Actual Sales Predicted Sales % Error

2004 $743,203,941.31 $740,336,170.05 -0.39%

2005 $826,969,138.75 $825,148,749.58 -0.22%

2006 $975,724,449.00 $1,002,965,878.32 2.79%

2007 $987,896,799.86 $956,907,932.59 -3.14%

2008 $1,034,431,846.94 $1,043,719,368.00 0.90%

2009 $752,005,333.88 $743,286,006.38 -1.16%

2010 $768,882,644.62 $762,552,247.79 -0.82%

2011 $885,405,400.95 $895,815,601.36 1.18%

2012 ? ? ?

Average (+/-)1.32%

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How accurate is our Model?

• 95% of the time it is within 3%. Once every 20 years it will be off by more

• The odds of it being off are the same as the odds the Dallas Cowboys have to win the Super Bowl this year according to Vegas

• Think about it the Cowyboys COULD win the Super Bowl, but we are pretty sure that won’t happen. if anybody wants to bet heads-up with 1 to 1 odds, I’ll take your bet right now

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