Chard AppliancesBecca Carlson

Bethany HaefnerJack Lim

Liang Wen

Opportunity to increase annual revenue by 20% to $137M

Current New $100,000,000

$110,000,000

$120,000,000

$130,000,000

$140,000,000 Chard’s Annual Revenue

$114M

$137M

Opportunity

+$23M

Agenda

The ProblemThe Root CauseThe SolutionConclusion

Problem: Underselling Market Potential

If Chard Appliance would have retained market share it had in 1981, sales would have grown to over $250 million in 1993

1981198219831984198519861987198819891990199119921993$0

$50$100$150$200$250$300

Chard Appliance Sales (in millions of $) Expected

Actual

Chard’s identified potential reasons why have sales not increased

Should more salespeople

be hired?

More communicatio

n between sales and

distribution?

Are the prices not

representative of the

market price?

Are lead times too

long?

Are the lead times too variable?

Is the company

adequately advertised?

Why does it matter?

Internal: Profitability

External: Supplier Relationships

• Cost increase • Advertising• Hiring/Firing• Pricing• Customer Service

• Cost increase needs to be coupled with an increase in revenue

• Threats from major manufacturers to stop supplying

• Small supplier base of 4

$47.7M sales opportunity was lost

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 -

2,000

4,000

6,000

8,000

10,000

12,000

14,000

Sold

Demanded256,821

181,173

$47.7M

Missed sales

Units

Week

Appliance Units Demanded vs. Sold

What affects how much we sell?

Units Sold

Price

# Sales people

Adv. Expense

Order Cycle Time

Order Cycle

Variability

Credit Adj.

Fill RateDependent VariableIndependent Variables

Price and Order Cycle Variability significantly impact the number of units sold

  Multiple R-Square Adjusted StErr of    Summary R R-Square Estimate      0.8 0.7 0.7 642.5                   Degrees of Sum of Mean of F-Ratio p-Value  ANOVA Table Freedom Squares Squares  

Explained 2.0 39578885.319789442.

6 47.9 0.0  Unexplained 40.0 16512182.8 412804.6                   

  Coefficient Standard t-Value p-ValueConfidence

Interval 95%Regression Table Error Lower UpperConstant 12516.5 999.6 12.5 0.0 10496.3 14536.7Price -10.7 1.1 -9.6 0.0 -12.9 -8.4Order Cycle Variability -300.0 91.9 -3.3 0.0 -485.7 -114.3

Units sold = 12,516.5 - 10.7(Price) - 299.9(Variability)

SOLUTION: Integrate and strengthen business process management

Ideally, eliminating variability would increase revenue 37%...

Decreased OCV by 100%, • Unit Sold: +37%; +67,173 units • Fill Rate: +26%• Revenue : +37%; +$42,298,829

“Perfect World” Solution

…Realistically, optimizing variability increases revenue 20%

• Minimum OCV – 2.82 (25%) and $560 (-11%)

• Unit Sold - +35% (63,015 units)• Fill Rate - +25%• Revenue - +20% ($22,585,604)

Optimized Solution

Recommendation: Invest money in increasing customer service levels

75%Increase Safety Stock Levels

25%Improve

Forecasting Methods

$400,000 per week can be spent to improve customer service levels in order to maintain a 33% return on

investment

CONCLUSION

Missed Sales - $47.7 MillionProblems - Price & Order Cycle

VariabilityOptimized Solution: 20% (+$23M)

Back Slide

Back Slide – OCV -100%

Back Slide –Optimum

Appendix: Add’l costs for new safety stock levels Assume inventory carrying costs are 10% of price

Found inventory carrying costs are 25% of costs (http://bstocksolutions.com/blog/carrying-cost-of-excess-inventory/)