Best in Market Pricing

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Best in Market Pricing
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What is Best in Market Pricing ?

• An extension of parametric modeling for
• negotiating lowest pricing for a statement of
  work
• consisting of many items
• assessing a suppliers market position
• in absence of competitive bids
• A mathematical model which
• simulates the competitive bid process
• shows the effect of bidding scenarios
• quantifies the likelihood of achieving desired
  pricing

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Why is Best in Market Pricing Needed ?

• Cost analysis approach is expensive and
  ambiguous
• extensive analysis of suppliers costs
• suppliers reluctant to share costs
• costs often obscured by suppliers accounting
• timeliness and resources dictate different
  approach
• Estimate of market competition is needed
• potential market is not reflected in current
  costs
• RFI/RFQs often requested to establish target
• no dominate low cost supplier exists

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An Extension of Parametric Models

• Mathematical equations used to predict the prices
  of parts as a function of one or more of their
  attributes.

Predicted Price Constant s x Attribute 1
.
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How Are Parametric Models Developed ?

• Samples of price data are drawn from a population
• Prices are regressed against part attributes to
  identify equations that explain the pricing
  variation
• Equations are used to predict the prices of
  remaining parts

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Parametrics Used to Predict Market Prices

• Average market prices vary from actuals
• due to market competition
• variance represented by bell curve.
• market s are at center
• width is determined by variance
• between actual and predicted s.
• Regression models can be used to predict
• average market prices
• competitive range of market.
• lowest Probable s

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Best in Market Pricing Sets the Negotiation Range

• The Strategy
• For a suppliers Statement of Work,
• estimate range of lowest pricing likely to
• be seen in the marketplace
• i.e. 5 RFQs extended with lowest accepted
• Negotiate price within competitive range
• Initial position
• Dont leave money on table 10 probability
• Maximum position
• Dont reject a reasonable offer 80
  probability

The Model
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Similar to Best-in-Class (BIC) Method

• The Strategy
• For each group of similar parts find the
• supplier with lowest market ratio
• (observed/predicted) broad model coverage
• Offer BIC supplier prices for all parts.
• Negotiate between market and BIC

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Determine BIC Market Ratio
Calculate each suppliers relationship to market
BIC is the lowest market ratio demonstrated over
the relative range of the parametric model
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How Best in Market Price is Determined
First Estimate the Distribution of Supplier
Prices of Each Part

• Predicted prices vary from actual
• due to market competition
• Variance represented by bell curve.
• Market is at center
• Width is determined by variance
• between actual and predicted s.
• Regression models can be used to predict
• Average market prices
• Competitive range of market.

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Normal Distribution is Assumed for Predictions
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How Best in Market Price is Determined
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Forming Similar Mega-Parts
The variance summation of individual parts
assumes independence. To validate this
assumption, similar parts with identical pricing
are considered to be the same part.
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Distribution of Total Market s.
 
Note Independence assumption requires parts to
be into similar Mega-parts
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Basics behind Order Statistics
Odds of receiving at least one low price out
of n independent bids
i.e. 1- odds of not getting any bids that low
Example If the odds of getting a single
supplier bid ? 100 is 5 then, the odds of at
least one bid in 6 supplier bids being that low is
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Applying Order Statistics to Best in Market
Pricing
Let 1, 2, ., n be a random sample of mutually
independent bids from the population of suppliers
capable of producing the statement of work. It
is assumed that the suppliers bids would follow
the same cumulative normal distribution.
Arrange bids in ascending order so that 1, 2,
., n. It is considered unlikely that any two
bids would be equal.
Although order Statistics would be concerned with
the properties of all bids, Best in Market
Pricing is concerned primarily with the smallest.
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Applying Order Statistics to Best in Market
Pricing
The cumulative and probability distribution
functions of the smallest of n bids i for some
value The mean and variance of i are
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Distribution of Lowest s in Sample
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Distribution of Lowest s in Sample
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Comparison of Lowest s to Normal Distribution

• Conclusions
• Skewness Kurtosis are insignificant at
• levels of interest.
• As bids increase
• Negative skew increases
• Peak sharpens, tails fatten
• Differences decrease further out in tails
• At 95 interval difference for 5 bids
• is .00036

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Computing Negotiation Range for Lowest ...
Using expectation rules to convert back to the
Lowest distribution
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Computing Negotiation Range for Lowest ...
To determine the desired negotiation range,
select the normalized mean and standard deviation
from table which corresponds to the expected
number of bids, translate them into units and
apply standard confidence interval techniques
using the critical values associated with the
desired management risk of .10 for the initial
offer and .8 for the final.
For the case when n 5 bids and total market
dollars is Normal(1M,100K)
1. Select Normalized Extreme Value Constants for
n5
2. Compute Distribution Statistics for Lowest
Bid s
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Computing Negotiation Range for Lowest ...
To determine the desired negotiation range,
select the normalized mean and standard deviation
from table which corresponds to the expected
number of bids, translate them into units and
apply standard confidence interval techniques
using the critical values associated with the
desired management risk of .10 for the initial
offer and .8 for the final.
3. Compute Negotiation Range for Lowest Bid
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Applying Best in Market Pricing

• The Strategy
• For a suppliers Statement of Work, estimate
• range of lowest supplier pricing likely to be
  
• seen if competitively bid.
• i.e. 5 RFQs extended with lowest accepted
• - Negotiate price within competitive range
• Initial position
• Dont leave money on table 10 probability
• Maximum position
• Dont reject a reasonable offer 80 probability

The Model
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An Example Statement of Work
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Verify Estimates of Cost Passengers
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A Single Part Number Case Study
Decision To RFI parts
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Analyzing the Resulting RFIs for Outliers
One bid identified as outlier
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Comparison of RFI Data to Parametrics
Difference being investigated for potential model
enhancement
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Important Best in Market Price Considerations

• Does not always predict lowest prices
• May be a dominant supplier (Best-in-Class)
• Limited Market Place
• Other supplier requirements precludes low cost
• Current supplier may not be capable of low
  prices
• Strategy may be to bid instead of negotiate
• Non-recurring costs should be considered
• Expected gains may be driven by a few parts
• Pareto techniques are valuable
• Predicted prices for high drivers should be
  verified

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Best in Market Price Summary

• Logical Extension of Parametric Modeling
• Models Competitive Bidding Process
• Provides estimate of Market Competition
• without RFI/RFQ data or existence of
• Best in Class Supplier
• Quantifies likelihood of obtaining desired
• price in marketplace

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QUESTIONS ANSWERS