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Generic Prices  Forecasting  and Mega Trends
 
How do companies forecast generic prices? Time Price Generic Launch
Some just react to market changes
Fitting trends to prices
But reality was different
Same Therapeutic Cat Same Therapeutic Category, But Different Decay Rates Selective Serotonin Re-Uptake Inhibitors
Same Market Value Same Value, But Different Decay Rates £450,000 - £550,000
Same molecule Same Molecule But No Uniformity Fluconazole Caps
But actual patterns are very complex
SWAGs   ,[object Object]
 
Data Collection ,[object Object],[object Object],[object Object],[object Object],[object Object]
Trend in average generic price
Each product follows a pattern
Patterns   & Relationships £ ?  Generic Price Volume Market Share Value Brand Generic Spilt No of Manufacturers Reimbursement 
Statisticians found each product can be modelled
Multiple models were produced, one for each product
Each with its own formula
First Model (2005)   ,[object Object],[object Object],[object Object],[object Object],[object Object]
Further development (2006) ,[object Object],[object Object],[object Object],[object Object],[object Object]
Current model completed (2007)   ,[object Object],[object Object],[object Object],[object Object]
 
Does it work? ,[object Object],[object Object],[object Object],[object Object]
 
 
 
 
 
 
 
Other Markets ,[object Object],[object Object],[object Object],[object Object]
USA
USA vs UK
Other Products
 
The ‘Dead Cat Bounce’
Key Bounce factors ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
How often bounces happen ,[object Object],[object Object],[object Object],[object Object],[object Object]
Bounces after generic launch
Bounces – the real picture
Bounce frequency
Seasonality – Omeprazole Apr 02- Mar 06
Seasonality - Omeprazole Oct 02- Sep 06
Seasonality - Ciprofloxacin Jan 03 – Dec 05
Seasonality - Levothyroxine Oct 00-Sep 06
Seasonality - Atenolol Oct 00-Sep 06   Jan 01- Dec 05
Seasonality - Simvastatin Oct 03-Sep 06
Seasonality - Lisinopril Jan 01 – Dec 05
Clusters ,[object Object],[object Object],[object Object]
Clusters 1 - 4
Clusters 5 - 8
Highs and Lows ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Possible reasons ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Summary ,[object Object],[object Object],[object Object],[object Object],[object Object]
Many Thanks www.wavedata.biz

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New Pr Ofesy Conference Presentation

Hinweis der Redaktion

  1. Research project Trying to find the hidden patterns behind Long term Points – interesting / surprising / unexpected
  2. How do companies set or predict prices. What would you do?
  3. A guess could be based on a particular competitor, or a key supplier, or an average, but it is still at heart a guess
  4. Trend lines can be added to what data you have, but often more than one line can be fitted to the same trend, and if you only have a few months of post patent generic prices, many trends could be fitted to the same data – each one a guess.
  5. The price went up, as it was following a hidden pattern, not immediately obvious to the uninformed
  6. Trend lines can be added to what data you have, but often more than one line can be fitted to the same trend, and if you only have a few months of post patent generic prices, many trends could be fitted to the same data – each one a guess.
  7. Trend lines can be added to what data you have, but often more than one line can be fitted to the same trend, and if you only have a few months of post patent generic prices, many trends could be fitted to the same data – each one a guess.
  8. But sometimes they don't
  9. The range of decline curves possible is very wide
  10. Information revolution Bill gates Email Design around the planet Ebay Lack of science in one area of pharma – pricing Aunt story
  11. The range of decline curves possible is very wide
  12. Each of the 100+ decline curves we looked at was based on a pattern
  13. Each could be modelled
  14. Each could be connected with the underlying factors
  15. Even the rises in price due to availability and price could be predicted
  16. Even the rises in price due to availability and price could be predicted
  17. The range of decline curves possible is very wide
  18. The range of decline curves possible is very wide
  19. Patterns are complex but predictable Not as complicated as shares But pharmaceuticals is an island – doesn't pick up techniques from other industries easily Why has no one done it before – not enough pricing history Norton healthcare SPSS experience
  20. So the method works Will get better as more examples are analysed Sugar traders / stocks and shares But bounces still an issue ….. But what about other markets
  21. Nothing is new But pharma is still left with vulnerability in those companies that don’t take this on board No reason why techniques won’t work in other states
  22. Pebble in a pond