COMPUTERIZED METHOD, COMPUTER PROGRAM PRODUCT AND COMPUTER ENVIRONMENT

• US 20090018935A1
• Filed: 06/25/2007
• Published: 01/15/2009
• Est. Priority Date: 05/04/2007
• Status: Active Grant

• Current Assignee: Gewofag IT Servicegesellschaft mbH, SAP SE
• Original Assignee: SAP AG (SAP SE)
• Inventors: REISER, Rudolf, OTTE, Kurt-Ulrich, von RECHBERG, Ulrich, NEUMAIER, Florian
• Granted Patent: US 8,060,397 B2
• Time in Patent Office: Days
• US Class Current: 705/30
• CPC Class Codes: G06Q 10/0635 Risk analysis of enterprise or organisation activities
• CPC Class Codes: G06Q 30/08 Auctions
• CPC Class Codes: G06Q 40/00 Finance; Insurance; Tax strategies; Processing of corporate or income taxes
• CPC Class Codes: G06Q 40/03 Credit; Loans; Processing thereof
• CPC Class Codes: G06Q 40/12 Accounting

Abstract

Summarizing, the present invention relates to a computerized method for processing and displaying data of a business unit comprising the steps:

• • extracting a set of past data of said business unit from a past data base using a closed computer environment and introducing said set of past data into said closed computer environment;
  • extracting a set of future plan data of said business unit from a future plan data base using said closed computer environment and introducing said set of future plan data into said closed computer environment, wherein
  • extracting of said set of future plan data comprises:
    • automatic determining and extracting of at least one preconfigured subset of said set of future plan data;
    • automatic extracting the remaining data of said future plan data from said data base;
  • determining at least one estimate value from said set of past data and said set of future plan data, using said closed computer environment and
  • displaying said at least one determined estimate value, as well as a computer program product, a computer environment and a method for determining a value of a business unit.

Claims

1. Computerized method for processing and displaying data of a business unit comprising the steps:

extracting a set of past data of said business unit from a past data base using a closed computer environment and introducing said set of past data into said closed computer environment;

extracting a set of future plan data of said business unit from a future plan data base using said closed computer environment and introducing said set of future plan data into said closed computer environment, wherein

extracting of said set of future plan data comprises;

automatic determining and extracting of at least one preconfigured subset of said set of future plan data;

automatic extracting the remaining data of said future plan data from said data base;

determining at least one estimate value from said set of past data and said set of future plan data, using said closed computer environment and

displaying said at least one determined estimate value.

2. Computerized method according to claim 1, wherein the step of automatically determining and extracting said at least one preconfigured subset of said future plan data comprises:

automatic evaluating of contract data;

automatic determining and extracting of said at least one preconfigured subset of said set of future plan data from said contract data.

3. Computerized method according claim 1, wherein said at least one estimate value is calculated using one or more of the following equations:

${\mathrm{DCF}}_c=\underset{t=1}{\overset{\mathrm{\infty <br><br>}}{\sum <br><br>}}<br><br><br><br>\frac{f_c<br><br>\left(t_0+t\right)}{{\left(1+i_{t_0+t}\right)}^t};<br><br><br><br>f_c<br><br>\left(t\right);<br><br>={\widehat{\mathrm{\beta <br><br>}}}_0$

wherein

i_t represents a market traded interest at time index t and

t₀;

0≦

t₀≦

n represents the index of a predetermined date.

{circumflex over (β

)}₀ represents the statistical estimator for parameter β

₀

and;

${\mathrm{DCF}}_l=\underset{t=1}{\overset{\mathrm{\infty <br><br>}}{\sum <br><br>}}<br><br><br><br>\frac{f_l<br><br>\left(t_0+t\right)}{{\left(1+i_{t_0+t}\right)}^t};<br><br><br><br>f_l<br><br>\left(t\right);<br><br>={\widehat{\mathrm{\beta <br><br>}}}_0+{\widehat{\mathrm{\beta <br><br>}}}_1<br><br>t,$

wherein

i_t represents a market traded interest at time index t and

t₀;

0≦

t₀≦

n represents the index of a predetermined date.

{circumflex over (β

)}₀ represents the statistical estimator for parameter β

₀

{circumflex over (β

)}₁ represents the statistical estimator for parameter β

₁

and;

${\mathrm{DCF}}_n=\underset{t=1}{\overset{\mathrm{\infty <br><br>}}{\sum <br><br>}}<br><br><br><br>\frac{f_n<br><br>\left(t_0+t\right)}{{\left(1+i_{t_0+t}\right)}^t};<br><br><br><br>f_n<br><br>\left(t\right);<br><br>={\widehat{\mathrm{\beta <br><br>}}}_0+{\widehat{\mathrm{\beta <br><br>}}}_1<br><br>t^{\widehat{\mathrm{\beta <br><br>}}<br><br>2},$

wherein

i_t represents a market traded interest at time index t and

t₀;

0≦

t₀≦

n represents the index of a predetermined date.

{circumflex over (β

)}₀ represents the statistical estimator for parameter β

₀

{circumflex over (β

)}₁ represents the statistical estimator for parameter β

₁

{circumflex over (β

)}₂ represents the statistical estimator for parameter β

₂.

4. Computerized method according to claim 3, wherein for the at least one estimate value, a limit factor l_m is calculated using the equation:

$l_m;<br><br>=\frac{{\mathrm{DCF}}_m^r}{f_m<br><br>\left(t_0+1\right)}.$

5. Computerized method according to claim 4, wherein a risk premium is between approximately 80% and approximately 99%, in particular approximately 95%.

6. Computerized method according claim 1, wherein said step of extracting said set of past data and said step of extracting said set of future plan data comprise the step:

displaying an activating device for simultaneously activating said extraction of at least one subset of said set of past data and at least one subset of said set of future plan data.

7. Computerized method according to claim 1, wherein said step of extracting said set of past data and said step of extracting said set of future plan data comprises the step:

displaying an activating device for simultaneously activating said extraction of a plurality of subsets of said set of past data and a plurality of subsets of said set of future plan data.

8. Computerized method according to claim 1, wherein after extracting said at least one set of past data and said at least one set of future plan data, said data are displayed to a user for manual inspection.

9. Computerized method according to claim 1, wherein a choice of a present date is made manually, and wherein, after choice of the present date, the set of past data and the set of future plan data are determined automatically.

10. Computerized method according to claim 9, wherein a choice of a present interval is provided manually and wherein said set of past data is provided for a past interval and wherein said set of future plan data is provided for a future interval automatically.

11. Computerized method according to claim 10, wherein the set of past data and the set of future plan data is automatically determined using at least one of the present date and the present interval.

12. Computerized method according to claim 1, wherein for the future plan data an individual estimate value is determined automatically for every time unit of said future interval.

13. Computer program product which, when loaded in the memory of a computer carries out the method according to claim 1.

14. Computer environment for processing and displaying data of a business unit comprising:

a past data extractor which is adapted for extracting a set of past data of said business unit from a past data base, wherein the computer environment is a closed computer environment and wherein said extractor is adapted for introducing said set of past data into said closed computer environment,

a future plan data extractor which is adapted for extracting a set of future plan data of said business unit from a future plan data base and wherein said future plan data extractor is adapted for introducing said set of future plan data into said closed computer environment, wherein

said future plan data extractor comprises;

a determinator which is adapted for automatically determining and extracting at least one preconfigured subset of said set of future plan data

a remaining data extractor which is adapted for automatically extracting the remaining data of said future plan data from said data base

an estimate value determinator which is adapted for determining at least one estimate value from said set of past data and said set of future plan data, using said closed computer environment and

a display which is adapated for displaying said determined estimate value.

15. Computer environment according to claim 14, further comprising

a reader which is adapted for automatically reading contract data, wherein

said determinator is adapted for automatically determining and extracting said at least one preconfigured subset of said set of future plan data from said contract data.

16. Computer environment according to claim 14, wherein said display is adapted to display said extracted past data and said extracted future plan data.

17. Computer environment according to claim 14, wherein said display is adapted to display at least one estimate value.

18. Method for determining a value of a business unit, comprising the steps:

providing past data of the business unit for a given first period of time in the past;

providing future plan data of said business unit for a given second period of time in the future;

determining from the past data and the future plan data at least one value of the business unit using one or more business-calculation-models of the set comprising a constant business-calculation-model and a linear business-calculation-model and a non-linear business-calculation-model.

19. Method according to claim 18, wherein the past data of the business unit and/or the future plan data of said business unit are extracted from an external database.

20. Method according to claim 18, wherein the business unit includes one or more rental objects.

21. Method according to claim 18, wherein the determination of said value of said business unit includes at least one of planning positions and profit center groups.

22. Method according to claim 21, including at least one of the following five categories of plan positions:

revenues;

operating costs;

maintenance;

building operation; and

building administration,

wherein said five categories constitute a cash flow.

23. Method according to claim 20, wherein the future plan data includes individual rental contract data of the business unit.

24. Method according to claim 18, wherein at least one of the past data of the business unit and the future plan data of said business unit are provided on an annual basis.

25. Method according to claim 24, wherein said value of said business unit includes a discounted cash flow.

26. Method according to claim 25, wherein for calculating the discounted cash flow, market traded annual interest rates are taken into account for a period of 50 years.

27. Method according to claim 26, wherein using said constant business-calculation-model said discounted cash flow, as a specific value of said business unit is calculated using the equation:

${\mathrm{DCF}}_c=\underset{t=1}{\overset{\mathrm{\infty <br><br>}}{\sum <br><br>}}<br><br><br><br>\frac{f_c<br><br>\left(t_0+t\right)}{{\left(1+i_{t_0+t}\right)}^t};<br><br><br><br>f_c<br><br>\left(t\right);<br><br>={\widehat{\mathrm{\beta <br><br>}}}_0$

wherein

i_t represents a market traded interest at time index t and

t₀;

0≦

t₀≦

n represents the index of a predetermined date.

{circumflex over (β

)}₀ represents the statistical estimator for parameter β

₀.

28. Method according to claim 26, wherein using said linear business-calculation-model said discounted cash flow, as a specific value of said business unit is calculated using the equation:

${\mathrm{DCF}}_l=\underset{t=1}{\overset{\mathrm{\infty <br><br>}}{\sum <br><br>}}<br><br><br><br>\frac{f_l<br><br>\left(t_0+t\right)}{{\left(1+i_{t_0+t}\right)}^t};<br><br><br><br>f_l<br><br>\left(t\right);<br><br>={\widehat{\mathrm{\beta <br><br>}}}_0+{\widehat{\mathrm{\beta <br><br>}}}_1<br><br>t,$

wherein

i_t represents a market traded interest at time index t and

t₀;

0≦

t₀≦

n represents the index of a predetermined date.

{circumflex over (β

)}₀ represents the statistical estimator for parameter β

₀

{circumflex over (β

)}₁ represents the statistical estimator for parameter β

₁.

29. Method according to claim 26, wherein using said non-linear business-calculation-model said discounted cash flow, as a specific value of said business unit is calculated using the equation:

${\mathrm{DCF}}_n=\underset{t=1}{\overset{\mathrm{\infty <br><br>}}{\sum <br><br>}}<br><br><br><br>\frac{f_n<br><br>\left(t_0+t\right)}{{\left(1+i_{t_0+t}\right)}^t};<br><br>$

wherein

i_t represents a market traded interest at time index t and

t₀;

0≦

t₀≦

n represents the index of a predetermined date.

{circumflex over (β

)}₀ represents the statistical estimator for parameter β

₀

{circumflex over (β

)}₁ represents the statistical estimator for parameter β

₁

{circumflex over (β

)}₂ represents the statistical estimator for parameter β

₂.

30. Method according to claim 18, wherein the future cash flow developments are compared by calculating the limit factor l_m is calculated using the equation:

$l_m;<br><br>=\frac{{\mathrm{DCF}}_m^r}{f_m<br><br>\left(t_0+1\right)}.$

31. Computer program product which, when loaded in the memory of a computer carries out the method according to claim 18.

32. Computer environment for determining a value of a business unit, comprising:

a past data provider for providing past data of the business unit for a given first period of time in the past;

a future plan data provider for providing future plan data of said business unit for a given second period of time in the future and

a determinator for determining from the past data and the future plan data at least one value of the business unit using one or more business-calculation-models of the set comprising a constant business-calculation-model and a linear business-calculation-model and a non-linear business-calculation-model.