System and method for multi-dimensional average-weighted banding status and scoring

• US 20060161471A1
• Filed: 01/19/2005
• Published: 07/20/2006
• Est. Priority Date: 01/19/2005
• Status: Abandoned Application

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Yang, Vincent Feng, Hulen, Corey James, Chau, Carolyn K.
• Application Number:
  US11/039,714
• Publication Number:
  US 20060161471A1
• Time in Patent Office: Days
• US Class Current: 705/10
• CPC Class Codes: G06F 16/283 Multi-dimensional databases or data warehouses, e.g. MOLAP or ROLAP
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: G06Q 10/06393 Score-carding, benchmarking or key performance indicator [KPI] analysis

Abstract

Method and system for generating summary scores from heterogeneous measures retrieved from multi-dimensional data structures for monitoring organizational performance. Scorecards are created for each group of tree-structured measures branching from Parent nodes to child nodes based on Key Performance Indicators (KPI). Scores for each parent node may be obtained by rolling up scores for child nodes reporting to the parent node. KPI'"'"'s at the lowest level are mapped on first scale, then mapped to a normalized scale, and score values determined. KPI scores are weight-averaged for roll-up to a parent node determining the score for that node. Multiple parent nodes may be rolled-up to a higher level node in a similar way. Multiple dimensions of the measure such as geographic and temporal may be scored simultaneously.

Claims

1. A computer-implemented method for generating summary scores from heterogeneous measures, the method comprising:

determining a first position of a first value within a first scale, wherein the scale is banded by a lower bound value and an upper bound value and the first value corresponds to a first measure of the heterogeneous measures;

translating the first value to a second normalized value, wherein the second normalized value corresponds to a second position within a second scale such that the second normalized value corresponds to a score for the first value; and

translating the second normalized value to a third weighted value, wherein the third weighted value takes into consideration an assigned weight relative to other measures of the same parent node;

rolling up the third weighted value with additional weighted values corresponding to additional measures of the heterogeneous measures such that the summary score is generated.

2. The computer-implemented method of claim 1, wherein rolling up the third weighted value with additional weighted values further comprises translating the third weighted value to another weighted value, wherein the other weighted value takes into consideration an assigned relative weight of the other parent nodes.

3. The computer-implemented method of claim 1, wherein the first value is substantially equal to the normalized second value.

4. The computer-implemented method of claim 3, wherein the second normalized value is a Key Performance Indicator (KPI) score and the summary score is an Objective.

5. The computer-implemented method of claim 4, wherein the KPI score is associated with a trend type, and wherein the trend type includes at least one of an “

increase is better”

, a “

decrease is better”

, and an “

on-target is better”

.

6. The computer-implemented method of claim 4, further comprising:

determining another summary score based on weighted averaging of at least two summary scores in a substantially similar way as determining the summary score, wherein the second summary score is associated with a parent node of the evaluated KPI.

7. The computer-implemented method of claim 6, further comprising:

presenting the KPI score, the Objective, and the Perspective to a user.

8. The computer-implemented method of claim 6, further comprising:

presenting a plurality of KPI scores, Objectives, and Perspectives to a user, wherein a subset of KPI scores are grouped in a Theme and another subset of KPI scores are grouped in an Initiative.

9. The computer-implemented method of claim 1, wherein each band within the first scale and each band within the second scale is assigned an indicator.

10. The computer-implemented method of claim 9, wherein the indicators include one of a set of predetermined default symbols and a color-coded scale.

11. The computer-implemented method of claim 1, wherein the number of bands within the first scale, the number of bands within the second scale, the indicators, and the boundaries of the bands are determined by one of a set of default parameters and a set of user-defined parameters.

12. The computer-implemented method of claim 1, wherein the first scale and the second scale are determined based on one of the lower bound value and the upper bound value of the measure, normalized lower bound and upper bound values of the measure, Multi-Dimensional eXpression (MDX) determined lower bound and upper bound values of the measure, and user-defined lower bound and upper bound values for the measure.

13. The computer-implemented method of claim 1, wherein the data associated with the heterogeneous measures is received from at least one of a multi-dimensional database, a regular database, and user input.

14. A computer-readable medium that includes computer-executable instructions for generating summary scores from heterogeneous measures stored in a multi-dimensional hierarchy structure, the instructions comprising:

retrieving data associated with at least one measure from a multi-dimensional database;

determining an actual scale between a lower bound value and an upper bound value for the measure that includes a predetermined number of actual bands;

assigning a value within one of the actual bands to the retrieved data based on a comparison of the data with the actual scale;

determining a band percentage value based on dividing a distance between a lower boundary of the actual band, in which the value is assigned and the value by a length of the actual band;

establishing an evenly distributed scale comprising a number of evenly distributed bands, wherein a number of the evenly distributed bands is the same as the number of actual bands, and wherein boundaries of the evenly distributed bands are equidistant;

mapping a new value on the evenly distributed scale to the value on the actual scale;

determining a total band distance by subtracting a lower boundary value of an evenly distributed band, to which the new value is assigned, from an upper boundary of the same band;

determining an in-band distance by multiplying the total band distance with the band percentage value; and

determining a KPI score based on adding the lower boundary value of the evenly distributed band to the in-band distance.

15. The computer-readable medium of claim 14, the instructions further comprising:

determining a parent node score by multiplying each of at least two KPI scores with a weighting factor that is assigned to each KPI score, wherein each KPI score is associated with a different measure, and wherein the parent node is one of an Objective and a KPI Group;

adding the at least two KPI scores multiplied with the weighting factors; and

dividing the sum of weighted KPI scores by a sum of all weighting factors.

16. The computer-readable medium of claim 15, the instructions further comprising:

determining another parent node score based on one of at least two parent node scores in a substantially similar way as determining the parent node score, wherein the other parent node is one of a Perspective and a parent KPI Group;

presenting the KPI score, the parent node score, and the other parent node score to the user.

17. The computer-readable medium of claim 16, wherein a subset of the KPI scores are grouped in a Theme and another subset of the KPI scores are grouped in an Initiative.

18. The computer-readable medium of claim 14, wherein the actual scale and the evenly distributed scale are determined based on one of actual lower bound and upper bound values of the measure, normalized lower bound and upper bound of the measure, Multi-Dimensional eXpression (MDX) determined lower bound and upper bound values of the measure, and user-defined lower bound and upper bound values for the measure.

19. A system for generating summary scores from heterogeneous measures stored in a multi-dimensional hierarchy structure, the system comprising:

a first computing device configured to store a multi-dimensional database that includes data associated with the heterogeneous measures;

a second computing device in connection with the first computing device configured to receive user input associated with processing the data associated with the heterogeneous measures;

a third computing device that is configured to execute computer-executable instructions associated with processing the heterogeneous measures, the computer-executable instructions comprising;

retrieving data associated with at least one measure from a multi-dimensional database;

determining an actual scale between a worst case value and a best case value for the measure that includes a predetermined number of actual bands;

assigning a value within one of the actual bands to the retrieved data based on a comparison of the data with the actual scale;

determining a band percentage value based on dividing a distance between a lower boundary of the actual band, in which the value is assigned and the value by a length of the actual band;

establishing an evenly distributed scale comprising a number of evenly distributed bands, wherein a number of the evenly distributed bands is the same as the number of actual bands, and wherein boundaries of the evenly distributed bands are equidistant;

mapping a new value on the evenly distributed scale to the value on the actual scale;

determining a total band distance by subtracting a lower boundary value of an evenly distributed band, to which the new value is assigned, from an upper boundary of the same band;

determining an in-band distance by multiplying the total band distance with the band percentage value; and

determining a KPI score based on adding the lower boundary value of the evenly distributed band to the in-band distance; and

a fourth computing device that is configured to present the summary scores generated by the third computing device to at least one of a user and a network.

20. The system of claim 19, wherein the first, the second, the third, and the fourth computing devices are integrated into one device.