FORM 2
THE PATENTS ACT, 1970
(39 of 1970)
&
THE PATENT RULES, 2003
COMPLETE SPECIFICATION
(See Section 10 and Rule 13)
Title of invention:
A SYSTEM AND METHOD FOR PERFORMANCE ANALYSIS OF A BUSINESS
FUNCTION IN AN ENTERPRISE
Applicant
TATA Consultancy Services Limited A Company Incorporated in India under The Companies Act, 1956
Having address:
Nirmal Building, 9th Floor,
Nariman Point, Mumbai 400021,
Maharashtra, India
The following specification particularly describes the invention and the manner in which it is to be performed.

FIELD OF THE INVENTION
The present invention relates generally to a method and system for deriving and measuring user modifiable key performance indicators and more particularly relates to methods and systems for facilitating business intelligence analytics based on a set of pre-defined key performance indicators derived from disparate heterogeneous data sources.
BACKGROUND OF THE INVENTION
Business Analytics tools play an important role in analyzing the performance of business functions in various units of an enterprise. Business functions can be analyzed by defining key performance indicators for a specific business function. Key Performance Indicators (KPI's) are typically pre-defined for business functions and are then used to track performance of business functions. At present, there are a plethora of Enterprise products which use key performance indicators for analysis of the performance of business function. However; these KPI's are not standard across an industrial domain and there is no unified way of measuring and acting upon them. KPI's represent a holistic view of performance of a business function - however, KPIs should be available in a format where they can be in a readily used or customized for analysis.
Each business domain has its inherent complexities. Often, enterprises do not get the requisite insight, as KPIs are not presented into a format where they can be readily used for analysis. A consolidated set of KPIs that cut across industry domains or business functions supported with a data model comprising granular level of data will help provide real-time business insights. Analyzing any business function based on a real-time view of a set of influencing KPIs helps uncover performance of sub-processes that lead to a specific KPI being analyzed. An arrangement of KPIs also indicate cross relation of KPIs between business functions and the direction of measure (positive or negative) that govern the KPI. Thus a KPI arrangement coupled with a data model comprising data that is consolidated from various sources and harmonized to a required granular level provides a customized real-time logical presentation of performance of a business function to gain valuable business insights.
SUMMARY OF THE INVENTION

The present invention provides a system for analyzing performance of one or more business functions from a predetermined set of key performance indicators for a plurality of business functions of an enterprise. The system comprises of a development module configured to develop a customizable illustrative structure in real time for a business function such that the illustrative structure is developed in a hierarchical manner to indicate a relation between various key performance indicators. The system further comprises of a construction module configured to create a plurality of data models corresponding to each key performance indicator and an integration module configured to integrate the data models with said illustrative structure for each key performance indicator, such that for each key performance indicator or combination thereof at least one externalized data structure of the corresponding data model is dynamically mapped and invoked. Moreover, the system comprises of a comparison module configured to perform a comparative analysis for each key performance indicator towards a specific business function of said enterprise by using a user defined corresponding threshold value and an output generation module configured to rendera visual display towards said comparative analysis for each key performance indicator, such that the visual display indicates a relation among the business functions.
The present invention also provides a computer implemented method for analyzing performance of one or more business functions from a predetermined set of key performance indicators for a plurality of business functions of an enterprise. The method comprises steps of developing a customizable illustrative structure in real time for a business function such that the illustrative structure is developed in a hierarchical manner to indicate a relation between various key performance indicators. The method further comprises steps of creating a plurality of data models corresponding to each key performance indicator and integrating the data models with said illustrative structure for each key performance indicator, such that for each key performance indicator or combination thereof at least one externalized data structure of the corresponding data model is dynamically mapped and invoked. Also, the method comprises steps of generating a comparative analysis for each key performance indicator towards a specific business function of said enterprise by using a user defined corresponding threshold value and rendering a visual display towards said comparative

analysis for each key performance indicator, such that the visual display indicates a relation among the business functions.
OBJECTS OF THE INVENTION
The principle object of the present invention is to provide a consolidated set of key performance indicators across plurality of business functions arranged in a logical manner to enable users to traverse through KPIs for assessing a business function at any given point in time.
Another object of the present invention is to develop a customizable illustrative structure in real time for a business function such that the illustrative structure is developed in a hierarchical manner to indicate relation between various key performance indicators.
Yet another object of the present invention is to provide key performance indicators in a readily usable format for the end users to draw meaningful business insights.
Another significant object of the invention is to generate an enterprise specific data model to derive Key Performance Indicators across the entire functioning of this industry.
Yet another object of the present invention to provide a system that harmonizes data of varying granularity across an enterprise to arrive at an illustrative structure that offers deeper insights into performance of critical business functions.

BRIEF DESCRIPTION OF DRAWINGS
Figure 1 illustrates the architecture of system for analyzing performance of one or more business functions in accordance with an embodiment of the invention.
Figure 2 illustrates the flowchart for the method for analyzing performance of one or more business functions from a predetermined set of key performance indicators in accordance with an alternate embodiment of the invention.
Figure 3 represents an illustrative structure developed in a hierarchical manner to indicate a relation between various key performance indicators in an exemplary embodiment of the invention.
DETAILED DESCRIPTION
Some embodiments of this invention, illustrating its features, will now be discussed:
The words "comprising", "having", "containing", and "including", and other forms thereof, are intended to be equivalent in meaning and be open ended in that an item or items following any one of these words is not meant to be an exhaustive listing of such item or items, or meant to be limited to only the listed item or items.
It must also be noted that as used herein and in the appended claims, the singular forms "a", "an", and "the" include plural references unless the context clearly dictates otherwise. Although any systems, methods, apparatuses, and devices similar or equivalent to those described herein can be used in the practice or testing of embodiments of the present invention, the preferred, systems and parts are now described. In the following description for the purpose of explanation and understanding reference has been made to numerous embodiments for which the intent is not to limit the scope of the invention.
One or more components of the invention are described as module for the understanding of the specification. For example, a module may include self-contained component in a hardware circuit comprising of logical gate, semiconductor device, integrated circuits or any other discrete component. The module may also be a part of any software programme executed by any hardware entity for example processor. The implementation of module as a

software programme may include a set of logical instructions to be executed by the processor or any other hardware entity. Further a module may be incorporated with the set of instructions or a programme by means of an interface.
The disclosed embodiments are merely exemplary of the invention, which may be embodied in various forms.
The present invention relates to a system and method for analyzing performance of one or more business functions from a predetermined set of key performance indicators for a plurality of business functions of an enterprise. A customizable illustrative structure is developed in real time for a business function in a hierarchical manner to indicate a relation between various key performance indicators. A plurality of data models are integrated with the illustrative structure so that for each key performance indicator the corresponding data model is dynamically mapped and invoked. A comparative analysis for each key performance indicator is generated with respect to the user-defined threshold values and the results thereof are displayed which further reflects the growth of the enterprise with respect to a particular Key performance indicator.
In this description, an embodiment of the current invention is described with reference to the consumer product group (CPG) industry, whose operations are spread across the globe and is complex in nature. However, those skilled in the art will acknowledge that the invention is not limited to one industry domain alone.
In accordance with an embodiment, referring to figure 1, the system (100) comprises of a development module (102) configured to develop a customizable illustrative structure in real time for a business function. The system (100) further comprises of a construction module (104) which creates a plurality of data models and an integration module (106) which is configured to integrate the data models with the illustrative structure for a business function. The system (100) further comprises of a comparison module (108) and an output generation module (110).
In accordance with an embodiment, still referring to figure 1 and figure 2, the development module (102) is configured to develop a customizable illustrative structure in real time for a

business function such that the illustrative structure indicates relation between various key performance indicators (as shown in step 201) that influence and later map to the business function. The illustrative structure is customizable, such that a user can define weight ages and a threshold value for a set of KPIs that govern a business function. User also has the flexibility to select a set of KPIs for a business function, and these KPIs in turn can be defined to comprise lower level KPIs, thus establishing drill-down logic for a business function. Such a hierarchical arrangement addresses provides a view of what is happening in a business function and at the same time also helps to identify the root cause, that is, 'why part' or reason for a particular level of performance. This KPI arrangement makes the performance of a business function available in a ready to read format and also helps drill down deeper and analyze reason for performance.
Business functions may typically be from a key group of functions such as sales, marketing, retail execution, manufacturing, procurement, logistics and distribution, customer service, finance or a combination thereof and the like. This is to be further understood that these are mere examples of business functions and the intent is not to limit the scope of the invention.
Referring to figure 1 and figure 2, the system (100) also includes the creation of a weightage table that associates a user defined weightage to key performance indicators to indicate strength of key performance indicator that influences a business function (as shown in step 202). Weights for all the focus areas and the sub-process level KPIs are obtained from the weightage table. In order to generate illustrative structure of KPI's, depending on the weights for the respective KPIs, the development module (102) dynamically builds the illustrative structure of KPI's in real time.
The system (100) further comprises a construction module (104) which is configured to create a plurality of data models corresponding to each key performance indicator (as shown in step 203).
The system (100) further comprises of an integration module (106) which is configured to integrate the data models on user login with said illustrative structure for each key performance indicator, such that for each key performance indicator or combination thereof

at least one externalized data structure of the corresponding data model is dynamically mapped and invoked (as shown in step 204).
In a preferred embodiment, the unified central data model created by the construction module (104) and the integration module (106) of the present invention is employed to arrive at a consolidated set of KPl's (Key Performance Indicators) across the various Key functions of an enterprise. KPI's along with their complete definitions, calculations and the logic based on which these have been derived from data sources that have been extracted from the data model drive business insight. KPI is a unit of measure that determines health of a business function. Various data attributes are used to derive a KPI.
By way of a specific example, a data model can be consolidated from disparate data sources and further harmonized to create granular levels of data that can then be integrated to key function areas of a business and to their corresponding associated KPIs in order to form an illustrative structure of KPI's to derive key business insights.
The central data model created by the construction module (104) addresses the challenge of varying granularity of data by calculating the KPI value at the atomic level. The lowest possible level of granularity is determined by the individual elements involved. Among the individual elements involved, the one which is available at the highest hierarchy level determines the granularity of the KPI. For example - if a KPI has elements available at Brand level and SKU level then, the KPI is calculated at a Brand level. Similar logic is followed for other dimensions. All the elements which are required for calculation are then picked up from the data model. The calculation is then made and rendered through various rendering mechanisms.
From the illustrative structure of KPI's, factors that influence a business function in an enterprise can be traversed in a logical manner and drilled down into. Starting from the primary level, sub-level KPIs can be delved into. This navigation and drill down logic is an integral part of the present invention which is enabled within the Application / Business Logic. This logic is controlled through various role based security layers that are applicable in an enterprise. For example, the VP Sales or Country Head of Sales will have the utmost authority to navigate and drill down from the Primary KPIs such as Value Growth to the

Secondary KPIs such as Gross Sales of Current Year versus Gross Sales of Last Year and Planned Gross Sales versus Actual Gross Sales.
In an exemplary embodiment, the KPIs that are measured are maintained at different levels. For example, the KPI "Shrink Rate" that can be defined as the rate at which CPG Products are damaged and diminished at the Retailer Store level. But this Shrink Rate as a Secondary KPI will contribute to the Primary KPI of Retail Execution. Thus while navigating through the illustrative structure of KPFs from Primary KPIs to the Secondary KPIs, the level of measurement will vary across, which is driven as an integral part of the underlying data model.
Illustrative structure of KPIs is constructed through a Weighted Average means of calculating the KPIs at all levels of the illustrative structure through tables which are part of an enterprise specific data model. In the above example, the Weightage for Shrink Rate KPI
which contributes to the overall Retail Execution function is driven by the rules that can be
set up in the data model.
In an exemplary embodiment of the invention, the illustrative structure of the KPIs built from the underlying data model performs analysis in three basic steps:
• WHAT is happening
• WHY is it happening
• HOW can it be corrected
At the WHAT stage and the WHY stage, visual indicators in the illustrative structure of KPIs which supplement the KPIs play a role. Visual indicators corresponding to KPIs in the illustrative structure of KPI's indicate the health of KPI and are configured based on required threshold levels corresponding to KPI.
For example, if a primary KPI's indicator is "red" in color, it represents the fall in the KPI level below a threshold value. Upon examining the KPI, the trend would become clear. Comparing the trend with the trend in previous year also gives an idea of seasonality. Taking

these three inputs into consideration, a scenario gets established for which reasons are later found and correction(s) made. This completes the "WHAT is happening" stage.
"WHY is it happening" stage is essentially finds out the reasons for the established scenario. This stage is also assisted by indicators. The color of indicators on other KPIs indicates the possible reasons for the "WHAT" stage. The colors along with inputs such as seasonality, direct or indirect dependence help establish the reasons for the scenario.
Now that scenario has been established and the reasons have been found, the "HOW should it be corrected" phase begins. This requires comprehensive insights which can help in guided decision making. The central data model has role based dashboards and reports which give specific insights about the various KPIs in a way that helps decide the course of correction. Specific insights are provided at all the possible granular levels of Dimensions across KPIs.
Still referring to figure 1, the construction module (104) employs a star Schema for developing a central data model. Star Schema requires presencm of Tact and dimension tables. By way of a specific example the "Facts" can be Sales, Inventory Levels, Shipments, etc. which is represented across "Dimensions" that for example comprises Product, Geography and Time. For example - Product hierarchy may comprise Category, Sub-Category, Brand, Product or SKU. Data model is developed in such a way that it is possible to accommodate each of the data elements at the lowest hierarchical level of that particular element.
The construction module (104) initially, analyzes data formats, including column values and table structures for source data and categorizes data. By way of a specific example, categories may include Syndicated Data Providers, Direct Data feeds from Retailers and other such. Further, the data staging module 104(a) stages data associated with sources to be integrated into one solution and edited for conformance to quality. Most sophisticated data profiling and auditing activity of data is conducted at the staging area. Data is prepared, standardized, matched or otherwise manipulated for data quality checks. Once the module harmonizes various levels of data available from disparate data sources the data gets further stored in data warehouse module 104(b).

The transformation logic is applied to analyze the characteristics; structure and format of data in the destination tables and is thereon mapped between staged source data and the destination table. The data gets mapped, translated and transformed from the source data fields into data with the same characteristics and format of that in the externalized target data structure for developing there from a unified data model specific to an industry domain. The central data model so developed signifies relationship and association between multiple entities. These entities are identified and defined specific to the relevant industry domain. By way of a specific example, some entities specific to the CPG industry could be : brand, category, country, time, region, manufacturer, product, market share, customer profitability, store, out of stock, sell through, share of shelf, shipment volume, shrink rate, SKU share, display share, user role, retail execution weightage, KPI (key performance indicator) list and KPI threshold.
The integration module (106) thus, enables data transformation and quality management for harmonization of structured and unstructured data at the object level. The module processes the data to create a multi-dimensional data model that includes dependency information within and among various attributes of entities across different functional sectors of an enterprise. The multi-dimensional data model addresses functional towers of an enterprise comprising facts and dimension tables that reference to structured and unstructured datasets within and outside of the enterprise; tables covering functional areas like, and not limited to sales, retail execution, marketing, manufacturing, procurement, logistics and distribution, customer service, finance, cost efficiency and new product development. This data model is then utilized to predict the business performance in said functional areas by utilizing key performance indicators.
The integration module (106) of the present invention harmonizes and enables analysis of data received at various granularity of time, region, organization and multiple hierarchies of time, region, and product through highly normalized dimensional data structures uniquely identified through a surrogate key. Based on the understanding of a specific domain and data usage pattern, data structure is designed to support multiple hierarchies. Each of the fact metrics are linked to these dimension structures through appropriately linked surrogate keys.

This design enables seamless aggregation and apportionment of values depending on the level at which the analysis is done.
In an exemplary embodiment of the invention, consider CPG industry as an enterprise. The system establishes a unique cross reference between multiple functional sectors of a CPG industry by way of mapping across their Product Category, Sub Categories, Brand or SKUs. For example, the product Shampoo may be considered as Personal Care category in the first enterprise, whereas the same product Shampoo can be considered under Hair Care in the second enterprise. By establishing the cross reference between Products and Categories, the system helps the CPG companies to seamlessly generate Business Insights across functions. CPG internal data structure is designed to be stored at the Universal Product Code (UPC) and weekly granularity by Retail store. Syndicated data is received at various time intervals across regional hierarchies defined. This data structure is designed to be stored in weekly or half-yearly time hierarchy along with the regional hierarchy of country granularity while the Social media data is received and stored at Brand level in the data base.
CPG companies cater to consumers across the globe although most of the products have been adapted to suit region-specific needs of consumers. The database design of the present invention supports a global deployment, and in addition accommodates region specific customization through configuration tables comprising attributes such as KPI Thresholds, access control, language, currency, conversion and other such metadata. Appropriate level of aggregate tables combined with role based security is enabled to provide data security and access restrictions for various roles for internal as well as external users. Out-of-the-box data extractors are designed to process data from the standard ERP providers and Syndicated data providers. There is no Industry standard Point of Sale (POS) data exchange data structure available for data exchange between CPG & Retailers. POS Data extractors are designed as canonical data structure based on heuristic analysis and the solution can be extended and customized for a specific retailer format.
The system (100) further comprises of a comparison module (108) which is configured to perform a comparative analysis for each key performance indicator towards a specific business function of said enterprise by using a user defined corresponding threshold value (as

shown in step 205). All the tables of the data model are referenced while deriving the key performance indicators.
The system (100) further comprises of an output generation module (110) which is configured to render a visual display towards said comparative analysis for each key performance indicator, such that the visual display indicates a relation among the business functions (as shown in step 206).
The visual display depicts the relation among the business functions by way of differentiating indicators and also by way of reports, charts that facilitate in trend analysis and dashboard creation. At the time of dynamically creating an illustrative structure for a business function, data from fact and dimension tables for a corresponding set of KPIs or a metric are also represented in the form of user defined charts, graphs and dashboards, so as to enable decision makers and stakeholders easily view comparisons, trends for various dimensional
attributes such as time, geography etc for a KPI. Granular data model coupled with a
hierarchical illustrative structure facilitates in performing a real time analytics which otherwise would be time consuming and cumbersome given the volume of data present in an enterprise.
In an exemplary embodiment of the invention, the threshold values are preconfigured in KPI threshold table of the model. All the KPFs are pre-built and maintained at the output generation module (110). The value of the KPI is then compared to the threshold value that is stored and is then assigned a visual indicator that represents the health of the KPI. Thus, health of the KPIs that can be explored in a drill down structure enables a business to track important KPIs along with the KPIs of the sub processes to identify root cause of prevailing issues for their remediation.
In order to gain insights about a KPI, the user has to perform analysis on that KPI. Each KPI has a metadata definition. Depending on the definition and the nature of KPI, the value is calculated during run-time by the development module (102), at the output generation module (110). Nature of KPI can be either composite or non-composite - which means a business function is monitored by means of a function of KPIs.

Composite KPIs are defined as any KPIs that are derived out of either one or more Base Metrics and one or more Derived Metrics through calculations and aggregations. Example is Market Share = XI * (Volume Share) + X2 * (Value Share), where XI & X2 are Weightages.
Non Composite KPIs are defined as any base facts and plan values available at granular levels, or a simple aggregation from metrics, or any KPIs derived out of Base Metrics by simple aggregations. Examples are Value Share and Volume Share.
Each KPI is tagged using a unique ID that is obtained from a KPI list defined in a KPILIST table. In case of a composite KPI, for each KPI (KPIID), weights are defined (if a KPI is composite) from the corresponding KPI Weight table. The composite KPI value is then calculated based on the weights. In case of non-composite KPI, the KPI is directly calculated by the output generation module (110). By way of a specific example, the KPI is calculated over a certain pre-determined time period (daily, weekly, monthly or yearly). The values calculated during each period are represented graphically, which displays the trend. Each KPI is also associated with a threshold level. On data consolidation and integration during run time, the value thus aggregated is compared to the threshold defined and a visual indicator is displayed adjacent to the KPI in the illustrative structure.
By way of a specific example, threshold values are derived from the KPI_THRESHOLD table, and visual indicators may be defined in three colors: Red, Amber and Green. Each color has a From and To value corresponding to each KPI. The From and To values are configurable for each KPI which gives a flexibility to change thresholds depending on the business requirements.
The KPI along with their thresholds are then logically represented via the illustrative structure of KPI's in a manner so that the Business Insights can be gleaned by a business user by traversing through the structure in a hierarchical fashion.
BEST MODE/EXAMPLE FOR WORKING OF THE INVENTION

The invention is described in the example given below which is provided only to illustrate the invention and therefore should not be construed to limit the scope of the invention.
The example here is referring to retail execution sector of the CPG industry. A CPG enterprise major sales channel is through Retailers and it is one of the primary medium of connects with end consumers, thus Retail execution excellence is imperative to a CPG enterprise. Some of the fundamental challenges however, which effect CPG enterprise performance with regards to Retail Execution are Inventory shortage, product availability issues and compliance violations thus effecting market consumption which in turn translates to dip in share, overall performance at the Retailers and end consumer sentiment, a critical factor that affects market image.
Efficient access to multiple data sources like enterprise data, syndicated data, demand data and social media data, an integrated view of the these various forms of data through windows of KPIs and Illustrative structure of KPIs can help CPG enterprises in tracking the performance, consumption trends and consumer sentiment for decision making.
The various modules of the present invention aids in dashboard and score carding, scenario and root cause analysis thus enabling Retail execution excellence. Below is one of typical business scenario addressed by the present invention.
Considering a scenario wherein the market share (value share) of the enterprise is dipping. The present invention, here, will facilitate scenario analysis through its constituting modules. The first step is to assess "what" is happening in the business", which for the present scenario is dip in market share of a category during one of the weeks. The next step is to identify the possible root causes "why it is happening" the reasons (like) include dip in Share of SKU, Lower Sell Through at Retailer, Competitors running Price Promotions, and Low Shipments to Retailer resulting in Out of Stocks. The final step is to find ways to correct "How can we correct it". Correction steps include (like) Improving Value Share and Improving Supply Chain execution parameters with help of analytical models which helps in establishing causal relationship between step 1 and 2.

For logging in to the application, once the user enters user id and password, depending on the user role dimensional attributes such as REGION, COUNTRY, CATEGORY, ZONE, RETAILER, a the user can view KPIs for the categories and zones.
Now, for example, a Category manager for a particular category (e.g.: Snack) logins to the system of the present invention using manger's specific login. Manager will have access to entitled selection related to various dimensions and underlying hierarchical levels for e.g.: Category Manager will have choice of selection of one or more categories, corresponding brands along product dimension and to various Retailers, stores etc under location dimension, Account manger will have privilege of selection among various categories, brands along product dimension but have selection to a specific Retailer, store etc under location region.
Configuration related to user login and entitled access based on user and role for various dimensions (product, location, time) is achieved through underlying tables provided by
central data model of the present invention. The data model facilitates access restrictions and
privileges mentioned above, the table under discussion being USERROLE where user details (such as user id, role, category, country, Retailer for which user would have the access are available). Data attributes from a data model will be visible to a user and the illustrative structure will be constructed dynamically at run time, based on user role.
For e.g.: A Category manger will have user specific login credentials and access to various (one or few) specific categories, brands, SKUs which are under his purview. Category manager in the above scenario after logging in with user credentials selects "Snack" as the category. The Manager's subsequent selection of various aspects such as brands, SKUs, Retailers, store etc is based on configured details.
Referring to Figure 3, Manager then reaches Illustrative structure of KPFs window where he can view a KPI represented along with an indicator to instantly gauge the health of a function in the KPI Tree. For example, a red mark indicates levels that are below the defined threshold as shown in Figure 3.
Visual indicators are configured after a comparison with defined thresholds in the KPITHRESHOLD table of the data model where various KPIs, corresponding ranges and

associated visual indicators are stored (data can be modified/included as per business requirements). Also underlying tables of data model facilitates configuration of KPI definition (combination of other KPIs).
For eg: Market share is a composite KPI formed as a weighted product of volume share and values share KPIs, weights corresponding to Volume share and Value share for calculation of Market share KPI and corresponding thresholds can be configured in relevant tables as required. In the above scenario Market share KPI has turned "red" indicating it has fallen below certain threshold thus seeking immediate corrective actions. The KPIs forming Market share KPI are fetched from KPILIST table and corresponding weights for calculation of Market share are fetched from MARKETSHAREWEIGHTAGE table.
The Category manager upon receiving the red signal traverses to Market share KPI details dashboard from the illustrative structure of KPI's to observe the Market share of the category (volume and value share) and dip in share of display. Category manager then returns to the illustrative structure of KPI's to identify the possible causes for dip in market share. Category manager observes a dip in Share of SKU KPI during the same period and details from the dashboard, for example shows increase in the number of SKUs by competitors. The other KPI's (say for example) showing similar trend correlated to dip in market share are dip in Sell through KPI at certain Retailers that can be correlated to increase in average price at the Retailers inferred from pricing corridor KPI dashboard.
Suppose, Shipments KPI dashboard shows lower shipments resulting out of stocks (increase in Out of stock KPI, and also a negative sentiment is observed in social media related to product availability during same period. All the discussed factors help the manager identify the reasons that contribute to dip in market share and display share thus enabling him to take the corrective action.
Now, the volume share and value share KPIs are calculated for respective dimensions (Geography, Product and Time) selected. The data elements corresponding to calculation of Volume share and Value share KPI are fetched from Syndicated data into the data model.

Volume Share is defined as "For each Category or Brand, the number of units of enterprise's sales in the Category or Brand divided by the total sales of that Category or Brand": formulae being [Category or Brand sales in Units (A) / total sales in Units (B)] .The elements sales of Category or Brand (A & B) are fetched from syndicated data sources.
Value Share is defined as "For each Category or Brand, the Currency (Dollar) value of the enterprise's sales in the Category or Brand divided by the total sales of that Category or Brand": formulae [Category or Brand sales in dollar value (A) / total sales in dollar value (B)] .The elements sales of Category or Brand (A & B) are fetched from syndicated data sources. The formulae of various KPI are pre-configured.
Granularity of data elements corresponding to Volume share and Value share across various dimensions form the base for data modeling and reporting. For example, for Volume share and Value share KPIs the time dimensions can be configured as yearly, half yearly, Quarterly, Monthly, Weekly, and Daily; product dimensions as category, manufacturer, brand, products while the geography dimensions can be region, country, retailer, zone, state, store etc.
Volume share is summation of Quantity Sold, and Value share is summation of the Sales Amount for CPG enterprise under a given category for selected time period. Quantity Sold and Sales Amount values are fetched from a corresponding fact table for Market Share (available at brand level) and then aggregated. Similar data is obtained from dimension tables and aggregated.
The volume share and value share can be aggregated or rolled up to Category along the product dimension. For e.g.: Lowest granularity at which data is available is at brand level and Sales of snacks category used for Volume share and Value share and thus Market share KPI calculation is a summation of sales of various brands under the category corresponding to manufacturer. Similar procedure is adapted for other KPIs as well.
Likewise, share of display KPI is calculated for respective dimensions (Geography, Product and Time) selected. The data elements corresponding to calculation of Share of Display KPI are fetched from Syndicated data into the data model. Share of Display is defined as "For

each Category or Brand, the Currency (Dollar) value of the enterprise's sales in the Category or Brand divided by the total sales of that Category or Brand": The KPI is a static value sourced from syndicated data sources.
The data elements granularity available corresponding to Share of Display along various dimensions is fetched which form the base for data modeling and reporting. Share of Display is a value given by the Syndicated data sources. Share of Display values are fetched from FB_Display_Share table which (available at category level) is displayed in the Micro Strategy reports; for which the data gets fetched from the underlying tables in the Oracle layer. The other tables which facilitate the above information fetch are DIMWEEK, D1MCOUNTRY, DIMCATEGORY and DIMMANUFACTURER.
Similarly, the share of shelf is calculated for respective dimensions (Geography, Product and
Time) selected. The data elements corresponding to calculation of Share of Display KPI are
fetched from Internal Audit data into the data model.
Share of Shelf is defined as "Ratio of product facings to the overall facings with respect to Display": formula being [(Shelf Space available for products of a Consumer Good Company at Category level at a retail store) / (Total Shelf Space available for the category across Consumer Goods Companies at a retail store)]. The elements are sourced from Internal Audit data sources.
The data elements granularity available corresponding to Share of Shelf along various dimensions is fetched to form the base for data modeling and reporting. Share of Shelf is the ratio of the Consumer Goods Shelf Space to the Total Shelf Space available at the Retailer.
CPG Shelf Space and Total Shelf Space values are fetched from F_Share_ofjShelf table which (available at category level) is displayed in the Micro Strategy reports for which the data gets fetched from underlying tables in the Oracle layer. The other tables which facilitate the above information fetch are DIMWEEK, DIMSTORE, DIMSTATE, DIM_ZONE, DIM_RETAILER, DIM_COUNTRY and DIMCATEGORY.
Next, the Share of SKU KPI is calculated for respective dimensions (Geography, Product and Time) selected. The data elements corresponding to calculation of Share of Display KPI are

fetched from Syndicated data into the data model. Share of Shelf is defined as "Ratio of number of SKUs from the enterprise compared to the overall SKUs from all enterprises (including Private Labels)": formula being [(Sum total of number of SKUs from the CPG Company / Sum total of number of SKUs from all CPGs including Private Labels)* 100]. The elements are sourced from Syndicated data sources.
The data elements granularity available corresponding to Share of SKU along various dimensions is fetched to form the base for data modeling and reporting. Share of SKU is the ratio of number of Consumer Goods SKUs. Share of SKU values are fetched from FSKUShare table which (available at product level) are summed up to category in the Micro Strategy reports; sql queries which aggregate the data are generated in the Micro Strategy layer using which the required data gets fetched from the underlying tables present in the Oracle layer. The other tables which facilitate the above information fetch are DIMWEEK, DIMCOUNTRY, DIMCATEGORY, DIMMANUFACTURER, DIMBRAND and DIM_PR0DUCT,
On the same lines, sell through at a retailer is calculated for respective dimensions (Geography, Product and Time) selected. The data elements corresponding to calculation of Sell Through at Retailer KPI are fetched from Internal data and Retailer POS data into the data model.
Sell Through at Retailer is defined as "% of units shipped which are actually sold during a period either at a brand or category level": formula being [No. of units sold / (No. of Units sold + OH + No. of Units Shipped)]* 100. The elements - Number of Units Sold and On Hand Inventory are sourced from Retailer POS data sources and Number of Units Shipped is sourced from Internal data sources.
The data elements granularity available corresponding to Sell Through at Retailer along various dimensions is derived to form the base for data modeling and reporting. Sell Through at Retailer is the ratio of number of units sold by the Retailer to the number of units shipped by the CG Enterprise. Number of Units Sold and On Hand Inventory values are obtained from FSellThrough table; Number of Units Shipped values are obtained from FShipmentVolume table which (available at product level) are summed up to category in

the MICRO STRATEG reports; sql queries which aggregate the data are generated in the Micro Strategy layer using which the required data gets fetched from the underlying tables present in the Oracle layer. The other tables which facilitate the above information fetch are DIM_WEEK, DIM_DAY, DIMSTORE, DIM STATE, DIMZONE, DIMRETAILER, DIMCOUNTRY, DIM_CATEGORY, DIM_MANUFACTURER, DIM_BRAND and DIMPRODUCT.
Again, the Pricing Corridor KPI is calculated for respective dimensions (Geography, Product and Time) selected. The data elements corresponding to calculation of Pricing Corridor KPI are fetched from Retailer POS data into the data model.
Pricing Corridor is "identifying the range of price for a SKU at a Retail store over a week period": The element is a static value sourced from Retailer POS data sources. The data elements granularity available corresponding to Pricing Corridor along various dimensions is -derived to form the base for data modeling and reporting. Pricing Corridor is the represented by the minimum and maximum prices during a particular time period and the day level variations of the prices during the same time period. Shelf Price Values are obtained from FSellThrough table which (available at product level) are summed up to category in the Micro Strategy reports; sql queries which aggregate the data are generated in the Micro Strategy layer using which the required data gets fetched from the underlying tables present in the Oracle layer. The other tables which facilitate the above information fetch are DIMWEEK, DIMDAY, DIM STORE, DIMSTATE, DIMZONE, DIMRETAILER, DIMCOUNTRY, DIMCATEGORY, DIMJVIANUFACTURER, DIM_BRAND and DIMPRODUCT.
Also, the Shipments KPI is calculated for respective dimensions (Geography, Product and Time) selected. The data elements corresponding to calculation of Sell Through at Retailer KPI are fetched from Internal data and Syndicated data into the data model.
Shipment is defined as "Total number of units shipped during a period either at a brand or category level": The elements - Number of Units Shipped is sourced from internal data sources. The data elements granularity available corresponding to Shipments along various dimensions is fetched to form the base for data modeling and reporting. Shipment is the

number of units shipped by the CG Enterprise during a time period. Shipped quantity values are obtained from FShipmentVolume table which (available at product level) are summed up to category in the Micro Strategy reports; sql queries which aggregate the data are generated in the Micro Strategy layer using which the required data gets fetched from the underlying tables present in the Oracle layer. The other tables which facilitate the above information fetch are DIMWEEK, DIM DAY, DIMSTORE, DIMSTATE, DIMZONE, DIM_RETAILER, DIMCOUNTRY, DIM CATEGORY, D1M_MANUFACTURER, DIM_BRAND and DIMPRODUCT.
Finally, the Out of Stock KPI is calculated for respective dimensions (Geography, Product and Time) selected. The data elements corresponding to calculation of Out of Stock KPI are fetched from Syndicated data into the data model.
Out of Stock is defined as "% of tracked items Out of Stock (audit or customer-based data)": formula being [(Count of Products not present at the store (or retailer) level/Count of total audited products)]* 100. The elements are sourced from Syndicated data sources. The data elements granularity available corresponding to Out of Stock along various dimensions is derived to form the base for data modeling and reporting. Out of Stock is the number of items not present at the store out of the total audited times during a time period. Out of stock values are obtained from F_Out_of_Stock table which (available at product level) are summed up to category in the reports; sql queries which aggregate the data are generated in the using which the required data gets fetched from the underlying tables. The other tables which facilitate the above information fetch are DIM_WEEK, DIMSTORE, DIMSTATE, DIM ZONE, DIMRETAILER, DIMCOUNTRY, DIMCATEGORY, DIMMANUFACTURER, DIMBRAND and DIMPRODUCT.
The data structure of the present invention is designed to store data from various sources and of varying granularity as each of the entities have their own hierarchy specific to their businesses; to handle the SKU reuse; and to accommodate information fusion by bringing together structured and unstructured data. The system undertakes the challenge of unifying data present at various levels of hierarchy and granularity and integrating data from these entities into one common data model. Also, the system establishes cross reference between

the ways the products are categorized across the various entities. The system works in coordination with an operating database to provide a unified view of the data to the end user by logically organizing the information obtained from retrieved structured and unstructured data. The end user can then draw meaningful insight from the stored data through a user interface as the unified view indicates a significant relationship across different entities wherein these entities originates from different data hierarchies and/or data sources. These relationships can be displayed in a unified view and entities along with their associated relationships may be updated and stored in the operating database.
The foregoing description has been directed to one or more specific embodiments of this invention. It will be apparent; however, that other variations and modifications may be made to the described embodiments, with the attainment of some or all of their advantages. For instance, it is expressly contemplated that the teachings of this invention can be implemented as software, including a computer-readable medium having program instructions executing on a computer, hardware, firmware, or a combination thereof. In addition, it is understood that the data structures described herein can include additional information while remaining within the scope of the present invention. Accordingly this description is to be taken only by way of example and not to otherwise limit the scope of the invention. Therefore, it is the object of the appended claims to cover all such variations and modifications as come within the true spirit and scope of the invention.

WE CLAIM:
1. A computer implemented method for analyzing performance of one or more business functions from a consolidated predetermined set of key performance indicators for a plurality of business functions of an enterprise, the method comprising steps of:
developing a customizable illustrative structure in real time for a business function such that the illustrative structure is developed in a hierarchical manner to indicate a relation between various key performance indicators;
creating a plurality of data models corresponding to each key performance indicator;
integrating the data models with said illustrative structure for each key performance indicator, such that for each key performance indicator or combination thereof at least one externalized data structure of the corresponding data model is dynamically mapped and invoked;
generating a comparative analysis for each key performance indicator towards a specific business function of said enterprise by using a user defined corresponding threshold value; and
rendering a visual display towards said comparative analysis for each key performance indicator, such that the visual display indicates a relation between the business functions.
2. The method as claimed in claim 1, wherein the method also includes creating a weightage table that associates a user defined weightage to at least one key performance indicator to indicate strength of key performance indicator that influences a business function.
3. The method as claimed in claim 1, wherein the data model is defined by determining data drilled down to the atomic level which is further mapped to plurality of key performance indicators and base metrics that relate to a business function.

4. The method as claimed in claim 3, wherein said data model comprises an aggregation of data from a combination of fact tables and dimension tables, further which said data model is invoked dynamically to create an illustrative structure leading to said business function.
5. The method as claimed in claim 1 further comprises defining a threshold upper and lower limit for each key performance indicator, which is further compared to data integrated from said data model at run time and assigned visual indicators indicating health of a business function and related sub processes.
6. The method as claimed in claim 1, wherein the business functions are selected from at least one group of sales, marketing, retail execution, manufacturing, procurement, logistics and distribution, customer service, finance or a combination thereof and the like.
7,The method as claimed in claim 1, wherein each data model is configured and
designed at an atomic level by integrating and harmonizing data from a plurality of" heterogeneous data sources corresponding to a business function.
8. The method as claimed in claim 1, wherein the hierarchical level of the Key Performance Indicators comprises a granular level representation of Key Performance Indicators that influence a business function.
9. The method as claimed in claim 1, wherein the visual display further comprises generation of reports and dashboards in a user defined format based on dimension attributes of a plurality of key performance indicators and base metrics at each level of the illustrative structure related to a business function.
10. A system for analyzing performance of one or more business functions from a predetermined set of key performance indicators for a plurality of business functions of an enterprise, the system comprising:

a development module configured to develop a customizable illustrative structure in real time for a business function such that the illustrative structure is developed in a hierarchical manner to indicate a relation between various key performance indicators for a plurality of business functions;
a construction module configured to create a plurality of data models corresponding to each key performance indicator;
an integration module configured to integrate the data models with said illustrative structure for each key performance indicator, such that for each key performance indicator or combination thereof at least one externalized data structure of the corresponding data model is dynamically mapped and invoked; and
a comparison module configured to perform a comparative analysis for each key performance indicator towards a specific business function of said enterprise by using a user defined corresponding threshold value;
an output generation module configured to render a visual display towards said comparative analysis for each key performance ~ Indicator, such that the visual display indicates a relation among the business functions.
11. The system as claimed in claim 10, wherein the system also includes the creation of a weightage table that associates a user defined weightage to key performance indicator to indicate strength of key performance indicator that influences a business function.
12. The system as claimed in claim 10, wherein the data model comprises granular levels of data that are a combination of fact tables and dimension tables for a plurality of base metrics comprising a key performance indicator.
13. The system as claimed in claim 10, wherein the business functions are selected from a group of sales, marketing, retail execution, manufacturing, procurement, logistics and distribution, customer service, finance or a combination thereof and the like.

14. The system as claimed in claim 10, wherein each data model is configured by integrating data from a plurality of heterogeneous data sources of the functional sector.
15. The system as claimed in claim 10, wherein the hierarchical level of the Key Performance Indicators comprises a granular level representation of Key Performance Indicators that influence a business function.
16. The method as claimed in claim 10, wherein the visual display further comprises generation of reports and dashboards in a user defined format based on dimension attributes of a plurality of key performance indicators and base metrics at each level of the illustrative structure related to a business function.