# Patent

## Title
Method and system for providing business intelligence data

## Abstract

A machine implemented method for accelerating access to business intelligence data including providing a master database table on a computer readable medium and accessible by an analytics server, accessing rows in the master database table by the analytics server, with each row in the master database table containing at least a partial set of measures for a plurality of dimensions, identifying by the analytics server a set of one or more dimensions from the plurality of dimensions subject to a query by one or more computing devices in communication with the analytics server, extracting the set of one or more dimensions and associated measures for each of the one or more dimensions from the master database table, and forming a baby fact table such that each row in the baby fact table contains the set of one or more dimensions subject to the query and the associated measures derived from the extracting step.

## Background
FIELD OF THE INVENTION
The present invention relates generally to a method and system for providing business intelligence data, and in particular to increasing the efficiency of and resources using in obtaining business intelligence data by using data storage structures in the form of baby fact tables.
BACKGROUND OF THE INVENTION
Business intelligence is a set of methodologies, processes, architectures, and technologies that transform raw data into meaningful and useful information used to enable more effective strategic, tactical, and operational insights and decision-making for an organization. Better decision-making is the driver for business intelligence. Generally, the process of providing business intelligence data starts with the determination of what kinds of summaries and reports a user may be interested in. Key business users are queried to determine the types of reports and summaries that they may be interested in. Due to the amount of resources required to effect changes in the types of reports and summaries that are generated, significant care is taken in designing these reports and summaries.
Once the required reports and summaries have been identified, the data required to generate these summaries and reports is determined. The data is typically stored by one or more Enterprise Information Systems (“EISes”), such as an Enterprise Resource Planning (“ERP”) system. These EISes are referred to herein as “source systems”. The particular location of the data in the source systems is noted, and extraction functions are coded to extract the data from the specific locations. In general, the goal of the extraction phase is to convert the data into a single format that is appropriate for transformation processing. Thus, the extraction functions not only retrieve the data from the source systems, but they parse and align the data with other data from the same or other source systems. As extraction functions have to be manually coded and tested for the data from each specific location, this step can be lengthy.
Transformation functions are then designed to transform and structure the data extracted from the source system(s) to enable rapid generation of the desired summaries and reports. The transform stage applies a series of rules or functions to the data extracted from the source system(s) to derive the data for loading into the end target. Some data sources require very little or even no manipulation of data. In other cases, one or more transformations may be required to be applied to the extracted data to meet the business and technical needs of a target database that is used to generate reports and summaries. Depending on the amount of transforming, the design and testing of the transformation functions can be a lengthy procedure.
Transformed data is then loaded into an end target, typically a data warehouse, that can be queried by users via business intelligence clients. Depending on the requirements of the organization, this process varies widely. Some data warehouses may overwrite existing information with cumulative information, frequently updating extract data is done on daily, weekly or monthly basis. Other data warehouses (or even other parts of the same data warehouse) may add new data in a historicized form, for example, hourly.
This process of providing business intelligence data is very manually intensive and requires significant expertise. The entire process typically takes from two to six months. Further, changes to the structure and/or format of the data in the source systems to be extracted can require significant manual recoding of the extraction functions. Further, changes to the information desired from the summaries and reports can require significant recoding of the extraction, transformation and load functions. As this is generally performed manually, the effort required can be substantial and is very sensitive to human error.
Accordingly, it is an object of the invention to provide a novel method and system for providing business intelligence data.
SUMMARY OF THE INVENTION
According to one embodiment of the invention there is provided a machine implemented method for collecting business intelligence data including the steps of providing a master database table on a computer readable medium and accessible by an analytics server; accessing rows in the master database table by the analytics server, where each row in the master database table contains at least a partial set of measures for a plurality of dimensions; identifying by the analytics server a set of one or more dimensions from the plurality of dimensions subject to a query by one or more computing devices in communication with the analytics server; extracting the set of one or more dimensions and associated measures for each of the one or more dimensions from the master database table; and, forming a baby fact table, where each row in the baby fact table contains the set of one or more dimensions subject to the query and the associated measures derived from the extracting step.
According to one aspect of the invention, the master database table comprises a plurality of tables, each the plurality of tables related to a category of business intelligence data information.
According to another aspect of the invention, the method further includes identifying by the analytics server further sets of one or more dimensions from the plurality of dimensions subject to additional queries; forming one or more additional baby fact tables, wherein each row in each the one or more additional baby fact tables contains a unique set of one or more dimensions subject to one of the additional queries and measures associated with dimensions in the unique set.
According to another aspect of the invention, the method further includes prior to the providing step, importing by the analytics server data aggregated from one or more computer readable data sources in the form of source data, generating one or more dimensions from the source data, wherein the one or more dimensions define categories into which portions of the normalized data can be grouped, generating one or more measures from the source data linked to the one or more dimensions, and storing the one or more dimensions and one or more measures in the master database table.
According to another aspect of the invention, the master database table comprises a plurality of tables, each the plurality of tables related to a category of business intelligence data information.
According to another aspect of the invention, the method further includes storing a record of the query and the each additional queries on the computer readable medium.
According to another aspect of the invention, the method further includes forming the plurality of baby fact tables based on the record of the query and the each additional queries prior to a subsequent step of importing source data.
According to another aspect of the invention, the method further includes creating a facts table on the computer readable medium storing an identification of each the plurality of baby fact tables.
According to another aspect of the invention, the method further includes generating a data cube based on information in any one of the baby fact tables.
According to another aspect of the invention, the method further includes upon a condition in which the data cube can be generated by more than one of the baby fact tables, selecting the baby fact table having the fewest rows for generating the data cube.
According to another embodiment of the invention, there is provided a system for collecting business intelligence data including a master database table on a computer readable medium and accessible by an analytics server and computer readable instructions on the computer readable medium for carrying out the method according to the first embodiment, including all its aspects and variations as herein described.



BRIEF DESCRIPTION OF THE DRAWINGS
Embodiments will now be described, by way of example only, with reference to the attached Figures, wherein:
FIG. 1 shows a high-level architecture of an analytics server for providing business intelligence data in accordance with an embodiment of the invention and its operating environment;
FIG. 2 shows a schematic diagram of the analytics server of FIG. 1;
FIG. 3 is a flowchart of the general method of initializing the analytics server of FIG. 1;
FIG. 4 shows a schematic diagram of various data sets of the analytics server and an ERP system of FIG. 1 in relation to the method of FIG. 3;
FIG. 5 shows the normalized data model into which data from the ERP system of FIG. 1 is imported;
FIG. 6A shows the schema for the master data tables for sales representatives, business partners, and plants in the normalized data tables of FIG. 4;
FIG. 6B shows the schema for the master data tables for materials, resources, and activities in the normalized data tables of FIG. 4;
FIG. 6C shows the schema for the transaction data tables for production and sales in the normalized data tables of FIG. 4;
FIG. 7 illustrates a material activity tree generated by the analytics server of FIG. 1 for a product that relates the data imported from the ERP system to activities;
FIG. 8 is a flowchart of the calculation of measures during the method of FIG. 3;
FIG. 9 illustrates an exemplary material activity tree similar to that illustrated in FIG. 7;
FIG. 10 shows a summary of the materials used in the material activity tree of FIG. 9.
FIG. 11 shows an exemplary timeline illustrating the determination of revenue for a material in a production transaction by the analytics server of FIG. 1;
FIG. 12A shows an exemplary method of determining the production cost for a material in a sales transaction used by the analytics server of FIG. 1;
FIG. 12B shows an exemplary method of determining the revenue for a material in a production transaction used by the analytics server of FIG. 1;
FIG. 13 represents an exemplary, simplified star schema generated by the analytics server of FIG. 1;
FIG. 14 is a flowchart of the method of deriving measures used by the analytics server 20 of FIG. 1;
FIG. 15 illustrates the generation of a baby fact table by the analytics server of FIG. 1;
FIG. 16 illustrates the use of baby fact tables to generate cubes by the analytics server of FIG. 1;
FIG. 17 shows a bill of materials screen presented by the business intelligence client of FIG. 4 using data processed by the analytics server;
FIG. 18 shows a portion of the corresponding table containing the parent and child relationships for the materials shown in FIG. 17;
FIG. 19 shows a portion of a material table corresponding to the materials of FIG. 17;
FIG. 20 shows a portion of the material-activity table corresponding to the materials of FIG. 17;
FIG. 21 shows a portion of the activity table corresponding to the materials of FIG. 17;
FIG. 22 shows a portion of the activity-resource table corresponding to the materials of FIG. 17;
FIG. 23 illustrates a portion of the resource table corresponding to the materials of FIG. 17;
FIG. 24 shows a portion of the material-measure table corresponding to the materials of FIG. 17;
FIG. 25 shows a portion of the resource-measure table corresponding to the materials of FIG. 17;
FIG. 26 illustrates a portion of the measure-function table corresponding to the materials of FIG. 17; and
FIG. 27 shows a BOM screen generated by the business intelligence client of FIG. 4 showing material costs.



DETAILED DESCRIPTION OF THE EMBODIMENTS
The invention is generally applicable to a system for providing business intelligence data using an analytics server specially adapted for this purpose. Accordingly, the description that follows below describes the general system to which the invention may be applied, followed by the use of such baby fact tables in this application and various optimizations and variations in implementation.
An analytics server 20 for providing business intelligence data and its operating environment is shown in FIG. 1. In particular, the analytics server 20 is a computer system that is configured to extract, transform and load data from a source system 24 to which it is coupled. The source system 24 in this embodiment is an ERP system that integrates internal and external management information across an entire organization. It will be appreciated, however, that the invention can be used with other source systems, such as customer relationship management systems, manufacturing execution systems, etc. The source system 24 ties together finance/accounting, manufacturing, sales and service, customer relationship management, etc., automating this activity with an integrated software application. Its purpose is to facilitate the flow of information between all business functions inside the boundaries of the organization and manage the connections to outside stakeholders, such as suppliers, distributors, customers, etc. The source system 24 operates in real time or next-to-real time, and includes a common database that aggregates all data across all internal and outside stakeholder systems.
One or more client computing devices 28 are in communication with the analytics server 20, either directly or over a large communications network, such as the Internet 32. Client computing devices 28 may be desktop computers, notebook computers, mobile devices with embedded operating systems, etc. While, in this particular embodiment, the Internet 32 is shown, any other communications network enabling communications between the various devices can be substituted. The client computing devices 28 are personal computers that execute a business intelligence client for connecting to, querying and receiving response data from the analytics server 20.
FIG. 2 shows various physical elements of the analytics server 20. As shown, the analytics server 20 has a number of physical and logical components, including one or more central processing units 44 (referred to hereinafter as “CPU”), random access memory (“RAM”) 48, an input/output (“I/O”) interface 52, a network interface 56, non-volatile storage 60, and a local bus 64 enabling the CPU 44 to communicate with the other components. The CPU 44 executes an operating system, an analytics engine, and two business intelligence applications. RAM 48 provides relatively-responsive volatile storage to the CPU 44. The I/O interface 52 allows for input to be received from one or more devices, such as a keyboard, a mouse, etc., and outputs information to output devices, such as a display and/or speakers. The network interface 56 permits communication with other computing devices. Non-volatile storage 60 stores the operating system and programs, including computer-executable instructions for implementing the analytics engine and the business intelligence applications, as well as data being processed by both. During operation of the analytics server 20, the operating system, the programs and the data may be retrieved from the non-volatile storage 60 and placed in RAM 48 to facilitate execution.
Data Modeling
FIG. 3 illustrates the general method 100 of initializing the analytics server 20. In order to reduce effort to handle queries with expanded data requirements generated after initialization, all relevant data is extracted from the source system 24 and imported into the analytics server 20. The analytics server 20 then normalizes the imported data and generates dimension and fact tables from it. Formula tables are then generated to adjust the data and to capture knowledge not necessarily present in the source data.
Referring now to FIGS. 3 and 4, the method 100 commences with the extraction of source data from the source system 24 (110). A series of templates for modeling the data for various industries, such as chemical and manufacturing, are made available to an administrator of the source system 24. The administrator of the source system 24 selects the most suitable template and uses it to structure data extraction procedures from the source system 24. For example, all of the relevant data is thus extracted periodically in comma-separated value (“CSV”) format matching the structures set out in the selected template. As the extraction process may be time consuming, the extraction of data from the source system 20 is scheduled to be performed during off-peak hours. The extracted CSV-formatted data is stored in an intermediate data file 212. The intermediate data file 212 may be used for data correction and auditing.
Once the source data is extracted from the source system 24 at 104, the source data is imported into import tables 214 (120). The analytics server 20 parses the intermediate data file 212 and constructs the import tables 214 using the source data contained therein. The import tables 214 (whose names are prefixed with “PVI”) generally match the data layout of the intermediate data file 212. These import tables 214 are stored by the analytics server 20.
Once the source data is imported into the import tables 214, it is normalized (130). The analytics engine parses the import tables 214 and identifies the organization's structure, such as its sales and plants, and the structure and recipe for its products. As the layout of the import tables 214 is defined by the established templates used to extract data from the source system 24, the data can be normalized via a set of scripts to generate normalized data tables 216. The data from the import tables 214 is reorganized to minimize redundancy. Relations with anomalies in the data are decomposed in order to produce smaller, well-structured relations. Large tables are divided into smaller (and less redundant) tables and relationships are defined between them. The normalized data tables 216 (whose names are prefixed with “PVN”) are stored by the analytics server 20.
FIG. 5 illustrates the normalized data tables 216 that the analytics engine populates with the imported source data. The imported source data relating to the organization's structure, then product structures and recipes (i.e., products) are used to populate a set of master data tables 300, and the imported source data relating to the organization's transactions (e.g., sales and production) is used to populate a set of transactional data tables 302.
The master data tables 300 store non time-phased elements. These include organizational data 304, inbound materials data 308, resource data 312, activity data 316 and outbound materials data 320. The organizational data 304 includes the following tables:

    
    
        PVN_BusinessPartner—business partners, such as customers, customer groups, market segments, suppliers, vendor groups, other third-parties, etc.
        PVN_SalesRep—sales representatives
        sales regions/divisions
        production plants
        time (e.g., where an organization's fiscal year does not coincide with the calendar year)
    
    


FIG. 6A shows the schema for the master data tables for sales representatives, business partners, and plants.
Referring again to FIG. 5, the inbound materials data 308 and outbound materials data 320 are stored in a table called PVN_Material and include product groups, intermediates, raw material groups, standard costs, etc. Materials are any goods that are consumed and produced by an activity. Outbound materials can include by-products, such as scraps, that may offset the costs of the inbound materials when determining the total cost of an activity. Each material is uniquely identified.
The resource data 312 is stored in PVN_Resource and includes a list of resources. A resource is any entity that is required to perform the activity but is not materially consumed by the activity (e.g., shifts, equipment, warehouses, etc.). Resources have costs that are usually time-based.
The activity data 316 is stored in PVN_Activity and its associated tables include activity recipes, bills of materials, and routings. An activity is a required task that is needed to add economic value to the product that an organization sells (e.g., press alloy 6543 with die #1234). An activity type is a group of activities that performs similar tasks (e.g., extrusion, painting, cooking, etc.). The activities are linked together via inbound materials and outbound materials. For example, a first activity can produce a widget. A second activity for making a multi-widget may require widgets as inbound materials. As a result, the first activity is performed to produce a widget that is then an inbound material for the second activity.
FIG. 6B shows the schema for the master data tables for materials, resources, and activities.
Returning again to FIG. 5, the transactional data 302 is data that is linked to the materials. Sale transaction data 324 relating to materials sold is stored in PVN_Sales, and includes:

    
    
        quantity and price
        customer that purchased the materials
        sales person who sold the materials
        plant that the material is being sourced from
        sales-specific charges and costs (e.g., freight)
        time of sale
    
    


Production transaction data 328 relating to materials produced is stored in PVN_Production, and includes:

    
    
        materials built/processed (quantity/batch size)
        time of production
        run duration (cycle-time)
        plant
    
    


FIG. 6C shows the schema for the transaction data tables for production and sales.
Referring again to FIG. 5, the other transaction data 332 is stored in PVN_Transaction, and includes data for user-defined transactions, such as quotes.
Each master and transaction table has its own associated attribute table 336. For example, PVN_Material has an associated PVN_MaterialAttribute table. These attribute tables 336 allow the analytics engine to aggregate the data in different ways. User-defined attributes can be introduced in these attribute tables 336. In this manner, the data can be aggregated in user-defined manners. The name and description of the attributes are defined in the table PVN_AttributeCategory. The maximum length of the name of an attribute is 25 characters and the maximum length of the value is 50 characters.
Each entity (activity, material, resource, sales, production) that participates in the manufacturing process has its own associated measure table 340. These measure tables 340 record the data for that entity when it takes part in the process. The name and description of the measures have to be defined in the table PVN_MeasureCategory. The maximum length of the name of a measure is 25 characters and the value is always stored as decimal (20.6).
FIG. 7 illustrates a hierarchical structure referred to as a material activity tree that represents the production of a product, the providing of a service, etc. The material activity tree is a set of activities chained together according to dependencies. In the production of a product, the activities can include, for example, the production of subcomponents and solutions to make the subcomponents, packaging, etc. In the provision of a service, the activities can include, for example, internal tasks performed by various employees, such as the completion of documents, and external tasks, such as client meetings. The material activity tree is effectively a bill of materials. A bill of materials (sometimes “bill of material” or “BOM”) is a list of the raw materials, sub-assemblies, intermediate assemblies, sub-components, components, parts and the quantities of each needed to manufacture an end product. As a result, the material activity tree is sometimes also referred to as a BOM. The same approach can be used for a bill of equipment and routings for a sequence of work centers.
As illustrated, the final activity, Activity N, has two pre-requisite activities, Activity N-1 (branch 1) and Activity M (branch 2). Each activity along the upper and lower branches of the material activity tree requires that the preceding activity along the branch be completed before it can be performed. A simplified example of such a process may be the production of a simple product, wherein the upper branch represents the assembly of the product from simple components bought from external suppliers, and the lower branch represents the production of packaging for the product. Thus, the last activity, Activity N, may be the placement of the product in the packaging.
The pseudo-code for the importation of source data from the intermediate data file 212 is shown in Appendix A.
Turning back to FIGS. 3 and 4, once the source data has been imported, dimensions are generated (140). The dimensions are stored in dimension tables 220 whose names are prefixed by the letters “PVD”. Each dimension table 220 represents an axis on which data can be aggregated and grouped. In effect, each dimension table 220 is like a hierarchy. These dimensions include sales representative, plant, partner, material, resource, activity, and time. Custom attributes can be added to any of these dimensions.
The master and transaction data tables 300, 302 contain pre-defined attributes that can be used by analysis. For example, the “PartnerName” attribute of the PVN_BusinessPartner identifier for a partner allows business intelligence applications to aggregate the data according to the customer name instead of customer ID.
The following normalized data tables 216 are used to help generate the dimension and fact tables.

    
    
        PVN_AttributeCategory
        PVN_AttributeValueDescription
        PVN_Activity
        PVN_ActivityGrouping
        PVN_ActivityAttribute
        PVN_BusinessPartner
        PVN_BusinessPartnerAttribute
        PVN_Material
        PVN_MaterialBOM
        PVN_MaterialAttribute
        PVN_Production
        PVN_ProductionAttribute
        PVN_Resource
        PVN_ResourceAttribute
        PVN_Sales
        PVN_SalesAttribute
        PVN_SalesRep
        PVN_SalesRepAttribute
    
    


These dimension-related tables are normalized in order to provide flexibility to add extra attributes. In order to add an extra attribute to a dimension (say PVN_Production), the attribute is first specified in the PVN_AttributeCategory. The attribute values are then added to the corresponding attribute table (i.e., PVN_ProductionAttribute).
Without further processing, the normalized data tables 216 (i.e., the master and transaction data tables named PVN_*) with their associated attribute tables form the “snowflake schema” for a basic data cube.
These dimension-related tables then are de-normalized to form a “star schema”. Only one joint operation is needed to form the cube from the de-normalized dimension-related tables during runtime, increasing efficiency. Also, the attribute value can be transformed to provide more meaningful grouping. For example, a custom de-normalization process can classify the weight (an attribute) of a material to be “heavy” or “light” if the value is “<50 kg”. Further, some entities like material (using PVN_MaterialBOM) and activity (using PVN_ActivityGrouping) have hierarchy support. The hierarchy is flattened during the de-normalization process.
For example, for the following material related tables,
PVN_Material:




















Material
Material 



PV_MaterialID
PlantID
MaterialID
MaterialName
Description
Type
MaterialUOM







M1
P1
M1
M1
Computer
Chair
1






chair




M2
P1
M2
M2
Chair arm
Chair
1









PVN_AttributeCategory:
















AttributeID
AttributeName
AttributeDescription









weight
Weight
Weight per piece



color
Color
Color of the base










PVN_MaterialAttribute:
















PV_MaterialID
AttributeID
AttributeValue









M1
weight
60



M1
color
Black










PVN_MaterialBOM:


















PV_MaterialID
BOMLevel
PV_OutMaterial
OutQty 
PV_InMaterial 
InQty 
InUom
OutUOM







M1
0
M1
1
M2
1
1
1









the generated material dimension table, PVD_Material, is:























Dim
PV_Material
Plant 
Material
Material
Material
Material 
Material
PV_
PV_
PV_LEVEL_




ID
ID
ID
ID
Name
Description
Type
UOM
LEVEL_0
LEVEL_1
LEAF
weight
color







1
M1
P1
M1
M1
Computer
Chair
1
M1

M1
60
Black







chair









2
M1
P1
M1
M1
Computer
Chair
1
M1
M2
M2
60
Black







chair









3
M2
P1
M2
M2
Chair arm
Chair
1
M2

M2









The PVD_Material (or any hierarchical) dimension table includes the attributes for the TOP (i.e., PV_MaterialID) and the LEAF (PV_LEVEL_LEAF). The attributes for the LEAF are prefixed by “PV_”. For example, attribute “Description” refers to the TOP and “PV_Description” refers to the LEAF.
The following attributes that are added if the level is greater than 1.


















PV_Plant
PV_Material
PV_Material
PV_Material 
PV_Material




dimID
ID
Name
Description
Type
UOM
PV_weight
PV_color







1
P1
M1
Computer chair
Chair
1
60
Black


2
P1
M1
Computer chair
Chair
1
60
Black


3
P1
M2
Chair arm
Chair
1









The pseudo-code of standard de-normalization process is included in Appendix B.
Once the dimensions are generated, measures are calculated (150). Measures include both raw and calculated values. An example of a raw value is the sale price of a material. An example of a calculated value is the cost of a piece of machinery allocated to the manufacture of the material.
FIG. 8 illustrates the process of calculating measures in greater detail. First, rate/driver measures are calculated (151). Rate/driver measures include costs that directly affect the production of a material. These can be time-based, volume-based, etc. For example, a rate/driver measure can be the cost of a worker that operates a piece of machinery used to manufacture a material. This measure is calculated by determining an effective hourly rate for the worker (after consideration for wages, benefits, etc.) and then multiplying that by the number of hours, or fractions thereof, of the worker's time are spent during the manufacture of a unit of the material. Another example of a rate/driver measure is the cost of a material consumed during the manufacture of the material. Dye used to color T-shirts may be purchased in gallon containers, and a fraction of that gallon may be spent manufacturing each T-shirt, leading to a cost of the dye per T-shirt. Lookups of values may be employed to generate rate/driver measures.
Next, allocation measures are calculated (152). Allocation measures include costs that indirectly affect the production of a material. For example, an allocation measure can be the portion of the cost of the construction of a plant or the company's human resources department allocated to the manufacture of the material. Like rate/driver measures, allocation measures can be time-based, volume-based, etc.
Finally, hierarchical structure measures are calculated (153). Hierarchical structure measures are composite measures corresponding to a set of related hierarchically-related objects, events, etc.
A beneficial feature of the analytics server 20 is that it can perform rapid simulation or recalculation of costs that are derived from the bill of materials of a particular product or service. For example, the analytics server 20 can quickly determine what is the updated cost for the end-material if the cost of one or more raw materials of that product is updated. The same is true for other measures associated with the BOM components, such as durations, equipment costs, customer costs, other group-by-combination costs, etc.
In order to determine a revised cost for the end-material for an updated component (material, equipment and/or resource) cost, the BOM of the end-material may be traversed with the updated component (material and equipment) costs. A traversal of the BOM means that the BOM structure is repeatedly queried every time a cost update is being performed. Since this is an often-performed use case, it can be inefficient to continue to traverse the BOM every time. This can become particularly evident when the impact of a change in a raw material is measured across the business of an entire organization.
The analytics server 20 facilitates recalculation of measures related to a BOM by determining the cost relationship between the finished product (i.e., the end-material) and its components (i.e., the raw material inputs).
The total product cost (minus the transfer fees, taxes, etc.), consists of all the material and processing costs of that product. The cost contribution of a component material or equipment can be represented by the following:

PRODUCT_COSTcom=COMper×COMcost 

PRODUCT_COSTcom represents the cost of a single component relative to a single unit of the finished product. COMper represents the fraction or percentage representing the weight of the COMcost to PRODUCT_COSTcom. For example, if both the finished product and the component have the same unit of measure, then it would be Qcom (quantity of the component used in the BOM to make a single unit of the finished product). COMcost is the per-unit cost of the component used in the BOM of the finished product. Thus,

TOTAL_PRODUCT_COST=Σ PRODUCT_COSTcom 

for all components in the BOM.

The above technique can even be more generalized. Since any group-by's consume or are related to products, the use of percentages can be applied to the group-by's themselves. For example, if it is desired to determine the raw material cost impact to a list of customers, the COMper can be calculated for each component and customer combination.
During the calculation phase, the engine actually calculates the following fields PV_FinishedQty, and PV_ComponentQty for every component used in all the product's BOM. The division of these two measures effectively yields the COMper value. These two measures are stored in the base fact table, and also exist within other derived fact tables.
A similar technique can be used for equipment or resources, but instead of using quantity, the total hours and component durations are stored.
It is during the process of updating scenario, when the value of the corresponding COMper is required.
This technique is more efficient, and scaleable, especially when the intermediate dimensions between the components and group-by's are significantly large. For example, consider a scenario with ten plants, where it is desired to determine the marginal impact when the cost of ten raw materials is changed. Without the above approach, all the products from the ten plants must be located, and the BOM of each product that uses the ten raw materials must be traversed to determine the impact of the cost changes. If the number of products are in the thousands, the effort required to traverse each related BOM is significant.
FIG. 9 illustrates a material activity tree 400 similar to that shown in FIG. 7. As shown, the material activity tree 400 includes four activities. A first activity 404 is shown having three inputs, material Ml, material M2 and resource R1, and outputs a material M3. A second activity 408 has three inputs, material M4, material M3 from activity 404, and resource R2, and outputs a material M5. A third activity 412 has three inputs, the same material M1 that is an input for activity 404, material M6 and resource R3, and outputs a material M7. A fourth activity 416 has three inputs, resource R4, material M5 from activity 408 and material M7 from activity 412, and outputs a material M8. The cost in dollars for each material at each activity is shown.
FIG. 10 shows a summary of the materials used in the BOM of FIG. 9. The end-material, M8, is shown, as well as the total cost to produce material M8. In addition, a list of the raw materials and resources used to produce material M8 is also shown, together with the total cost of the raw material used to produce material M8. The percentage of the total cost of the material M8 has been calculated for each raw material by dividing the raw material cost for each by the total cost to produce material M8. These percentages are presented beside the total cost for each raw material.
Referring again to FIGS. 3 and 4, the measure calculation process generates fact tables 224. The fact tables 224 include PVF Facts, which is the fact table used in the star schema.
The pseudo-code for the measure calculation process is in Appendix C.
Production and Sales Matching
In some cases, it can be desirable to more closely relate production costs for specific materials or batches of materials to specific sales. In these cases, the analytics engine attempts to match specific sales to the production of specific materials.
The common attributes that reside in both the sales documents and the production documents are product code and order number. All attributes are used first to determine the most accurate match. The attribute list is reduced with the biggest number first when a match cannot be found, yielding more general matches.
The following parameters in table PVC_FunctionParameters are used to control whether match occurs or not:












FunctionID
ParameterName
ParameterValue







calc
addSalesForProduction: add the
NO



weighted average of sales measure to



production record; default is “YES”


calc
addProductionForSales: add the
NO



weighted average of production



measures to sales record; default is



“YES”









The following parameters in table PVC_FunctionParameters are used to control matching:















FunctionID
ParameterName
ParameterValue









match
criteria.0
OrderNum, OrderLine,





PV_ActivityID



match
criteria.1
*lot, PV_ActivityID



match
cutoff
360



match
range
120



match
production
before



match
sales
after










Entries in the ParameterName field of the form criteria.0, criterial.1, etc. specify the order of matching criteria. Each criteria consists of a list of common attributes (pre-defined or used-defined in PVN_AttributeCategory). If there are no matching transactions using criteria.n (say criteria.0), the analytics engine uses criteria.n+1 (i.e. criteria.1) to find the matching transactions. The analytics engine will stop finding until all criteria are processed. This parameter supports a special attribute “*lot”. In make-to-stock situations, the set of match transactions can be obtained by comparing the “Lot” number. The table “PVS_Lot” is used to store the lot information.















LotNumber
TransactionType:
TransactionID:
LotUsage: ‘I’ for



“S” for sales,
SalesID if
input, ‘O’ for



“P” for
TransactionType
output, ‘B’ for



production
is “S” and
both




ProductionID




if TransactionType




is “P”









Lots are used in multiple production steps. For example,




the resulting PVS_Lot table is as follows:














LotNumber
TransactionType
TransactionID
LotUsage







Lot1
P
P1
O


Lot1
P
P2
B


Lot1
P
P3
I









Cutoff refers to the time interval (in days) in which the analytics engine will try to find a match.
Range defines a time period after the first matched transaction in which other transactions occurring in the time period are included in the matched set for calculating the weighted average.
Production determines the direction of the range for matched production transactions. The values can be “before”, “after” or “both”. The default is “before”, as production transactions usually occur before sales transactions.
Sales determines the direction of the range for matched sales transactions. The values can be “before”, “after” or “both”. The default is “after”, as sales transactions usually occur after production transactions.
FIG. 11 shows an exemplary timeline with a production transaction, P1, for a material. A series of sales transactions, S1 to S5, falling before and after the production date are also shown. A time period (P1−cutoff, P1+cutoff) is searched by the analytics engine to locate matches. Sales transaction S2 is chosen as it is the earliest sales transaction within the cutoff period. As shown, sales transactions S2 and S3 are within the defined range and, thus, are included in the calculation of a weighted average revenue per unit. Although sales transaction S4 lies within the cutoff interval, it is not included because it is not in the range.
FIG. 12A shows an exemplary method 500 of determining a production cost for a material sold using the parameters for PVC_FunctionParameters shown in the table above. In particular, production transactions are assumed to occur before sales transactions. The method 500 commences by starting from the sales date of the sales document and trying to find an exact match based on common attributes shared by sales and production documents (504). It is then determined if a match is found within the six months previous to the sales date (508). If no match is found at 508, it is determined if the number of common attributes is reducible (512). If the number of common attributes cannot be reduced, no match is found and the method 500 ends. If the number of common attributes is reducible, the match is generalized by removing the most specific attribute (516). Upon reducing the number of common attributes sought, the method 400 returns to 404 to search for matches using the loosened constraints. If, instead, a match was found in the six months previous to the sales date at 508, other matches within the three months prior to the first match are looked for (524). A weighted average production cost is then calculated (528). The production cost is then pro-rated to the amount sold (532). For example, if the total production cost for the matched range is $1000 for 100 units produced, and the matched sales revenue is for 50 units, then the production costs allocated to the matched revenue will be 50/100*$1000=$500. Upon pro-rating the production cost, the method 500 ends.
FIG. 12B shows an exemplary method 600 of determining revenue for a material produced using the parameters for PVC_FunctionParameters shown in the table above. In particular, sales transactions are assumed to occur after production transactions. The method 600 is similar to the method for sales view matching illustrated in FIG. 10A, except that the searches are forward-looking, as sales are completed after production of a material. The method 600 commences by starting from the production date of the production document and trying to find an exact match based on common attributes shared by sales and production documents (604). It is then determined if a match is found within the six months after the production date (608). If no match is found at 608, it is determined if the number of common attributes is reducible (612). If the number of common attributes cannot be reduced, no match is found and the method 600 ends. If the number of common attributes is reducible, the match is generalized by removing the most specific attribute (616). Upon reducing the number of common attributes sought, the method 600 returns to 604 to search for matches using the loosened constraints. If, instead, a match was found in the six months after the production date at 608, other matches within the three months after the first match are looked for (624). A weighted average revenue per unit is then calculated (628). The revenue for the production run is then projected based on the amount produced (632). For example, if the total production cost for the matched range is $1000 for 100 units produced, and the matched sales revenue is for 50 units, then the production costs allocated to the matched revenue will be 50/100*$1000=$500. Upon pro-rating the revenue, the method 600 ends.
The fact tables 224 include PVF_Match, which stores the matching between the sales and production records.
The following optional parameters in table PVC FunctionParameters are used to control the calculation process of the analytics engine.












FunctionID
ParameterName
ParameterValue







engine
calculation: a java class that
com.pvelocity.rpm.calc.StandardMeasureCalculation



implements the interface for the




calculation




(com.pvelocity.rpm.calc.Measure-




CalculationLiaison),




allowing the user to substitute




a new calculation process



engine
matchAlgorithm: a java class that
com.pvelocity.rpm.calc.StandardMatchMachine



implements the interface for the




matching algorithm




(com.pvelocity.rpm.calc.Match-




FunctionLiaison),




allowing the user to implement




new matching algorithm



engine
matchCalculator: a java class that
com.pvelocity.rpm.calc.StandardMatchCalculator



implements the interface for the




calculating the weighted average




of the matched transactions




(com.pvelocity.rpm.calc.Match-




CalculationLiaison),




allowing the user to determine




the matched transaction measures



engine
unMatchCalculator: a java class that




implements the interface for




calculating the measures when there




are no matched transactions




(com.pvelocity.rpm.calc.UnMatch-




CalculationLiaison),




allowing the user to assign some




default production/sales costs



engine
sharedInstallDirectory: used if the
Microsoft Windows UNC pathname to the analytics



analytics engine and the database
engine installation directory, which is set up



are deployed on different servers
as a shared directory accessible with read




permission from the database server









The master entities (i.e. material, resource and activity) that participate in the manufacturing process have their own associated measure tables (i.e., PVN_MaterialMeasure, PVN_ResourceMeasure and PVN_ActivityMeasure). During the calculation process, these measures are calculated and added to the transactions.
For example, the following fields are used in the PVN_ActivityMeasure tables:

    
    
        PV_ActivityID—the activity
        MeasureID—the name of the measure, which is defined in PVN_MeasureCategory
        MeasureFunction—the index of the function that is used to calculate the amount of the measure; the Java function is defined in the table PVN_MeasureFunction
        Measure—the argument that is passed to the MeasureFunction
    
    


The measures for transaction entities are applied directly to the transactions using PVN_ProductionMeasure and PVN_SalesMeasure. If there are some measures that apply to all transactions, these measures are specified in the PVN_GlobalTransactionMeasure. The following fields are used in these tables:

    
    
        MeasureID—the name of the measure; defined in PVN_MeasureCategory
        MeasureFunction—the index of the function that is used to calculate the amount of the measure; the Java function is defined in the table PVN_MeasureFunction
        Measure—the argument that is passed to the MeasureFunction
        EntityType—stores the origin of the measure; e.g.,
        
            1—material cost
            2—resource cost
            3—activity cost
        
        
        EntityID—stores the key of the origin of the measure; e.g.,
        
            MaterialID if EntityType=1
            ResourceID if EntityType=2
            ActivityID if EntityType=3
        
        
        MeasureUOM—stores the unit of measure for the measure
    
    


Using EntityType and EntityID, the analytics engine attributes the measure to a specific material, activity or resource. However, if a measure is associated with two or more entities (like activity and resource) at the same time, the following fields can be used instead of EntityType and EntityID:

    
    
        PVM_Activity—the activity
        PVM_Resource—the resource
        PVM_Material—the material

These fields can be specified at the same time.

    
    


All the functions that are used to compute the measures are defined in PVN_MeasureFunction. Some of these functions are pre-defined by the analytics engine, namely com.pvelocity.rpm.calc.ConstantMeasure and com.pvelocity.rpm.calc.TableLookupMeasure.
When ConstantMeasure function is specified, the “Measure” column represents the final value of the measure. For the following record in PVN_ProductionMeasure:


















Measure






ProductionID
MeasureID
Function
Measure
EntityType
EntityID
MeasureUOM







P-1
ProducedWeight
1
7.2
3
Ext
Lbs









Thus, the “ProducedWeight” for the production “P-1” is 7.2.

Sometimes, the values of the measure are collected periodically. For example, the total freight cost in a month. In this case, the cost (or measure) is distributed among the transactions according to other measures. Table PVN_PeriodLookup and the TableLookupMeasure function are used to provide this capability. For example, PVN_GlobalTransactionMeasure:















TransactionType
MeasureID
MeasureFunction
Measure
EntityType
EntityID







4
PlantSupplies
2
0.0
3
Ext









and PVN_PeriodLookup:































SplitBy



TotalBy





UnitOf
SplitBy 
SplitBy
Measure


PlantID
EntityType
TotalEntityID
TargetMeasureID
StartDate
Amount
Currency
Measure 
EntityType
EntityID
ID







P1
3
Ext
PlantSupplies
Jan. 1, 2006
50000
US
Lbs
3
Ext
Material












Qty





where:


PlantID—the plantID


TotalByEntityType—the entity type associated with the measure


TotalByEntityID—the entity ID of the measure


TargetMeasureID—the name of the measure


StartDate—the start date of the monthly data


Amount—the total amount for the month


Currency—the currency of the monthly total


UnitOfMeasure—the unit of measure


SplitByEntityType—the entity type of the other measure that determines the distribution; e.g., the MaterialQty (SplitByMeasureID) of the Extrusion “Ext” (SplitByEntityID) activity (SplitByEntityType)


SplitByEntityID—the entity ID of the other measure that determines the distribution


SplitByMeasureID—the measure that determines the distribution






FIG. 13 illustrates an exemplary, simplified star schema 700. At the center of the star schema is a fact table 704 that contains all the measures for the imported data. Each record in the fact table 704 includes a series of foreign keys 708 referencing a set of dimension tables 712A to 712D.
Returning again to FIGS. 3 and 4, once the measures have been calculated, a formula table 232 is generated (160). The formula table includes formulas such as for calculating commission rates, discount rates, freight costs, etc.
Updating of Transaction Data
The analytical information that is used by the analytics engine is updated periodically in order to include the newly generated transaction data. Since the calculation is time-consuming, the analytics engine provides a way to generate the new analytical information incrementally. When new transaction (i.e. sales and production) records are generated, a new database (e.g., PVRPM_Inc) is created to store the new data. It merges the original analytical information stored in another database (e.g., PVPRM_Base). It may also point to a separate user database (e.g., PVRPM_User) that stores the user preferences. The following parameters in table PVF FunctionParameters are used by incremental measure calculation:












FunctionID
ParameterName
ParameterValue







engine
userdb: database that stores
jdbc:sqlserver://localhost:1433;



user configuration
DatabaseName=PVRPM_User;



information; e.g.
user=sa;password=sa



PVC_DerivedMeasure,




App_*, etc.



engine
basedb: database that stores
jdbc:sqlserver://localhost:1433;



the original analytical
DatabaseName=PVRPM_Base;



information
user=sa;password=sa


engine
incrementalPreprocessor: a
com.pvelocity.rpm.calc.In-



java class that implements
crementalUpdateByDateRange



the interface for the




incremental preprocessor,




com.pvelocity.rpm.calc.In-




crementalPreprocessorLiaison;




allows the user to remove the




obsolete data in the original




analytical database



engine
remove.Sales.0: the parameters
1,2004/03/01,2004/03/14



that are processed by the




incrementalPreprocessor;




specifies the plant and the




date range of the obsolete




data in the original




analytical database



engine
remove.Production.0: the
1,2004/03/01,2004/05/01



parameters that are processed




by the incrementalPreprocessor;




specifies the plant and the




date range of the obsolete




data in the original




analytical database



engine
lockCondition: data that




satisfy the lockPeriod and




lockCondition in the orginal




analytical database will be




moved to the new database




without changing



engine
lockPeriod: see comments on




lockCondition









Cube Generation

Using the dimension tables 220 and the fact tables 224, the analytics engine generates one or more cubes 228 that represent aggregated data used in the analysis. The cubes are defined using SQL tables and are named with the prefix PV3_*. These cubes are cached to improve performance. The table PVF_CachedCubes is used to implement the cache.














CubeSignature
CubeTableName
CubeCreator
Hits
LastUsedTime







1027105|1027263|1|PVD_Material|
PV3_1149100238625_15606014
Admin
1
5/31/2006


D31|MATERIAL|MATERIAL|



2:30:43 PM


MATERIALID|MaterialID






varchar(25)|Q|null









The following parameters in PVF_FunctionParameters are used to control the cache:












FunctionID
ParameterName
ParameterValue







cache
cacheSize: the maximum cache size,
 0



with a value of “0”




indicating an unlimited cache size



cache
cacheTableName: the cache table
PVF_CachedCubes



name, by default




“PVF_CachedCubes”



cache
disable: used to turn off caching
false


cache
expiredCheckPeriod: interval (in
 0



day) to check expired cached;




default value is 2



cache
expiredDays: number of days of no
 0



use after which a cube; default




is 10



cache
initialCacheSize: initial cache
100



size when the engine starts up;




if the existing cache size is




more than the “initialCacheSize”




when the engine starts up, the




least recently used or least




frequently used cubes are




removed; default is 100



cache
initialExpiredDays: specifies
 10



the expired day when the engine




starts up; default is 10



cache
replacementPolicy: LRU (Least
LRU



Recently Used) or LFU (Least




Frequently Used) policy can




be implemented when




cacheSize is reached









Cubes 228 aggregate the data in the dimension tables 220 and fact tables 224.
Once the cubes 228 and the formula table 232 have been generated, business intelligence applications 236 enable users to query, manipulate and view the data therein.
Baby Fact Tables
The master facts table PVF_Facts contains data for both sales and production categories. As the master facts table can be very large in many cases, all cube formations (except drill-down and summary cube formations) accessing this master facts table can lead to very slow cubing performance. In order to accelerate the generation of cubes, the analytics engine generates subsets of the fact tables for handling specific queries. These smaller fact tables are referred to as baby fact tables.
Baby fact tables are generally much smaller than the master fact table, thus greatly accelerating access to it. In addition, its clustered index is smaller and is more prone to be cached in memory. Moreover, there is no need to include the category column, a low-selectivity column with only values ‘S’ and ‘P’, as part of the index. Often many measures only apply to one category (e.g. freight cost only meaningful in Sales queries), so a baby fact table can forgo the irrelevant measures, and no time is wasted in aggregating these measures. Often some dimensions only apply to one category (e.g. SalesRep dimension is often not populated in production records). Therefore, the clustered index can forgo irrelevant dimensions, resulting in a narrower index. Additional non-clustered indexes can be applied on each baby fact table according to runtime query patterns. □In multi-user environment, I/O convention is reduced since we have two separate, independent fact tables.
FIG. 15 shows the generation of a baby fact table. The fact tables and dimension tables that define the star schema of FIG. 13 are shown at 900. The records of the main facts table are filtered to distill a baby fact table 904.
The fact tables for some implementations continue can grow very large, as they put in data for greater time span, or because the nature of an organization's business and intended usage tend to result in large fact tables (e.g., the organization has lots of BOM and they need to see material linkages). The more specific the baby fact table, the better the performance. In order to make a baby fact table very specific, it should contain only the set of dimension(s) that encompass all the query group(s), plus the plan and time dimensions if not already in the set. Also, the formal semantic for a set applies here, in that the order of dimensions is insignificant. With that in mind, the basis for creating baby fact tables should not just be the category, but be generalized to include a dimension set. The following passages outline an optimization scheme based on this idea.
Similar to the way the analytics engine keeps a cache of generated cubes, the analytics engine has a fact table pool. For each query, based on its category and referenced dimension set, the analytics engine tries to find the most specific baby fact table available in the pool that can satisfy the query. Note if such a pooled table exists, it may not be the optimal one for that query, because the pooled table may still contain more dimensions than is minimally needed by the query. The analtics engine may fall back to use the category baby fact table (i.e. the one with all dimensions), if no pooled table can satisfy the request. However, the engine keeps track of the number of such fact table pool misses, and the resultant cube creation time. This information is useful to determine whether it is worthwhile to pre-generate the missing baby fact table. Also, the analytics engine maintains statistics on pool hits as well, so that it can further optimize the most-used baby fact tables (e.g. by creating more indexes or subdivide them further).
There are two ways the analytics engine populates the fact table pool. First, parameters in the PVC_RuntimeParameter table specify what baby fact tables are to be pre-generated. The category baby fact tables are automatically created at the end of the generation of measures by the analytics engine. Pool population code is also stored by the analytics server. The pool population code creates all baby fact tables explicitly specified in the PVC_RuntimeParameter table. In addition, the pool population code looks at the fact table pool misses information and creates any baby fact tables deemed worthwhile to pre-generate.
In addition, the analytics engine analyzes the statistics for the most-used baby fact tables to see if it is possible and worthwhile to create more specific baby fact tables from them.
There are 3 levels of Baby Facts Tables for improving performance:

    Category Baby Facts Tables: The master facts table is divided based on category, namely, there is one baby fact table for category Sales (PVF_Sales_Facts), and one for category Production (PVF_Production_Facts). The engine can use either baby table based on the category of the current query.
    Time Aggregated Baby Facts Tables: Since the master and category fact tables contain facts at the day level, but queries are mostly by month or week, generating baby fact tables based on category baby facts tables that are aggregated by month or by week will result in baby facts tables with fewer rows, thus improving cubing performance.
    Generalized Baby Facts Tables: To further reduce the required number of aggregations, the basis for creating baby fact tables can be further generalized to include only the set of dimensions that encompass all the query groups for a query.


The following time dimensions are used to support monthly and weekly aggregated facts tables:

    
    
        TimeMth—time month dimension, table PVD_TimeMth
        TimeWk—time week dimension, table PVD_TimeWk

These additional time dimension tables are automatically created/populated following generation of the time dimension (PVD_Time) during dimension generation and incremental dimension generation.

    
    


The following PVC_FunctionParameters table enables specification of whether baby fact tables are generated and used at runtime:
















Default





if not


FunctionID
ParameterName
ParameterValue
defined







calc
aggregateMonthlyFacts -
YES or NO
NO



control whether the





monthly baby facts tables





should be generated and





used at runtime




calc
aggregateWeeklyFacts -
YES or NO
NO



control whether the





weekly baby facts tables





should be generated and





used at runtime




calc
generateBabyFactsTables -
YES or NO
YES



control whether category,





time aggregated and





generalized baby facts





tables should be generated





during calculation and





incremental calculation









Time aggregated category facts tables are created for the sales and production categories only, and is not applicable for user-defined categories. After the category baby fact tables (PVF_Sales_Facts, PVF_Production_Facts) are updated, the monthly and weekly aggregated category baby fact tables are generated if the corresponding function parameter described above is set to YES. These table names have the following format:












Table Name Format
Used For
Example







PVF_category_Facts_Month
corporate currency
PVF_Sales_Facts_Month



monthly aggregated facts



PVF_category_Facts_Month_currency
other display currency
PVF_Sales_Facts_Month_EUR



monthly aggregated facts



PVF_category_Facts_Week
corporate currency
PVF_Production_Facts_Week



weekly aggregated facts



PVF_category_Facts_Week_currency
other display currency
PVF_Production_Facts_Week_EUR



weekly aggregated facts









The time aggregated category baby fact tables have the same set of dimensions as the base category baby fact table (e.g. PVF_Sales_Facts) except for the time dimension. The monthly aggregated baby fact tables reference the TimeMth dimension (column name TimeMthDim) instead of the time dimension. The weekly aggregated baby fact tables reference the TimeWk dimension (column name TimeWkDim) instead of the time dimension. The clustered index created for these time aggregated category baby fact tables is the set of active dimension columns in descending order of the corresponding PVD table row count. That is, the first column in the clustered index is the dimension column of the dimension with the most entries in its dimension table. No non-clustered index is created.
Creation of generalized baby fact tables is invoked after generating time aggregated category baby facts tables. This is driven by the content of the new facts table pool table PVF_FactsTablePool. The generalized baby fact tables may be aggregated by month or week if the corresponding function parameter is set to YES. The fact table pool entries in PVF_FactsTablePool table are manually defined by the implementer of the application. If the PVF_FactsTablePool table does not exist in the database, it will be created automatically upon first use. This table consists of two columns:











Column Name
Comment







FactsTableName
There is no required format for the table name. The



table is dropped and re-created during calculation.


FactsSignature
Must be specified in the format described below.









The format for the fact table signature specified in column PVF_FactsTablePool.FactsSignature is:

    
    
        category|currency|Comma separated dimension names
        The signature entry is not case-sensitive.
        The dimension names must be these values defined in the dimension class: Activity, BusinessPartner, Material, Plant, Production, Resource, Sales, SalesRep, Time, TimeMth, TimeWk
        The signature must include one of the three time dimensions Time, TimeMth, or TimeWk. The implementer ensures ensure that the aggregateMonthlyFacts or aggregateWeeklyFacts Function Parameter is set if the TimeMth or TimeWk is referenced in the signature respectively.
        The signature must include the Plant dimension.
    
    


A non-clustered index will be created on the baby fact table based on the order of the dimension names in the signature. Examples of fact table signatures:

    
    
        Sales|USD|Material,Plant,TimeMth
        SALES|EUR|SALES, SALESRE,TIME,PLANT
        Production|GBP|Plant,TimeWk,Material,Activity
    
    


The clustered index created on generalized baby fact tables is the set of active dimension columns in descending order of the corresponding PVD table row count. That is, the first column in the clustered index is the dimension column of the dimension with the most entries in its dimension table. One non-clustered index is created based on the order of dimension names in the fact table signature.
The generalized baby fact tables specified in the PVF_FactsTablePool table are loaded into memory at startup of the analytics engine. When a query requires a new cube to be created, the engine will try to find a baby fact table in the pool that will satisfy the query and give the best cube population performance. The choice of baby fact table is based on the following factors:

    
    
        The baby fact table row count. Tables with fewer rows are considered first.
        The baby fact table must include all the dimensions required by the query. If two baby fact tables have the same row count and both include all the required dimensions, the baby fact table with the fewer dimensions will be selected.
    
    


If no suitable generalized baby fact table is found, cube population will try to use a time aggregated category fact table if the time dimension requirement matches, and the corresponding PVC_FunctionParameters is set. If still not suitable, the base category fact table will be used.
The factors that determine the time dimension required for the query are:

    
    
        Date range by month, week or YTD:
        
            If query is by month, TimeMth and Time dimension baby fact tables can be considered.
            If query is by week, TimeWk and Time dimension baby fact tables can be considered.
            YTD date range is considered as by month.
        
        
        Time dimension attribute in group by:
        
            If attribute is Year or Month, TimeMth and Time dimension baby fact tables can be considered.
            If attribute is Year or Week, TimeWk and Time dimension baby fact tables can be considered.
        
        
        Query filters:
        
            If a role-based query filter or a global query filter contains a PV_DATE variable, only the Time dimension baby fact tables will be considered.
        
        
        Plant released time restricted query condition
        
            Plant released time is defined by month. If query is by week, both TimeMth and TimeWk dimension baby fact tables cannot be used. In this case, can only consider the Time dimension baby fact tables.
        
        
    
    


FIG. 16 shows two baby fact tables 904A, 904B that were generated by the analytics engine. The first baby fact table 904A is used to generate two cubes 228A and 228B. The second baby fact table 904B is used to generate cube 228C.
Measure Derivation
An enterprise business intelligence application 236 allows the user using the business intelligence client 240 to pick a cube 228 for profit analysis by choosing different measures using the values in PVN_MeasureCategory, and different grouping using the values in PVN_AttributeCategory. It also allows the user to add extra measures that are derived from the existing data to the selected cube 228 by using the formula table 232 for profit analysis.
After the PV Measures Tables are de-normalized, each “MeasureID” in the table “PVN_MeasureCategory” is represented by a column (or measure) in the final measure table. If the “MeasureID” is related to a resource, the “Duration” associated with the resource is also represented too. A user can add a derived measure by using the existing measures. For example, “paintLabor” and “drillLabor” may be two existing measures. A user can add a derived measure called “totalLabor” by adding “paintLabor” and “drillLabor”. A PVC_DerivedMeasure table is used to store the formula of the derived measures. There are two columns in the table:

    
    
        DerivedMeasureID—the name of derived measure; e.g., totalLabor
        DerivedMeausreExpr—the expression of derived measure; e.g., “paintLabor+drillLabor”
    
    


FIG. 14 is a flowchart of the method 800 of deriving measures used by the analytics server 20. The method 800 commences with the selection of dimensions and range (804). Next, the measures are selected (808). Upon selecting the measures, it is determined if a cube matching the selected dimensions, range and measures exists (812). If it is determined that a cube matching the criteria does not exist, a cube is generated (816). Then, SQL is generated based on the range (820). Once the SQL is generated at 820, the resulting records are retrieved (824). Next, derived measures are calculated and added to the result (828). Upon calculating the derived measures, the information is transmitted to the business intelligence client 240.
In order to calculate the derived measures during runtime, Java bytecode is generated, loaded and executed directly using technology like Jasper or Janino (http://www.janino.net).
These derived measures are evaluated at runtime after the raw measures are aggregated. The derived measures are used to calculate the profit of a set of transactions after the revenue and cost of the transactions are aggregated.
There are two challenges to evaluating the derived measures at runtime. First, some measures have to be calculated by the extract, transform, and load (“ETL”) process for each transaction. For example, there is a need to calculate the discount for a sales transaction whose formula is: Discount$=SalePrice×DiscountRate. The ETL process can capture the discount rate and the sales price. Since the discount rate for each transaction is different, the total discount for a set of transactions may not be determined using the runtime-derived measures after the measures for the transactions are aggregated. The only way to calculate the total discount for the group is to determine the individual discount for each transaction and sum them up.
Second, some measures have to be calculated after matching. For example, if there is a desire to calculate Tax$, whose formula is: Tax$=EBIT×Tax %. Tax % is time dependent and can be different for each transaction. EBIT is a derived measure, which depends on the measures calculated from the matched production records. This situation may not be pushed to the import process because that would mean the entire import process would have to mimic the engine model and configurations to calculate EBIT. This is thus performed at the transaction level, since Tax % cannot be aggregated. The logical spot to add this functionality in is the calculation step within the analytics engine. These types of calculations can be added as the new last step of the calculation process. Further, the calculations can depend on derived measures, which means these calculation steps depend on the evaluation of other derived measures.
To overcome these challenges, the analytics engine is enhanced to calculate derived measures during the calculation phase for each transaction. These derived measures are treated as raw measures during the runtime. If a derived measure is calculated after import but before matching, the calculated value is projected to the matched transactions during the matching phase. If a derived measure is calculated after matching, the formula can include both sales and production measures.
A new column, “EvaluationPoint”, is added to the PVC_DerivedMeasure table. It can contain the follow values:

    
    
        ‘I’—the derived measure is calculated after import but before matching for each transaction
        P—the derived measure is calculated after matching for each transaction
        R—the derived measure is calculated during the runtime
    
    


If the value of “EvaluationPoint” is NULL or the whole “EvaluationPoint” column is missing, it is treated as R and behaves the same as before.
There are three measures that control segmentation. These three measures are specified in the PVC_FunctionParameters. For example,












FunctionID
ParameterName
ParameterValue







segment
SegmentM: specifies the measure for
TotVarContribution



the calculation of the margin




contribution



segment
SegmentR: specifies the measure for
CMHr



the rate used on the Y-axis



segment
SegmentV: specifies the measure for
PQty



the volume used on the X-axis









The formula for these derived measures have to be specified in PVC_DerivedMeasure as well. For example,











DerivedMeasureID
DerivedMeasureExpr







CMHr
TotVarContribution_PUnit * NetLbsPHr


PQty
EXTSalesWeight


TotVarContribution
Margin - DirectLaborCost - Utilities -



PlantSupplies - MaintSupplies -TotDieCost -



PackLabor - PackCost - OtherExpense - Freight









The default presentation of segmentation is to use standard cumulative view with percentile of 15% and 80%. The user can change the view by using the administrative tools, which will modify the file PVC_SegmentsSetup accordingly.
The administrative tools are used to modify the PVC_SegmentsSetup. The following fields exist in the file:













ABCType
Type of the Segment. ‘STD’ or ‘FLOOR’


CumQtyAPct
Accumulative Volume Percentage for Region A; e.g., 80


CumQtyBPct
Accumulative Volume Percentage for Region B; e.g., 15


DPHPaAB
Rate (Y-axis) Percentage for Region A in ‘FLOOR’ segment; e.g., 20


DPHPaBC
Rate (Y-axis) Percentage for Region B in ‘FLOOR’ segment; e.g., 80


FloorValue
The calculator Floor Value in ‘FLOOR’ segment


BFloorInc
The Floor increment for Region B


CFloorInc
The Floor increment for Region C


FixedOH
The ‘FixedOH’ for ‘FLOOR’ calculator


Depreciation
The ‘Depreciation’ for ‘FLOOR’ calculator


SGA
The ‘SG&A’ for ‘FLOOR’ calculator


HoursPay
The ‘Hours/Day’ of Working Periods for ‘FLOOR’ calculator


DaysPWeek
The ‘Days/Week’ of Working Periods for ‘FLOOR’ calculator


WeeksPYear
The ‘Weeks/Year’ of Working Periods for ‘FLOOR’ calculator


ProdPer
‘Production %’ for ‘FLOOR’ calculator


NumOfEquipment
‘Number of Equipment” for ‘FLOOR’ calculator


IncludeAll
This column is optional.



If set to ‘True’, calculation of standard derivation will include the



extreme data points.



(Note: The extreme data points are defined as followed:



The standard derivation (sigma) of the whole data set is calculated. All



data that is larger than (average + 4 * sigma) or smaller than (average − 4



* sigma) are treated as extreme data points.)









Scenarios

A scenario business intelligence application allows the user to change the measures (or derived measures) of the cube (one at a time) and examine the impact to the other measures. When this application is invoked, the data of the cube (generated as PV3_* files) is stored in memory. The updated cube information is also kept in the memory. When the scenario is saved, the most up-to-date data is saved in a table named PV4_*.
If the value of a raw measure is changed in the scenario business intelligence application, it only affects the values of the related derived measures. Since the values of the derived measures are computed during runtime, there is no extra work that the analytics engine has to do. However, if the value of a derived measure is changed, it can impact the other raw measures.
Considering the following derived measure:

labCostPerHr=Labor/Duration

The analytics engine can calculate the average labor cost using the derived measure. However, if the user would like to know what will be the impact if the labor cost is increased by 10%, the user has to make some assumptions regarding which raw measure will be kept constant.

The PVC_DerivedMeasureCallback table specifies how the raw measures will be affected if a derived measure is changed. There are 3 columns in the table












DerivedMeasureID:
CallbackMeasureID:



the name of the
the name of the raw
CallbackMeasureExpr:


derived measure
or derived measure
the expression that


whose value is
whose value will be
is used to reevalulate


changed
reevaluated
the CallbackMeasureID







labCostPerHr
Labor
labCostPerHr * Duration









The analytics engine has the ability to model any production process consisting of multiple steps (activity), equipment (resource), and materials. The analytics engine in fact does not limit the number of steps that are involved in the process. The analytics engine employs a model that consists of materials, activities, and resources to facilitate interaction with the data via a standard BOM view.
Using the above modeling techniques, the analytics engine models the production of any product or services. Each step in the production process is in effect an activity that loads zero or more resources that consumes zero or more inbound materials and produces zero or more outbound materials. The materials can further be qualified by terms that are unique to an organization, such as “finished product”, “part”, etc. This is also true for the other modeling elements. Activities can be qualified as extrusion, paint, etc. Resources can be qualified as press, saw, oven, shift, employee, etc. The qualifications are referred to as attributes.
This BOM information can then be married with a sales transaction, a forecast, budget, a customer order, etc. The same can be said with a production order as well. The customer order refers to the material (or finished product) and the activity (or the last step) that is used to produce it. The activities are linked by the materials that are produced in the preceding step.
The model includes the following tables:

    
    
        PVN_Material—contains the list of all materials which includes finished products, intermediate products and raw materials
        PVN_MaterialBOM—contains the parent and child relationship for the materials; the quantities of the ratio have to be specified
        PVN_MaterialActivity—contains the relationship of the material and its production steps; the batch size of the production step is also specified
        PVN_Activity—contains the list of production steps
        PVN_ActivityResource—contains the relationship of a production step and the equipments that it uses; the duration or amount of the equipment that utilized can be specified
        PVN_Resource—contains the list of the equipment
        PVN_MaterialMeasure, PVN_ActivityMeasure and PVN_ResourceMeasure—contain the measures for the related costs
    
    


Using the relationship specified between activities via the inbound and outbound materials, the analytics engine can determine the unit cost to produce a material. For example, FIG. 17 shows a BOM screen presented by the business intelligence client 240 for a finished product, BA_111677.
The portion of the corresponding table containing the parent and child relationships for the materials, PVN_MaterialBOM, is shown in FIG. 18. At shown, the top level (i.e., BOMLevel is 0), the PV_OutMaterial is equal to PV_MaterialID. At each level, the OutQty should be the same for the same PV_OutMaterial. If the batch size is not specified in PVN_MaterialActivity as MaterialQty, the OutQty will be used as the batch size.
FIG. 19 shows a portion of the corresponding PVN_Material table.
FIG. 20 shows a portion of the corresponding PVN_MaterialActivity table. As shown, the MaterialQty is used as a batch size.
FIG. 21 illustrates a portion of the corresponding PVN_Activity table. Here, the PVN_Constraint is not used and shows a value of 0. If the duration is not specified, the activity duration is calculated using the resources that the activity used.
FIG. 22 shows a portion of the corresponding PVN_ActivityResource table. The “Duration” specifies the time that it takes to produce one batch size for the activity. The batch size is specified in PVN_MaterialActivity (MaterialQty) or PVN_MaterialBOM (OutQty) in the unit of MaterialUOM in PVN_Material. If PV_Constraint is 0, the duration of the resource used will not be added to the activity duration.
FIG. 23 illustrates a portion of the corresponding PVN_Resource table.
FIG. 24 shows a portion of the corresponding PVN_MaterialMeasure table. As can be seen, the unit raw material cost is specified. The naming convention for standard material cost is that the name starts with “S_”. The MeasureFunction should point to “com.pvelocity.rpm.calc.QuantityRateMeasure”. The unit of measure for the unit material cost is the unit of MaterialUOM in PVN_Material.
FIG. 25 shows a portion of the corresponding PVN_ResourceMeasure table. As will be noted, the unit resource cost is specified.
FIG. 26 illustrates a portion of the corresponding PVN_MeasureFunction table. The PVN_MeasureFunction table stores the names of the functions that are used to calculate the BOM related cost.
FIG. 27 shows a BOM screen generated by the business intelligence client 240 showing material costs.
The analytics engine can calculate the unit material cost by using the following steps:

    1. Store all the material costs associated with the material by looking up PVN_MaterialMeasure.
    2. Calculate the unit material cost for its components recursively.
    3. Total the materials costs of all components of the material using the BOM ratio specified in PVN_MaterialBOM.


The analytics engine can also determine the unit equipment cost for a material by using the following steps:

    1. Check whether there is any activity associated with the material by looking up PVN_MaterialActivity.
    2. Store all the activity costs associated with the material by looking up PVN_ActivityMeasure.
    3. Retrieve all the resources that are used by the activity by looking up PVN_ActivityResource.
    4. For each resource, calculate the equipment costs by multiplying the duration specified in PVN_ActivityResource with the resource rate specified in PVN_ResourceMeasure.
    5. The equipment costs obtained from the steps above are used to produce a batch of material. The unit equipment cost will be divided by the batch size specified in PVN_MaterialActivity.

The equipment cost for the finished product can then be determined by sum up its own unit equipment cost and the equipment costs of all its components using the BOM ratio specified in PVN_MaterialBOM.



During the fact table calculation, the standard cost can be added using the following parameters in PVC_FunctionParameter:












FunctionID
ParameterName
ParameterValue







calc
addBOMCostsForSales
YES


calc
addBOMCostsForProduction
YES









On the production perspective, it should record the actual transaction cost.

In a scenario generated via the scenario business intelligence application, a user can change the BOM in order to simulate the following impacts:

    
    
        changes in raw material cost
        changes in the composition of a material
        changes in equipment rate
        changes in batch sizes (or yield)
        switching equipment

The business intelligence client 240 calculates the updated production costs and the associated profitability analysis.

    
    


In order to evaluate the impact of the changes in the BOM, “PV_MaterialID” is included in the cube as one of the “group by”. If the scenario created by a user does not contain this column, the scenario business intelligence application automatically generates another scenario by adding this extra column and storing the linking in a file called PVF_StoredScenarioMaterial.
The changes in the BOM for scenarios are stored in a set of files that correspond to the files storing the BOM information. These files are:

    
    
        PVS_Material
        PVS_MaterialBOM
        PVS_MaterialActivity
        PVS_Activity
        PVS_ActivityResource
        PVS_Resource
        PVS_MaterialMeasure, PVS_ActivityMeasure and PVS_ResourceMeasure
    
    


The layouts of these files are exactly the same as the layouts for the corresponding files except the fact that there are two extra columns:

    
    
        ScenarioID—this stores the name of the scenario which the changes apply to
        PV_Remove—the entry is removed for the scenario
    
    


The capacity display shows the duration that an equipment entity has been used for during a transaction and its associated profitability measures. This information can be invoked at the main menu, which shows the actual usage, or in a scenario, which shows the standard BOM usage. In order to show the actual usage, the PVN_ProductionMeasure is populated with actual data. In the scenario, the capacity display shows the equipment usage for standard BOM.
In the PVC_FunctionParameter, three parameters are added:












FunctionID
ParameterName
ParameterValue







calc
ResourceDuration: measure for the
ResourceDuration



resource duration used in the




standard BOM



calc
TransactionResourceDuration: measure
Duration



for the transaction resource duration.




This data has to be supplied in the




PVN_ProductionMeasure



calc
ProfitMeasures: measure for
CM,CMPHr



profitability analysis




obtained by joining the




PVD_TransactionResource table;




the profitability of a resource




is determined by all the




transactions that utilize that




resource









In the scenario business intelligence application, both raw material rate and equipment rate can be changed. In some cases, it can be convenient to group a set of materials/resources and present as a single unit to the user so that when the rate is changed, individual element within the group are updated with the single changed value. This is especially true for cases when rates are maintained in PV by periods, such as by week or month.
Material and/or resource grouping is controlled by parameters defined in the PVC_FunctionParameters table.












FunctionID
ParameterName
ParameterValue







calc
RawMaterialGroupField:
MaterialID



specifies the Material Attribute




Name for Material grouping



calc
ResourceGroupField: specifies
PV_ ResourceID



the Resource Attribute Name for




Resource grouping









In querying the rate for the defined group, the record associated with the largest ID is returned. It is accomplished using the SQL function MAX( ) on the ID of the group. That is, for material grouping, MAX(PV_MaterialID) is used and for resource grouping, MAX(PV_ResourceID) is used.
Computer-executable instructions for implementing the analytics engine and/or the method for providing business intelligence data on computer system could be provided separately from the computer system, for example, on a computer-readable medium (such as, for example, an optical disk, a hard disk, a USB drive or a media card) or by making them available for downloading over a communications network, such as the Internet.
While the analytics server described in the embodiment above provides business intelligence data from a single source system, those skilled in the art will appreciate that the analytics server may receive and aggregate data from two or more source systems in providing business intelligence data.
While the invention has been described with specificity to certain operating systems, the specific approach of modifying he methods described hereinabove will occur to those of skill in the art.
While the analytics server is shown as a single physical computer, it will be appreciated that the analytics server can include two or more physical computers in communication with each other. Accordingly, while the embodiment shows the various components of the server computer residing on the same physical computer, those skilled in the art will appreciate that the components can reside on separate physical computers.
The above-described embodiments are intended to be examples of the present invention and alterations and modifications may be effected thereto, by those of skill in the art, without departing from the scope of the invention that is defined solely by the claims appended hereto.







APPENDIX A





Pseudo-code for Source Data Importation















Sales Record


 Populate the PVN_Sales using sales records from ERP.


 For each sales record in PVN_Sales


  Call routine “Add/Update BusinessPartner” for CustomerID and CustomerType


  Call routine “Add/Update BusinessPartner” for ShipToID and ShipToType


  Call routine “Add/Update SalesRep” for SalesRepID


  Call routine “Add/Update Material” for MaterialID using the PlantID of the sales record.


  Call routine “Add/Update Activity” for ActivityID using the PlantID of the sales record.


  For each attribute of the Sales record that is interested


   Check whether the AttributeID is in PVN_AttributeCategory. If not, add a record. For


    example, discount code:












AttributeID
AttrbuteName
AttributeDescription





discount
Discount Code
Discount Code










   Add a record to PVN_SalesAttribute to store the attribute values for the SalesRep. For


    example, if the attribute value is a code, add a record to


    PVN_AttributeValueDescription to store the description of the value:















PlantID
InvoiceID
InvoiceLine
Shipment
AttributeID
Attribute Value





ABC
INV00001
1
0
discount
Summer










  End-for


  Retrieve the list of resources (e.g. freight) associated with the Sales


   For each resource, call routine “Add/Update Resource”


   Add a record to PVN_SalesResource to indicate the usage.


 End-for


Production Record


 Populate the PVN_Production using production records from ERP.


 For each production record in PVN_Production


  Call routine “Add/Update Material” for MaterialID using the PlantID of the production


  record.


  For each attribute of the Production record that is interested


   Check whether the AttributeID is in PVN_AttributeCategory. If not, add a record. For


    example, production type












AttributeID
AttrbuteName
AttributeDescription





prodtype
Production Type
Production Type










   Add a record to PVN_ProductionAttribute to store the attribute values for the


    production. For example,













PlantID
ProductionID
AttributeID
Attribute Value





ABC
P10000
prodtype
T1










   If the attribute value is a code, add a record to PVN_AttributeValueDescription to store


    the description of the value.


  End-for


  Call routine “Add/Update Activity” for ActivityID using the PlantID of the production


  record.


 End-for


Routine “Add/Update BusinessPartner”


 If the “PartnerID/PartnerType” in PVN_BusinessPartner does not exist or the modified date is old


  Retrieve the customer record from ERP


  Update the PVN_BusinessPartner record information


  For each customer grouping that is interested


   Check whether the PartnerGroupID is in PVN_BusinessPartnerGroupCategory. If not,


    add a record. For example, Sales Organization, Distribution Channel, Division,


    Customer Hierarchy, Market, Submarket, etc.












PartnerGroupID
PartnerGroupName
PartnerGroupDescription





salesorg
Sales Organization
SAP Sales Organization


hierarchy
Customer Hierarchy
SAP Customer Hierarchy


market
Market Segment
Market segmentation










   Check whether the value exists in PVN_BusinessPartnerGroup. If not, add a record.


    For example,












PartnerGroupID
PartnerGroupValue
PartnerGroupParent





salesorg
3000



market
Electronics










   For hierarchical grouping like “Customer Hierarchy”, add its parent and all its ancestors.


   Add a record in PVN_BusinessPartnerGrouping













PartnerGroupID
PartnerGroupValue
PartnerID
PartnerType





salesorg
3000
1234










  End-for


  Call routine “Add/Update Region” for RegionID of PVN_BusinessPartner record


  Update the fields “ParentID/ParentType” according to the “PartnerType”. (Note:


  ParentID/ParentType is used to model only the ship-to or bill-to relationship.)


  Update the modified date of PVN_BusinessPartner


 End-if


Routine “Add/Update Region”


 If the “RegionID” in PVN_Region does not exist or the modified date is old


  Add a record to PVN_Region and checks its parent.


 End-if


Routine “Add/Update Sales Rep”


 If the “RepID” in PVN_SalesRep does not exist or the modified date is old


  Retrieve the sales rep record from ERP


  Update the PVN_SalesRep record information


  For each attribute of the SalesRep that is interested


   Check whether the AttributeID is in PVN_AttributeCategory. If not, add a record. For


    example, Cost Center












AttributeID
AttrbuteName
AttributeDescription





costcenter
Cost Center
SAP Cost Center










   Add a record to PVN_SalesRepAttribute to store the attribute values for the SalesRep.


    For example,












SalesRepID
AttributeID
Attribute Value





7777
costcenter
12345










   If the attribute value is a code, add a record to PVN_AttributeValueDescription to store


    the description of the value.


  End-for


  Retrieve the list of the regions for the sales rep


   For each of the region, call “Add/Update Region” and add a record to


    PVN_SalesRegionRep.


  Update the modified date of PVN_SalesRep


  If the sales rep has a parent, recursively call this routine for the parent


 End-if


Routine “Add/Update Material”


 If the “MaterialID” in PVN_Material does not exist or the modified date is old


  Retrieve the material (or item) record from ERP


  Update the PVN_Material record information


  For each attribute of the Material that is interested


   Check whether the AttributeID is in PVN_AttributeCategory. If not, add a record. For


    example, color, classification












AttributeID
AttrbuteName
AttributeDescription





weight
Weight
Weight in Kg


color
Color
Color


classification
Product group
Product group










   Add a record to PVN_MaterialAttribute to store the attribute values for the Material. For


    example,













PlantID
MaterialID
AttributeID
Attribute Value





ABC
PRODUCT1
color
Red


ABC
PRODUCT1
classification
Electronics










   Add a record to PVN_AttributeValueDescription if the value is a code.


  End-for


  If the material is purchased


   Retrieve the list of the suppliers (Note: the differences in the cost for using different


    suppliers are not modeled.)


   For each supplier, add a record to PVN_MaterialSupplier


  End-if


  If the material is manufactured


   Retrieve BOM for the material from ERP


   For each component, call “Add/Update Material” recursively.


   Traverse the BOM and add the records to PVN_MaterialBOM.


   Retrieve the list of activities (or recipes) for the material.


    For each activity, call routine “Add/Update Activity”


  End-if


  For each type of material cost (or measure)


   Check whether the MeasureID is in PVN_MeasureCategory. If not, add a record. For


    example, standard cost















Measure
Measure
Measure
Measure


MeasureID
Description
Type
Currency
CalcUOM





stdcost
Standard
1
USD
kg



Cost










   Add a record to PVN_MaterialMeasure to store the cost for the material. For example,













PlantID
MaterialID
MeasureID
Measure





ABC
PRODUCT1
stdcost
12.50










  End-for


  Update the modified date of PVN_Material.


 End-if


Routine “Add/UpdateActivity”


 If the “ActivityID” in PVN_Activity does not exist or the modified date is old


  Retrieve the activity (or recipe/routing) record from ERP


  Update the PVN_Activity record information


  For each attribute of the Activity that is interested


   Check whether the AttributeID is in PVN_AttributeCategory. If not, add a record. For


    example, actgroup












AttributeID
AttrbuteName
AttributeDescription





actgroup
Activity Group
Activity Grouping










   Add a record to PVN_ActivityAttribute to store the attribute values for the Activity. For


    example,













PlantID
ActivityID
AttributeID
Attribute Value





ABC
painting
actgroup
Decoration










   Add a record to PVN_AttributeValueDescription if the value is a code.


  End-for


  For all the sub-activities (like operations in SAP), recursively call this routine “Add/Update


  Activity” for the sub-activities.


  Retrieve the list of resources associated with the Activity


   For each resource, call routine “Add/Update Resource”


   Add a record to PVN_ActivityResource to indicate the usage.


  Update the modified date of PVN_Activity


 End-if


Routine “Add/UpdateResource”


 If the “ResourceID”in PVN_Resource does not exist or the modified date is old


  Retrieve the resource record from ERP


  Update the PVN_Resource record information


  For each attribute of the Resource that is interested


   Check whether the AttributeID is in PVN_AttributeCategory. If not, add a record. For


    example, resource category












AttributeID
AttrbuteName
AttributeDescription





Rscgroup
Resource Group
Resource Grouping










   Add a record to PVN_ResourceAttribute to store the attribute values for the Resource.


    For example,













PlantID
ResourceID
AttributeID
Attribute Value





ABC
PS-10
rscgroup
Paint Shop










   Add a record to PVN_AttributeValueDescription if the value is a code.


  End-for


  For eachtype of resource cost or measure (production cost)


   Check whether the MeasureID is in PVN_MeasureCategory. If not, add a record. For


    example, standard cost















Measure
Measure
Measure
Measure


MeasureID
Description
Type
Currency
CalcUOM





paintlabor
Painting
2
USD
hr











labor












   Add a record to PVN_ResourceMeasure to store the cost for the resource. For example,













PlantID
ResourceID
MeasureID
Measure





ABC
PS-10
paintlabor
20.00










  End-for


  Update the modified date of PVN_Resource


 End-if
















APPENDIX B





Pseudo-code for Denormalization of Dimension Related Tables















PVND_BusinessPartner Record


 Create a new table PVND_BusinessPartner like PVN_BusinessPartner (only schema, no data)


 For each record in PVN_BusinessPartnerGroupCategory


  Add a new column to PVND_BusinessPartner using the PartnerGroupID


 End-for


 Populate the data in PVND_BusinessPartner using the left join table of PVN_BusinessPartner,


   PVN_BusinessPartnerGrouping and PVN_BusinessPartnerGroupCategory. (Note: the


   hierarchical relationship remains in PVN_BusinessPartnerGroup.)


PVND_SalesRep Record


 Create a new table PVND_SalesRep like PVN_SalesRep (only schema, no data)


 For each attribute record (AttributeID) in PVN_AttributeCategory that exists in


 PVN_SalesRepAttribute


  Add a new column to PVND_SalesRep using the AttributeID


 End-for


 Populate the data in PVND_SalesRep using the left join table of PVN_SalesRep,


   PVNSalesRepAttribute and PVN_AttributeCategmy.


Use the similar algorithm of PVND_SalesRep to generate PVND_Material, PVND_Resource,


  PVND_Activity.
















APPENDIX C





Pseudo-code for Measure Calculation Process















Routine “Calculate Measure”


 For each PVN_Production record


  Call “Calculate Material Measure” for the MaterialID


  Call “Calculate Activity Measure” for the ActivityID


  Calculate the Production measures using PVN_ProductionMeasure and add them to


    PVF_Facts.


  Use the Matching Algorithm to locate one or more PVN_Sales records.


   Calculate the sales measures of PVN_Production using the weighted average of


      PVN_Sales records.


 End-for


 For each PVN_Sales record


  For each PVN_SalesResource that belongs to this PVN_Sales


   For each PVN_ResourceMeasure that belongs to the resource


     Calculate the amount by using “Measure”, “Duration”, “MaterialQty”, etc... 


       (Note: this calculation will be MeasureType dependent)


     Add a record to PVF_Facts.


   End-for


  End-for


  Calculate the Saleas Measures using PVN_SalesMeasure and add them to PVF_Facts.


  Use the Matching Algorithm to locate the PVN_Production record.


   Calculate the production measures of PVN_Sales using the weighted average of


      PVN_Production records.


   Call “Calculate Activity Measure” for the ActivityID using the MaterialID and


      MaterialQty


 End-for


Routine “Calculate Material Measure”


 For each PVN_MaterialMeasure record that belongs to


  Calculate the amount


  Add a record to PVF_Facts.


 End-for


 Use PVN_MaterialBOM to retrieve the first level of components.


  For each component, recursively call “Calculate Material Measure”.


 Use PVN_MaterialActivity to retrieve all the activities that can produce the material.


  For each activity, call “Calculate Activity Measure”.


 Add a record to PVF_Facts (with EntityType set to 1) where


  Amount = average(?) of activity cost + sum of the components cost. (Note: this 


    amount is used only if standard cost is not available).


Routine “Calculate Activity Measure” for MaterialID, MaterialQty


 For each PVN_ActivityResource that belongs to this PVN_Activity


  For each PVN_ResourceMeasure that belongs to the resource


   Calculate the amount by using “Measure”, “Duration”, “MaterialQty”, etc... 


      (Note: this calculation will be MeasureType dependent.)


   Add a record to PVF_Facts.


  End-for


 End-for


 For each sub-activity,


  recursively call “Calculate Activity Measure”.


  Add a record to PVF_Facts.


 End-for

## Claims

1. A machine implemented method for efficient retrieval of derived data, comprising:
importing source data into an analytics server executing an analytics engine;
normalizing, by the analytics engine, the imported data to create normalized data tables;
generating, by the analytics engine, from the normalized data tables, a plurality of dimension tables, including a time dimension table, followed by a time month dimension table and a time week dimension table;
generating, by the analytics engine, from the normalized data tables, a plurality of measures and a master facts table containing data for two or more categories;
generating, by the analytics engine, a plurality of baby fact tables, each comprising a subset of the master facts table;
generating, by the analytics engine, a plurality of cubes from the baby fact tables, the cubes aggregating data in the baby fact tables by at least one of the categories;
creating, by the analytics engine, a pool of generalized baby fact tables,
receiving a query;
upon receiving the query,
searching for the most specific baby fact table available in the pool to satisfy the query;
failing to find the most specific baby fact table;
upon failing to find the most specific baby fact table, recording a miss, creating a new cube and recording the cube creation time for the new cube; and
based on said recording of the miss, pre-generating the most specific baby fact table for use in subsequent queries;
wherein, the cube creation time using baby fact tables is smaller than the cube creation time using the master fact tables, thereby speeding up the generation of the cubes.

2. The method of claim 1, wherein the most specific baby fact table includes a plurality of dimensions required by the query.

3. The method of claim 1, further comprising loading, by the analytics engine, the pool of generalized baby fact tables into memory at startup of the analytics engine.

4. The method of claim 1, wherein selecting the generalized baby fact table from the pool of generalized baby fact tables is based on the generalized baby fact table row count.

5. The method of claim 4, wherein selecting the generalized baby fact table based on the generalized baby fact table row count comprises selecting a first generalized baby fact table over a second generalized baby fact table having more rows than the first generalized baby fact table.

6. The method of claim 4, wherein if the first and second generalized baby fact tables have the same row count, selecting the generalized baby fact table with fewer dimensions.

7. The method of claim 1, further comprising if the generalized baby fact table that can satisfy the query is not found, recording a fact table pool miss.

8. The method of claim 7, wherein the analytics engine pre-generates the missed generalized baby fact table.

9. The method of claim 1, further comprising storing, by the analytics engine, information on a most used generalized baby fact table that can satisfy the query, and creating more indexes for the most used generalized baby fact table.

10. The method of claim 1, wherein the time aggregated category baby fact tables are created for the sales and production categories only.

11. A system for efficient retrieval of derived data, comprising:
an analytics server in communication with one or more computing devices, accessing a master database table stored on a computer readable medium and executing computer readable instructions for an analytics engine on said computer readable medium for:
importing the source data into an analytics server executing an analytics engine;
normalizing, the imported data to create normalized data tables;
generating, from the normalized data tables, a plurality of dimension tables, including a time dimension table, followed by a time month dimension table and a time week dimension table;
generating, from the normalized data tables, a plurality of measures and a master facts table containing data for two or more categories;
generating, by the analytics engine, a plurality of baby fact tables, each comprising a subset of the master facts table;
generating, by the analytics engine, a plurality of cubes from the baby fact tables, the cubes aggregating data in the baby fact tables by at least one of the categories;
creating a pool of generalized baby fact tables,
receiving a query;
upon receiving the query, searching for the most specific baby fact table available in the pool to satisfy the query;
failing to find the most specific baby fact table;
upon failing to find the most specific baby fact table, recording a miss, creating a new cube and recording the cube creation time for the new cube; and
based on said recording of the miss, pre-generating the most specific baby fact table for use in subsequent queries;
wherein, the cube creation time using baby fact tables is smaller than the cube creation time using the master fact tables, thereby speeding up the generation of the cubes.

12. The system according to claim 11 wherein the most specific baby fact table includes a plurality of dimensions required by the query.

13. The system according to claim 12 wherein the computer readable instructions further include instructions for loading the pool of generalized baby fact tables into memory at startup of the analytics engine.

14. The system according to claim 11 wherein selecting the generalized baby fact table from the pool of generalized baby fact tables is based on a row count of the generalized baby fact table.

15. The system according to claim 14 wherein selecting the generalized baby fact table based on the row count comprises selecting a first generalized baby fact table over a second generalized baby fact table having more rows than the first generalized baby fact table.

16. The system according to claim 11 wherein the computer readable instructions further include instructions for if the first and second generalized baby fact tables have the same row count, selecting the generalized baby fact table with the fewer dimensions.

17. The system according to claim 11 wherein the computer readable instructions further include instructions for if the generalized baby fact table that can satisfy the query is not found, recording a generalized fact table pool miss.

18. The system according to claim 17 wherein the computer readable instructions further include instructions for pre-generating the missed generalized baby fact table.

19. The system according to claim 11 wherein the computer readable instructions further include instructions for storing information on a most used generalized baby fact table that can satisfy the query, and creating more indexes for the most used generalized baby fact table.

20. The system according to claim 11 wherein the time aggregated category baby fact tables are created for the sales and production categories only.