1. Business Intelligence
INVENTORY CASE STUDY

2. Introduction
Optimized inventory levels in stores can have a major impact on chain profitability:
minimize out-of-stocks
reduce overall inventory carrying costs

3. We will examine this in our Analysis Services project
Value chain example
We will examine this in our Analysis Services project
Value chain
What is the primary objective of most analytic decision support systems ?
 monitor the performance results of key business processes
each business process produces unique metrics at unique time intervals with unique granularity and dimensionality
each process typically spawns one or more fact tables
value chain provides high-level insight into the overall enterprisedata warehouse

4. Some Common Questions related to Inventory
How did the inventory level changed per product, per warehouse over time?
How is the profitability of products in our inventory?
How many times have we placed a product into an inventory bin on the same day we picked the product from the same bin at a different time?
How many separate shipments did we receive from a given vendor, and when did we get them?
On which products have we had more than one round of inspection failures that caused return of the product to the vendor?
… etc.
 BI helps answering these questions

5. BI Inventory Models The three main models discussed:
Inventory Periodic Snapshot
Inventory Transactions
Inventory Accumulating Snapshot
They are complementary models, and provide different information about the Inventory

6. Periodic Snapshot The most common inventory scheme
Example of Retail Store Chain Inventory:
The assumed atomic level of detail is:
Inventory per product
Per day
Per Store
Basic dimensions:
Product
Day
Store
Fact:
Inventory

7. Simple Inventory Periodic Snapshot
Usage:
Provide information about inventory levels:
Daily Inventory level
Average Inventory level over a time period
Problems:
Inventory levels are semi-additive (i.e. NOT additive through each dimension)
 Through the Date dimension the quantity on hand is NOT additive
Historical Inventory data using daily granularity results in unreasonably huge amount of data over time
 Suggestion to define distinct atomic time period for short and long term measures

8. Enhanced Inventory Periodic Snapshot
Extra recorded facts
Velocity of inventory movement becomes measurable
Key concepts:
Number of Turns
Number of days’ supply
Growth Margin Return on Inventory (GMROI)

9. Enhanced Inventory Periodic Snapshot
Extra recorded facts
measure
daily
Over a period
Number of Turns
Number of days’ supply
GMROI

10. Enhanced Inventory Periodic Snapshot
GMROI - Growth Margin Return on Inventory
Turns
Gross margin
High GMROI
lots of turns
high gross margin
Low GMROI
low turns
low gross margin
GMROI is a standard metric used by inventory analysts to judge a company’s quality of investment in its inventory.
We do not store GMROI in the fact table because it is not additive!!!

11. Inventory Transactions
Receive product
Place product into inspection hold
Release product from inspection hold
Return product to vendor due to inspection failure
Place product in bin
Authorize product for sale
Pick product from bin
Package product for shipment
Ship product to customer
Receive product from customer
Return product to inventory from customer return
Remove product from inventory
Record every transaction that affects inventory:

12. Inventory Transactions
Use: Measure the frequency and timing of specific transaction types
Example:
How many times have we placed a product into an inventory bin on the same day we picked the product from the same bin at a different time?
How many separate shipments did we receive from a given vendor, and when did we get them?
On which products have we had more than one round of inspection failures that caused return of the product to the vendor?

13. Inventory Accumulating Snapshot
In progress!!!
In a single fact table row we track the disposition of the product shipment until it has left the warehouse
only possible if we can reliably distinguish products delivered in one shipment from those delivered at a later time
also appropriate if we are tracking disposition at very detailed levels, such as by product serial number or lot number

14. Inventory Accumulating Snapshot

15. Fact Table Type Comparison
Periodic Snapshot
Transaction
Accumulating Snapshot
Time period represented
Regular predictable intervals
Point in time
Indeterminate time span, typically short lived
Grain
One row per period
One row per transaction event
One row per life
Table loads
Insert
Insert and update
Row updates
Not revisited
Revisited whenever activity
Date dimension
End-of-period
Transaction date
Multiple dates for standard milestones
Facts
Performance for predefined time
interval
Transaction activity
Performance over finite time

16. Value Chain Integration
Integrating business processes together benefits:
Intelligence aspects:
Better understand customer relationships from an end-to-end perspective
Observe information across business processes
Technological aspects:
Reusability
Less resources used
Question: How do we properly integrate all business processes in the enterprise?
Answer: Data Warehouse Architecture

17. Data Warehouse Bus Architecture
“Common structure to which everything can and is connected”
Data Warehouse Bus Architecture:
Defining a standard warehouse architecture (bus interface) to which different data marts can connect.
Standardizes dimensions and facts that have uniform interpretation across the enterprise.
Architectural framework for the overall design and separate data marts following the framework.

18. Data Warehouse Architecture Kimball vs. Inmon
Bill Inmon and Ralph Kimball – the co-founders of the data warehouse concept and their views on data warehouse architecture
Dependent Data Mart Structure (Inmon)
Let everyone build what and when they want and we will integrate it if we need it.
Each data mart gets information from the operational data base and then data is loaded in the data warehouse
Data Warehouse Bus Structure (Kimball)
Design everything then build.
The data warehouse is responsible for loading data in the data marts from the operational database.

19. Bus Matrix
The tool we use to document the Data Warehouse Bus Architecture
A part technical, part management, part communication tool
Business processes as ROWS
Common dimensions as Columns

20. Bus Matrix (cont.) Rows : Columns:
Business processes
A business process translates into a First-Level Data Mart
Each Data Mart spanning over multiple business processes translates into a Consolidated Data Mart (E.G. Profitability)
Columns:
Common Dimension used across the enterprise
Consequences of improper or non-existent bus matrix:
Isolated data marts blocking the coherent warehouse environment, narrowing down the scope of information to be viewed.
Expansion of the data warehouse is nearly impossible

21. Conformed Dimensions What are conformed dimensions:
The cornerstone of the Bus Architecture
A single, coherent view of data across the enterprise that can be reused across different Data Marts.
Conformed dimensions have:
Consistent dimension keys
Consistent attribute values
Consistent naming, attribute definitions.

22. Conformed dimensions (cont.)
Some characteristics of conformed dimensions
Each conformed dimension has the same meaning in each Data Mart
They are defined at the most granular level possible

23. Conformed dimensions (cont.)
Some considerations when defining conformed dimensions
Rolled-up dimensions
Rolled-up dimensions – having higher level of granularity
Rolled-up dimensions conform to the base-level atomic dimension if they are a strict subset of that dimension

24. Conformed dimensions (cont.) - Considerations (cont.)
Dimension subsetting
Two dimensions with same level of detail but representing different subsets of rows or columns
Rolled-up dimensions are another example of dimension subsetting
Advised Solution – dimension authority
Has responsibility for defining, maintaining and publishing dimensions and their subsets to all Data Marts

25. Conformed Facts Conformed facts are:
Facts used living in more that one data mart.
Same rules and characteristics apply in designing and implementing them as with conformed dimensions
Few more considerations are:
Units of measure for the fact
Identical labeling
Underlying definitions and equations