Business Intelligence

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

9. Enhanced Inventory Periodic Snapshot Extra recorded facts

10. Enhanced Inventory Periodic Snapshot GMROI - Growth Margin Return on Inventory • 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 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

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 • Bus: • “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 : • 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