inventory case study. introduction optimized inventory levels in stores can have a major impact on...
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Business IntelligenceINVENTORY CASE STUDY
IntroductionOptimized inventory levels in stores can
have a major impact on chain profitability:minimize out-of-stocksreduce overall inventory carrying costs
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
Value chain
We will examine this in
our Analysis Services
project
Value chain example
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
BI Inventory ModelsThe three main models discussed:
Inventory Periodic SnapshotInventory TransactionsInventory Accumulating Snapshot
They are complementary models, and provide different information about the Inventory
Periodic SnapshotThe most common inventory schemeExample 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
Simple Inventory Periodic Snapshot
Usage:Provide information about inventory levels:1. Daily Inventory level 2. Average Inventory level over a time period
Problems:
1. Inventory levels are semi-additive (i.e. NOT additive through each dimension)
Through the Date dimension the quantity on hand is NOT additive
2. 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
Enhanced Inventory Periodic Snapshot
Velocity of inventory movement becomes measurable
Key concepts:Number of TurnsNumber of days’ supplyGrowth Margin Return on Inventory (GMROI)
Extra recorded facts
measure daily Over a period
Number of Turns
Number of days’ supply
GMROI
Enhanced Inventory Periodic Snapshot
Extra recorded facts
final quantityon hand
average quantity sold
total quantity sold
dayily averagequantityonhand
quantity sold
quantityonhand
quantityon hand
quantity sold
totalquantitysold x (valueat latest selling price - valueat cost)quantityon hand
daily average quantityon hand x value at the latest selling price
Enhanced Inventory Periodic SnapshotGMROI - Growth Margin Return on Inventory
totalquantitysold x (valueat latest selling price - valueat cost)quantityon hand
daily average quantityon hand x value at the latest selling price
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!!!
Inventory TransactionsR
eco
rd e
very
tra
nsa
ctio
n
that
aff
ect
s in
ven
tory
:
Remove product from inventory
Return product to inventory from customer return
Receive product from customer
Ship product to customer
Package product for shipment
Pick product from bin
Authorize product for sale
Place product in bin
Return product to vendor due to inspection failure
Release product from inspection hold
Place product into inspection hold
Receive product
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?
Inventory Accumulating SnapshotIn 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
In progress!!!
Inventory Accumulating Snapshot
Fact Table Type ComparisonPeriodic Snapshot Transaction Accumulating Snapshot
Time period represented
Regular predictable intervals
Point in timeIndeterminate time span, typically short lived
Grain One row per periodOne row per transaction event
One row per life
Table loads Insert Insert Insert and update
Row updates
Not revisited Not revisited Revisited whenever activity
Date dimension
End-of-period Transaction dateMultiple dates for standard milestones
FactsPerformance for predefined timeinterval
Transaction activity
Performance over finite time
Value Chain IntegrationIntegrating 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
Data Warehouse Bus ArchitectureBus:
“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.
Data Warehouse ArchitectureKimball vs. Inmon
Bill Inmon and Ralph Kimball – the co-founders of the data warehouse concept and their views on data warehouse architectureDependent 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.
Bus MatrixThe tool we use to document the Data
Warehouse Bus ArchitectureA part technical, part management, part
communication toolBusiness processes as ROWSCommon dimensions as Columns
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
Conformed DimensionsWhat are conformed dimensions:
The cornerstone of the Bus ArchitectureA single, coherent view of data across the
enterprise that can be reused across different Data Marts.
Conformed dimensions have:Consistent dimension keysConsistent attribute valuesConsistent naming, attribute definitions.
Conformed dimensions (cont.)Some characteristics of conformed
dimensionsEach conformed dimension has the same
meaning in each Data MartThey are defined at the most granular level
possible
Conformed dimensions (cont.)Some considerations when defining
conformed dimensionsRolled-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
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 authorityHas responsibility for defining,
maintaining and publishing dimensions and their subsets to all Data Marts
Conformed FactsConformed 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 factIdentical labelingUnderlying definitions and equations