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