INVENTORY CASE STUDY

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

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

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 Turns  Number of days’ supply  Growth Margin Return on Inventory (GMROI) Extra recorded facts

measuredailyOver a period Number of Turns Number of days’ supply GMROI Enhanced Inventory Periodic Snapshot Extra recorded facts

Enhanced Inventory Periodic Snapshot GMROI - Growth Margin Return on Inventory TurnsGross margin High GMROIlots of turnshigh gross margin Low GMROIlow turnslow 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 Transactions Record every transaction that affects inventory: 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 Snapshot 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 In progress!!!

Inventory Accumulating Snapshot

Fact Table Type Comparison Periodic SnapshotTransactionAccumulating Snapshot Time period represented Regular predictable intervals Point in time Indeterminate time span, typically short lived GrainOne row per period One row per transaction event One row per life Table loadsInsert Insert and update Row updatesNot revisited Revisited whenever activity Date dimension End-of-periodTransaction date Multiple dates for standard milestones Facts Performance for predefined time interval Transaction activityPerformance over finite time

General Notes Analysis services presentation: P – semi additive facts

Notes CH6 Aggregate Functions Types in Analysis Services 2008: 1. Additive a) SUM b) COUNT 2. Pseudo-additive a) MIN b) MAX 3. Non-additive a) DistinctCount b) None 4. Semi-additive a) FirstChild b) LastChild c) FirstNonEmpty d) LastNonEmpty e) AverageOfChildren f) ByAccount The functions can be used as a out-of-the-box functions in Analysis services in creating measures

Additive, Pseudo- and Non-Additive Aggregate Functions Aggregate FunctionCategoryDescription SUMAdditive

Cube demo Data Source specifications The Data Source wizard

Cube demo Data Source View specifications The Data Source View wizard

Cube demo Implementing named calculations Creating a user friendly date

Cube demo Implementing named queries

Cube demo Creating dimensions with the Dimension wizard