Operation Data Analysis Hints and Guidelines EGN 5621 Enterprise Systems Collaboration Summer B, 2014

Operation Data Analysis Operation Data Analysis Hints and GuidelinesHints and Guidelines

EGN 5621 Enterprise Systems CollaborationSummer B, 2014

Tools to Analyze DataTools to Analyze DataTools to analyze data range from

simple to complexReports and graphsAdvanced statistics forecasting modelsAdvanced optimization models and

toolsHaving the right people mattersHaving data modeling

A Large Quantity of Quality DataA Large Quantity of Quality Data

All analytic methods feeds on data – in large quantity and good quality

Having good data can be turned into a competitive advantage

Integrated organizations have a lot of data available, they must learn to exploit it

Interpreting DataInterpreting Data

Skills are required to create appropriate graphs, reports, and statistical analysis

Skills are required to interpret correctly graphs, reports and statistics

Skills are required to make the appropriate decisions from the analytics

Using Queries to Analyze DataUsing Queries to Analyze DataA primary key is an attribute, or a combination of attributes, that identify in a unique way each row in a table. A primary key has to always contain a value; that is to say, it cannot be empty

A foreign key is an attribute of a table (that can be composed) that is a primary key of the table to which is linked.

There is an important concept associated with a foreign key: referential integrity. We say that a relation has referential integrity if all the values of a foreign key attribute in a table exist in the table where this attribute is a primary key.

Using Queries to Analyze DataUsing Queries to Analyze Data


A logical data model of a 1 to N relationship


A logical data model of an N to N relationship


A logical data model of a relational database


Using Queries to Analyze DataUsing Queries to Analyze DataMetadata of a relational databaseMetadata (from the Greek "meta" "after, beyond, with" and the Latin word "data" "information") is data about data.

Metadata is used to describe or describe another data. In a database, the metadata correspond to the information about the data in the fields of the tables. They define the shell containing the data. Thus, prior to populate a database of its content, it is important to create the shell or envelope that will contain and describe the data of the database.

In practice, metadata describes the list of tables, the list of attributes, the format (or data types) of the attributes, the restrictions on the data, the consistency rules to apply (e.g., referential integrity and mandatory field rules), the type of relations and joins between tables etc.



Queries contain 2 basic elements: (i)Key Figures, KPI(ii) Dimensions.

Margins as a function of time

Sales by country

An ExampleAn Example

MeasuresDimensionsDimensions

Elements of an Info CubeElements of an Info Cube

Key figuresDimensions

Types of MeasuresTypes of MeasuresAdditive : it makes sense to sum the measures

across all dimensions◦ Quantity sold across Region, Store, Salesperson, Date,

Product …semi additive : additive only across certain

dimensions◦ Quantity on hand is not additive over Date, but it is

additive across Store and Productnon additive : cannot be summed across any

dimensions◦ A ratio, a percentage

A measure that is non additive on one dimension may be the object of other data aggregations◦ Average, Min, Max of quantities on hand over time

How DW Differs from a Transactional DB?How DW Differs from a Transactional DB?

Characteristic DB DW

Operation Real-time, transactional Decision support, strategic analysis

Model Entity-Relationship Star Schema

Redundant data Designed to avoid Permitted

Data Raw data, current Aggregated, Historical data,

# of users Many Few

Update Immediate Deferred

Calculated fields None stored Many stored

Mental model Tabular Hypercube

Queries Simple, some saved Complex, many saved

Operations Read / Write Read Only

Size Go (Gigabytes) To(Terabytes)

Exploring Data

Plant A: An overviewPlant A: An overview

Plant B : an OverviewPlant B : an Overview

Plant C an OverviewPlant C an Overview

Trying to Maintain Stocks for All Trying to Maintain Stocks for All ProductsProducts

Large Variations in Sales per StepLarge Variations in Sales per Step

Manipulating Graphs

Key Figure or KPIKey Figure or KPI

Graph type: Scattered BarsGraph type: Scattered Bars

Graph Type: Scattered LinesGraph Type: Scattered Lines

Graph Type: LinesGraph Type: Lines

Graph Type: 3D BarsGraph Type: 3D Bars

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BI Questions

BI Question 1BI Question 1

Current assets include(i) cash(ii) receivables(iii) raw material inventory (for mfg game)(iv) finished product inventory

How well have the teams performed in managing the current assets over time?

Hint: Use the financial data

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Did the winning team bring their highest margin product to market first?

Did they charge a price premium while they were first to market?

Can you see the impact of a competitor entering the market?

Hint: Use the operational data

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One objective of materials management is to make sure that raw materials are available for production when needed

Which company has managed this process well as shown by having the largest variety of products in stock?

Hint: Use inventory data by products over time

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Companies may have different strategies for production management◦Some may prefer long productions to minimize

setup losses, while others may prefer shorter runs to respond more quickly to market opportunities

Can you determine what strategies were used by each team?

Where there any production disruptions?Hint: Use production data over time and

products. Filter for each individual company.

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Companies want to maximize sales◦If sales are too high, the price may be too low,

and vice versaCan you tell sales is affected by prices?

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Who owns the market (as measured by market share) for each product?

Hint: Use sales data filtered by product with drilldown across plant◦Use a stacked area chart

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Documents

Operation Data Analysis Hints and Guidelines EGN 5621 Enterprise Systems Collaboration Summer B, 2014