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

2. Tools to Analyze Data Tools to analyze data range from simple to complex Reports and graphs Advanced statistics forecasting models Advanced optimization models and tools Having the right people matters Having data modeling

3. A 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

4. Interpreting 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

5. Using Queries to Analyze Data A 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.

6. Using Queries to Analyze Data

7. A logical data model of a 1 to N relationship

8. Using Queries to Analyze Data A logical data model of an N to N relationship

9. Using Queries to Analyze Data A logical data model of a relational database

10. Using Queries to Analyze Data

11. Metadata of a relational database Metadata (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.

12. Using Queries to Analyze Data

13. Queries contain 2 basic elements: (i) Key Figures, KPI (ii) Dimensions. Margins as a function of time Sales by country

14. An Example Measures Dimensions

15. Elements of an Info Cube Key figures Dimensions

16. Types of Measures Additive : 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 Product non 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

17. How DW Differs from a Transactional DB? CharacteristicDBDW OperationReal-time, transactionalDecision support, strategic analysis ModelEntity-RelationshipStar Schema Redundant dataDesigned to avoidPermitted DataRaw data, currentAggregated, Historical data, # of usersManyFew UpdateImmediateDeferred Calculated fieldsNone storedMany stored Mental modelTabularHypercube QueriesSimple, some savedComplex, many saved OperationsRead / WriteRead Only SizeGo (Gigabytes)To(Terabytes)

18. Exploring Data

19. Plant A: An overview

20. Plant B : an Overview

21. Plant C an Overview

22. Trying to Maintain Stocks for All Products

23. Large Variations in Sales per Step

24. Manipulating Graphs

25. Key Figure or KPI

26. Graph type: Scattered Bars

27. Graph Type: Scattered Lines

28. Graph Type: Lines

29. Graph Type: 3D Bars 29

30. BI Questions

31. BI 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 31

32. BI Question 2 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 32

33. BI Question 3 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 33

34. BI Question 4 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. 34

35. BI Question 5 Companies want to maximize sales ◦If sales are too high, the price may be too low, and vice versa Can you tell sales is affected by prices? 35

36. BI Question 6 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 36