1. Operation Data Analysis Hints and Guidelines EIN 6133 Enterprise Engineering Fall, 2015

2. A Large Quantity of Quality Data Integrated organizations have a lot of data available, they must learn to exploit it All analytic methods feeds on data – in large quantity and good quality Having good data can be turned into a competitive advantage

3. 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 data modeling

4. Interpreting Data Skills are required to ◦create appropriate graphs, reports, and statistical analysis ◦interpret correctly graphs, reports and statistics, and ◦make 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. It cannot be empty. A foreign key is an attribute of a table (that can be composed), meanwhile, the foreign key is a primary key of another table. These two tables are linked each to other by the foreign key. 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. An example will be shown later.

6. Using Queries to Analyze Data

7. Using Queries to Analyze Data (Star Schema) A logical data model of a 1 to N relationship A star schema is diagramed by surrounding each fact with its associated dimensions.

8. Using Queries to Analyze Data

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

10. An Example Measures Dimensions

11. Elements of an Info Cube Key figures Dimensions

12. 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

13. 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) SAP HANA: high-performance analytic appliance

14. Exploring Data

15. Plant B : an Overview

16. Plant C: an Overview

17. Try to Maintain Stocks for All Products

18. Large Variations in Sales per Step

19. Manipulating Graphs

20. Graph type: Scattered Bars

21. Graph Type: Lines

22. Graph Type: 3D Bars 22

23. Questions

24. 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 (F.01) 24

25. 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 25

26. 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 26

27. 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. 27

28. 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? 28

29. 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 29