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Operation Data Analysis

Operation Data Analysis. EGN 5621 Enterprise Systems Collaboration (Professional MSEM) Fall, 2011. 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

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Operation Data Analysis

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  1. Operation Data Analysis EGN 5621 Enterprise Systems Collaboration (Professional MSEM)Fall, 2011

  2. 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 Tools to Analyze Data

  3. 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 A Large Quantity of Quality Data

  4. 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 Interpreting Data

  5. Queries contain 2 basic elements: • Key Figures, KPI • Dimensions. Margins as a function of time Using Queries to Analyze Data Sales by country

  6. Dimensions Dimensions Measures An Example

  7. Key figures • Dimensions Elements of an Info Cube

  8. 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 Types of Measures

  9. How DW Differs from a Transactional DB?

  10. Doing Business Intelligence (BI) with ERPsim Data in MS Access

  11. Step 1: • Download the ACCESS file ERPsimData.accdb from the site provided by your instructor • Save the file ERPsimData.accdb on your hard drive • You may open it to check its content How to use ERPsimData.accdb

  12. Step 2: • Use Pivot Table or normal table in Excel to analyze data • Open an Excel file • In the Excel file, on the “Data” tab, click on the “From Access” button. • Look for ERPsimData.accdb on your hard drive • Select the query or table you want to analyze How to use ERPsimData.accdb

  13. Step 2 (cont’d): • Select Pivot or normal Table report • Select the fields you want to use in your report How to use ERPsimData.accdb

  14. Exploring Data

  15. Plant A: An overview

  16. Plant B : an Overview

  17. Plant C an Overview

  18. Trying to Maintain Stocks for All Products

  19. Large Variations in Sales per Step

  20. Small Production Runs

  21. Long production runs

  22. Manipulating Graphs

  23. Key Figure or KPI Y-dimension

  24. X (Row) dimension

  25. Multiple Series: Column Dimension

  26. Graph type: Scattered Bars

  27. Graph Type: Scattered Lines

  28. Graph Type: Lines

  29. Graph Type: 3D Bars 29

  30. An example

  31. BI Questions

  32. Current assets include (i) cash (ii) receivables (iii) raw material inventory (iv) finished product inventory • How well have the teams performed in managing the current assets over time? • Hint: Use the financial data BI Question 1 32

  33. 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 BI Question 2 33

  34. 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 BI Question 3 34

  35. 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. BI Question 4 35

  36. 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? BI Question 5 36

  37. 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 BI Question 6 37

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