1. Pierre-Majorique LÉGER Jacques ROBERT Gilbert BABIN Robert PELLERIN Bret WAGNER Version : August 2011 Copyright © 2011 HEC Montréal ERP Simulation Game Manufacturing game Part 2 – Chapter 6 Reporting

2. OLTP Reporting with SAP

3. Version : August 2011 Procurement management

4. Version : August 2011 Inventory management

5. Version : August 2011 Production execution

6. Version : August 2011 Sales order report

7. Version : August 2011 Sales summary report

8. Version : August 2011 Market report

9. Version : August 2011 Financial statements

10. Version : August 2011 Profit center analysis

11. Version : August 2011 Raw material cost per PO

12. Version : August 2011 Product cost planning

13. Version : August 2011 Liquidity planning

14. OLAP Reporting with SAP and MS Access

15. Version : August 2011 5 styles of business intelligence Enterprise Reporting – Broadly deployed pixel-perfect report formats for operational reporting and scorecards/dashboards targeted at information consumers and executives. Cube Analysis – OLAP slice-and-dice analysis of limited data sets, targeted at managers and others who need a safe and simple environment for basic data exploration within a limited range of data. Ad Hoc Query and Analysis – Full investigative query into all data, as well as automated slice and- dice OLAP analysis of the entire database – down to the transaction level of detail if necessary. Targeted at information explorers and power users. Statistical Analysis and Data Mining – Full mathematical, financial, and statistical treatment of data for purposes of correlation analysis, trend analysis, financial analysis and projections. Targeted at the professional information analysts. Alerting and Report Delivery - Proactive report delivery and alerting to very large populations based on schedules or event triggers in the database. Targeted at very large user populations of information consumers, both internal and external to the enterprise. Source: The 5 Styles of Business Intelligence: Industrial-Strength Business Intelligence, A White Paper Prepared by MicroStrategy, Inc.

16. Version : August 2011 5 styles of business intelligence Source: The 5 Styles of Business Intelligence: Industrial-Strength Business Intelligence, A White Paper Prepared by MicroStrategy, Inc.

17. Version : August 2011 Building an organisation around analytics Decisi ons

18. Version : August 2011 Start with the right business questions Successful analytic organization must focus on the right questions, those that help make the right decision and provide a competitive edge: Who are the most profitable/desirable customers? Which clients are we more likely to lose? What has been sold and where? What should be our pricing and advertising strategy? How my markets have evolved through time? Have we reduced in-process inventories ? Were there major production disruptions? Why ?

19. Version : August 2011 Tools to analyse 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

20. Version : August 2011 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

21. Version : August 2011 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

22. Version : August 2011 Using queries to analyse the data Queries contain 2 basic elements: (i) Key Figures, KPI (ii) Dimensions. Margins as a function of time Sales by country

23. Version : August 2011 An example Measures Dimensions

24. Version : August 2011 Elements of a Info cube Key figures Dimensions

25. Version : August 2011 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

26. Version : August 2011 How the 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)

27. Doing BI with ERPsim data in MS Access

28. Version : August 2011 How to use ERPsimData.accdb 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

29. Version : August 2011 How to use ERPsimData.accdb Step 2: Use Pivot 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

30. Version : August 2011 How to use ERPsimData.accdb Step 2 (cont’d): Select Pivot Table report Select the fields you want to use in your report

31. Exploring data

32. Version : August 2011 Plant A: An overview

33. Version : August 2011 Plant B : an overview

34. Version : August 2011 Plant C an overview

35. Version : August 2011 Trying to maintain stocks for all products

36. Version : August 2011 Large variations in sales per step

37. Version : August 2011 Small production runs

38. Version : August 2011 Long production runs

39. Manipulating graphs

40. Version : August 2011 Key Figure or KPI Y-dimension

41. Version : August 2011 X (Row) dimension

42. Version : August 2011 Multiple series: Column dimension

43. Version : August 2011 Graph type: scattered bars

44. Version : August 2011 Graph type: scattered lines

45. Version : August 2011 Graph type: lines

46. Version : August 2011 Graph type: 3D bars 46

47. Version : August 2011 An example

48. BI Questions

49. Version : August 2011 BI Question 1 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 49

50. Version : August 2011 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 50

51. Version : August 2011 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 51

52. Version : August 2011 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. 52

53. Version : August 2011 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? 53

54. Version : August 2011 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 54