1. Enterprise Business Processes and Reporting (IS 6214) MBS MIMAS 3 rd Feb 2010 Fergal Carton Business Information Systems

2. Last week Pivot tables and revenue reporting Downloading data and parsing in Excel –Using parsing tools –Macros Importing files from other applications Interfaces via Excel ERP and reporting Decision support Compare plan to actual Data cubes Types of data Data collection

3. This week Data cubes Types of data –Dynamic / Static –Cucina example Soft information Data recording Data storage Deciding what information to collect

4. Example : sales figures Sales dashboard is a key tool: –Allocate responsibility for poor performance with more accuracy –Break down per product / market –Present both volumes, gross revenues and contribution figures Use colour coding to indicate where results are good or bad Use sales maps for visual impact Comparison with: –Budget figures (e.g. weekly figures) –Competitors –Previous period –Same period previous year in case of seasonality

5. Example of data cubes

6. Types of data 1 Volume data (production) consumption data (raw material, packaging…) personnel data maintenance data time related measurements productivity data All form the basis of the calculations used to monitor manufacturing activities …

7. Type of data 2 Primary data: –taken straight from the floor (input and output) –e.g. production, consumption, labour, maintenance –ad-hoc reports - e.g. accidents, defects Secondary data or calculated data: –allocated costs –productivity –pay bonuses –variances High level data: –investigations of variances –soft information about staff morale etc...

8. Type of data : Cucina What are the types of data you have for Cucina?

9. Type of data : static What are the types of data you have for Cucina?

10. Type of data : dynamic What are the types of data you have for Cucina?

11. Type of data: soft information Data collection - –Grapevine –factory tours (talking and observing) Data storage - –managers’ minds –special reports Data usage: –ad-hoc basis –decision making

12. Bad data recording No data! Too costly - e.g. in equipment or time not timely - lack of speed (e.g. weekly measure) inaccurate (e.g. procedure not well designed) wrong incentive / instructions given lack of control - open to dishonesty

13. Data storage Series of ad-hoc systems manual and computer- based (spreadsheet, filed forms…) Dedicated databases for manufacturing data (QC, shipping etc…) Process Control Systems (technical parameters) Other specialised proprietary systems (integration may not be easy). ERP system with its own data structure or fed by existing systems

14. Deciding what information to collect Information cost + overload mean not all data are useful Some framework can be used – e.g. Critical Success Factors (CSF) Questions that must be answered: –How is it measured and broken down? –How often should it be measured? –Who should know about it? –Where can the data be found? –How should it be presented?