IS Consulting Process (IS 6005) Masters in Business Information Systems 2009 / 2010 Fergal Carton Business Information Systems

Last week UCC integration framework PCB exercise –Analysis method, importance for budgeting –Structure of requirements spec Families of IS –TPS, Reporting, DSS and dashboards A framework for integration Dashboards: from data to decisions

This week UCC integration framework PCB exercise –Analysis method, importance for budgeting –Structure of requirements spec Dashboard design –Data cubes –Types of data –Data recording, A framework for transition of data to decisions Dashboards: Good Food Limited

Integration framework for Uni (1)

Integration framework for Uni (2)

Proposal structure - example Executive summary Your understanding of our requirements Your recommended approach to undertaking this initiative including high-level project phases, key milestones and challenges which must be overcome Key personnel including brief biographies Project costs and any relevant terms and conditions Profile of the organization, including –Overall consultancy services methodology –Key consultancy offers –Client portfolio –Suitability for this contract, including details of similar engagements previously carried out

A vendor proposal evaluation

The nature of office work: decision making (managerial level) data manipulation (preparing lists, meetings, agendas...) document handling (letters, memos, budgets...) communication (attending meetings, telephone...) storage (recording, storing and filing documents, archiving...)

Decisions compare plan to actual Compare –Plan to –Actual figure Decide on course of action

What is a Decision?

Decision Support Systems: set of tools provided to managers to assist them in solving problems in their own, personalised way. not necessarily totally computer-based support manager as opposed to replace them originally targeted at top managers interactive and flexible

Dashboards: from data to decisions

Data for monitoring activities Norm or budget is put together: –based on experience, gut feeling or statistical analysis –corresponds to expected levels the more complete the model the more complete the monitoring measurement methods and procedures are also put together: the structure of the budget tells you what data to collect

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

Example of data cubes

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 …

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...

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

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

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

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?