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Enterprise Business Processes and Re porting ( IS 6214 )

Enterprise Business Processes and Re porting ( IS 6214 ). MBS MIMAS 2010 / 2011 13 th October 2010. Fergal Carton Bu siness Information Systems. Last week. Lessons from clean-up process exercise Seasonality in business affecting processes Sandwich / ice cream / cider sales

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Enterprise Business Processes and Re porting ( IS 6214 )

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  1. Enterprise Business Processes and Reporting(IS 6214) MBS MIMAS 2010 / 2011 13th October 2010 Fergal Carton Business Information Systems

  2. Last week • Lessons from clean-up process exercise • Seasonality in business affecting processes • Sandwich / ice cream / cider sales • Can technology help automate processes? • Where structured data is used eg. dates, times, temperatures, humidity, … • In class exercise: where bad data has a business impact (€!) • ATM: retrieves notes automatically if not collected with no credit to a/c • Paddy Power: error on kick-off time allowed betting when result known • Bar sales: late night price increment not applied automatically • Novartis: time taken tracking IT equipment due to poor inventory coding • Allied Irish Banks: unannounced charges on bank account • Tesco: product promotion price not at till resulting in poor service / refund • Homework: write these up • Some examples close to home: UCC • Unidentified monies coming into the organisation: inefficiency and embarassment • Recommended reading

  3. This week • Homework: where bad data might have resulted in a process breakdown • Sample processes • A process requires … • Good and bad processes • Technology can help • Example: Applied Research Project (Cucina) • In class exercise

  4. A process requires … • Someone to carry it out … • Some parameters … • Some master data … • Some communication … • Some deadlines …

  5. Processes = information + goods + € Customer Sales orders information Shipments goods Invoice information Payments cash Returns and repairs information & goods Suppliers Purchase orders information Deliveries goods Delivery discrepancies information & goods PPV information Invoice information Supplier payments cash

  6. Sample processes … • Registering as a student • Buying and using top-up credit for phone • Registering as a user for on-line services • Getting educated • Moving house • …

  7. Good and bad processes • Paper based • Slow • Inefficient • Poor customer service • Poor quality products • Expensive

  8. Technology can help, but … • What makes system data wrong? • System clocks, data entry problems, … • Virtual not paper, but access? • Instantaneous, when system is up! • Very efficient, can store data and use triggers • Requires greater data integrity • Creates reliance on skills • Expensive?

  9. Tesco example • Use of bar-coding on products • What happens when product doesn’t scan? • What should the process be? • What is the advantage of the technology? • Technology imposes data integrity norms

  10. Applied Research Project IS6216 • Analysis of a sales process • Understanding flows of information • Highlighting areas of inefficiency • Designing new processes supported by IT • Providing visibility and control for decisions

  11. In class exercise • Pick a business idea • Describe customer requirements • How these requirements can be met? • What processes will the business need? • What are the characteristics of a process?

  12. 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

  13. 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...

  14. 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

  15. Process mapping criteria • Represents a transformation • Must be clear: one A4 sheet • Should eliminate ambiguity • Workflow or automation • Has rules

  16. “Kicking the tyres” of a process • What is unique about this process? • Is there seasonality? • What happens to the process under duress? • What are customers complaining about? • What are competitors doing? • Is the business model changing? • Is the way of doing business evolving?

  17. Bad data examples • Examples of where bad data can result in customer dissatisfaction? • Situations where you see poorly managed information can result in frustration for customers? • What the implications might be for processes?

  18. Bad data held by the bank (PW) • Overcharged for a service provided by Bank of Ireland. • I noticed the charge and mention it to the Bank. I was told it was an automatic payment due to the Government. • A week later I was told that a mistake was made and the 5 euro they took every month was a malfunction in the computer. • I was given the money back and an extra fee. • Bank maintained that it was an error by the computer system and not a hidden charge.

  19. Poor itemisation of invoice (AW) • Received an invoice for a product which had the wrong information provided on the docket • e.g. invoiced for boxes of Walkers crisps • The total price on the invoice was €12.10 per box. • The invoice was missing the breakdown of • Net price of €10.00 per box • Plus €2.10 of VAT (crisps are rated at 21% VAT). • VAT paid out by a business can be reclaimed • If VAT information is missing or inaccurate then cost to the business = money and time in chasing it up

  20. Markets don’t believe our data (BC) • Financial market distrust of figures for the final cost of bailing out our banks • Reflected by poor Irish performance at a recent bond auction • Consequence = huge interest rate Ireland pays for raising money at bond auctions • Ireland purchased ten year bonds at interest rates of 6.28%, in stark contrast to the 2.47% the German’s incurred for raising money at the same auction • Financial markets don’t believe data coming from our banks via our Government • Impose substantial interest rates to counteract the high risk associated with doing business with Ireland. • Ireland currently borrowing €19 billion every year • A process for presenting sound reliable data has not been achieved. • Takeover by the IMF?

  21. Poor processes cost more (SO) • Installation of new window blinds in apartment by supplier in Midleton • Visit by supplier, measurements taken, price, quality and colour agreed, payment made and date for installation given • On day of installation, technician arrived with different set of blinds from those ordered (technician sure of product match to order). • Poor process between order and installation, technician ending up with the wrong data : poor record keeping and bad data management • Result: higher cost to the supplier as right blinds were eventually installed.

  22. Process failures (FC) • Example 1: Quality team process failure? • Software development company whose customers have a unique version of the company's software. Training manuals are sent out to users. • A client company had different set-ups of the system in different locations, so therefore needed unique manuals. First 'LIVE' days of new system, users had wrong manuals, causing increased helpline workload. • Example 2: student email account status update • 'mycit.ie' account still receiving information regarding registration and freshers week etc. even though no longer registered

  23. Process control failures (RW) • Example 1 • ESB department sent out two compensation cheques for customer with ESB masts on land. • Letter was sent out: • there had been a mistake and that • one of the cheques would be cancelled • if they had been both cashed it would come out of next compensation • Example 2 • Speeding offence: driver details taken. • False licence number entered on form received by post • At post office, actual licence not checked against form • No penalty points on his licence.

  24. Poor data or poor prep (OB)? • Example 1: Data wrong or process rushed, or both? • During a transplant operation, an ill-prepared doctor removed the wrong kidney from a patient. The data given to the doctor told him to remove the incorrect kidney and the doctor did. • Example 2: Data accuracy, recording or communication? • Replacing windows in a high rise building, sometimes windows were wrong size. This was caused by the wrong data recorded initially and sent to the suppliers. • Installation of window frames without glass. Customer had to wait another 2 weeks for replacement glass, living with drafty boarded up windows in the meantime.

  25. Wrong data or poor service (COD)? • A supermarket promotes a special offer for a particular brand (e.g. buy 3 Colgate products for €6) but the offer is not recognised when goods scanned at the cash register • Customer may be over charged but may only notice after they have returned home. Customer would have to return to the supermarket with the receipt to get their money back, assuming they have the time. • Customer might not return to the supermarket, as the business is unorganised • Where was the process shortcoming?

  26. Multiple views of price data (KS) • If the price of an item drops within 30 days of your purchase, Amazon.co.uk will refund you the difference. • Issues occurred when the wrong item was listed on the website as having dropped in price. From our internal data we could see no drop in price, however it was clearly visible on the website that the item had dropped in price. • Customer Frustration occurred when they contacted the Amazon customer service team to seek a refund in the price difference. • Customers were told by customer service agents that they would non receive a refund as no change in price for this item was visible from their internal data system. • Eventually Amazon did refund the customer the difference in price due to the high volume of complaints

  27. Consistently poor data (MOD) • Product scanned at till, incorrect amount displayed, not corresponding with shelf price • When I looked over my receipt, I noticed that one of my items was overpriced. • I returned to the shop and showed my receipt to the manager and was given the difference between the amount I paid and the actual price of the product. • However, this problem occurred two weeks later and I had to be given another refund. The shop did not fix the problem when it was brought to their attention. • The implications that might occur are a loss of a customer and / or reputation.

  28. Consistently poor data (IW) • Receiving the wrong food at a fast food outlet, like McDonald's • Shoe purchase online, three weeks later when the were back in stock and shipped out to me, they were the wrong shade colour I had selected. • Printing quota not working in Lab 1.111

  29. Communication of vital data (MOL) • Return train tickets booked to Kerry from Dublin. • At train station on Monday to return to Dublin, the train I had booked had left early. • According to the station manager all passengers should have received an email with the change in the timetable. • All reservations lost, so seating on next train was on a first come first serve basis

  30. Static data loses customers (YL) • Head-hunters: for candidate profile, age of that person was compulsory • In HR database, age data static, no matter how many years had passed • This problem could result in introducing an unsuitable candidate as a target employee to the customer company. • Cost and inconveniences impact to headhunting company, the hiring company and the potential candidate: lost customers and reputation. • To solve this problem and to improve the service quality of the company, the database system should be modified • with an age calculation function, which will run a calculation each year to present the current age of the person • Otherwise, they could simply type in the exact date of birth of the candidates instead (i.e. dd/mm/yy format).

  31. Slow response loses sales (PB) • Football team's website www.football-shirts.co.uk. • When ordering a shirt from this site, a customer must first choose home or away and then what size he/she required. • The customer than had to fill out then all relevant details such as pay-pal number, e-mail  and address. • Having completed the transaction I received a confirmation e-mail. • Four days later I received another e-mail from the website stating that they were currently out of stock for that particular shirt and sorry for the inconvenience.

  32. Bad data or bad promotion (SS)? • McDonalds: data on the menu board does not match the data on the till system. • It states on the menu board that all medium drinks cost €2.10, this is true for all drinks on the till system except medium Fanta. • When medium Fanta is pressed on the till the customer is actually charged €2.40 as the systems do not match. • The customer is being charged €0.30 extra wrongfully. • The breakfast menu board states that there is a hash brown included with every meal, but this is not true for the pancake meal as it only includes the pancakes and a drink. • This has brought about many arguments for both me and my staff on the breakfast shift.

  33. Poor process documentation (SB) • Redeveloping AIB intra-net site for the Project and Change Management team • Process flows available on-line that were used by other AIB departments were out of date: • Departments that no longer existed. • Broken links. • Incorrect flowing of processes. • And documents that where no longer used. • This information was accessible by all AIB employees therefore it showed the team in an unprofessional manner to their customers. • Customers would get frustrated as they would try to follow the correct procedure for completing a project only to discover it was incorrect. • Over the six months I worked on improving these process flows and working with the different departments e.g. risk and finance, to determine the correct flow of business during a project.

  34. Poor IT inventory data costs (EL) • Novartis IT inventory system had problems, mainly due to data either being entered incorrectly or just not being entered at all. • The inventory system was there to keep track of material, such as, computers, phones, keyboards, etc. • The materials are entered into the inventory system and identified by their unique bardcode. • While I was at Novartis I was involved in a plant wide roll-out of new pc's, during this project we noticed that the inventory system was full of bad data. • This bad data could cost the company a lot of money and makes it very difficult for the IT department to identify the users and the location of the materials.

  35. Billing data impacts cash flow (JC) • Incorrect billing addresses or lack of a billing address can cause major problems for a company as the finance department can’t send out invoices to customers. • The finance department may contact the sales people to find the correct billing address but this may take time delaying the process of sending the invoice. • The break down in the normal business process can increase costs and the delay in payment can affect cash flow.

  36. Poor control results in abuse (JN) • Fergal O'Malley was a lecturer in electronic engineering in the National University of Ireland, Galway and 90km away in the Athlone Institute of Technology. • Between the two jobs he was earning approximately €170,000 a year as well as building up separate pension entitlements. • He was forced to resign from the two colleges after it was discovered he had been working full-time for both colleges for the past eight years. • Bad data in the department of education information systems can let someone like Fergal O’Malley slip through the net without being noticed • One of the consequences of this bad data is the loss of annual revenue. • However, another major consequence was the impact it had on the students at both colleges, who didn’t receive due interest or consideration

  37. Slow process or bad data (CF)? • I rang Apple as an employee to order an iPad on staff discount. • Firstly the staff database system was down and the sales rep was unable to confirm whether or not I was an actual employee. • Sales rep was unable to store my details as the Oracle system they use was experiencing difficulties. • The initial delivery time of items purchased was 2-3 days but due to the difficulties the order was expected to, and did, take 5 working days to arrive. • Result: recipient did not receive gift in time for birthday

  38. Is the data or the process bad? • If the data is bad, can you identify the process issue? • Data requires maintenance • Easy to implement solutions • Less exciting to maintain data • Only good data results in good systems

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