Dienstleistungsmanagement Übung 3

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1 Dienstleistungsmanagement Übung 3 Univ.-Prof. Dr.-Ing. Wolfgang Maass Chair in Economics Information and Service Systems (ISS) Saarland University, Saarbrücken, Germany

2 ANKÜNDIGUNG Veranstaltung Enterprise Software as a Service Diese Veranstaltung beschreibt Software as a Service als ein neues Paradigma für die Auslieferung und den Betrieb als Unternehmenssoftware. Trends in Unternehmenssoftware und Cloud Computing werden aus betriebswirtschaftlicher und technischer Sicht beleuchtet und anhand von Praxisbeispielen in SAP Business ByDesign veranschaulicht. Behandelte Inhalte und Themenbereiche sind hierbei: - Betriebswirtschaftliche Bewertungskriterien & Erfolgsfaktoren von Enterprise SaaS - Technik von Enterprise SaaS - Customizing und Integration von Enterprise SaaS - Fallstudien - Zertifikat in Praxisfallstudie mit SAP Business ByDesign Termine: Blockveranstaltung zu je 4 ganztägigen Terminen im Januar / Februar 2012 Dozenten: Dr. Dirk Werth (DFKI), Dr. Kristof Schneider (SAP AG) Nachweis: 3 Credit Points + offizielles Zertifikat in SAP Business ByDesign Webseite: iwi.uni-sb.de/enterprise-saas/ 2

3 Übung 3 Control Charts Example: A hospital recorded the following samples regarding the response times of its ambulances. Task: Construction of an - chart. Ambulance response time in minutes Day Morning 3,6 4,5 2,9 7,1 4,3 6,7 2,8 Afternoon 5,2 6,3 4,7 6,2 2,8 5,8 5,6 Evening 6,5 7,2 3,8 3,9 5,9 6,9 3,8 Night 4,9 6,9 4,3 5,6 3,2 4,9 4,9 5,1 6,2 3,9 5,7 4,1 6,1 4,3 Range 2,9 2,7 1,8 3,2 3,1 2,0 2,8 Calculation of the mean (day 1): Calculation of the range (day 1): Slide 3

4 Control Charts Calculation of the estimated population mean: Calculation of the estimate of population range: Slide 4

5 Control Charts Variable control charts constants Sample size R-chart n A 2 D 3 D 4 2 1,880 0,000 3, ,023 0,000 2, ,729 0,000 2, ,577 0,000 2, ,483 0,000 2, ,419 0,076 1, ,373 0,136 1, ,337 0,184 1, ,308 0,223 1, ,266 0,283 1, ,235 0,328 1, ,212 0,363 1, ,194 0,391 1, ,180 0,415 1, ,167 0,434 1, ,157 0,451 1,548 Slide 5

6 Control Charts Calculation of R-chart control limits (daily sample size of 4): Calculation of -chart control limits (daily sample size of 4): Mean response time in minutes UCL (6,9) 5,0 LCL (3,1) Time (days) Slide 6

7 Control Charts Example: The chief operator of a mechanized sorting machine in a post office records the following data on his employees: Error rate of skilled operators: 5% Number of parcels in post office: 100 Task: Construction of a control chart (p-chart) for new operators to see which operators are unsuited for the job Fraction of errors of new sorting operators Day Fraction of errors of sample 0,14 0,10 0,07 0,09 0,06 0,03 0,06 0,07 0,03 Slide 7

8 Control Charts Calculation of p-chart control limits: Fraction of errors (percentage) UCL (0,1154) 0,05 LCL (0) Random samples of observations Slide 8

9 Übung3 Huff Modell Example: A department store (j=1) has customers in four geographic areas. The manager wants to calculate the total consumer expenditures per year in the category food. Regarding the number of consumers living in the different areas, he has the following figures: C1=1500, C2=2500, C3=1000, C4=3000. The average shopping budgets for food per month are as follows: B1=100, B2=150, B3=125, B4=75. There is another supermarket (j=2) close by, which is two times larger. The travel times from the different areas are in minutes: T11=20, T12=15, T21=35, T22=20, T31=30, T32=25, T41=20 and T42=25. What is the total expenditure per year, assuming = 1? Slide 9

10 Huff Modell Attraction A ij ij A ij = Attraction to facility j for consumers from area i S j = Size of the facility j T ij = Travel time from consumers of area i to facility j = Parameter estimated empirically to reflect the effect of travel time on different kinds of shopping (large shopping mall vs. small convenience store) S1=1, S2=2 Example for existing store 1, customers from area 1 (A11): Attraction Community (=area) i Facility j ,0500 0,0286 0,0333 0, ,1333 0,1000 0,0800 0,0800 Slide 10

11 Huff Modell Probability P ij ij P ij = Probability of consumer from statistical area i to travel to shopping facility j Example for existing store 1, customers from area 1 (P11): Probability Community (=area) i Facility j ,1833 0,2222 0,2941 0, ,7273 0,7777 0,7059 0,6154 Slide 11

12 Huff Modell Expenditures E jk jk E jk = Estimate of total annual consumer expenditures for product class k at a shopping facility j C i = Number of consumers at area i B ik = Average annual shopping budget of a consumer at area i for product class k m = Number of areas Example for existing store 1, customers from area 1, per year (E11): Expenditure Community (=area) i Facility j Total Slide 12

13 Univ.-Prof. Dr.-Ing. Wolfgang Maass Chair in Information and Service Systems Saarland University, Germany Univ.-Prof. Dr.-Ing. Wolfgang Maass