DienstleistungsmanagementÜbung 3

Univ.-Prof. Dr.-Ing. Wolfgang Maass

Chair in Economics – Information and Service Systems (ISS)

Saarland University, Saarbrücken, Germany

ANKÜNDIGUNG

Veranstaltung „Enterprise Software as a Service“

Diese Veranstaltung beschreibt Software as a Service als ein neuesParadigma für die Auslieferung und den Betrieb alsUnternehmenssoftware. Trends in Unternehmenssoftware und Cloud Computing werden aus betriebswirtschaftlicher und technischer Sichtbeleuchtet und anhand von Praxisbeispielen in SAP Business ByDesignveranschaulicht.

Behandelte Inhalte und Themenbereiche sind hierbei:- Betriebswirtschaftliche Bewertungskriterien & Erfolgsfaktoren von

2

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/

- Betriebswirtschaftliche Bewertungskriterien & Erfolgsfaktoren vonEnterprise SaaS

- Technik von Enterprise SaaS- Customizing und Integration von Enterprise SaaS- Fallstudien- Zertifikat in Praxisfallstudie mit SAP Business ByDesign

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 minutesAmbulance response time in minutesAmbulance response time in minutesAmbulance response time in minutesDayDayDayDay 1111 2222 3333 4444 5555 6666 7777MorningMorningMorningMorning 3,6 4,5 2,9 7,1 4,3 6,7 2,8AfternoonAfternoonAfternoonAfternoon 5,2 6,3 4,7 6,2 2,8 5,8 5,6Evening Evening Evening Evening 6,5 7,2 3,8 3,9 5,9 6,9 3,8NightNightNightNight 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

Univ.-Prof. Dr.-Ing. Wolfgang Maass24.11.2011 Slide 3

• Calculation of the mean (day 1):

• Calculation of the range (day 1):

5,1 6,2 3,9 5,7 4,1 6,1 4,3RangeRangeRangeRange 2,9 2,7 1,8 3,2 3,1 2,0 2,8

Control Charts

• Calculation of the estimated population mean:

• Calculation of the estimate of population range:

Univ.-Prof. Dr.-Ing. Wolfgang Maass24.11.2011 Slide 4

Control Charts

Variable Variable Variable Variable controlcontrolcontrolcontrol chartschartschartscharts constantsconstantsconstantsconstantsSample Sample Sample Sample sizesizesizesize RRRR----chartchartchartchartnnnn AAAA2222 DDDD3333 DDDD4444

2 1,880 0,000 3,2673 1,023 0,000 2,5744 0,729 0,000 2,2825 0,577 0,000 2,1146 0,483 0,000 2,0047 0,419 0,076 1,9248 0,373 0,136 1,864

Univ.-Prof. Dr.-Ing. Wolfgang Maass24.11.2011 Slide 5

8 0,373 0,136 1,8649 0,337 0,184 1,81610 0,308 0,223 1,77712 0,266 0,283 1,71714 0,235 0,328 1,67216 0,212 0,363 1,63718 0,194 0,391 1,60820 0,180 0,415 1,58522 0,167 0,434 1,56624 0,157 0,451 1,548

Control Charts

Calculation of R-chart control limits (daily sample size of 4):

Calculation of -chart control limits (daily sample size of 4):

Univ.-Prof. Dr.-Ing. Wolfgang Maass24.11.2011 Slide 6

Time (days)1 2 3 4 5

LCL (3,1)

UCL (6,9)

Mean response time in minutes

5,0

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

Univ.-Prof. Dr.-Ing. Wolfgang Maass24.11.2011 Slide 7

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 operatorsFraction of errors of new sorting operatorsFraction of errors of new sorting operatorsFraction of errors of new sorting operators

DayDayDayDay 1111 2222 3333 4444 5555 6666 7777 8888 9999Fraction of Fraction of Fraction of Fraction of errors of errors of errors of errors of samplesamplesamplesample

0,14 0,10 0,07 0,09 0,06 0,03 0,06 0,07 0,03

Control Charts

Calculation of p-chart control limits:

Univ.-Prof. Dr.-Ing. Wolfgang Maass24.11.2011 Slide 8

Random samples of observations 1 2 3 4 5

LCL (0)

UCL (0,1154)Fraction of errors (percentage)

6 7 8 9

0,05

Übung 3 – 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.

Univ.-Prof. Dr.-Ing. Wolfgang Maass24.11.2011 Slide 9

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?

Huff Modell

Attraction Attraction Attraction Attraction AAAAijijijij– Aij = Attraction to facility j for consumers from area i

– Sj = Size of the facility j

– Tij = 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

Univ.-Prof. Dr.-Ing. Wolfgang Maass24.11.2011 Slide 10

– S1=1, S2=2

– Example for existing store 1, customers from area 1 (A11):

AttractionCommunity (=area) i

Facility j 1 2 3 41 0,0500 0,0286 0,0333 0,05002 0,1333 0,1000 0,0800 0,0800

Huff Modell

ProbabilityProbabilityProbabilityProbability PPPPijijijij

− Pij = Probability of consumer from statistical area i to travel to shopping facility j

− Example for existing store 1, customers from area 1 (P11):

Univ.-Prof. Dr.-Ing. Wolfgang Maass24.11.2011 Slide 11

− Example for existing store 1, customers from area 1 (P11):

ProbabilityCommunity (=area) i

Facility j 1 2 3 41 0,1833 0,2222 0,2941 0,38462 0,7273 0,7777 0,7059 0,6154

Huff Modell

ExpendituresExpendituresExpendituresExpenditures EEEEjkjkjkjk– Ejk = Estimate of total annual consumer expenditures for product class k at a shopping facility j

– Ci = Number of consumers at area i

– Bik = Average annual shopping budget of a consumer at area i for product class k

– m = Number of areas

Univ.-Prof. Dr.-Ing. Wolfgang Maass24.11.2011 Slide 12

– Example for existing store 1, customers from area 1, per year (E11):

ExpenditureCommunity (=area) i

Facility j 1 2 3 4 Total1 329.940 999.900 441.150 1.038.420 2.809.4102 1.309.140 3.499.650 1.058.850 1.661.580 7.529.220

Univ.-Prof. Dr.-Ing. Wolfgang Maass

Univ.-Prof. Dr.-Ing. Wolfgang Maass

Chair in Information and Service SystemsSaarland University, Germany