From Robert Kaplan (Accounting) and Michael Porter (Strategy), 

HBR, September 2011 

Question (Title): “How to Solve the Cost Crisis in Health Care” 

Answer: Does not require medical science breakthroughs or new governmental 

regulation. It simply requires a new way (TDABC = Time‐Driven Activity‐Based 

Costing) to accurately measure costs and compare them to outcomes. 

Indeed, accurately measuring costs and outcomes is the single most powerful lever 

we have today for transforming the economics of healthcare. 

A TDABC budgeting process starts by predicting the volume and types of patients 

the provider expects. 

 

The new approach engages physicians, clinical teams, administrative staff and 

financial professionals in creating process maps and estimating the resource costs 

involved in treating patients over their care cycle. 

 

Introduction: 

Goal of Heath care delivery system: Improve the value delivered to patients. 

Value = measured in terms of outcome achieved per dollar expended (cost). 

Medical outcome: has enjoyed growing attention. 

Cost to deliver outcomes: received much less attention ‐ the FOCUS here. 

 

Opportunities to Improve Value: 

 

‐ Eliminate unnecessary process variations and processes that don’t add value. 

‐ Improve resource capacity utilization. 

‐ Deliver the right processes at the right location. 

‐ Match clinical skills to the process. 

‐ Speed up cycle time.  

‐ Optimize over the full cycle of care. 

 

 

The Challenge of Health Care Costing:  

‐ Heath care today is a highly customized job shop 

‐ Any accurate costing system must, at a fundamental level, account for the 

total costs of all the resources used by a patient as she or he traverses the 

system. That means tracking the sequence of and duration of clinical and 

administrative processes used by individual patients – something the 

most hospital information systems today are unable to do. (In the future: 

RFID etc.) 

‐ With good estimates of the typical path an individual patient takes for a 

medical condition, providers can use the Time‐Driven Activity‐Base 

Costing (TDABC) to assign costs accurately and relatively easily to each 

process step along the path.  

‐ Requires that providers estimate only two parameters at each process 

step: the cost of each resource used in the process and the quantity of 

time the patient spends with each resource.  

 

 

 

The Cost Measurement Process: 

‐ Select the medical condition 

‐ Define the care delivery value chain (CDVC), which charts the principal 

activities involved in a patient’s care for a medical condition along with 

their location.  

‐ Develop process maps of each activity in patient care delivery. 

‐ Obtain time estimates for each process. 

‐ Estimate the cost of supplying patient care resources.  

‐ Estimate the capacity of each resource and calculate the capacity cost rate.  

‐ Calculate the total cost of patient care. 

 

 

Reinventing Reimbursement: Abandon the current complex fee‐for‐service 

payment schedule. Instead, payors should introduce value‐based reimbursement, 

such as bundled payment, that covers the full care cycle and included care for 

complications and comorbidities (=several deseases). 

 

 

From “Managing Business Process Flows”, by Anupindi, Chopra, Deshmukh, Van Mieghem, Zemel (Kellogg, Northwestern) 

 

‐ Job‐shops typically display jumbled work flows with large amounts of 

storage and substantial waiting between activities. 

‐ Thus, it is more practical to represent a jobshop with a  

Network of Resources, instead of  

Network of Activities. 

 

On Financial Measures: Though the ultimate judge of process performance, 

financial measures are inherently lagging, aggregate, and more results oriented than 

action oriented. They also are reported infrequently. 

 

The operations manager, however, needs Operational Measures – more detailed 

and more frequent measures that can be controlled and that ultimately have an 

impact on financial measures.  

 

Ideally, companies want operational measures to be leading indicators of financial 

performance. The three types of financial measures (absolute performance, 

performance relative to asset utilization, cash‐flow) would then mirror operational 

measures and provide daily support to process management.   

 

Uncharted Territory: Information Technology (e.g. RFID), Statistics, Operations 

Research/Management plus Professionals (Physicians, Marketing,…) can jointly 

“close the gap” between financial and operational measures. 

 

 

Research Questions: 

‐ Operational Models at the “right” level of resolution (individual transaction) 

‐ Imputed / Surrogate for Costs (Profits) or Quality, inferred from the more 

easily observable operational measures. 

o Tardiness costs via newsvendor o Clinical quality via return‐to‐hospitalization o Waiting costs from Constraint Satisfaction (e.g. 20‐80 rule in call centers)  

o Waiting/Abandonment costs? (There is literature on the “Cost of Waiting”)  

Conceptual Model:Service Networks = Queueing Networks

8

Service Networks = Queueing Networks • People, waiting for service: teller, repairman, ATM

• Telephone-calls, to be answered: busy, music, info.

• Forms, to be sent, processed, printed; for a partner

• Projects, to be developed, approved, implemented

• Justice, to be made: pre-trial, hearing, retrial

• Ships, for a pilot, berth, unloading crew

• Patients, for an ambulance, emergency room, operation

• Cars, in rush hour, for parking

• Checks, waiting to be processed, cashed

• Queues Scarce Resources, Synchronization Gaps

Costly, but here to stay

– Face-to-face Nets (Chat) (min.)

– Tele-to-tele Nets (Telephone) (sec.)

– Administrative Nets (Letter-to-Letter) (days)

– Fax, e.mail (hours)

– Face-to-ATM, Tele-to-IVR

– Mixed Networks (Contact Centers)

38

15

Stochastic Systems

q

PATIENT FLOW IN HOSPITALS: A DATA-BASEDQUEUEING-SCIENCE PERSPECTIVE

By Mor Armony, Avishai Mandelbaum, Yariv Marmor,Yulia Tseytlin, and Galit Yom-Tov

Patient flow in hospitals can be naturally modeled as a queueingnetwork, where patients are the customers, and medical staff, bedsand equipment are the servers. But are there special features of sucha network that sets it apart from prevalent models of queueing net-works? To address this question, we use Exploratory Data Analysis(EDA) to study detailed patient flow data from a large Israeli hospi-tal.

EDA reveals interesting and significant phenomena, which are notreadily explained by available queueing models, and which raise ques-tions such as: What queueing model best describes the distribution ofthe number of patients in the Emergency Department (ED); and howdo such models accommodate existing throughput degradation dur-ing peak congestion? What time resolutions and operational regimesare relevant for modeling patient length of stay in the Internal Wards(IWs)? While routing patients from the ED to the IWs, how to con-trol delays in concert with fair workload allocation among the wards?Which leads one to ask how to measure this workload: Is it propor-tional to bed occupancy levels? How is it related to patient turnoverrates?

Our research addresses such questions and explores their opera-tional and scientific significance. Moreover, the above questions mostlyaddress medical units unilaterally, but EDA underscores the need forand benefit from a comparative-integrative view: for example, com-paring IWs to the Maternity and Oncology wards, or relating EDbottlenecks to IW physician protocols. All this gives rise to additionalquestions that offer opportunities for further research, in QueueingTheory, its applications and beyond.

CONTENTS

1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31.1 Anonymous Hospital . . . . . . . . . . . . . . . . . . . . . . . 51.2 Some Hints to the Literature . . . . . . . . . . . . . . . . . . 81.3 Data Description . . . . . . . . . . . . . . . . . . . . . . . . . 10

2 Summary of Results . . . . . . . . . . . . . . . . . . . . . . . . . . 123 Emergency Department . . . . . . . . . . . . . . . . . . . . . . . . 18

3.1 Empirical findings . . . . . . . . . . . . . . . . . . . . . . . . 193.2 What simple queueing model best fits the ED environment? . 24

1imsart-ssy ver. 2011/05/20 file: Patient_flow_main_230811.tex date: August 23, 2011

PATIENT FLOW IN HOSPITALS 5

and significance (practical or statistical).

1.1. Anonymous Hospital. The data we rely on was collected at a largeIsraeli hospital, referred to here as “Anonymous Hospital”. This hospitalconsists of about 1000 beds and 45 medical units, with about 75,000 patientshospitalized annually. The data includes detailed information on patientflow throughout the hospital, over a period of several years (2004-2008). Inparticular, the data allows one to follow the paths of individual patientsthroughout their stay at the hospital, including admission, discharge, andtransfer between hospital units.

Traditionally, hospital studies have focused on individual units, in isola-tion from the rest of the hospital; but this approach ignores interactionsamong units. On the flip side, looking at the hospital as a whole is complexand may lead to a lack of focus. Instead, and although our data encompassesthe entire hospital, we chose to focus on a sub-network that consists of theEmergency Department (ED) and five Internal Wards (IW), denoted by Athrough E; see Figure 1. This subnetwork, referred to as ED+IW, is more

Arrivals EmergencyDepartment

Abandonment

Services

IW A

IW C

IW B

IW D

IW E

Dischargedpatients

Dischargedpatients

InternalDepartment

OtherMedical

Units

53%

13.6%

"JusticeTable"

Blocked at IWs3.5%

69.9%

16.5%

5%

15.7%

1%

23.6%

84.3%

75.4%

245 pat./day

161 pat./day

Fig 1. The ED+IW system as a queueing network

imsart-ssy ver. 2011/05/20 file: Patient_flow_main_230811.tex date: August 23, 2011

PATIENT FLOW IN HOSPITALS 7

90×

90Matrix,Sub-W

ardResolution

InternalMedicine

119

Fig 2. Transition probabilities between hospital wards

imsart-ssy ver. 2011/05/20 file: Patient_flow_main_230811.tex date: August 23, 2011

DataMOCCA DATA MOdel for Call Center Analysis

Volume 5.1 Skills-Based- Routing in US Bank

Mr Pablo Liberman Dr Valery Trofimov

Professor Avishai Mandelbaum

Created: February 2008

Skills Groups Definitions

Grouping Several factors influence the characterization of an agent’s skills-set. Here we explain, via examples, the factors that we have been using. When there are several types of calls served by an agent, one must decide if these types characterize a skill or, alternatively, they are random assignments due perhaps to random circumstances. (For example, an unforeseen increase in load that enforces unqualified agents to serve calls beyond their skill-set.) Our grouping decisions are based on the different services types which the agents take, the percentage of the agent calls from each service type, the percentage of the service type calls that flows to each agent group, the agent skills characteristics over the different months and the number of agent with the same skills characteristics. Grouping Examples, the May 2001 Case On May 2001, 1851 agents worked in the call center within 17 different skills-groups. The largest group in May 2001 is Group 1, consisting of 575 agents. This group consists of all the agents that take mainly Retail service. In Table 2 we see that this group serves 36.26% of the Retails calls, and a very small percentage of others services. This small percentage is negligible because the number of calls is small and the number of agents is large, so it does not influence agents performance. (In Table 1 we see that this fraction is 0.01% of the agents calls). Still, the question arises whether these call types should affect the characterization of these agents’ skills-set. To this end, we observe that, in later months, none of such call-types were served by these agents. Hence, we deduce that the service-types in question are not elements of these agents-skills-set. There are 252 agents who serve mainly Retail group that form Group 2. The difference between this group and Group 1 is that the Group 2 agents take a small number of Premier, Business and Telesales calls, but in these cases we identify predictable patterns of those calls routing (in most of them, we see a small number of these service types calls to each agent on each month of the successive months). The smallest group is Group 38, which is formed by only one agent. This one agent is very important because he or she serves 15.24% of the Subanco calls, and there are no others agents in the call center with the same skills characteristic. Main Service Our Main Service decision is based on only two important parameters: the percentage of the agent calls from each service type and the percentage of the service type calls in each agent group. Examples of Main Services, the May 2001 Case Group 12 is grouping 58 agents, who take 7.24% of the Retails calls; these 7.24% of the Retail calls represent 93.44% of those agents work, therefore the main service of this group is Retail service. Group 31 is grouping 43 agents; 84.15% of their calls are Business calls and 15.62% are Platinum calls but, on the other hand, this group takes 39.5% of the Business calls and 95.51% of the Platinum calls. This is the reason that the main service of this group is Platinum calls.

12

Table 1 (Groups work description): group code, total number of agents, main service, total number of calls and the percentage of the agent calls from each service type.

Group Code

Total # Agents Main Services Retail Premier Business Platinum Customer

Loans Online

Banking EBO Telesales Subanco Summit Total # Calls

1 575 Retail (1) 99.97 0.01 0 0 0.01 0 0 0 0 0 2540752 252 Retail (1) 97.38 0.3 1.67 0 0 0.06 0 0.59 0 0 2058754 17 Retail (1) 69.06 19.62 5.79 0 0 0 0 5.53 0 0 6387 6 94 Retail (1) 98.62 0.25 0.9 0 0 0.01 0 0 0.22 0 86529 9 44 Retail (1) 96.53 0.19 0 0.15 0.01 3.12 0 0 0 0 36369 10 78 EBO (7) 66.99 0.35 0.62 0 0 0 31.93 0.11 0 0 55452 12 58 Retail (1) 93.99 0.17 1.26 0 0 0 0 4.58 0 0 53943 15 43 Retail (1) 98.73 0.14 0.12 0 0 0.01 0 0 1 0 24996 19 89 Premier (2) 0.68 99.29 0 0.03 0 0 0 0 0 0 40681 29 64 Business (3) 0.58 0 98.89 0.47 0 0.02 0 0.02 0.02 0 37705 31 43 Platinum (4) 0.23 0 84.15 15.62 0 0 0 0 0 0 33493 33 83 Customer Loans (5) 7.35 0 0 0 92.65 0 0 0 0 0 67803 34 6 Subanco (9) 0.02 0 0 0 68.67 0 0 0 31.31 0 5273 35 178 Online Banking (6) 8.67 0.23 0 0 0 91.1 0 0 0 0 35404 36 129 Telesales (8) 0 0 0 0 0 0 0 100 0 0 74765 38 1 Subanco (9) 0 0 38.15 0 0 0.16 0 0 61.69 0 616 45 97 Summit (14) 0 0 0 0 0 0 0 0 0 100 111948

Note: Each row sums up 100%.

Table 2 (Calls flow description): main service, group code, total number of agents, the percentage of the service type calls that flows to each agent group, and the number of calls arriving from each service.

Main services Group Code

Total # Agents

Retail Premier Business Platinum Customer Loads

Online Banking EBO Telesales Subanco Summit

Retail (1) 1 575 36.26 0.07 0.01 0.02 0.03 0.01 0 0 0.16 0 Retail (1) 2 252 28.62 1.43 4.81 0 0.01 0.38 0.01 1.55 0.32 0 Retail (1) 4 17 0.63 2.92 0.52 0 0 0 0 0.45 0 0 Retail (1) 6 94 12.18 0.5 1.09 0 0 0.02 0 0 7.78 0 Retail (1) 9 44 5.01 0.16 0 0.99 0 3.39 0 0 0 0 EBO (7) 10 78 5.3 0.45 0.48 0.04 0 0 99.99 0.08 0 0 Retail (1) 12 58 7.24 0.21 0.96 0 0 0 0 3.13 0 0 Retail (1) 15 43 3.52 0.08 0.04 0 0 0.01 0 0 9.98 0 Premier (2) 19 89 0.04 93.99 0 0.22 0 0 0 0 0 0 Business (3) 29 64 0.03 0 52.26 3.23 0 0.02 0 0.01 0.28 0 Platinum (4) 31 43 0.01 0 39.5 95.51 0 0 0 0 0 0 Customer Loans (5) 33 83 0.71 0 0 0 94.51 0 0 0 0 0 Subanco (9) 34 6 0 0 0 0 5.45 0 0 0 66.2 0 Online Banking (6) 35 178 0.44 0.19 0 0 0 96.18 0 0 0.04 0 Telesales (8) 36 129 0 0 0 0 0 0 0 94.79 0 0 Subanco (9) 38 1 0 0 0.33 0 0 0 0 0 15.24 0 Summit (14) 45 97 0 0 0 0 0 0 0 0 0 100

Total # Calls 700703 43282 72149 35068 71742 36810 55458 78874 7965 111948

Note: Each column sums up 100%.

13

Chart 1

Note: The width of the arrows is proportional to the number of calls for all the arrows that represent more than 5000 calls. The width of all the arrows that represent less than 5000 calls is equal.

14

Chart 2

Note (1): The hold Service Type in each Skill-Group represents the Main-Service. Note (2): The above codes of groups-of-agents-by-skills are part of a list of 48 codes, which we have produced for the whole period of our analysis. In the above chart we describe only the codes relevant to May 2001. The full list appears in the SBR manual, which is under preparation.

15

Chart 3

Note: The width of the arrows is proportional to the number of calls for all the arrows that represent more than 5000 calls. The width of all the arrows that represent less than 5000 calls is equal.

17

Chart 5

Note: The width of the arrows is proportional to the number of calls for all the arrows that represent more than 5000 calls. The width of all the arrows that represent less than 5000 calls is equal.

20

1

Service Engineering

Recitation 4, Part 1: Processing Networks. An Emergency Department Example

The tutorial objective is to teach how to model a queueing network as a “Fork-Join network”.

Join Networks-ForkU

A fork-join network consists of a group of service stations, which serve arriving customers simultaneously and

sequentially according to pre-designed deterministic precedence constraints. More specially, one can think in terms of

"jobs" arriving to the system over time, with each job consisting of various tasks that are to be executed according to

some preceding constraints. The job is completed only after all its tasks have been completed. The distinguishing features

of this model class are the so-called "fork" and "join" constructs. A "fork" occurs whenever several tasks are being

processed simultaneously. In the network model, this is represented by a "splitting" of a task into multiple tasks, which

are then sent simultaneously to their respective servers. A "join" node, on the other hand, corresponds to a task that may

not be initiated until several prerequisite tasks have been completed. Components are joined only if they correspond to

the same job; thus a join is always preceded by a fork. If the last stage of an operation consists of multiple tasks, then

these tasks regroup (join) into a single task before departing the system.

ModelingU

We model our “fork-join network” using 4 specific flow-charts: activities, resources, activities plus resources, and

information. To draw these 4 flow charts one must list all resources of the network and all activities as well, and then

write which activity is using which resource. Next, one draws the flow charts, using the following “language”:

Chart Legend-FlowU

Often times, reality is too complex to capture with the above “language”. Then one must be creative, hence introduce,

ad hoc, the notation that will tell one’s specific story. (As an example, see page 2 where the “red-dot” is such a special

notation)

resource

decision

resources queue

synchronization queue

Job’s “flow”

“fork”

“join”

) 08/01/2009(

4

Figure 1 - Activity (Flow) Chart

Labs

Treatment

Awaiting

evacuation

Administrative reception

Vital signs & Anamnesis

First

Examination

Imagine:

X-Ray, CT,

Ultrasound

Follow-up

Instruction prior

discharge

Waiting

hospitalized

Administrative discharge

Consultation

Decision

A A A B

B

B

C

C C C

C

Alternative Operation -

Ending point of alternative operation -

C

Figure 67: Activities flow chart in the ED

134

) 08/01/2009(

5

Administrative secretary

Nurse

Labs

Physician

Consultant

CT

X-Ray

Ultrasound

Alternative Operation -

Recourse Queue - Synchronization Queue –

Ending point of alternative operation -

C

Figure 2 - Resource (Flow) Chart

A

A

A

A

A A B

B

B

B

B

C

C D

D

Figure 66: Resources flow chart in the ED

133

Alternative Operation -

Recourse Queue - Synchronization Queue –

Ending point of alternative operation -

C

PhysicianNurse Imaging Lab Other

Follow-up

Instruction Prior to Discharge

Administrative Release

Decision

Labs Treatment

Administrative Reception

Vital signs & Anamnesis

Imaging: X-Ray, CT, Ultrasound

Treatment Consultation

First Examination

AAAA

B

B

B

C

C

C

Figure 7: Activities-Resources flow chart in the ED

13

) 08/01/2009(

6

Ending point of alternative operation -

Figure 3 - Information (Flow) Chart

Backgrounds

Receptions

Family doctor/ Internet/

Community

Clinical Information

Nursing Information

Nurse

Physician

Test tube and results

Labs

Shot result or prognosis

Imagine

Clinical Information

Consult

Collecting ResultNurse

Clinical Information

Coordination with outsources

Nurse / ED Receptions

Figure 68: Information flow chart in the ED

135

8

Part 3: Applications and Results

The data is taken from an ED simulator written in Arena12.

Triage

External X-Ray Multi-

Trauma

Trauma

Customer Types

Departures

Released

Admitted

Arrivals Queue

… and more

33975

4221

2006

2886

2741

361

354697

1111

43

1049

994

319

162

2281

3908

160

504

266

193

122

158

118

7

1

32246

35916

1259

2006

2823

3798

876 352

370

107 491

545

566

127

48

48

2052

47

59

33

307

19

17

390

547

14

3

192

221

20

12

60

282

174

355

1405

169 91

23 1

1022

73

35

112

27

3

119

4

34

243

2935

3908

7

23

17

1

44

71

2

516

267229

266

11

78

8 132

132

172

5

11

41

67

7

17

8

8

118

45

27

13

22

8

135

150

24

1

8

3

3

37

24

16

19

26

67

32

10

3

4

2516

1 11

11

11

8

35

6

7

11

6

4

13

6

3

1

6

13

1

11

2

18

1

7

1

9

33

2 7

2

4

2

6

6

4

3

4

1

1

11

6

1

3

8

1

2

1

4

2

2

1

1

1

1

1

1

1

1

1

2

2

3

1

1

1

1

1

1

1

34

3

1

1

26102

735

4489

1704

2232

2240

53

782 95

10

604

393

24134

105

32

2194

82

1369

1362

33

71

501

22

57

205

757

545

3

208

4

1497

2561

34

254

176

25

82

115

153

2843

3071 560

327

14

1

17

1058

26

18932

15

28

1215

34

46

31 94

2

58

15

49

39

9

94 36

52

5916

70

5

1

8

27

8

4

4

2

10

9

6

1

71

3

5

86

7

5

4

1

1

1

4

21

1

1

1

1

3

1

2

3

1

1

Entry

VRURetail

VRUPremier

VRUBusiness

VRUConsumer Loans

VRUSubanco

VRUSummit

Business LineRetail

Business LinePremier

Business LineBusiness

Business LinePlatinum

Business LineConsumer Loans

Business LineOnline Banking

Business LineEBO

Business LineTelesales

Business LineSubanco

Business LineSummit

AnnouncementRetail

AnnouncementPremier

AnnouncementPlatinum

AnnouncementConsumer Loans

AnnouncementOnline Banking

AnnouncementTelesales

AnnouncementSubanco

MessageRetail

MessagePremier

MessageBusiness

MessagePlatinum

MessageConsumer Loans

MessageTelesales

NonBusiness LineBusiness

NonBusiness LineSubanco

NonBusiness LinePriority Service

NonBusiness Line NonCC ServiceCCO

NonCC ServiceQuick&Reilly

Overnight ClosedRetail

Overnight ClosedConsumer Loans

Overnight ClosedEBO

TrunkRetail

TrunkPremier

TrunkBusiness

TrunkConsumer Loans

TrunkOnline Banking

TrunkEBO

TrunkTelesales

TrunkPriority Service

Trunk

Trunk

Trunk

Trunk

Trunk

InternalTotal

OutgoingTotal

NormalTermination

NormalTermination

UndeterminedTermination

AbandonedShort

Abandoned

OtherUnhandled

Transfer

Transfer

33975

4221

2006

2886

2741

361

354697

1111

43

1049

994

319

162

2281

3908

160

504

266

193

122

158

118

7

1

32246

35916

1259

2006

2823

3798

876 352

370

107 491

545

566

127

48

48

2052

47

59

33

307

19

17

390

547

14

3

192

221

20

12

60

282

174

355

1405

169 91

23 1

1022

73

35

112

27

3

119

4

34

243

2935

3908

7

23

17

1

44

71

2

516

267229

266

11

78

8 132

132

172

5

11

41

67

7

17

8

8

118

45

27

13

22

8

135

150

24

1

8

3

3

37

24

16

19

26

67

32

10

3

4

2516

1 11

11

11

8

35

6

7

11

6

4

13

6

3

1

6

13

1

11

2

18

1

7

1

9

33

2 7

2

4

2

6

6

4

3

4

1

1

11

6

1

3

8

1

2

1

4

2

2

1

1

1

1

1

1

1

1

1

2

2

3

1

1

1

1

1

1

1

34

3

1

1

26102

735

4489

1704

2232

2240

53

782 95

10

604

393

24134

105

32

2194

82

1369

1362

33

71

501

22

57

205

757

545

3

208

4

1497

2561

34

254

176

25

82

115

153

2843

3071 560

327

14

1

17

1058

26

18932

15

28

1215

34

46

31 94

2

58

15

49

39

9

94 36

52

5916

70

5

1

8

27

8

4

4

2

10

9

6

1

71

3

5

86

7

5

4

1

1

1

4

21

1

1

1

1

3

1

2

3

1

1

Entry

VRURetail

VRUPremier

VRUBusiness

VRUConsumer Loans

VRUSubanco

VRUSummit

Business LineRetail

Business LinePremier

Business LineBusiness

Business LinePlatinum

Business LineConsumer Loans

Business LineOnline Banking

Business LineEBO

Business LineTelesales

Business LineSubanco

Business LineSummit

AnnouncementRetail

AnnouncementPremier

AnnouncementPlatinum

AnnouncementConsumer Loans

AnnouncementOnline Banking

AnnouncementTelesales

AnnouncementSubanco

MessageRetail

MessagePremier

MessageBusiness

MessagePlatinum

MessageConsumer Loans

MessageTelesales

NonBusiness LineBusiness

NonBusiness LineSubanco

NonBusiness LinePriority Service

NonBusiness Line NonCC ServiceCCO

NonCC ServiceQuick&Reilly

Overnight ClosedRetail

Overnight ClosedConsumer Loans

Overnight ClosedEBO

TrunkRetail

TrunkPremier

TrunkBusiness

TrunkConsumer Loans

TrunkOnline Banking

TrunkEBO

TrunkTelesales

TrunkPriority Service

Trunk

Trunk

Trunk

Trunk

Trunk

InternalTotal

OutgoingTotal

NormalTermination

NormalTermination

UndeterminedTermination

AbandonedShort

Abandoned

OtherUnhandled

Transfer

Transfer

Logged Off

Available (7.36)

Consulting (562.54)

Consulting (208.00)

Consulting (175.42)

General Banking (218.31)

General Banking (136.89)

General Banking (97.22)

Idle (185.11)

Internet Support (313.66)

Internet Support (114.49)

Internet Support (166.50)

Investments (252.68)

Investments (211.41)

Logged In

Logged Off

Long Break (1,384.28)

Managers VIP (227.05)

Managers VIP (149.36)

Medium Break (507.26)

None (209.00)

Paperwork (140.57)

Paperwork (140.62)

Paperwork (140.97)

Paperwork (313.19)

Pension (758.00)

Pension (307.14)

Private Call (27.11)

Sales (177.14)

Sales (329.00)

Securities (278.20)

Securities (532.00)

Shifting (209.71)

Short Break (12.97)

Virtual Branches (195.46)

Virtual Branches (133.67)

Wrap Up (7.46)

Wrap Up (8.71)

Wrap Up (8.11)

Wrap Up (8.03)

ED

Pediatric

Neurology Nephrology Derrmatology

Internal

IntensiveCare

Cardiology

Rheumatology

UrologyOtorhinolaryngology Orthopedics

Surgery

Maternity

Gynecology

Psychiatry Ophthalmology

R

L

D

Out

R

R

R

R

R R

R

RR RR

R

R

R

R

R

L

LL

L

L

L

L

L

L

D

D

D

D

D DD

D

DD

D

D

Out

Out

Out

Out Out

Out

OutOut Out OutOut

Out

Out

115

17

5

43

2

1

1

1

8

6

2

1

1

2

23

3

15

1

7

1

11

1

2

1

6

1

1

1 13

1

1

1

1

1

1

1 3

1

1

1

1

1

1 1

1 1

1

1

11

1 1

1 11

1

11 11 1

1

3

1

1

1

1

1

2

1

3

1 1

2

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

2

1

1

1

1

1

1

1

1

1

1

1

11

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

11

1 1

1

1

1

1

1

1

1

1

1

1

2

11

1

1

1

1

1 1 1 1

1

1

2

1

1

1

6

1

2

1

3

1 1

11

1

3

1

61

1

1 1

1

1

1

1

1

1

1 2

1

1

1

1

2 1

1 1

1

1

1

1

1

1 11

1

11 11 1

1

1

1

1

1

1

1

2

23

1 1

1

1

1

1

1

1

11

1

1

1

1

1

1 11

1 1

2 2

1

2

1

1 1 1 11

2

1

1

serveng

lectures

references

predictionmay1809.pdf

homeworks

hw1_amusement_park_mgt.pdf

recitations

hazard_phase_type.pdf

course2011winter

icpr_service_engineering.pdfon_cc_data_frommsom.pdf pivot_table.pdf

thesis_polyna.pdf

proposal_polina.pdf

moss.pdf

files

nurse_proj_psu_tech.pdf

national_cranberry_1.pdf

hw9par_sol_2012w.pdf

syllabus8.pdfexams

statanalysis.pdf

agent-heterogeneity-brown-book-final.pdf

1_1_qed_lecture_introduction_2011w.pdf

course2004

retail.pdf hall_6.pdf

jasa_callcenter.pdf dantzig.pdf

bacceli.pdf

avishai-class-april-2003.ppt

hw7_2011w_par_sol_part2.pdf

ibmrambam technion srii presentation final.ppt

dimensioning.pdf

palmannoyance.pdf

yair_sergurywaitingtimeanalysis.pdf

nejmsb1002320.pdf

qed_qs_scientific_generic_caschina.pdf

web_summary13_2011s.pdf

4callcenters_coverpage.pdf

documentation.pdf rec9part2.pdf

web_summary9_2010w.pdf

qed_qs_scientific_wharton_stat_seminar.pdf

l2_measurements_class_2009s.pdf

ccreview.pdfvdesign_ecompanion.pdf

wharton_lecture_avi_printout.pdflarson_atoa.pdf

proposal_michael.pdf

stanford_seminar_avi_printout.pdf

solver_guide.pdf

fitz.pdf

rec10_part2.pdf rec5.pdf

paxson.pdf

connect_to_see_terminal.pdf

project_ug_simulationinterface.pdf revenue_manage.pdf

hw7_2009s_forecast.xls

syllabus2.pdf

infocom04.pdf

hall_transportation.pdf

whitt_handout_chapter1.pdf

fromprojecttoprocess.pdf

nytimes.2007-06-23.pdf ds_pert_lec1.pdf syllabus6.pdf hw9_2011w.pdf hw7_2011w.pdf

mmng_constraint_supp_or.pdf

awam-april_2011.pdfwhitt_scaling_slides.pdfmmng_seminar.pdf

syllabus4.pdf

syllabus12_2012w.pdf

syllabus5.pdf

mm1_strong_apprximations.pdf seestat_tutorial.ppt

kaplan_porter_2011-9_how-to-solve-the-cost-crisis-in-health-care_hbr.pdf

rec9_part2.pdf

callcenterdata

bocaraton.ps

gccreport 20-04-07 uk version.pdf hall_manpower_planning.pdf

bi18.pdf

syllabus12.pdf fluidview_2010w_short_version.pdf hw7_2009s_par_sol_part2.pdf

moed_b_2007w_solution.pdf

newell.pdf

rec11.pdf

moedb_2009w.pdf

rec3.pdf

hw10_par_sol_2011s.pdf

thirdlevel support ijtm1_cs.pdf

hw4_full.pdf

moedb_2010s_sol.pdf

chandra.pdf

gurvich_seminar.pdf

ed_sinreich_marmor.ppt regimes_supplement.pdf

web_summary10_2011s.pdf hw6par_sol_2009s.xls

exam_2005s_sol.pdf

servicefull.pdf

witor09_empirical_adventures_sep2009.pdf

web_summary12_2011s.pdf

heb_summary_yulia.pdf

hw9par_sol_2011s.pdf

seminar_yulia_9_2.pdf

bpr_2003.pdf staffing_italian_us_2011w.xlsdesignofqueues.pdf thepsychologyofwaitinglines.pdf

erlangabc.pdf

studentsevaluations.pdf hw8_2011s.pdf

hw9_2012w.pdf

edie.pdf

syllabus9.pdf

ds_pert_lec2.pdf

zohar_seminar.ppt

hist-normal.pdf

vandergraft.pdf

rec13.pdf

desighn_ivr.pdf

cohen_aircraft_failures.pdf

sbr.pdf

nano.pdf

hw2_2011w.pdf seminar_presentation_boaz_estimatinger load.ppt

project_ug_meirav.pdf see_report_output_june_2011.pdf

see_report_2010.pdf

470

228

5

101

8 4 3

30

53

44 35

6 33

7334 3

4

4

321

3

314 93

3

4 43

19

4

3 3

5

48 34

64

3 3 4

14

7 3

5 4

3

6

7

335 3 33 3

58 4 3

53

44 35 7334 3

4

3

3

5

14 93

3

4 43

4

3

5

48 34

64

3 3 45 4

3

6 335 3 33 3

serveng

lectures

hall_flows_hospitals_chapter1text.pdf

references

4callcenterscourse2004 homeworks

icpr_service_engineering.pdf keycorp.pdf

files

revenue_manage.pdfhw1_amusement_park_mgt.pdf

recitations

setup.exe

garnett.pdf abandon02.pdf erlang_a.pdf ccbib.pdf

us7_cc_avi.pdf

jasa_callcenter.pdfsbr.pdf

hazard_phase_type.pdf

mazeget_noa_yul.pptexams technion-call-center-report-sept-2004.pdfbrandt.pdf statanalysis.pdfmjp_into_erlang_a.pdf loch.pdf sbr_stolyar.pdf

callcenterdata

larson_atoa.pdf sivanthesisfinal.pdf columbia05.pdf crmis.pptfluid_systems.rar

januarytxt.zip

sbr_wharton_ccforum_short_may03.pdf predictingwaitingtime.pdf avishaicourse_munichor.ppt vdesign_pub.pdf hierarchical_modelling_chen_mandelbaum_part1.pdf

solver_guide.pdf

datamocca_february_2008_cc_forum_lecture.ppt seminar_presentation_galit.pptintro_to_serveng_teaching_note.pdf

pivot_table.pdf

project_ug_simulationinterface.pdf

course2010winter

ed_simulation_modeling_supp.pdf avim_cv.pdfdantzig.pdf yariv_phd.pdf

rec10_part2.pdf

fr6.pdfnejmsb1002320.pdf

hebrew_syllabus_2011s.pdf

patientflow main.pdf

hw9_2012w.pdf

avim_cv_18_12_2011.pdf

470

228

5

101

8 4 3

30

53

44 35

6 33

7334 3

4

4

321

3

314 93

3

4 43

19

4

3 3

5

48 34

64

3 3 4

14

7 3

5 4

3

6

7

335 3 33 3

58 4 3

53

44 35 7334 3

4

3

3

5

14 93

3

4 43

4

3

5

48 34

64

3 3 45 4

3

6 335 3 33 3

serveng

lectures

hall_flows_hospitals_chapter1text.pdf

references

4callcenterscourse2004 homeworks

icpr_service_engineering.pdf keycorp.pdf

files

revenue_manage.pdfhw1_amusement_park_mgt.pdf

recitations

setup.exe

garnett.pdf abandon02.pdf erlang_a.pdf ccbib.pdf

us7_cc_avi.pdf

jasa_callcenter.pdfsbr.pdf

hazard_phase_type.pdf

mazeget_noa_yul.pptexams technion-call-center-report-sept-2004.pdfbrandt.pdf statanalysis.pdfmjp_into_erlang_a.pdf loch.pdf sbr_stolyar.pdf

callcenterdata

larson_atoa.pdf sivanthesisfinal.pdf columbia05.pdf crmis.pptfluid_systems.rar

januarytxt.zip

sbr_wharton_ccforum_short_may03.pdf predictingwaitingtime.pdf avishaicourse_munichor.ppt vdesign_pub.pdf hierarchical_modelling_chen_mandelbaum_part1.pdf

solver_guide.pdf

datamocca_february_2008_cc_forum_lecture.ppt seminar_presentation_galit.pptintro_to_serveng_teaching_note.pdf

pivot_table.pdf

project_ug_simulationinterface.pdf

course2010winter

ed_simulation_modeling_supp.pdf avim_cv.pdfdantzig.pdf yariv_phd.pdf

rec10_part2.pdf

fr6.pdfnejmsb1002320.pdf

hebrew_syllabus_2011s.pdf

patientflow main.pdf

hw9_2012w.pdf

avim_cv_18_12_2011.pdf

8 AM - 9 AM

55888

1113 43320 160505194 7

36512 23633845 3792

845 103

120

314

3314

155

43

13

4

34

594 140

547

191

16

10 60 303

6

73

432

242

3052 3908

7

23

12

60

472131 202

19

8

9

150

14 46 10

8

78

30924

873

20863633 3536

562

867

40

260

294

8

78

20

666

198 35148

29

213

3005

6

3671

11

19 356

126

10

46

4

12

3

Entry

Entry Entry Entry Entry EntryEntry Entry

VRU

Retail Premier Business PlatinumConsumer Loans EBOTelesales Subanco Summit

VRU

Retail PremierBusiness PlatinumConsumer LoansEBO Telesales SummitOut

VRU

Retail

C

A

C

CC C

C

C

C

C

C

C

C

C C

C

C

C

C

C

C

AA

A

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ChangePassword

Agent

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Query Transfer

Securities

StockMarket

PerformOperation

Credit

Error

43123

234

270

1352

35315

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7895

830

68

65

273

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231

768

133

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897

40

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71

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165

53

114

29

16

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9

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107

6

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26091

4115

12317

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54

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3315

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1

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7

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4

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6

2

26

1

2

2

3

2

9

1

1

1

3

3

52

1

1

2 3

22

1

2

2

2 1

2

1

1

3

1

11

18

1

3

1 12

1

3

1 2

1

3

2

4

1

3

3

1

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1

2

13

1

1

1 1

1

2

7

3 2813

5

18

32

1

10

14

23

3

5

1

Logged Off

Allocate For Call (0.39)Allocate For Call (1.29)Allocate For Call (0.67)

Available (4.15)

Completed (0.29)

Completed (1.00)

Consult (148.43)

Consult (94.50)

Dial (38.67)

Disconnect (50.19)

Disconnect (47.50)

Disconnected (39.00)

Disconnected (52.00)

Held (21.00)

Held (144.33)

Hold (127.00)

Idle (69.87)

Inbound Call (160.47)

Logged In

Logged Off Long Break (1,225.50)

Make Agent Dialer (43.82)

Make Call (192.00)

Make Call (53.67)

Medium Break (273.50)

Other Party Disconnected (2.33)

Other Party Disconnected (54.00)

Paperwork (140.57)

Paperwork (77.48)

Paperwork (37.00)

Paperwork (97.20)

Private Call (31.90)

Ready (61.17)

Ready (16.00)

Ready (2.00)

Reschedule (0.31)

Retrieve (34.00)

Retrieved (67.50)

Retrieved (108.33)

Short Break (6.29)

Transfer Done (39.24)Transfer Done (41.19)

Wrap Up (7.03)

Wrap Up (5.53)

Wrap Up (2.50)

Wrap Up (14.53)

44 15

3

1 11233

1

2 1

42

2

14

9

1

11

3

1 1

1

1

2

12

1

1

2

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3

1 1 21 3

12

1

3

2

2

2

1

33

7

18 32

1

10

14 23

3

5

1

Logged Off

Available (4.15)

General Banking (190.48)

General Banking (297.50)

General Banking (61.25)

Idle (69.87)

Investments (184.00) Investments (125.00)

Logged In

Logged Off Long Break (1,225.50)

Managers VIP (119.08)

Medium Break (273.50)

None (209.00)

Paperwork (140.57) Paperwork (77.48)

Paperwork (37.00)

Paperwork (97.20)

Private Call (31.90)

Sales (176.09)

Short Break (6.29)

Wrap Up (7.03) Wrap Up (5.53)

Wrap Up (2.50)

Wrap Up (14.53)

2 11 12

3

1 1

1

1

1 1

1

2

1

2

2 1

1

1

4

1

2

2

2

2 2

Logged Off

Available (4.50)

General Banking (93.50) General Banking (19.00)

Idle (23.00)

Investments (184.00) Investments (125.00)Managers VIP (129.50)

Medium Break (154.00)

Paperwork (340.50)

Paperwork (103.50) Paperwork (53.00)Idle

Idle (9.00)

Sales (125.50)

Short Break (10.00)

Wrap Up (11.00)

Wrap Up (3.50) Wrap Up (5.50)

Private Prepaid

PrivatePrivate VIP Private Customer RetentionBusiness Business VIP Business Customer Retention

Language 2 Prepaid Language 2 Prepaid Low PriorityLanguage 2 PrivateLanguage 3 Internet Surfing Internet

Overseas

1 24 5 810 11 12 1315161718 20 21 24 25

2104

2208 26264592 445 3411432049 1484 8734785 4658 411 444480 43 847 181 974

1253823 843343 1390109630 4454 1240792 3793 49 735

1433 193

1 24 5 810 11 12 1315161718 20 21 24 25

2104

2208 26264592 445 3411432049 1484 8734785 4658 411 444480 43 847 181 974

1253823 843343 1390109630 4454 1240792 3793 49 735

1433 193

8 AM - 9 AM

Entry

EntryEntry Entry Entry EntryEntry Entry

VRU

RetailPremier Business PlatinumConsumer Loans EBOTelesalesSubanco Summit

VRU

RetailPremier Business PlatinumConsumer Loans EBO Telesales SummitOut

VRU

Retail

C

A

C

C C C

C

C

C

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C

C

C

CC

C

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C

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C

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8 AM - 9 AM

Entry

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RetailPremier Business PlatinumConsumer Loans EBOTelesalesSubanco Summit

VRU

RetailPremier Business PlatinumConsumer Loans EBO Telesales SummitOut

VRU

Retail

C

A

C

C C C

C

C

C

C

C

C

C

CC

C

C

C

C

C

C

AA

A

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A

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Out OutOut

9 AM - 10 AM

Entry

EntryEntry Entry Entry EntryEntry Entry

VRU

RetailPremier Business PlatinumConsumer Loans EBOTelesalesSubanco Summit

VRU

RetailPremier Business PlatinumConsumer Loans EBO Telesales SummitOut

VRU

Retail Business

C

A

C

C C C

C

C

C

C

C

C

C

C

CC

C

C

C

C

C

C

AA

A

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10 AM - 11 AM

Entry

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RetailPremier Business PlatinumConsumer Loans EBOTelesalesSubanco Summit

VRU

RetailPremier Business PlatinumConsumer Loans EBO Telesales SummitOut

VRU

Retail Business

C

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C

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C

C

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C

C

C

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11 AM - 12 PM

Entry

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RetailPremier Business PlatinumConsumer Loans EBOTelesalesSubanco Summit

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RetailPremier Business PlatinumConsumer Loans EBO Telesales SummitOut

VRU

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C

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Conceptual Model:Bank Branch = Queueing Network

23

Teller

Entrance

Tourism

Xerox

Manager

Teller

Entrance

Tourism

Xerox

Manager

Bottleneck!

16

Bank Branch: A Queuing Network

Bank: A Queuing Network

Transition Frequencies Between Units in The Private and Business Sections:

Private Banking Business

To Unit Bankers Authorized Compens - Tellers Tellers Overdrafts Authorized Full Exit

From Unit Personal - ations Personal Service

Bankers 1% 1% 4% 4% 0% 0% 0% 90%

Private Authorized Personal 12% 5% 4% 6% 0% 0% 0% 73%

Banking Compensations 7% 4% 18% 6% 0% 0% 1% 64%

Tellers 6% 0% 1% 1% 0% 0% 0% 90%

Tellers 1% 0% 0% 0% 1% 0% 2% 94%

Services Overdrafts 2% 0% 1% 1% 19% 5% 8% 64%

Authorized Personal 2% 1% 0% 1% 11% 5% 11% 69%

Full Service 1% 0% 0% 0% 8% 1% 2% 88%

Entrance 13% 0% 3% 10% 58% 2% 0% 14% 0%

Legend: 0%-5% 5%-10% 10%-15% >15%

Dominant Paths - Business:

Unit Station 1 Station 2 Total Parameter Tourism Teller Dominant Path

Service Time 12.7 4.8 17.5 Waiting Time 8.2 6.9 15.1

Total Time 20.9 11.7 32.6

Service Index 0.61 0.41 0.53

Dominant Paths - Private:

Unit Station 1 Station 2 Total Parameter Banker Teller Dominant Path

Service Time 12.1 3.9 16.0 Waiting Time 6.5 5.7 12.2

Total Time 18.6 9.6 28.2

Service Index 0.65 0.40 0.56

Service Index = % time being served

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Mapping the Offered Load (Bank Branch)

Mapping Offered Load (Branch of a Bank)

Business

Services

Private

Banking

Banking

Services

Department

Time Tourism Teller Teller Teller Comprehensive

8:30 – 9:00

9:00 – 9:30

9:30 – 10:00

10:00 – 10:30

10:30 – 11:00

11:00 – 11:30

11:30 – 12:00

12:00 – 12:30

Break

16:00 – 16:30

16:30 – 17:00

17:00 – 17:30

17:30 – 18:00

Legend:

Not Busy

Busy

Very Busy

Note: What can / should be done at 11:00 ? Conclusion: Models are not always necessary but measurements are !

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Conceptual Model: The Justice Network, orThe Production of Justice

“Production” Of Justice

Queue

Mile Stone

Activity

Appeal

Proceedings

Closure

Prepare AllocateOpen File

Avg. sojourn time ≈ in months / years

Processing time ≈ in mins / hours / days

Phase Transition

Phase

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Conceptual Model: Burger King Bottlenecks

Bottleneck Analysis: Short – Run ApproximationsTime – State Dependent Q-Net

3 Minimal:Drive-thruCounterKitchen

Add:#4 Kitchen#5 Help

Drive -thru

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