Simulation Based Prospective Productivity Assessment of Complex Services

doi:10.4236/ib.2013.53B008

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iBusiness, 2013, 5, 36-40

http: //dx.doi.org/10.4236/ib.201 3.53B008 Published Online September 2013 (http://www.scirp.org/journal/ib)

Simulation Based Prospective Productivity Assessment of

Complex Services

Andreas Petz, Sönke Duckwitz, Susanne Mütze-Niewöhner, Christopher M. Schlick

Work Organization Department, Chair and Institute of Industrial Engineering and Ergonomics at the RWTH Aachen University,

Aachen, Germany.

Email: [email protected]

Received May, 2013

ABSTRACT

Although services play a crucial role in developed countries, service productivity management is not as much re-

searched as productivity management in production systems. Especially complex, knowledge inten sive service systems

have specific characteristics that have to be considered, in order to comprehensively evaluate their performance. A

novel productivity model considering quantitative as well as qualitative aspects from both customer and provider side

lays the foundation for a simulation based prospective productivity assessment of complex services. The paper con-

cludes by presenting a use case scenario from the chemical industry.

Keywords: Complex Service Productivity; Key Performance Indicators; Simulation

1. Introduction

Services, especially complex, knowledge intensive services

are gaining more and more importance not only because

of the high workforce rate in this sector but also due to

their cont ribut ion on grow th and w elf are in emerg ing an d

developed countries. In Germany, the gross added value

in this sector is growing slower than the workforce rate

and offers an evidence for slower productivity gains in

comparison to the production sector [13]. Although ser-

vices play an important role, service productivity man-

agement is not as much investigated as productivity

management in the production industry mostly due to the

specific characteristics of services and due to a meas-

urement mismatch [4,13].

1.1. Complex, Knowledge Intensive Services

Defining services is a challenging task. The most suitable

approach is given by Donabedian and Hilke [1,16]. Ac-

cording to them, services can be specified from three

dimensions: the potential, the process and the outcome

dimension. From the potential perspective a service is

defined as the availability of resources for service provi-

sion or the willingness to perform a specific service. In

the process dimension, services are defined as joint or

standalone activities during the service provision. Finally,

from the outcome perspective, services are defined as the

material or immaterial result of the service prov ision.

The traditional definition is supp lemented by generally

accepted service specific characteristics known as the

IHIP characteristics: Intangibility, Heterogeneity, In-

separability and Perishab ility [9].

Following this three dimensional differentiation, com-

plex services such as research and development or con-

sultancy are characterized by a high degree of these spe-

cific attributes and are usually organized in projects. In

the potential dimension the amount of the composing

elements (input factors), their heterogeneity as well as

their interrelations are highly time and space dependent

resulting in an increase of complexity [10,11]. In the

process dimension an intensive customer to service pro-

vider interaction and thereby the induced uncertainty and

changeability of the process over time (dynamics) in-

creases complexity. Finally, the outcome is also hetero-

geneous, vague beforehand and the service perception is

highly immaterial as an evidence of high complexity.

1.2. Service Productivity

The traditional productivity definition as a ratio of vol-

umes of output and input has its origins in the assessment

of isolated production systems [6]. Thus, service inherent

characteristics like customer integration and the simulta-

neity of production and consumption (service perishabil-

ity) are not considered at all. In complex service systems

the simultaneity is considered especially when exchang-

ing information in terms of communication or collabora-

tion. Some scholars tried to close this gap by developing

service specific productivity models with focus on some

Simulation Based Prospective Productivity Assessment of Complex Services 37

specific aspects[2,5,12].

In order to enhance these concepts, the authors of this

paper conducted empirical studies and identified success

factors and phase specific value drivers for a comprehen-

sive definition of service productiv ity [3]. Based on these

findings, a novel p roductivity model was develope d. The

novel productivity model considers the influence of ser-

vice provider as well as customer from an objective,

quantitative (directly quantifiable measures e.g. costs) as

well as subjective, qualitative (indirectly quantifiable

measures e.g. communication) point of view. Further-

more it defines partial productivities with regard to ser-

vice effectiveness and efficiency in every single service

dimension.

The conceptual model is static in its nature and does

not completely fulfill the need for a prospective assess-

ment particularly due to the dynamic characteristic of

services. Simulation is considered to be a suitable solu-

tion for a dynamical analysis and assessment of a service

system.

2. Methodology

The service system modeling and simulation approach

aims to bridge the gap b etween traditional manufacturing

productivity management and knowledge worker pro-

ductivity management. Therefore, based on the novel

service productivity model, a service process simulation

tool as decision support for service planners and manag-

ers is developed.

2.1. Service Process Modelling

In order to prospectively evaluate a service system or a

single service provision it is prerequisite to represent the

underlying system in a formal manner as a partial repre-

sentation of the real system [7,8]. Consequently the ser-

vice system model should build up upon the service defi-

nition in all relevant dimensions considering also a role

specific differentiation (at least customer and service

provider).

Furthermore, it is important to consider only relevant

attributes of the modeled en tities. In the potential dimen-

sion we focus on human resources as input factors due to

their high degree of influence within knowledge inten-

sive services. Human resources are characterized by their

availability, suitability for given roles as well as costs per

hour. In the process dimension the service process is

mapped by tasks and their parameters in terms of tempo-

ral effort, interrelations (communications effort) and ad-

ditional effort due to an overlapping processing of the

tasks. Finally, the outcome dimension is represented by

the simulation results presenting the overall process

schedules and costs, as well as resource allocation tables.

The actual representation is done by using the Design

Structure Matrixes (DSM) method for modeling complex

systems, their elements and their interdependencies [14].

2.2. Service Process Simulation

A Monte-Carlo simulation environment was developed

and implemented in MATLAB. It is an adaptation of the

simulation approach developed by Gärtner for new de-

velopment projects in the automotive industry [15].

After the parameterization of the DSM-model, simula-

tion runs can be performed. The simulation algorithm

starts with the determination of the normal temporal ef-

fort of the service tasks and the availability of the work-

ing persons by random value generation through the

latin-hypercube sampling method. The normal temporal

effort and the availability are inpu t variables modeled by

statistical triangular distribution s.

A working person is associated to a working task

based on his availability and his suitability for the re-

quired role. There are three different assignment strate-

gies implemented so far: random, best fit (in terms of the

worker’s competence to the required role) and in a given

order. Based on the assignment of the working persons to

a specific task, the final effort for processing the given

task is computed. This final task effort is also dependent

on the head count of the assigned persons to the task as

well as their interactions in terms of communication and

collaboration effort. The task flow is determined by the

allowable degree of task overlapping as well as their in-

terrelations according to the DSM. If the starting crite-

rion is fulfilled, the activated task is executed and avail-

able working persons are associated for processing the

given task. Furthermore, iterations are also considered by

their probability of occu rren ce. The cu stomer influ ence is

considered by a customer specific degree of task accu-

racy. Finally, the durations of the tasks and the resulting

service schedule are computed.

2.3. Prospective Service Assessment

Table 1 presents an excerpt of the implemented key per-

formance indicators in accordance to the novel produc-

tivity model. These indicators refer to partial productiv-

ities of the underlying service system.

Table 1. Key performance indic a tor s.

Service Dimensions

Partial service

productivities Potential Process Outcome

Efficiency Capacity

utilization Task effort Overall costs

Effectiveness Degree of task

incompleteness Task duration/

planned duration Adherence to

schedule

Simulation Based Prospective Productivity Assessment of Complex Services

3. Case Study probability after each iteration loop.

In this case study the ten tasks are assigned to four

different worker roles (T1: project leader, T2: project

controller/financial officer, T3 and T8: specialist process

engineering, T4 – T7 and T9 – T10: chemical process

engineer) and 30 workers from the company are consid-

ered to be suitable for working on this project, each of

them covering different roles with different levels of

competence and availability. Thereby, the availability of

the workers is limited due to commitments in other pro-

jects or holidays.

3.1. Study Design

The case study presented in this paper refers to the early

planning phases of a chemical facility. This section of the

project consists of ten tasks representing the preparatory

work on information gathering and the generation of ba-

sic data for the two following phases “basic engineering”

and “detail engineering”.

The structure of the project’s tasks is given in the

DSM in Figure 1. In this DSM, entries on the principle

diagonal describe the normal temporal effort (effort to be

performed by one average skilled worker) that has to be

provided to fulfill the respective task in form of a trian-

gular distribution (e.g. T4 best case=6 hours, most likely

case=13 hours, and worst case=15 hours), while entries

below the diagonal of the matrix describe the minimum

degree of completion of predecessor tasks that is neces-

sary to start the task (e.g. T5 can be started once T4

reaches a degree of completion of 20%). Entries above

the diagonal represent possible iterations in the process.

In this case study after each of the other nine tasks addi-

tional effort has to be spen t on the task T1 “Project man-

agement” to consolidate the information for the follow-

ing project pha ses.

3.2. Simulation Results

Based on the previously defined data, a simulation study

is carried out. The study aims to assess the duration and

costs of the proposed project and defines a set of valid

project structures as decision support for the project

manager. The results of 1000 simulation runs have been

analyzed and are presented in the following.

In the first step, a most likely assignment of the ten

project tasks to workers according to their suitability is

given in Figure 2. According to their engagement the

simulated capacity utilizatio n can be computed.

The corresponding Gantt-diagram presenting task du-

rations of the project is depicted in Figure 3, showing the

overlapping of the tasks and the iterations of T1 “Proj ect

management”.

In addition to the parameters given in Figure 1, further

variables are considered in form of a multidimensional

input-DSM not visualized here. The amount of rework or

additional effort resulting from an overlapping of tasks

has to be defined as well as the decrease of the iteration The cloud plot in Figure 4 presents the frequency of

occurrence of different cost-d urat i o n c ombinations.

T 1T 2T 3T 4T 5T 6T 7T 8T 9T 10

T 1Projec t management3/8/151.0 1.0 1.0 1.0 1.0 1.01.0 1.0 1.0

T 2Pro jec t assignment / cust omer order0.5 1/2/15

T 3Kick- Off0.1 1/3/5

T 4Gathering/ pro cessing Info rmation0.1 0.16/13/15

T 5Defining streams0.2 6/13/15

T 6Calculating0.20.2 6/13/15

T 7Meetings with the customer/

job-site inspection0.2 0.2 0.210/30/35

T 8Reporting0.2 0.2 0.2 0.2 0.210/13/17

T 9Traveling1.0 1.0 1.0 1.0 1.0 0.21/5/7

T 1 0Do cumentation1.0 1.0 1.0 1.0 1.0 1.01/2/3

Figure 1. DSM representing the case study’s project tasks.

Simulation Based Prospective Productivity Assessment of Complex Services 39

Figure 2. Most likely assignment of tasks to workers (WPxxx) based on the working persons suitability and availability for

the given task.

Figure 3. Gantt-chart of the project plan.

Figure 4. Duration and costs of different project settings.

Simulation Based Prospective Productivity Assessment of Complex Services

Based on this information, the project duration is ap-

proximately between 33 and 48 days, the most likely

duration is 41 days. The costs of the project vary between

17 and 25 thousand Euros and are most likely 21 thou-

sand Euros. These results represent valuable information

for the project planner. He is able to assess the risk of the

project and select a project organization that will with a

high probability lead to a successful project outco me.

4. Outlook

In order to verify and validate the conceptual productiv-

ity model and the simulation system broader studies will

be conducted. Furthermore, more detailed use cases from

our project partners will be gathered and will assist to

prove the usefulness of a dynamical assessment of ser-

vice productivity. This will enable service planners and

managers to take reliable decisions based on a system-

atical support.

As a further enhancement an optimization algorithm

will be developed and connected to the simulation tool.

This will close the loop by identifying optimal organiza-

tional settings under given circumstances of uncertainty.

5. Acknowledgements

The research is funded by the German Federal Ministry

of Education and Research (BMBF), Project: ProLoDi,

according to Grant No. 01FL10050, Research Program

“Productivity of Services” supervised by the Project

Management Agency of the German Aerospace Center

(PT-DLR). The authors would like to express their grati-

tude for the support given.

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