Although the importance of flexibility has long been recognized in the service industry, scholars and practitioners alike still struggle to express the value of flexible services in economic terms. We perceive that many service providers tend to strive for very flexible service processes no matter in which ecosystem they are embedded. They invest huge amounts of money in flexibilization projects without being able to justify their decisions in line with economic criteria. Scholars, in contrast, advise against investing as much as possible in flexibilization. Concrete recommendations, however, are missing. Especially insights into the positive economic effects of flexible service processes require more attention. Against this backdrop, we propose an economic optimization model as a first step to capture the general relationships that govern the flexibilization of service processes. The optimization model enables service providers to estimate ap propriate levels of volume and functional flexibility and to select flexibilization projects accordingly. We also provide first insights into the applicability of the optimization model via a demonstration example.

1. University of Augsburg
Department of Information Systems Engineering
& Financial Management
Research Center
Finance & Information Management
Journals Business & Information Systems
Engineering and WIRTSCHAFTSINFORMATIK
Felix Krause
Maximilian Röglinger
Lydia Neuhuber
www.fim-online.eu
II/2010-101007
Flexibilization of service
processes:
toward an economic
optimization model

2. 2 • FLEXIBILIZATION OF SERVICE PROCESSES • Felix Krause © FIM Research Center
Research Question
How much should a service provider invest
in the flexibility of its services process?

3. 3 • FLEXIBILIZATION OF SERVICE PROCESSES • Felix Krause © FIM Research Center
Basic Relationships for the Model
Flexibility
Total Service Time
Additional Sales /
Cash Inflows
Cashflows /
Objective Function
Cash Outflows
- - +
+ -

4. 4 • FLEXIBILIZATION OF SERVICE PROCESSES • Felix Krause © FIM Research Center
We focus on volume flexibility and
functional flexibility
• Flexible working hours
• Short time work
• Optimization of degree of
automatization
Exemplary measures
• Reference models
• Professional training of employees
• Modular composition of service
portfolio
Exemplary measur
VOLUME FLEXIBILITY
The ability to respond to a numerical
fluctuation in demand.
Exemplary measures
FUNCTIONAL FLEXIBILITY
The ability to respond to numerous
different and possibly unknown
customer requests
(Johnston et al. 2012; OECD 1998)

5. 5 • FLEXIBILIZATION OF SERVICE PROCESSES • Felix Krause © FIM Research Center
Flexibilization – Reality vs. Model
ModelReality
vol 1
Decision between discrete pre-selected
and pre-ordered flexibilization projects
Decision with continuous variables
for volume & functional flex.
vol 2 vol 3 vol 4
vol = volume fun = functional
0
10
1
fun 1 fun 2 fun 4fun 3

6. 6 • FLEXIBILIZATION OF SERVICE PROCESSES • Felix Krause © FIM Research Center
The Impact of Flexibility Projects on
Total Service Time
Strangers
Repeaters
Runners





Waiting Time Processing TimeSet-Up
Time
Total Service Time = + +
Volume
Flexibility
Functional
Flexibility
-- (fixed)
(Johnston et al. 2012)

7. 7 • FLEXIBILIZATION OF SERVICE PROCESSES • Felix Krause © FIM Research Center
The Impact of Total Service Time on
Additional Sales
T = total time needed for the service
t’= max. time where all customers are willing to wait
t’’= min. time where no customers are willing to wait
Not interested
Market Potential
Interested and happy
with Total Service Time
Interested but unhappy
with Total Service Time

8. 8 • FLEXIBILIZATION OF SERVICE PROCESSES • Felix Krause © FIM Research Center
Basic Relationships for the Model
Flexibility
Total Service Time
Additional Sales /
Cash Inflows
Cashflows /
Objective Function
Cash Outflows
- - +
+ -

9. 9 • FLEXIBILIZATION OF SERVICE PROCESSES • Felix Krause © FIM Research Center
Demonstration example
Starting situation
Volume FP Functional FP
1 Flexible employment contracts + 0.4 0.4 1
Reference
process model
+ 0.1 0.1
2 Part time employee system + 0.1 0.5 2 Multi-skilling of employees + 0.3 0.4
3
Outsourcing of
process activities
+ 0.2 0.7 3
Expert training of
employees
+ 0.2 0.6
4
Dynamic optimization of the
degree of automatization
+ 0.3 1.0 4
Extension of information
system
+ 0.4 1.0
Service Requests:
60% from Strangers
40% from Runners and Repeaters
Possible Time Reductions
Set-Up Time: From 40 min to 10 min
Waiting Time: From 120 min to 40 min
0
x(T)
70 min 130 min

10. 10 • FLEXIBILIZATION OF SERVICE PROCESSES • Felix Krause © FIM Research Center
Volume FP Functional FP
1 Flexible employment contracts + 0.4 0.4 1
Reference
process model
+ 0.1 0.1
2 part time employee system + 0.1 0.5 2 Multi-skilling of employees + 0.3 0.4
3
Outsourcing of
process activities
+ 0.2 0.7 3
Expert training of
employees
+ 0.2 0.6
4
Dynamic optimization of the
degree of automatization
+ 0.3 1.0 4
Extension of information
system
+ 0.4 1.0
Demonstration example
Result
Volume FP Functional FP
1 Flexible employment contracts + 0.4 0.4 1
Reference
process model
+ 0.1 0.1
2 Part time employee system + 0.1 0.5 2 Multi-skilling of employees + 0.3 0.4
3
Outsourcing of
process activities
+ 0.2 0.7 3
Expert training of
employees
+ 0.2 0.6
4
Dynamic optimization of the
degree of automatization
+ 0.3 1.0 4
Extension of information
system
+ 0.4 1.0

11. 11 • FLEXIBILIZATION OF SERVICE PROCESSES • Felix Krause © FIM Research Center
Conclusion, Limitations and Outlook
• It is not reasonable to invest as much as possible in flexibility
• The optimal level of flexibility depends on business environment and
internal conditions
Conclusion
• Assumption of certain demand
• Consideration of one point of time
• No dependencies of service requests
• Volume flexibility independent from functional flexibility
Limitations
• Address limitations
• Application to real world settings
Outlook