1. Development of a framework for
improvement of business
processes in a service operation:
An Action Research Approach
2. • Introduction
• Literature Review
• Objective of the study
• Methodology
• Case Description and Analysis
• Findings of the Study
3. • Increase in economic contribution of the service based
industries (Machuca et al., 2007; Bamford and Forrester,
2010):
• Emphasis on improvements in these service businesses.
• Quality and standard of the final service provided
depends on the business processes (Dale et al., 2007) :
• Improvements in these processes will lead to considerable
improvements in customer end service provided.
• These, along with many other studies and practical
experiences, emphasize on Business Process
Improvement.
1. Bamford, D. R. and Forrester, P. L. (2010) Essential guide to operations management: concepts and case notes. 1st edn.
Chichester :Wiley.
2. Dale, B.G., Iwaarden, J and Wiele, A. (2007) Managing Quality. 5th edn. Oxford, UK: Blackwell.
3. Machuca, J.A.D, González-Zamora, M.D and Aguilar-Escobar, V.G. (2007) ‘Service operations management research’,
Journal of Operations Management, 25, pp. 585-603.
4. • Steps in Business Process Improvement:
• Understanding of the system
• Understanding the process in the first requirement for suggesting improvement
(Slack, 2006)
• Paradiso (2003) – Process Maps leads to improvements through identification
of bottlenecks and redundant steps
• Making the system leaner – through Value Stream (VSA)
• First popularized by Womack and Jones (2003)
• Mehta and Fargher (2005) - Aim of process improvement is to increase the
ration of Value Adding Activities to Non-Value Adding Activities
• Abdullah and Rajgopal (2009) – Gap in the use of VSMs and Lean in Service
Sector.
1. Abdullah, F., Rajgopal, J. (2003) ‘Lean manufacturing in the process industry’, Proceedings of the IIE Research Conference,
CD-ROM, Portland, OR, IIE, Norcross, GA.
2. Mehta, M. and Fargher, J. (2005) ‘Goodwill mapping’, Industrial Engineer, September, pp. 34-9.
3. Slack, N. (2006) Operations and process management: principles and practice for strategic impact. 1st edn. Harlow. UK:
Financial Times Prentice Hall.
4. Paradiso, J. (2003) ‘The essential process’, Industrial Engineer, 35(4), pp. 46-8.
5. Womack, J. P. and Jones, D.T. (2003) Lean thinking: banish waste and create wealth in your corporation. Revised and
Updated edn. New York : Free Press.
5. • Steps in Business Process Improvement:
• Process Simulation
• Pidd (2003) – Simulation can be used effectively to:
• Increase process understanding
• Find out problems in the system and suggest improvements
• Justify quantitatively the improvement steps needed
• Banks et al. (2005) – Simulation shows the changes in the process
performances real-time
• Need for a framework:
• Hall and Johnson (2010) – The existing frameworks lack in exact steps that needs
to be taken.
• Zellnor (2011) – Some frameworks suggest steps for improvements, but lack the
means of feasibility test.
1. Banks, J., Carson, J.S., Nelson, B.L., Nicol, D.M. (2005) Discrete-Event System Simulation. 4th edn. Upper Saddle River,
NJ : Prentice Hall.
2. Hall, J.M. and Johnson, M.E. (2010) ‘When should a process be art, not science?’, Harvard Business Review, 87(3), pp. 58-
65.
3. Pidd, M. (2003) Tools for thinking: modelling in management science. 2nd edn. Chichester : Wiley.
4. Zellnor, G (2011) ‘A structured evaluation of business process improvement approaches’, Business Process Management
Journal, 17(2), pp. 203 – 237.
6.
7. • Action Research (Costello, 2003):
• Incorporates learning from practical experiences into development of
academic contributions:
• Why Action Research?
• Is real-time and incorporates feedback from steps already taken
(Coughlan and Coghlan, 2002).
• Appropriate when a series of actions are required (Coghlan and
Brannick, 2010).
• Action Research vs. Case Study
• Case Study – One time incorporation of the findings of a practical
situation into academic research
• Action Research – Cyclical process of learning from practical
context and making changes to the context
1. Coughlan, P. and Coghlan, D. (2002) ‘Action research for operations management’, International Journal of Operations and
Project Management, 22(2), pp. 220-240.
2. Coghlan D. and Brannick, T. (2010) Doing Action Research in your Own Organization. 3rd edn. London: Sage.
8. • Security ID Centre – Manchester Airport
• Managed by Manchester Airport Group
• Processes Applications for Security Passes
• Issues Security Passes for different areas of Airport
• Processes Applications of Companies for getting involved in the
Pass scheme
• Manages existing companies in the scheme and outstanding
Passes
• Objectives
• Create better understanding of the office processes.
• Finding out outstanding problems for the office processes.
• Suggest short term ‘Quick Wins’ to improve the process
• Look for the feasibility of long term strategic changes of the office
operations
9. • Most accessible data available:
• The guidelines and procedures for application of security passes
and company registration (Input)
• Qualitative data – mostly forms, handbooks and website
instructions
• A log of rejected applications for passes with the record of missing
data or mistake in the relevant section of the application that
resulted in rejection (Output)
• Quantitative data – a log count, with checkmarks of sections
that are missing
10. • Tools used: Benchmarking and Pareto Analysis
• Benchmarking areas:
• The pass application procedures of other internationally acclaimed
and heavy traffic airports
• Standards set for improvement:
• Improve on:
• Provision of Booking Appointments Online.
• More detailed guidelines for the type of Pass required.
• Checklists of documents and information required.
• Pareto Procedure:
• Analysis of Monthly and then Quarterly counts for data available of
year 2011 to March 2012
• Outcome:
• Some sections were found to be troublesome
• Some sections were missing more often
11. • ‘Kaizen’ Workshops:
• Focus on process understanding and improvement, including the
recommendation of the employees.
• Participants were asked to discuss and detail out the steps
involved in each of the processes.
• They were also asked to provide input on problems of the
individual stages and ways of improving them
• The outputs of the Kaizen workshops were used to
develop As-Is Process Maps of the processes of the
Security ID centre.
• After that Value Stream Analysis (VSA) was carried out to
check for:
• Redundant Activities
• Non Value Adding Activities
12. • After the implementation of improvements suggested by VSA,
the process timings were expected to reduce by:
• On average – 19.44%
• Minimum - 7%
• Maximum – 31%
• After the implementation of a fully computerized system, the
improvement in process times will be:
• On average – 81%
• Minimum – 48%
• Maximum – 100%
• The final step was to check whether these changes will
actually cause significant improvements
• This was done through building a simulation model for the operations
of the Security ID Centre.
13. • Simulation model of the Security ID Centre was
developed to:
• Reflect the present scenario, and create a better understanding of
the processes
• Look into the state of the operations after the improvements
suggested by VSA and automation are implemented
• Provide concrete means of deciding whether the changes
suggested should be implemented or not.
14. • Plotting of application input data over a period of 4 years to
look for trend patterns:
• From the plot, it could be ssen that the data shows pattern
wise variation
• As such, input data for the peak and trough seasons were
taken for building two separate models.
0
50
100
150
200
250
1
55
109
163
217
271
325
379
433
487
541
595
649
703
757
811
865
919
973
1027
1081
1135
1189
1243
1297
1351
1405
1459
1513
1567
1621
1675
1729
1783
15. • The data for the two seasons were then used to fit in
statistical distributions:
• To incorporate stochasticity and randomness in the system
• To incorporate different uncertainties and possibilities in the real
life model
• Statistical plots and tests were used to validate the
choice of distributions:
• Chi-Square Tests
• Anderson Darling Tests
• P-P (Probability Plots)
• A similar methodology was used for the case of modelling
processing times.
16.
17. • Steady state is required for accurate representation and accurate
predictions.
• 50 trials were required to obtain stable output:
• 450 hours of warm time to generate stable results:
90
92
94
96
98
100
5 10 15 20 25 30 40 50 60 70
93
94
95
96
97
98
99
100
101
0 20 40 60 80 100 130 160 190 220 250 350 450
18. • Black box validation
• Results from model statistically checked with results from real life
scenario
• Chi-Square Goodness of Fit Test
• Null hypothesis (The datasets belong to the same population)
could not be rejected, at significance = 0.01
• White box validation
• Turing Test – The Business Analyst was thoroughly briefed on the
internal workings of the model
• Data seasonality validation
• Two seasons – Busy and Free Period
• Difference of Means (T-tests) were carried out
• Alternate hypothesis (The mean of the datasets are statistically
different from each other) could not be rejected, as significance =
0.01
19. • Two Scenarios for Two Periods
• Busy/Peak Period
• After implementation of improvements by VSA
• After implementation of fully automated system
• Free/ Trough Period
• Same as Busy Period
• Focus on:
• The average resource (FTE) required for each scenario
• The average number of Applications processed out in a set time
period – 3 hours
• Results were statistically tested for their difference of
means (T-tests)
20. • Post VSA
• 84% of applications processed within time
• Further Automation
• 98% of applications processed within time
• For resource usage:
• Decision for the Security ID Centre:
• Implement changes suggested by VSA
• Do not go for further automation and computerization of the systems:
• Investments required for specialist software and design in costly
• The benefits do not far outweigh the costs
Busy Period Free Period
9.78
6.986.91
5.59
4.14 3.45
Original Post VSA Post Automation
21. Inputs & Outputs
Exploratory
Improvement
Tools
7 Management
Tools
Benchmarking
Other simple
tools
Quality Function
Deployment
Use:
Exploratory
analysis
Target:
Identify what is
wrong.
Find out quick wins
Suggest the direction
of furtheranalysis.
Pareto
Analysis
Process Mapping – As Is Use:
Process
Understanding
Target:
Involvement of staff
to increase
understanding of the
processes.
Clearrepresentation
of present scenario
Use of Mapping
Tools
As-Is MappingKaizen Events Validation with
existing system
Process Mapping – To Be Use:
Process
Improvement
Target:
Elimination of non-
valueadding activities.
Improvements in
necessary activities.
Computerization
Optimization.
As-Is Maps
VSA
improvements
BPM
Improvements
To-Be Maps
Simulation Modelling Use:
Optimization and
Validation.
Target:
Increaseunderstanding
of processes.
Find out further areas
of improvement.
Quantify the
improvements to
validatethe use of
approaches.
Collect Data for
model creation
Fitting necessary
statisticaldistributions
Creating model for
present scenario
Validate the
model
Experiment
with the model
Implement the
improvements suggested
Record results
and interpret
Decide on Final
Improvement Strategy
22. • Limitations:
• Time
• Access to more detailed data
• Testing the suggested framework in different contexts
• Further Directions of Research
• This framework can be further clarified into detailed steps of
implementation
• Suggestion of testing out other different tools
• Putting this framework to use in other contexts and compare the
results:
• Other contexts similar to application handling
• Other completely different contexts
