Application for Lecturer Presentation

Application for Lecturer PresentationDissertation Topic Development of a framework for improvement of business processes in a service operation: An Action Research Approach

Outline of Presentation• Introduction• Literature Review• Objective of the study• Methodology• Case Description and Analysis• Findings of the Study

Generic Context• 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.

Literature Review (1)• 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.

Literature Review (2)• 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.

Research Objectives

Methodology and Justification• 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 context1. 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.

Introduction• 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

Preliminary Data• 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

Input and Output Analysis• 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

Detailed Data Collection and Process Mapping• ‘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

Benefits from mapping and improvements

• 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.

Final Stage - Modelling• 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.

Modelling Inputs – Pattern Analysis• 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.

2 126 250 374 498 622 746 870 994 1118124213661490161417380

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Modelling Inputs – Distribution Fitting• 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.

Screenshot of Model

Trial Run and Warm Up Time Determination• 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:

5 10 15 20 25 30 40 50 60 7090

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0 20 40 60 80 100 130 160 190 220 250 350 45093949596979899

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Model Validation• 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

Scenario Analysis• 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)

Results of Modelling• 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 6.98 6.91 5.59 4.14 3.45

Original Post VSA Post Automation

Findings of Study - Framework Development

Inputs & Outputs

Exploratory Improvement

Tools

7 Management Tool s

Benchmarking

Other simple tools

Qual ity Function Depl oyment

Use: Exploratory analysis

Target: Identify what is wrong. Find out quick wins Suggest the direction of further analysis.

Pareto Analysis

Process Mapping – As Is Use: Process Understanding

Target: Involvement of staff to increase understanding of the processes. Clear representation of present scenario

Use of Mapping Tools

As-Is Mapping Kaizen Events Validation with existing system

Process Mapping – To Be Use: Process Improvement

Target: Elimination of non-value adding activities. Improvements in necessary activities. Computerization Optimization.

As-Is Maps

VSA improvements

BPM Improvements

To-Be Maps

Simulation Modelling Use: Optimization and Validation.

Target: Increase understanding of processes. Find out further areas of improvement. Quantify the improvements to val idate the use of approaches.

Collect Data for model creation

Fi tti ng necessa ry sta tistical distributions

Creating model for present scenario

Validate the model

Experiment with the model

Impl ement the improvements suggested

Record results and interpret

Decide on Final Improvement Strategy

Limitations and Further Research Directions• 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

End of PresentationThank You for your patience