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GlaxoSmithKline Business Intelligence Strategy and Performance Management

Info: 24436 words (98 pages) Dissertation
Published: 11th Dec 2019

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Tags: BusinessBusiness Strategy

ABSTRACT

With the rapid advancements in technology and increased competition between large corporations due to globalization, the importance of Business Intelligence as a tool to aid performance management is now at the forefront of business strategy. The aim of this project is to provide a proposal of a potential alternative Business Intelligence strategy and software to be adopted by GSKs Customs Duty Management department

The main focus of this study is a service within a large organisation, more specifically the Customs Duty Management service within GlaxoSmithKline. The service is a brand new one and is still in its infancy so as expected they haven’t fully adopted the culture of performance management and are not monitoring their Key Performance indicators in the most effective way.

This study looks at the need and importance of Performance Management using Key Performance indicators and what benefits are derived from embedding performance management within a service. In order to do this, a picture of the services current practice will be built, this will be done by using a variety of methods including observation and evaluation of the current ways of working by utilising Process Mapping for illustration, surveys and interviews will also be utilized in order to gauge the users attitudes towards the existing method of performance management. An evaluation of the current Business Intelligence maturity level of the department shall be conducted and proposals to improve the current level of BI maturity shall be put forward along with a comparison of possible software alternatives to the existing method.

Key Words: Key Performance Indicators, Performance Management, Business Intelligence

ACKNOWLEDGEMENTS

TABLE OF CONTENTS

1.0 CHAPTER OVERVIEW

1.1 Company Background

1.2 Current Ways of Working

1.2.1 Problems with Current Practices

1.2.2 How is the Department Affected?

1.3 Project Aims & Objectives

1.4 Structure of Study

1.5 CHAPTER SUMMARY

2.0 CHAPTER OVERVIEW

2.1 Performance Measurement & Management

2.2 Key Performance Indicators (KPIs)

2.2.1 What is a KPI?

2.2.2 Uses of KPI metrics within organisations

2.2.3 Characteristics of KPIs

2.2.4 Key Performance Management for Performance Management

2.2.5 Key Performance Indicators for Business Intelligence

2.3 Service Maturity Model

2.4 CHAPTER SUMMARY

3.1 CHAPTER OVERVIEW

3.2 Research

3.3 Research Methodology

3.4 The Research Paradigm

3.5 Positivist vs Interpretivist Paradigms

3.5.1 Positivist Paradigm

3.5.2 Interpretivist Paradigm

3.6 The Research Design

3.6.1 Quantitative Research

3.6.2 Qualitative Research

3.7 Data Collection Approach

3.7.1 Primary Data

3.7.2 Secondary Data

3.8 Methods & Techniques

3.9 GEMBA

3.10 Survey

3.10.1 Questionnaire

3.11 Interview

3.12 Structured Interview

3.12.1 Closed Questions

3.12.2 Open Questions

3.13 Semi-Structured Interview

3.14 Business Process Modelling

3.15 SIPOC Diagrams

3.16 CHAPTER SUMMARY

4.0 CHAPTER OVERVIEW

4.1 GEMBA Results

4.2 Classification GEMBA

4.2.1 Classification Process SIPOC

4.2.2 Classification Process Output

4.3 Customs Authority Request (CAR) GEMBA

4.3.1 Customs Authority Request SIPOC

4.3.2 Customs Access Request Output

4.4 Preferential Origin (PO) GEMBA

4.4.1 Preferential Origin SIPOC

4.4.2 Preferential Origin Output

4.5 Duty Optimization & Special Regimes GEMBA – FOR REVIEW BY KD

4.5.1 Duty Optimization & Special Regimes (DOSR) SIPOC

4.5.2 Duty Optimization & Special Regimes (DOSR) Output

4.6 Survey Findings

4.7 Results of the interview process

4.7.1 Interview Procedure & Justification

4.7.2 Interview Responses

4.8 CHAPTER SUMMARY

APPENDICIES

ABBREVIATIONS

LIST OF FIGURES

Figure 1: Relationship between Performance Management & Performance Measurement (Lebas, 1995)

Figure 2: Business Goals, KPIs & Micro-Transactions (RocketMill.co.uk, 2015)

Figure 3: Strategic alignment with KPIs (Bauer, 2004)

Figure 4: A basic understanding of BI  (Ranjan, 2009)

Figure 5: Different Stages of Service Maturity  (Little, 2009)

Figure 6: Approach to defining the Research Methodology (Tuli, 2011)

Figure 7: Comparison between Quantitative & Qualitative research (Greener, 2008)

Figure 8: Likert Scale in KPI Questionnaire, 2017

Figure 9: KPI Questionnaire, 2017

Figure 10: Amount of Control the Interviewer Has Over the Interview (Harrell & Bradley, 2009)

Figure 11: Illustration of a Business Process (Lampathaki, et al., 2013)

Figure 12: Benefits of BPM within an Organisation (Alzubidi, et al., 2011)

Figure 13: SIPOC Diagram Illustration (Tasko Consulting, 2017)

Figure 14: Classification SIPOC

Figure 15: Classification Metrics

Figure 16: Customs Access Request SIPOC

Figure 17: Open CARs by country of origin

Figure 18: Open CARs by Year Opened

Figure 19: Preferential Origin SIPOC

Figure 20: Preferential Origin Metrics

Figure 21: Duty Optimisation & Special Regimes SIPOC

Figure 22: Duty Optimisation & Special Regimes Metrics

Figure 23: The Respondents Service Line

Figure 24: Time Spent Compiling Metrics

Figure 25: Likert Scale responses

Figure 26: Question 3 Response percentages

Figure 27: Question 4 Response percentages

Figure 28: Question 5 Response percentages

Figure 29: Question 6 Response percentages

Figure 30: Question 7 Response percentages

Figure 31: Question 8 Response percentages

Figure 32: Question 9 Response percentages

Figure 33: Question 9 Response percentages

Figure 34: Question 11 Responses

Figure 35: Question 12 Responses

LIST OF TABLES

Table 1: Example of how KPIs links Business Goals & Micro Transactions in order to facilitate decision making

Table 2: Functions served by having KPI metrics (Melnyk, et al., 2004)

Table 3: Aims of Conducting the Research

Table 4: Classification Service Process Information

Table 5: Customs Authority Request Process Information

Table 6: Preferential Origin Process Information

Table 7: Duty Optimization & Special Regimes KPI Information

Table 8: Interview Questions Rationale

Table 9: Response to Question 2

Table 10: Response to Question 3

Table 11: Response to Question 4

Table 12: Response to Question 5

Table 13: Response to Question 6

Table 14: Response to Question 7

Table 15: Response to Question 8

Table 16: Response to Question 9

Table 17: Response to Question 10

Table 18: Response to Question 11

Table 19: Response to Question 12

CHAPTER 1: INTRODUCTION

1.0 CHAPTER OVERVIEW

This chapter introduces the company and department that are central to this project; it presents the reader with a bit of background about the company and department that the project is about, along with the motivation behind the project. The current Performance Management process is introduced along with a summary of the problems with the current process and how said problems are affecting the department. Following on from that the researcher will set out the project the aims and objectives before setting out the structure of the study.

1.1 Company Background

GlaxoSmithKline (GSK) is one of the world’s largest research-based pharmaceutical organisations that discovers, develops, manufactures and markets branded human health products. The beginning of this incredible organisation began in 2000 when Glaxo Wellcome and SmithKline Beecham formed through a merger after a series of acquisitions of other pharmaceutical and scientific organisationsInvalid source specified..

Within GSK there are a number of different departments, the one that is central to this project is a brand new service called Customs Duty Management. The Customs Duty Management department is responsible for ensuring GSKs customs duty compliance globally. Within the Customs Duty Management department there are 4 main services, they are:

  • Preferential Origin (PO)
  • Classification
  • Customs Authority Request (CAR)
  • Duty Optimisation & Special Regimes (DOSR)

1.2 Current Ways of Working

With the Customs Duty Management department still being a relatively new and immature service, it was to be expected that new ways of working would need to be introduced. The issue that forms the basis of this project was the lack of a standard process for the compilation of the departments Key Performance Indicator (KPI) metrics as each different service within the department has adopted a different process for the compilation of their own services KPI metrics. In addition to this system that is used to compile the metrics often involve very time consuming & complicated process and inefficient process which involves manually capturing, analysing large amounts of data to be exported onto a spreadsheet document before being manually transformed visual dashboards to be used in the monthly departmental performance management meetings. The problems with the current Performance Management process are summarized below:

1.2.1 Problems with Current Practices

  1. There is not a standardized method of compiling the Performance Management metrics throughout the department as the, different services within Customs Duty Management using different processes compile their service metrics.
  2. The performance management process is very inefficient in terms of time taken to compile the metrics.
  3. The manual nature of the extraction and manipulation data to from multiple sources in order compile the metric current process makes the compilation process prone to human errors occurring.
  4. There is a lack of accountability for the compilation of the service metric, the compilation of the Performance Management metrics is done in silos meaning each service is responsible for compiling and presenting their individual service metrics.
  5. The department utilizes spreadsheet software to collate the raw data collected in a single location in order for it be manually manipulated and transformed into visual dashboards. The decision to manually manipulate the data using spreadsheet software effectively simulating the functionality Business Intelligence is also questionable.

1.2.2 How is the Department Affected?

  1. The lack of standardization makes the Performance Management process inefficient as the different departments are effectively taking different routes to the same end goal.
  2. The amount of time taken to manually capture and sort the data from the various sources, as well as having to manually compile visual dashboards, could be used to tend to more business critical tasks.
  3. The manual nature and highly labour intensive nature of the Performance Management process makes it prone to human error.
  4. The non-accountability when it comes to the compilation of the departmental metrics puts strain on the service leads ability to compile the metrics on a weekly basis, in the event they are unable to compile the metrics, there isn’t anyone else within the service with the knowledge to be able to compile the metrics.
  5. The dashboards & reports created using spreadsheet software is only as good as the data used to create them, if the data used to create the dashboards is inaccurate; the metrics produced will be of low quality and therefore will not provide much business value.

1.3 Project Aims & Objectives

The main objective of the project is to provide a proposal of a potential alternative Business Intelligence strategy and software to be adopted by GSKs Customs Duty Management department. In order to achieve the main objective set out, the researcher aims to:

  1. Conduct research into the importance of performance management within organizations and the business value that it provides.
  2. Utilize data collection techniques and process mapping tools to illustrate the current Business Intelligence system that is currently being used by Customs Duty Management to compile the departmental Performance Management metrics. This is done in order to gain a better understanding of the current process.
  3. Carry out an in depth analysis of the data gathered along with a detailed evaluation of the current Business Intelligence system used to compile the departmental in order to determine the suitability of the current Business Intelligence strategy being adopted by Customs Duty Management in order to Compile their Performance Management Metrics.
  4. Establish the current Business Intelligence maturity level of the Customs Duty Management department.
  5. Establish how the current level of Business Intelligence maturity can be increased whilst also solving the issued with the current Business Intelligence system that is in Place.
  6. Provide a comparison of potential Business Intelligence software solutions that the department can implement to replace the current Business Intelligence system in place.

1.4 Structure of Study

The structure of the entire study is as follows. A literature review is presented to provide an introduction to Performance management and Key Performance indicators and their importance in business decision making and overall organizational performance. The next chapter is an exploration of the research methodology used in this report; this contains the appropriateness and justification of the research and data collection methods and tools that the researcher utilized. The next chapter displays the results of the data collection process that occurred as a result of the utilization of the methods discussed in the methodology chapter. The penultimate section presents an in depth discussion of the results collated in the results chapter, the results are interpreted, backed up with literature and the implications to Customs Duty Management department are explored. Finally the researcher makes a case for the adoption of a Business Intelligence software solution that will replace that which is currently being utilized by Customs Duty Management with the aim of increasing the Business Intelligence maturity of the department and realizing the potential value derived from an effective Performance Management process & Business Intelligence strategy.

1.5 CHAPTER SUMMARY

In this chapter the company that is central to this project has been introduced, the current performance management process being currently adopted along with the issues and consequences they faced were also explored. The motivation for the project and the identification of the project aims and objectives were also defined before finally setting out the structure of the study to be carried out. At this point, the reader should be familiar with: who the end user is, the reasoning behind the project and what the researcher aims to achieve by undertaking this project.

CHAPTER 2: LITERATURE REVIEW

2.0 CHAPTER OVERVIEW

By the end of this chapter, the researcher aims to introduce relevant literature and background knowledge on the main concepts that are related to this report which includes:

  1. The theories of performance management and performance measurement and the difference between the two concepts.
  2. Key Performance Indicators (KPIs), their characteristics and the benefits their utilization brings to an organization.
  3. The concept of Business Intelligence (BI) as well as an exploration into the relationship between BI and KPIs.
  4. Services and service maturity will be defined along with the different stages of service maturity model.

The successful completion of this chapter will satisfy objective 1 set out in section 1.3 of this report

2.1 Performance Measurement & Management

The term performance is defined in the Business Dictionary (2017) as being the “accomplishment of a given task measured against standards of accuracy, completeness, cost and speed”, hence performance can be said to be the ability to complete a given task whilst also meeting a pre-determined criteria set to measure how well the task has been carried out. The concept of performance is a subjective one meaning that its meaning depends on the context for which it is being used, the definition of performance in a sporting context will greatly differ its meaning in an organizational context. Taking that into consideration, the general principle of being able to complete a task to a pre-determined standard can be applied in all instances of the word.

This report looks at the concept of performance from an organizational lens. Gaining insight into the level of business performance being achieved by the company has long been an area of interest for managers and management of organizations (Otley, 1999) but for many years, financial measures have been seen as the main method of organisational performance measurement. Due to the increase in the number of factors that now have an effect on organizational performance in terms of the complexity of organisations and the markets in which they operate, corporations soon realised that the sole utilization of financial measures as the criteria for assessing business success was not feasible (Kennerley & Neely, 2002) hence the traditional paradigm of utilizing financial performance as a means of measuring business success was judged  to be flawed as it failed to look past profit as being the overriding goal.

In order to move away from the traditional model of measuring business success, the concepts of performance measurement and performance management were. Although used interchangeably, the two terms are different, Verweire & Van Den Berghe (2004) viewed performance management as being the process of measuring and reporting on how well the organization is performing by qualifying how efficiently and effectively an organization deliver its goods or services, Amaratunga & Baldry (2002) expands on this view by saying that performance measurement is seen as a basis for an organisation to assess how well they are performing when compared to predetermined objectives. With that in mind the role that performance measurement has to play in the enhancement of business performance cannot be understated, Waggoner, et al. (1999) supports this by stating that effective performance measurement plays an important role in focusing people and resources on particular aspects of the business with the aim of enhancing the overall business performance.

Having the data that shows the effectively and efficiently a business delivers its goods or services is all well and good, but the real business value of performance measurement comes through harnessing the data captured and utilizing it as a key decision making tool to drive business strategy. With that said, Performance Measurement can be seen as a tool to aid Performance management. Performance management is viewed as the utilization of Performance Measurement information to effect positive organizational change (Amaratunga & Baldry, 2002), performance management is said to precede and follow performance measurement (Fig 1) in the sense that the information that is derived from the performance management process provides the context for performance measurement. (Lebas, 1995). In essence, Performance Management aims to use the data collated to make strategic organizational decisions, hence contributing to the increase in organizational performance (Neely, 2007)

Figure 1: Relationship between Performance Management & Performance Measurement (Lebas, 1995)

2.2 Key Performance Indicators (KPIs)

2.2.1 What is a KPI?

Key Performance Indicators (KPIs) are defined as quantifiable measures which aim to reflect the performance of a business in comparison to its pre-defined goals and objectives (Bauer, 2004) Parmenter adds to this view by stating his view of KPIs as being asset of measures that focus on a particular aspect of an organization that the organization sees as its most critical (Parmenter, 2015). Although KPIs and metrics are often used interchangeably, it is important to differentiate the two as they are not the same. Metrics are defined by Melnyk, et al (2004) as being verifiable quantitative or qualitative measures which exist as tools for decision making, with KPIs already defined above, it is worth noting that KPIs are metrics, but the defining feature that differentiates of a KPI from a metric is that a KPI is linked to a strategic objective/goal (Eckerson, 2009). So it can be concluded that a KPI is a type of metric, but not all metrics are  KPIs, the words ‘KPI’, ‘KPI Metrics’ & ‘Performance Management metrics’ will be used interchangeably throughout this paper, in order to avoid confusion it is worth noting that they all refer to the same concept.

In order to gain a better insight into the meaning of KPIs, Segen (2011) breaks the word “Key Performance Indicators” down further:

  1. Key: The word ‘KEY’ indicates that it is of fundamental importance in achieving business objectives and creating competitive advantage.
  2. Relating to Performance: This means that it can be quantified, measured and easily influenced by the organisation.
  3. An Indicator: Indicators provide information on current/future performance

Parameter (2015) builds on the point above made by Eckerson by pointing to KPI being the only thing that links the day to day business performance (Micro-transactions) with the critical success factors of the organisations (Business Goals) the business goals (Fig 2), the link  between the Business objectives and businesses micro-transactions in the form of KPIs forms an integral part of business performance management as it enables the business/service to measure their day to day performance against their critical success factors, having a quantifiable measure of business performance facilitates data backed decision making as the data provides information to the management that enables them to see if the business goals & objectives are being met or not (Rodriguez, et al., 2009). With that in mind it is very important to note that KPIs only provide Quantitative data, that on its own is not enough to inform decision making, it is therefore down to management to decide how to interpret that data and turn it into information needed to aid strategic decision making.

Figure 2: Business Goals, KPIs & Micro-Transactions (RocketMill.co.uk, 2015)

In order to gain a better understanding of how KPIs are linked to business goals and micro-transactions, an example of the KPIs used by a service provider is used (Table 1). A business goal for the service provider could be to increase customer satisfaction in the provision of the service, in order to measure customer satisfaction the KPI for the service provider could be the amount of complaints received from the customers over a period of time. The Micro-Transactions could be the level of customer service offered to the customer. If the amount of complaints received exceeds the pre-defined amount set as the KPI, this is flagged as a cause for concern as the service provider is not meeting the pre-determined level of customer service that is expected. As an action the company could then resort to increasing the amount of customer service training carried out within the company in order to improve the performance in relation to the KPI

Business Goal Key Performance Indicator Micro Transaction Below KPI Decision
Increase Customer Satisfaction Amount of customer complaint received Level of customer service provided by the employees Increase the amount of employee training on customer service

          Table 1: Example of how KPIs links Business Goals & Micro Transactions in order to facilitate decision making

2.2.2 Uses of KPI metrics within organisations

Function Benefit
Control

 

Enables the control of resources that are available to managers and workers.
Communication

 

Metrics can be seen as a basis for communicating performance to both internal & external stakeholders by acting as a benchmark for business performance.
Improvement Metrics identify gaps (between performance and expectation) that ideally point the way for intervention and improvement.

As stated above, KPI metrics are primarily used by organizations to aid management in making decision making, but Melnyk, et al (2004)  pointed out that the use of KPI metrics provides additional functionality which help the organization to achieve their goals; these are illustrated in table 2 below:

Table 2: Functions served by having KPI metrics (Melnyk, et al., 2004)

2.2.3 Characteristics of KPIs

As mentioned above, KPIs link the Business goals to the daily business operations, in order to gain the potential value that the KPIs provide, it is important to have the right KPIs, in order to do this, a report produced by the Public Records Office Victoria (2015) has established some key characteristics of good KPIs that eill effectively aid decision making. The key defining characteristics of KPIs are that:

  1. KPIs should be relevant and consistent with the specific agency’s vision, strategy and objectives (Figure 3)
  2. KPIs should measure departmental progress as  part of a wider and more strategic view of business progress is captured  instead of (Looks at the progress of functions as contributors to the progress of business as whole)
  3. KPIs should be realistic in terms of fitting into the business constraints with regards to resource capabilities and cost limitations.
  4. KPIs should be agreed upon and understood by the all the business stakeholders that it directly affects in order to ensure all parties know the role they have to play in the achievement of the targets set and are capable of meeting them.
  5. KPIs should be regularly reported, governed and assessed to ensure they are relevant and are continually driving the performance measurement and management effort.

Figure 3: Strategic alignment with KPIs (Bauer, 2004)

2.2.4 Key Performance Management for Performance Management

Having already differentiated between performance management and performance measurement earlier in the chapter, it is important to clarify the link between Performance Management and Key Performance Indicators. Performance Measures were defined as a way to quantify the performance of an organization, taking that into consideration, it can be concluded that Key Performance Indicators are Performance Measures. With that link made, the next logical conclusion to make would be that Key Performance Indicators are utilized as a tool aid the Performance management process.

2.2.5 Key Performance Indicators for Business Intelligence

Golfarelli, et al (2004) thought of Business Intelligence (BI) as being a process carried out by businesses in order to turn organizational data into information and then into knowledge which is utilized ad a tool to aid business decision making. The relationship between KPIs and business intelligence are similar to the relationship between performance management and performance measurement in the sense that KPIs play a significant role in a Business Intelligence strategy as BI systems utilize KPIs in order to assess the state of business and to “prescribe a course of action” (Ranjan, 2009).

One of the key features of BI systems is its ability to turn raw data collected from multiple sources (Fig 4) into reports and real time interactive visual dashboards (Azvine, et al., 2006). KPIs are utilized by BI systems to act as a basis for comparison of actual business performance against the business KPI, the task is made easier by the BI systems ability to create creation of interactive visual dashboards and reports which presents a quick & comprehensible overview of the performance of the business in comparison to the business goals and objectives (Butler, 2007). Boyer, et al (2010) added to this sentiment by calling the combination of a BI process with KPIs which are aligned to business strategy a comprehensive and consistent “strategic planning mechanism” as it requires collaboration from business managers who define the KPIs and the BI & IT teams who bring the KPIs to life by connecting the metrics to the data by leveraging BI systems

Figure 4: A basic understanding of BI  (Ranjan, 2009)

2.3 Service Maturity Model

In the first chapter of the report, it was established that Customs Duty Management is a brand new service therefore it is classed as immature, the concept of service maturity relates to how well a part of a business or a service is able to effectively deliver its service to their customers. The IT service maturity model aims to allocate a stage of maturity (Fig 5) that a service/department is currently operating at by analysing the way they carry out their processes and functions (Alexos, 2013):

  1. Stage 1(Chaotic) – Processes or functions are ad hoc, disorganized or chaotic.
  2. Stage 2(Reactive) – Processes or functions follow a regular pattern.
  3. Stage 3 (Stable) – The process or function has been recognized and procedures have been standardized, documented and communicated through training.
  4. Stage 4 (Proactive) – The process or function has now been fully recognized and accepted throughout IT.
  5. Stage 5 (Value Driven) – Leading practices are followed and automated.

Figure 5: Different Stages of Service Maturity  (Little, 2009)

The aim of the service should be to move through the various stages and end up in stage 5 which sees the service processes and decision making leveraged by the utilization of fully integrated and automated  IT systems, effectively making the service a strategic business partner.

2.4 CHAPTER SUMMARY

This chapter introduced some context and relevant academic literature on the following subjects:

  • The theories of performance management and performance measurement and the difference between the two concepts.
  • Key Performance Indicators (KPIs), their characteristics and the benefits their utilization brings to an organization.
  • The concept of Business Intelligence (BI) as well as an exploration into the relationship between BI and KPIs.
  • Services and service maturity will be defined along with the different stages of service maturity model.

By conducting the research on the importance of performance management within companies and the values it brings to the company, objective 1 set out in section 1.3 of this report has been achieved.

CHAPTER 3: METHODOLOGY

3.1 CHAPTER OVERVIEW

The main focus of this chapter is to gain a greater insight to the differing methods of research used by the author to delve deeper into the inner workings of Customs Duty Management and the justification of each method used. The author explores the research methodologies and research designs available to choose from and also the choices of research paradigms that were considered before choosing one of each along with a justification for the decision. A comprehensive review of the research, data collection approaches and process-modelling techniques to be adopted in this report will be conducted in order to present the motivation and reasoning and justification behind the selection of said approaches.

3.2 Research

Kothari (2004) Describes research as being “A scientific and systematic search for pertinent information on a specific topic” in the context of this report, the pertinent information that the author is searching for relates to delving deeper into the clients current Performance Management process in order to gain a better understanding of 5 main things which are summarized in this table 3 below:

  1. Determine the efficiency of the current method of compiling the Performance Management metrics

 

  1.           Time taken to compile reports
  2.           Quality of Metrics with regards to the possibility of human errors occurring in the process.
  1. Gauge the users attitudes towards the current ways of Woking

 

  1.           Non-Accountability in the compilation of the metrics
  2.           The Non-Standardized nature of the current process
  3.           Determine their expectation a proposed Business Intelligence solution

Table 3: Aims of Conducting the Research

3.3 Research Methodology

The words ‘method’ & ‘methodology’ are often used interchangeably because of their similarities, but it is worth noting that they are not the same. The methodology in the context of research, as written by Chilisa & Kawulich (2015) , is a summary of the research process in the sense that it highlights how the research will be conducted. Kothari (2004) goes on to explain the importance of the definition of the methodology by stating that the it relates to the thinking behind each of the chosen methods that the author has decided to adopt during the research, along with the logic and rationale behind each chosen & unchosen method available (Fig 6). Methods however relate to the particular activities that are designed & utilized by the author in order to generate the data (Greener, 2008). A survey for example would be classified as a method of data collection. In summary, the methodology relates to the reasons & Justifications of the chosen methods of research whilst the methods represent the specific activities which facilitate the gathering of data, these will be discussed in more detail later on in the chapter.

Figure 6: Approach to defining the Research Methodology (Tuli, 2011)

3.4 The Research Paradigm

A research paradigm as described by Patton (1990), represents world view or way of thinking in the sense that it provides a general perspective and a way of “breaking down the complexity of the real world”, Mackenzie & Knipe (2006) added to this by suggesting that the paradigm influences the way that information is collected and interpreted before going on to emphasize the importance of choosing the paradigm from the outset of the project as the chosen paradigm highlights the “intent, motivation & expectations” of the research to be carried out.

3.5 Positivist vs Interpretivist Paradigms

Building a methodology of a study needs to begin with picking a research paradigm and there are there are two main paradigms that can be chosen when carrying out research which are positivist & constructivist/interpretivist paradigms (Chilisa & Kawulich, 2015); it is worth nothing that although these are the main paradigms, there are a number of additional paradigms that can be adopted such as Transformative & Post-Colonial/Indigenous research.

3.5.1 Positivist Paradigm

The first potential paradigm to be explored is the positivist/post positivist paradigm; this paradigm is described as being a realist paradigm as it takes a scientific approach to research and it is based on the view that “science is the only foundation for knowledge” (Chilisa & Kawulich, 2015). The positivist view basically states that only knowledge that can be seen and measured should be trusted. In a bid to ensure that the research done can be scientifically proven, a positivist approach aims to test a theory or describe an experience through thorough observation and measurement. Cohen & Crabtree (2008) were of the opinion that in order for knowledge to be deemed as “objective & true”, the positivist approach normally relies heavily on experimental & manipulative methods of data collection/Analysis. The use of hypothesis generation & testing means that qualitative methods are generally more favourable when adopting this paradigm during research.

3.5.2 Interpretivist Paradigm

The interpretivist paradigm bares stark contrast to the positivist paradigm, in the sense that whilst positivism looks for a single objective reality that can be measured and analysed, Interpretivism holds that meaning is hidden and must be brought to surface through deep reflection/research. Interpretivism as described by Ponterotto (2005) as being concerned with the gaining of a deeper meaning of the research subject through increased interaction between the researcher and the object of investigation. The fundamental reasoning behind the adoption of an interpretative research paradigm is to conduct research in a way that enables the author better understand the users/clients experience. Qualitative methods of research are normally preferred when conducting this type of research as it captures the subjects/objects in, as Ponterotto called it their ‘natural setting’. Cohen & Crabtree (2008) highlighted the need to monitor the dialogue between the research subjects and objects by noting that findings and knowledge are created and interpreted through continuous dialogue, as a meaningful reality can only be constructed through meaningful dialogue between the researcher and the subjects.

After a comparison of the available paradigms to choose from, it is established that in order to fully understand the current method of compiling the Performance Management metrics for the Customs Duty Management department, the researcher has to conduct a thorough observation of metrics being compiled in its ‘natural setting’, the service leads who are currently in charge of the compilation of the metrics must also be observed and consulted in order to gauge their feelings and attitudes towards the current method of compiling the Performance Management metrics. Taking that into consideration the interpretivist paradigm is the most applicable paradigm to use during the course of this report.

3.6 The Research Design

When deciding the methodology that the author will utilize, the main factors that should be considered are the type of data being collected and the aim of the research collected. The first decision to make should be weather to use qualitative or quantitative research (Fig 7) approach to the collection of data.

3.6.1 Quantitative Research

The meaning of quantitative research lies in its name, ‘quantity’, meaning it this method of research relates to numbers and measuring quantity or amounts, and is normally utilized where there are data sets that are able to be measured/quantified Kothari (2004). Quantitative therefore can be said to be aligned with the positivist paradigm of research, in the sense that due to the post positivist approach being concerned with providing scientific evidence to solidify knowledge. Sukamolson (2007) supported this thought by stating that describing quantitative research simply as being `realist’ or sometimes `positivist’, seemingly due to the main emphasis of the research being numbers and being able to quantify the results of the research to prove a hypothesis.

Figure 7: Comparison between Quantitative & Qualitative research (Greener, 2008)

3.6.2 Qualitative Research

On the opposite end of the spectrum is Qualitative research; this research method is mostly subjective in the sense that it mostly deals with the study of objects in their natural environment (Hughes, 2008). Bogdan & Biklen (2007) defined qualitative research as an “approach to social science research” that puts emphasis on the collection of “Descriptive data in natural settings” whilst making use of indicative thinking and places emphasis on the “understanding of the subjects point of view”. Hox & Boeije (2005) added to this definition by explaining that qualitative research looks to delve deeper into how people learn to make sense of themselves and their surroundings with the aim of giving meaning to their daily lives. Whilst quantitative research mostly deals with theories, numbers, hypothesis and statistics, Qualitative research mainly deals with collecting text based data with the aim of utilizing the data collected to explore practices & patterns in the way the chosen research subject thinks and behaves (Smith, 2010).

As discussed above, the adopted paradigm for this research paper is the interpretivist paradigm, which looks to gain deeper meaning through increased interaction with the subject. Although no single qualitative or quantitative method is seen as being better, Chadwick, et al (1984) recommended that the method that is utilized should be entirely dependent on what is appropriate in relation to the topic at hand, taking that into consideration it would seem that due to the nature of the research and type of data that will be collected, the adoption of a qualitative approach would complement the interpretative paradigm a when carrying out this research.

3.7 Data Collection Approach

When conducting a research project, it is inevitable that the author will look to collect a large amount of data for analysis. During the course of the project the author must decide the approach to data collection that will be the most suitable for the data set that is needed. The two options that will be explored are primary and secondary methods of data collection.

3.7.1 Primary Data

The collection of primary data during research refers to the collection of data for analysis from the original source (Glass, 1976). Bogdan & Biklen (2007) added to this stating that primary research data is collected for the specific research problem at hand, in other words the data did not exist before the research is conducted. The main reason for choosing to collect primary data is said to be in order to ensure that the study is coherent by making sure that the data collected helps solve the issue that forms the basis of the research (Hox & Boeije, 2005). Some popular methods of primary data collection include conducting observations, interviews & questionnaires (Kothari, 2004). The methods to be utilised in this report are discussed in more detail below.

3.7.2 Secondary Data

The second approach to data collection that could be adopted during research the collection of data from secondary sources, which basically involves the reuse of pre-existing data (Heaton, 2008), or simply the use of data, which is not collected from the original source by the researcher. Secondary data could also be data collected to fit or utilized for a purpose other than that that for which it was originally collected (‘t Hart, et al., 2005). Data retrieved from secondary sources could be collated from a wide array of existing sources such as official organisational data archives, research papers and the Internet (Hox & Boeije, 2005).

There are a number of reasons why a researcher might adopt a secondary method of data collection, firstly this method of data collection is normally easier and cheaper to collect, as the data is readily available for use by the author, but it is worth noting that due to the data sets being historical, there is less of a chance that they are as current, relevant or as reliable as the primary sources (Greener, 2008). Greener (2008) also notes that due to the data being collected by someone other than the author and being used for reasons other than why it was originally collected, the credibility of secondary data sources such as those collected on the Internet is questionable due to the vast amount of un-moderated secondary data sources available. Taking that into consideration, it is up to the researcher to double check their secondary sources in order to ensure the data collected is credible.

In the context of the research being carried out by the author of this report the utilization of primary data collection such as surveys, interviews and observations will also be required in order to get a better understanding of the current process of compiling the Performance Management metrics, Also the primary methods of data collection seems to be the most feasible method of gauging the attitudes and feelings towards the current process of the service leads who are tasked with the compilation of the metrics.

3.8 Methods & Techniques

In order to be able to effectively apply the data collection approach that has been adopted by the researcher, the correct data collection methods must be utilized, below the researcher introduces and justifies the different methods of data collection and process mapping tools and techniques that will be employed with the aim of gathering and analysing data from Customs Duty Management department in order to better understand the current process of compiling metrics for use in the Performance Management process.

3.9 GEMBA

The word ‘GEMBA’ as described by Imai (1997) derives from the Japanese word for “Where things happen”, Suárez-Barraza et.al (2012) goes on to put this into the business context by translating it to “the shop floor” or “workplace”. Going to the GEMBA or conducting a GEMBA is a term is used to describe a form of ethnography that requires the researcher to go to the true source of information when approaching a problem. Ethnography assumes that in order to effectively understand a problem and develop a suitable solution, the researcher enters should go to the field/environment as an “invited guest” (LeCompte & Schensul, 1999), during a GEMBA the researcher is essentially a fly on the wall as he/she observes the how the subject behaves in their natural setting whilst recording what they see and how by the research subject behaves in their natural setting. A GEMBA follows the same path as ethnography in the sense that the researcher goes to the work area and gets first-hand experience of the problem. Conducting a GEMBA is described by Mazur (2003) as being an extremely effective method of understanding a problem as the it requires the researcher to rely on their own personal experience other than data collected from secondary sources, this provides better understanding of the problem being researched.

Taking the above into consideration, conducting a GEMBA is deemed as the best way to gain a better understanding of the effects that the current method of compiling the metrics has on the departments Performance management process, the GEMBA will also be used as a means of determining the extent of non-standardization and non-integration of the current method of compiling the Performance Management metrics as it enables the researcher to take a holistic view of the process of compiling the metrics from start to finish, whilst also enabling the researcher to observe & compare the different methods of compiling the KPI metrics adopted by the different sub services within Customs Duty Management.

Conducting a GEMBA will ultimately enable the Researcher to gauge how effective the process is in terms of the time spent compiling the reports and the effect of the current process on the value provided by the compiled metrics when being utilized as a tool to aid the departmental Performance Management process.

3.10 Survey

Having established that a structured and open ended, interview will be conducted as a means to gauge the attitudes of the employees towards the current method of performance management. In order accomplish this; a questionnaire will be developed by the researcher, which will provide the line of questioning for each of the interviews.

3.10.1 Questionnaire

A questionnaire is a tool for data collection that can be utilized in order to gauge attitudes and behaviours of selected respondents on a given subject matter (Mathers, et al., 2007). In order to develop an effective questionnaire to provide valuable and reliable results Diem (2002) provided a framework in the form of a step-by-step guide to developing an effective questionnaire. The development process of the questionnaire was done with reference to the steps provided within the guide and is illustrated below.

Step 1: Determine the Purpose

The first step in the framework was is to look at the reason that the questionnaire is being conducted. As set out in chapter 1, one of the aims of the project is to conduct an in-depth analysis into the current method of performance management being adopted by Customs Duty Management and also to utilize data collection and process mapping techniques to show how the current method of performance management is adversely affecting the service. In order to meet these aims, the researcher will need to gauge the attitudes of the service leads who are tasked with the compilation of the KPI metrics on a monthly basis, they are chosen as the respondents of the interview due to the fact that they will have first-hand experience of working with the system and will have a clear idea of how the how the current method of compiling the Performance Management metrics affects the department.

The Aims of the questionnaire process are summarized in Table 3 located in section 3.2 of this paper.

Step 2: Determine what you are measuring

After defining the purpose of the questionnaire, Diem explains that the next logical step to take will be to decide what exactly is being measured. The questionnaire aims to measure the attitudes of the employees within the duty management service with regards to the current method of compiling their performance management metrics. Finding a method of gauging attitudes towards a given subject matter is a generally thought to be a difficult task because after all attitude as defined by Wyse (2011) is simply a “group of opinions, values and dispositions to act associated with a particular object or concept” hence making it difficult to measure due to it being very subjective.

Step 3: Who should be asked?

The service leads that are in charge of compiling the monthly service Performance Management metrics are the target respondents of the questionnaire.

Step 4: Choose an appropriate data collection method

The researcher will be utilizing a popular online surveying site called Survey Monkey to administer the questions to the respondents; this method of administering the questionnaire was adopted mainly due to the geographical distance between the respondents and the researcher but the site was chosen also chosen was due to its analytical capabilities and its ability to collate the results and present them in a graphical manner that will be easily understood by the researcher, hence making the process of evaluating the results easier.

As mentioned in section 3.12.1 below, the use of closed questions when administering a questionnaire makes the dataset that is collected easier to analyse because of the lack of variability of the answers, but the closed nature of the responses don’t allow further probing into the reasoning behind the answers this is why as mentioned above, the responses from the questionnaire will form the line of questioning for the interview that is going to be conducted, this enables the interviewer to probe deeper into the responses received from the questionnaire by asking open ended follow up questions in order to gain a better understanding of their responses. This method of combining methods such as a structured questionnaire and a semi structured interview is seen by Denscombe (2008) as being an ideal way to improve the accuracy of the research being done as it enables the researcher to gain a holistic view of the research subject. Additionally there will be a mix of open and closed questions within the questionnaire.

Step 5: Choose Measurement scale

A Likert scale (Fig 8) has been utilized by the researcher as a means of gauging the employee attitudes towards the current method of performance management. A Likert scale is said to be a useful tool to utilize when measuring “constructs such as attitudes, images and opinions” (Wu, 2007).

Figure 8: Likert Scale in KPI Questionnaire, 2017

Likert scales aren’t the only method included in the questionnaire; the respondent is also able to add to utilize check boxed and can also type their own responses the response that they have provided if the answer they want is not listed in the available options provided (Fig 9)

Figure 9: KPI Questionnaire, 2017

A  Likert scale is a suitable method of quantifying qualitative data and the amount of respondents replying to a specific reply can represented using graphic data display methods such as bar charts, the visual nature of these charts make the data easier to analyse and evaluate.

3.11 Interview

An interview is described by as being a “face to face” interaction that occurs between two or more individuals where the actors in place are; the ‘respondent’ being the person being questioned and the ‘interviewer’ being the individual that carries out the line of questioning (Henerson, et al., 1987). Probing during an interview is a method that is adopted by the interviewer in order to get the respondent to provide more information on a line of questioning (Bernard, 2006). The aim of an interview is to obtain information that is relevant to the subject of research objectives (Chadwick, et al., 1984). In order to make the most of the interview process the researcher/interviewer probes the subject/interviewee by using two main types of interview techniques, the researcher explains this in more detail below.

3.12 Structured Interview

The first type of interview that the researcher can conduct is a structured interview, this type of interview occurs when the interviewer adopts a rigid line of questioning when posing questions to the interviewees (Guthrie, 2010), the reason for conducting a structured interview is to ensure that the line of questioning and the stimuli that is received is the same for each interviewee this is done in order to ensure that the replies of the interviewees can be grouped and analysed, structured interview questions are seen to be highly specific and the range of answers/options of answers are often fixed/closed (Bryman & Bell, 2011).

3.12.1 Closed Questions

A question is said to be ‘closed’ when the respondent is limited in the responses that they can provide to a question that is asked by the interviewer, or as Reja, et al (2003) noted, the close ended question “limits the respondent to the set of alternatives being offered”. It has been suggested that close ended questions might be suitable in cases where a conclusion is being made about a larger population (Aberback & Rockman, 2002), the reasoning behind adopting a strategy of asking closed ended questions when there are a large amount of correspondents is that the specific  responses to these questions enables the researcher produce a summary of the results easily and quickly (Henerson, et al., 1987).

Although having closed responses to questions enables the data to be easily and quickly analysed by the researcher, due to the high amount of control with regards to the line of questioning exerted by the interviewer (Fig 10) the questions bieng closed eliminates the possibility of gaining deeper understanding into the reasoning behind the responses that are provided by the resopndents, this is supported by Boynton & Greenhalgh (2006)  who indicated that having closed ended questions could cause the respondent to get frustrated due to the as they do not have the option of elaborating on why they chose a specific resopnse.

3.12.2 Open Questions

In an attempt to gain a better insight into the reasoning behind the responses of the respondents and to gain a deeper meaning from the responses aquired from the study, the  researcher can adopt a strategy of asking open ended questions when conducting an interview. Labuschagne (2003) described the use of  open ended questions in interviews as bieng an enabler for the researcher to delve deeper into the phyche of the respondent in the sense that it helps them gain a better understanding of how the respondent sees the world. The fact that the participants are able to give answers outside of the options that are provided by the researcher could prove to be beneficial for the research as it allows them to fully express their viewpoints and experiences about the chosen subject (Turner, 2010).

Although open ended questions seem the perfect approach to adopt when gaining insight into participant behaviours Bryman & Bell (2011) correctly point out that the nature of open ended questions mean that looking through the differing answers/opinions provided by each of the participants will be very time consuming and also it is more difficult to detect themes/trends as no two answers will be worder in the exact same manner. But it is concluded that asking open questions also explores the possibility of steering the conversation towards a subject that the researcher didn’t have knowledge of before (Bryman & Bell, 2011) this could form the basis of gaining a deeper understanding about a problem/subject.

3.13 Semi-Structured Interview

In order to accommodate open ended questions, a different approach to interviewing must be adopted, the second approach is called a semi structured-interview. This approach to interviewing as described by Guthrie (2010) is one that uses pre-detrmined questions or an interviw guide (Bernard, 2006) as done in structured interviews, but the key difference is that the interviewer can utilize open ended follow up questions probes to gain better understanding of the interviewees answer. While & Barriball (2013) illustrated what they thought to be the main use of semi structure by stating that the use of this interviewing approach is suited to the exploration of perceptions and opinions of the participants with regards to a chosen subject by enabling the researcher to delve deeper into the reasoning behind an answer by probing and asking for clarification.

Figure 10: Amount of Control the Interviewer Has Over the Interview (Harrell & Bradley, 2009)

When conducting qualitative research, there is an emphasis placed on understanding the participants point of view or as Van Teijlingen (2004) described it, the approach attempts to understand the world view from the perspective of the research participants by addressing ‘How & Why’ questions, rather than ‘How Many’. Taking that into consideration, the approach to interviewing that is adopted by the researcher must be one that allows the subjects to express their viewpoints in a way that enables better understanding of their attitudes and behaviour with regards to the research subject. Putting that into the context of this research, the objective of this research is to gain a better insight into the current Performance Management process that is adopted by the Customs Duty Management department within GSK. In order to do this it has been established that an interview shall be used in order to gain the attitudes of the users towards the current system.

Earlier in this report, the authour established that there are two main types of interview approaches that can be adopted; Structured interviews with closed questions or Semi Structured interviews with open ended questions. Due to the nature of the type of information that needs to be gathered from conducting the interview, conducting a semi structured interview with open ended questions would be the most sutable approach to take in this research as it enables the researcher to meet the objective of gaining a better understand the attitudes and feelings of the service leds towards the current Performance Management Process.

3.14 Business Process Modelling

Before getting into the concept of business process modelling as a concept, it would be useful to firstly understand the meaning of the terms ‘Process’,  ‘Business Process’ & ‘Modelling’ as individual entities before finally bringing them all together as a combined concept. The first term that should be understood is what a process is; Hanna & Rance (2011) described a process as being a “structured set of activities that are designed to accomplish a specific objective”, a process in essence is a definition of ‘what’ needs to be done in order to achieve the desired output. For example in order to make a sandwich the inputs required are the raw materials, such as bread, spread, lettuce and a filling. These are separate entities. The sandwich making process defines what needs to be done in order to turn the raw materials into a sandwich such as putting the spread on the bread and putting the filling and the lettuce in-between the pieces of bread. When the process is followed, the output that is achieved is a sandwich.

Putting the concept of a process into a business context, a business process is described by spark systems (2004) in their tutorial as being a “collection of activities” that are carried out by a business in order to produce a specific output for their customer segment or target market, Weske (2007) elaborates on this by stating that the aim of the activities that make up the business process is to achieve a specific goal or output. A business process as illustrated by  is made up of three distinct features (Zigiaris, 2000) and they are (Fig 11):

  1. Inputs: These are the raw material/data that forms the basis of the process to be carried out. The nature of the input depends on the industry/function of the business. For example the inputs in a bakery could be the flour, sugar and eggs. In contrast the inputs in an IT service could be customer data gathered from Customer Relationship Management software.
  2. The process:  This is the means of adding value by transforming the raw materials using a specified method. The process of making a cake involves whisking the eggs, adding the sugar and flour and baking.
  3. The Output: This is the finished product that is the outcome of the processing/transformation of the raw materials/inputs.

Figure 11: Illustration of a Business Process (Lampathaki, et al., 2013)

With the definitions of ‘Process’ & ‘Business Process’ now established, the subject of business process modelling can now be addressd. Business process modelling is a approach that is utilized in different industries and organisations, it is described as a way for organisations to graphically display the activities, events and actors in their business processes (Indulska, et al., 2009). Business process modelling is said to be a method of performance enhancement through the optimisation of business process by pointing out the ways that connecting activities within specific processes can be streamlined hence bieng made more efficient (Chapman, 2017).

The need for organisational process modelling is encapsulated perfectly in the idiom that reads “a picture is worth a thousand words”, this perfectly describes the reasoning behind process modelling as it basically means that one picture can be a better method of conveying meaning than through text. This view is backed up by Kesari, Chang, & Sheddon (2003) who were of the opinion that having a graphical representation of processes aids communication throughout the organisation as it is easier to understand than trying to convey meaning through text. There are a number of other reasons why businesses diagramatically model their processes, some of these are illustrated in a study done by Alzubidi, Recker, & Bernhard (2011) where employees of a company called suncorp were asked what benefits they thought were derived from modelling their processes and the top 3 responses (Fig 12) included:

  1. The use of modelling to identify “hand offs” to create leaner process (Process Understanding)
  2. The use of modelling to identify areas for improvement (Process analysis)
  3. The use of modelling to explaini how the departmemnt works to outsiders (Process Communication)

As seen above from the responses of the interviewed employees of Suncorp, they were in agreement that the use of process modelling aids Process analysis, process improvement & process communication throughout the organisation. The focus of the organisation on the improvement/management of their processes will be sure to lead to organisational/departmental improvement.

Figure 12: Benefits of BPM within an Organisation (Alzubidi, et al., 2011)

Putting this into the context of Customs Duty Management, one of the issues that the department is facing is that they do not have a standardized or integrated process of compiling their Performance Management metrics, for the purpose of this report the most effective way of illustrating the different processes bieng utilized by the diffeerent services is to create a business process model for each of services, this diagramatical represenation of the differing processes will help the researcher gain a holistic view of the current method of compiling the metrics, which will help when it comes to identifying improvement opportunities and  in the suggestion of a new method of the the compilation of the KPI metrics for Performance Management.

There are a number of different methods that could be utilized when graphically illustrating processes within an organisation/department, but the method that is to be adopted will depend on the reason for wanting to model the processes because each method of process modelling puts emphasis on different aspect of the process. Aldin & de Cesare (2009) illustrated this by using the examples of Role Activity Diagrams placing emphasis on the interaction between the different roles in the organisation, and also the use of Data Flow Diagrams to show how data flows through the different aspects of a system.

The use of different diagrams to convey different meanings leads the researcher to think about what meaning needs to be conveyed by utilising a method of graphically representing the processes. With the main objective of the project to recommend an improved method of compiling the the departments KPI metrics for Performance Management, the reason therefore for utilizing a method of illustrating the processes graphically will be to show that there is no standardized or integrated method of managing their KPIs making their current method inefficient in terms of time taken to compile the reports needed.

3.15 SIPOC Diagrams

Taking the above into consideration, the author decided that developing a SIPOC diagram will be the best way to graphically illustrate the current process being used by the different services within Customs Duty Management. A SIPOC is an acronym for Supplier, Input, Process, Output & Customer, it is described as Rasmusson (2006) as being a high level illustration of any processes within a business, Gibson & Gebken (2010) expands on this by stating that the SIPOC can be used to map out the relationship between the different entities that interact in order to complete a task or a group of interelated tasks. It is basically a way of mapping out what is involved in the whole process and  can be utilized for process analysis and ultimately for process improvement as it shows how the various sections that make up a process  interact with each other (Mishra & Sharma, 2014), this enables the parts of the process that arent adding value to be identified and eliminated (Parkash & Kaushik, 2011).

A SIPOC models is broken into 5 main segments that illustrate the journey from input to output and beyond, it places emphasis on the sources of the inputs (Suppliers) and the destination of the outputs (Customer) by dividing them into segments. Each of the components are explained in more detail as illustrated  below (See Fig 13):

  • Supplier: The first segment of the SIPOC looks at the supplier of the inputs that are to be used in the process, this is essencially the source of the data that is to be processed by the service in order to produce the metrics, or put simply the supplier segment of the diagram shows “where do we get the data from ?” or “who do we get the data set to be processed from ?”
  • Inputs: The second aspect of the SIPOC diagram represents the inputs to the process, in other words the data/raw materials that are going to be processed.
  • Process: This is the transformation procedure that will turn the inputs into the outputs. So In other words it answers the question of “What should be done in order to turn the inputs into outputs?”
  • Output: The outputs represent the finished product after the inputs have been processed, in other words they are the result of the processing of the inputs.
  • Customer: The customer represents the user of the outputs or put simply “who will be the user of the outputs”

D:Final Year ProjectDissertation Picturessipoc-diagram.jpg

Figure 13: SIPOC Diagram Illustration (Tasko Consulting, 2017)

As shown above, utilizing a SIPOC diagram will be of great benefit to the researcher as it enables the mapping out of all aspects of the process involved in creating the metrics from the sources of the data to the users of the compiled metrics. The compilation of SIPOC diagrams for each of the services will enable the researcher to clearly see any processes that are repeated by the different services, which make the whole process inefficient as the sub services would be essentially doing the same task more than once. The aim essentially is to show that using a non-standardized method of managing KPIs is not the most efficient method of performance management.

3.16 CHAPTER SUMMARY

This chapter has covered the research approach that is being adopted by the researcher, firstly the interpretivist research paradigm was deemed to be the most suitable, by the researcher, next the a qualitative research design was chosen as it was the most feasible due to the type of data that is to be collected. Next the methods of data collection and process mapping were established along with the justification of each method. The next chapter presents the results gathered by the utilization of the methods outlined in this chapter.

CHAPTER 4: RESULTS

4.0 CHAPTER OVERVIEW

This chapter aims to display the results that were gathered through the utilization of the methods of data collection outlined in chapter 3. The reasoning behind the utilization of an interview and questionnaire for each of the Customs Duty Management service leads was to gain a better insight into the attitudes of the service leads with relation to their current method compiling the Performance Management metrics with regards to the efficiency of the current process, their opinions on the effects of non-accountability in the Performance Management process and their requirements from a potential replacement of the current system. In addition to this, a method of ethnography called a conducting a GEMBA was adopted in order to get a better understanding of the level of non-standardization of the processes currently being utilized by the different services within Customs Duty Management in order to compile their service KPI metrics. Upon the completion of this chapter, project objective number 2 as set out in section 1.3 of this report would have been fulfilled.

4.1 GEMBA Results

As mentioned in the methodology (Chapter 3) the researcher decided to conduct a GEMBA of the service leads in their natural environment whilst they compiled the monthly KPI metrics. Some of the GEMBAs took more than one session because the metrics had to have to be transferred to multiple parts of the business i.e. sales, for analysis, modification & approval.

Before conducting the GEMBAs, the researcher had two main objectives that were to be achieved at the end of the process, these objectives were:

  1. The first objective was to gain a better understanding about each of the services and their process of compiling the metrics with regards to:
    1. What role does the service play within Customs Duty Management?
    2. What inputs go into their metrics (Where they get the data from)
    3. What are the outputs of the process? (What do the metrics look like)
    4. Who uses the metrics (Value provided by the metrics)
  2. The second objective was to map out the process by utilizing a process mapping tool called a SIPOC in order to visualize the process.

As explained above, a SIPOC was utilized in order to present the process in a graphical format, this was done so that the process would be easily understood by not only the researcher but it would also be helpful when explaining the inefficiencies of the process to the client, the SIPOC would also aid the researcher in gaining a greater understanding as to why the issues identified in section 1.3 are occurring.

All the information collected during the GEMBA process was compiled in conjunction with the service leads after the process had been carried out, the leads were very willing to explain the reasoning behind every step that was taken within the process, and this aided the researcher when mapping out the process.

4.2 Classification GEMBA

Service What Data is Collected What KPIs are monitored?
Classification The data collected is extracted form a database called CDRT, from this the tickets are sorted by:

  • The amount of tickets received by business unit.
  • The amount of tickets by service level.
  • One day ticket by BO?
  1. Amount if tickets received by BU
  2. Amount of tickets by service level
  3. Amount of tickets by BO

Table 4: Classification Service Process Information

What is the function of the Classification service?

The classification service is responsible for ensuring that duties and taxes incurred by GSK are paid correctly. Non Compliance can result in overpayment/underpayment of duties/import taxes, supply chain disruption, financial penalties & other sanctions.

Who uses the KPI metrics that are compiled by the Classification Team?

There are three main users of the metrics that is compiled by the classification lead, these are:

  1. The first use of these metrics is that it is presented in monthly the governance board meetings.
  2. The metrics for the service is also presented in the weekly duty management team meetings; this is done as part of the Performance Management Process for monitoring progress.
  3. The third use of these metrics is to put up on the duty management ‘performance wall’; this is basically a section of the department dedicated to visualization of the Performance Management metrics which are printed out each month.

How often is the process of compiling the Classification metrics carried out?

The metrics are compiled by the Classification lead on a monthly basis

4.2.1 Classification Process SIPOC

After the conducting the GEMBA on the classification service, the process was mapped out by the researcher by utilizing the SIPOC (Fig 14). The main aim of the process is to classify the tarrif codes used within the organisation. The tarrif codes represent a universally accepted convention that is used to categorise products that GSK transports/recieves.

The SIPOC below shows a completely mannual process of manipulating the data. The data is firstly extracted from the Classification  site which is the front end site where requests for product classification gets input by the customer. Once the data is extracted, the Classification lead then proceeds to mine the data to extract what is needed which includes:

  • Tickets by Business Unit: This shows how many requests for the classification of products were received and what part of the business (GSK) the request came from.
  • Tickets by service level: This shows the amount ofclassification requests that were resolved within the Service Level Agreement time preiod
  • One Day Tickets by BO: A one day ticket represents tickets that are require responses on the same day, this may be due to the importance and these take priority.

Figure 14: Classification SIPOC

4.2.2 Classification Process Output

Once the data that is needed is extracted from the site it is then exported into an excel sheet before getting converted into a pivot table which contain the raw data that is then converted into a stacked bar graph  (Fig 15). The bar graph as shown below presents the monthly figures for the amount of:

  • Classification requests that were responded to required within a day
  • Classification requests responded to within a month
  • Classification requests responded to  within a month

The bar graph also shows the amount/percentage of tickets that were solved within the Service Level Agreement (an agreed acceptable number between the service and the customers) & also contains some commentary about the metrics with the aim of explaining the reason the figues are as they are. Once the bar graph has been completed, it is ready to be displayed in the team meetings & in the monthly governance board meting.

Figure 15: Classification Metrics

4.3 Customs Authority Request (CAR) GEMBA

Service What Data is Collected What KPIs are monitored?
Classification The data collected is extracted form a database called CDRT, from this the tickets are sorted by:

  • The amount of tickets received by business unit.
  • The amount of tickets by service level.
  • One day ticket by BO?
  1. Amount if tickets received by BU
  2. Amount of tickets by service level
  3. Amount of tickets by BO

Table 5: Customs Authority Request Process Information

What is the function of the CAR service?

The Customs Access Request (CAR) service is the main escalation point for any requests made by customs to GSK. This could be; Requests for an audit, requests for the payment fees/penalties, Notice of an issue, correspondence for any challenges/ appeals made to the customs authorities. The CAR service is also responsible for managing the customs requests received and allocating them to suitable team members/sub teams to deal with.

Who uses the KPI metrics compiled by the CAR team?

  1. The first use of these metrics is that it is presented in monthly the governance board meetings.
  2. The metrics for the service is also presented in the weekly duty management team meetings; this is done as part of the Performance Management Process for monitoring the amount of CARs received along with any new CAR requests and who deals with them.
  3. The third use of these metrics is to put up on the duty management ‘performance wall’; this is basically a section of the department dedicated to visualization of the Performance Management metrics which are printed out each month.
  4. The metrics are also used by CAR specialists to manage the requests as it is standardized method of viewing the amount of cars received, resolved, and how important they are.

How often is the process of compiling the CAR metrics carried out?

The metrics are compiled by the CAR lead on a monthly basis

4.3.1 Customs Authority Request SIPOC

The SIPOC (Fig 16) below represents the process of compiling the KPI metrics for the CAR service, as mentioned above, the main aim of the process is to illustrate the amount of requests that customs has made to the company. The CARs received are of different significance levels which are determined by the service lead. The data contained within these metrics will be used to track the amount of open CARs within the service to enable better management of the requests.

The SIPOC diagram shown below maps out the KPI metric compilation process carried out by the CAR lead. Due to car lead having considerable experience using the spreadsheet software, some aspects of the process such as the data capture from the Customs Duty Request Ticket (CDRT) SharePoint database has been automated, this eliminates the need to manually export the data into excel the data every month. The CDRT database is automatically updated to reflect the most up to date data to be input into a pivot table called the CAR tracker; this table contains all the open requests within the organisation. Once the CAR tracker has been updated, the CAR lead then updates the graphs to be used by the service.

Figure 16: Customs Access Request SIPOC

4.3.2 Customs Access Request Output

After the process of compiling the metrics has been completed by the CAR lead, there are three main outputs that are produced:

  1. The first output that is produced is a stacked bar chart (Fig 17) that shows the number of open CARs that were received by the service by country of origin; these are also displayed by order of significance using a RAG status* to group the requests for each country.

Figure 17: Open CARs by country of origin

  1. The second output that is produced by the CAR service lead is a stacked bar chart that (Fig 18) that shows the amount of open CARs that were received, these are grouped by the year the request was made, and it contains historical data that is obtained from the CAR tracker along with the updated CAR metrics from the CDRT database. This metric is also categorised by order of significance by utilizing a RAG status* to show the number of low, medium & high priority cars are open.

Figure 18: Open CARs by Year Opened

 

Red : High Priority CAR

Amber: Medium Priority CAR

Green: Low Priority CAR

  1. The third output that is produced is present on both charts and it shows the commentary compiled by the CAR leads explaining the reason for the open CARs shown in the graphs. The commentary also states any actions that have been taken in order to respond to the requests. Once the bar graph has been completed and the commentart added, the CAR metrics are now ready to be displayed in the team meetings & in the monthly governance board meting.
Service What Data is Collected What KPIs are monitored?
Preferential Origin The data used to develop the KPI metrics for Preferential origin is obtained from the:

  • Preferential Origin Database (POD)
  • JDE Invoicing Database
The main KPIs that are monitored is the amount of accumulated duty savings & potential losses achieved by utilizing/underutilizing specific SKUs.

4.4 Preferential Origin (PO) GEMBA

 Table 6: Preferential Origin Process Information

 

What is the function of the Preferential Origin arm of the department?

The Preferential Origin (PO) team within Duty Management at GSK is responsible for leveraging and managing sites (also known as Stock Keeping Units/SKUs) that the company has signed a Free Trade Agreement (FTA) with. By signing an FTA with specific countries, the company can obtain preferential rates when it comes to paying import duties for the goods that they transport.  As well as managing existing FTAs the PO team is in charge of negotiating new FTAs in order to optimize GSKs duty cost globally.

Who uses the KPI metrics compiled by the Preferential Origin team?

  1. The first use of these metrics is that it is presented in monthly the governance board meetings.
  2. The metrics for the service is also presented in the weekly duty management team meetings; this is done as part of the Performance Management Process specifically to aid decision making about the benefits derived/losses incurred by utilizing a specific SKU.
  3. The third use of these metrics is to put up on the duty management ‘performance wall’; this is basically a section of the department dedicated to visualization of the Performance Management metrics which are printed out each month.
  4. The metrics are also used by PO specialists to monitor and manage the FTAs held by monitoring the benefits/losses they bring on a monthly basis.

How often are the metrics compiled by the Preferential Origin team?

The metrics are compiled by the Preferential Origin lead on a monthly basis

4.4.1 Preferential Origin SIPOC

After conducting a GEMBA with the lead of the Preferential Origin service, the process was mapped out by utilizing a SIPOC (Fig 19). The main aim of the process is to measure potential savings and losses incurred as a result of the utilization of Stock Keeping Units (SKUs) within the Free Trade Agreement (FTA) region. Before getting into the process of compiling these metrics, it is worth noting that the figures that are derived are non-transactional, meaning that no physical transfer of money to/from the business, the metrics represent money that is saved by utilizing a SKU and the benefits lost by not utilizing a FTA within a region.

With that taken into consideration, the process of compiling the metrics begins with the PO team has to capture data from three main sources they are the Preferential Origin Database (POD) which is a store of the regions that GSK has signed an FTA with therefore enabling them to preferential duty rates. The second source of data that is utilized is called the JDE database which is where all the sales records incurred by GSK are stored. The PO lead extracts the invoices from the SKUs which are contained within the JDE database this shows how much GSK is to pay for utilizing the SKU.

The data from the two databases are then transferred onto an excel spreadsheet in order to be compared, so the PO lead performs some conditional formatting by utilizing excel functions to determine if the amount to be paid will be influenced by an existing FTA or if the amount to be paid can be reduced by utilizing an FTA (Potential Losses). Once the data has been compared and the benefits and potential losses have been determined, the metrics are then added to the graph under a new month and then sent off to the preferential origin specialists for analysis.

Figure 19: Preferential Origin SIPOC

4.4.2 Preferential Origin Output

On a monthly basis the sum of the amount saved by the utilization of SKUs within FTA regions is calculated and the amount not saved (Potential losses) are calculated and presented in the form of a stacked bar graph (Fig 20), the bar chart shows three main bits of information:

  1. The first figure that is contained within the bar graph is the total amount of savings that has been realised from the existing FTAs for the month.
  2. Second figure that is presented on the graph is the amount of new cost savings that have come as a result of signing new FTAs within new regions.
  3. The third amount is the potential loss that the company could incur by not having a FTA within a particular region, this would act as a basis for decision making within the service as they decide if the benefits of signing the FTA outweigh the potential losses they could incur by not having one in place.

Figure 20: Preferential Origin Metrics

Service What Data is Collected What KPIs are monitored?
Duty Optimization & Special Regimes The data used to develop the KPI metrics for Duty Optimization is obtained from the:

  • JDE Sales reports
  • Data capture form on SharePoint
There is a baseline set on the benefits tracker metric that is flagged when the figure achieved is lower than the expected amount.

 4.5 Duty Optimization & Special Regimes GEMBA – FOR REVIEW BY KD

Table 7: Duty Optimization & Special Regimes KPI Information

 

What is the function of the Duty Optimization service?

The duty optimisation service looks to identify and implement special government authorised programmes & provisions to reduce, defer & eliminate customs duty payments. The Duty Optimization team is in place to ensure compliance with regulatory authorities by making sure that special regimes are applied consistently & correctly.

Who uses the KPI metrics compiled by the Duty Optimization team?

  1. The first use of these metrics is that it is presented in monthly the governance board meetings.
  2. The metrics for the service is also presented in the weekly duty management team meetings; this is done as part of the Performance Management Process specifically to monitor if expected duty savings are being achieved on a monthly basis.
  3. The third use of these metrics is to put up on the duty management ‘performance wall’; this is basically a section of the department dedicated to visualization of the Performance Management metrics which are printed out each month.

How often are the metrics compiled by the Duty Optimization team?

The metrics are compiled by the Duty Optimization lead on a monthly basis

4.5.1 Duty Optimization & Special Regimes (DOSR) SIPOC

After conducting the GEMBA for the Duty Optimisation (DOSR) service, the researcher then went about mapping out the process by utilizing a SIPOC (Fig 21). The main aim of the PO metric compiling process is to determine how many special regimes that the company is signed up to and to determine the total monetary savings/benefits that the company gains by utilizing the special regimes.

Before getting into the process, it is worth noting that the figures presented in the metrics shows the total amount of money saved by utilizing the special regimes, therefore they are non-transactional meaning that no physical transfer of money to/from the business. The process is revolved around looking into the data that is captured from the JDE sales reports database, this where the information about all the sales transactions that GSK makes are stored. Another source of data is a SharePoint data capture form where the Duty Optimization lead enters the information regarding to the amount of duty to be paid on each transaction. The duty optimisation lead sends the sales reports for the month to the sales department who verify the amounts on the reports, points out the costs that are eligible for discounts on the grounds of special regimes and sends the report back. The Duty Optimization lead then enters populates the SharePoint data capture form inputting the amount on the sales report and the amount of discount on duty that the company is to                               receive.

4.5.2 Duty Optimization & Special Regimes (DOSR) Output

          Figure 21: Duty Optimisation & Special Regimes SIPOC

Once the comparison has been done by the duty optimisation lead, the raw data is captured from the JDE database and exported as an excel spreadsheet that is ready to be turned into the metrics for the Duty Optimisation service (See Fig 22). There are two main metrics that are compiled for the service and they are:

  1. The first metric is the duty under management within GSK; this figure represents the total amount of duty payments that are being reviewed for optimisation in that month, this is represented by a bar graph, the total cumulative duty payment being considered for optimization is also represented by using a line graph.
  2. The second metric that is compiled by the duty management service is one that shows the benefits derived from utilizing the duty optimization strategies employed, this is represented in terms of the amount of money saved per month by utilizing special regimes. There is also a forecast amount which represents the amount of savings that the company is targeted to achieve by the year end from the utilization of the duty optimization strategy.

Figure 22: Duty Optimisation & Special Regimes Metrics

4.6 Survey Findings

The online survey was completed by the 4 service leads that are in charge of compiling the monthly metrics for Duty Management. The surveys were all completed on the same day as an interview was conducted straight afterwards, the responses from the survey formed the basis for the questions that were asked during the interview.

Question 1: Which Service of Customs Duty Management do you help compile the metrics?

The purpose of this question was to gauge what the differing experiences of compiling the metrics were between the different services. The leads from each of the 4 services within Duty Management (25%) took part in the survey.

Figure 23: The Respondents Service Line

Question 2: How many hours on average does it take to compile the metrics for your service per month?

The purpose of this question was to get a better idea about how long the service leads spend compiling the reports per month. (Fig 24) shows that 50% of the respondents that took the survey spend 5 hours or more compiling their monthly service metrics. The remaining respondents spent between 1.5 and 4.5 hours per month compiling their service metrics, the reason for the amount of time spent compiling the metrics takes as they is looked at in greater detail in the chapter 5.

Figure 24: Time Spent Compiling Metrics

Questions 3 – 10: Select how much you agree/disagree with the following statements about Duty Managements current method of Performance Management.

The next series of questions were designed to delve into the attitudes of the service leads within duty management with regards to their current method of performance management. As explained in the methodology section of this report, the researcher chose to use a universially recognised method of gauging attitudes called the likert scale. The line of questioning was designed to determine how they felt about 4 main issues:

  1. The amount of time it took to compile the reports.
    1. Questions were designed to identify the parts of the process the service leads felt took the most time.
    2. Questions were designed to identify the effects that automation would have on the overall effeciency of the process in terms of time taken and quality of the metics provided.
  2. The convenience of using the current system.
    1. Questions within the survey were asked to explore the effects the manual nature of the process has on the quality & reliabiliy of the metrics.
  3. The lack of standardization or integration of their performance management  process.
    1. Questions were designed to gauge their thoughts on the lack of a standard method of compiling the Performance Management metrics throughout the department.
    2. Questions were also designed to shed light on the lack of an accountable entity within the service with regards to the compillation of the Performance Management metrics and the consequences of not having more than one person carrying out the process.
    3. Questions were designed to shed light on the differing sources of data used by the different services in order to compile their metrics.
  4. Their expectations from a potential replacement of the current method of performance management.
    1. A question was designed to explore what the users expect from a potetial software solution, the question was also meant to shed a light into what they felt the current method is lacking.

Figure 25 below shows a summary of the responses that were received from the likert scale questions:

Figure 25: Likert Scale responses

Question 3: The amount of time spent compiling the business KPIs could be reduced by the automation of the process.

This question aims look deeper into the process of the compilation of the metrics and gauge whether the users of the system feel the speed in which the metrics are compiled can be increased by the automation of the process. The response was overwhelmingly for the automation of the process, all 4 of the service leads (100%) strongly agreed with the thought that the automation of the process would reduce the amount of time spent compiling the metrics.

Figure 26: Question 3 Response percentages

Question 4: The automation of the performance management process by the utilization of a business intelligence software would enable me spend time on more business critical tasks.

This question sticks to the theme of gauging how the automation of the process can reduce the amount of time spent compilling the reports. But unlike question 2 above, this question takes a different spin on the same line of questioning by trying to determine what effect the time taken to compile the metrics has on the service leads ability to perform other business critical tasks. Yet again, the service leads mostly feel that the time spent compiling the monthly metrics can be better spent on more business critical tasks as three out of the four respondents asked (75%) “agreed” with the statement with the remaining service lead (25%) strongly agreeing with the statement.

Figure 27: Question 4 Response percentages

Question 5: The mannual nature of the current method of performance managent makes it the most efficient method of monitoring the services Key Performance Indicators.

This reasoning behind this questioning was to gather the thoughts of the service leads as to weather they thought the current method of compiling the metrics was the most efficient. In other words, it aimed to gauge if they thought the process could be imporved. The responds mostly disagreed with the statement with 75% of the leads strongly disagreeing that the current method is the most efficient method of compiling the KPI metrics.

Figure 28: Question 5 Response percentages

Question 6: Mistakes can be made when compiling the mothly service metrics that has an effect on the reliability, usability and reporting of the metrics.

This question was designed to assess the effects that the manual nature of the compilation of the metrics has on the quality & usability of the metrics produced. This aimed to shed light on the effects that manually compiling the metrics has on performance management. The leads who answered to this question mostly agreed with the statement with 3 out of 4 (75%) of the respondents agreeing with the statement and the final service lead (25%) strongly agreeing with this statement. This lead the researcher to conclude that the manual nature of the current method of compiling & reporting the KPI metrics leave it sussiptable to mistakes which can sometimes have an adverse effect on the performance management process.                                                      

Figure 29: Question 6 Response percentages

Question 7: Compiling the monthly metrics is a simple and straightfoward process.

This question was aimed at gauging the thoughts of the service leads on the simplicity of the current performance management process. Thhe response to this question was mostly disagreement with the 50% of the respondents disagreeing and 50% strongly disagreeing with the statement that the compillation of the monthly metrics is a simple and straightfoward process.

Figure 30: Question 7 Response percentages

Question 8: The performance management process for the service line should be compiled by a single entity that is accountable for the metrics.

The aim of this question was an attempt to shed light into the non accountability of any part of the dapertment when it comes to performance management, this question was asked as currently the exercise of performance management is done is silos with each of the leads in charge of their own services metrics. All the respondents (100%) “agreed” with the statement that a single accountable entity should be accountable and therefore responsible for the compillation of the the service metrics.

Figure 31: Question 8 Response percentages

Question 9: In the event of my absence, someone else within the service is able to compile the monthly metrics on my behalf.

Similarly to question 8 above, this question aims to look into the effect of not having a dedicated entity within the department to compile the KPI metrics. All of the respondents (100%) “agreed” that due to them bieng soley responsible for the compilation of their own performance metrics, in the event of their absense no one else would be able to compile the service metrics due to them not having the necessary skills/knowledge to compile the metrics on their behalf, This is bound to have an adverse effect on the whole performance management process ad there wont be any useful metrics to use in the team meetings.

Figure 32: Question 9 Response percentages

Question 10: A knowledge transfer of the process required to compile the monthly KPI metrics for my service can be done easily & quickly.

This question was aimed at looking into how easily a knowledge transfer of the KPI compilation process could be done. This question would also gauge the complexity of the current process of performance management. The respondent overwhelmingly “disagreed” with the statement, leading the researcher to conclude that the current performance management process is very complicated & hence difficult to understand.

Figure 33: Question 9 Response percentages

Question 11: Of the following data sources, state all that you need in order to compile your monthly service metrics.

This question aims to shed further light the non-standardisation or integration of the processed by pointing out the different sources of data used by each of the sub service. Having to manually gather data from a variety of different sources would inevitably lead to a more time consuming process that is susceptible to human errors. The response pointed out that the respondents used the service catalogue and team site databases to capture data with 75% of the respondents using those sources of data, SharePoint databases and the JDE sales report database were both used by 25% of the respondents each. There were also additional sources of data such as documentation collected from the service specialists in the case of preferential origin and Customs Authority Requests.

Figure 34: Question 11 Responses

Question 12: In your opinion, what do you think the most important criteria are when adopting business intelligence software in terms of functionality?

This line of questioning aimed to gauge the user expectations of a potential business intelligence solution. The respondents were of the opinion that the most important functionality that a potential business intelligence software solution should have should be its ability to automate the performance management process and the creation of reports with 100% of the service leads mentioning this functionality as being important. In addition the respondents thought the use of visual dashboards to show the data and the ease of use of the BI software is also an important factor to consider when choosing a potential software solution with 75% of the respondents choosing said functionality. Quite surprisingly none of the respondents thought the cost of the software was an important factor when choosing a BI software solution. One of the service leads also stated that the “compatibility of the software solution with SharePoint and other databases” is also an important factor when considering investing in BI software.

Figure 35: Question 12 Responses

Question 13: Please add any further comment that you believe is pertinent to the questionnaire.

This question was added as an opportunity for the service leads to express any other opinions about what they thought of the current method of performance management. Most of the respondents did not chose to add any further comments, but one was on hand to comment that the process is very “tedious and repetitive” and mentioned that they had to manually clean up the database as “there is normally some invalid data contained within the database” this extra task of cleaning up the data is sure to add to the time taken to compile the KPI metrics.

4.7 Results of the interview process

Before getting into the results of the interview, it is important to point out the main aims of conducting the interview, or put simple what did the researcher aim to achieve by conducting the interview, the aims of the research process are located in section 3.2 of this report (Table 3). The aims of the questionnaire/interview utilizing a questionnaire style approach of structuring the interview with predetermined questions which, whilst adopting an open style of questioning. This enabled the researcher to prompt and probe the subjects in order to gain a deeper insight into their responses.

The individuals chosen to be interviewed were the Customs Duty Management service leads who were in charge of compiling the monthly KPI metrics to be used in Performance Management process. Profiles for each of the employees interviewed can be found in Appendix 1.

4.7.1 Interview Procedure & Justification

The interviews for each sub service were conducted after the questionnaire had been filled out by each of the service leads; this enabled the interviewer to begin each of the questions asked as a probe into the responses that were provided in the questionnaire.

The rationale behind each of the questions asked in the interview is illustrated in table 8 below. The table also shows which of the research aims that were established in section 3.2 (Table 3) were met by asking the each of the questions.

Question Rationale Interview Aim
Question 1 This question was aimed at identifying service the interviewee represented.
Question 2 Based on the respondents answer on the questionnaire, this question aimed to uncover the underlying reason why the process was said to take as long as it did. 1.1
Question 3 The literature discussed in chapter 2 showed that in order for a service to be considered mature, there should be integration & automation of their processes, The user is prompted to identify stages of the process they see as being automatable. 2.2
Question 4 This question was aimed at gauging the effect that having a manual performance management process has on the service leads ability to perform other tasks to the best of their ability. 1
Question 5

 

 

This question looks to gain an insight into how the current process is adversely affecting the quality of KPI metrics that are compiled. 1.2
Question 6 This question looks to gain an insight into how the current process is adversely affecting the quality of KPI metrics that are compiled. 1.2
Question 7 The aim of this question was to gain an insight into the thoughts of the employees on the possible benefits/drawbacks of using the excel spreadsheet to manually compile the Performance Management metrics 1.2 & 1.1
Question 8 These two questions aim to address the issue of a lack of an accountable entity within Customs Duty Management, it looks to prove that having one person per service with all the knowledge of the performance management processes will have an adverse effect on the service.

This aims to address problem number 4  established in chapter 1

2.1
Question 9
Question 9

 

Same as with questions 7 & 8, this question looks to show that specialist knowledge that cannot be easily or quickly transferred is required to build the monthly metrics. It makes another case for making a single entity/ sub service accountable for the production of the monthly metrics. 2.1
Question 10

 

 

This question aims to establish the non-standardisation of the processed by pointing out the different sources of data used by each of the sub service, this would inevitably lead to a less efficient process 2.2
Question 11 This question was written with the aim of gauging what the user expectations were with regards to the adoption of Business Intelligence software. 2.3

Table 8: Interview Questions Rationale

4.7.2        Interview Responses

Q2: In the questionnaire that you answered your response indicated that the process of compiling the metrics took (Response) hours per month; please explain why it takes that long?
Respondent 1 1.5-2.5 hrs “Due to my excel knowledge I have managed to automate some aspects of the process, so I have to refresh the data to get the updated metrics”

“Most of the time is spent making the graphs to be shown in the meetings also doing the analysis and providing insights and commentary”

Respondent 2 3-4.5 hrs “there is a lot of transferability of the data, once I have gathered and sorted the raw data, it has to be sent off to another part of the business for confirmation, this normally takes 2-3 days”

“When I get the data set back I then have to make the required changes before I can create the metrics and the commentary”

Respondent 3 5+ hrs “There is a lot of cross checking of the new dataset with historic data, the dataset is not automatically added to the historic dataset therefore the new data has to be extracted from the team site manually and then added to the historical excel sheet”

“I have to copy the new data into an excel sheet with the historic data and then paste the data under the specific column names, whilst cross checking that the data matches”

Respondent 4 5+ hrs “I have to extract the raw data from 2 different databases and paste them into a new spreadsheet”

“I have to use a number of excel functions such as V-Lookups to find the relevant data from the extracted dataset”

“A lot of comparison of last month’s data with the current month’s data has to be used; conditional formatting functions are also added to make sure the data matches, all the conditions need to be met in order for the data to be considered valid.”

Table 9: Response to Question 2

Q3: You (Response) that the amount of time spent compiling the monthly service metrics could be reduced by the automation of the process, what aspects of the current process do you feel can be automated to reduce the time spent compiling the metrics?

 

Respondent 1 SA “As I mentioned above, I have automated some aspects of the process, but I think the automatic creation of the charts  would make my job a whole lot easier”
Respondent 2 SA “Personally speaking, the whole process could benefit from being automated, at the moment, I find myself conducting data analysis on large data sets which could be done automatically.”

“I honestly haven’t got the excel expertise to come up with a function to automate some aspects of the process, but I assume it shouldn’t be too hard to do”

Respondent 3 SA “At the moment I have to create a number of “check-if” functions to compare the new data collected with the historical data, I feel this could be automated”
Respondent 4 SA “The extraction of the data from the different sources onto a single location is a task that I think can be automated, the current manual process of doing this is very repetitive and tedious”

Table 10: Response to Question 3

Q4: You (Response) that the automation of the performance management process can would enable you better focus on more business critical tasks. Do you then think that the time taken to compile these metrics manually is having an adverse effect on your ability to perform more business critical tasks effectively?
Respondent 1 A “I wouldn’t say that not automating the process is affecting my other work adversely per se due to having been able to automate some aspects of the current process, it’s not as bad as it the other services. But yes I do feel that the automation of the process would enable me to spend my time be better spent elsewhere”
Respondent 2 A “I don’t think it would have that much of an effect on me doing my work well, but the amount of time that would be freed up by the automation of this process would be substantial, enabling me focus more on other more important tasks”
Respondent 3 A “I agree that the automation of the process will free up time for other tasks, but I do not think that it the amount of time currently being spent compiling these metrics is adversely affecting my ability to effectively perform business critical tasks”
Respondent 4 SA “the extracting, analysing and the reporting of the data takes far too long, especially as they are very repetitive tasks, I feel that the time spent could be better spent elsewhere, especially as I am also responsible for a large number of staff under my management”

Table 11: Response to Question 4

Q5: You (Response) that the manual nature of the current method of performance management is the most efficient method of monitoring KPIs can you explains why you think this?
Respondent 1 D “Put quite simply I do not think it is efficient at all, there is a lot that can be automated & streamlined”
Respondent 2 SD “There is a lot of sending the data back and forth between myself and other parts of the business for approval and amending, this ruins the flow of the process, making it inefficient”
Respondent 3 SD “having to extract the data from the database & create new excel files for analysis each month is not very efficient, if these can be done automatically and all functions be entered only once, that would make the process more streamlined”
Respondent 4 SD “the manual creation of the functions to be used such as V-Lookups and inserting conditional formatting every time that I analyse the data is not very efficient, the process is very repetitive and could be more streamlined for sure.”

Table 12: Response to Question 5

Q6: You (Response) that the sometimes mistakes can be made in the compilation of the monthly metrics that affect its reliability & usability.

  1. Can you provide me an example of a time when this has occurred?
  2. What were the consequences of this occurring?
Respondent 1 A “Mistakes do occur from time to time, the most mistakes occur due to failing to update the existing data before adding in the new, without this being done, the data is invalid, making all the analysis invalid.”
Respondent 2 A “when the sales data is extracted, it is up to me to get the data from and to a specific date, sometimes the data overlaps and I end up getting data from the wrong dates, this makes the dataset to be sent for review by the other business function worthless, therefore the information they send back to me for analysis is also void. It’s like a domino effect really.”
Respondent 3 SA “Once the data has been extracted from the team site, it is inserted into the columns in the historical spreadsheet, Due to the amount of repetition involved there is bound to be a mistake here and there. The data has to match the headings if not the dataset that is analysed becomes useless.”
Respondent 4 A “the comparison manual comparison of the historical data with the new data is a very long process, I am only human so I am bound to miss a couple of things from time to time”

“most of the time I am covered as I am a stickler for double checking the metrics before the meetings”

Table 13: Response to Question 6

Q7: You (Response) that the process of compiling the metrics is a simple and straightforward process. What aspects of the process prove difficult for you?
Respondent 1 SD “The most challenging aspect of compiling the metrics is the process of adding the insights and the commentary as part of the slides that are looked at during the meetings, these are very important as they are related to our SLAs, so take my time to make sure that they are reliable and useful.”
Respondent 2 D “In my opinion the most difficult bit about compiling these reports is the amount of re working that needs to be done before the business are happy with the metrics”

“I have to send it to the sales department to make sure that the metrics are accurate, they add notes and send back for amending. This process could happen 3 or 4 times over until they are happy with the metrics. This is important as the metrics are to do with money, and the sales department always want to make sure that we have the right amount on record.”

Respondent 3 SD “The amount of time it takes to finally match all the data with the correct columns and then compiling the metrics to be reported is probably the most challenging aspect of the process”
Respondent 4 D “The comparison manual comparison of the historical data with the new data is a very long process, also the manual entry of various excel functions such as “check if” proves difficult”

Table 14: Response to Question 7

Q8: You (Response) that there should be a single accountable sub service to deal with the metrics of the whole service. Why do you think this would (or wouldn’t) be beneficial to the service?
Respondent 1 A “This  would ensure that the metrics are being calculated by a person/team of people with the right amount of knowledge/expertise to compile these metrics on a weekly basis would make for higher quality of metrics being produced”
Respondent 2 A “I’m speaking for myself when I say this, but when I am creating my metrics I try my best to make my service look good”

“I feel like having people that do not work within the service will enable there to be less bias when compiling the reports, which will in turn give a true and fair view of the performance of the service”

Respondent 3 A “This would free up a lot of time for me to use on more important tasks, positive statements on the performance of the service is more satisfying when it is coming from someone else, it is also more urgent when someone else says you aren’t doing well, hence there is a better chance of improving the performance.”
Respondent 4 A “if a single accountable part of the service is responsible for this, I think their methods would be more standardized, and it is more efficient than each service having their own method of compiling these metrics”

“It would also make our review meetings run a lot smoother, as we would have one/two people talking about the service metrics they compiled instead of having 4 people talk one after the other.”

Table 15: Response to Question 8

Q9: You (Response) that in the event of our absence, someone else would be able to compile the monthly reports. Why would this be the case?
Respondent 1 SD “No one within the service currently has the knowledge of how to compile these metrics, I have been compiling them on my own for some time now, we haven’t felt the need to change this”
Respondent 2 SD “the uniqueness of every metric compiled means that in order for someone else to know how to do them requires a long and complicated knowledge transfer, I don’t think the man hours that would take would be worth it”
Respondent 3 SD “I am the only one with the knowledge to compile the metrics, the person who used to compile them before has now left the company”
Respondent 4 SD “No one within the service has the required experience manipulating the Preferential Origin (PO) database, it is full of thousands of lines of data which needs to be carefully filtered and sorted before the extraction occurs”

Table 16: Response to Question 9

Q10: You (Response) that knowledge transfer of the KPI metrics compilation process can be conducted easily and quickly. What aspects of the process make it difficult to teach to others?
Respondent 1 D “There are a couple of crucial steps that requires historical knowledge of the metrics gathered, for someone with no experience, this might prove challenging and it might take some time to get their head around it”
Respondent 2 D “teaching someone else how to do this might take some time, as they have to have prior knowledge and experience of working with and manipulating the service database in order to know where and how to extract the right data set”
Respondent 3 D “I think knowledge of the functions that are to be used and the matching up of the newly extracted dataset with the historical data on an excel sheet takes a bit of getting used to”

“The reasoning behind why some things are done within the process such as why the headings need to be matched, will take some explaining & time to understand”

Respondent 4 D “Knowledge of creating a number of functions including V-Lookups, and also conditional formatting to compare the new sales data that is extracted from the JDE database with the PO database needs to be acquired, this might need some excel expertise/knowledge”

“The data does not make sense unless knowledge of some of the products & shipping to & from locations are acquired”

Table 17: Response to Question 10

Q11: From your response I can see that you gather raw data from (Sources) in order to compile your reports. Do you feel the process of consolidating all the data from all the different sources is the time consuming or can it be done quite quickly?
Respondent 1 Service team site + service catalogue + other documentation “As I have mentioned before, I have managed to automate the data collection process so gathering the metrics from these sources isn’t really a problem”

“Sometimes I do have to consult with the CAR specialists when I see a request is taking too long, it normally gets settled by email and they send me required documentation to add to the metrics once the issue is solved”

Respondent 2 JDE Forms “This can be done relatively quickly as the only source of data is the JDE forms, once I extract the required sales information that is pretty much it concerning the extraction of data. There’s not that much more to it”
Respondent 3 Service Team site & Service Catalogue “The data that I need is usually exported as an excel document from the teams SharePoint site, the service catalogue is also used to gather some information on SLAs.”

“The process is done quite quickly as the data can be exported as an excel document at the click of a button”

Respondent 4 Service team site, service catalogue, SharePoint Database & other sources “This process is very time consuming, I have to extract the data that I need from about 5 different sources, I then need to filter and sort the data that I need, and then transfer onto the PO database before being able to compile the metrics. This process is what takes up most of my time”

Table 18: Response to Question 11

Q12: You mentioned that (Factors) are the most important when adopting business intelligence software, can you state your reasons why?
Respondent 1 Dashboard visualization + Report Creation +

Automation of process

“All three of these are very important when looking at the functionality of a software solution, currently the visualization of the metrics using dashboards/graphs is being done but it is manually compiled, this links into the report creation and the automation of the process.”
Respondent 2 Ease of use + Report Creation + Automation of process + Compatibility “Ease of use is very important as the system should be easy to navigate and have a user friendly interface that makes it easy to operate”

“The automatic creation of reports in my opinion is a must when looking BI software”

“I also mentioned compatibility with SharePoint and other databases, this is because the system will be gathering data from a number different systems/databases hence compatibility with the different sources of data is crucial”

Respondent 3 Ease of use + Dashboard visualization +

Report Creation +

Automation of process

“The automatic creation of reports is a key factor when looking at potential software solutions to compile the metrics; this is because new data is created on a daily basis, so automation of the process saves me manually updating the reports.”

“Dashboard visualization is also a good way to present the metrics as they are shown in way that is easily understandable”

Respondent 4 Ease of use + Dashboard visualization +

Report Creation +

Automation of process

“It is pretty straight forward isn’t it? the system should be easy to use, it should show the metrics in an easily understandable format & the creation of reports and capture of data from the different sources should be automated”

Table 19: Response to Question 12

4.8 CHAPTER SUMMARY

By the employing multiple data collection techniques such as surveys, interviews and conducting a GEMBA of the current process in practice, as well the utilization of a business process modelling technique called a SIPOC, to map out the process being used by the different services within Duty Management at GSK, the researcher was able to present the findings of the research. An in-depth evaluation and analysis of said findings will be conducted in the next chapter.

Due to the utilization of all the data collection methods and process mapping techniques to gather data that will enable the researcher to analyse Custom Duty Managements current method of compiling the KPI metrics, Project objective number 2 as set out in section 1.3 of this report has now been achieved.

BIBLIOGRAPHY

Alexos, 2013. ITIL Maturity Model. [Online]
Available at: https://www.axelos.com/Corporate/media/Files/Misc%20Qualification%20Docs/ITIL_Maturity_Model_v1_2W.pdf
[Accessed 10 November 2016].

Amaratunga, D. & Baldry, D., 2002. Moving from performance measurement to performance management. Facilities, 20(6), pp. 217 – 223.

Azvine, B., Cui, Z., Nauck, D. & Majeed, B., 2006. Real time business intelligence for the adaptive enterprise. E-Commerce Technology, p. 29.

Bauer, K., 2004. The Metrics That Drive Performance Management. DM Review, pp. 63-64.

Boyer, J. et al., 2010. Business Intelligence Strategy: A Practical Guide for Achieving BI Excellence. Ketchum: MC Press Online.

BusinessDictionary.com, 2017. Performance. [Online]
Available at: http://www.businessdictionary.com/definition/performance.html
[Accessed 24 April 2017].

Butler, L., 2007. New Dashboard Reports Help Institutions Gauge their Performance, New England: NEW ENGLAND BOARD OF HIGHER EDUCATION.

Eckerson, W., 2009. Performance management strategies. Business Intelligence Journal, 14(1), pp. 24-27.

Golfarelli, M., Rizzi, S. & Cella, I., 2004. Beyond data warehousing: what’s next in business intelligence?. Washington DC, ACM, pp. 1-6.

Kennerley, M. & Neely, A., 2002. A framework of the factors affecting the evolution of performance measurement systems. International Journal of Operations & Production Management, pp. 1222-1245.

Lebas, M., 1995. Performance measurement and performance management. Int. Journal of Production Economics, Volume 41, pp. 23-35.

Little, K. F., 2009. Deloitte Consulting LLP Commonwealth of Massachusetts Statewide Strategic IT Consolidation (ITC) Initiative Helpdesk and Desktop and LAN Strategy September. [Online]
Available at: http://slideplayer.com/slide/4713303/
[Accessed 10 November 2016].

Melnyk, S., Stewart, D. & Swink, M., 2004. Metrics and performance measurement in operations management: Dealing with the metrics maze. Journal of Operations Management, 22(3), pp. 209-218.

Neely, A., 2007. Business Performance Measurement. Cambridge: Cambridge University Press.

Otley, D., 1999. Performance management: a framework for management control systems research. Management Accounting Research, Volume 10, pp. 363-382.

Parmenter, D., 2015. Key Performance Indicators: Developing, Implementing, and Using Winning KPIs. New Jersey: John Wiley and Sons inc.

Public Records Office Victoria, 2015. Guideline 3: Key Performance Indicators, Victoria: Victoria State.

Ranjan, J., 2009. Business Intelligence: Concepts, Components, Techniques and Benefits. Journal of Theoretical and Applied Information Technology, 9(1), pp. 60-70.

RocketMill.co.uk, 2015. Doing Measurement Frameworks The Right Way. [Online]
Available at: https://www.rocketmill.co.uk/doing-measurement-frameworks-the-right-way
[Accessed 26 April 2017].

Rodriguez, R., Saiz, J. & Bas, A., 2009. Quantitative relationships between key performance indicators for supporting decision-making processes. Computers In Industry, pp. 104-113.

Segen, 2011. Key performance indicator. [Online]
Available at: http://medical-dictionary.thefreedictionary.com/key+performance+indicator
[Accessed 22 November 2016].

Verweire, K. & Van Den Berghe, L., 2004. Integrated Performance Management. London: Sage Publications.

Waggoner, D., Neely, A. & Kennerley, M., 1999. The forces that shape organisational performance measurement systems: An interdisciplinary review. Int. J. Production Economics, Volume 60-61, p. 53—60.

 INTERVIEW PROFILES

Respondent Service
Respondent 1 Classification
Respondent 2 Customs Authority Request
Respondent 3 Preferential Origin
Respondent 4 Duty Optimization & Special Regimes

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