Ioannis Fotopoulos, Exploring product portfolio management processes in
generic pharmaceutical companies.
School of Social Sciences
Master of Business Administration
Postgraduate Dissertation
Exploring product portfolio management processes in generic
pharmaceutical companies.
Ioannis Fotopoulos
Supervisor: Dr Maria Argyropoulou
Patras, Greece, June 2018
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Ioannis Fotopoulos, Exploring product portfolio management processes in
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© Hellenic Open University, 2018
The content of this thesis/dissertation along with its results is owned by the Hellenic Open University and
his/her author, where each of them has the sole and exclusive right to use, reproduce, and publish it (totally
or partially) for educational or research purposes, with the obligation to make reference to the thesis’s title,
the author’s name and to the Hellenic Open University where the thesis / dissertation was written.
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Ioannis Fotopoulos, Exploring product portfolio management processes in
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Exploring product portfolio management processes in generic
pharmaceutical companies.
Ioannis Fotopoulos
Supervising Committee
Supervisor:
Co-Supervisor:
Dr Maria Argyropoulou
Dr Vasiliki Grougiou
Patras, Greece, June 2018
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ACKNOWLEDGEMENTS
I would like to express my sincere gratitude to my first supervisor, Dr. Maria
Argyropoulou. Her advice, her academic as well as practical guidance and genuine interest
through the whole preparation of the dissertation have helped me to develop my skills and
knowledge and successfully complete this endeavor. Moreover, I would like to thank my
second supervisor, Dr. Vasiliki Grougiou for her insightful comments.
I would also like to thank all the professional respondents that participated and
devoted some of their precious time for this dissertation, hoping to genuinely give back
new knowledge and insights to this industry.
Most of all I would like to thank my family, this dissertation is devoted to them. To
my beloved wife Tanya for all the patience and effort she had to exert in order to fill the
gap created by my absence and to my three wonderful children for their support and
understanding.
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Abstract
How to stay competitive in a fast-moving global market is a challenge many generic
pharmaceutical companies are faced with today. Generic pharmaceutical companies are
implementing Portfolio Management processes in order to better manage their new
product development projects from Idea-to-Launch and thus be successful. There are
various tools, risks, approaches and other aspects of Portfolio Management that generic
pharmaceutical companies must take into account in order to achieve this. Some
companies are better positioned and more mature regarding Portfolio Management and
thus receive more benefits.
In this research study, the main research question is what is the current level of project
portfolio management maturity among generic pharmaceutical companies and what is the
impact of the different PfM maturity levels to various PfM aspects.
The research was based in a quantitative study, using primary data obtained from
Portfolio Management professionals working in generic firms worldwide through a web
administered questionnaire.
According to the conclusions of this research better portfolio management does not
happen as a result of chance. High-maturity organizations have developed specific
portfolio management practices and decision-making capabilities that help them to be
successful. They have set up Project Management Offices, they used special computerized
systems, they use incentives, they incorporate risks, they use various tools to evaluate and
optimize the portfolios and they tend to rely less in non-rational decision making practices.
These high-maturity generic firms report greater benefits from their PfM processes.
Keywords
Product
portfolio
management,
generics,
pharmaceuticals,
project,
evaluation,
optimization
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Περίληψη
Σήμερα πολλές εταιρίες γενοσήμων φαρμάκων αντιμετωπίζουν την πρόσκληση του
να παραμείνουν ανταγωνιστικές σε μία ταχέως κινούμενη παγκόσμια αγορά. Οι εταιρίες
γενοσήμων φαρμάκων εφαρμόζουν διεργασίες Διαχείρισης Χαρτοφυλακίου ώστε να
διαχειρίζονται καλύτερα τα έργα ανάπτυξης νέων προϊόντων από την αρχική ιδέα εώς το
λανσάρισμα και έτσι να είναι επιτυχημένες. Υπάρχουν διάφορα εργαλεία, κίνδυνοι,
προσεγγίσεις και άλλες πλευρές της Διαχείρισης Χαρτοφυλακίου που οι εταιρίες
γενοσήμων φαρμάκων πρέπει να λάβουν υπόψη τους ώστε να το επιτύχουν αυτό. Κάποιες
εταιρίες είναι σε καλύτερη θέση και πιο ώριμες σχετικά με την Διαχείριση
Χαρτοφυλακίου και έτσι λαμβάνουν περισσότερα οφέλη.
Σε αυτην την έρευνα, το κύριο ερευνητικό ερώτημα είναι ποιο είναι το επίπεδο της
ωριμότητας σχετικά με την Διαχείριση Χαρτοφυλακίου των εταιριών γενοσήμων
προϊόντων και ποια είναι η επίδραση των διαφορετικών επιπέδων ωριμότητας Διαχείρισης
Χαρτοφλακίου στις διάφορες πλευρές της Διαχείρισης Χαρτοφυλακίου.
Η έρευνα βασίστηκε σε μια ποσοτική μελέτη, με την χρήση πρωτογενών δεδομένων
που προέκυψαν από επαγγελματίες Διαχείρισης Χαρτοφυλακίου που εργάζονται σε
εταιρίες γενοσήμων σε παγκόσμιο επίπεδο μέσω ενός ερωτηματολογίου που μοιράστηκε
μέσω διαδικτύου.
Σύμφωνα με τα συμπεράσματα αυτής της έρευνας η καλύτερη Διαχείριση
Χαρτοφυλακίου δεν συμβαίνει από τύχη. Οι οργανισμοί με υψηλή ωριμότητα έχουν
αναπτύξει συγκεκριμένες πρακτικές Διαχείρισης Χαρτοφυλακίου και δυνατότητες λήψης
αποφάσεων που τους βοηθούν να είναι επιτυχημένοι. Έχουν Γραφεία Διαχείρισης Έργων,
χρησιμοποιούν ειδικά λογισμικά προγράμματα, χρησιμοποιούν κίνητρα, ενσωματώνουν
τους κινδύνους, χρησιμοποιούν διάφορα εργαλεία για την αξιολόγηση και βελτιστοποίηση
των χαρτοφυλακίων και τείνουν να βασίζονται λιγότερο σε μη ορθολογικές πρακτικές
λήψης αποφάσεων. Αυτές οι υψηλής ωριμότητας εταιρίες γενοσήμων αναφέρουν
μεγαλύτερα οφέλη από τις διεργασίες Διαχείρισης Χαρτοφυλακίου τους.
Λέξεις – Κλειδιά
Διαχείριση
χαρτοφυλακίου
προϊόντων,
διαχείρηση,
γενόσημα,
φάρμακα,
έργο,
αξιολόγηση, βελτιστοποίηση
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Table of Contents
Abstract ................................................................................................................................. v
Περίληψη.............................................................................................................................. vi
Table of Contents ................................................................................................................vii
List of Figures ......................................................................................................................ix
List of Tables.................................................................................................................. xiiiiii
List of Abbreviations & Acronyms ..................................................................................xivv
Chapter 1. Introduction ......................................................................................................... 1
1.1 Introduction.................................................................................................................. 1
1.2 Background .................................................................................................................. 1
1.3 Aims and objectives ................................................................................................... 2
1.4 Research questions....................................................................................................... 3
1.5 Overview of the research ............................................................................................. 3
Chapter 2. Literature review ................................................................................................ 4
2.1 Portfolio Management ................................................................................................. 5
2.2 Portfolio Management in Pharmaceuticals ................................................................. 9
2.3 Portfolio Management in Generic Pharmaceuticals .................................................. 20
2.4 Literature Gap ........................................................................................................... 24
Chapter 3. Research questions and methodological deployment ...................................... 24
3.1 Key research questions .............................................................................................. 24
3.2 Methodology ............................................................................................................. 28
3.3 Research design ......................................................................................................... 28
3.4 Collection Of Primary Quantitative Data-Surveying techniques .............................. 29
3.5 Respondents’ profile ................................................................................................. 30
3.6 Condusting the research ............................................................................................ 30
3.7 Preparation of questionnaire ..................................................................................... 32
3.8 Method of Analysis ................................................................................................... 37
Chapter 4. Results .............................................................................................................. 42
4.1 Descriptive statistics.................................................................................................. 42
4.2 Inferential statistics ................................................................................................... 92
4.3 Conclusions and contribution to academia ............................................................. 110
4.4 Managerial implications ......................................................................................... 112
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4.5 Limitations ............................................................................................................. 113
4.6 Future research directions ...................................................................................... 114
Bibliography ...................................................................................................................... 115
Appendix A: “Questionnaire” ........................................................................................... 121
Appendix B: “ANOVA - PfM maturity – Alignment with corporate strategy” ............... 129
Appendix C: “MANOVA – PfM Maturity – tools to align strategy” ............................... 131
Appendix D: “MANOVA – PfM maturity – PfM outcomes (success)” ........................... 133
Appendix E: “MANOVA – PfM Maturity – tools to evaluate individual projects” ......... 137
Appendix F: “MANOVA – PfM Maturity – tools to prioritize projects and optimize the
portfolio” ........................................................................................................................... 148
Appendix G: “MANOVA – PfM Maturity - benefits of current PfM processes” ........... 149
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List of Figures
Figure 2.1: Portfolio Management conceptual framework for literature review
Figure 2.2: Portfolio relationships example
Figure 2.3: Schematic of Drug Development Process
Figure 2.4: Total R&D Pipeline size by year 2001-2017
Figure 2.5: Example decision trees
Figure 2.6: Example of bubble chart
Figure 2.7: Cumulative return vs. cumulative investment
Figure 2.8: PfM in fully integrated firms
Figure 2.9: Originator (NDA) versus Generic (ANDA) Review Process Requirements (in
the USA)
Figure 2.10: Selected pharma models
Figure 4.1: Respondents’ education level
Figure 4.2: Respondents’ professional seniority level
Figure 4.3: Respondents’ general pharmaceutical industry experience
Figure 4.4: Respondents’ experience in generic pharmaceuticals
Figure 4.5: Respondents’ experience in Portfolio Management
Figure 4.6: Respondents’ current organization size
Figure 4.7: Respondents’ current organization HQ location
Figure 4.8: Importance of PfM in achieving strategic objectives
Figure 4.9: PfM alignment with corporate strategy
Figure 4.10: Mean frequency of use of PfM and strategy alignment tools by descending
order
Figure 4.11: Frequency of use of Strategic buckets
Figure 4.12: Frequency of use of Strategic Roadmaps
Figure 4.13: Frequency of use of Strategic fit criteria
Figure 4.14: Frequency of use of “Gut feel” of senior decision makers
Figure 4.15: Mean frequency of data used in order to evaluate individual new projects
Figure 4.16: Frequency of use of revenues
Figure 4.17: Frequency of use of development (or in-licensing) cost.
Figure 4.18: Frequency of use of development (or in-licensing) time
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Figure 4.19: Frequency of use of cost of goods
Figure 4.20: Frequency of use of cost of marketing
Figure 4.21: Frequency of use of cost of capital
Figure 4.22: Mean frequency of risks considered in evaluating individual new projects by
descending order
Figure 4.23: Frequency of consideration of market risk
Figure 4.24: Frequency of consideration of intellectual property risk
Figure 4.25: Frequency of consideration of technical risk
Figure 4.26: Frequency of consideration of clinical risk
Figure 4.27: Frequency of consideration of regulatory risk
Figure 4.28: Frequency of consideration of supplier/partner
Figure 4.29: Mean frequency of tools used in order to evaluate individual new projects
Figure 4.30: Frequency of use of Discounted Cash Flow (DCF)
Figure 4.31: Frequency of use of Net Present Value (NPV)
Figure 4.32: Frequency of use of Internal Rate of Return (IRR)
Figure 4.33: Frequency of use of Return Of Investment (ROI)
Figure 4.34: Frequency of use of Profitability Index (PI)
Figure 4.35: Frequency of use of Technical Probability of Success (TPS)
Figure 4.36: Frequency of use of Commercial Probability of Success (CPS)
Figure 4.37: Frequency of use of Expected Net Present Value (eNPV)
Figure 4.38: Frequency of use of decision trees in project evaluation
Figure 4.39: Frequency of use of Real Options
Figure 4.40: Frequency of use of Expert Opinion
Figure 4.41: Frequency of use of “gut feel” of senior decision makers for project
evaluation
Figure 4.42: Mean frequency of use project prioritization and portfolio optimization tools
by descending order
Figure 4.43: Frequency of use project interdependencies evaluation
Figure 4.44: Frequency of use of simple checklists
Figure 4.45: Frequency of use of forced ranking models
Figure 4.46: Frequency of use of weighted scoring models
Figure 4.47: Frequency of use of bubble charts
Figure 4.48: Frequency of use of decision trees for portfolio optimization
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Figure 4.49: Frequency of use of Simulation Approach
Figure 4.50: Frequency of use of marginal analysis
Figure 4.51: Frequency of use of Analytical Hierarchy Process
Figure 4.52: Frequency of use of Sensitivity & scenario analysis
Figure 4.53: Frequency of use of efficient frontier
Figure 4.54: Frequency of use of Tornado Charts
Figure 4.55: Frequency of use of “Gut feel” of senior decision makers for portfolio
optimization
Figure 4.56: Mean frequency of approaches to review the portfolio in descending order
Figure 4.57: Frequency of use of periodical review approach
Figure 4.58: Frequency of use of Stage-gate or milestone review approach
Figure 4.59: Frequency of use of ad-hoc review approach
Figure 4.60: Frequency of use of combined review approach
Figure 4.61: Operation of a Project Management Office.
Figure 4.62: Overall level of PfM maturity
Figure 4.63: Mean of projects outcomes in descending order
Figure 4.64: Projects on time
Figure 4.65: Projects within budget
Figure 4.66: Projects meet goals and business intent
Figure 4.67: Means of benefits of PfM processes with descending order
Figure 4.68: Benefits of PfM processes – Common basis for discussion
Figure 4.69: Benefits of PfM processes – Focus on breakthrough projects
Figure 4.70: Benefits of PfM processes – Better strategic fit of portfolio
Figure 4.71: Benefits of PfM processes – Balance short term vs. long term projects
Figure 4.72: Benefits of PfM processes – Fewer but more worthwhile projects
Figure 4.73: Benefits of PfM processes – Improved times to market
Figure 4.74: Benefits of PfM processes – Provides unified support, better buy-in
Figure 4.75: Benefits of PfM processes – Improves strategic planning
Figure 4.76: Use of special computerized systems for PfM
Figure 4.77: Contribution of specialized computerized systems for PfM
Figure 4.78: Incentives structured on the project and portfolio level.
Figure 4.79: PfM maturity and strategic fit
Figure 4.80: PfM maturity and strategy tools – Strategic buckets
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Figure 4.81: PfM maturity and strategy tools – Strategic roadmaps
Figure 4.82: PfM maturity and strategy tools – Strategic fit criteria
Figure 4.83: PfM maturity and strategy tools - Gut feel
Figure 4.84: PfM maturity and PfM outcomes – Projects on time
Figure 4.85: PfM maturity and PfM outcomes – Projects within budgets
Figure 4.86: PfM maturity and PfM outcomes – Projects meet goals & business intent
Figure 4.86: PfM maturity and PfM outcomes – Projects meet goals & business intent
Figure 4.87: PfM maturity and PfM benefits – Common basis for discussion
Figure 4.88: PfM maturity and PfM benefits – Focus on breakthrough projects
Figure 4.89: PfM maturity and PfM benefits – Better strategic fit
Figure 4.90: PfM maturity and PfM benefits – balance short-term vs. long-term projects
Figure 4.91: PfM maturity and PfM benefits – Fewer but more worthwhile projects
Figure 4.92: PfM maturity and PfM benefits – Improved times to market
Figure 4.93: PfM maturity and PfM benefits – Provides unified support
Figure 4.94: PfM maturity and PfM benefits – Improves strategic planning
Figure 4.95: PfM maturity and PMO operation
Figure 4.96: PfM maturity and incentives
Figure 4.97: PfM maturity and use of special computerized systems
Figure 4.98: PfM maturity and size of organization
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List of Tables
Table 3.1: Key research variables and references summary
Table 3.2: Research questions summary table
Table 4.1: Chi-square test - PfM maturity and PMO operation
Table 4.2: Chi-square test - PfM maturity and incentives
Table 4.3: Chi-square test - PfM maturity and special computerized systems
Table 4.4: Chi-square test - PfM maturity and Organization size
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List of Abbreviations & Acronyms
DCF
Discounted Cash Flow
eNPV Expected Net Present Value
IRR
Internal Rate of Return
NCE
New Chemical Entity
NPD
New Product Development
NPV
Net Present Value
PI
Profitability Index
PfM
Portfolio Management
PMI
Project Management Institute
PMO
Project Management Office
ROI
Return On Investment
TPP
Target Product Profile
WHO World Health Organization
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Chapter 1. Introduction
1.1
Introduction
Organizations today operate in a highly complex and dynamic environment in
which the pace of change is faster than ever before. Technology, globalization, and
evolving customer behavior, force organizations to constantly strive for strategic and
operational excellence. In many cases the activities undertaken are in the form of
projects of various purposes like new product development or organizational reengineering.
In this environment, the management of a collection of projects which is called
portfolio management, is an increasingly critical component of success. Portfolio
management spans across multiple industries and is one of the major business
functions within an innovative firm. Over the last two decades academic research
related to project management, new product development management as well as
companies' management practices, have focused steadily on portfolio management.
However most companies suffer from too many projects and not enough resources
If not managed proficiently and in line with the firm’s strategy, the negative impact of
poor portfolio decisions can be significant.
Portfolio management (PfM) must help leaders avoid “missing the forest for the
trees.” With effective PfM processes organizations can align projects with overall
objectives, prioritize their projects, and organize their execution. In addition they can
identify the resources needed and assure they are appropriately allocated.
1.2
Background
The pharmaceutical industry leads all industries in terms of R&D spend Healthcare
demands are increasing due to growing and ageing populations and the rise of chronic
diseases. Total global pharmaceutical R&D spending is increasing year by year.
There is a broad consensus among pharmaceutical firms that successful portfolio
management of new drug projects is a necessary condition for long-term survival
Portfolio management in new drug development is extremely challenging due to long
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drug development cycles and high probabilities of failure. Decisions on how to allocate
resources to these projects in order to achieve the maximum returns are very
challenging. For example, decisions on how to evaluate the value and risk of each
project, how to choose new projects for both short-term cash flow and long-term
development, how to decide which projects to prioritize and which projects to remove
from the portfolio are crucial. As pharmaceutical companies are getting larger through
mergers and acquisitions and more global, this is creating a need for better control and
streamlining of processes.
During their development activities generic pharmaceutical companies do not need
to repeat risky and huge resource consuming activities like discovery, pre-clinical and
clinical studies as the innovative pharma companies. Different product development
times, portfolios that have 10 to 100 times more products, more aggressive and cost
based competition and different business models impact the new product development
decision making compared to innovative pharma companies. Generic pharmaceutical
companies of all sizes, which vary in a great extent, are implementing portfolio
management processes in order to optimize project and portfolio management in order
to launch and commercialize successfully new products that support their strategic
objectives and corporate growth. There are various tools, risks, approaches and other
aspects of PfM that generics companies must take into account in order to achieve this.
Some generic companies may be better positioned and more mature regarding PfM and
thus receive more benefits.
1.3
Aims and objectives
This general aim of this research is to explore the processes related to portfolio
management of new product development of generic pharmaceutical companies.
The specific objective of the study was to assess the current level of project
portfolio management maturity among generic pharmaceutical companies and to
explore how do the different PfM maturity levels relate to PfM aspects such as:
•
the strategic approaches to portfolio management
•
the strategic alignment tools used
•
the project evaluation and portfolio optimization tools used
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•
the outcomes of PfM processes
•
the benefits of the PfM processes
•
the use of Project Management Offices, specialized computer systems and
incentives
1.4
Research questions
The key research question of this dissertation is: what is the impact of the different
levels of overall PfM maturity of generic pharmaceuticals companies on various PfM
aspects. Specific research questions were developed and elaborated in Chapter 3.
1.5
Overview of research
The dissertation consists of four chapters.
Chapter 1 is the introductory chapter which includes a general introduction, the
background of the research, the research objectives and key research questions.
Chapter 2 offers the literature review organized in three sections Portfolio
Management, Portfolio Management in Pharmaceuticals, and Portfolio Management in
Generic Pharmaceuticals. It also includes the identified gap in literature.
Chapter 3 presents the research questions stemming from the key research questions
which are divided into three sections. The specific questions are built based on the
literature review. It also outlines the research methodology, analyzing the method used,
the instrument constructed as well as the data collection process and analysis approach.
Chapter 4 is about the results of the dissertation. It includes the descriptive statistics
of the data collected which provide useful first level conclusions. It also includes
inferential statistics related to the key research questions. Finally chapter 4 hosts the
conclusions of the dissertation, in respect to the impact of PfM maturity to the various
PfM aspects and processes. Contribution to academia, managerial implications and
limitations of the study are noted and suggestions for future research are made.
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Chapter 2. Literature review
This section provides an overview of the research topic in order to explain to the
reader the completed research performed by this time point, to identify various trends
in literature and show the connection with the questions and objectives related to the
specific research.
This dissertation is concerned with the Portfolio Management (PfM) area and
focuses on the implementation of PfM in pharmaceutical companies and especially
generics pharmaceuticals companies.
The following literature review is based on the conceptual model as depicted in
Figure 2.1 and is accordingly structured on the topics of Portfolio Management (PfM),
Pharmaceutical Portfolio Management and finally Generic Pharmaceutical Portfolio
Management.
Figure 2.1: Portfolio Management conceptual framework for literature review.
(Source: author)
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2.1 Portfolio Management
The term ‘‘portfolio’’ has come to represent different meanings, as it has been used
throughout many different organizations and for quite some time. For example if we
consider the financial industry, a portfolio is defined as a collection of investment
instruments (stocks, bonds, mutual funds, commodities, etc.) (PMI, 2005). For the
purpose of this research, the focus is only on project portfolio management related to
new product development (NPD).
Portfolio is defined as a collection of projects and/or programs (a group of related
projects managed in a coordinated way to obtain benefits and control not available
from managing them individually) and other work that are grouped together to
facilitate the effective management of that work to meet strategic business objectives.
Projects are temporary endeavors undertaken to create a unique product, service, or
result while programs are a group of related projects managed in a coordinated way to
obtain benefits and control not available from managing them individually (PMI,
2005),.
The projects or programs of a portfolio can be measured, ranked, and prioritized
while on the other hand there does not have to be a direct relation or dependence
between them. An illustrative example of the relationship of a portfolio and the
components of the portfolio are shown in Figure 2.2 (PMI, 2013)
Figure 2.2: Portfolio relationships example (reproduced from PMI, 2015)
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According to the definition from Cooper (2011 p. 232) “Portfolio Management is a
dynamic decision process, whereby a business´s list of active new-product (and
development) projects is constantly updated and revised. In this process, new projects
are evaluated, selected, and prioritized; existing projects may be accelerated, killed, or
de-prioritized; and resources are allocated and reallocated to active projects. The
portfolio decision process is characterized by uncertain and changing information,
dynamic opportunities, multiple goals and strategic considerations, interdependence
among projects, and multiple decision-makers and locations. The portfolio decision
process encompasses or overlaps a number of decision-making processes within the
business, including periodic reviews of the total portfolio of all projects (looking at all
projects holistically, and against each other), making Go/Kill decisions on individual
projects on an ongoing basis, and developing a new-product strategy for the business,
complete with strategic resource allocation decisions.”
Over the last two decades academic research related to project management, new
product development management as well as companies' management practices, have
focused steadily on portfolio management. As a result project portfolio management
has been consolidated in the form of global standards (PMI, 2013, ISO, 2015) as well
as practical tool books (Cooper et al., 2001) that may help companies organize and
implement their own project portfolio management. Many PfM practitioners rely on
the Project Management Institute (PMI) organization, which provides guidance and
frameworks (tools, techniques, and processes) for managing projects, programs and
portfolios as well as training and professional certification schemes (Jones, 2016).
Various project evaluation and decision criteria procedures and tools have been used
by organizations in order for them to standardize and formalize their project PfM
processes. (Martinsuo, 2013).
Portfolio management spans across multiple industries and is one of the major
business functions within an innovative firm. (Kester et al., 2011). Effective portfolio
management must be an integral part of the process in order to keep the right projects
in the pipeline, but most companies suffer from too many projects and not enough
resources (Cooper, 2011). If not managed proficiently and in line with the firm’s
strategy, the negative impact of poor portfolio decisions can be significant (Kester et
al., 2011).
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Companies are still having great difficulties in effectively sharing resources across
projects and keeping up with constant changes in their portfolios, even though there is
much guidance regarding ways to select and include projects to the portfolio, regarding
resource allocation, strategic alignment and assessing portfolio success. According to
Martinsuo (2013) the management practice (what managers actually do) and the
context and micro-level dynamics of PfM question the applicability of “traditional”,
rational decision making centered project portfolio management, particularly in rapidly
changing business environments.
Recently Meifort (2016) conducted a thorough review of pertinent literature in the
area of portfolio management by systematizing prior findings in a construct adopted by
Cooper et al. (1998) after evaluation of possible alternatives. The following main four
categories were established: optimization perspective, strategic perspective, decisionmaking perspective and organizational perspective, which are not integrated or
connected in most cases. Conceptual, qualitative and quantitative methods have been
used by researchers in order to provide empirical evidence related to portfolio
management, which will be discussed in the subsequent paragraphs.
2.1.1 Optimization Perspective
The previous research regarding PfM, from an optimization perspective, is
concerned with individual project evaluation, selection and prioritization in a
continuous portfolio value maximization process, aiming to have the best projects that
will lead to business success, in a constrained resources framework. The optimization
problem was commonly focused on decision events determining the portfolio contents,
using conceptual approaches illustrated by case studies or experiments (Meifort, 2016).
The identified challenges of models and tools used (e.g. discounted flow analysis, real
options analysis) were data requirements, analytical complexity and difficulty to
handle interdependencies between projects, time dependency of decisions or industry
specifics (Meifort, 2016). According to Cooper et al (1999) financial approaches are
the most popular and dominate the portfolio decision. But dubious results are achieved
via financial approaches. Benchmark businesses stand out from the rest as they place
less emphasis on financial approaches and more on strategic methods, and they tend to
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use multiple methods more so than the rest. Solely optimization approaches were
unable to address strategic needs. (Meifort, 2016).
2.1.2 Strategic Perspective
According to the strategic perspective, apart from the optimization approach, PfM is
also considered as a tool of implementation of corporate strategic plans, by allocating
resources to specific projects. In that sense, PfM is regarded as a strategic capability, a
source of competitive advantage, which must be overseen by top-level executives.
Empirical evidence from benchmarking studies identified four main goals of PfM
followed by successful firms: maximizing portfolio value, achieving balance between
projects, alignment with strategy and selecting the right number of projects. Empirical
results confirm that firms that have moved from purely financial to strategic PfM tools
are more successful. (Meifort, 2016).
2.1.4 Decision-Making Perspective
PfM is considered as dynamic decision process, involving decisions on which
opportunities to pursue, how to share resources across products and constantly
updating and revising the list of active NPD projects. Due to the fact that economic
estimates are uncertain and some contingencies are not known, resource allocation
decisions during PfM can be challenging. In addition, technological, market or
resource interdependencies between projects can be a major source of competitive
advantage (Meifort, 2016). Four key decision areas are distinguished: project selection,
project prioritization, resource allocation across projects and implementation of
business strategy (Cooper et al., 1998). Conceptual studies on both the decision
making stages and tools used as well as the dynamics of non-rational and deliberately
political dimensions, which appear to be typical for PfM decision-making, have been
conducted (Morcos, 2008; Kester et al., 2011). The effects of factors other than formal
PfM systems that influence choices made at portfolio meetings have not been
quantitatively investigated so far (Meifort, 2016). Kester et al. (2014) in one of the few
empirical quantitative studies in this section, find that a combination of effective
portfolio decision-making dimensions (i.e., portfolio mindset, focus, agility) positively
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impacts portfolio success and in turn market performance. Most prior research in the
decision-making perspective does not take into account that PfM is performed by
different parties in a complex network of relationships among multiple hierarchical
levels. These arguments point to the relevance of an organizational perspective on
PfM. (Meifort, 2016).
2.1.4 Organizational Perspective
PfM involves several decision makers and parties which belong to various levels of
the organizational matrix. Usually there is a committee that requires consensus among
a group of managers, because in most firms there is no single function responsible for
all new product development activities. Effective PfM processes require organizationwide information sharing and buy-in to avoid strong political and psychological
pressures. High-quality data on projects can only be gathered if multiple subunits and
hierarchical levels collaborate (Meifort, 2016). On one hand information availability
and project management efficiency at the project level by project and functional
managers (Martinsuo & Lehtonen, 2007) and on the other portfolio managers’
transformational leadership, positively impact the project success (Kissi et al., 2013).
A formalized process for single projects has a positive impact on project portfolio
management success.
Structured portfolio governance in general and Project
Management Offices (PMO) in particular increase project portfolio management
quality and in turn portfolio success. (Unger et al., 2012). According to a quantitative
survey by PMI (2015) on 466 project portfolio practitioners, the PfM maturity
correlates to portfolio and organizational success. Five maturity levels were defined:
Ad hoc, Getting Started, Structured and improving, Established and Optimized for
continuous improvement.
2.2 Portfolio Management in pharmaceuticals
Development of new drugs is a complex and costly process as it takes 10–15 years
and costs from US$800 million up to US$2 billion to get a new drug to market.
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Development of biopharmaceuticals (or else biologics) entails similar, or even higher,
costs. Research and Development involves drug discovery (preclinical studies) and
development (clinical studies) of New Chemical Entities (NCEs) or alternatively called
New Molecular Entities (NMEs) (Dunne et al, 2013). Out of 10,000 NMEs that are
initially investigated targeting a specific disease, about 250 might qualify for animal
testing and, of these, approximately 5 to 10 will make it for testing in humans (Dunne
et al, 2013). Experimental drugs, also known as an Investigational Medicinal Products
(IMPs), are first tested in in-vitro laboratory studies and in-vivo animal studies. If they
are successful, tests move to the clinical phase where the IMP will be used for the first
time in human clinical trial volunteers. According to statistical results about 19 to 30%
of Investigational Medicinal Products (IMPs) that begin Phase 1 trials make it to
marketing. Eventually 1–2 of the initial 10,000 NMEs will lead to a product entering
the markets (Dunne et al, 2013). Refer to Figure 2.3,
Figure 2.3: Schematic of Drug Development Process. (source Dunne et al, 2013)
Consequently a company developing innovative drugs typically has many projects,
several molecules that form a pipeline. In a drug development pipeline, new products
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in different phases of development form repeating sequences of similar projects. Single
projects are separate and focused to each individual drug, but the projects encompass
very similar activities that have to be repeated. The pipeline produces a drug portfolio,
which can include both approved drugs and candidate drugs. (Jekunen 2014). There is
a broad consensus among pharmaceutical firms that successful portfolio management
of new drug projects is a necessary condition for long-term survival (Ding, 2014).
In order to spread the risk of failure of projects Portfolios of pharmaceutical firms
usually include compounds in diversified therapeutic categories. The top 25 innovative
firms have between 66 and 251 compounds in their portfolio (PharmaProjects, 2017).
As of Feb 2017, there are 14.872 pipeline projects (Pharmaprojects, 2017) under active
development or launch. As shown in Fig. 2.4 there is vast increase during the last 5
years.
Figure 2.4: Total R&D Pipeline size by year 2001-2017 (reproduced from
Pharmaprojects, 2017).
According to Ding (2014) Pharmaceutical portfolio management is classified and
reviewed into two main areas: portfolio evaluation and portfolio optimization. During
portfolio evaluation specified metrics, such as value and risk, are used for measuring
the state of a portfolio. Portfolio optimization aims to fulfill the firm’s objectives
through the optimal selection of portfolio strategies.
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2.2.1 Portfolio Evaluation
2.2.1.1 Valuation of Individual Projects
The earliest PfM evaluation techniques applied in the pharma industry were based
on economic analysis according to Chapman and Ward (as cited by Blau et al., 2004).
A classical tool used is the discounted cash flow (DCF) analysis, which is calculated
by dividing projected annual earnings over an extended period by an appropriate
discount rate, which is the weighted cost of raising capital by issuing debt or equity.
The net present value (NPV) and internal rate of return (IRR) values can be calculated
in order to make decisions based on the cash flows which are dependent on various
parameters. For instance, factors like the cost of development (schedule, resource, and
cost), projected sales and revenue (market size and share, unit price, cannibalization),
recurring costs (cost of goods sold (COGS) and advertising) and the cost of capital are
often considered in the valuation process. (Ding, 2014).
However there is considerable uncertainty in all costs and revenue projections
(Ding, 2014). When only NPV is used for evaluation of project with comparable
returns but different levels of risk, the tool cannot distinguish between them and it also
fails to provide a cumulative measure of risk and returns at the whole-portfolio level
Evans et al. (2009).
Decision tree analysis is an effective tool used to illustrate R&D decision points, the
probabilities of uncertain outcomes at each milestone, and potentially resulting
decision options (Bode-Greuel & Nickisch, 2008). In Fig. 5, two example decision
trees are reproduced from Ding and Eliashberg (2002). The first tree shows a singlestage decision, while the second tree shows how a phased approach can account for
probabilities of success or failure along with the expected final payoff. Calculated
probabilities of success depend on the risks associated with each phase. The decision
which maximizes the expected (or probabilistic) net present value (eNPV) can then be
identified. (Ding, 2014).
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Figure 2.5: Example decision trees (reproduced from Ding and Eliashberg 2002)
Pharma portfolio managers rely heavily on expert opinion and in-house calculations
obtained by sensitivity and scenario analysis (Hartmann & Hassan, 2006). Expert
opinion is defined as a statement from a specialist on a particular subject. According to
Martino (1995) sensitivity and scenario analysis involve changing one or more of the
values supplied for the payoffs, costs, and probabilities, then rerunning the procedure
for selecting optimum portfolio.
The real options approach is used in capital market theory to determine valuation of
risky R&D projects. Real options are defined as the situation in which an investor can
choose between two different investments, where both choices are tangible assets
(Jones, 2016). It helps the pharmaceutical portfolio manager to factor in the potential
upsides of a drug investment that may not necessarily be predictable in advance. (Ding,
2014). According to Hartmann and Hassan (2006) real options pricing, have not seen a
high rate of adoption within the pharma industry.
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2.2.1.2 Valuation of Portfolios
Apart from the necessary valuation of individual projects, pharmaceutical firms also
need to measure the value of their portfolios. A common approach is to roll-up
individual project valuations into an aggregate valuation. Grewal et al. (2008) measure
the value of new drug portfolios using shareholder expectations derived from stock
market-based indicators. They argue that shareholders have positive expectations of
firms with higher portfolio breadth (number of different markets (therapeutic
categories) and a blockbuster strategy (Portfolio targeting a few diseases with high
expected market potential). For most firms, they find that the final stage of the drug
development process is most critical for shareholders to form their expectations and
portfolio depth (variation in the number of diseases targeted across therapeutic
categories) is usually de-emphasized. In general, the literature in the area of developing
suitable descriptors to measure market value of portfolios is sparse (Ding, 2014)
2.2.1.3 Portfolio Risk
Managers are also concerned with the risk associated with NPD projects and the
related possible outcomes in their portfolio. The classical measures of portfolio risk
originate from the financial economics literature and include Beta from the Capital
Asset Pricing Model (CAPM) and mean-variance (Ding, 2014). However Devinney
and Stewart (1988) argue that CAPM fails to capture projects interactions in a portfolio
and that risk and return of new products may be less related than in financial assets as
managers have more control over product development than financial assets.
Taggart and Blaxter (1992) have developed a methodology of assessing the risk
associated with a firm’s research portfolio by separating the technical risk (whether the
development product results in a marketable product) and market risk (whether the
new product can be introduced to a suitable market niche where it will generate the
required revenues) components.
Sax et al, (2015) highlight the clinical risks of
pharmaceutical portfolio management, which contribute to estimation of the
Probability of Technical Success. The clinical risks associated with NCEs developed
by innovative pharma are significantly greater compared to generic drug companies.
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Other indicative risk categorization from practitioners and consulting companies
(Steward-Long, 2016; Thakkar, 2015) includes technical, operational, regulatory,
commercial (competition), financial, resource/personnel, intellectual property, partner
/supplier/ subcontractor and launch (time to market) risks.
2.2.2 Portfolio Optimization
2.2.2.1 Strategic fit & Portfolio Balance
The portfolio must be aligned with the overall corporate strategy and priorities, so
that value is created from resources being appropriately deployed to maintain revenue
from marketed and future products (“value creation”). This includes both current and
future strategies. (Sax et al. 2015). In that sense alignment with the overall corporate
strategy (“strategic fit”) is included as a single project evaluation criterion in relevant
checklists and scoring models (Ding, 2014).
Srivannaboon, S. and Milosevic. Z. (2006) using a case-study methodology,
developed an empirically based theoretical framework to address the configuration of
PfM as influenced by the business strategy. Business strategy (low cost, differentiation,
or best cost) realizes its influence on PfM via the competitive attributes of the business
strategy (time-to-market, quality, and cost). In some organizations, roadmaps were
included in the strategic plan as the guidance for the company’s (or department’s)
future interests, such as a product roadmap and an information technology roadmap to
assist them in aligning PfM with Strategy.
The “big picture” view of the new drug portfolio and how it fits with corporate
objectives must be available to managers in order to define the appropriate balance
between incremental and radical innovation and having the right mix of short, medium,
and long-term developments. If short term goals of the portfolio take priority over
long-term investment in the development pipeline, it might cause erosion of the
strategic perspective and value in the future
Chao and Kavadias (2008) use a theoretical framework based on the strategic
buckets approach to examine the balance between incremental and radical innovation.
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Strategic buckets divide the R&D budget into smaller groups each of which is aligned
with a particular innovation strategy.
2.2.2.2 Optimal Project Selection and Prioritization
Once corporate strategy has defined the areas and types of projects that must be
included, then the selection among possible specific projects that can be resourced and
their relative prioritization must follow, as resources for all business cases is never
enough (Ding, 2014). There are many tools such as simple criterion checklists,
weighted scoring models, dynamic rank ordered lists and mapping tools (e.g., bubble
charts related to e.g. risk, reward, cost, probability of success) to guide managers and
their teams to make decisions about portfolio prioritization. (PMI, 2015). An example
of a bubble chart is shown in Figure 2.6.
Figure 2.6: Example of bubble chart (reproduced from Blau et al. (2004)
Loch and Kavadias (2002) used marginal analysis in a programming model in order
to demonstrate optimal selection across projects and resource allocation of a limited
budget.
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Other researchers used decision trees for project selection and sequencing using
analytical or simulation approaches (Ding, 2014). Dahan and Mendelson (2001)
conclude that the optimal number of projects for a pipeline is the ratio of the scale
parameter of profit uncertainty to the cost per project, which means that greater profit
uncertainty or lower cost per project drive a fatter pipeline.
According to Ding and Eliashberg (2002), the pipeline with optimal number of
projects at each stage is determined by the cost of developing a project, its success
probability and its expected reward. Comparing their normative results with empirical
practice data, they find that firms tend to have fewer projects in their pipelines than the
optimal structure, according to comparison with empirical data. However, even if the
optimal number of projects in the pipeline is determined, a sequencing of funding these
projects may be needed as resources are usually scarce (Ding, 2014)
According to Bilyard and Markland (2008), optimizing the portfolio by resource
and return is typically done by ranking projects in terms of some productivity measure
(e.g., return divided by investment and then plotting the cumulative return vs. the
cumulative investment). This produces a so-called efficient frontier. Figure 2.7 shows
two such plots—the first represents the existing portfolio and the second represents an
enhanced portfolio based on improved development strategies for each of the projects.
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Figure 2.7: Cumulative return vs. cumulative investment (reproduced from Bilyard
K. and Markland D., (2008))
Blau et al. (2004) propose a simulation-based approach to selecting sequences of
projects in a portfolio, which maximizes the expected economic returns for a given
level of risk and budget. They do not obtain closed form optimal solutions, but
demonstrate an improvement of 28 % in expected return using the simulation approach
as compared to a traditional bubble chart approach, which however think it is very
useful in general practice. They have also included in their simulation model the
internal interactions among projects.
The importance of considering project interdependencies in portfolio selection
decisions has been recognized (Ding, 2014). (Gear and Cowie, 1980 as cited by Ding,
2014) identified both internal and external interactions. Internal interaction exists when
the resource requirements and benefits of a project are impacted (in magnitude and/or
timing) by the selection or rejection decisions of other projects.
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2.2.3 Portfolio Execution Issues
While accurate portfolio evaluation and effective portfolio optimization is necessary
for developing a successful new drug portfolio, execution is equally important as it
translates the strategy to actions.
According to Bode-Greuel and Nickisch (2008), a typical pharmaceutical PfM
process includes the evaluation of development milestones and probabilities (decisiontree meetings). A typical PfM process is displayed in Figure 2.8.
Figure 2.8: PfM in fully integrated firms (reproduced from Bode-Greuel &
Nickisch, 2008)
Bode-Greuel (2008) identified four common tools that are applied to align
pharmaceutical project management with portfolio decisions: target product profile
(TPP), a stage-gate decision process, timeline and budget management, and sales
forecast aligned with TPP and development plan. A target product profile (TPP) serves
as a blueprint of the desired future product. The stage-gate decision process is related
to the major preclinical and clinical development milestones and is also a well-
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established principle in the pharma industry (Bode-Greuel & Nickisch, 2008). At each
stage-gate, it is decided whether the achieved results support continuation of
development, and the project may be reprioritized depending on other projects
competing for resources (Bode-Greuel & Nickisch, 2008). According to Datamonitor
(2003) there could be milestone-triggered reviews (during stage-gates) or periodic
portfolio reviews, reactionary ad-hoc reviews or combining review processes.
According to Ding (2014) firms should carefully consider the history of changes
made in the portfolio, to assess whether further change is likely to help or hinder
overall performance. A balance needs to be struck between very infrequent portfolio
rebalancing (not reacting enough to changes in the economic environment) and overly
frequent rebalancing (comes at a cost).
Incentives affect how organizational strategies are carried out by the people tasked
with execution. Firms need to ensure that those responsible for strategic choices and
executing on them are rewarded appropriately for their decision making, especially in
the high risk world of new drug portfolios. Szydlowski (2012) (as cited by Ding, 2014)
suggests that performance-related bonuses at the project level lead to more optimal
managerial behavior than issuing firm-level equity in the form of shares.
2.3 Portfolio Management in generic pharmaceuticals
Generic medicines can be produced by manufacturers, other than the original
innovator (patent-holding) company, when the original patent of the originator drug
has expired (Dunne et al, 2013). The term “generic drug” or “generic medicine” can
have varying definitions in different market. The definition by the World Health
Organization (WHO) means “a pharmaceutical product which:
– is usually intended to be interchangeable with an innovator product,
– is manufactured without a license from the innovator company, and
– is marketed after the expiry date of the patent or other exclusive rights”
The main difference with originators regarding product development is that it is not
necessary for generic drug manufacturers to repeat discovery, pre-clinical and clinical
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studies,
but bioequivalence to the originator or “reference” medicine must be
demonstrated, where necessary (Figure 2.9). Bioequivalent drugs are pharmaceutical
equivalents whose rate and extent of absorption are not statistically different when
administrated to patients or subjects at the same molar dose under similar experimental
conditions Dunne et al (2013)
Figure 2.9: Originator (NDA) versus Generic (ANDA) Review Process
Requirements (in the USA) (reproduced from Dunne et al, 2013)
Development costs, associated complexities, failure risks and required time for
bringing a generic product to market are thus significantly lower. Variations in generic
medicine prices are mostly shaped by local regulations and reimbursement
arrangements that may, in some cases, be disassociated from the costs of manufacture
and distribution. Fierce competition in the sector is putting further pressure for
reducing prices (Dunne et al, 2013). On the other hand, as both originators and
generics are manufactured under the same industry standards and conditions, the cost
of manufacturing will probably be similar, with minor differences (Dunne et al, 2013).
According to Sommerfeld (2007) the go-to markets for generics and innovative
drugs are very different. Different product development times, portfolios that have 10
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to 100 times more products, more aggressive and cost based competition. Generic
R&D is focused on achieving numerous product approvals ready to launch on the day
of patent expiry at low risk (which means high quality registration files used in various
submissions). The business models of generics are different and more complex than
that of the classic R&D pharma model (Figure 2.10), leading in various strategic routes
which shape portfolio decisions.
Figure 2.10: Selected pharma models (reproduced from Sommerfeld, 2007)
Moreira and Cheng (2010) present the results of a research case study of four
different Brazilian generic drug companies in selecting and prioritizing their new
product development projects. The results of the field study confirmed that these
companies had a non-structured Product Development System. The research identified
key criteria for the selection of projects of new pharmaceutical products, a managerial
standard for application of New Product Portfolio Management was proposed, without
further empirical validation.
Suchak and Murray (2016) address the key considerations of portfolio management
of generic medicines. The authors are portfolio management professionals in Sandoz,
one of world’s largest generic companies. The role of strategic focus covering the
entire product selection process is emphasized. Company’s strengths and competitive
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advantage should be taken into account and a risk based process implemented. The
factors discussed based on Market dynamics, Technical risks, Partner/Supplier risks,
Intellectual Property and Regulatory Risks. Finally quantitative modeling activities are
discussed like using standard financial criteria like NPV, IRR and “risk weight”
adjustments. Value and risk can be mapped (bubble charts) per product for evaluation.
“Stages gates” at pre-planned points are proposed for continuous review.
According to Shargel and Kanfer (2005) the main driving force for the selection of
generic drug products for manufacture is the estimated sales volume and the potential
market share. Patent and legal considerations are also very important. The generic drug
manufacturer needs to consider the lead time that is needed to develop the product and
submission for approval as well as knowledge of anticipated competitors. The
availability of technology and the cost of acquiring technology to manufacture the
product will also impact on the choice of generic drug. Formulation considerations as
well as experience with certain drug product dosage forms will also affect the choice of
generic drug product development. Niche drug products, such as transdermal drug
products, may be difficult to make and also riskier, but may have a greater financial
reward due to less competition from other generic drug firms (Shargel and Kanfer,
2005).
Weyand (2006) investigates the advantages, disadvantages and the factors of
decision making regarding the choice to either internally develop or to in-license
generic pharmaceutical products, which should be taken into account by Generic
companies during NPD Portfolio Management.
Other empirical indications regarding PfM processes in generic drug companies
come from Steingrimsdottir (2017) who investigates the potential benefits from the use
of specialized software (Planisware) for Pharmaceutical Project Portfolio Management
in Actavis, one the largest generics companies worldwide. The dissertation provides
useful insights like specific “stage gate” points for decisions, evaluation factors like
strategic fit, risk, investment, value, technical difficulties, financial evaluation (NPV,
IRR, COGS, discount rate), business case preparation. Jósepsson (2017) has performed
a study which focuses on tools for performing project prioritization and especially
focus on generic product launches and their success taking limited resources into
account. Methods mentioned are Analytical Hierarchy Process and Truth table. Product
case studies are used from MEDIS generics, a TEVA owned pharmaceutical company.
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2.4 Literature gap
Considering the differences in size, R&D processes and business models between
innovative pharmaceutical companies and generic pharmaceutical companies, it is
reasonable to question whether the portfolio management processes are diversified in
terms of approaches, evaluation criteria and tools used and other operational issues.
Almost all industry specific literature is based on the innovative pharmaceutical sector,
while on the other hand the academic literature related to generic drug companies is
scarce and there is no empirical verification of the level of PfM maturity, the data
taken into account, the tools that are used, their relative importance, and other elements
of PfM identified by the general or innovative pharma sector.
There is no quantitative empirical research related to exploring NPD portfolio
management processes of generic companies and more specifically for assessing the
current level of project portfolio management maturity among generic pharmaceutical
companies and the impact of different PfM maturity levels to various PfM aspects.
Chapter
3.
Research
Questions
and
Methodological
Deployment
3.1 Key research questions
Following the literature review and in order to meaningfully establish the specific
research questions stemming from the key research question about PfM maturity, a
structured approach was taken based on the fundamental perspectives of PfM identified
by Meifort (2016), namely the strategic perspective, the decision making/optimization
perspective and the organizational/execution perspective.
3.1.1 Portfolio Management strategic perspective in generic pharmaceuticals
According to the strategic perspective PfM is considered as a tool of
implementation of corporate strategic plans, by allocating resources to specific
projects. The portfolio must be aligned with the overall corporate strategy and
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priorities, so that value is created from resources being appropriately deployed to
maintain revenue from marketed and future products (“value creation”) (Sax et al.
2015).
Strategic alignment tools used (strategic buckets, strategic roadmaps and strategic fit
criteria) were identified in literature review. In addition firms that have moved from
purely financial to strategic PfM tools are more successful (Meifort, 2016). Companies
with high-maturity portfolio management are far more likely to indicate that their
processes help them link projects in the portfolio to strategic initiatives (PMI, 2015).
In light of the above analysis we frame the following research questions:
R1: What is the effect of the different levels of overall PfM maturity of generic
pharmaceuticals companies on the alignment of PfM with corporate strategy?
R2: What is effect of the various levels of overall PfM maturity of generic
pharmaceuticals companies on the frequency of the various tools used to align with
corporate strategy?
3.1.2 Portfolio Management optimization and decision making perspective in
generic pharmaceuticals
According to the literature review there are various individual project evaluation
tools which are purely financial (DCF, NPV, IRR, ROI, PI) and others which
incorporate the identified uncertainties and risks (Technical Probability of Success,
Commercial Probability of Success, eNPV, Decision Trees, Real Options, Expert
Opinion). According to Cooper et al (1999) financial approaches are the most popular
and dominate the portfolio decision. But benchmark businesses stand out from the rest
as they place less emphasis on financial approaches and more on other methods, and
they tend to use multiple methods more so than the rest. In addition there are various
portfolio prioritization and optimization tools which are relatively simple (simple
criterion checklists, forced ranking, weighted scoring models, bubble charts) or more
advanced (project interdependencies evaluation, Decision Trees, Simulation Based
Analysis, Marginal Analysis, Analytical Hierarchy Process, Sensitivity and Scenario
Analysis, Efficient Frontier Analysis, Tornado charts).
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In light of the above analysis we frame the following research questions:
R3: What is the effect of the various levels of overall PfM maturity of generic
pharmaceuticals companies on the frequency of the various tools used to evaluate
individual projects?
R4: What is effect of the various levels of overall PfM maturity of generic
pharmaceuticals companies on the frequency of the various tools used to prioritize
and optimize the portfolio?
3.1.3 Portfolio Management organizational and execution perspective in generic
pharmaceuticals
According to PMI (2015), the overall PfM maturity correlates to portfolio success.
The research survey used the PMI (2015) five levels in order to measure the PfM
maturity: 1) Ad hoc, 2) Getting Started, 3) Structured and improving, 4) Established
and 5) Optimized for continuous improvement. The research survey also used the PMI
(2015) criteria for measuring Portfolio success through the following outcomes: 1)
timely completion of projects, 2) projects completed within budget and 3) projects
meeting goals and business intent. The expected benefits of portfolio management
were studied by Cooper (2001). The unaided answers were: 1) Creates a common
basis for discussion, discipline & consistency, 2) Helps us to focus on major projects,
breakthrough projects, 3) Leads to better strategic fit (of the portfolio), 4) Provides
balance between short and long term projects, 5) Helps us to concentrate on fewer but
more worthwhile projects, 6) Achieves improved times to market, 7) Unified support
& creates better buy in and 8) Improves strategic planning.
In light of the above analysis we frame the following research questions:
R5: What is the effect of various levels of overall PfM maturity of generic
pharmaceuticals companies on project portfolio success?
R6: What is the effect of the various levels of overall PfM maturity on the various
benefits of PfM processes?
According to (Meifort, 2016) Project Management Offices (PMO), increase project
portfolio management quality and in turn portfolio success. The question is whether
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Project Management Offices are associated mostly with increased PfM maturity
companies.
In light of the above analysis we frame the following research question:
R7: What is the relationship of overall PfM maturity in generic pharmaceuticals
companies and operation of PMOs?
Steingrimsdottir (2017) mentions the use and positive contribution of special
computerized systems to pharmaceutical PfM. The main advantage according to
scholars and supported by this research results, is the integration of the portfolio
management processes with the Idea-to-Launch process and the automation in the PPM
tool. However in what percentage do generic pharmaceutical companies use
specialized PfM software and are they mostly in high PfM maturity companies?
In light of the above analysis we frame the following research question:
R8: What is the relationship of overall PfM maturity in generic pharmaceuticals
companies and operation of special computerized systems?
Szydlowski (2012) (as cited by Ding, 2014) suggests that performance-related
bonuses at the project level lead to more optimal managerial behavior. However do
these bonuses exist mainly in high PfM maturity companies or are they part of industry
practice for all organizations?
In light of the above analysis we frame the following research question:
R8: What is the relationship of overall PfM maturity in generic pharmaceuticals
companies and the use of incentives structured on project and portfolio level?
And the final research question is whether bigger organizations are more mature in
relation to PfM processes. There is no such bibliographical reference and thus would
provide a useful insight on the topic. In light of the above analysis we frame the
following research question:
R9: What is the relationship of overall PfM maturity in generic pharmaceuticals
companies and the size of the organization?
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3.2 Methodology
According to Jonker & Pennink (2010) the word methodology is derived from the
Greek ‘meta hodos’ meaning ‘the way along which’. In more everyday language it
means ‘. . . a system of methods and principles for doing something’. A methodology
assumes there is a logical order the researcher needs to follow in order to achieve a
certain predetermined result (e.g., knowledge, insight, design, intervention, change)
(Jonker & Pennink, 2010).
A methodology does not specify all individual steps but rather indicates the main
path to the end point. Methodology helps make the research approach visible to
readers. Methods and techniques on the other hand prescribe what one should do (or
not) in a specific situation or a particular moment in time (Jonker & Pennink, 2010).
3.3 Research Design
A research design describes a flexible set of guidelines that connect theoretical
paradigms to strategies of inquiry and methods for collecting empirical material
(Denzin and Lincoln 1994, p. 14).
According to the research questions developed in the previous section, it is apparent
that the research has a descriptive as well as an explanatory (non-experimental)
approach. Explanatory research is a study that goes beyond description and attempt to
explain the reasons for the phenomenon. In an explanatory study, the researcher uses
theories or at least hypothesis to account for the forces that caused a certain
phenomenon to occur (Jonker and Pennink, 2010).
A first decision regarding research design is whether the research will be a
quantitative or qualitative. Qualitative is the empirical research in which the researcher
explores relationships using textual, rather than quantitative data. Case study,
observation, and ethnography are considered forms of qualitative research. Results are
not usually considered generalizable, but are often transferable. Quantitative is the
empirical research in which the researcher explores relationships using numeric data.
Survey is generally considered a form of quantitative research. Results can often be
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Ioannis Fotopoulos, Exploring product portfolio management processes in
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generalized, though this is not always the case. Theory is most often translated into a
conceptual model and elaborated predominantly by means of hypotheses. For the
researcher conducting quantitative research implies carefully operationalising a theory
and subsequently measuring it by means of variables and questions. (Jonken and
Pennink, 2010).
The distinction between quantitative versus qualitative research actually deals with
the distinction between closed and open questions, between testing and discovering or
between positivism and constructivism (Jonker and Pennink, 2010). According to
Jonker and Pennink (2010) a closed question is referring to the validity of existing
theory indicates the need for quantitative research while qualitative research is
considered as more appropriate to open questions.
The aim of this dissertation is to study what are the current practices of generic
pharmaceuticals companies with respect to new product Portfolio Management. This
general question seems to be open however the specific questions as well as the
hypotheses developed during the structural examination of the main research question
reveal that the questions developed are actually closed. There is a degree of knowledge
re-structuring and uniqueness in the combination of the specific PfM topics covered
but as long as these are a result of testing existing suggestions and findings, although in
a specific industry section, the research question may be considered as a closed one.
Therefore, a quantitative, research seems to be the appropriate strategy for this study.
In case there are contradictory results these can indicate questions for future research,
either quantitative or pure qualitative based on concepts generated due to these results.
(Jonker and Pennink, 2010).
3.3 Collection of primary quantitative data-surveying techniques
The second decision regarding methodology is whether the source of data for the
quantitative research would be primary or secondary. Although the internet has
undoubtedly made accessing some secondary data easier, the use of secondary data is
not selected because during the literature review no databases or surveys were found
which could provide data related to the research question. In addition any secondary
data present in companies’ databases and archives would might be non accessible due
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to confidentiality business reasons or the time needed to gain access would out of the
time limits of this study. On the contrary primary data can provide the exact and
current information required to answer the questions and test the hypotheses developed
and therefore is selected as the data source.
The survey is one of the most frequently used methods in quantitative research for
collecting primary data. The survey provides the ability to collect large amounts of
standardized, comparable data in an economical and efficient way (Saunders, et al.,
2009, p. 144).
Taking into account the time and resource constraints of this
dissertation as well as the fact that other similar studies on the topic of PfM practices
used the survey as a technique (Cooper, 2001; PMI, 2015), the use of the survey
method for the purposes of the current study is considered as a safe approach.
3.5 Respondents’ profile
The target profile of prospective respondents was determined in order to assure the
reliability and validity of answers. “Portfolio Management related professionals in
Generic pharmaceutical companies” was the general description established as the
target profile. Portfolio related professionals would either have a job title and
description clearly including portfolio management or would be Marketing or Senior
Executive Management professionals related to PfM operations. The seniority level of
professionals could span from specialist, to managers, directors and up to C-level
executives in order to be able to gain the opinions and perspectives of practitioners as
well as senior decision makers. The professionals’ current organization would ideally
have global dispersion and size.
3.6 Conducting the research
3.6.1 Ethics
The general ethical issue of the research design should not subject the research
population to embarrassment, harm or any other material disadvantage (Saunders, et
al., 2009, p. 191). For this study the ethical issues are mostly related to the confidential
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nature of the information provided and the guaranty of anonymity of the organizations
and individual participants during and after the primary data collection.
The
respondents should be properly informed before participating in the survey through an
official sheet and the anonymity assured with appropriate means.
3.6.2 Participant Information sheet
In order to comply with the ethics principles and ensure an acceptable number of
respondents, a participant information sheet was prepared and approved by the
responsible supervising academic tutor.
The questionnaire included the text in its first part after the title and before the
questions and answers section, explaining the academic purpose of the study, reassuring about the anonymity of the responses and stressing the confidential treatment
of the data. Moreover it included an advance thanking note to the respondents. See
Appendix A.
3.6.3 Duration – reminders - responses collected
The questionnaire’s link was sent to the employees of generic pharmaceutical
companies matching the desired respondents’ profile. In order to increase the response
rate the link was also posted to specific Project and Portfolio Management groups in
LinkedIn professional social website. The posts included a note stating that the
questionnaire should be completed only by employees of generic pharmaceutical
companies with experience in Product Portfolio Management.
After one week to ten days of first completion request, the prospective respondents
were kindly reminded through a follow up message to complete the questionnaire.
The period of completion was between April and May 2018 and the final result was
seventy two (72) completed and usable questionnaires. No questionnaires were rejected
as according to the respondents profile as they all had experience in generics and
portfolio management and would thus be reliable for the relevant answers.
A specific response rate cannot be calculated as the link was also posted on
professional groups where the actual number of prospective respondents cannot be
estimated.
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Two (2) respondents raised confidentiality reasons for not answering the
questionnaire.
3.7 Preparation of the questionnaire
3.7.1 Questionnaire Design and Platform
For the collection of primary data through a self-administered questionnaire was
constructed. The self-administered questionnaire was created and hosted using the
Google Forms tool. One of the reasons for selecting an online surveying tool is that it
allowed for a fast and extensive reach of respondents in a global scale. Secondly, it
promoted the anonymity assurance as there was no way to find out the respondent’s
identity, which in turn decreased the risk of the respondents giving socially desirable
responses (Saunders, et al., 2009, pp. 363-365). Moreover, the self-administered
attribute of the questionnaire helped in avoiding any observer errors and biases, an
especially important issue for this study, given the professional occupation of the
writer within a generic pharmaceutical company. It has to be noted though that
according to Cook et al. (2000) web-based questionnaires have generally lower
response rates compared to other methods.
3.7.2 Questionnaire Length
According to Edwards et al. (2002) shorter questionnaires have better response rates
than longer ones while approximately 13 minutes is considered as a reasonable time
that respondents are willing to devote on the filling of a questionnaire (Handwerk, et
al., 2000)
Taking the above findings into consideration, the questionnaire included 20
questions and consisted of four parts, forming four successive pages. The average time
needed to complete it was about 6 minutes, as measured on its testing by the pilot
testers. Extensive use of matrix questions was adopted in order to save space in the
questionnaire
after
proper
consideration
of
presentation
attractiveness
and
disambiguation avoidance.
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3.7.3 Questions
In order to be able to easily analyze data all the questions used were closed. In
addition according to Saunders et al. (2009) close-ended questions are easier to
complete by respondents and thus lead to increased response rates. Most of the
variables used were supported by pertinent theory.
The questionnaire is presented in Appendix A. The first part of the questionnaire
after the participant information sheet consisted of the multiple choice category
questions about the respondents’ and their organizations’ attributes. More specifically:
Question 1 aimed to record the respondent’s highest level of education received
(e.g.Bsc, Msc etc). Question 2 aimed to record the respondents’ professional seniority
level (e.g. Supervisor, Manager, etc). Questions 3, 4 and 5 aimed to record how many
years of experience do the respondent has in Pharmaceuticals (in general), in generics
and in Portfolio Management accordingly. Questions 1 to 5 aimed to provide the
respondent’s profile which would be used both for validity and reliability reasons as
well as for inferential statistical analysis purposes. Question 6 aimed to record the size
of the respondents’ current organization. The size of the organization was decided to
be determined only with number of employees, as other classification criteria like sales
volume, might not be available and thus hinder the questionnaire responses. The
company classification according to the number of employees was based on the
LinkedIn’s classification range and related information, in order to facilitate easily
accessible and reliable data collection. Question 7 aimed to record the headquarters’
location of the respondent’s current organization.
Question 8 aimed to record the opinions of the respondents regarding Strategy and
PfM. A rating category was preferred, using a 5-point Likert scale to measure the level
of agreement with the proposition that Portfolio Management is very important in
achieving an organization’s strategic objective (Question 8.1) and whether PfM is
aligned with corporate strategy in their current organization (Question 8.2). In
continuation question 9 using a 5-point Likert scale (and a “I do not know” option) in a
matrix format aimed to measure the frequency of use of different strategy alignment
tools (9.1 to 9.4) found in literature as well as the involvement of non rational decision
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making of senior managers. The strategy tools listed were collected from the literature
review.
Question 10 using a using a 5-point Likert scale (and a “I do not know” option) in a
matrix format aimed to measure the frequency of use of essential data found in
literature (Questions 10.1 to 10.6) for the evaluation of individual new product projects
like projected sales, development costs, cost of capital etc. The data listed were
collected from the literature review.
Question 11 using a using a 5-point Likert scale (and a “I do not know” option) in a
matrix format aimed to measure the frequency of use of risk assessment of various
factors found in literature (Questions 11.1 to 11.6) for the evaluation of individual new
product projects like intellectual property risk, technical risk etc.
Question 12 using a using a 5-point Likert scale (and a “I do not know” option) in a
matrix format aimed to measure the frequency of use of various tools found in
literature (Questions 12.1 to 12.12) for the evaluation of individual new product
projects like intellectual NPV, ROI, Decision trees, simulation etc.
Question 13 using a using a 5-point Likert scale (and a “I do not know” option) in a
matrix format aimed to measure the frequency of use of various tools found in
literature (Questions 13.1 to 13.13) for the prioritization of projects and portfolio
optimization like intellectual bubble charts, analytical hierarchy process, efficient
frontier etc.
According to Martinsuo (2013) the management practice (what managers actually
do) and the context and micro-level dynamics of PfM question the applicability of
“traditional”, rational decision of making centered project portfolio management,
particularly in rapidly changing business environments. For this reason in Questions 9,
12 and 13 one of the available tools mentioned was the “Gut feel of senior managers”
in order to measure the non-rational and deliberately political dimension, following the
example of the survey of PMI (2015).
Question 14 using a using a 5-point Likert scale (and a “I do not know” option) in a
matrix format aimed to measure the frequency of use of various PfM review methods
found in literature (Questions 14.1 to 14.4) like periodic portfolio, stage gate and adhoc reviews. The methods listed were adopted from Datamonitor (2003).
Question 15 aimed to record whether there is a Project Management Office
operating or not in the current organization (including an “I do not know” option).
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Question 16 using a 5-point category multiple choice aimed to record the opinion of
the respondents regarding the maturity level of their current organization in respect to
PfM, choosing between “ad-hoc” up to “Optimized for continuous improvements”.
The questions where adopted from the PMI (2015) survey.
Question 17 using a 5-point Likert scale in a matrix format aimed to measure
portfolio success by recording project outcomes in current organizations regarding
timely completion of projects, completion within budget and whether projects meet the
business intent. The questions where adopted from the PMI (2015) survey.
Question 18 using a using a 5-point Likert scale in a matrix format aimed to
measure opinions on the benefits of PfM processes in current organizations regarding
various. The perceived benefits listed where adopted from Cooper (2001).
Question 19 using a multiple choice format (including an “I do not know” option)
aimed to record the use (or not) of specialized computerized systems like Planisware
etc. In parallel it also aimed to record using a 4-point Likert scale the perceived
contribution, if such a system was used.
Finally question 20 using a multiple choice format (including an “I do not know”
option) aimed to record the use (or not) of incentives structured specifically to the
project and portfolio level.
In general all Likert scales were symmetric in terms of positive and negative
options. Table 3.1 summarizes the key research variables along with references to
related authors.
Table 3.1: Key research variables and references summary
Variable
Author(s)
PfM Maturity
PMI (2015)
Alignment of PfM with Corporate strategy
Sax et al. 2015
Tools used to evaluate individual projects
•
DCF
Ding, 2014
•
NPV
Ding, 2014
•
IRR
Ding, 2014
•
ROI
Ding, 2014
•
Profitability index
Ding, 2014
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•
Technical Probability of Success
Bode-Greuel & Nickisch, 2008
Planisware (2018)
•
Commercial Probability of Success
Planisware (2018)
•
eNPV
Ding, 2014
•
Decision tree (project)
Bode-Greuel & Nickisch, 2008
•
Real Options
Hartmann and Hassan, 2006
•
Expert opinion
Martino, 1995
•
Gut feel - project
PMI, 2015
Tools to prioritize projects and optimize the portfolio
•
Project interdependecies
Gear and Cowie, 1980
•
Simple checklists
PMI, 2015
•
Forced ranking models
PMI, 2015
•
Weighted scoring models
PMI, 2015
•
Bubble charts
PMI, 2015
•
Decision trees -optimization
Ding and Eliashberg, 2002
•
Simulation approach
Blau et al., 2004
•
Marginal analysis
Loch and Kavadias, 2002
•
Analytical Hierarchy Process
Jósepsson, 2017
•
Sensitivity & scenario analysis
Martino, 1995
•
Efficient frontier
Bilyard and Markland, 2008
•
Tornado charts
Planisware, 2018
•
Gut feel - optimization
PMI, 2015
Tools used to align PfM with corporate strategy
•
Strategic buckets
Chao and Kavadias, 2008
•
Strategic roadmaps
Cooper and Edgett, 2010
•
Strategic fit criteria
Ding, 2014
•
“Gut feel” of senior decision makers
PMI, 2015
Outcomes of PfM
(Projects on time, Projects within Budget, Projects meeting
goals and business intent)
Benefits of PfM
(Common basis for discussion, Focus on breakthrough
projects, Better Strategic fit of portfolio, Balance short-term
vs. long-term projects, Fewer but more worthwhile projects,
Improved times to market, Provides unified support, Improves
strategic planning)
PMI, 2015
Cooper (2001)
Operation of Project Management Office (PMO)
Unger et al., 2012
Incentives structured in the project and portfolio level
Szydlowski (2012)
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Use of special computerized systems
Steingrimsdottir (2017)
3.7.4 Pilot test and feedback
The questionnaire was sent for pilot testing to two accessible qualified
professionals working in generic pharmaceutical companies related to Strategic
Portfolio Management. It was answered and there was no negative feedback regarding
the structure and the content of the questionnaire. No ambiguities or errors were
reported.
3.8 Method of Analysis
The collected data from the questionnaire were exported to Excel 2010 and an initial
visual test confirmed that the 72 questionnaires could be used. The first step was the
coding of the questionnaire in a manner that facilitated their management and analysis
(code book creation). The data were then transferred to SPSS 24 for Windows through
which their analysis was conducted. The “I do not know” answers were transformed as
:missing values in SPSS in order for them to be excluded from statistical analysis. The
exclusion was “pair wise”, meaning that the cases (persons) are excluded only for the
specific analysis where the value is missing, and not for all the variables.
The choice of analysis techniques was made on the basis of both the type of data
available as well as the research objectives.
3.8.1 Descriptive statistics
Firstly, an exploratory analysis using descriptive statistics was conducted, as
suggested by Saunders et al. (2009, p. 429), in order to get a first view of the data.
According to Pallant (2007) descriptive statistics can be used for checking violations of
assumptions underlying the statistical techniques and to address specific research
questions. Mostly bar charts were created in order to describe the data collected. In
order to provide meaningful observations in certain parts, means were calculated
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(considering the Likert scales data as continuous) and relevant bar charts with
descending order where prepared.
3.8.1 Inferential statistics
For the part of inferential statistics where the hypotheses were tested, the type of
data determined the techniques. Most of the data derived either from Likert Scales and
individual items or from multiple choice questions. A first question that needed to be
answered referred to the type of data.
In our case data from Likert Scales are treated as continuous data and the other data
are categorical (Incentives, PMO, Computerized systems, Organizational size, PfM
maturity).
Following Pallant’s (2007) summary table of the characteristics of the main
statistical techniques in pages 116 and 117, for group comparisons related only to
categorical variables Chi-square non parametric tests were used. For group
comparisons involving a categorical variable with more than two levels and one
continuous variable, one way analysis of variance parametric tests were used
(ANOVA). For group comparisons involving a categorical variable with more than two
levels and several continuous variables of the same scale, multivariate analysis of
variance parametric tests were used (MANOVA) followed by univariate tests using
ANOVA. Table 3.2 summarizes the research questions and test used for each case.
Table 3.2: Research questions and tests summary
Research
question
What is the
effect of the
different levels
of PfM
maturity on
the PfM
alignment
with strategy
score?
What is the
effect of the
various levels
Test used
One way
between
groups
ANOVA
Test type
Variable used
Parametric PfM Maturity
PfM alignment
with Strategy
Multivariate Parametric PfM Maturity
ANOVA
(MANOVA)
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Variable
dependent or
independent
Independent (X)
Dependent (Y)
Independent (X)
Type of
variable
Categorical
(3 or more
levels)
Continuous
Categorical
(3 or more
levels)
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Research
question
Test used
of PfM
maturity on
the frequency
of the various
tools used to
evaluate
projects?
What is effect
of the various
levels of PfM
maturity on
the frequency
of the various
tools used to
optimize the
portfolio?
Test type
Variable used
Type of
variable
DCF
Dependent (Y)
Continuous
NPV
Dependent (Y)
Continuous
IRR
Dependent (Y)
Continuous
ROI
Dependent (Y)
Continuous
Profitability
index
TPS
Dependent (Y)
Continuous
Dependent (Y)
Continuous
CPS
Dependent (Y)
Continuous
eNPV
Dependent (Y)
Continuous
Decision tree
(project)
Real Options
Dependent (Y)
Continuous
Dependent (Y)
Continuous
Expert opinion
Dependent (Y)
Continuous
Gut feel - project
Dependent (Y)
Continuous
Independent (X)
Dependent (Y)
Categorical
(3 or more
levels)
Continuous
Simple checklists
Dependent (Y)
Continuous
Forced ranking
models
Dependent (Y)
Continuous
Weighted scoring Dependent (Y)
models
Continuous
Bubble charts
Dependent (Y)
Continuous
Decision trees optimization
Dependent (Y)
Continuous
Simulation
approach
Marginal
analysis
Analytical
Hierarchy
Process
Sensitivity &
scenario analysis
Dependent (Y)
Continuous
Dependent (Y)
Continuous
Dependent (Y)
Continuous
Dependent (Y)
Continuous
Efficient frontier
Dependent (Y)
Continuous
Tornado charts
Dependent (Y)
Continuous
Gut feel optimization
Dependent (Y)
Continuous
Multivariate Parametric PfM Maturity
ANOVA
(MANOVA)
Project
interdependecies
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independent
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Ioannis Fotopoulos, Exploring product portfolio management processes in
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Research
question
Test used
Test type
Variable used
What is effect
of the various
levels of PfM
maturity on the
frequency of
the various
tools used to
align with
corporate
strategy
Multivariate Parametric PfM Maturity
ANOVA
(MANOVA)
Strategic buckets
What is the
effect of
various levels
of PfM
maturity on
the scores of
the various
portfolio
outcomes?
Multivariate
ANOVA
(MANOVA)
What is the
effect of the
various levels
of PfM
maturity on
the various
benefits of
PfM
processes?
What is the
relationship of
operation of
Strategic
roadmaps
Strategic buckets
Gut feel strategy
Parametric PfM Maturity
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Non
parametric
Independent (X)
Type of
variable
Dependent (Y)
Categorical
(3 or more
levels)
Continuous
Dependent (Y)
Continuous
Dependent (Y)
Continuous
Dependent (Y)
Continuous
Independent (X)
Projects on time
Dependent (Y)
Categorical
(3 or more
levels)
Continuous
Projects within
Budget
Dependent (Y)
Continuous
Dependent (Y)
Continuous
Independent (X)
Dependent (Y)
Categorical
(3 or more
levels)
Continuous
Focus on
breakthrough
projects
Better Strategic
fit of portfolio
Dependent (Y)
Continuous
Dependent (Y)
Continuous
Balance shortterm vs. longterm projects
Fewer but more
worthwhile
projects
Improved times
to market
Provides unified
support
Improves
strategic
planning
PfM Maturity
Dependent (Y)
Continuous
Dependent (Y)
Continuous
Dependent (Y)
Continuous
Dependent (Y)
Continuous
Dependent (Y)
Continuous
Dependent (Y)
Categorical
(3 or more
levels)
Projects meeting
goals and
business intent
Multivariate Parametric PfM Maturity
ANOVA
(MANOVA)
Common basis
for discussion
Chi-square
test
Variable
dependent or
independent
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Research
question
PMOs and
PfM
maturity?
What is the
relationship of
incentives
structured on
project and
portfolio and
PfM
maturity?
What is the
relationship of
special
computerized
systems and
PfM
maturity?
What is the
relationship of
organizational
size and PfM
maturity?
Test used
Chi-square
test
Chi-square
test
Chi-square
test
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Test type
Non
parametric
Non
parametric
Non
parametric
Variable used
Variable
dependent or
independent
Type of
variable
PMO operation
Independent (X)
Categorical
(two levels)
PfM Maturity
Dependent (Y)
Incentives
Independent (X)
Categorical
(3 or more
levels)
Categorical
(two levels)
PfM Maturity
Dependent (Y)
Special computer
systems
Independent (X)
PfM Maturity
Dependent (Y)
Organization size
Independent (X)
Categorical
(3 or more
levels)
Categorical
(two levels)
Categorical
(3 or more
levels)
Categorical
(3 or more
levels)
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Chapter 4. Results
4.1 Descriptive statistics
4.1.1 Respondents profile
According to the actual responses the following respondents profile was established:
All the respondents have a higher academic qualification, at least a bachelor’s
degree. The vast majority has a master’s degree (65%) and rest, bachelor and doctorate
degree (Fig 4.1). Most probably this is consistent with the respective prerequisites of
jobs related to PfM practitioners as they have to perform complex tasks in matrix
multi-functional collaborating teams.
Figure 4.1: Respondents’’ education level
The respondents are mainly managers (68%) and directors (24%) with a few
respondents (7%) at C-level and one respondent only having a supervisor (lower than
manager) status (Fig. 4.2). In any case it is evident that the respondents are highly
qualified senior professionals which increase the degree of reliability of the results.
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Figure 4.2: Respondents’ professional seniority level
The years of general pharmaceutical experience as well as experience in generics is
normally distributed with the group of 11-15 years of experience to be the most
frequent answer (37% and 43% respectively) (Fig. 4.3 and Fig 4.4). However the years
of PfM experience are not normally distributed. Most of the respondents (46%) have 05 years of experience and 33% 6-10 years, which means that they are probably fairly
newly appointed in PfM positions although their general experience is higher)(Fig 4.5).
A possible explanation could be also that Generic firms have been gradually
implementing PfM processes and creating PfM roles in a greater extent than in the past
as they realize the benefits of structured PfM processes. In any case overall it can be
concluded that the respondents’ profile shows that the sample population is reliable for
the purpose of the study.
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Figure 4.3: Respondents’ general pharmaceutical industry experience
Figure 4.4: Respondents’ experience in generic pharmaceuticals
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Figure 4.5: Respondents’ experience in Portfolio Management
Approximately 31% of respondents’ current organization has over 10.000
employees (Fig. 4.6). This can be attributed to the fact that bigger companies are
expected to have more established PfM processes and more professionals specialized
specifically in this area. However the respondent’s organizations size covers from
small (50 employees) to very large ones (10.000+) thus providing an increased degree
of reliability to the survey.
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Figure 4.6: Respondents’ current organization size
The biggest proportion of respondents’ organization HQ location is Europe (47%)
followed by Asia-Pacific (19%) and North America (18%) which collectively account
approximately for 84% of respondents. Organizations with headquarters in the other
regions were significantly lower in the survey sample (Fig 4.7).
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Figure 4.7: Respondents’ current organization HQ location
4.1.2 PfM and strategy
The first part of main section of the questionnaire was designed to explore whether
PfM practitioners believe that PfM is important in achieving an organization’s strategic
objectives. A 5-point Likert scale, ranging from 1 - I strongly disagree to 5 - I strongly
agree was used with 3 being the neutral point (neither agree nor disagree). Figure 4.1
shows the respondents’ distribution regarding their level of agreement. All respondents
(100% of the sample) agree and the bigger part, 65% of them, strongly agrees with the
stated proposition. There were no neutral or disagreement answers. These results (Fig.
4.8) confirm that generic firm PfM practitioners consider PfM as very important in
achieving an organization’s strategic objective, without further analysis.
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Figure 4.8: Importance of PfM in achieving strategic objectives
The next question aimed to explore the level of alignment of PfM with corporate
strategy in their current organizations. Figure 4.9 shows the respondents’ distribution
regarding their level of agreement. Almost 70% of the sample agrees and strongly
agrees that PfM is aligned with corporate strategy in their current organizations, 23%
remains neutral while only almost 7% disagree showing a clear trend of the situation.
There was no response of strongly disagreement.
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Figure 4.9: PfM alignment with corporate strategy
Next question aimed to explore the tools used in order to align Portfolio
Management with corporate strategy. (Fig 4.10, 4.11, 4.12, 4.13, 4.14). Strategic fit
criteria are more frequently used and strategic buckets less often It is also confirmed
that non-rational practices (“gut feel”) indeed have a substantial impact on decision
making, as noted in conceptual studies (Meifort, 2016).
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(1=Never, 2=Seldom, 3=Sometimes, 4=Often, 5=Always)
Figure 4.10: Mean frequency of use PfM and strategy alignment tools by
descending order.
Another conclusion, which is general throughout this survey, is that indeed the
frequency of use varies depending on the available tools of PfM. Otherwise the
answers would be at situated only at the extreme (Never, Always) which is not the case
according to the answers. The preference of one tool over the other is revealed through
the frequency of use scale. The choice of the frequency scale instead of a Yes or No
dichotomous answer is thus confirmed as a value adding survey feature.
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Figure 4.11: Frequency of use of Strategic buckets
Figure 4.12: Frequency of use of Strategic Roadmaps
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Figure 4.13: Frequency of use of Strategic fit criteria
Figure 4.14: Frequency of use of “Gut feel” of senior decision makers
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4.1.3 Data used in order to evaluate individual new projects in current
organization
The next section aimed to record the frequency of use of specific data for individual
new project evaluation. Figure 4.15 shows the mean of frequencies in a descending
order.
As it is logical forecast of potential revenues (Fig. 4.16) is always included as data
in every evaluation as 100% of respondents use it always. Likewise the cost of
development (Fig. 4.17) and the cost of goods (GOGS) (Fig. 4.19) are always used at a
90% and 86% respectively, allowing for the basic financial evaluation of projects.
However other data such time for development (or licensing) (Fig. 4.18), the cost of
marketing (Fig. 4.20) and the cost of capital (Fig. 4.21) have a wider distribution of
frequencies showing essentially that they are taken less frequently into account, with
the cost of capital used less frequently than all the rest.
(1=Never, 2=Seldom, 3=Sometimes, 4=Often, 5=Always)
Figure 4.15: Mean frequency of data used in order to evaluate individual new projects
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Figure 4.16: Frequency of use of revenues
Figure 4.17: Frequency of use of development (or in-licensing) cost.
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Figure 4.18: Frequency of use of development (or in-licensing) time
Figure 4.19: Frequency of use of cost of goods
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Figure 4.20: Frequency of use of cost of marketing
Figure 4.21: Frequency of use of cost of capital
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4.1.4 Risks considered in evaluating individual new projects in current
organization
The responses of this section recorded frequencies of consideration of the various
risks. However it was on the scope of the study to investigate thoroughly on the ways
these risks are estimated and incorporated and whether this is done in a structured
manner (e.g. bubble charts, risk adjusted NPV) or unofficially by the PfM
practitioners. This could be the scope of further investigation.
Figure 4.22 shows the mean of frequencies with descending order in order to show
the most frequently used risks taken into consideration.
Intellectual property risk is “often” and “always” considered at an approximately
93% collective rate (Fig. 4.24) which is explained by the fact that generic medicines
can be marketed only after the patent expiry of the originator drug. Firms in order to be
able to launch first and gain a head start in acquiring market share are monitoring for
patent expiries in various regions and formulate market access strategies as there are
variations in the patent expiry status across the globe.
Then the most frequently used risks are Technical risk (Often and Always – 89%)
(Fig. 4.25) and Regulatory risk (Often and Always – 79%) (Fig.4.27). This is
justifiable since generic firms must be able to develop or in-license robust product
formulations which can consistently provide products with the required safety, efficacy
and purity in order to be able to comply with quality, regulatory as well as marketing
requirements at the same time.
Market risk follows (Often and Always – 78%) (Fig. 4.23) as market dynamics
cannot be accurately predicted and must be considered on a risk basis, apart from the
basic market data collection and evaluation.
Clinical risk is less frequently taken into consideration (Fig. 4.26). Generic
medicines do not need to repeat the initial clinical studies; they only need to prove their
bioequivalence to originator products through clinical studies which however are not
necessary for all pharmaceutical dosage forms and the risks of failure as not compared
to the high clinical risks of discovering new therapeutic agents.
Finally supplier and partner risk is considered less frequently than the others (Fig.
4.28).
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(1=Never, 2=Seldom, 3=Sometimes, 4=Often, 5=Always)
Figure 4.22: Mean frequency of risks considered in evaluating individual new projects
by descending order
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Figure 4.23: Frequency of consideration of market risk
Figure 4.24: Frequency of consideration of intellectual property risk
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Figure 4.25: Frequency of consideration of technical risk
Figure 4.26: Frequency of consideration of clinical risk
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Figure 4.27: Frequency of consideration of regulatory risk
Figure 4.28: Frequency of consideration of supplier/partner risk
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4.1.5 Tools used in order to evaluate individual new projects in
current organization
The most frequently used tools to evaluate individual new projects are financial
tools. As we can see in Fig. 4.29, NPV, ROI, Profitability Index and DCF are most
frequently used that the other tools, except for IRR. Net Present Value is the most
frequently used tools with 95% of respondents using it). Tools that incorporate risk and
estimation of uncertainties such as eNPV, TPS, CPS, Expert opinion, Decision trees
are used less frequently than the purely financial tools. Real Options is the least
frequently used tool and it is not used at all by 53% of respondents, which coincides
with former literature findings in general pharmaceutical PfM. It is revealed also in this
case that non-rational practices (“gut feel”) have a substantial impact on project
evaluation, regardless of the other tools used, with a normal distribution of answers
(“sometimes” accounts for most of the answers).
(1=Never, 2=Seldom, 3=Sometimes, 4=Often, 5=Always)
Figure 4.29: Mean frequency of tools used in order to evaluate individual new projects
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Figure 4.30: Frequency of use of Discounted Cash Flow (DCF)
Figure 4.31: Frequency of use of Net Present Value (NPV)
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Figure 4.32: Frequency of use of Internal Rate of Return (IRR)
Figure 4.33: Frequency of use of Return of Investment (ROI)
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Figure 4.34: Frequency of use of Profitability Index (PI)
Figure 4.35: Frequency of use of Technical Probability of Success (TPS)
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Figure 4.36: Frequency of use of Commercial Probability of Success (CPS)
Figure 4.37: Frequency of use of Expected Net Present Value (eNPV)
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Figure 4.38: Frequency of use of decision trees in project evaluation
Figure 4.39: Frequency of use of Real Options
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Figure 4.40: Frequency of use of Expert Opinion
Figure 4.41: Frequency of use of “gut feel” of senior decision makers for project
evaluation
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4.1.6 Tools used in order to prioritize projects and optimize the
portfolio in current organization
As shown in Fig. 4.42 the most frequently used tools to prioritize projects and
optimize the portfolio are the more simple tools like simple checklists, weighted
scoring models, forced ranking models and the “gut feel” of senior decision makers.
More demanding, complex and advanced tools are less frequently used. The least
frequently used tools are tornado charts, efficient frontier analysis, Analytical
hierarchy process and simulation approach. Figures 4.34 up to 4.45 depict the
frequencies of use of all the specified tools.
(1=Never, 2=Seldom, 3=Sometimes, 4=Often, 5=Always)
Figure 4.42: Mean frequency of use project prioritization and portfolio optimization
tools by descending order
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Figure 4.43: Frequency of use project interdependencies evaluation
Figure 4.44: Frequency of use of simple checklists
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Figure 4.45: Frequency of use of forced ranking models
Figure 4.46: Frequency of use of weighted scoring models
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Figure 4.47: Frequency of use of bubble charts
Figure 4.48: Frequency of use of decision trees for portfolio optimization
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Figure 4.49: Frequency of use of Simulation Approach
Figure 4.50: Frequency of use of marginal analysis
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Figure 4.51: Frequency of use of Analytical Hierarchy Process
Figure 4.52: Frequency of use of Sensitivity & scenario analysis
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Figure 4.53: Frequency of use of efficient frontier
Figure 4.54: Frequency of use of Tornado Charts
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Figure 4.55: Frequency of use of “Gut feel” of senior decision makers for portfolio
optimization
4.1.7 Approaches used to review the portfolio in current organization
According to Fig. 4.56, the most frequently used approach to review the portfolio is
the periodical review (Fig. 4.57) followed by the stage gate / milestone triggered
review approach (Fig. 4.58). It appears specifically for stage gate reviews, that they are
indeed used by generic firms when reaching significant steps in the development
process of new generic products, apart from the innovative big pharmaceutical
companies that use stage gates in pre-clinical and clinical stages of NCE. However the
formality and exact procedure of the process was not investigated in this survey and
could be a subject of further study. Ad-hoc reviews (Fig. 4.59) are used as well along
with other approaches while combined reviews are less frequently as an approach than
the rest (Fig 4.60).
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(1=Never, 2=Seldom, 3=Sometimes, 4=Often, 5=Always)
Figure 4.56: Mean frequency of approaches to review the portfolio in descending
order
Figure 4.57: Frequency of use of periodical review approach
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Figure 4.58: Frequency of use of Stage-gate or milestone review approach
Figure 4.59: Frequency of use of ad-hoc review approach
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Figure 4.60: Frequency of use of combined review approach
4.1.8 Project Management Office (PMO) operation
Project management offices (PMO) are operating according to 62,5% of
respondents (Fig. 4.61). PMOs have been associated with increased project portfolio
management quality and in turn portfolio success. (Meifort, 2016).
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Figure 4.61: Operation of a Project Management Office.
4.1.8 Maturity level regarding PfM in current organization
In Fig 4.62 we can see that there is distribution of answers in all PfM maturity
categories however they are not normally distributed and they are negatively skewed,
with most answers collected the higher maturity categories.
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Figure 4.62: Overall level of PfM maturity
4.1.9 Outcomes of PfM in current organization
As a general picture we can see that regarding the basic outcomes of PfM most
answers are collected in the Agree and Strongly Agree responses – 50% collectively
for Projects on time (Fig. 4.64), 57% for Projects within budget (Fig. 4.65) and 62%
for Projects that meet goals and business intent (Fig 4.66). The most negative
answers are for Projects on time proposition (29% disagree and 50% for disagree
and neutral answers collectively). Figure 4.63 shows the means of the responses in
the 5 point Likert scale. These findings indicate that timely delivery of projects
appears to be a slightly bigger challenge for generic firms while the focus on
business goals has probably been a priority and has greater success.
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(1=Strongly disagree, 2=Disagree, 3=Undecided/Neutral, 4=Agree, 5=Strongly agree)
Figure 4.63: Mean of projects outcomes in descending order
Figure 4.64: Projects on time
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Figure 4.65: Projects within budget
Figure 4.66: Projects meet goals and business intent
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4.1.11 Benefits of PfM in current organization
Figure 4.67 shows that respondents agree the most that their current PfM practices
lead to “Better strategic fit of portfolio” followed by increased “Focus on breakthrough
projects” and “Balance between short term and long term projects”. The benefit less
experienced according to the respondents’ perception is “Improved times to market”
which coincides with the lower ratings on “Projects on time” question in Portfolio
Success Section. Figures 4.68 to 4.75 show the distribution of answers regarding the
benefits of PfM in current organizations as perceived by respondents.
(1=Strongly disagree, 2=Disagree, 3=Undecided/Neutral, 4=Agree, 5=Strongly agree)
Figure 4.67: Means of benefits of PfM processes with descending order
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Figure 4.68: Benefits of PfM processes – Common basis for discussion
Figure 4.69: Benefits of PfM processes – Focus on breakthrough projects
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Figure 4.70: Benefits of PfM processes – Better strategic fit of portfolio
Figure 4.71: Benefits of PfM processes – Balance short term vs. long term projects
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Figure 4.72: Benefits of PfM processes – Fewer but more worthwhile projects
Figure 4.73: Benefits of PfM processes – Improved times to market
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Figure 4.74: Benefits of PfM processes – Provides unified support, better buy-in
Figure 4.75: Benefits of PfM processes – Improves strategic planning
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4.1.12 Specialized computer applications for PfM
Almost 46 % of respondents confirmed that no specialized computerized systems
are used while the rest 54% they do, revealing a normal distribution of this aspect (Fig.
4.76). The ones that use such systems rated what they think is the level of contribution
to PfM processes. Most of them (46%) answered that it contributes moderately, 35%
significantly (slightly 18%, not at all 0%) showing a rather positive opinion on their
contribution (Fig. 4.77).
The use of advanced methods for portfolio optimization has increased
computational needs. Special computerized systems (e.g. Planisware or MS Project)
facilitate the creation and evaluation of different business scenarios in very short time
incorporating all related parameters (financial and non financial data) which would be
too hard to generate and re-calculate with non automated calculation means.
Figure 4.76: Use of special computerized systems for PfM
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Figure 4.77: Contribution of specialized computerized systems for PfM
4.1.13 Incentives on the project and portfolio level
Regarding incentives on the project and portfolio level, 3 out of 72 respondents
answered that they did not know whether incentives such schemes are used and were
classified as missing data from the SPSS data file. Roughly the same number of
respondents (approximately 50 %) answered that incentives were either used or not,
showing a normal distribution regarding this aspect (Fig. 4.78). We conclude that a
large part of generic firms have not decided to use such incentives despite the positive
impact that incentives have on the alignment of decision making of managers with the
success of PfM and ultimately fulfillment of company objectives.
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Figure 4.78: Incentives structured on the project and portfolio level.
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4.2 Inferential statistics
4.2.1 Normality of data
For parametric techniques it is assumed that the populations from which the
samples are taken are normally distributed. Kolmogorov-Smirno and Shapiro-Wilk
tests were conducted and showed some violation of normality in certain cases.
However, according to Pallant (2007) this is quite common in social sciences and in
large samples (30+) the violation of this assumption should not cause any major
problems, especially where we expect scores that are skewed.
In addition skewness values were much less than -2 and all kurtosis values were
less than 2. According to Curran et al. (1996) the cutoff point values are for skewness
< ± 2 and for kurtosis < ± 7. This fact allowed the use of parametric tests.
4.2.2 ANOVA - PfM maturity – Alignment with corporate strategy
A one-way between groups analysis of variance (ANOVA) was conducted to
explore the impact of overall PfM maturity on the levels of alignment of PfM with
corporate strategy. Subjects were divided by the five groups (1:Ad-hoc, 2: Getting
Started, 3: Structured and improving, 4: Established, 5: Optimized for continuous
improvement). Figure 4.79 shows the mean scores of PfM – Strategy alignment for the
various PfM maturity groups.
The full range of calculation tables generated by SPSS is presented in Appendix B.
The Levene’s test for the homogeneity of variance showed that there was a violation of
the relevant assumption (sig. < 0,05). So instead of taking into account the ANOVA
results, the Robust Tests of Equality of Means were preferred. For both Welsh and
Brown-Forsythe tests there was statistically significant difference at the p<0.05 level in
Strategy Alignment scores for the five groups. Post-hoc comparisons using Tukey
HSD test indicated that there is statistically significant difference between group 1 and
2 with groups 3, 4 and 5, but there is no statistical difference between group 1 and 2 or
between groups 3, 4 and 5.
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We can generally conclude, using the PMI (2015) survey grouping and
terminology, that “high-maturity” PfM generic companies align their PfM with
corporate strategy more so than the “low-maturity” ones.
Figure 4.79: PfM maturity and alignment with corporate strategy
4.2.3 MANOVA – PfM Maturity – tools to align strategy
The residual means plots (Fig. 4.80, 4.81 and 4.82) generated from MANOVA
indicates that high-maturity generic companies tend to use more frequently specific
tools for alignment of PfM with corporate strategy more so than the low-maturity. On
the contrary low-maturity companies tend to use gut feel of senior decision makers
more frequently then the high-maturity companies (Fig. 4.83). See Appendix C for
SPSS tables.
There is overall statistically significant difference in the combined variables.
When the results for the dependent variables were considered separately using a
Bonferroni adjusted lower alpha level of 0.013 (derived from calculation of 0,05/4
where 4 is the number of dependent variables), statistically significant difference
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between various PfM levels was found for all four dependent variables (Sig. less than
0,013).
Figure 4.80: PfM maturity and strategy tools – Strategic buckets
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Figure 4.81: PfM maturity and strategy tools – Strategic roadmaps
Figure 4.82: PfM maturity and strategy tools – Strategic fit criteria
Figure 4.83: PfM maturity and strategy tools - Gut feel
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4.2.4 MANOVA – PfM maturity – PfM outcomes (success)
A one-way between groups multivariate analysis of variance was performed to
investigate PfM maturity differences in the scores of PfM outcomes. Three dependent
variables were used: Projects on time, Projects within budgets, Projects meet goals and
business intent. The independent variable was PfM maturity.
See Appendix D for full results. An inspection of the mean scores indicated that
higher-maturity generic companies reported higher levels of success in PfM outcomes
than the low-maturity ones.
The Sig. value is 0,167 which is larger than 0,01 and so the assumption on the
homogeneity of variance-covariance matrices is not violated.
The only Sig. value that is less than 0,05 is for Projects On Time variable which
indicates a violation of assumption of equality of variances. A more conservative alpha
level for determining significance for this variable in the univariate F-test was set.
The Sig. value for Wilks’ Lambda is 0,000 which lower than 0,05 and indicates that
there is statistically significant difference between the various PfM maturity levels on
combined dependent variables. As there is a violation of an assumption the more
robust Pillai’s trace was checked also which however has the same result.
When the results for the dependent variables were considered separately using a
Bonferroni adjusted lower alpha level of 0.017 (derived from calculation of 0,05/3,
where 3 is the number of dependent variables), statistically significant difference
between various PfM levels was found for all 3 dependent variables for all (Sig. less
than 0,05).
As the independent variable (PfM maturity) has five levels (more than two), one
way analysis of variance (ANOVA) was conducted (with a similar approach as in the
previous section) for each variable separately.
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Results for Projects in time showed statistically significant differences between Adhoc and Getting Started subgroup (Low-maturity performers) and Structured and
Improving, Established and Optimized for Continuous Improvement subgroup (highmaturity performers). There were not statistically significant differences between
groups within each subgroup, just like the case of PfM maturity and Strategy alignment
ANOVA results.
Figure 4.84: PfM maturity and PfM outcomes – Projects on time
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Results for Projects within budgets showed statistically significant differences
between Ad-hoc and Structured and Improving, Established and Optimized for
Continuous Improvement. There was no statistically significant difference for Getting
Started. Also there were not statistically significant differences between groups within
each subgroup.
Figure 4.85: PfM maturity and PfM outcomes – Projects within budgets
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Results for Projects meet goals & business intent showed statistically significant
differences between Ad-hoc and Structured and Improving, Established and Optimized
for Continuous Improvement subgroup (high-maturity). Getting started was
statistically different only with Optimized for continuous improvement. Also there
were not statistically significant differences between groups within the high-maturity
subgroup.
Figure 4.86: PfM maturity and PfM outcomes – Projects meet goals & business
intent
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4.2.5 MANOVA – PfM Maturity – tools to evaluate individual projects
See Appendix E for the full range of tables and figures. The residual means plot
generated from MANOVA indicates that high-maturity generic companies tend to use
more frequently tools for evaluation of projects including tools apart from financial
tools, that incorporate risk such as CPS, TPS, eNPV, etc more so than the lowmaturity. On the contrary low-maturity companies tend to use gut feel of senior
decision makers more frequently then the high-maturity companies.
There is overall statistically significant difference in the combined variables.
When the results for the dependent variables were considered separately using a
Bonferroni adjusted lower alpha level of 0.004 (derived from calculation of 0,05/12
where 12 is the number of dependent variables), statistically significant difference
between various PfM levels was found for all dependent variables (Sig. less than
0,004) except for IRR, ROI, Expert Opinion.
4.2.6 MANOVA – PfM Maturity – tools to prioritize projects and optimize the
portfolio
See Appendix F for the full range of tables and figures. The residual means plot
generated from MANOVA indicates that high-maturity generic companies tend to use
more tools more frequently for portfolio prioritization and optimization including more
advanced tools such as Sensitivity and Scenario Analysis, etc more so than the lowmaturity. They also take into account more frequently project interdependencies
evaluation.
On the contrary low-maturity companies tend to more frequently use simple tools
such as simple checklists and forced ranking models as well as the gut feel of senior
decision makers than the high-maturity companies.
There is overall statistically significant difference in the combined variables.
When the results for the dependent variables were considered separately using a
Bonferroni adjusted lower alpha level of 0.004 (derived from calculation of 0,05/13
where 13 is the number of dependent variables), statistically significant difference
between various PfM levels was found for all dependent variables (Sig. less than
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0,004)
except for Forced ranking models, Weighted scoring models, Marginal
Analysis and Tornado Charts.
4.2.7 MANOVA – PfM Maturity - benefits of current PfM processes
See Appendix G for the full range of tables and figures. The residual means plots
generated from MANOVA (Fig. 4.87 to 4.
indicates that high-maturity generic
companies receive the expected benefits of PfM processes in a bigger scale than the
low-maturity ones
There is overall statistically significant difference in the combined variables.
When the results for the dependent variables were considered separately using a
Bonferroni adjusted lower alpha level of 0,063 (derived from calculation of 0,05/8
where 8 is the number of dependent variables), statistically significant difference
between various PfM levels was found for all eight dependent variables (Sig. less than
0,063).
Figure 4.87: PfM maturity and PfM benefits – Common basis for discussion
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Figure 4.88: PfM maturity and PfM benefits – Focus on breakthrough projects
Figure 4.89: PfM maturity and PfM benefits – Better strategic fit
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Figure 4.90: PfM maturity and PfM benefits – balance short term vs. long-term
projects
Figure 4.91: PfM maturity and PfM benefits – Fewer but more worthwhile projects
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Figure 4.92: PfM maturity and PfM benefits – Improved times to market
Figure 4.93: PfM maturity and PfM benefits – Provides unified support
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Figure 4.94: PfM maturity and PfM benefits – Improves strategic planning
4.2.8 Chi-square test - PfM maturity and PMO operation
Project Management Offices (PMO) are operated by all organizations but appear to
be operated mostly in more mature organizations compared to the others (Figure 4.95).
The results of Chi-Square test (Table 4.1) showed that there is statistically significant
difference among the groups (Asymp. Sig. is < 0,05) however there is a violation of
minimum expected cell frequency and so a significant differentiation cannot be
supported.
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Figure 4.95: PfM maturity and PMO operation
Table 4.1: Chi-square test - PfM maturity and PMO operation
Chi-Square Tests
Value
df
Asymptotic Significance (2-sided)
Pearson Chi-Square
44,678
a
4
,000
Likelihood Ratio
52,986
4
,000
Linear-by-Linear
40,105
1
,000
Association
N of Valid Cases
72
a. 2 cells (20,0%) have expected count less than 5. The minimum expected count is 4,50.
4.2.9 Chi-square test - PfM maturity and incentives
Almost all PfM maturity categories have incentives structured in the project and
portfolio level, however more mature generic companies have incentives at a greater
extent that less mature ones (Figure 4.96). The results of Chi-Square test (Table 4.2)
showed that there is statistically significant difference among the groups (Asymp. Sig.
is < 0,05),
there is no violation of minimum expected cell frequency and so a
significant differentiation can be supported.
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Figure 4.96: PfM maturity and incentives
Table 4.2: Chi-square test - PfM maturity and incentives
Chi-Square Tests
Value
df
Asymptotic Significance (2-sided)
Pearson Chi-Square
33,018a
4
,000
Likelihood Ratio
40,071
4
,000
Linear-by-Linear Association
28,605
1
,000
N of Valid Cases
69
a. 0 cells (,0%) have expected count less than 5. The minimum expected count is 5,91.
4.2.10 Chi-square test - PfM maturity and special computerized systems
More PfM mature generic companies use special computerized systems at a
greater extent that less mature ones (Figure 4.97). The results of Chi-Square test (Table
4.3) showed that there is statistically significant difference among the groups (Asymp.
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Sig. is < 0,05), there is no violation of minimum expected cell frequency and so a
significant differentiation can be supported.
Figure 4.97: PfM maturity and use of special computerized systems
Table 4.3: Chi-square test - PfM maturity and special computerized systems
Chi-Square Tests
Asymptotic Significance (2Value
df
sided)
Pearson Chi-Square
40,246a
4
,000
Likelihood Ratio
49,079
4
,000
Linear-by-Linear Association
38,033
1
,000
N of Valid Cases
72
a. 0 cells (0,0%) have expected count less than 5. The minimum expected count is 5,50.
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4.2.11 Chi-square test - PfM maturity and Organization size
According to Figure 4.98 it appears that there is a positive correlation of PfM
maturity and organization size. Bigger companies (from 1001 to 10.000+ employees)
have higher PfM maturity ratings compared to the smaller ones (up to 500 employees).
Medium sized companies (500-1000) have a normal distribution of PfM maturity
responses. The results of Chi-Square test (Table 4.4) showed that there is statistically
significant difference (Asymp. Sig. is < 0,05) however there is a violation of minimum
expected cell frequency and so a significant differentiation cannot be supported.
Figure 4.98: PfM maturity and size of organization
Table 4.4: Chi-square test - PfM maturity and Organization size
Chi-Square Tests
Value
df
Asymptotic Significance (2-sided)
Pearson Chi-Square
68,577a
24
,000
Likelihood Ratio
76,567
24
,000
Linear-by-Linear
36,029
1
,000
Association
N of Valid Cases
72
a. 34 cells (97,1%) have expected count less than 5. The minimum expected count is ,17.
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4.3 Conclusions and contribution to academia
4.3.1 Portfolio Management strategic perspective in generic pharmaceuticals
In Chapter 2, a review of the literature on portfolio management and strategy
revealed that PfM is regarded as a strategic capability and a tool of implementation of
corporate strategic plans, by allocating resources to specific projects (Meifort, 2016).
In this study it was similarly confirmed that generic pharmaceuticals practitioners
consider PfM as very important in achieving an organization’s strategic objectives.
However not all organizations have the same ability to align PfM with corporate
strategy. According to the present research results the overall PfM maturity of generics
firms has an impact on the level of this alignment. “High-maturity” PfM companies
align their PfM with corporate strategy more so than the “low-maturity” ones, which
coincides with the results with the PMI (2015) survey.
Regarding the tools used for this alignment strategic fit criteria are more frequently
used and strategic buckets less often. It is also confirmed that non-rational practices
(“gut feel”) indeed still have a substantial impact on decision making, as noted in
relevant studies (Martinsuo, 2013). However it was shown that high-maturity generic
companies tend to use more frequently specific tools for alignment of PfM with
corporate strategy more so than the low-maturity. On the contrary low-maturity
companies tend to use gut feel of senior decision makers more frequently then the
high-maturity companies.
4.3.2 Portfolio Management optimization and decision making perspective in
generic pharmaceuticals
Among the data used in order to evaluate new individual projects mentioned in
literature, this research recorded that the cost of marketing and the cost of capital are
used less frequently than revenues, COGS, development cost and time.
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On the other hand the risks reported as taken into account include in a descending
order: Intellectual property risk, Technical risk, Regulatory risk, Market risk , Clinical
risk and with supplier and partner risk considered less frequently than the others.
According to Cooper et al (1999) financial approaches are the most popular and
dominate the portfolio decision. Indeed it is confirmed in this research that the most
frequently used tools to evaluate individual new projects are financial tools. Tools that
incorporate risk and estimation of uncertainties are used less frequently than the purely
financial tools. It is revealed also in this case that non-rational practices (“gut feel”)
have a substantial impact on project evaluation, regardless of the other tools used.
Similarly the most frequently used tools to prioritize projects and optimize the
portfolio are the more simple tools like simple checklists, weighted scoring models,
forced ranking models and the “gut feel” of senior decision makers. More demanding,
complex and advanced tools are less frequently used.
But according to Cooper (1999) benchmark businesses stand out from the rest as
they place less emphasis on financial approaches and more on strategic methods, and
they tend to use multiple methods more so than the rest. It was confirmed in our
research that that high-maturity generic companies, apart from financial tools, they
tend to use more tools that incorporate risk. On the contrary low-maturity companies
tend to use gut feel of senior decision makers more frequently than the high-maturity
companies.
4.3.3 Portfolio Management organizational and execution perspective in generic
pharmaceuticals
A new contribution is that Project Management Offices (PMO) are operated by
organizations from every maturity level but appear to be operated mostly in more
mature organizations compared to the less mature.
The group comparisons focused on the levels of success in PfM outcomes showed
that higher-maturity generic companies reported higher outcomes than the lowmaturity ones for all the three metrics: Results for Projects Results for Projects within
budgets Results for Projects meet goals & business intent. Our findings also indicate
that timely delivery of projects appears to be a slightly bigger challenge for generic
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firms while the focus on business goals has probably been a priority and has greater
success. Both findings confirm PMI’s (2015) general industrial survey.
One of the organizational conclusions of this research is that a large part of generic
firms have not decided to use such incentives despite the positive
impact that
incentives have on the alignment of decision making of managers with the success of
PfM and ultimately fulfillment of company objectives. What was also newly evident is
that high maturity generic companies tend to use incentives structured in the project
and portfolio level more so that the low-maturity ones.
Similar to the findings of PMI (2015) survey, high-maturity generic companies
receive the expected benefits of PfM processes in a bigger scale than the low-maturity
ones.
Another organizational conclusion that is new is that more PfM mature generic
companies use special computerized systems at a greater extent that less mature ones.
It also appears that there is a positive correlation of PfM maturity and organization size
however a statistically significant differentiation could not be supported due to sample
size limitations.
In summary of the above the main contributions to academia were the
confirmation of previous portfolio management research which were not focused
solely in a single sector or industry) in various topics as discussed above. This was
backed by solid primary data and quantitative tools. The research combined aspects
and topics from various sources and references bringing together complementary and
cross-explanatory measuring scales usefull for further research on this topic.
Another contribution is the addition of new industry specific (generic
pharmaceuticals) information like relative frequency of data, risks, evaluation and
optimization tools used etc regarding new product Portfolio Management which could
be the source of further industry specific or industry non-specific research questions
and generation of conceptual models for further studies.
The findings of the study could also contribute to a more comprehensive and valid
managerial standard or framework for the application of new product portfolio
management in generic pharmaceuticals.
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4.4 Managerial implications
Given the broadly accelerated pace of change, organizations are responding by
adding projects and programs. Since resources are limited the ability to allocate these
resources to the most important projects and see them through successfully is critical
(PMI, 2015).
According to the conclusions of this research better portfolio management does
not happen as a result of chance. High-maturity organizations have developed specific
portfolio management practices and decision-making capabilities that help them to be
successful. They have set up Project Management Offices, they used special
computerized systems, they use incentives, they incorporate risks and use various tools
to evaluate and optimize the portfolios and they tend to rely less in-non rational
decision making practices. These high-maturity generic drug firms report superior
returns and greater agility to respond to opportunities and changing competitive needs.
Generic pharmaceutical companies can follow these benchmark practices and
leverage their size and financial capabilities in order to gain the increased benefits of
portfolio value maximization, successful project execution, strategic objectives
realization and ultimately organizational success.
However given the continuously changing dynamic environment, generic
pharmaceutical companies need to frequently update and adapt their strategy and PfM
processes according to their specific characteristics—agility is crucial. They must also
in parallel run PfM processes, the actual business, and continuously improve the way
PfM is performed.
4.5 Limitations
Securing the validity, reliability and usefulness of this dissertation was a constant
goal however some points must be stressed.
The main limitation of this study is the sample size. Although it was large enough
(72 cases) in order to gain statistical important results the generalization of these
results cannot be supported.
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4.6 Future research directions
Future research could cover the limitations mentioned in the previous section by
doing a more robust quantitative study and by in depth analysis on each of the issues
discussed in this study.
Examples are the effects of factors other than formal PfM systems that influence
PfM decisions of generic pharmaceutical companies like the organizational culture or
micro-level political and emotional decision processes instead of rational ones.
One the major issues of PfM is resource allocation, which was not investigated in
depth in this industry specific research and could be a field of study focused on generic
pharmaceuticals.
In addition this research was not involved in Portfolio Management processes of
generic pharmaceutical companies of already commercialized products during their
whole lifecycle until their discontinuation. This could be a similarly interesting and
valuable area of further study both for academia and generic pharmaceutical managers
alike.
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Appendix A: “Questionnaire”
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Appendix B: “ANOVA - PfM maturity – Alignment with
corporate strategy”
Test of Homogeneity of Variances - PfM maturity and alignment with corporate
strategy
Test of Homogeneity of Variances
Strategy and PfM alignment
Levene Statistic
df1
df2
2,597
4
67
Sig.
,044
ANOVA - PfM maturity and alignment with corporate strategy
ANOVA
Strategy and PfM alignment
Sum of Squares
Between Groups
26,403
Within Groups
26,208
Total
52,611
df
4
67
71
Mean Square
6,601
,391
F
16,875
Sig.
,000
Robust Tests of Equality of Means - PfM maturity and alignment with corporate
strategy
Robust Tests of Equality of Means
Strategy and PfM alignment
Statistica
df1
df2
Welch
20,377
4
31,900
Brown-Forsythe
16,315
4
51,728
a. Asymptotically F distributed.
Sig.
,000
,000
Post Hoc Tests - PfM maturity and alignment with corporate strategy
Multiple Comparisons
Dependent Variable: Strategy and PfM alignment
Tukey HSD
95% Confidence Interval
Mean
(I) Maturity of
Difference
Std.
Lower
Upper
PfM
(J) Maturity of PfM
(I-J)
Error Sig.
Bound
Bound
Ad hoc
Getting started
-,314
,250 ,720
-1,02
,39
Structured and improving
-1,012*
,246 ,001
-1,70
-,32
Established
-1,521*
,239 ,000
-2,19
-,85
Optimized for continuous
-1,495*
,236 ,000
-2,16
-,83
improvement
Getting started
Ad hoc
,314
,250 ,720
-,39
1,02
Structured and improving
-,698*
,241 ,039
-1,37
-,02
Established
-1,207*
,234 ,000
-1,86
-,55
Optimized for continuous
-1,181*
,230 ,000
-1,83
-,53
improvement
Structured and
Ad hoc
1,012*
,246 ,001
,32
1,70
improving
Getting started
,698*
,241 ,039
,02
1,37
Established
-,509
,229 ,184
-1,15
,13
Optimized for continuous
-,483
,226 ,215
-1,12
,15
improvement
Established
Ad hoc
1,521*
,239 ,000
,85
2,19
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Optimized for
continuous
improvement
Getting started
Structured and improving
Optimized for continuous
improvement
Ad hoc
Getting started
Structured and improving
Established
1,207*
,509
,026
,234
,229
,218
1,495*
1,181*
,483
-,026
,236
,230
,226
,218
,000
,184
1,00
0
,000
,000
,215
1,00
0
,55
-,13
-,58
1,86
1,15
,64
,83
,53
-,15
-,64
2,16
1,83
1,12
,58
*. The mean difference is significant at the 0.05 level.
Homogeneous Subsets
Strategy and PfM alignment
Tukey HSDa,b
Subset for alpha = 0.05
Maturity of PfM
N
1
2
Ad hoc
12
2,92
Getting started
13
3,23
Structured and improving
14
3,93
Optimized for continuous
17
4,41
improvement
Established
16
4,44
Sig.
,670
,206
Means for groups in homogeneous subsets are displayed.
a. Uses Harmonic Mean Sample Size = 14,164.
b. The group sizes are unequal. The harmonic mean of the group sizes is
used. Type I error levels are not guaranteed.
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generic pharmaceutical companies.
Appendix C: “MANOVA – PfM Maturity – tools to align
strategy”
Box's Test of Equality of Covariance
Matricesa
Box's M
87,281
F
1,868
df1
40
df2
7912,570
Sig.
,001
Tests the null hypothesis that the observed covariance
matrices of the dependent variables are equal across
groups.
a. Design: Intercept + matur
Multivariate Testsa
Partial
Hypothesis
Effect
Intercept
Value
df
Error df
Sig.
Squared
,983
891,541b
4,000
60,000
,000
,983
,017
891,541b
4,000
60,000
,000
,983
Hotelling's Trace
59,436
891,541b
4,000
60,000
,000
,983
Roy's Largest Root
59,436
891,541b
4,000
60,000
,000
,983
Pillai's Trace
,927
4,754
16,000
252,000
,000
,232
Wilks' Lambda
,223
7,282
16,000
183,941
,000
,313
Hotelling's Trace
2,837
10,374
16,000
234,000
,000
,415
Roy's Largest Root
2,610
41,110c
4,000
63,000
,000
,723
Pillai's Trace
Wilks' Lambda
matur
F
Eta
a. Design: Intercept + matur
b. Exact statistic
c. The statistic is an upper bound on F that yields a lower bound on the significance level.
Levene's Test of Equality of Error Variancesa
F
df1
df2
Sig.
Strategic buckets
5,584
4
63
,001
Strategic roadmaps
6,775
4
63
,000
Strategic fit criteria
1,153
4
63
,340
Gut feel - strategy
1,384
4
63
,250
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Tests the null hypothesis that the error variance of the dependent variable is equal across
groups.
a. Design: Intercept + matur
Tests of Between-Subjects Effects
Type III
Sum of
Source
Dependent Variable
Corrected Model
Strategic buckets
92,994a
4
23,249
24,773
,000
,611
Strategic roadmaps
81,188b
4
20,297
28,211
,000
,642
Strategic fit criteria
30,752c
4
7,688
15,192
,000
,491
Gut feel - strategy
35,486d
4
8,871
12,408
,000
,441
Strategic buckets
455,954
1
455,954
485,850
,000
,885
Strategic roadmaps
645,323
1
645,323
896,936
,000
,934
Strategic fit criteria
998,953
1
998,953 1974,045
,000
,969
Gut feel - strategy
684,109
1
684,109
956,819
,000
,938
Strategic buckets
92,994
4
23,249
24,773
,000
,611
Strategic roadmaps
81,188
4
20,297
28,211
,000
,642
Strategic fit criteria
30,752
4
7,688
15,192
,000
,491
Gut feel - strategy
35,486
4
8,871
12,408
,000
,441
Strategic buckets
59,123
63
,938
Strategic roadmaps
45,327
63
,719
Strategic fit criteria
31,881
63
,506
Gut feel - strategy
45,044
63
,715
Strategic buckets
650,000
68
Strategic roadmaps
819,000
68
Strategic fit criteria
1111,000
68
Gut feel - strategy
754,000
68
Strategic buckets
152,118
67
Strategic roadmaps
126,515
67
Strategic fit criteria
62,632
67
Gut feel - strategy
80,529
67
Intercept
matur
Error
Total
Corrected Total
Squares
Partial Eta
df
Mean Square
F
Sig.
Squared
a. R Squared = ,611 (Adjusted R Squared = ,587)
b. R Squared = ,642 (Adjusted R Squared = ,619)
c. R Squared = ,491 (Adjusted R Squared = ,459)
d. R Squared = ,441 (Adjusted R Squared = ,405)
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generic pharmaceutical companies.
Appendix D: “MANOVA – PfM maturity – PfM outcomes
(success)”
Descriptive Statistics
Maturity of PfM
Mean
Std. Deviation
N
Projects on
Ad hoc
2,08
,289
12
time
Getting started
2,46
,519
13
Structured and improving
3,64
,745
14
Established
3,69
,602
16
Optimized for continuous improvement
3,94
,659
17
Total
3,25
,931
72
Projects
Ad hoc
2,67
,492
12
within
Getting started
3,31
,630
13
budgets
Structured and improving
3,93
,616
14
Established
3,75
,577
16
Optimized for continuous improvement
3,82
,636
17
Total
3,54
,730
72
Projects
Ad hoc
2,75
,452
12
meet goals
Getting started
3,23
,725
13
& business
Structured and improving
4,07
,616
14
intent
Established
3,94
,680
16
Optimized for continuous improvement
4,29
,849
17
Total
3,72
,876
72
Box's Test of Equality of Covariance Matricesa
Box's M
34,020
F
1,274
df1
24
df2
10854,935
Sig.
,167
Tests the null hypothesis that the observed covariance matrices of the
dependent variables are equal across groups.
a. Design: Intercept + matur
Levene's Test of Equality of Error Variancesa
F
Projects on time
Projects within budgets
Postgraduate Dissertation
df1
df2
Sig.
2,558
4
67
,047
,294
4
67
,881
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generic pharmaceutical companies.
Projects meet goals &
1,226
4
67
,308
business intent
Tests the null hypothesis that the error variance of the dependent variable is
equal across groups.
a. Design: Intercept + matur
Multivariate Testsa
Partial Eta
Effect
Value
Intercept
Hypothesis df
Error df
Sig.
Squared
Pillai's Trace
,982
1163,535b
3,000
65,000
,000
,982
Wilks' Lambda
,018
1163,535b
3,000
65,000
,000
,982
53,702
1163,535b
3,000
65,000
,000
,982
53,702
1163,535b
3,000
65,000
,000
,982
Pillai's Trace
,724
5,325
12,000
201,000
,000
,241
Wilks' Lambda
,339
7,256
12,000
172,265
,000
,303
Hotelling's Trace
1,769
9,387
12,000
191,000
,000
,371
1,661
27,827c
4,000
67,000
,000
,624
Hotelling's Trace
Roy's Largest Root
matur
F
Roy's Largest Root
a. Design: Intercept + matur
b. Exact statistic
c. The statistic is an upper bound on F that yields a lower bound on the significance level.
Tests of Between-Subjects Effects
Type III
Sum of
Source
Corrected
Model
Intercept
matur
Dependent Variable
Squares
Mean
df
Square
Partial Eta
F
Sig.
Squared
Projects on time
37,760a
4
9,440
26,641
,000
,614
Projects within budgets
14,040b
4
3,510
9,867
,000
,371
Projects meet goals & business intent
22,491c
4
5,623
11,790
,000
,413
Projects on time
708,648
1
708,648 1999,941
,000
,968
Projects within budgets
865,210
1
865,210 2432,094
,000
,973
Projects meet goals & business intent
946,997
1
946,997 1985,680
,000
,967
Projects on time
37,760
4
9,440
26,641
,000
,614
Projects within budgets
14,040
4
3,510
9,867
,000
,371
Projects meet goals & business
22,491
4
5,623
11,790
,000
,413
Projects on time
23,740
67
,354
Projects within budgets
23,835
67
,356
Projects meet goals & business intent
31,953
67
,477
Projects on time
822,000
72
Projects within budgets
941,000
72
intent
Error
Total
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generic pharmaceutical companies.
Projects meet goals & business intent
1052,000
72
Corrected
Projects on time
61,500
71
Total
Projects within budgets
37,875
71
Projects meet goals & business intent
54,444
71
a. R Squared = ,614 (Adjusted R Squared = ,591)
b. R Squared = ,371 (Adjusted R Squared = ,333)
c. R Squared = ,413 (Adjusted R Squared = ,378)
Projects on time
Tukey HSD
a,b
Subset for alpha = 0.017
Maturity of PfM
N
1
2
Ad hoc
12
2,08
Getting started
13
2,46
Structured and improving
14
3,64
Established
16
3,69
Optimized for continuous
17
3,94
improvement
Sig.
,446
,671
Means for groups in homogeneous subsets are displayed.
a. Uses Harmonic Mean Sample Size = 14,164.
b. The group sizes are unequal. The harmonic mean of the group sizes
is used. Type I error levels are not guaranteed.
Projects within budgets
Tukey HSD
a,b
Subset for alpha = 0.017
Maturity of PfM
N
1
2
Ad hoc
12
2,67
Getting started
13
3,31
Established
16
3,75
Optimized for continuous
17
3,82
14
3,93
3,31
improvement
Structured and improving
Sig.
,043
,054
Means for groups in homogeneous subsets are displayed.
a. Uses Harmonic Mean Sample Size = 14,164.
b. The group sizes are unequal. The harmonic mean of the group sizes is used. Type I
error levels are not guaranteed.
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generic pharmaceutical companies.
Projects meet goals & business intent
Tukey HSD
a,b
Subset for alpha = 0.017
Maturity of PfM
N
1
2
3
Ad hoc
12
2,75
Getting started
13
3,23
Established
16
Structured and improving
14
4,07
Optimized for continuous improvement
17
4,29
Sig.
3,23
3,94
,353
,061
3,94
,646
Means for groups in homogeneous subsets are displayed.
a. Uses Harmonic Mean Sample Size = 14,164.
b. The group sizes are unequal. The harmonic mean of the group sizes is used. Type I error
levels are not guaranteed.
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generic pharmaceutical companies.
Appendix E: “MANOVA – PfM Maturity – tools to evaluate
individual projects”
Box's Test of Equality of
Covariance Matricesa
Box's M
500,999
F
1,752
df1
156
df2
4101,396
Sig.
,000
Tests the null hypothesis that the
observed covariance matrices of the
dependent variables are equal across
groups.
a. Design: Intercept + matur
Multivariate Testsa
Partial Eta
Effect
Intercept
matur
Value
F
Hypothesis df
Error df
Sig.
Squared
Pillai's Trace
,988
338,894b
Wilks' Lambda
,012
338,894b
12,000
48,000
,000
,988
Hotelling's Trace
84,723
338,894b
12,000
48,000
,000
,988
Roy's Largest Root
84,723
338,894b
12,000
48,000
,000
,988
1,492
2,527
48,000
204,000
,000
,373
,073
3,793
48,000
186,940
,000
,480
6,299
6,102
48,000
186,000
,000
,612
5,373
22,835c
12,000
51,000
,000
,843
Pillai's Trace
Wilks' Lambda
Hotelling's Trace
Roy's Largest Root
12,000
48,000
,000
,988
a. Design: Intercept + matur
b. Exact statistic
c. The statistic is an upper bound on F that yields a lower bound on the significance level.
Levene's Test of Equality of Error Variancesa
F
df1
df2
Sig.
DCF
1,307
4
59
,278
NPV
13,022
4
59
,000
IRR
2,414
4
59
,059
ROI
3,898
4
59
,007
,346
4
59
,846
TPS
9,522
4
59
,000
CPS
6,325
4
59
,000
Profitability index
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eNPV
4,455
4
59
,003
Decision tree (project)
6,236
4
59
,000
15,549
4
59
,000
Expert opinion
2,243
4
59
,075
Gut feel - project
2,049
4
59
,099
Real Options
Tests the null hypothesis that the error variance of the dependent variable is equal
across groups.
a. Design: Intercept + matur
Tests of Between-Subjects Effects
Dependent
Type III Sum of
Squares
Mean
df
Square
Partial Eta
Source
Variable
F
Sig.
Squared
Corrected
DCF
54,408a
4
13,602
8,228
,000
,358
Model
NPV
41,676b
4
10,419
11,541
,000
,439
IRR
52,241c
4
13,060
14,076
,000
,488
ROI
6,368d
4
1,592
1,500
,214
,092
Profitability
4,459e
4
1,115
,645
,633
,042
TPS
104,664f
4
26,166
22,448
,000
,603
CPS
89,434g
4
22,358
20,086
,000
,577
eNPV
87,759h
4
21,940
13,735
,000
,482
Decision tree
52,358i
4
13,089
14,414
,000
,494
27,403j
4
6,851
6,162
,000
,295
4,064k
4
1,016
1,312
,276
,082
16,507l
4
4,127
7,497
,000
,337
DCF
749,799
1
749,799
453,586
,000
,885
NPV
1104,066
1
1104,066
1223,010
,000
,954
IRR
552,361
1
552,361
595,308
,000
,910
ROI
1052,409
1
1052,409
991,373
,000
,944
853,676
1
853,676
493,669
,000
,893
TPS
480,148
1
480,148
411,916
,000
,875
CPS
465,773
1
465,773
418,429
,000
,876
eNPV
616,650
1
616,650
386,057
,000
,867
Decision tree
353,667
1
353,667
389,444
,000
,868
Real Options
221,842
1
221,842
199,533
,000
,772
Expert opinion
626,599
1
626,599
809,207
,000
,932
index
(project)
Real Options
Expert opinion
Gut feel project
Intercept
Profitability
index
(project)
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Gut feel -
557,298
1
557,298
1012,428
,000
,945
DCF
54,408
4
13,602
8,228
,000
,358
NPV
41,676
4
10,419
11,541
,000
,439
IRR
52,241
4
13,060
14,076
,000
,488
ROI
6,368
4
1,592
1,500
,214
,092
Profitability
4,459
4
1,115
,645
,633
,042
TPS
104,664
4
26,166
22,448
,000
,603
CPS
89,434
4
22,358
20,086
,000
,577
eNPV
87,759
4
21,940
13,735
,000
,482
Decision tree
52,358
4
13,089
14,414
,000
,494
27,403
4
6,851
6,162
,000
,295
4,064
4
1,016
1,312
,276
,082
16,507
4
4,127
7,497
,000
,337
DCF
97,530
59
1,653
NPV
53,262
59
,903
IRR
54,744
59
,928
ROI
62,632
59
1,062
102,026
59
1,729
TPS
68,773
59
1,166
CPS
65,676
59
1,113
eNPV
94,241
59
1,597
Decision tree
53,580
59
,908
Real Options
65,597
59
1,112
Expert opinion
45,686
59
,774
Gut feel -
32,477
59
,550
DCF
950,000
64
NPV
1268,000
64
IRR
689,000
64
ROI
1158,000
64
999,000
64
TPS
714,000
64
CPS
667,000
64
eNPV
858,000
64
project
matur
index
(project)
Real Options
Expert opinion
Gut feel project
Error
Profitability
index
(project)
project
Total
Profitability
index
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Decision tree
496,000
64
Real Options
318,000
64
Expert opinion
700,000
64
Gut feel -
631,000
64
DCF
151,938
63
NPV
94,938
63
IRR
106,984
63
ROI
69,000
63
106,484
63
TPS
173,438
63
CPS
155,109
63
eNPV
182,000
63
Decision tree
105,938
63
Real Options
93,000
63
Expert opinion
49,750
63
Gut feel -
48,984
63
(project)
project
Corrected Total
Profitability
index
(project)
project
a. R Squared = ,358 (Adjusted R Squared = ,315)
b. R Squared = ,439 (Adjusted R Squared = ,401)
c. R Squared = ,488 (Adjusted R Squared = ,454)
d. R Squared = ,092 (Adjusted R Squared = ,031)
e. R Squared = ,042 (Adjusted R Squared = -,023)
f. R Squared = ,603 (Adjusted R Squared = ,577)
g. R Squared = ,577 (Adjusted R Squared = ,548)
h. R Squared = ,482 (Adjusted R Squared = ,447)
i. R Squared = ,494 (Adjusted R Squared = ,460)
j. R Squared = ,295 (Adjusted R Squared = ,247)
k. R Squared = ,082 (Adjusted R Squared = ,019)
l. R Squared = ,337 (Adjusted R Squared = ,292)
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generic pharmaceutical companies.
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generic pharmaceutical companies.
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generic pharmaceutical companies.
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generic pharmaceutical companies.
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generic pharmaceutical companies.
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generic pharmaceutical companies.
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generic pharmaceutical companies.
Appendix F: “MANOVA – PfM Maturity – tools to prioritize
projects and optimize the portfolio”
Box's Test of
Equality of
Covariance
Matricesa
Box's M
206,958
F
1,183
df1
91
df2
2820,702
Sig.
,117
Tests the null hypothesis
that the observed
covariance matrices of the
dependent variables are
equal across groups.
a. Design: Intercept +
matur
Multivariate Testsa
Partial
Eta
Square
Effect
Intercept
Value
Hypothesis df
Error df
Sig.
d
,978
164,711b
13,000
48,000
,000
,978
,022
164,711b
13,000
48,000
,000
,978
Hotelling's Trace
44,609
164,711b
13,000
48,000
,000
,978
Roy's Largest Root
44,609
164,711b
13,000
48,000
,000
,978
1,464
2,266
52,000
204,000
,000
,366
Wilks' Lambda
,109
2,786
52,000
188,014
,000
,425
Hotelling's Trace
3,938
3,521
52,000
186,000
,000
,496
Roy's Largest Root
2,934
11,510c
13,000
51,000
,000
,746
Pillai's Trace
Wilks' Lambda
matur
F
Pillai's Trace
a. Design: Intercept + matur
b. Exact statistic
c. The statistic is an upper bound on F that yields a lower bound on the significance level.
Levene's Test of Equality of Error Variancesa
F
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df2
Sig.
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generic pharmaceutical companies.
Project interdependecies
3,366
4
60
,015
Simple checklists
6,342
4
60
,000
Forced ranking models
1,940
4
60
,115
Weighted scoring models
2,141
4
60
,087
Bubble charts
6,636
4
60
,000
Decision trees -optimization
2,802
4
60
,034
Simulation approach
3,241
4
60
,018
Marginal analysis
7,171
4
60
,000
Analytical Hierarchy Process
8,364
4
60
,000
Sensitivity & scenario analysis
2,835
4
60
,032
Efficient frontier
9,070
4
60
,000
19,620
4
60
,000
3,057
4
60
,023
Tornado charts
Gut feel - optimization
Tests the null hypothesis that the error variance of the dependent variable is equal across groups.
a. Design: Intercept + matur
Tests of Between-Subjects Effects
Type III
Sum of
Source
Dependent Variable
Squares
Mean
df
Square
Partial Eta
F
Sig.
Squared
Corrected
Project interdependecies
31,372a
Model
Simple checklists
19,866b
4
4,967
5,027
,001
,251
Forced ranking models
11,262c
4
2,816
1,731
,155
,103
Weighted scoring models
19,265d
4
4,816
3,696
,009
,198
Bubble charts
55,878e
4
13,969
16,005
,000
,516
Decision trees -optimization
39,534f
4
9,883
9,782
,000
,395
Simulation approach
32,123g
4
8,031
6,681
,000
,308
Marginal analysis
26,369h
4
6,592
3,925
,007
,207
Analytical Hierarchy Process
33,910i
4
8,477
5,472
,001
,267
Sensitivity & scenario analysis
65,473j
4
16,368
13,955
,000
,482
Efficient frontier
24,703k
4
6,176
4,435
,003
,228
Tornado charts
18,174l
4
4,544
4,370
,004
,226
Gut feel - optimization
34,606m
4
8,652
9,767
,000
,394
Project interdependecies
497,194
1
497,194
457,235
,000
,884
Simple checklists
812,125
1
812,125
822,094
,000
,932
Forced ranking models
555,963
1
555,963
341,783
,000
,851
Weighted scoring models
682,955
1
682,955
524,133
,000
,897
Bubble charts
355,326
1
355,326
407,106
,000
,872
Decision trees -optimization
348,473
1
348,473
344,908
,000
,852
Simulation approach
245,550
1
245,550
204,275
,000
,773
Intercept
Postgraduate Dissertation
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7,843
7,213
,000
,325
149
Ioannis Fotopoulos, Exploring product portfolio management processes in
generic pharmaceutical companies.
matur
Error
Marginal analysis
271,696
1
271,696
161,773
,000
,729
Analytical Hierarchy Process
231,258
1
231,258
149,276
,000
,713
Sensitivity & scenario analysis
417,542
1
417,542
355,995
,000
,856
Efficient frontier
198,494
1
198,494
142,556
,000
,704
Tornado charts
154,690
1
154,690
148,789
,000
,713
Gut feel - optimization
575,962
1
575,962
650,224
,000
,916
Project interdependecies
31,372
4
7,843
7,213
,000
,325
Simple checklists
19,866
4
4,967
5,027
,001
,251
Forced ranking models
11,262
4
2,816
1,731
,155
,103
Weighted scoring models
19,265
4
4,816
3,696
,009
,198
Bubble charts
55,878
4
13,969
16,005
,000
,516
Decision trees -optimization
39,534
4
9,883
9,782
,000
,395
Simulation approach
32,123
4
8,031
6,681
,000
,308
Marginal analysis
26,369
4
6,592
3,925
,007
,207
Analytical Hierarchy Process
33,910
4
8,477
5,472
,001
,267
Sensitivity & scenario analysis
65,473
4
16,368
13,955
,000
,482
Efficient frontier
24,703
4
6,176
4,435
,003
,228
Tornado charts
18,174
4
4,544
4,370
,004
,226
Gut feel - optimization
34,606
4
8,652
9,767
,000
,394
Project interdependecies
65,244
60
1,087
Simple checklists
59,272
60
,988
Forced ranking models
97,599
60
1,627
Weighted scoring models
78,181
60
1,303
Bubble charts
52,369
60
,873
Decision trees -optimization
60,620
60
1,010
Simulation approach
72,123
60
1,202
100,769
60
1,679
Analytical Hierarchy Process
92,952
60
1,549
Sensitivity & scenario analysis
70,373
60
1,173
Efficient frontier
83,543
60
1,392
Tornado charts
62,380
60
1,040
Gut feel - optimization
53,147
60
,886
Project interdependecies
652,000
65
Simple checklists
936,000
65
Forced ranking models
712,000
65
Weighted scoring models
802,000
65
Bubble charts
512,000
65
Decision trees -optimization
494,000
65
Simulation approach
393,000
65
Marginal analysis
Total
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Ioannis Fotopoulos, Exploring product portfolio management processes in
generic pharmaceutical companies.
Marginal analysis
433,000
65
Analytical Hierarchy Process
399,000
65
Sensitivity & scenario analysis
607,000
65
Efficient frontier
341,000
65
Tornado charts
260,000
65
Gut feel - optimization
697,000
65
Corrected
Project interdependecies
96,615
64
Total
Simple checklists
79,138
64
108,862
64
97,446
64
Bubble charts
108,246
64
Decision trees -optimization
100,154
64
Simulation approach
104,246
64
Marginal analysis
127,138
64
Analytical Hierarchy Process
126,862
64
Sensitivity & scenario analysis
135,846
64
Efficient frontier
108,246
64
Tornado charts
80,554
64
Gut feel - optimization
87,754
64
Forced ranking models
Weighted scoring models
a. R Squared = ,325 (Adjusted R Squared = ,280)
b. R Squared = ,251 (Adjusted R Squared = ,201)
c. R Squared = ,103 (Adjusted R Squared = ,044)
d. R Squared = ,198 (Adjusted R Squared = ,144)
e. R Squared = ,516 (Adjusted R Squared = ,484)
f. R Squared = ,395 (Adjusted R Squared = ,354)
g. R Squared = ,308 (Adjusted R Squared = ,262)
h. R Squared = ,207 (Adjusted R Squared = ,155)
i. R Squared = ,267 (Adjusted R Squared = ,218)
j. R Squared = ,482 (Adjusted R Squared = ,447)
k. R Squared = ,228 (Adjusted R Squared = ,177)
l. R Squared = ,226 (Adjusted R Squared = ,174)
m. R Squared = ,394 (Adjusted R Squared = ,354)
Estimated Marginal Means
Maturity of PfM
95% Confidence Interval
Dependent Variable
Maturity of PfM
Project interdependecies
Ad hoc
1,667
,301
1,065
2,269
Getting started
2,615
,289
2,037
3,194
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Mean
Std. Error
Lower Bound
Upper Bound
151
Ioannis Fotopoulos, Exploring product portfolio management processes in
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Structured and improving
3,000
,369
2,263
3,737
Established
3,375
,261
2,854
3,896
Optimized for continuous
3,625
,261
3,104
4,146
Ad hoc
4,167
,287
3,593
4,741
Getting started
4,462
,276
3,910
5,013
Structured and improving
3,250
,351
2,547
3,953
Established
3,188
,248
2,690
3,685
Optimized for continuous
3,188
,248
2,690
3,685
Ad hoc
3,083
,368
2,347
3,820
Getting started
3,769
,354
3,062
4,477
Structured and improving
2,500
,451
1,598
3,402
Established
3,063
,319
2,425
3,700
Optimized for continuous
2,688
,319
2,050
3,325
Ad hoc
2,417
,330
1,758
3,076
Getting started
3,385
,317
2,751
4,018
Structured and improving
4,250
,404
3,443
5,057
Established
3,625
,285
3,054
4,196
Optimized for continuous
3,063
,285
2,492
3,633
Ad hoc
1,083
,270
,544
1,623
Getting started
1,615
,259
1,097
2,134
Structured and improving
2,750
,330
2,089
3,411
Established
3,313
,234
2,845
3,780
Optimized for continuous
3,313
,234
2,845
3,780
Ad hoc
1,250
,290
,670
1,830
Getting started
1,769
,279
1,212
2,327
Structured and improving
2,625
,355
1,914
3,336
Established
3,188
,251
2,685
3,690
Optimized for continuous
3,125
,251
2,622
3,628
Ad hoc
1,417
,316
,784
2,050
Getting started
1,308
,304
,699
1,916
Structured and improving
1,750
,388
,975
2,525
Established
2,500
,274
1,952
3,048
Optimized for continuous
3,063
,274
2,514
3,611
improvement
Simple checklists
improvement
Forced ranking models
improvement
Weighted scoring models
improvement
Bubble charts
improvement
Decision trees -optimization
improvement
Simulation approach
improvement
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generic pharmaceutical companies.
Marginal analysis
Ad hoc
1,500
,374
,752
2,248
Getting started
1,308
,359
,589
2,027
Structured and improving
2,250
,458
1,333
3,167
Established
2,875
,324
2,227
3,523
Optimized for continuous
2,625
,324
1,977
3,273
Ad hoc
1,000
,359
,281
1,719
Getting started
1,615
,345
,925
2,306
Structured and improving
1,750
,440
,870
2,630
Established
3,063
,311
2,440
3,685
Optimized for continuous
2,313
,311
1,690
2,935
1,333
,313
,708
1,959
Getting started
1,692
,300
1,091
2,293
Structured and improving
3,000
,383
2,234
3,766
Established
3,063
,271
2,521
3,604
Optimized for continuous
4,000
,271
3,458
4,542
Ad hoc
1,000
,341
,319
1,681
Getting started
1,462
,327
,807
2,116
Structured and improving
1,625
,417
,790
2,460
Established
2,250
,295
1,660
2,840
Optimized for continuous
2,688
,295
2,097
3,278
Ad hoc
1,000
,294
,411
1,589
Getting started
1,154
,283
,588
1,720
Structured and improving
1,500
,360
,779
2,221
Established
1,938
,255
1,428
2,447
Optimized for continuous
2,375
,255
1,865
2,885
Ad hoc
4,333
,272
3,790
4,877
Getting started
3,538
,261
3,016
4,061
Structured and improving
2,375
,333
1,709
3,041
Established
2,688
,235
2,217
3,158
Optimized for continuous
2,438
,235
1,967
2,908
improvement
Analytical Hierarchy Process
improvement
Sensitivity & scenario analysis Ad hoc
improvement
Efficient frontier
improvement
Tornado charts
improvement
Gut feel - optimization
improvement
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generic pharmaceutical companies.
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generic pharmaceutical companies.
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generic pharmaceutical companies.
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generic pharmaceutical companies.
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Appendix G: “MANOVA – PfM Maturity
- benefits of
current PfM processes”
Box's Test of Equality
of Covariance
Matricesa
Box's M
318,062
F
1,601
df1
144
df2
7274,075
Sig.
,000
Tests the null hypothesis that
the observed covariance
matrices of the dependent
variables are equal across
groups.
a. Design: Intercept + matur
Multivariate Testsa
Partial Eta
Effect
Intercept
Value
Hypothesis df
Error df
Sig.
Squared
,992
909,486b
8,000
60,000
,000
,992
,008
909,486b
8,000
60,000
,000
,992
Hotelling's Trace
121,265
909,486b
8,000
60,000
,000
,992
Roy's Largest Root
121,265
909,486b
8,000
60,000
,000
,992
1,392
4,205
32,000
252,000
,000
,348
Wilks' Lambda
,089
6,465
32,000
222,864
,000
,454
Hotelling's Trace
5,714
10,446
32,000
234,000
,000
,588
Roy's Largest Root
4,999
39,369c
8,000
63,000
,000
,833
Pillai's Trace
Wilks' Lambda
matur
F
Pillai's Trace
a. Design: Intercept + matur
b. Exact statistic
c. The statistic is an upper bound on F that yields a lower bound on the significance level.
Levene's Test of Equality of Error Variancesa
F
df1
df2
Sig.
Common basis for discussion
1,297
4
67
,280
Focus on breakthrough projects
3,348
4
67
,015
Better Strategic fit of portfolio
3,837
4
67
,007
Balance short-term vs. long-term
3,244
4
67
,017
projects
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Ioannis Fotopoulos, Exploring product portfolio management processes in
generic pharmaceutical companies.
Fewer but more worthwhile
7,501
4
67
,000
Improved times to market
1,045
4
67
,390
Provides unified support
2,125
4
67
,087
,571
4
67
,685
projects
Improves strategic planning
Tests the null hypothesis that the error variance of the dependent variable is equal across groups.
a. Design: Intercept + matur
Tests of Between-Subjects Effects
Type III Sum
Source
Dependent Variable
Corrected Model
Mean
of Squares
df
Square
Partial Eta
F
Sig.
Squared
Common basis for discussion
45,005a
4
11,251
21,016
,000
,556
Focus on breakthrough projects
44,652b
4
11,163
23,525
,000
,584
Better Strategic fit of portfolio
42,075c
4
10,519
25,868
,000
,607
Balance short-term vs. long-term
30,020d
4
7,505
22,000
,000
,568
62,675e
4
15,669
26,114
,000
,609
Improved times to market
36,207f
4
9,052
19,920
,000
,543
Provides unified support
37,401g
4
9,350
20,557
,000
,551
Improves strategic planning
35,339
h
4
8,835
15,881
,000
,487
Common basis for discussion
883,040
1
883,040
1649,399
,000
,961
Focus on breakthrough projects
962,811
1
962,811
2029,052
,000
,968
Better Strategic fit of portfolio
999,807
1
999,807
2458,777
,000
,973
Balance short-term vs. long-term
891,234
1
891,234
2612,627
,000
,975
750,897
1
750,897
1251,482
,000
,949
Improved times to market
676,224
1
676,224
1488,122
,000
,957
Provides unified support
803,553
1
803,553
1766,676
,000
,963
Improves strategic planning
765,763
1
765,763
1376,511
,000
,954
Common basis for discussion
45,005
4
11,251
21,016
,000
,556
Focus on breakthrough projects
44,652
4
11,163
23,525
,000
,584
Better Strategic fit of portfolio
42,075
4
10,519
25,868
,000
,607
Balance short-term vs. long-term
30,020
4
7,505
22,000
,000
,568
62,675
4
15,669
26,114
,000
,609
Improved times to market
36,207
4
9,052
19,920
,000
,543
Provides unified support
37,401
4
9,350
20,557
,000
,551
projects
Fewer but more worthwhile
projects
Intercept
projects
Fewer but more worthwhile
projects
matur
projects
Fewer but more worthwhile
projects
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generic pharmaceutical companies.
Error
Improves strategic planning
35,339
4
8,835
Common basis for discussion
35,870
67
,535
Focus on breakthrough projects
31,792
67
,475
Better Strategic fit of portfolio
27,244
67
,407
Balance short-term vs. long-term
22,855
67
,341
40,200
67
,600
Improved times to market
30,446
67
,454
Provides unified support
30,474
67
,455
Improves strategic planning
37,273
67
,556
Common basis for discussion
1027,000
72
Focus on breakthrough projects
1104,000
72
Better Strategic fit of portfolio
1135,000
72
999,000
72
923,000
72
Improved times to market
795,000
72
Provides unified support
929,000
72
Improves strategic planning
886,000
72
Common basis for discussion
80,875
71
Focus on breakthrough projects
76,444
71
Better Strategic fit of portfolio
69,319
71
Balance short-term vs. long-term
52,875
71
102,875
71
Improved times to market
66,653
71
Provides unified support
67,875
71
Improves strategic planning
72,611
71
15,881
,000
projects
Fewer but more worthwhile
projects
Total
Balance short-term vs. long-term
projects
Fewer but more worthwhile
projects
Corrected Total
projects
Fewer but more worthwhile
projects
a. R Squared = ,556 (Adjusted R Squared = ,530)
b. R Squared = ,584 (Adjusted R Squared = ,559)
c. R Squared = ,607 (Adjusted R Squared = ,584)
d. R Squared = ,568 (Adjusted R Squared = ,542)
e. R Squared = ,609 (Adjusted R Squared = ,586)
f. R Squared = ,543 (Adjusted R Squared = ,516)
g. R Squared = ,551 (Adjusted R Squared = ,524)
h. R Squared = ,487 (Adjusted R Squared = ,456)
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,487
Ioannis Fotopoulos, Exploring product portfolio management processes in
generic pharmaceutical companies.
Author’s Statement:
I hereby declare that, in accordance with article 8 of Law 1599/1986 and article 2.4.6 par. 3 of Law
1256/1982, this thesis/dissertation is solely a product of personal work and does not infringe any
intellectual property rights of third parties and is not the product of a partial or total plagiarism, and the
sources used are strictly limited to the bibliographic references.
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