As a Masters degree student, YOu Have a project and the brief goes thus:


Component A is a team presentation of 25 minutes (with 5 minutes of questions).

As a group you will be required to create a PowerPoint presentation which will detail the planning and management issues of a global project.

1.Choose a project you know off or you are interested in provided it is global, whether completed or in progress, and make sure that you are able to gather the information that is necessary for your analysis;

2.There are two sets of slides in Blackboard > Learning Material > week 1. These slides give you directions how to use UWE resources to find a case project.

3.Use the tools as described below in section 3. Content below and participate in the workshops to practice each tool with your group.

The presentation will be live, and all group members will present ONE part of the Assignment for which they will be individually marked. Not all group members will get the same mark necessarily, it depends on the quality of their work and presentation in their own part. Group members who do not participate and contribute to the Assignment will have to re-sit as they will get 0. .

This assessment strategy is employed to produce the type of output required by professional project management practice that is developed by a typical project team; and to maximize learning opportunities with added values for students’ development and character building.

This assignment requires you to develop research skills and find information that is useful for the purpose of detecting organizational issues related to the global environment and construct a plan. You will have to work with University databases and search through a large volume of information from Professional associations.

If you have difficulty to choose a global project here are some choices:

SMART CITIES
EXAMPLES
https://www.apm.org.uk/blog/five-of-the-uk-s-most-exciting-smart-city-projects/
https://publications.iadb.org/en/international-case-studies-smart-cities-santander-spain

GLOBAL HEALTHCARE PROJECTS
EXAMPLES
https://www.bond.org.uk/news/2017/04/7-projects-improving-healthcare-around-the-world

GLOBAL MEDICAL AND BIOTECH RESEARCH PROJECTS
EXAMPLES
https://globalprojects.ucsf.edu/
https://www.nihr.ac.uk/explore-nihr/funding-programmes/global-health.htm
https://www.tendersinfo.com/healthcare-and-medical-pharma-projects.php
https://www.pharmaceuticalonline.com/doc/top-10-international-pharmaceutical-biotech-c-0001

GLOBAL HUMANITARIAN PROJECTS
EXAMPLES
https://borgenproject.org/5-top-humanitarian-aid-organizations/
https://www.humanitarianprojectsinc.com/

These are some examples, you can find global projects in most industries.

Please do not just use Apple, Google or Toyota or any of these famous large organizations because you cannot have access to information about their projects. Choose projects about which you can discover information.

I strongly suggest that you attend the workshops and be proactive in forming groups in week 1 and use the Direction files in the Learning Materials folders showing you case study books to select project case studies.








2.Presentation Content & Structure
The live team presentations should not exceed 30 minutes including Q&A and feedback from the tutor. This means that the presentation should be approximately 25 minutes maximum. In a team of 5 people this means that each person will have to present their contribution for 5 minutes.

It is the responsibility of the team to allocate the parts of the work amongst them.


Content

Your task is to present your analysis & plan of a global project. You will choose the project as described in the section above.

STRUCTURE OF THE PRESENTATION - PARTS:

INTRODUCTION: introduce your project and your team and each team member’s contribution you are going to have a slide where you will attach next to each part the name of the team member who worked at it and who will present it.

ANALYSIS PART 1. Evaluate the challenges regarding such a project using the dimensions model from Binder
Evaluate the impact of environmental factors on this project.
oCollect information about the project environment through industry & professional reports and company literature or case studies. You do not need to do primary research and collect information from the organization people.

PLAN PART 2: Recommend the most appropriate method for managing your project using the Global Project Management Framework from Binder. This part will focus on:
1.a risk assessment based on the Global Project Management Framework
2.explain how you will deal with the issues of: international and intercultural communications, global team structures, and collaborative relationships with other organizations in the particular project
3.The team and project manager roles & skillset [competency maps/leadership framework/Belbin roles]

PLAN PART 3: Construct a Project Plan with objectives, a phase plan and collaboration tools and collaboration techniques as advised by Binder
*Note: be careful that the type of plan you present is linked to the methodology you chose. For example, if you select a specific agile methodology your plan should be based on the type of iterations of this method.

References: reports from industry or from organization/firm, company material and information from reputable sources (case studies, professional magazines articles etc.) and the sources from the models you use to conduct analysis






3.Preparation for Assignment A (mandatory)

Students are advised to attend the workshops because the Assignment consists of the use of a series of tools, each tool belonging to a part of the Assignment. Please look at the table below to see which workshops correspond to which tools and which parts of the Assignment.


Workshops Section in Assignment A Tool to use Workshop Session
Find a project
Introduction Use the slides Session 1
Detect the challenges Part 1 The Radar Chart Session 2
Detect success factors Part 1 The PIP Session 3
Describe the team competencies needed Part 2 Competency map Session 4
Design the project manager role & leadership Part 2 leadership framework Session 5
Describe the teams’ behavioural roles Part 2 Belbin team roles Session 6
Choose the way your teams will be creative Part 2 Brainstorming Techniques Session 7
Describe the cultural differences and challenges in your project Part 2 Hofstede cultural scores Session 8
Explain how you qill uaw technologies to deal with challenges Part 3 Collaboration platforms Session 9
Design project plan for your presentation Part 3 Already known tools Session 10

In preparing for this assessment, teams will be able to undertake a series of formative exercises and gain feedback in response. Expectations concerning the presentation will be discussed in workshops, and teams will also be able to present ONE draft presentation plan for formative review on the final workshop on week 10.



Your part of the project is to do the sessions 7 & 8; that is, Choose the way your teams will be creative (Brainstorming Techniques) and Describe the cultural differences and challenges in your project (Hofstede cultural scores)




In the best possible way, give me a more detailed structure for my ppt presentation. Break it down into slides. The chosen global project is below, with all the information available.


Project Title:AI IN ACTION (HOW THE HONGKONG DEVELOPMENT BUREAU BUIL THE PSS —
AN EARLY - WARNING - SIGN SYSTEM FOR PUBLIC WORKS PROJECTS)



EXECUTIVE SUMMARY
Hong Kong has a large volume of construction works
currently taking place; this will increase further and faster
in the coming years.
Hong Kong’s projects typically come in 15% under
budget. However, ten per cent of local projects over-run
their budgets, with a total overspend of eight percent.
Some flagship projects have over-run their budgets and
completion dates, causing public embarrassment.
It is crucial to get these cost and schedule estimates under
control in order that the booming infrastructure environment in Hong Kong can flourish to its potential while
carefully monitoring costs and performance.
We investigated whether Artificial Intelligence (AI) can play
a part in accurately predicting these outcomes and identifying ahead of time when a project is going off track, allowing
early intervention.
The large volume of projects in Hong Kong needs to be monitored collectively, with a system that allows leaders to focus
their efforts on the projects most likely to fail. An automated
system simplifies this process, and in our studies was found
to be as accurate as the best-in-class human cost estimates.
High quality data is available in Hong Kong, and we studied a total of 2,700 years of combined construction activity
and HKD 450bn in construction spent to trial this innovative project management approach. We unexpectedly discovered, however, that the AI needs only a very small amount
of data in order to make good predictions.
WE BELIEVE THESE EARLY FINDINGS WILL
INFORM A NEW APPROACH TO PROJECT PERFORMANCE MONITORING AND LEAD THE WAY IN
CAPITAL WORKS PROJECTS INTERNATIONALLY



THE CHALLENGE
Hong Kong expects project spending to increase by 42% between
2021 and 2031. The expected annual expenditure will reach up to
HKD 325bn by 2031.
However, these projects are not without their problems.
To spot challenges early, the Project Strategy and Governance
Office established a system to support supervision and governance of projects by providing early-warning signals to senior
leaders of projects that are at risk.
Projects are used to deliver the
most significant initiatives in the world;
from the Olympics, the artificial island
of Lantau, to the International Space
Station.
One of the challenges currently
faced by project leaders and investors is
that projects do not perform well.
They underperform in terms
of execution, with significant cost and
schedule overruns. 50% delays are common.
Our research has shown this
woeful underperformance is so consistent that we’ve coined the iron law
of megaprojects: they are ‘over budget,
over time, over and over again’.
This poor performance is not a
recent phenomenon: it has been fairly
consistent for over 70 years. It is also
common to different countries, sectors
and delivery in the private and public
sectors.
The scale of projects has been
increasing for decades. The largest
megaprojects have budgets that match
the gross domestic products of the largest countries.
The GDP of Hong Kong
in 2020 was USD 347bn, a similar
amount to the budget for China’s high
speed rail of USD 300bn and the USA’s
joint strike fighter (USD 400bn). This
large scale means that it is crucial for
the money spent on projects to be better
managed.
The impacts of funds of this
size being mismanaged can be felt on
a national and even international scale.
Over the period 2008 to 2017, performance on Government projects
under the Capital Works Programme
has generally been to a good standard, particularly from a budget perspective. Although certain projects
required additional funding owing
to project specific circumstances, the
majority of approved projects were
delivered at or under the original
Approved Project Estimates (APE).
Additional funding was required in approximately 10% of the
projects and the amount represented
some 8% of the total provision of
these projects.
Also of importance, this period of time was particularly active
for the Industry, with approximately
850 Category A projects having their
final accounts settled. In terms of
cost, the original APE of these projects totalled approximately HKD
240bn as compared with cumulative final accounts of approximately
HKD 210bn – leading to an overall
surplus of HKD 30bn.
Effectively, this means the
surplus generated at the portfolio
level was more than able to offset
the cost overruns incurred by a select
number of projects.
To reinforce the positive nature of this performance, Professor
Bent Flyvbjerg of the University of
Oxford pointed out that Hong Kong
is better than many other regions
when it comes to project cost estimation, based on his findings in a study
covering over 100 international jurisdictions.
Megaprojects are those that
come with high levels of procurement, design and/or construction
complexity as well as scale. They
typically take multiple years to implement and often involve high levels
of risk to the participants involved.
Unfortunately, a number of complex
megaprojects in Hong Kong have
suffered from underperformance in
the form of delays, cost overruns
and/or quality failures in recent
years. These incidents have been
well documented through extensive
media coverage – leading to reduced
levels of public confidence in the industry.
The early stages of a project
are crucial and can set the tone and
influence its efficacy. One of the
challenges of project performance
is the sensitivity to timescales in taking corrective action. The later such
actions are undertaken, the harder
and more resource intensive it is to
rectify problems.
However, it is not always
easy to identify risky projects before
things have gone wrong and they require substantial and costly remedy.
The Construction Industry Council
estimates that construction expenditure will increase in real terms by up
to 42% to HKD 325bn, compared
to 2021. The Development Bureau
of the Hong Kong Special Administrative Region has launched their
vision for Construction 2.0 to support effective delivery.
A key policy objective of the
Development Bureau is ‘to ensure
the effective planning, management
and implementation of public sector infrastructure development and
works programmes in a safe, timely
and cost-effective manner and to
maintain high quality and standards.’
In order to do this, it is vital
for senior managers to notice problems and ensure efficient, effective
and timely intervention when necessary. However, senior managers lack
the daily and deep involvement in
their projects. They need the right
monitoring tools and early-warning
-sign systems to help them in their
project supervision.


THE EXISTING SYSTEM
EARLY WARNINGS
A WEB-BASED MONITORING SYSTEM BASED
ON CASHFLOWS OF HISTORIC PROJECTS
The Development Bureau
uses the PSS, or Project Supervision
System, to monitor capital works
projects and provide early-warning
signals to senior management. The
PSS is a web-based application comprising a series of standardized project cashflow graphs.
The graphs are based on the
past cashflows of capital works projects. The PSS covers different categories for building and non-building
projects with various construction
durations.
All active projects submit
their actual cashflows to date and
their planned cashflows for the remainder of the project. The PSS uses
this information to identify any unusual patterns.
There are 3 types of patterns
which then trigger different followup actions.
First, normal performance.
The project cashflow falls within the
green zone. There is no follow-up
action for projects with normal performance. The data of these projects
will be stored for future use and refinement of the system.
Second, yellow performance
raises an alert. The project cashflow
falls within the yellow zone, meaning
heads of departments need to review
these projects. The review takes in the
risk of cost overrun and programme
delay. The heads of departments are
responsible for the project assessment
and, if necessary, for the planning
and implementation of appropriate
recovery measures.
Third, red performance flags
the strongest warning. When the
project cashflow falls within the red
zone, the Permanent Secretary for
Development (Works) chairs a top
management meeting, which monitors all projects in this category.

The monitoring system
provides a better understanding of
various elements of a project’s performance. Here are some insightful
examples from the first year of using
the system.
The first project falls into the
lower yellow zone, which indicates
slow progress. The question to investigate is whether the project might
be able to take actions to catch up
(purple dotted line), or will be delayed (blue dotted line).
The second project falls
into the upper yellow zone, which
indicates fast progress or cost overrun. The team needs to investigate
whether the project is indeed making very good progress and will be
completed early (purple dotted line),
or if the project will experience cost
overrun (blue dotted line). In either
case project plans and budgets might
need to be adjusted as a result of the
investigation.
The third project falls into
the lower red zone and has stayed
persistently in this zone for more
than a year.
This can signal a very long
delay or an overinflated budget due
to excessive contingency. In this case,
the team investigated the project indepth and found that the project carried too much contingency.
The project team reviewed
the cost and developed a more realistic project budget. Once the cashflow
was adjusted according to the new
budget, the cashflow fell back into
the normal zone.



After using our monitoring system for over a year, we discovered
that instead of it being a mere monitoring tool, the system
provides a benchmark and even means to set better target for the
project teams’ delivery of their projects. The data of these
projects, once completed, will form part of the database
creating a self-learning and self-updating system. Hong Kong’s
project governance will be enhanced by the positive and
interactive feedback between the PSS and the ongoing projects.



THE DATA
These closed projects have a total outturn cost of HKD 460bn (in
2019 HKD terms) and individual projects range from HKD 10m to 13bn.
The mean project value is HKD 550m.
The project durations range from 10 months to 18 years, with an
average 3-year duration. In total, the portfolio contains more than 2,700
years of construction activity.
The average project costs 15% less than forecast, and the range of
cost outcomes is from 69% below the budget to 59% over budget. There’s
a far greater range in project duration, ranging from 41% shorter than
planned to 317% longer than planned. The average delay is 34% for the
delayed projects.
The historic 2008-17 dataset is representative of current performance, according to the Development Bureau’s response to the Legislative
Council for projects in 2018-19.



FIRST FINDING OF THE AI
PROJECT TYPES
AI ALGORITHM IDENTIFIED THE GROUPINGS OF PROJECTS THAT
HAVE A DISTINCTIVE CASHFLOW PATTERN
To analyse the cashflow data,
two methods were selected: tracking
annual performance as a percentage
of the (1) outturn performance and
(2) the original plan.
A relative timeline was determined for each project, with the
percentage of the total duration assigned to each year. For instance, in a
four-year project the first year would
have 25% progress, the second 50%
etc.
The data for the analysis
comprised completed projects containing actual outcomes, but the
forecasted outcomes will be used
in the future, so the dual reporting
will reflect cost and schedule performance as a percentage of the most
recent forecast and the original plan.
The analysis used random
forrests, an artificial intelligence
method to analyse the data. This unsupervised learning algorithm considered all possible characteristics to
group the projects then extracted the
typical cashflows from the s-curves
and compared the fit of the curves.
Analysis of the PSS identified that four types of projects had
statistically significantly different
outcomes. The analysis resulted in
a decision tree, where the first decision divides all projects along the
lines of building or non-building
projects. Then the building projects
are categorised by whether the original budget was smaller than HKD
150m or not and the non-building
projects were categorised by whether
the planned duration was less than
six years or not.


RESULTS FROM THE AI
W A R N I N G
FLAGS
Each project was assigned a
warning flag status; red, amber or
green based on the outcome of the
project. If the project finished in the
middle 40% of the historic projects
(30th–70th percentile) the project
got a green flag. The bottom and
top 5% of the project got a red flag
(<5th or >95th percentile). All other
projects got an amber warning flag
(5th–30th and 70th–95th percentile).
After assigning the warning
flag based on the project outcomes
we trained three AI algorithms
(gradient booster, neural network, rolling discretizer) to find out the
warning flag status based on progress
and cashflows.
The first AI assigns a warning flag for cost performance. The
algorithm looks at progress and then
the share of dispensed cash to date
The second AI assigns a
warning flag for schedule performance. The algorithm looks at the
share of dispensed cash and then at
the progress made
The rolling discretizer, an algorithm specifically created for this task, achieved the most accurate
predictions in unseen test data. To
validate the predictive power of the
algorithm the data were split into a
training and a test set. The AI learned
from 70% of the data how to assign
warning flags. Then the accuracy
of the warning flags was measured
on the remaining 30% of the data,
which the algorithm had not seen
before.
The AI achieves an excellent
performance, especially in identifying the red projects correctly. The
performance is well balanced across
the different flags.
53%–69% of all the flags
assigned to the observations are accurate. This means, for example, that
if the AI assigns a red flag for a cost
overrun the algorithm is accurate
nearly 7 out of 10 times.
The accuracy of the schedule
flags is slightly worse. Here the AI is
accurate in its prediction 18%–63%
of the time.
The AI outperforms the split
by project type, which was only accurate in 8%–40% of predictions.
The AI vastly outperforms
randomness. If we simply made an
uninformed “lucky” guess about the
project’s risk we would get it right in
3%–25% of times.
Especially for the very high
risk category “red” the AI is more
than 20–times better than a lucky
guess.
In addition to the validation with a test and training set of
the data, we tested how little data
the AI needs to achieve this level of
accuracy. The analysis shows that is
possible with as little as 10% of the
data. We were surprised by this finding, because we always assumed that
AI needs lots of data. It turns out we
were wrong. Any organisation wishing to integrate AI into their early
-warning-sign system needs some but
not vast amounts of data.
The AI is far from perfect;
the accuracy is surpsingly good even
though the algorithm only knew the
cashflow and type of a project.
Adding more data in the future will certainly improve the accuracy



L O O K I N G F O R W A R D
WITH THE AI
P R E D I C T I N G
COST
The accuracy of the warning flag AI is promising enough to train
more algorithms on an even more detailed task: forecast the
outturn cost of projects. A more complex AI, a deep artificial
neural network, predicted the final outturn cost with an average
error of only ±8%. The accuracy is as good as a Class 1 estimate.
The next challenge we set for
the AI algorithms was to predict the
final outturn cost of projects.
The AI only knew the percentage of progress the project had
achieved so far, measured against the
planned project duration at final investment decision and the cashflow
spent to date as percentage of the
budget, that was approved at the final investment decision.
For this challenge we tested
many different AI and non-AI algorithms, including linear regressions,
support vector machines, gradient
boosters, random forrest regressions,
k-nearest neighbours, multilayer
percepetrons, simple deep neural
networks and complex deep neural
networks.
We measured the error of the
predicted outturn cost in the training data, which tells us how good
the AI is at summarising the data it
knows. We then gave the AI unseen
data to test the prediction. As before,
the AI used 70% of the data to train
and 30% for testing.
The best performing algorithm was the simple deep neural
network. The mean squared error
of the predictions for the test data
was 1.6 percentage points. The mean
absolute error in the test data was 7.9
percentage points.
This means that on average the forecasted outturn cost was
within ±8% at the portfolio level.
The mean squared error, which is
similar to the standard deviation, is
only ±1.6%, thus 68% of predictions
are within a range of ±1.6%, 95%
of predictions are within a range
of ±3.2% and 99.7% of predictions
are within ±4.8% of the predicted
cost. The mean absolute error of the
prediction is ±7.9%, therefore an
indication of the skew in the data,
where very large overruns and fat
tails are not uncommon.
The AI estimate is in line
with what conventional cost estimation practice considers to be the
best cost estimate achievable. The
Association for the Advancement of
Cost Engineering (AACE) suggests
that international best practice for
a best-in-class cost estimate has an
error between ±3% and ±15%; to
achieve an estimate with this narrow
error range, 100% of design needs to
be completed and detailed information of unit cost and prices needs to
be available. The estimate of the AI
falls within the range expected for a
Class 1 cost estimate.




CASE STUDIES
SPOTTING RISK
ALL HONG KONG PROJECTS ARE MONITORED BY THE PSS.
THESE ARE TWO REAL-LIFE CASES WHERE THE AI SPOTTED RISKY
PROJECTS EARLY
We selected two capital works projects from the portfolio to run the AI analyses. Project 1 has an approved project
cost estimate (APE) of HKD 5.2bn with completion within 3.5 years. Project 2 has an approved budget of HKD
1.3bn with a construction duration of 3.9 years. Project 1 has completed its first year of construction. Project 2
has completed its first two years of construction.
The latest cashflow information shows that project 1 has spent 1% of its cash and is nearly 29% complete when
measured against the originally planned duration. Project 2 is 51% complete and has spent 21% of its cash.
First, the AI assigned warning flags. Project 1 has a red flag. Project 1 has been spending the budget slower than
the AI anticipates based on the typical spending pattern of capital works projects in Hong Kong. The project’s own
forecast of spend (dotted line) shows
that this trend will reverse in the next
two years and exceed usual patterns
in years 4 and 5. The AI’s flag raises
questions about this unusual profile
of acceleration and suggests further
investigation.
Project 2 receives a yellow flag. The
project is spending its budget more
slowly than typical projects but it
forecasts that it will soon be in line
with others. The schedule flags show a similar pattern. Project 1 gets a red
flag for being very late if it can keep
up the expenditure as is (under the
assumption that this is not indicating
a big cost underrun).
Project 2 is progressing like a typical
project in Hong Kong and the AI assigned a green schedule flag. But the
AI raises questions about the plans
after year 5.
The AI also produces a cost forecast
of the final outturn cost. At the mo- ment Project 1 is forecasted to cost
HKD 4.0bn. For this estimate, the
AI assumes that the project will not
follow its own forecasted trajectory
but behave more like a typical project
in the future. However, the unusual
pattern of expenditure means that if
the project does indeed accelerate its
spend the cost are likely HKD 5.1bn
close to the APE. Project 2 has similar issues. The AI forecasts outturn
cost of HKD 1.0bn. The project’s
own forecasts show a spike in spending near the end; if that holds true
the AI forecasts spending of HKD
1.3bn. Again the readiness for acceleration should be investigated.


The enhancement of the PSS does not stop here. Our future plans
include adding more detailed project data, further developing
the AI to enhance the predictive power and extending the PSS to
railway projects in collaboration with other international regions. Our innovations will not only support Hong Kong’s ambitious vision but also benefit projects worldwide.