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    To sign up to the event please go to our meetup page here: https://bit.ly/32hk47O Risk management has existed for decades. But how many times do we go back and analyse how many times we got it right? How good were we at managing risk? We leave a forensic data plume that evaporates, yet it contains a huge amount of insight. Rather than relying on expert judgement, can we use risk to augment our instinct and counter inbuilt bias? What will the impact of this be on traditional project based risk management roles? Within this session Sue Kershaw, President of the APM, will be c
  4. Earlier
  5. The first part of this challenge is to visualise the emissions associated with with vehicle and flight travel. Vehicle emissions can be extracted from the expenses data. Based on the logic recorded by the challenge sponsor,  you should report:

    Total Own car emissions (CO2e) 
    Total Own car energy (kWhr) 
    Total Vehicle rent and running emissions (CO2e) 
    Total Vehicle rent and running energy (kWhr) 

    Emissions should be reported by project, cost centre and location where possible. Flight data reports an associated emission with each flight and should be reported similarly to vehicle data, along with any suitable geographical insights.

    The second part of this challenge is to build a data capture tool to record relevant data for the reporting of emissions. It is suggested that this would be a Microsoft PowerApp.

    The structure of the data captured should be consistent with the visualisations created in Part 1, but should also strive for a gold standard of environmental reporting for business related travel, i.e. you should liaise with the challenge sponsor if possible to ensure all that their "dream data scenarios" are catered for.

    CHALLENGE 13.pptx Demo Data.xlsx Demo Solution.txt

    Free
  6. SPS - Subsea Production Systems - delivers EPC Projects (Engineering, Procurement, Construction) for Oil Majors, National Oil Companies and Independent O&G Operators. These projects are managed through Primavera P6 and are made of multiple schedules, each with hundreds of activities.

    Actual Progress in our many types of Project Schedule activities is accrued according to varying profiles, which deviate a lot from the assumed template profiles we use in our baseline schedules. We need a comprehensive template vs actual comparison and suggestions as to what kind of clustering / refinement we need to put in place to make them more accurate. We project the % Progress to be accrued over the lifetime of each Activity based on Curve profiles that represent when we expect to comply with each of the steps towards activity completion, i.e., when rules of credit are fulfilled.

    You should compare how our theoretical Activity Progress curves compare with the Actual Activity Progress from 14 Projects. You will need to develop a measurement of the quality of the fit. We would like to know which of the theoretical curves fit better than others. We would also like you to provide advice on changes to our theoretical curves to better fit reality.

    CHALLENGE 11.pptx Demo Data.xlsx Demo Solution.txt

    Free
  7. We have a variety of H&S data and would like insights into the causes of HSE incidents. The first part of the challenge involves understanding the environment in which a HSE incident is most likely to occur. Are there any correlations between HSE incidents occuring and competency levels, disciplines, contracts/locations, absence or training levels? Are there any seasonal or other trends within the HSE incident data? When production is lower during maintenance periods are incidents more or less likely to occur? This part of the challenged could be solved though a interactive Power BI dashboard.

    The second part of the challenge involves building a model to predict when a HSE incident is likely to occur. You should highlight the attributes in the model that are most predictive and any potential reason for this. If we are able to understand the conditions that are likely to lead to a HSE incident occuring then we are able to take action to mitigate the risks. How useful are our HSE metrics in predicting whether an incident is likely to occur?

    CHALLENGE 6.pptx Demo Data.xlsx Demo Solution.txt

    Free
  8. The first part of this challenge is to visualise the emissions associated with with vehicle and flight travel. Vehicle emissions can be extracted from the expenses data. Based on the logic recorded by the challenge sponsor,  you should report:

    Total Own car emissions (CO2e) 
    Total Own car energy (kWhr) 
    Total Vehicle rent and running emissions (CO2e) 
    Total Vehicle rent and running energy (kWhr) 

    Emissions should be reported by project, cost centre and location where possible. Flight data reports an associated emission with each flight and should be reported similarly to vehicle data, along with any suitable geographical insights.

    The second part of this challenge is to build a data capture tool to record relevant data for the reporting of emissions. It is suggested that this would be a Microsoft PowerApp.

    The structure of the data captured should be consistent with the visualisations created in Part 1, but should also strive for a gold standard of environmental reporting for business related travel, i.e. you should liaise with the challenge sponsor if possible to ensure all that their "dream data scenarios" are catered for.

     

     

    CHALLENGE 13.pptx Demo Data.xlsx Demo Solution.txt

    Free
  9. There are very few established tools available to project team to allow easy and clear monitoring of the performance of the design teams, despite a large amount of design-related data being available through our Common Data Environment. Two particular strands of this data have been selected as potential candidates for creating metrics for monitoring & reporting on design team performance:

    RFI Data - 'Requests for Information', where additional clarification and information is required on specific elements of the design. While these are a normal part of the design process, it is thought that  an excessive amount of RFIs indicate a design is badly thought-out of incomplete. In addition, RFIs that take a long time from being raised to being resolved would suggest a design team that is not attentive to the needs of the project team.

    Drawing Data - 'General Arrangement' and 'Layout' drawings as a representative sample of the type of drawing that all projects are likely to have. The key focus here is on revisions - an excessive amount of revisions indicate a poorly co-ordinated drawing while revisions taking place over a long period suggest an overly complex design process. Additionally, extensive revisions to 'Construction Issue' drawings can be particularly disruptive to project teams and instigate costly and time-consuming change processes.

    In addition, some basic project information has been provided on the projects shown in the datasets to allow context on scale, status etc.

    Using the above data you should develop a set of metrics to assess and model design partner performance and also look to explore trends across data - such as what stage of a project sees the largest volume of RFIs. Consider this data in the context of what a main contractor could do to improve the process - for example if a large volume of RFIs occur at a certain stage in the project, they might commit additional design staff during this period.

    You should also consider:
    - Which designer had a disproportionate amount of RFIs?
    - Where did RFIs take a long time to close out and require multiple iterations?
    - How are RFIs distributed over time and what is the implication of this?
    - Which designer partners required a large amount of change to their design drawings and how long did this change process last?

     

     

    CHALLENGE 7.pptx Challenge 7b - Demo Dashboards.pbix Drawing Data.xlsx Drawings.ipynb ML Rev 3.ipynb RFI Data.csv RFI.ipynb

    Free
  10. As a Cost Manager it is important to be able to understand the price trends of key materials components, such as steel, concrete and copper and currency fluctuations. Currently it is a very manual process to extract this information, and this data isn't well recorded so we aren't able to understand trends in prices.

    This challenge involves creating a webscraping tool to track the price trends of key materials over time using the sources provided. The materials we are interested in include steel, copper and concrete as well as currency fluctuations. You should create a dashboard to display these prices as well as key analytics such as change in month/qtr. You could also include some guages that will alert the user when prices rise above a certain threshold. The data sources must be displayed on the dashboard.

    You should include a plan to deploy the solution using an Azure Function (or similar) so that the webscraping tool can run on a daily basis to ensure that the data extracted is consistently up to date.

     

     

    CHALLENGE 5.pptx Demo Data.xlsx Demo Solution.txt

    Free
  11. Checking specification documents for a project is a manual and time-consuming process, with the potential for human error. We would like to build a tool that improves the efficiency of this process by automatically creating a requirements list from a document based on a series of key words and phrases.

    The challenge involves creating a script in Python that is able to 'read' in a specification document in PDF form and search through the document for key words and phrases that have been provided. This script should pull out a requirements list (the long list) from the document to then be checked by a Quality Manager. If possible the script should highlight the location in the document of items in the requirements list to enable someone to then go through the document and review the requirements.

    The second part of the challenge involves creating a User Interface to this tool - the majority of people who will find this tool useful will not have experience using Python and will not want to open a Python script and press run. We recommend creating either a Power App or Power Automate flow. A Power Automate flow could allow you to email a specification document to a certain email address, and the flow return a list of requirements along with their locations in the document.

     

     

    CHALLENGE 2.pptx Demo Data.xlsx Demo Solution.txt

    Free
  12. SPS - Subsea Production Systems - delivers EPC Projects (Engineering, Procurement, Construction) for Oil Majors, National Oil Companies and Independent O&G Operators. These projects are managed through Primavera P6 and are made of multiple schedules, each with hundreds of activities.

    Actual Progress in our many types of Project Schedule activities is accrued according to varying profiles, which deviate a lot from the assumed template profiles we use in our baseline schedules. We need a comprehensive template vs actual comparison and suggestions as to what kind of clustering / refinement we need to put in place to make them more accurate. We project the % Progress to be accrued over the lifetime of each Activity based on Curve profiles that represent when we expect to comply with each of the steps towards activity completion, i.e., when rules of credit are fulfilled.

    You should compare how our theoretical Activity Progress curves compare with the Actual Activity Progress from 14 Projects. You will need to develop a measurement of the quality of the fit. We would like to know which of the theoretical curves fit better than others. We would also like you to provide advice on changes to our theoretical curves to better fit reality.

     

     

    CHALLENGE 11.pptx Demo Data.xlsx Demo Solution.txt

    Free
  13. We have a variety of H&S data and would like insights into the causes of HSE incidents. The first part of the challenge involves understanding the environment in which a HSE incident is most likely to occur. Are there any correlations between HSE incidents occurring and competency levels, disciplines, contracts/locations, absence or training levels? Are there any seasonal or other trends within the HSE incident data? When production is lower during maintenance periods are incidents more or less likely to occur? This part of the challenged could be solved though a interactive Power BI dashboard.

    The second part of the challenge involves building a model to predict when a HSE incident is likely to occur. You should highlight the attributes in the model that are most predictive and any potential reason for this. If we are able to understand the conditions that are likely to lead to a HSE incident occurring then we are able to take action to mitigate the risks. How useful are our HSE metrics in predicting whether an incident is likely to occur?

     

     

    CHALLENGE 6.pptx Demo Data.xlsx Demo Solution.txt

    Free
  14. In the wake of the Covid-19 pandemic, social distancing has become the 'new normal' for people across the world. Now that the majority of construction sites have re-opened after brief shutdowns, social distancing is even more important here due to the often a large number of people working on a site at any one time. This challenge involves creating a tool that alerts managers when workers are breaking social distancing regulations and detecting transmission hotspots on the site itself. 


    Can you use existing technology to determine the number of times social distancing is broken in certain video clips? If a set rate of social distancing breaches is detected, can an alert tool be built to inform a supervisor that an area of site monitored by a specific video feed is a potential hotspot for Covid-19 transmission?


    An advanced part of the challenge involves identifying transmission hotspots in the video. Can you identify areas where workers were closer together for longer periods of time, and overlay a heatmap over a still image from the video?
    The article attached in Useful Resources give an example of how this problem can be solved by mapping the camera frame to bird eye view. It is reasonable to assume the pedestrians are 6 feet tall for the purpose of calculating distances between people.

     

     

    CHALLENGE 1.pptx Demo Data.xlsx Demo Solution.txt

    Free
  15. This tool is a Microsoft Power App designed to help you work through your backlog and see what tasks are important to the core team, and then bring that list for and Executive level review.

    Instructions.docx PrioritisationAppDataBuild_20210310191333.zip ProjectingSuccess-PrioritisationTool_20210310191303.zip

    Free
  16. I had written above article on LinkedIn.I would appreciate to receive feedback & thoughts on the same.
  17. The possibilities of Big Data Analytics are exciting in the construction industry. It will open up new horizon of opportunities for construction companies by helping them in improving project management by reducing costs, mitigating risks involved, and time taken in completing the project. Note :One of the biggest challenge would be gathering such data and organizing the same in format so it can be analyzed(cleansing) .But I am confident its not impossible with advancement in technology.
  18. Hi Everyone, A huge thanks to everyone who supported project Hack 8. I have attached the consolidated feedback and scores from >20 judges. We have bracketed the scores for the last 10 places to avoid disenfranchising anyone. We hope that you find the feedback helpful in preparing for Project:Hack 9. Hope to see you at the next one! What do you think? What would you do to improve? Feedback Hack 8.xlsx
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    To sign up to the event: https://bit.ly/3sDj7C4 The Road to Project Data Analytics Greatness is back! After popular demand, we have decided to rerun our interactive session for those who couldn't attend! Within this session James Smith, PhD will be walking you through all the components of project data analytics, through the lens of a project delivery professional. Many existing roles will look radically different in a few years time; for some it will be a matter of adapt or become obsolete. We will cover everything that we'll need in our armoury to adapt over the coming years.
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    To attend the event please go to Meetup: https://bit.ly/39qKNTh Infrastructure projects are rich in data, but we often struggle to leverage it. The data isn’t just on performance metrics and cubic metres of soil, we also capture data on supplier performance, compensation events, and a wide variety of other data. This data is held at every level of our supply chain. Within his talk Alan Perkins, Head of Complex Infrastructure Programme, will be providing an overview of how Highways England are beginning to transform how they leverage this data. He will walk us through a number of c
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    To attend the event please go to: https://bit.ly/2PmjYsy When extracting insights from project data, a symptom of not understanding the problem statement, is that we often get overwhelmed and dive in without any substantial planning. We end up with several shallow takes on what our data might suggests, but we lack the depth to support and really drive our business case. This talk will showcase the importance of meticulously planning your approach to optimise your implementation of data analytics. Rishi will provide a demonstration in identifying the use case, conceptually designin
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    We have all worked in organisations that suffer from MI overload, that create constant reporting cycles and seem to have endless data but yet are not very data-driven when it comes to decision making. Very few of the many project reports and portfolio dashboards that we create today are actually used effectively by stakeholders to either stay informed or make decisions (still heavily relying on meeting discussions and ad-hoc verbal updates). As we accelerate advances in project data analytics and the application of machine learning in project predictability, we need to ensure the outputs ar
  23. Effective project data analytics requires data; lots of data. There is a split across industry into how this challenge is tackled, divided into various camps. I’ve summarised them into 5 areas but I recognise that it is difficult to capture all the nuances within such a short blog. 1. Closed systems. This is where a few companies are heading, creating their own vaults of data to shape their own models and analysis. We are seeing these starting to proliferate, from waste to project performance, schedules to risk. Each is individual silos, with bespoke T&Cs. Beliefs: Data is
  24. In 2017 we met with people from the NHS and Network Rail to discuss the challenge of learning from experience and how advanced data analytics holds the key. I then developed a paper with Dr Stephen Duffield that summarised our research into 20,000 lessons lesson; we concluded that the process just doesn’t work. We take the complexity of a project and boil the experience down into a few trite paragraphs that are often statements of the obvious. Even today, a recent paper from Grant Mills et al highlights that “the industry as a whole is failing to learn from known failures“. We saw then th
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    Our second masterclass of 2021 will be led by Chris Huntingford! This class will focus on giving you a full overview of the components within Microsoft's Power Platform and how they can be leveraged in your specific areas. We will take a look at the technical architecture and then snap this to several use cases to bring the technology to life. Some more information about Chris and his work: “I’m a geek and proud to admit it! I’m also a rather large, talkative South African who plays drums, wears horrendous Hawaiian shirts and has an affinity for engaging in as many social gatherings a
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    In the field of Project Data Analytics a lot of what is possible is unrecognised by the majority of the project community. We developed Project:Hack to show the art of the possible within a weekend. Project:Hack was created to provide a platform for professionals and students to get hands-on experience working with data on real business challenges. Our 8th instalment of Project:Hack is on the horizon and we're looking to optimise your experience by building your competitive appetite and preparing you for the upcoming event. This talk will feature guest speakers and advocates of Project:H
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