Regardless of the Artificial Intelligence (AI) platform used to predict future project outcomes or results based on today’s achievements and actions, the risk-simulated integrated resource-loaded project schedule (IPS) and daily progress reports will always be part of the input data needed by the AI platform. Of course, many other sources of data also needed to be fed to AI platform such as weather conditions, price escalations, risks, supply chain, and procurement updates, past projects performance data as well as data captured from IoT sensors, RIFD, and GPS tags, digital site cameras just to list a few.
The integrated risk-simulated resource-loaded schedule represents the most-likely plan to deliver the capital construction project at the target completion date using the estimated labor and equipment resources. The risk probability cumulative distribution curve generated using Monte Carlo Risk simulation will be used to determine what should be the project’s target completion date at 80% confidence level where the difference between the 80% and 100% confidence level will be the schedule buffer. Of course, organizations can create an integrated resource-loaded project schedule with a higher confidence level by increasing the resources planned for the project.
Regardless of how perfect is the risk-simulated integrated resource-loaded project schedule (IPS) in predicting the project’s completion date, the only completion date prediction that will be of interest is when the construction project moves from the planning stage to the execution stage. During the execution stage, the project schedule needs to be updated on daily basis with actual progress achieved against the planned activities and what was consumed as labor and equipment resources in achieving this progress to enable providing a meaningful prediction.
In addition, the schedule needs to be updated with all events that could have disrupted or interfered with the achieved progress and whether those would be a progressive trend. Those could be safety incidents, severe weather conditions, actions by entities involved in delivering the project, and other events that could have had an impact. Further, copies of all communications, pictures, videos and other types of documents that are associated with those disruption events need to be captured in digital format where attributes and notes can be added to their digital version.
The timely capturing and reporting of this actual progress information is of great importance. IoT and RIFD Sensors, Time Attendance Systems and other methods used for capturing labor resource hours will not be the right solution for progress capturing. Maybe it will work for equipment resources but sure not labor resources. What is needed is a solution that will not only captures the labor resource hours but also against what project schedule activities they were spent and what was the work quantity completed by those resources. In addition, all observations of events that could have disrupted or interfered with the resources while performing their works need to be captured. Further, any notes and comments made by the resources crew supervisors and site engineers associated with what has happened during the day are also vital to be captured.
Of course, there is a challenge in achieving this daily progress capturing and reporting if it will be done the old-fashioned way where the reported progress from each crew supervisor needs to be captured, consolidated, reviewed, and formally submitted as the project daily report. Not only this is a high-effort consuming process that will delay sharing this important information, but also it has the risk that some of the captured details could be lost or disregarded when reported. Therefore, there is a need to capture and share the daily report progress data in their raw format before they are edited and presented.
Using a Project Management Information System (PMIS) like PMWeb will enable capturing Daily Reports in their Raw format to achieve real-time feeding of the daily progress information to the Artificial Intelligence (AI) platform. Each project supervisor or site engineer will be responsible for capturing the daily progress details for the work package or packages assigned to them. For each created daily report, the weather conditions of that data need to be captured.
In addition, the name of the company submitting the report as well as the name of the supervisor or site engineer needs to be provided. Although there will be separate business process forms for safety incidents and safety violations to capture their details, nevertheless a summary of incidents needs to be captured in the daily report. In addition, the report will include the top five issues or events that have either disrupted or interfered with the reported work progress.
The daily report will be also used for the details of the completed works for the assigned work package or work packages. For each reported work in place item, the supervisor or site engineer needs to drag the location from the list that had been predefined for the daily report. In addition, the reported workplace will be used to capture the name of the company that has completed the work, description of the completed works, classification of the completed work from a pre-defined list of values, quantities of the completed work, and unit of measure.
In addition, details of the project schedule activity associated with this work, Work Breakdown Structure (WBS) level for the work package associated with the work in place, cost code, or Cost Breakdown Structure (CBS) for the completed work will be also captured. The project schedule activities and Work Breakdown Structure (WBS) levels will be imported from Primavera P6 or MS Project to make them readily available to select from.
The captured data will also include on which date this work has started and finished which will be the Actual Start (AS) and Actual Finish (AF) dates of the associated activity, notes on the completed works among others. In addition of the completed or progress work will be captured and attached.
The details of the actual labor and equipment resources for the completed work in place will be captured on the timesheet tab. For each labor or equipment resource, the data to be captured will be the resource name, cost account, or Cost Breakdown Structure (CBS) that the resource hours will be charged against, the project schedule activity that the resource hours is associated with, start and finish time, whether the labor resource hours are regular pay, weekend pay, overtime pay or another type of pay, for the equipment hours whether the equipment was idle, description of the complete works, Work Breakdown Structure (WBS) level for the work package associated for the work in place, notes on the spent resource hours among others.
In addition to the pictures of the completed works, all other pictures, videos, and documents are relevant to the reported daily report. As a best practice, all pictures, videos, and documents need to be uploaded and stored in the PMWeb document management repository. Folders and subfolders will be created to ensure that those documents are stored in their specified location.
In addition, links to records for business processes managed in PMWeb can be also added to the daily report. Those could include for examples work inspection requests, safety incidents, non-conformance reports, site work instructions, confirmation of verbal instructions, submittals, and others.
To ensure the quality of the reported daily data, a basic workflow will be added to the daily report form. The workflow will be for the supervisor or project engineer who is also the submitter will submit the workflow to their direct supervisor, who could be the site manager who will approve the daily report. The objective of the workflow is not for the site manager to review, comment, approve or reject the daily report submission but more for the submitter to confirm that he/she is responsible for the submitted data.
For the artificial intelligence (AI) platform to function and predict future outcomes of today’s actions and results, need to capture the actual labor and equipment resources hours spent on the project to achieve the reported work in place for each trade. In addition, it needs to capture the details of the disruption and interference events that could impact the performance of those resources in achieving the reported work in place. Those events would include exceptional weather events.
In addition, the AI platform will capture the details of the risk-simulated integrated resource-loaded project schedule (IPS), project risk register, and other details associated with the project. This data will be further enriched by other data sources that the AI platform will be integrated with. This data, past projects historical data, and the analysis learning fed to the AI platform will become the basis for the AI algorithms to determine the performance indices that need to be applied to each trade to enable predicting the future results and outcomes based on each project own progress and performance particulars.
About the Author
Bassam Samman, PMP, PSP, EVP, GPM is a Senior Project Management Consultant with 40-year service record providing project management, project controls services, and project management information systems to over 200 projects with a total value over the US $100 Billion. Those projects included Commercial, Residential, Education and Healthcare Buildings and Infrastructure, Entertainment, Hospitality, and Shopping Malls, Oil and Gas Plants and Refineries, Telecommunication, and Information Technology projects. He is thoroughly experienced in complete project management including project management control systems, computerized project control software, claims analysis/prevention, risk analysis/management (contingency planning), design, supervision, training, and business development.
Bassam is a frequent speaker on topics relating to Project Management, Strategic Project Management, and Project Management Personal Skills. Over the past 40 years, he has lectured at more than 350 events and courses at different locations in the Middle East, North Africa, Europe, and South America. He has written more than 300 articles on project management and project management information systems that were featured in international and regional magazines and newspapers. He is a co-founder of the Project Management Institute- Arabian Gulf Chapter (PMI-AGC) and has served on its board of directors for more than 6 years. He is a certified Project Management Professional (PMP) from the Project Management Institute (PMI), a certified Planning and Scheduling Professional (PSP), and Earned Value Professional (EVP) from the Association for the Advancement of Cost Engineering (AACE) and Green Project Management (GPM).
Bassam holds a Masters in Engineering Administration (Construction Management) with Faculty Commendation, George Washington University, Washington, D.C., USA, Bachelor in Civil Engineering – Kuwait University, Kuwait and has attended many executive management programs at Harvard Business School, Boston, USA, and London Business School, London, UK.