Digitally Enabled Organizations- Leveraging New Age Technologies
Bibliographic record
Abstract
Abstract When inadequate information appears via a long-winded channel, project leaders usually struggle to make timely decisions. There is frequently a lack of visibility, contractual and organizational fragmentation, and genuine facts being segregated and concealed due to an optimism bias. Despite the finest planning and estimation efforts, projects frequently exceed their budgets or experience delays of more than 30%. The paper outlines the importance of data and of data use to improve the performance in projects planning and delivery. The data value and hierarchy are reviewed in the context of the construction industry and the importance of a smooth digitalization process for ensuring acceptance and adoption is discussed. The concept of ‘digital construction blocks’TM and Lean thinking is introduced to address the problem of complexity which is commonly recognized as the main cause of cost overruns, time delays, and poor quality and safety for the construction industry. To capture the footprint of how the asset was built, the authors propose the Digital Twin of Execution adding dynamism to the commonly discussed Digital Twin of Asset, which is more static when the asset has already been constructed. The project is organized into digital blocks, allowing all project functions and disciplines to focus on a common path of construction, allowing for an earlier start of a constraint-free construction and, as a result, de-risking and compressing the total execution schedule. Data from existing systems and technologies is unlocked and placed in automated processes, allowing thousands of documents, activities, and fast-moving events to be collected in digital blocks of construction. The digital block is connected throughout project stages and taken across all aspects of the project, including plot plans, activity plans, drawings, 3D, materials, and so on, resolving the project's disarray caused by manual and analogue procedures. The entire planning, project setup, and execution process is aided by GIS, which provides visibility at various levels of magnification via an interactive geo spatial map superimposed with plot plans, timetables, and work packages. Artificial intelligence (AI) and machine learning (ML) can be used to forecast the probability of danger in various field operations. It's done by using IoT devices implanted in employees’ PPE and in the environment to process data collected on the system. Digital Control Tower can provide a smart dashboard that not only displays the KPIs but also helps the user prioritize his next steps. It may provide an overall view of the project's progress and KPIs, as well as get to the root of a problem in a specific installation area, raise red flags and alerts, and function as a user's assistant by predicting errors early on.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".