Digitally Enabled Organizations- Leveraging New Age Technologies
Notice bibliographique
Résumé
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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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».