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	= Building Information Modeling (BIM) Analysis 3D =		= Building Information Modeling (BIM) Analysis 3D =
−	[[File:BIM 1.jpg|frameless|right|300 px]]	+	[[File:BIM 1.jpg|frameless|right|300 px]] Building Information Modeling (Revit, AutoCAD Civil 3D or ArchiCad) is a technological tool usually to represent 3D models in a cloud computing collaborative environment in order to avoid human mistakes in the phase of designs, particularly between teams of the same project but from different study line.
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		+	Usually, in the common market the design flaws are caused due to fact that designers are using CAD as a tool to represent their sketches and no BIM. In that order of ideas designers don’t understand the magnitude of the whole project, the stakeholders involved and the visualization in detail of small and big modifications that others members of the designer team (structural team, architectural team, mechanical team, etc) have performed in the project. Additionally, any change in the designs that is not opportunely informed in the pre-operative phase of the project might cause a high cost overrun during the operative phase of the project. That is why the management team should comprehend the main difference between CAD models and BIM models besides the risk related to each method during the design phases.
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		+	CAD model is based in Data that it is a collection of symbols randomly put together that does not convey in any sense in contrast BIM is based in information, if data is organized in a way that convey in any sense or meaning will create knowledge that can be defined as information with the addition of a person’s own world view, Kumar (2015)
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	= Time scheduling Analysis 4D =		= Time scheduling Analysis 4D =

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Revision as of 13:12, 14 February 2018

Contents 1 Abstract 2 Building Information Modeling (BIM) Analysis 3D 3 Time scheduling Analysis 4D 3.1 Monte-Carlo Analysis PERT Distribution 4 Project Finance Analysis 5D 4.1 Time Value of Money 4.2 Free Cash Flow in the Project 4.3 Equity and Debt 5 Business Analytics vs Intelligence Analytics 6 common problems in industry construction Colombian case 7 Buisness analytics good case 8 References 8.1

Abstract

Business Analytics in Civil Engineering Projects and Civil Engineering Companies has become a significant solution in problems of operational management and in decision making. Nowadays with the development of the Information Technology is possible for management team to do the pricing of any kind of project (Infrastructure, Housing and Building, Energy, Environmental) taking into account stakeholders, time, costs, risk, macroeconomic and political context. However, to be able to use the methodology is necessary to understand the importance of using analytical tools such as Project Finance, Monte-Carlo Analysis and in some cases Statistics Linear Methods. The outstanding fact about doing pricing to a civil engineering project with business analytics is that managers and investors will have resume data and factual information about a predicted project, moreover to forecast the project would not only create future indicators for the project but also will support managers in the planning of other areas like legal area (Contracts with suppliers, Governmental, Institutional client, customer) and Risk Management Area, the forecasted information(indicators) after being processed is beneficial to the organization specially when it is compared with the reality of the project in a Business Intelligence atmosphere.

The reader must comprehend that the role of the manager or management team of a civil engineering company is to create and generate the most amount of value possible to the organization and therefore to the investors. In order to be able to achieve that objective, knowledge in business analytics (probability, accounting, finance, statistics, programing) and data analysis is essential and compulsory. This article will focus on how managers should apply business analytics in construction companies and civil engineering projects and will illustrate how to build Models an abstraction of reality that are useful for make good decision, Saxena & Srinivasan (2013) states.

For business analytics, a decision model is an abstraction that shows key variables and relationships. We use models to learn about their real-life counter-parts. We judge how well a model corresponds with reality by manipulating its variables and observing the results, and then we use it as a proxy for the complex reality—a useful proxy that can be used to generate insights and optimal decisions. (p.31) ^[1]

Building Information Modeling (BIM) Analysis 3D

Building Information Modeling (Revit, AutoCAD Civil 3D or ArchiCad) is a technological tool usually to represent 3D models in a cloud computing collaborative environment in order to avoid human mistakes in the phase of designs, particularly between teams of the same project but from different study line.

Usually, in the common market the design flaws are caused due to fact that designers are using CAD as a tool to represent their sketches and no BIM. In that order of ideas designers don’t understand the magnitude of the whole project, the stakeholders involved and the visualization in detail of small and big modifications that others members of the designer team (structural team, architectural team, mechanical team, etc) have performed in the project. Additionally, any change in the designs that is not opportunely informed in the pre-operative phase of the project might cause a high cost overrun during the operative phase of the project. That is why the management team should comprehend the main difference between CAD models and BIM models besides the risk related to each method during the design phases.

CAD model is based in Data that it is a collection of symbols randomly put together that does not convey in any sense in contrast BIM is based in information, if data is organized in a way that convey in any sense or meaning will create knowledge that can be defined as information with the addition of a person’s own world view, Kumar (2015)

Time scheduling Analysis 4D

Monte-Carlo Analysis PERT Distribution

Project Finance Analysis 5D

Time Value of Money

Free Cash Flow in the Project

Equity and Debt

Business Analytics vs Intelligence Analytics

common problems in industry construction Colombian case

Buisness analytics good case

References

^[1]

1. ↑ ^{1.0 1.1} R. Saxena and A. Srinivasan, Business Analytics, International Series in Operations Research & Management Science 186 (2013), DOI: 10.1007/978-1-4614-6080-0_10, © Springer Science+Business Media New York 2013.