Waste Data Processing Algorithm and Data Collection Integration System (DCIS) in Singular Construction Activities for Aiding the Quantification of Overruns in Construction Process

The construction process is marred by project delays that tend to ruin the economies of nations worldwide. Studies pertaining to causes and effects of construction delays are plentiful, but solutions that was supposed to provide the results of this phenomenon (such as lean construction), is not seen to that scale. One reason for this is that delays are caused by time wastes at activity levels, and scholarly studies primarily don't deal at activity levels. Experience based heuristics play the most important role in fixing the duration of activates by managers. But, construction activities are prone to highly improbable & complex process flows, making heuristics unreliable. The reason being probability of one construction site condition and stakeholders, being similar to the next project is meagerly low. Thus experience gained by the project management personnel involved may not be handy in predicting actual durations and costs in the forthcoming project. The only practical solution would be fixation of cost and time standards for singular construction activities basing on the overall history of projects and personnel involved. On the long run globalizing or at least nationalizing heuristic data of delays and wastes would help in predicting future process. It can be achieved by proving a mechanism of the centralizing construction process into a single data entity at national level- Data Collection System (DCIS). As part of the system, synchronization of personnel and construction site data takes place at every instance a new construction process is activated anywhere in the concerned boundary of DCIS. Collection of inventory data, material data, labor data, stakeholder data, delay data, time waste data etc. is to form the epicenter of this data center. Data from heuristics should then be converted to mathematical distributions that can be used for predictions in future construction sites. This would result in giving better and better results as the process of data entry begins.