On budget and on time – with data analytics
By Digital News Asia February 27, 2017
- Construction company turns to advanced big data analytics to spot problems sooner
- Flawless project management with advanced analytics shrinks response time from one week to one day
[Since October 2016, in a commercial arrangement, DNA is featuring articles from a special 2015 pdf book, the Malaysian Digital Economy Corporation (MDEC) produced called, Exposing the Promise of Big Data Analytics: Case Studies in Malaysia. Where relevant, a link is provided at the end of article to download the full version. Please note that today's case study was not part of the original pdf book and there is no download link.]
Which harried and under pressure construction site managers wouldn’t love to have at their disposal, a set of data with visibility of decisions taken that help pinpoint accountability and highlight potential problems before they happen?
Well one construction company wanted to give its managers such visibility to allow them to deal with problems much faster – even narrowing response time from one week to within a day. The construction company turned to Quandatics and its advanced data capabilities.
The following article lays out what Quandatics was able to do, including bringing clear visualization across various spreadsheets spread across different departments through a centralized database system that not only offered a web interface, but mobile as well.
IN an industry where the cost of a single nail can make the difference between delivering a project on budget and on time or irreparably ruining every player connected to the project, a great amount of effort is usually devoted to making sure that every single variable is accounted for – from resource scheduling to manpower allocation, progress updates to machinery placements.
To add to that, many construction companies typically juggle more than one project at any given time; which multiplies the amount of data that they have to collect, collate and analyze. By working with big data analytics (BDA), this construction company has been able to surmount all of these challenges; managing all its projects and delivering them on time and on budget through a centralized system that enabled different levels of executives to monitor, benchmark and make data-driven decisions.
A construction project typically deals with a vast array of stakeholders each generating their own set of data. Prior to implementing Quandatics’ advanced analytics solution, this data was scattered around in spreadsheets in different departments. This made it difficult for the developer to obtain a complete overview of important metrics without spending additional resources to consolidate and process this data.
In addition to that, old-fashioned spreadsheet visualizations also became a limitation – it constrained the amount of understanding all the users could obtain from available data, problems are only detected after weeks of ongoing events and consequently, implemented corrective measures were much less effective.
The first step toward a solution was the consolidation of historical datasets into a centralized database system, with mobile and web interfaces for supervisors to append up-to-date data of on-going projects into the system. This allows for extraction of descriptive insights, and benchmarking to be performed on a number of important metrics that constitutes the project schedules, so that senior management can make data-driven decisions.
A key component in the implementation is a project management portal for schedule, progress, and cost monitoring to optimize resource allocation. An interactive infographic service with multi-level access is provided for personnel with different access rights to visualize or append the data and results according to their job functions.
Thanks to Quandatic’s centralized database system, project managers at all levels now have instant, up-to-date access to progress visualizations for all on-going projects. The standardised reporting and progress update interface for all site supervisors have also minimised the likelihood of human error in reading, processing and making well-informed decisions.
With these capabilities, the management team is now able to respond to potential deficiencies or problems much faster – narrowing their response time from one week to within a day.
By consolidating data from past and current piling projects, correlations between their attributes and the project outcome, profits and schedules can be studied. Moving forward, more data sources – such as ground survey data and weather data – can also be incorporated to make the system more comprehensive.
Future implementation with machine learning features will generate best-action recommendations for on-going projects. These outputs could potentially lead to the minimization of late project delivery penalties, reduction of material wastage, and optimization of machinery placement.
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