A Fortune 500 company had constructed various building/ manufacturing units in the past across multiple locations. They are still expanding aggressively however, are not able to benchmark the historic construction costs due to lack of a database which could provide them key insights. They wanted to build a construction cost database which would factor historic costs, inflation and other construction metrices to estimate and benchmark future construction costs.
KEY CHALLENGES
Construction Modules – Historic construction costs for previous modules not available.
Data in multiple files – No single source to fetch historic project cost data.
Multiple Formats – Cost data resides in multiple formats and difficult to compare.
Multiple Taxonomy – Each data source has a different taxonomy for classification.
Missing Project Info – Cost data sources lacked project information & details.
Multiple Currency – Project currency varied in different geographies.
Cost Comparison – Due to the above factors it was very difficult to compare costs.
APPROACH
Data Digitization – Data from multiple sources such as PDF, excel, word was digitized in standard excel templates.
Taxonomy Normalization – Multiple taxonomies such as WBS, Procurement, Finance was normalized for cross comparison.
Unit Cost Comparisons – Analyzing digitized data sets using normalized taxonomy led to the possibility of unit cost comparison.
Project Comparison – Analyzing multiple projects using capacity & Production.
KEY BENEFITS
Project Cost Comparison – Historic constructions cost comparison was possible across multiple regions / buildings/ currency.
Construction Taxonomy – The exercise not only helped in benchmarking construction cost but also enhanced the existing construction taxonomy for better mapping of spend.
Unit Metrices Comparison – Key construction metrices such as $ per Sq. Meter, $ per L, etc. helped the client with apple to apple benchmarks.
Budgeting – The construction cost benchmark also helped the client in defining better budgets for future projects which would avoid project cost overruns.
Negotiation – The cost database also helped the client in negotiating better with its suppliers since the cost metrics were readily available for comparison.
