CASE STUDIES

Concrete Evidence

Adiona FlexOps for Construction Materials Logistics

Highlights

14%

increase in average loads per truck per day

50%

reduction in backoffice scheduling time

$7.8M

in annual savings

Lafarge Holcim, a global aggregates and concrete supplier, trucks millions of tonnes of heavy material daily around the world. Not only is it demanding on their vehicles and staff, but it’s a complex challenge for their highly customised IT stack.  That IT stack has grown more challenging to manage with each merger and acquisition as the company has grown, and the thought of replacing it with a single platform is enough to make the pulse quicken

Holcim wanted to work within the boundaries of their existing software and knew there were efficiencies to gain with the wealth of data they were collecting.  The first step was to import a set of sample data and let the FlexOps Diagnostics AI engine create a customised process map of their high level operation.  By mining historical data, this process map linked key steps in their physical operations to data that could be used in various optimisation algorithms.

By prioritising the potential value of optimisations available using FlexOps, it was clear that our predictive genetic scheduling algorithms were a great match for their existing delivery scheduling process. By customising it to use their booking data, fleet constraints, and ticketing system, the scheduling process was able to better predict where a truck should be placed for future jobs and ad hoc jobs, driving a huge efficiency gain.

Importantly, the FlexOps API was easy to integrate into their existing IT stack by writing a few scripts to import and export the necessary data.  This allowed FlexOps to be completely customised and also remain standalone for fast implementation.

Developers!

Recreate this case study using sample data from Tutorial #3.

Recreate this case study

Post Covid-19, the value of FlexOps has increased even more for Lafarge Holcim. The construction industry has slowed minimally in most areas, but with drivers now harder to recruit, efficiency with existing resources is more crucial than ever.  The ability to model processes, data flow, and automatically identify efficiency bottlenecks is a competitive advantage and continually uncovers new potential improvement initiatives.

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