Reducing Operational Costs & Machine Downtime with Data Insights

Element Six case study
2 minutes

Knowledge is power. Digital transformation is transforming the manufacturing industry into a smart one with the potential of, once again, becoming an economic powerhouse

Smart composite manufacture success relies on smart planning and delivery from the outset. With so many ways available to harvest and visualise production line data, it offers opportunities to significantly reduce costs. Unlocking these savings efficiently, however, requires in-depth experience and understanding of process data.

London-based Additive Flow, which established in 2017, strives to change this experience by developing and delivering advanced digital solutions for the additive manufacturing workflow.

Looking to reduce the operational downtime and development costs for manufacturers, its management team turned to The Data Analysis Bureau (T-DAB) to enhance its composite material production.


The solution’s control systems governed multiple materials inputs, with procedures that were inefficient, costly and slow to react.

Additive manufacturers work with experimental masses of materials and this can be a source of delays. Often, these types of operations are beyond the current understanding of how these composite materials behave.

Working with unknown quantities meant that – on a regular basis –teams needed to stop production to change, test and review the operational setup before it could continue.

Delays like this take a heavy toll on productivity, resulting in rising costs and placing delivery at risk, too.


Working in close collaboration with the Additive Flow team, T-DAB designed and developed an innovative solution to ensure maximum machine productivity – even with experimental masses.

Smart by design, the system incorporates machine learning driven AI into the manufacturer’s machinery’s calibration, enabling the dynamic adjustment and optimisation of plant equipment – without disrupting operations – saving development time and costs.

T-DAB’s holistic approach resulted in an ecosystem which can scale with the client. It incorporates a range of services including:

  • Azure cloud-distributed and localised computing services
  • Edge deployment
  • IoT (Internet of Things)
  • Cutting-edge deep learning frameworks (e.g.Deeplearning4j)


Since implementation, the manufacturer has seen a significant reduction in production delays. For example, machine-learning capabilities mean that calibration is now part of the system’s ongoing learning process.

Working autonomously, it trains algorithms on data of inputs and outputs during operation. This ongoing learning is key to ensuring the system works as smartly as it can.


Thanks to this smart solution from T-DAB, Additive Flow has unlocked its client-base’s manufacturing production potential.

Because the system enables machinery to dynamically adapt and optimise functionality – without relying on manual inputs – it has helped make operations more efficient.

Machine-learning driven capabilities mean that stoppages are no longer a regular occurrence.

Projects like these have the potential to transform operations and it certainly has for Additive Flow and its clients.

By ensuring non-stop operations, the solution has eliminated unnecessary delays, while ensuring more efficient development time and safeguarding delivery.