Businesses should start implementing technology solutions with business outcomes in mind. Better alignment between the business value and the implementation of Internet of Things (IoT) systems and projects will improve digital transformation projects in companies, says information technology consultancy Hitachi Vantara MD Alexander Jenewein.
“The business value of IoT systems is not typically determined prior to the deployment of such systems in South Africa, despite IoT increasingly being considered a key facet of digital transformation.”
IoT is a higher priority than robotics and artificial intelligence for 33% of global business executives, but they are still determining how best to benefit from this technology, he explains.
“Local appetite for IoT is growing, and many businesses have established a transformation charter. Yet the adoption of IoT is fairly slow because stakeholders are not clear on the outcomes of their digital transformation plan,” says Jenewein.
“Aligning the implementation process and the business value, rather than installing IoT solutions as another isolated vertical departmental project, enables businesses to integrate IoT into their digital transformation plan and to realise value from it.”
Hitachi Vantara has had great success with its IoT offering, recently increasing the yield of a global manufacturer by more than 90%.
“The manufacturer’s polymer mixing process was producing output of inconsistent quality, with yields sometimes dipping as low as 50%. Production engineers were unable to stabilise the process using traditional approaches, as polymer mixing was unstable and each new product formulation increased the problem.”
Hitachi Vantara delivered an advanced analytics platform that integrated a wide range of production data and sensor data outputs to visualise, analyse and diagnose the mixing process.
During the initial pilot project, the solution eliminated more than 50% of the poor-quality batches, increasing the average yields to above 90%. Hitachi Vantara’s solution optimised the process parameters and the system can now continuously adapt to changing conditions.
This new insight enabled the production engineering team to understand the correlations and cause and effect from a wide number of variables. By adding machine-learning functionality, the solution was also able to make continuous process adjustments to improve the yield over several months, details Jenewein.
“Most businesses have data silos that are difficult to correlate and analyse,” he emphasises.
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