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Ag tech: Data-led innovation drives international award win

by Beef Central, 22 September 2017

A COLLABORATION involving Meat & Livestock Australia and commercial partners in Queensland has been awarded the inaugural Hitachi Transformation Award for its work in using data to help drive innovation in the red meat industry.

The award was announced overnight by Hitachi Vantara at Hitachi’s NEXT 2017 digital conference in Las Vegas.

The Australian entry won from a field of five international finalists.

The Transformation Award recognises leading companies that are transforming their businesses in new and innovative ways, with the support of Hitachi group solutions. The MLA project was selected among five finalists who exhibited the substantial benefits they have achieved by accelerating business transformation, improving user experience, innovating with data and enabling the internet of things (IoT).

Hitachi technology is being used in a new research and development project underway in Queensland, aimed at allowing value chain partners to share and mine data to benefit their operations.

MLA’s Nigel Tomkins and Ben Dwyer from Australian Cattle and Beef Holdings receive the Hitachi Award in Las Vegas. They are pictured with (L-R) Hitachi executives Toahiaki Higashihara, Keiji Kojima, Phil Townsend and Ryuichi Otsuki.

The MLA Donor Company, in collaboration with vertically-integrated beef supply chain business, Australian Country Choice and Australian Cattle and Beef Holdings, is undertaking the project using Hitachi’s sensor-driven data collection and analytics technology.

“The objective is to evaluate the integration of data capture on farm and management systems to access improvements in processes, practices and technologies”

Collaboration with Hitachi includes an on-farm pilot evaluating a series of typical data streams generated by weather stations, soil moisture probes and water trough monitors that are now integrated with process intelligence and data analytics to support almost real time management decisions and forecasting.

The objective of the project is to evaluate the integration of data capture on farm and management systems to access improvements in processes, practices and technologies, to meet the increasing needs of the red meat industry.

MLA’s general manager for research, development & innovation, Sean Starling, said the project was a great example of working collaboratively across industry.

“I would particularly like to acknowledge our project partners – ACC, Australian Cattle and Beef Holdings and Hitachi for their ongoing work in undertaking this valuable project,” Mr Starling said.

Hitachi’s sensor-driven data collection and process analytics would assist Australia’s red meat industry to identify their own processes and ‘pain-points’ along the whole value chain to better manage, measure and improve supply chain operations as far as on-farm operations and down to individual animals, he said.

The award was recognition of the work MLA was doing in better utilising and sharing data across the entire Australian red meat industry.

“The outcomes and learnings from this project will be used to inform a number of current MLA industry projects and strategies under development including industry’s digital strategy, automation technology and sensing, and building data management systems to support current producer data,” Mr Starling said.

Brian Householder, Hitachi Vantara’s president and chief operating officer, said the award recognised the substantial benefits the collaboration had achieved through accelerating business transformation.

“Through data analytics and driving IoT- led decision-making, MLA is helping foster the productivity and profitability of the Australian red meat and livestock industry,” he said.

The NEXT 2017 conference also featured MLA’s Dr Nigel Tomkins presenting on the ‘The Power of Analytics and IoT To Drive Real-World Outcomes’.

Phase-one of the R&D project being supported by MDC in collaboration with Hitachi and ACC/ACBH is expected to be completed in January 2018.

 

Source: MLA



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