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Analytics Engines

Michaela Fox discusses the role of Analytics Engines in building an application to help food manufacturers and processes understand and manage risks i
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Analytics Engines specializes in data integration, data management, machine learning, artificial intelligence, operationalization of workflows, in-depth analytics, and visualizations. Analytics Engines supports clients throughout their data journey with full stack, customer-facing solutions designed to be scalable and deployable on-prem or via the cloud. Their high-value, end-to-end solutions have shaped data-driven, decision-making within government, biomedical science, media, health care, tourism, and the higher education spheres.
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According to one common definition, food fraud is a deliberate and intentional substitution, addition, tampering, or misrepresentation of food, food ingredients, or food packaging. Detecting food fraud is extremely challenging, because foods are exposed to risk at almost every stage of the supply chain. Food fraud has the potential to be a risk to public health, and can be detrimental to consumer trust and confidence in food. For example, following the horse meat scandal in 2013, consumer trust fell not only in beef products, but also in the food industry more generally. This led to the UK government commissioning the Elliot Review into the integrity and insurance of food supply networks.
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One of the recommendations arising from this review was the need to establish a shared focus between government and industry to collect, analyze, and disseminate information on intelligence. Such an intelligence hub will be central to the production and sharing of horizon-scanning analysis of commodities and markets considered most at risk from crime due to factors such as trade route complexity, price fluctuations, crop failures, fishing restrictions, development of premium markets, and criminal ingenuity. The EIT Food Fortress project aims to play a key role in gaining consumer trust through improving the transparency of the supply chain.
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By using a combination of technologies such as blockchain, multi-analytical tests, and predictive analytics, EIT Food Fortress aims to facilitate a rapid reactive and proactive risk management approach, specifically targeted at two supply chains– beef, and also herbs and spices.
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Analytics Engines– one of the project partners– is building an application to help food manufacturers and processors understand and manage risks in their supply chains. Powering this application are visual and predictive analytics designed to assess risk and predict food fraud in these supply chains. The information gathered can then be displayed via interactive web-based dashboards and reporting tools.
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Data visualization plays an important role in analytics to allow exploration, communication, and analysis of large, complex datasets. Importantly, these need to be well-designed to make it easy for our eyes to find the story the data is telling us, to extract insights, and draw conclusions. Effective and impactful visualizations should be customized to the needs, interests, and skills of the users, to capture and hold their attention. Analytics Engines is adopting a human-centric design approach, which aims to provide the maximum possible utility for EIT Food Fortress and its end users.

Analytics Engines is building an application to help food manufacturers and processors understand and manage risks in their supply chains.

Powering this application are visual and predictive analytics designed to assess risk and predict food fraud in these supply chains. The information gathered can then be displayed via interactive web-based dashboards and reporting tools.

This tool is an important aspect of analytics to allow exploration, communication and analysis of large complex data sets; predict and prevent food fraud; and help gain consumer trust in the food chain.

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