Thought Leadership Papers

Our papers present and discuss various technical subjects with a practical viewpoint.

DataProphet Papers

Restarting Production After COVID-19

As the Coronavirus mitigation measures begin to achieve some success and the economic impact of this crisis grows across the globe, governments are calling on manufacturers to restart their production and to provide leadership in the post lockdown world. For manufacturers, this is a time of great uncertainty as executives face increased challenges around supply and demand, workforce disruptions, and cash flow.

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Building a Foundation for Autonomous Manufacturing

When considering the implementation of AI and how a manufacturer can work towards achieving autonomous manufacturing, a few questions come to the forefront. This paper explores how manufacturers can begin the process of digitization and put the foundations in place for autonomous manufacturing.

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Four Best Practices to Optimize Plant Control Plans with AI

DataProphet has reduced spot weld defects in the body shop of a mature vehicle assembly plant by 75%. In this paper, we share key takeaways and tips from our spot weld experience and other real-world implementations of Artificial Intelligence (AI) to optimize manufacturing control parameters.

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Preparing your Factory for Artificial Intelligence

It is essential to lay the proper groundwork in order to achieve the benefits of Artificial Intelligence (AI) enabled factory optimization. This entails the digitization of existing data, the automation of data collection, and the implementation of a proper storage mechanism. In this paper, we outline these initial steps to prepare your factory for AI optimization.

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Maturity Self-Assessment for Industry 4.0

It is essential to lay the proper groundwork in order to achieve the benefits of Artificial Intelligence (AI) enabled factory optimization. This entails the digitization of existing data, the automation of data collection, and the implementation of a proper storage mechanism. In this paper, we outline these initial steps to prepare your factory for AI optimization.

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Data Requirements for AI in Manufacturing

At the core of today's state-of-the-art Artificial Intelligence (AI) algorithms is the ability to learn patterns from a sample of data. In the manufacturing context, an example of such a pattern might be the ways in which a set of parameters contained in that data, which are related to a process in a factory, vary together. It’s important to understand at the outset what the data requirements are, when considering using AI.

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The Journey Towards Autonomous Manufacturing

Within the industrial world, leaders are defining their journey to autonomous manufacturing. Manufacturers are focusing on digitization strategies that will help to drive efficiency across their plants. As data is increasingly aggregated across manufacturing lines, the reality of autonomous lines is likely to be reached in the next five years.

This paper explores the journey towards autonomous manufacturing—what a typical journey looks like and best practices for how manufacturers can begin to achieve this.

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The Value of Data in Mineral Processing Plants

Mineral process plants collect a huge amount of data, across hundreds of control parameters and quality measurements, due to the large volumes of production materials these plants typically process. Pre-industry 4.0 plants are constrained by their inability to capitalize upon this valuable resource, often resulting in poor operational performance and sub-optimal quality.

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Q&A: The Impact of AI on the Automotive Sector

From robots on the factory floor to autonomous cars on the road, AI is transforming the automotive sector. The new technology has plenty of room to expand, increasing efficiency, productivity, and safety throughout the process of automotive manufacturing. In the next five years, AI is expected to completely revolutionize the industry.

In this Q&A, DataProphet’s CEO talks about the impact of AI on the automotive industry, what steps manufacturers need to take to leverage the technology, and what the future of AI looks like.

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Considerations for Computer Vision Quality Control in Manufacturing

Computer vision systems are installed in manufacturing plants to assist with a wide variety of industrial quality assurance and control processes, and to a lesser extent in applications of process control.

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