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Smart Factory 101 A Data, AI, Cloud and Workforce Revolution in the Making

Acuvate

As with previous years, smart factory solutions are projected to contribute. However, the development of technologies like RPA, AI, and the Internet of Things is making up for these constraints, making production and supply chains more agile and bringing manufacturing well and truly into the era of Industry 4.0.

Data 52
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Smart Factory 101 A Data, AI, Cloud and Workforce Revolution in the Making

Acuvate

As with previous years, smart factory solutions are projected to contribute When considering the financial and environmental implications of manufacturing, smart factories are indispensable. Process Optimization With the help of AI, ML, and Big Data, production processes may be optimized, leading to greater efficiency with less cost.

Data 52
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Data Science: Why and How do you Invest in Data?

mjvinnovation

Data Science began with statistics and evolved to include concepts / practices such as Artificial Intelligence, Machine Learning, and the Internet of Things, to name but a few. Data scientists are trained to identify data that stands out in some way. How do you start a Data Science project in your company?

Data 40
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A Guide to Smart Cities

Innovation Walk

The prospect of diminishing issues, such as overcrowding and making life more aesthetic and convenient for citizens, is incredibly attractive for governments who both care about their citizens and seek reputation and profit. Residents want to know how and what data is being collected, and what is being done with it.

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The Innovation-Driven Disruption of the Automotive Value Chain (Part 2)

Corporate Innovation

While investing heavily in R&D, automotive OEMs had not been investing in technologies and business models that are now used by newcomers to disrupt them (software, big data, user experience, additive manufacturing/materials, energy storage, sharing economy, direct to consumer). But I think that the problem runs deeper.

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The Innovation-Driven Disruption of the Automotive Value Chain (Part 2)

Corporate Innovation

While investing heavily in R&D, automotive OEMs had not been investing in technologies and business models that are now used by newcomers to disrupt them (software, big data, user experience, additive manufacturing/materials, energy storage, sharing economy, direct to consumer). But I think that the problem runs deeper.

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The next challenges in the area of ??Logistics and Supply Chain: what the post-pandemic designs

mjvinnovation

Experts point to four major forces driving the movement: Surprising growth in data volume ( Big Data ) The emergence of tools, resources, and methods for data analysis The innovative possibilities of human-machine interaction Improvements in the transfer of digital instructions to the physical world. Data Governance.

Design 52