Remove IoT Remove Metrics Remove Optimization Remove Predictive Analytics
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Streamlining supply chain management: Strategies for the future

IBM Big Data Hub

Effective SCM initiatives offer several benefits: Lower operational costs : By optimizing inventory levels , improving warehousing efficiency and streamlining order fulfillment processes, companies can save on storage, labor and transportation expenses.

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How can CIOs Build Business Value with Business Analytics?

Smart Data Collective

However, the rapid technology change, the increasing demand for user-centric processes and the adoption of blockchain & IoT have all positioned business analytics (BA) as an integral component in an enterprise CoE. They are using analytics to help drive business growth. Extract Value From Customer.

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What is asset reliability?

IBM Big Data Hub

Enterprises are constantly looking for new ways to optimize performance, increase reliability and extend asset lifespans—all without adding unnecessary costs. In order to take a proactive approach to asset reliability, maintenance managers rely on two widely used metrics: mean time between failure, (MTBF) and mean time to repair (MTTR).

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Conversational AI use cases for enterprises

IBM Big Data Hub

Predictive analytics integrates with NLP, ML and DL to enhance decision-making capabilities, extract insights, and use historical data to forecast future behavior, preferences and trends. ML and DL lie at the core of predictive analytics, enabling models to learn from data, identify patterns and make predictions about future events.

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Six EAM trends pushing the oil and gas industries forward

IBM Big Data Hub

More recently, these systems have integrated advanced technologies like Internet of Things (IoT), artificial intelligence (AI) and machine learning (ML) to enable predictive analytics and real-time monitoring. Trend #5: The rise of mobile EAM solutions Mobile technology is making EAM more accessible than ever.

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How to Manage Risk with Modern Data Architectures

Cloudera

Incorporate data from novel sources — social media feeds, alternative credit histories (utility and rental payments), geo-spatial systems, and IoT streams — into liquidity risk models. Use predictive analytics and ML to formalize key intraday liquidity metrics and monitor liquidity positions in real time.

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Interview with Dominic Sartorio, Senior Vice President for Products & Development, Protegrity

Corinium

Life insurance needs accurate data on consumer health, age and other metrics of risk. And more recently, we have also seen innovation with IOT (Internet Of Things). Machine learning can keep up, by continually looking for trends and anomalies, or predictive analytics, that are interesting for the given use case.

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