Internet of Thing (IoT), Big Data & Analytics

Internet of Things (IoT)

The Internet of Things (IoT) will make manufacturing smarter. Manufacturing worldwide is on the dawn of a new disruptive revolution. Technologies based on the Internet of Things have the potential to radically improve visibility in manufacturing to the point where each unit of production can be “seen” at each step in the production process. Batch-level visibility is being replaced by unit-level visibility. This is the dawn of smart manufacturing.

Smart manufacturing is about creating an environment where all available information—from within the plant floor and from along the supply chain—is captured in real-time, made visible and turned into actionable insights. Smart manufacturing comprises all aspects of business, blurring the boundaries among plant operations, supply chain, product design and demand management. Enabling virtual tracking of capital assets, processes, resources and products, smart manufacturing gives enterprises full visibility which in turn supports streamlining business processes and optimizing supply and demand.

Smart manufacturing transforms businesses into proactive, autonomic organizations that predict and fix potentially disruptive issues, evolve operations and delight customers, all while increasing the bottom line.

Big Data & Analytics

Today we see the astonishing rise in data volume, velocity and variety; computational power; connectivity; the emergence of advanced analytics and business intelligence capabilities. The primary goal of big data analytics is to help companies make more informed business decisions by enabling data scientists, predictive modelers and other analytics professionals to analyse large volumes of transaction data, as well as other forms of data that may be untapped by conventional business intelligence (BI) programs. That could include Web server logs and Internet clickstream data, social media content and social network activity reports, text from customer emails and survey responses, mobile-phone call detail records and machine data captured by sensors connected to the Internet of Things.

Traditional data warehouses based on relational database may not cut it in dealing with semi-structured and unstructured data. Furthermore, data warehouses may not be able to handle the processing demands posed by sets of big data that need to be updated frequently or even continually – for example, real-time data on the performance of mobile applications or humidity measurement of the environment.

Big data can be analysed with the software tools commonly used as part of advanced analytics disciplines such as predictive analytics, data mining, text analytics and statistical analysis. Mainstream BI software and data visualization tools can also play a role in the analysis process.

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