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We can develop data science and data analytics for every measurement made as part of industrial engineering and basic engineering related to industrial engineering.
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https://www.youtube.com/watch?v=csG_qfOTvxw
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Data Science and Data Analytics to Support Process Productivity Improvement
Data Science in Operation Process Chart and Flow Process Chart Analysis and Improvement
Processing and Inspection Issues - Operation Process Chart
The Industrial products / manufacturing sector is undergoing a massive data and analytics driven transformation.
Technology can now bring together information that has traditionally been siloed or never before leveraged. Organizations are now looking for ways to use data to gain insights and work across departmental boundaries that have traditionally had limited collaboration.
At the same time, industries are using Internet of Things, analytics, cloud, and social data to:
• Modernize factories to become autonomous, connected and better controlled
• Optimize inventory logistics and distribution
• Use information from machines to improve operational efficiency and worker safety
• Improve operations through connected assets, resource optimization and integrated weather data
• Predict regulatory compliance risk, fraud detection in warranty claims
• Predict worker sentiments and product safety features
The complex ecosystem of internal and external data sources holds tremendous value for manufacturers, who can now bring together this data, analyze it, and use it to provide new and differentiated services for their customers.
Increase Productivity of Your Materials Handling Operations with Real-Time Data
Jim Rock, CEO of Seegrid, a leading provider of connected self-driving vehicles for materials handling.
JAN 18, 2018
From where do you get Materials Handling Operations Data?
self-driving vehicles can communicate valuable data from their routes and about their loads. This data can feed evaluations and provide insight into material flow efficiencies. By using easy-to-understand, visual charts, process designers and managers can quickly get up to speed on vehicle status in real time.
Initially, a baseline of data can be established for future comparison. Based on the data, we can create standard reports on material flow effectiveness, which will serve as a solid foundation for analyzing and comparing against for further optimization.
For example, an auto manufacturing customers had been using roughly estimated times for how long it took to replenish parts with a manually driven vehicle. But once self driving vehicles were put into operation, the data from self-driving vehicles was analyzed to understand the real time the process took. The insights were used to reduce the amount of time of transportation and improve takt time.
A management system that shows real-time status details allows for immediate corrections and communication of any issues. The right management system helps identify immediate actionable opportunities to reduce waste and increase throughput.
As we move forward into Industry 4.0 and smart, automated production, inspection, warehousing and transport systems become more intertwined, data produced from these systems will forever change manufacturing. Gathering data from these systems, analyzing the data and discovering valuable insights will accelerate efficiency and optimization.
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How data scientists use critical thinking to generate valuable processes
by John Weathington in Big Data on February 14, 2017,
To maximize the performance of your business processes, organize a group of data scientists and other experts to run a Value Stream Mapping effort. Get the specifics on what this entails.
Data Science and Data Analytics Solution Providers
Industrial IoT solution that uses data science to increase productivity, machine uptime and safety.
Data science that solves your biggest problems.
Keep your machines up and running when they are needed the most. Improve product quality. Increase production throughput.
Elisa Smart Factory advanced analytics solutions detect and resolve anomalies and defects early in the process, ensuring world-class quality in each batch. To keep your machines running, we go beyond traditional rule-based systems by using AI / machine learning to identify failures proactively. Our algorithms analyze all relevant information including sensor data, MES/PLC/SCADA data, asset management systems, plus structured and unstructured data such as technicians’ notes in Excel.
RQ1. What is the population of journals that focus on topics of data science?
RQ2. What disciplinary landscape of data science is reveal
Important - Table - Top keywords of disciplines
Interesting point from the above paper. Industrial engineering is not represented as an area. Productivity is not a top key word in engineering or business and economics. It shows industrial engineering discipline has not done significant work in this new discipline and technology.
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