Artificial Intelligence (AI) Facilitation Data Science Transformation and Productivity Improvement

 

How Artificial Intelligence Is Transforming Data Science

https://cranesvarsity.com/how-artificial-intelligence-is-transforming-data-science/

AI in Data Science: Uses, Examples, and Tools to Consider

Written by Coursera Staff • Updated on Sep 24, 2026

Learn how artificial intelligence is used in data science and explore ways to prepare your team to use the latest AI technologies.

https://www.coursera.org/articles/ai-in-data-science

How AI is transforming Data Science Model Development

Driving higher-value activities, democratization, and operational excellence

https://modern-cdo.medium.com/how-ai-is-transforming-data-science-model-development-67802b28fd35

AI Made Data Scientists Faster. Now It’s Expanding the Job.

The real shift is bigger than productivity: AI is reshaping ownership, judgment, and the career path of data scientists.

Yu Dong

September 29, 2026

https://towardsdatascience.com/ai-made-data-scientists-faster-now-its-expanding-the-job/

https://cloud.google.com/use-cases/ai-data-analytics

https://www.microsoft.com/en-us/copilot/ai-knowledge/ai-for-data-analysis

https://www.turing.com/blog/how-data-scientists-leverage-ai-for-enhanced-efficiency-and-effectiveness

https://www.databricks.com/blog/how-ai-transforming-data-analytics

https://www.linkedin.com/pulse/how-artificial-intelligence-transforming-data-science-a67ac

https://plotly.com/blog/practical-guide-ai-in-data-science-workflows/

https://solutions.nextcomputing.com/ai/how-to-use-ai-for-data-science-techniques-and-tools/?srsltid=AYCX9vI7aBd2jne0A3UUxkxFNKkjAlQTlq2w9tFIeUMq_4729g0CQYRs

https://www.researchgate.net/publication/404831816_AI-Powered_Data_Science_A_Review_of_Current_Developments_and_Emerging_Opportunities

https://www.simplilearn.com/ai-in-data-science-article

https://towardsdatascience.com/beyond-code-generation-ai-for-the-full-data-science-workflow/

https://www.usdsi.org/data-science-insights/how-can-ai-enhance-data-science-with-automation

https://www.iiba.org/business-analysis-blogs/how-ai-is-rewriting-the-rules-of-data-analysis/

https://www.wdsai.org/blogs/ai-and-data-science-working-together-for-better-results

https://www.nvidia.com/en-us/solutions/ai/

https://www.eicta.iitk.ac.in/knowledge-hub/data-science/ai-data-science-in-2025

Applied Industrial Engineering - IE Applications in AI and Agentic AI

 

Applied Industrial Engineering  - IE in New Technologies

Industrial Engineers have to develop productivity science, productivity engineering and productivity management for new technologies.   Are they doing it effectively? No industrial engineers are not doing it adequately.

Applied Industrial Engineering: Prof. Narayana Rao's Vision for Systemic Efficiency and Profitability
https://nraoiekc.blogspot.com/2025/07/applied-industrial-engineering-prof.html

Applied Industrial Engineering - Industrial Engineering 4.0 - Online Course Module

AI - Generative AI - LLMs - Applications in Software Development for Productivity and Quality Improvement

https://nraoiekc.blogspot.com/2026/11/ai-generative-ai-llms-applications-in.html

AI - LLM - Cost Reduction - Token Optimization - Industrial Engineering

https://nraoiekc.blogspot.com/2026/11/ai-llm-token-optimization-industrial.html

IE Applied to Agentic AI

AI and AI Agents are new technologies with application potential in many processes and systems. Industrial engineers have to learn those technologies and develop IE for those technologies.

Operations Function - The key areas where AI agents are making a significant impact
 

The Business Case

The adoption of AI is not just an upgrade to the technology stack; it is a disruption to operational processes and cost structure. With the real-time decision-making process,  manufacturers are seeing improvements in operational efficiency that were very difficult to achieve with rules-engine-based automation.

The key areas where AI agents are making a significant impact include:

Autonomous manufacturing operations (Smart Manufacturing): AI agents can oversee entire production processes, ensuring robotic systems operate at peak efficiency and managing deviations in schedules. They can handle most real-time decisions, with human workers intervening only for issues requiring judgment.

Predictive maintenance: By continuously monitoring machine performance and sensor data, AI agents can predict equipment failures before they occur. This allows for scheduling  maintenance. It is observed that  plants significantly reduced unplanned downtime by up to 40% and cut maintenance costs by 20-25% using predictive maintenance agents.

Quality control and defect detection: AI agents can be used for real-time inspection, using machine vision, sensor fusion and anomaly detection to spot subtle defects that human inspectors might miss, especially in high-speed production. They can also adjust processes in real-time to correct issues, leading to a 30-50% reduction in defect rates.

Automated Inspection - Introduction and Bibliography

Supply Chain Agents: AI agents can predict and react to supply chain disruptions by monitoring raw material availability, adjusting production schedules, optimizing resource use and even identifying alternative suppliers. They streamline logistics, forecast demand and manage inventory, helping to avoid bottlenecks and material shortages.

Energy optimization and sustainability: Manufacturers can significantly reduce energy waste as AI agents monitor consumption across machines and make real-time adjustments to minimize usage without compromising production targets. From our observations the implementation of AI tools at our plants, this can lead to energy savings of 15-20% and supports green manufacturing objectives.

Process automation and optimization: Beyond traditional robotics, AI agents enable cognitive process automation by improving decisions and workflows that were previously manual or rule-bound. They can dynamically adjust parameters like temperature and pressure in real-time based on historical data, ambient conditions and input materials, leading to less waste, fewer mistakes and consistent quality.

Workplace safety: AI agents can monitor environmental factors and safety metrics on the factory floor, predicting potential hazards and automatically triggering safety protocols—such as shutting down machinery or alerting workers—to ensure safe operations.

Intelligent manufacturing assistants: These agents integrate design intelligence into the engineering process, using generative design algorithms to explore product variants, analyzing customer data to recommend product tweaks and evaluating manufacturability before prototyping.

End-to-end automation: Advanced "super AI agents" can manage complex, cross-functional tasks across the entire manufacturing process, from material procurement and production planning to quality control and shipment. They integrate data from all aspects of the supply chain and manufacturing floor to ensure seamless automation.

https://www.industryweek.com/technology-and-iiot/emerging-technologies/article/55362524/were-data-experts-at-ford-heres-how-we-see-ai-agents-reshaping-the-shop-floor

A Dilemma of Marketing Managers - The First Customer Could be an AI Agent

“How do we remain visible and persuasive when the first ‘customer’ in the funnel is not a human, but an AI agent?”

McKinsey & Company

Our research estimates that by 2030, agentic commerce could orchestrate $3 trillion to $5 trillion globally, as AI agents increasingly influence discovery, decision-making, and transactions across categories.

As AI quickly becomes the first stop in the shopping journey in Europe and among industry leaders, the strategic question is shifting to: “How do we remain visible and persuasive when the first ‘customer’ in the funnel is not a human, but an AI agent?” https://mck.co/3Q7kKIM

https://www.linkedin.com/posts/our-research-estimates-that-by-2030-agentic-share-7444669940797755392-d0zm

Interesting LinkedIn Posts on AI and Agentic AI

https://www.linkedin.com/posts/andreashorn1_anthropic-claude-skills-ugcPost-7444286437992103936-HP3D

100 AI  agents

https://www.linkedin.com/posts/adamdanyal_i-analyzed-100s-of-ai-agent-use-cases-from-activity-7443990342397296640-AOJn

6 Imp AI Reports

https://www.linkedin.com/posts/adamdanyal_i-read-2000-pages-of-ai-research-heres-activity-7444352724369969152-qb44

"Global AI Leadership Summit- Edition 1"

https://www.linkedin.com/posts/shaikabdulkhadar_global-ai-leadership-summit-virtual-edition-activity-7443523890301206528-OzUG

https://www.linkedin.com/posts/aiforenterprise_generative-vs-agentic-ai-vs-ai-agents-%F0%9D%97%95-activity-7444383150631194624-dwTt

𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝗶𝘀 𝗰𝗼𝗺𝗶𝗻𝗴 𝗳𝗼𝗿 𝗽𝗿𝗼𝗰𝘂𝗿𝗲𝗺𝗲𝗻𝘁.

https://www.linkedin.com/posts/supplychainaipro_supplychain-ai-procurement-activity-7444635932672188416-Gw4X

Post included in

Industrial Engineering Encyclopedia - A to Z of Industrial Engineering - Principles, Methods, Techniques, Tools and Applications.

ud. 7.11.2026

Pub. 1.4.2026

Industrial Engineering in Information Technology (IT) Systems and Organizations

Lesson  of Industrial Engineering ONLINE Course.

Industrial Engineering and Information Systems

Larry E.Long
Computers & Industrial Engineering
Volume 2, Issue 1, 1978, Pages 1-6

Abstract
Industrial engineering techniques can increase productivity in computer center operations and user activities by as much as 50–100%.
https://www.sciencedirect.com/science/article/abs/pii/0360835278900025

The lean model of industrial engineering that focused on reducing inventory through solving issues that give rise to delays in job processing was implemented in software development from 1990s.

Industrial Engineering and TPS in Software Development.

Koichiro Toda

Director - TPS Certificate Institution, Agile&DevOps coach, Scrum Master, Software Engineering Consultant

https://www.linkedin.com/pulse/120-years-industrial-engineering-koichiro-toda-bk8xc/

Lean Software Strategies: Proven Techniques for Managers and Developers

Peter Middleton, James Sutton
CRC Press, 06-Mar-2020 (Original 2005 - now eEdition) - Business & Economics - 464 pages

Lean production, which has radically benefited traditional manufacturing, can greatly improve the software industry with similar methods and results. This transformation is possible because the same overarching principles that apply in other industries work equally well in software development. The software industry follows the same industrial concepts of production as those applied in manufacturing; however, the software industry perceives itself as being fundamentally different and has largely ignored what other industries have gained through the application of lean techniques.

https://books.google.co.in/books?id=HFDVDwAAQBAJ


Bibliography



Biblography

Fagan, M.E. 1976. Design and code inspections to reduce errors in program development, IBM Systems J. 15(3): 182-211.

Boehm, B. 1981. Software Engineering Economics, Englewood Cliffs, New York, Prentice-Hall.

Schonberger, R.J. 1986. World Class Manufacturing, New York, The Free Press.

Gilb, T. 1988. Principles of Software Engineering Management, Wokingham, England, Addison-Wesley.

Ohno, T. 1988. Toyota Production System: Beyond Large-Scale Production, Cambridge, MA Productivity Press.

Schulmeyer, G. 1990. Zero Defect Software, New York, McGraw-Hill.

Womack, J.P., Jones D.T., and Ross, D. 1990. The Machine that Changed the World, New York, Rawson Associates.

Paulk, M.C., Curtis, B., Chrissis, M.B., and Weber, C.V. 1993. Capability maturity model, version 1.1, IEEE Software 10(4): 18-27.

Gilb, T. and Graham, D. 1993. Software Inspection, Wokingham, England, Addison-Wesley.

Tierney, J. 1993. Eradicating mistakes from your software process through Poka Yoke, Proc. 6th Inte. Software Quality Week, Software Research Institute, San Francisco, CA, May, pp. 300-307.

Hou, A.C. 1995. Toward Lean Hardware/Software System Development: Evaluation of Selected Complex Electronic System Development Methodologies, Report—Lean 95-01, Lean Aircraft Initiative, Center for Technology, Policy and Industrial Development, Massachusetts Institute of Technology. http://lean.mit.edu/public/index.html.

Sutton, J.M. 1996. Lean software for lean aircraft, 15th Digital Avionics Systems Conference, New York, IEEE, pp. 49-54.

Morgan, T. 1998. Lean Manufacturing Techniques Applied to Software Development, MSc Thesis, Massachusetts Institute of Technology.

Raman, S. 1998. Lean software development: Is it feasible? 17th Digital Avionics Systems Conf., New York, IEEE, pp. 13-18.

Robinson, A.G. and Stern, S. 1998. Corporate Creativity: How Innovation and Improvement Actually Happen, San Francisco, CA, Berrett-Koehler.


Hamilton, T. 1999. A Lean Software Engineering System for the Department of Defense, Massachusetts Institute of Technology, MSc Thesis.










































Womack, J.P. and Jones, D.T. 1997. Lean Thinking, London, Touchstone Books.




Womack, J.P. and Jones, D.T. 1994. From lean production to the lean enterprise, Harvard Business Rev. 72(2): 93-103.


ud. 5.11.2026

Pub. 21/7/2020

Information Systems Industrial Engineering - Information Systems Engineering

Lesson 431  of  Industrial Engineering ONLINE Course - Applied Industrial Engineering Module

Ubiquity of Industrial Engineering Principle - Industrial Engineering is applicable to all branches of engineering.

In each branch of engineering the following three areas of industrial engineering are to be applied to increase productivity and reduce unit cost of output.

Facilities Industrial Engineering  -   Product  Industrial Engineering   -  Process Industrial Engineering 

           Picture Source: https://www.ecpi.edu/blog/what-are-computer-based-information-systems

Popular Articles

Lean Software Development and IT Enabled Services

https://nraoiekc.blogspot.com/2012/04/lean-software-development-and-it.html

$2 Trillion Waste In IT Systems? What are we doing?

https://nraoiekc.blogspot.com/2012/12/2-trillion-waste-in-it-systems-what-are.html

Google Engineering Productivity Department - Activities and Accomplishments

https://nraoiekc.blogspot.com/2019/03/google-engineering-productivity.html

Industrial Engineering and TPS in Software Development.

Koichiro Toda

Director - TPS Certificate Institution, Agile&DevOps coach, Scrum Master, Software Engineering Consultant

https://www.linkedin.com/pulse/120-years-industrial-engineering-koichiro-toda-bk8xc/

Read IEEE Computers Magazine

https://www.computer.org/csdl/magazine/co/past-issues/2020/2021

News Related to Information Systems Industrial Engineering - Information Systems Engineering 

2023

Generative AI by 2030 - KPMG article

https://www.communicationstoday.co.in/a-sneak-peek-into-2030-with-generative-ai/

IT Cost Reduction Strategies

https://www.leanix.net/en/wiki/ea/it-cost-reduction-strategies

HOW TO REDUCE SOFTWARE DEVELOPMENT COSTS?

https://www.linkedin.com/pulse/how-reduce-software-development-costs-harry-nguyen/

2022

Monq AIOps platform

World’s first topology-based AI-Ops automation platform

Monq is the world’s only self-hosted freemium platform for automated data collection and analysis. It turns your observability into proactive monitoring, helping IT teams prevent possible outages while minimizing costs and efforts.

https://www.producthunt.com/posts/monq-aiops-platform

https://monqlab.com/

Market Guide for AIOps Platforms

Summary

AIOps platforms enhance decision making across I&O personas by contextualizing large volumes of operational data. I&O leaders should use AIOps platforms to improve analysis and insights across the application life cycle, in addition to augmenting IT service management and automation.

Published: 06 April 2021

https://www.gartner.com/en/documents/4000217/market-guide-for-aiops-platforms

Information Systems Industrial Engineering

Costing Information Services

Raymond P. Lutz, Associate Professor of Industrial Engineering

Bull Med Libr Assoc. 1971 Apr; 59(2): 254–261.

In  the medical profession information centers, users can benefit directly  by having information searched by medical library professionals and readily available. If the users of an information system are to share in the operating expenses, some equitable system of charges must be established. The alternative systems have been reviewed, alternative methods of establishing prices are presented along with a typical example of what these prices might be, ranging from $7.50 to $2.50 per request. The implementation of the cost system is outlined.  

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC197574/

Industrial Engineering and Information Systems

Larry E.Long
Computers & Industrial Engineering
Volume 2, Issue 1, 1978, Pages 1-6

Abstract
Industrial engineering techniques can increase productivity in computer center operations and user activities by as much as 50–100%.

https://www.sciencedirect.com/science/article/abs/pii/0360835278900025

Areas of Information Systems Industrial Engineering

• Productivity in Computer Center Operations (1978)
• Minimizing Memory Time and Processing Time through Algorithms Improvement
• Programmer productivity
• Computer-aided Software Engineering Tools
• Software Cost Estimation and Reduction
• Lean Software Development
• Manpower Planning for Software Development
• Human Computer Interaction - Fatigue, Comfort and Health Aspects
• DevOps Automation and Productivity
• Data Center Industrial Engineering - Energy Consumption Reduction
• Internet Processes Productivity Improvement

Programmer Productivity

1978

Program Complexity and Programmer Productivity
E.T. Chen
IEEE Transactions on Software Engineering ( Volume: SE-4 , Issue: 3 , May 1978 )

 A set of empirical data showing programmer productivity as a function of program control complexity is also presented. The data reveals a step-function-like contour to programmer productivity with increasing program control complexity.
https://ieeexplore.ieee.org/document/1702520

Some basic determinants of computer programming productivity
Author: Earl  Chrysler
Communications of the ACM, Vol. 21, No. 6, June 1978 https://doi.org/10.1145/359511.359523
https://dl.acm.org/doi/abs/10.1145/359511.359523

1979

Impact of valid selection procedures on work-force productivity. (Remember F.W. Taylor)
Schmidt, F. L., Hunter, J. E., McKenzie, R. C., & Muldrow, T. W. (1979). Impact of valid selection procedures on work-force productivity. Journal of Applied Psychology, 64(6), 609–626. https://doi.org/10.1037/0021-9010.64.6.609

Used decision theoretic equations to estimate the impact of the Programmer Aptitude Test (PAT) on productivity if used to select new computer programers for 1 yr in the federal government and the national economy.  Based on the data and its interpretation, it is concluded that the impact of valid selection procedures on work-force productivity is considerably greater than most personnel psychologists have believed.

https://psycnet.apa.org/record/1981-02231-001

Human Factors in Software Engineering
December 1979, pp. 6-7, vol. 12
DOI Bookmark: 10.1109/MC.1979.1658569
J.D. Gannon


Software engineering is an effort to reduce the cost of producing software by raising programming productivity and lowering maintenance effort and to increase the benefits of software by extending application areas and improving service.
https://www.computer.org/csdl/magazine/co/1979/12/01658569/13rRUxBa5A8

1992
https://www.andrews.edu/~vyhmeisr/papers/progprod.html

2011
New metric for measuring programmer productivity
Marwa Solla, Ahmed Patel, Christopher Wills
https://www.researchgate.net/publication/241186592_New_metric_for_measuring_programmer_productivity


Study of Task Processes for Improving Programmer Productivity
Doctor of Philosophy Thesis by Damodaram Kamma, Under the Supervision of Prof. Pankaj Jalote
Indraprastha Institute of Information Technology Delhi
March, 2017
https://repository.iiitd.edu.in/jspui/bitstream/handle/123456789/513/PhD1004.pdf

Studying Task Processes for Improving Programmer Productivity
IEEE Transactions on Software Engineering ( Early Access )
Date of Publication: 11 March 2019
https://ieeexplore.ieee.org/document/8664196

What Predicts Software Developers' Productivity?
2019
Emerson Murphy-Hill, Ciera Jaspan, Caitlin Sadowski, David Shepherd, Michael Phillips, Collin Winter, Andrea
Knight, Edward Smith and Matthew Jorde
https://people.engr.ncsu.edu/ermurph3/papers/tse19.pdf
https://ieeexplore.ieee.org/document/8643844

2020

Steve Jobs promised a new age of programmer productivity. With serverless, it's here.
https://cloud.withgoogle.com/build/productivity/serverless-computing-accelerates-programmer-productivity/

Google Cloud’s VP of Apps Prabhakar Raghavan on workplace productivity
https://cloud.withgoogle.com/build/productivity/do-tools-automation-impact-creativity/

Measuring developer productivity in 2020 for data-driven decision makers
By Bill Harding, Programmer/CEO
Published February 25, 2019. Last updated January 29, 2020.
https://www.gitclear.com/measuring_developer_productivity_a_comprehensive_guide_for_the_data_driven

Data Center Productivity and Energy Efficiency

Industrial Engineering in Data Center Design and Processes

2020
WHAT IS DATA CENTER EFFICIENCY?
https://www.colocationamerica.com/data-center-metrics.htm

2017
Data center performance
https://w2.cleardb.net/best-practices-for-monitoring-and-measuring-data-center-performance/


------------------------------------------------------------------------------------------------------------------

Information Systems Engineering

Jeonbuk National University

Department of Industrial Information Systems Engineering

Department of Industrial Information Systems Engineering deals with the use of computer and information technology and systems  in industrial systems. Information system industry and  consulting use industrial information systems engineering for reasonable design and operation of IT systems in various economic, industrial and social systems.

Industrial Information Systems Engineering applies scientific principles and information technology to product design and manufacture process, and management of sales and distribution process. Industrial Information Systems Engineering innovates and systematize the corporate work process to provide high-quality products and services with low prices at the right time to the customers, draws reasonable decisions by scientific principles, and uses cutting-edge information technology and internet to establish and operate the latest management system that integrates information delivery and work process. . As the industrial system and social system become more high-level and high-tech, the role of Industrial Information Systems Engineering is expanding.

Department of Industrial Information Systems Engineering was established in March 1986, and aims to foster professionals who are creative and competent, with the basic knowledge and hands-on abilities to make industrial system and social system optimization and information-oriented; the four  education goals are:

•Cultivate the basic engineering knowledge as industrial information systems engineering student
•Foster engineering student to have the abilities to manage, plan, analyze, and design
•Cultivate engineering hands-on ability that is suitable for digital, information era
•Cultivate ethics as the righteous engineering student


Main Campus
567 Baekje-daero, Deokjin-gu, Jeonju-si, Jeollabuk-do, 54896 Republic of Korea +82-(0)63-270-2114
http://www.cbnu.edu/eng/?menuID=297&mode=view&pid=626&did=647


The Johns Hopkins Information Systems Engineering program

Courses


PREREQUISITES
605.101 - Introduction to Python
 
605.201 - Intro to Programming Using Java
 
605.202 - Data Structures
 
605.203 - Discrete Mathematics
 
605.206 - Introduction to Programming Using Python
 
If your prior education does not include the prerequisites listed under Admission Requirements, you may still be admitted under provisional status, followed by full admission once you have completed the missing prerequisites. All prerequisite courses are available at Johns Hopkins Engineering. These courses do not count toward the degree or certificate requirements.

FOUNDATION COURSES
Students working toward a master's degree in Information Systems Engineering are required to take the following three foundation courses before taking any other courses.

605.601 - Foundations of Software Engineering
 
635.601 - Foundations of Information Systems Engineering
 
695.601 - Foundations of Information Assurance
 
One or more foundation courses can be waived by the student’s advisor if a student has received an A or B in equivalent graduate courses. In this case, the student may replace the waived foundation courses with the same number of other graduate courses and may take these courses after all remaining foundation course requirements have been satisfied.

COURSES BY TRACK
The tracks offered represent related groups of courses that are relevant for students with interests in the selected areas. Students are required to choose a track to follow and to take at least three courses from the selected track, including at least one 700-level course. The tracks are presented as an aid to students in planning their course selections and are only applicable to students seeking a master’s degree. They do not appear as official designations on a student’s transcript or diploma.

CYBERSECURITY *
635.671 - Data Recovery & Continuing Operations
 
635.672 - Privacy Engineering
 
635.673 - Critical Infrastructure
 
635.676 - Cybersecurity in Information Systems
 
635.775 - Cyber Operations, Risk, and Compliance
 
635.776 - Building Information Governance
 
695.601 - Foundations of Information Assurance
 
695.611 - Embedded Computer Systems-Vulnerabilities, Intrusions, and Protection Mechanisms
 
695.614 - Security Engineering
 
DATA ENGINEERING *
605.635 - Cloud Computing
 
605.662 - Data Visualization
 
605.741 - Large-Scale Database Systems
 
605.744 - Information Retrieval
 
605.788 - Big Data Processing Using Hadoop
 
635.631 - Foundations of Data Analytics
 
635.632 - Foundations of Data Engineering
 
ENTERPRISE AND WEB COMPUTING *
605.681 - Principles of Enterprise Web Development
 
605.684 - Agile Development with Ruby on Rails
 
605.784 - Enterprise Computing with Java
 
605.785 - Web Services with SOAP and REST: Frameworks, Processes, and Applications
 
605.786 - Enterprise System Design and Implementation
 
605.788 - Big Data Processing Using Hadoop
 
635.682 - Website Development
 
635.683 - E-Business: Models, Architecture, Technologies, and Infrastructure
 
HUMAN-COMPUTER INTERACTION
605.662 - Data Visualization
 
635.661 - Principles of Human Computer Interaction
 
645.650 - Foundations of Human Systems Engineering
 
645.651 - Integrating Humans and Technology
 
INFORMATION MANAGEMENT *
605.641 - Principles of Database Systems
 
605.643 - Linked Data and the Semantic Web
 
605.644 - XML Design Paradigms
 
605.741 - Large-Scale Database Systems
 
605.744 - Information Retrieval
 
635.621 - Principles of Decision Support Systems
 
NETWORK ENGINEERING
605.772 - Network Security Management
 
635.611 - Principles of Network Engineering
 
635.711 - Advanced Topics in Network Engineering
 
For students with appropriate backgrounds, the following courses may be taken toward the network engineering track. Advisor approval and permission of the instructor is required.

605.677 - Internetworking with TCP/IP I
 
605.678 - Next Generation Mobile Networks with 5G
 
605.771 - Wired and Wireless Local and Metropolitan Area Networks
 
605.776 - Fourth Generation Wireless Communications: WiMAX and LTE
 
605.777 - Internetworking with TCP/IP II
 
605.778 - Voice Over IP
 
SOFTWARE ENGINEERING
605.601 - Foundations of Software Engineering
 
605.602 - Software Analysis and Design
 
605.604 - Object-Oriented Programming with C++
 
605.607 - Agile Software Development Methods
 
605.608 - Software Project Management
 
605.609 - DevOps Software Development
 
605.701 - Software Systems Engineering
 
605.704 - Object-Oriented Analysis and Design
 
605.705 - Software Safety
 
605.708 - Tools and Techniques of Software Project Management
 
SYSTEMS ENGINEERING
595.660 - Planning and Managing Projects
 
635.601 - Foundations of Information Systems Engineering
 
635.792 - Management of Innovation
 
645.650 - Foundations of Human Systems Engineering
 
645.662 - Introduction to Systems Engineering
 
645.667 - Management of Systems Projects
 
645.742 - Management of Complex Systems
 
645.753 - Enterprise Systems Engineering
 
645.754 - Social and Organizational Factors in Human Systems Engineering
 
645.757 - Foundations of Modeling and Simulation in Systems Engineering
 
645.761 - Systems Architecting
 
645.767 - System Conceptual Design
 
INDEPENDENT STUDY AND SPECIAL TOPICS
635.795 - Information Systems Engineering Capstone Project
 
635.801 - Independent Study in Information Systems Engineering I
 
635.802 - Independent Study in Information Systems and Technology II
 
*This track requires Data Structures.
https://ep.jhu.edu/programs-and-courses/programs/information-systems-engineering#quickset-program_pages_content_4



605.601 - FOUNDATIONS OF SOFTWARE ENGINEERING


Fundamental software engineering techniques and methodologies commonly used during software development are studied. Topics include various life cycle models, project planning and estimation, requirements analysis, program design, construction, testing, maintenance and implementation, software measurement, and software quality. Emphasized are structured and object-oriented analysis and design techniques, use of process and data models, modular principles of software design, and a systematic approach to testing and debugging. The importance of problem specification, programming style, periodic reviews, documentation, thorough testing, and ease of maintenance are covered.

https://apps.ep.jhu.edu/course-homepages?course_number=605.601




635.601 - Foundations of Information Systems Engineering


Creating and operating large-scale information systems requires a holistic approach that manages the blending of software, hardware, networks, and security inherent in modern systems. This course introduces key elements and processes required for designing, analyzing, developing, and integrating complex information systems. The course focuses on the systems engineering approach with specific emphasis on design, development, and deployment. Topics covered include requirements engineering, architecture development, security engineering, cost-benefit analysis, information and networking technologies, and operations.

https://apps.ep.jhu.edu/course-homepages?course_number=635.601

Technion - Information Systems Engineering

This program is administered jointly by the Industrial Engineering and Management and Computer Science faculties. It is an admissions track requiring application at the time of registration to the Technion.

Industrial Engineering and Management:
Professor Lavi Aharon  | 04-8294410 | ronlavi    at technion.ac.il
Saado Nili | 04-8294405 | nili    at    cc.technion.ac.il  | web.iem.technion.ac.il
Computer Science:
Shamir Hava & Assa Dorit | 04-8294344/4316 | ug     at cs.technion.ac.il

https://admissions.technion.ac.il/en/study-tracks/information-systems-engineering/

(C) 2020 Narayana Rao K.V.S.S.

Blog Posts on Information Systems Industrial Engineering

 #IT #MIS #productivity  #IndustrialEngineering

Information Systems Industrial Engineering - Information Systems Engineering

https://nraoiekc.blogspot.com/2020/07/information-systems-industrial.html

Industrial Engineering - Information Technology Systems - Productivity Improvement and Cost Reduction Processes

https://nraoiekc.blogspot.com/2013/01/information-technology-systems-cost.html

Industrial Engineering in Information Technology (IT) Systems and Organizations

https://nraoiekc.blogspot.com/2020/07/industrial-engineering-in-information.html

News - Information for Information Generation & Transmission - Operation Analysis

Productivity Improvement of Information Systems through Systems Level IE Studies and Operation Level IE Studies

https://nraoiekc.blogspot.com/2020/07/news-information-for-information.html

$2 Trillion Waste In IT Systems? What are we doing?

https://nraoiekc.blogspot.com/2012/12/2-trillion-waste-in-it-systems-what-are.html

Lean Software Development and IT Enabled Services

https://nraoiekc.blogspot.com/2012/04/lean-software-development-and-it.html

Programming Efficiency - Bibliography

https://nraoiekc.blogspot.com/2012/04/programming-efficiency-bibliography.html

Information Systems Industrial Engineering - IEPM 2020 Batch - NITIE

https://nraoiekc.blogspot.com/2020/08/information-systems-industrial.html

Data Center - Data Analytics and Management - Digital Transformation - Bibliography.

https://nraoiekc.blogspot.com/2020/09/unilever-data-centers-data-analytics.html

Productivity Analysis - IT Systems Developers, Developer - Users, and IT Users

https://nraoiekc.blogspot.com/2020/09/productivity-analysis-it-systems.html

Performance Evaluation of Computer Systems

https://nraoiekc.blogspot.com/2020/12/performance-evaluation-of-computer.html

Kubernetes - Cloud Computing - Cost Reduction and Cost Control

https://nraoiekc.blogspot.com/2021/02/kubernetes-cloud-computing-cost.html

Software Process Efficiency - Bibliography

https://nraoiekc.blogspot.com/2012/06/software-process-efficiency.html

API Based Services for Performance Improvement and Productivity Improvement

https://nraoiekc.blogspot.com/2021/06/api-based-services-for-performance.html

Is There a Cost Reduction Benefit in Using Hybrid Computers?

https://nraoiekc.blogspot.com/2021/06/is-there-cost-reduction-benefit-in.html

Document Databases - Alternative to Tabular, Relational Databases for Productivity Improvement

https://nraoiekc.blogspot.com/2021/08/document-databases-alternative-to.html

Google Engineering Productivity Department - Activities and Accomplishments

https://nraoiekc.blogspot.com/2019/03/google-engineering-productivity.html

Recent Articles

Google Cloud: 'We are a toolset to make companies better'

December 14, 2021 

https://www.zdnet.com/article/google-cloud-we-are-a-toolset-to-make-companies-better/

From Harness – Value-Stream Mapping (VSM): Your Software Delivery

By cdfoundationJune 26, 2020

https://cd.foundation/blog/2020/06/26/value-stream-mapping-vsm-your-software-delivery/

Updated on 5.11.2026, 20.4.2023, 28.3.2023,  24.9.2021,  9 Dec 2021,  26 July 2020
First published on 21 July 2020

Data Analytics Period in Productivity Improvement - Productivity Industrial Engineering and Management

New. Popular E-Book on IE,

Introduction to Modern Industrial Engineering.  #FREE #Download.

In 0.5% on Academia.edu for the year 2024. 15.000+ Downloads/Views  so far.

https://academia.edu/103626052/INTRODUCTION_TO_MODERN_INDUSTRIAL_ENGINEERING_Version_3_0

Lesson 446  of  Industrial Engineering ONLINE Course - Applied Industrial Engineering Module

 The Wave of Productivity Driven by Data and Analytics

Analytics is a new technology, a software based technology that give us support for improved decisions in the area of productivity improvement. So there is applied industrial engineering task of using analytics for improving productivity in processes and systems.

Intelligent Asset Management for Productivity

https://www.ibm.com/resources/guides/intelligent-asset-management/

Using  Industry 4.0 Technologies

Industry 4.0 has tremendous potential for productivity improvement in the world. The quantum is in trillions as per estimates and forecasts. This industrial revolution offers potential for totally new products. But its role in redesign of existing products and production systems producing them itself has great potential. Hence engineers working in new products and processes area and engineers working in productivity area have ample work and challenge. 

McKinsey 2021 Report on IoT Opportunity During 2020 to 2030

Internet of Things: Sensors and actuators embedded in things connected by networks to computing systems. These systems can monitor or manage the health and actions of connected objects and machines. Connected sensors can also monitor the natural world, people, and animals apart from parts and machines.

During 2015 - 2020,  the IoT has faced headwinds related to change management, cost, talent, and cybersecurity, particularly in enterprises despite potential benefits. These hurdles have prevented some organizations from scaling their IoT ambitions beyond pilots, with projects still stuck as pilots or abandoned to lack of technical and economic feasibility. 

McKinsey re-estimated the benefits, the estimated impact for all participants in the economic system in more  than 100 unique applications in nine settings. The estimates go beyond the pure GDP impact of IoT applications and include various forms of consumer surplus, which are not measured in GDP.

From a tailwind perspective, the verdict is clear. Customers, who are using,  see real value in deploying the IoT. Compared with 2015, the technology and networks needed to implement the IoT are available and sufficient. In general, technology is not the constraining factor (Economics could still be a barrier. IEs have to work in IoT supplier companies to reduce costs and prices).

11 Clusters of use cases

Operations optimization

Human productivity

Health

Condition-based maintenance

Sales enablement

Energy management

Autonomous vehicles

Environment management

Safety and security

Product development

Inventory management

The estimates in 2021-22 were revised downward from 2015 estimates at both the low and high scenarios. The present estimates for 2025 are: $2.8 trillion to $6.3 trillion in potential economic value in 2025 versus $3.9 trillion to $11.1 trillion from the 2015 work. 

5G is expected to cover about 60 percent of the global population by 2026.  We estimate that by 2030, up to 90 percent of the global population will have some level of 5G coverage.

IoT systems capture lot of data and big data analysis can capture lot of additional information from the data that can be used to increase effectiveness of the product or service and productivity. Data analytics, the computer based real time data analysis based machine intelligence will play a significant role in the industry 4.0 product and production systems and other engineering systems of enterprises.

Digital Analytics  -   Fuel for the next 20 percent Productivity Rise. Will your company achieve it in five years or ten years? 

https://www.mckinsey.com.br/en/our-insights/ops-4-0-fueling-the-next-20-percent-productivity-rise-with-digital-analytics

Four Areas for Productivity Improvement

Product Design

Manufacturing

Complete Value Chain

Planning and Budgeting Costs

Product Design

Product Part Complexity Management. Due to product proliferation, small variants in specifications generate hundreds of mostly overlapping SKUs. Current methods take vast amounts of time and effort still fall short. New digital analytics tools allowed a conglomerate to complete an analysis, in just two weeks. The company could use to reduce variations among product families, subsystems, and components.

Analytics and Automation. Data from the procurement and engineering department’s own bills of materials can be analyzed more effectively. New tools can combine thousands of records, held around the world in dozens of local languages and part-numbering structures, to find potential commonalities and opportunities to negotiate better pricing. Robotic process automation, allows software “robots” to take over tedious processes, such as collating information from disparate systems for complex forms. Technical and purchase persons can  to focus on work that uses their judgment and experience. Sophisticated algorithms optimize in real time dynamically many decisions, enabling constant adjustments as conditions change.

Powerful portfolio analysis. Revenue and cost analysis of an entire product portfolio can be done periodically to adjust prices and volumes.  A conglomerate found it could eliminate 15 to 80 percent of product variants within a category and reduce cost by approximately 30 percent.

Design optimzation

R&D -  Productivity Improvement through Machine Learning

Data Analytics for Product Design - Product Industrial Engineering

Manufacturing

Yield, Energy, Throughput Analysis
OEE Analysis
Experiential Learning for Digital Manufacturing (Simulators for Learning)
Smart Predictive Maintenance
Supply Chain 4.0

Complete Value Chain

End to End Digitization
Robotic Process Automation

Planning and Budgeting Costs

Smart Capital Investment Decisions
Zero Based Budgeting for All activities 

Industrial Engineering in the Big Data Era: Selected Papers from the Global Joint Conference on Industrial Engineering and Its Application Areas, GJCIE 2018, June 21–22, 2018, Nevsehir, Turkey

Fethi Calisir, Emre Cevikcan, Hatice Camgoz Akdag

Springer, 23-Jan-2019 - Technology & Engineering - 513 pages

This book gathers extended versions of the best papers presented at the Global Joint Conference on Industrial Engineering and Its Application Areas (GJCIE), held in Nevsehir, Turkey, on June 21-22, 2018. They reports on industrial engineering methods and applications, with a special focus on the advantages and challenges posed by Big data in this field. The book covers a wide range of topics, including decision making, optimization, supply chain management and quality control.

https://books.google.co.in/books?id=O_aEDwAAQBAJ

2016

The wave of productivity driven by data and analytics - McKinsey senior partners

"You had the wave of lean, you had the wave of outsourcing, and now we’re seeing the wave of productivity driven by data and analytics, enabling organizations to refine the way that people work together, the way that processes perform, and the way assets are productive. If you think about an oil well, for example, you’ve got more than 300 sensors downhole that are spewing out data at the rate of about a gigabit a second, in some cases." - Bill Wiseman, McKinsey senior partner

How advanced analytics can drive productivity
Podcast August 2016
Podcast transcript
http://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/how-advanced-analytics-can-drive-productivity

How Data Analytics Increases Productivity?

Technology and innovation drive productivity.  But transaction costs of new technology implementation are considerable.  Innovation requires capital and labor investment incurring transaction costs to build infrastructure and grow the market.

Analytics and decision science could provide the means to reduce the transaction costs and increase innovations to  improve productivity in the economy.

Analytics provide a process to monitor, measure, and benchmark performance. By integrating diverse datasets from remote monitoring (IoT), measuring, and benchmarking can be accomplished automatically and economically. Reporting by anomaly and exception could free resources and thereby reduce costs and improve productivity.

Application of analytics to monitoring energy through sensors resulted in 17% reduction in energy costs for the NJ DOT main office complex. In another application, propensity models were constructed from customer profile and behavior data to identify candidates with highest conversion rates for sending sales promotion communications. Such examples are now many, to illustrate applying analytics to reduce costs and improve operations.

Low cost cloud computing together with large, diverse, and growing datasets, from the web to internal sources, and available statistical visualization tools, have dramatically changed the cost of data analytics. The low cost of data analytics is allowing more companies to use analytics and improve the productivity.

Source: Onoly Analytics and Analytics 2 Insight - White Paper
Analytics at the Speed of Insight: Simple, Fast and Actionable Tools to Improve Productivity


-------------------------------------------------------------------------

Further Developments

Industrial Engineering through Process Mining 

https://nraoiekc.blogspot.com/2020/11/industrial-engineering-through-process.html

Industrial Engineering through Digital Twins 

https://nraoiekc.blogspot.com/2020/11/industrial-engineering-through-digital.html

Industrial Engineering through MES Data 

https://nraoiekc.blogspot.com/2020/11/industrial-engineering-through-mes-data.html

Harvard Data Science Review
https://hdsr.mitpress.mit.edu/

2023

Stardog's Enterprise Knowledge Graph platform delivers 320% ROI

Forrester determined an ROI of 320% and total benefits of over $9.86 million over three years for the Stardog Enterprise Knowledge Graph Platform.

Read this study to learn how several customers turned their data into knowledge, completed their data analytics projects faster, saved on infrastructure costs, and unlocked new business opportunities with Stardog.

https://www.stardog.com/resources/forrester-tei/

Data science productivity tools you need to have in 2023

Data scientists across the globe process approx 2.5 quintillion bytes of data everyday. Here are some of the most useful productivity tools that help them stay on top of their demanding roles.

https://www.oslash.com/blog/data-science-productivity-tools-you-need-to-have

Workplace Productivity Analytics – The Complete Guide

https://squeezegrowth.com/workplace-productivity-analytics/

2021

Big Data for Creating and Capturing Value in the Digitalized Environment: Unpacking the Effects of Volume, Variety,  and Veracity on Firm Performance*Francesco Cappa , Raffaele Oriani, Enzo Peruffo, and Ian McCarthy

J PROD INNOV MANAGEMENT, 2021;38(1):49–67

Big data can be a valuable resource when firms create and capture value from it that exceeds the costs of collecting, managing, and analyzing it (Björkdahl and Holmén, 2018; Chesbrough, Lettl, and Ritter, 2018; Lepak, Smith, and Taylor, 2007; Wamba et al., 2017).

While previous studies focused on the impact of big data on firm productivity (Müller et al., 2018; Tambe, 2014), short-term financial performance (Corte-Real et al., 2017; Wamba et al., 2017), or new product development (Johnson et al., 2017) to assess the impact of big data, we use a market-based proxy of firm performance, that is, Tobin’s Q.

Big data are central to digitalization of the busi-ness environment because it can provide informa-tion about customers’ preferences, feedback about a firm’s product and service performance, and insights about emerging trends

our study focuses in particu-lar on how private big data collected from customers through mobile applications (referred to as big data in the rest of the paper) impacts firm performance.

One estimate of the costs for the cre-ation, management, and analysis of a three-Terabyte database is approximately U.S. $ 1 million per month (CoolaData, 2014). 

© 2020

Data Science and Productivity Analytics

Editors: Charles, Vincent, Aparicio, Juan, Zhu, Joe (Eds.)

Table of contents (15 chapters)

Data Envelopment Analysis and Big Data: Revisit with a Faster Method Pages 1-34

Khezrimotlagh, Dariush (et al.)

Data Envelopment Analysis (DEA): Algorithms, Computations, and Geometry Pages 35-56

Dulá, José H.

An Introduction to Data Science and Its Applications  Pages 57-81

Rabasa, Alex (et al.)

Identification of Congestion in DEA Pages 83-119

Mehdiloo, Mahmood (et al.)

Data Envelopment Analysis and Non-parametric Analysis Pages 121-160

Villa, Gabriel (et al.)

The Measurement of Firms’ Efficiency Using Parametric Techniques Pages 161-199

Orea, Luis

Fair Target Setting for Intermediate Products in Two-Stage Systems with Data Envelopment Analysis

Pages 201-226

An, Qingxian (et al.)

Fixed Cost and Resource Allocation Considering Technology Heterogeneity in Two-Stage Network Production Systems Pages 227-249

Ding, Tao (et al.)

Efficiency Assessment of Schools Operating in Heterogeneous Contexts: A Robust Nonparametric Analysis Using PISA 2015 Pages 251-277

Cordero, Jose Manuel (et al.)

A DEA Analysis in Latin American Ports: Measuring the Performance of Guayaquil Contecon Port 

Pages 279-309

Morales-Núñez, Emilio J. (et al.)

Effects of Locus of Control on Bank’s Policy—A Case Study of a Chinese State-Owned Bank 

Pages 311-335

Xu, Cong (et al.)

A Data Scientific Approach to Measure Hospital Productivity Pages 337-358

Daneshvar Rouyendegh (B. Erdebilli), Babak (et al.)

Environmental Application of Carbon Abatement Allocation by Data Envelopment Analysis Pages 359-389

Yu, Anyu (et al.)

Pension Funds and Mutual Funds Performance Measurement with a New DEA (MV-DEA) Model Allowing for Missing Variables Pages 391-413

Badrizadeh, Maryam (et al.)

Sharpe Portfolio Using a Cross-Efficiency Evaluation Pages 415-439

Landete, Mercedes (et al.)

https://www.springer.com/gp/book/9783030433833

Special Issue on Data Science for Better Productivity

Data science for better productivity

Vincent Charles,Juan Aparicio &Joe Zhu 

Journal of the Operational Research Society 

Volume 72, 2021 - Issue 5: Special Issue Data Science for Better Productivity

Afsharian, M. (2019). A frontier-based facility location problem with a centralised view of measuring the performance of the network. Journal of the Operational Research Society, 72(5), 1058–1074. https://doi.org/10.1080/01605682.2019.1639476   

Bougnol, M.-L., & Dulà, J. (2020). Improving productivity using government data: The case of US Centers for Medicare & Medicaid's ‘Nursing Home Compare. Journal of the Operational Research Society, 72(5), 1075–1086. https://doi.org/10.1080/01605682.2020.1724056   

Del Vecchio, M., Kharlamov, A., Parry, G., & Pogrebna, G. (2020). Improving productivity in Hollywood with data science: Using emotional arcs of movies to drive product and service innovation in entertainment industries. Journal of the Operational Research Society, 72(5), 1110–1137. https://doi.org/10.1080/01605682.2019.1705194   

Grimaldi, D., Fernandez, V., & Carrasco, C. (2019). Exploring data conditions to improve business performance. Journal of the Operational Research Society, 72(5), 1087–1098. https://doi.org/10.1080/01605682.2019.1590136   

Ihrig, S., Ishizaka, A., Brech, C., & Fliedner, T. (2019). A new hybrid method for the fair assignment of productivity targets to indirect corporate processes. Journal of the Operational Research Society, 72(5), 989–1001. https://doi.org/10.1080/01605682.2019.1639477   

Jiang, R., Yang, Y., Chen, Y., & Liang, L. (2019). Corporate diversification, firm productivity and resource allocation decisions: The data envelopment analysis approach. Journal of the Operational Research Society, 72(5), 1002–1014. https://doi.org/10.1080/01605682.2019.1568841   

Li, Y., & Chen, W. (2019). Entropy method of constructing a combined model for improving loan default prediction: A case study in China. Journal of the Operational Research Society, 72(5), 1099–1109. https://doi.org/10.1080/01605682.2019.1702905   

Lin, S.-W., Lu, W.-M., & Lin, F. (2020). Entrusting decisions to the public service pension fund: An integrated predictive model with additive network DEA approach. Journal of the Operational Research Society, 72(5), 1015–1032. https://doi.org/10.1080/01605682.2020.1718011   

Routh, P., Roy, A., & Meyer, J. (2020). Estimating customer churn under competing risks. Journal of the Operational Research Society, 72(5), 1138–1155. https://doi.org/10.1080/01605682.2020.1776166   

Shi, Y., Zhu, J., & Charles, V. (2020). Data science and productivity: A bibliometric review of data science applications and approaches in productivity evaluations. Journal of the Operational Research Society, 72(5), 975–988. https://doi.org/10.1080/01605682.2020.1860661   

Summerfield, N. S., Deokar, A. V., Xu, M., & Zhu, W. (2020). Should drivers cooperate? Performance evaluation of cooperative navigation on simulated road networks using network DEA. Journal of the Operational Research Society, 72(5), 1042–1057. https://doi.org/10.1080/01605682.2019.1700766   

Zhu, J. (2020). DEA under big data: Data enabled analytics and network data envelopment analysis. Annals of Operations Research, 1–23. In press. https://doi.org/10.1007/s10479-020-03668-8 

Zhu, W., Liu, B., Lu, Z., & Yu, Y. (2020). A DEALG methodology for prediction of effective customers of internet financial loan products. Journal of the Operational Research Society, 72(5), 1033–1041. https://doi.org/10.1080/01605682.2019.1700188 [Taylor & Francis On 

https://www.tandfonline.com/doi/full/10.1080/01605682.2021.1892466

A Balanced Perspective on Prediction and Inference for Data Science in Industry

by Nathan Sanders
https://hdsr.mitpress.mit.edu/pub/a7gxkn0a

Reporting, analytics, harmonization, productivity and data access solutions for the SAP®-run enterprise.
Realize the operational efficiencies of your SAP® environment.

With a focus on enabling you to unify data from multiple applications and sources, delivering insights for critical – and trusted – business decisions, and extending the value of your infrastructure investments, Magnitude and our subsidiaries offer data management and productivity solutions for SAP-run businesses.
https://magnitude.com/solutions/sap-solutions/

Email Analytics
https://emailanalytics.com/21-productivity-analytics-tools-every-business-needs-to-be-using/


2020

Productivity of Aircraft increased on the basis of analytics

Lockheed Martin
Keeping aircraft ready for critical mission

ARTIFICIAL INTELLIGENCE FROM SAS HELPS TURN 35 DAYS OF DOWNTIME INTO 35 DAYS OF FLYING TIME – PER PLANE.

600 sensors on each C-130J

72,000 rows of data per flight hour

95% reduction in data cleanup time
With customer data streaming in, the SAS Platform enables Lockheed Martin to go from reactive to predictive and give its customers confidence through intelligent diagnostics.
https://www.sas.com/en_us/company-information/discover/lockheed-martin.html

Industry 4.0 based process data analytics platform: A waste-to-energy plant case study
James Clovis Kabugoa Sirkka-LiisaJäms ä-Jounela Robert Schiemann Christian Binder
Power & Energy Systems
Volume 115, February 2020, 105508
Open Access Paper
https://www.sciencedirect.com/science/article/pii/S0142061518336731


January 2020

Data sharing in the supply chain

https://www.bcg.com/publications/2020/manufacturers-unlock-value-from-data-sharing

2019

Mobilaris Productivity Analytics

Tunneling Productivity
https://mobilaris.se/mining-civil-engineering/Improvements-by-analytics-tunneling/



SEPTEMBER 17, 2019
Accenture Leverages Data Analytics Solutions to Help Deutsche Bahn Cargo and Mars Inc. Increase Productivity and Reduce Risk

New Splunk-based solutions help clients grow revenue and optimize operations
https://newsroom.accenture.com/news/accenture-leverages-data-analytics-solutions-to-help-deutsche-bahn-cargo-and-mars-inc-increase-productivity-and-reduce-risk.htm

Aug 11, 2019
10 Ways Machine Learning Is Revolutionizing Manufacturing In 2019
https://www.forbes.com/sites/louiscolumbus/2019/08/11/10-ways-machine-learning-is-revolutionizing-manufacturing-in-2019

Exponential Productivity: Why IRPA Is Here To Stay

IRPA: Intelligent Robotic Process Automation
February 27, 2019
Hosted by Bonnie D. Graham
https://www.voiceamerica.com/episode/113504/exponential-productivity-why-irpa-is-here-to-stay

https://blog-sap.com/analytics/2019/05/02/exponential-productivity-why-irpa-is-here-to-stay/


Exploiting Big Data and analytics to improve productivity in manufacturing
By TIM LAWRENCE, GLOBAL HEAD OF MANUFACTURING, PA CONSULTING . Feb 22, 2019,
https://www.manufacturingglobal.com/technology/exploiting-big-data-and-analytics-improve-productivity-manufacturing

How is Data Science Used in Manufacturing Companies?
May 30, 2019 | Manufacturing Analytics
https://www.sensrtrx.com/how-is-data-science-used-in-manufacturing-companies/

Prediction of Productivity and Energy Consumption in a Consteel Furnace Using Data-Science Models
International Conference on Business Information Systems
BIS 2019: Business Information Systems pp 85-99
https://link.springer.com/chapter/10.1007/978-3-030-20485-3_7


WeldCloud - Esab

WeldCloud is an online management system that connects welding power supplies to a software platform that manages data to be analyzed for maximum productivity.
https://nraoiekc.blogspot.com/2019/01/productivity-drivers-productivity.html

Smart welding – a practical research project
https://www.hera.org.nz/smart-welding/

2018

Data analytics and expert insights increase paper mill productivity
ABB Ability™ Collaborative Operations helps leading paper producer in Indonesia to optimize mill operations
https://new.abb.com/pulp-paper/abb-in-pulp-and-paper/articles/previous/data-analytics-and-expert-insights-increase-paper-mill-productivity

Main facts
Customer Major paper mill in East Java
Country Indonesia
Customer need: Automate production: Identify and address production, quality and cost issues to achieve better consistency, fewer rejects and more sales
Solution ABB Ability™ Collaborative Operations

Benefits:
Reduced cycle times on product grade changes
Stabilized moisture and other additive levels
Higher equipment availability, fewer sheet breaks
Lower chemical costs
Reduced paper quality variation
Improved return on capital
Year: 2018


Improve Employee Productivity With Internal Analytics
John Choi,  Jul 27, 2018
https://www.cmswire.com/digital-workplace/improve-employee-productivity-with-internal-analytics/


Uncovering cost savings hidden in administrative and operational data

Analyzing benchmarking data helped the company to save $26 million in a rational manner and maintain its competitiveness.
By Sally Akers, RN, MSN, CNS and Mike Chamberlain - April 26, 2018
https://www.ibm.com/blogs/watson-health/uncovering-cost-savings-hidden-administrative-operational-data/

2017

Ops 4.0: Fueling the next 20 percent productivity rise with digital analytics
By Mercedes Goenaga, Philipp Radtke, Kevin Speicher, and Rafael Westinner
Article April 2017
http://www.mckinsey.com/business-functions/operations/our-insights/ops-4-0-fueling-the-next-20-percent-productivity-rise-with-digital-analytics


Be agile, Be focused on results and Take up manageable data analytics projects
https://www.bcg.com/publications/2017/digital-transformation-transformation-data-driven-transformation.aspx

Digital transformation-driven productivity
Don't underestimate the incredible power of DX turned inward, toward enhancing internal organizational productivity.
http://www.cio.com/article/3200807/leadership-management/digital-transformation-driven-productivity.html

Digital Transformation Can Resolve the Productivity Paradox
EITN MALAYSIA, April 27, 2017
By Hu Yoshida, Chief Technology Officer, Hitachi Data Systems
http://www.enterpriseitnews.com.my/digital-transformation-can-resolve-the-productivity-paradox/

How digital transformation improves productivity in manufacturing
By Alex Walker and Arnd Simon, Senior Directors, Industry Services, Global Manufacturing Practice, Microsoft Corporation; Contributor: Dr. Lei Liu, Digital Advisor, Microsoft Deutschland GmbH on May 5, 2017
https://enterprise.microsoft.com/en-us/articles/industries/discrete-manufacturing/digital-transformation-improves-productivity-manufacturing/

Increasing Chemical Industry Productivity through Digital Transformation

May 12, 2017 | Showcase
Learn how Capgemini's ChemPath SAP – Certified Business All-in-One (BAIO) solution, will help your organization streamline and synchronize the operations, while achieving greater visibility and control over your core business processes.
https://www.capgemini.com/resources/video/increasing-chemical-industry-productivity-through-digital-transformation

IoT use cases with 2 year pay back period in global auto company mentioned.
https://www.linkedin.com/pulse/chinas-factories-want-digitize-heres-what-need-do-karel-eloot

Microsoft Workplace Analytics helps managers understand worker productivity
https://techcrunch.com/2017/07/05/microsoft-workplace-analytics-helps-managers-understand-worker-productivity/

Forecasting demand with limited information using Gradient Tree Boosting

Stephan Chang and Felipe Meneguzzi
Copyright
c 2017, Association for the Advancement of Artificial
Intelligence (www.aaai.org). All rights reserved.
https://www.aaai.org/ocs/index.php/FLAIRS/FLAIRS17/paper/download/15508/14934

Related article to above
A Gentle Introduction to the Gradient Boosting Algorithm for Machine Learning
by Jason Brownlee on September 9, 2016 in XGBoost
Last Updated on August 21, 2019
https://machinelearningmastery.com/gentle-introduction-gradient-boosting-algorithm-machine-learning/

2016

EXPLORING THE POTENTIAL OF DATA DRIVEN AGRICULTURE IN INCREASING FARM PRODUCTIVITY AND PROFITABILITY

November 2016

http://www.icrisat.org/exploring-the-potential-of-data-driven-agriculture-in-increasing-farm-productivity-and-profitability/

Smart data is the way to boost mining productivity

2 September 2016
https://home.kpmg.com/xx/en/home/insights/2016/09/smart-data-way-boost-mining-productivity.html

Improving dragline productivity and increasing reliability using big data

August 2016

Mining operations produce an enormous amount of data through numerous parallel, though diverse, monitoring systems. Data mining and analytics can be a major part of a successful mining improvement process.

In this case, the goal is to find a single target variable and its value that will drive operator behaviour to operate the dragline at maximum production capacity and speed while not exceeding machine fatigue.
https://www.ausimmbulletin.com/feature/improving-dragline-productivity-and-increasing-reliability-using-big-data/


A Gentle Introduction to the Gradient Boosting Algorithm for Machine Learning
by Jason Brownlee on September 9, 2016 in XGBoost
Last Updated on August 21, 2019
https://machinelearningmastery.com/gentle-introduction-gradient-boosting-algorithm-machine-learning/

2015

Analytics 4.0: The Scary Ago Of Automated Networks
Tom Davenport
December 31, 2015
https://www.iianalytics.com/blog/2015/12/31/analytics-40-the-scary-ago-of-automated-networks

Analytics 3.0
Thomas H. Davenport
FROM THE DECEMBER 2013 ISSUE of HBR
https://hbr.org/2013/12/analytics-30


Production Data Analytics – To identify productivity potentials
Master Thesis - 2015
MUKUND SUBRAMANIYAN
Department of Product and Production Development
CHALMERS UNIVERSITY OF TECHNOLOGY, Gothenburg, Sweden - 2015
http://publications.lib.chalmers.se/records/fulltext/225149/225149.pdf

Forecasting demand with limited information using Gradient Tree Boosting

Stephan Chang and Felipe Meneguzzi
Copyright
c 2015, Association for the Advancement of Artificial
Intelligence (www.aaai.org). All rights reserved.
Mentioned by Mr. Saikat Chakraborty, NITIE IE Conclave 15 October 2019)

2013

Analytics 3.0
Thomas H. Davenport
FROM THE DECEMBER 2013 ISSUE of HBR
https://hbr.org/2013/12/analytics-30


Data Analytics and Continuous Productivity
September 18, 2013
https://www.tibco.com/blog/2013/09/18/data-analytics-and-continuous-productivity/


July 2013

Big-data analytics as a productivity tool McKinsey Global Institute

Sectors across the economy can harness the deluge of data generated by transactions, medical and legal records, videos, and social technologies, sensors, cameras, bar codes, and transmitters embedded in the world around us. Advances in computing and analytics can transform this sea of data into insights that create operational efficiencies.

By 2020, the wider adoption of big-data analytics could increase annual GDP in retailing and manufacturing by up to $325 billion.

Additionally as much as $285 billion savings can occur in the cost of health care and government services.

http://www.mckinsey.com/global-themes/americas/us-game-changers


2011

MIT professor Erik Brynjolfsson discusses how companies can increase their productivity by making better use of their data.
by David Talbot  February 16, 2011
https://www.technologyreview.com/s/422753/using-it-to-drive-innovation/


2009
MIT professor Erik Brynjolfsson
Book Chapter: Business practices that enhance productivity along with IT investments.
https://books.google.co.in/books?id=WBYeChNzVo8C&pg=PA61#v=onepage&q&f=false

Google Books

https://books.google.co.in/books?id=G2keDQAAQBAJ

HBR Top 10 on AI
https://books.google.co.in/books?id=ckBbDwAAQBAJ

https://books.google.co.in/books?id=rNqdDwAAQBAJ

https://books.google.co.in/books?id=XDtbwwEACAAJ

Machining developments 2019
https://books.google.co.in/books?id=WTarDwAAQBAJ


https://books.google.co.in/books?id=0pY4DwAAQBAJ

https://books.google.co.in/books?id=4GHUxQEACAAJ

https://books.google.co.in/books?id=Qct2DwAAQBAJ



https://books.google.co.in/books?id=5FNwBQAAQBAJ


https://books.google.co.in/books?id=Q-OOBAAAQBAJ

https://books.google.co.in/books?id=5jqkBgAAQBAJ

https://books.google.co.in/books?id=EeDXCQAAQBAJ

https://books.google.co.in/books?id=tRnVBQAAQBAJ




Updated 12.4.2023,   10.3.2023, 4.5.2022, 11 Aug 2021, 16 July 2021

20 March 2020,  28 October 2019,  16 October 2019,  13 October 2019,  17 August 2019,  4 August 2019, 18 July 2019,  23 August 2017,  16 August 2017,  10 July 2017,  30 June 2017, 22 June 2017

Needs update