Taylor - Narayana Rao Principles of Industrial Engineering

Industrial Engineering for Society Prosperity through Productivity Improvement satisfying all constraints and limits. 

Online Education/Training Session on "Effective Industrial Engineering and Productivity Management."

I developed an online education/training session on "Effective Industrial Engineering and Productivity Management." I can present the session in one hour, one and half hour or two-hour long sessions. The sessions will be valuable when company industrial engineers and other engineers and managers attend as a group. Industrial engineers require active cooperation and participation of other engineers and managers in their studies and projects. Hence a common presentation and discussion on effectiveness will be very useful.

Supporting Information.

Effective Industrial Engineering - Some Thoughts by Narayana Rao K.V.S.S.

Effective industrial engineering has to satisfy management about the contribution it made to the organization year after year.

The prime contribution of IE has to be cost reduction through productivity improvement.

https://nraoiekc.blogspot.com/2025/07/effective-industrial-engineering-some.html

https://www.linkedin.com/in/narayana-rao-kvss-b608007/

New. Popular E-Book on IE,

Introduction to Modern Industrial Engineering.  #FREE #Download.

In 1% on Academia.edu. 12,000+ Downloads so far.

https://academia.edu/103626052/INTRODUCTION_TO_MODERN_INDUSTRIAL_ENGINEERING_Version_3_0

Online Free Access Handbook of Industrial Engineering includes all modules of IE Online Course Notes.

Industrial Engineers, Display Industrial Engineering Principles in Your Department. 

Practice them and Provide Value to the organization.

Taylor - Narayana Rao Principles of Industrial Engineering were developed Prof. Narayana Rao K.V.S.S. in two stages. In the first Stage, Taylor's principles of scientific management were converted into basic principles of industrial engineering.

Principles of Scientific Management - Taylor

The managers following scientific management thought do the following things.

First. They develop a science for each element of a man's work, which replaces the old rule-of.-thumb method.

Second. They scientifically select and then train, teach, and develop the workman, whereas in the past he chose his own work and trained himself as best he could.

Third. They heartily cooperate with the men so as to insure all of the work being done in accordance with the principles of the science which has been developed.

Fourth. There is an almost equal division of the work and the responsibility between the management and the workmen. The management take over all work for which they are better fitted than the workmen, while in the past almost all of the work and the greater part of the responsibility were thrown upon the men.

(From THE PRINCIPLES OF SCIENTIFIC MANAGEMENT - F.W.Taylor)

The principles explain the question what is industrial engineering (IE)?

Basic Principles of Industrial Engineering - Narayana Rao

1. Develop science for each element of a man - machine system's work related to efficiency and productivity.

2. Engineer methods, processes and operations to use the laws related to the work of machines, man, materials and other resources.

3. Select or assign workmen based on predefined aptitudes for various types of man - machine work.

4. Train workmen, supervisors, and engineers in the new methods, install various modifications related to the machines that include productivity improvement devices and ensure that the expected productivity is realized.

5. Incorporate suggestions of operators, supervisors and engineers in the methods redesign on a continuous basis.

6. Plan and manage productivity at system level.

(The principles were developed on 4 June 2016 (During Birthday break of 2016 - 30 June 2016 to 7 July 2016).

The principles were developed by Narayana Rao based on principles of scientific management by F.W. Taylor)

Principles of Industrial Engineering - Narayana Rao - Detailed List

Clicking on the link will take you to more detailed content on the principle

1. Productivity science

2. Productivity engineering

3. Industrial Engineering is applicable to all branches of engineering

4. Principles of (machine) utilization economy to be developed for all resources used in engineering systems.

5. Industrial engineering optimization

6. Industrial engineering economics

7. Implementation team membership and leadership

8. Human effort engineering for increasing productivity

9. Principles of motion economy to be used in all IE studies in the area of human effort engineering

10. Operator comfort and health are to be taken care of.

11. Work measurement

12. Selection of operators

13. Training of operators, supervisors and engineers

14. Productivity training and education to all

15. Employee involvement in continuous improvement of processes and products for productivity improvement.

16. Productivity incentives

17. Hearty cooperation

18. Productivity Management

19. System level focus for productivity

20. Productivity measurement

21. Cost measurement

The full paper on the principles by Prof. K.V.S.S. Narayana Rao is now available for downloading from IISE 2017 Annual Conference Proceedings in Proquest Journal Base.

https://www.proquest.com/docview/1951119980

Presentation on Principles of Industrial Engineering First made by Dr. Narayana Rao on 23 May 2017 in IISE Annual Conference, Pittsburgh, USA.

_________________

_________________

John Heap

Managing Director at Institute of Productivity

Principles of Productivity Science

Stimulated by recent exchanges in this group, on behalf of the World Confederation of Productivity Science, I have drafted the following principles of productivity science .... the principles that productivity leaders and practitioners should adopt to ensure their work is technically and ethically valid. Comments welcome!


Undertake a structured and systematic review of the social, environmental and economic value created by human endeavour;

Measure, at macro and micro levels, the value created - and the resources consumed to produce it;

Design products in ways which facilitate efficient manufacturing, distribution and subsequent disposal; Design services that are user-focused and scalable whilst remaining efficient in terms of their requirements for physical infrastructure and administrative support. 

Strive to identify and eliminate or minimize waste across the lifecycle of services and products (including throughout manufacturing and delivery processes);

Ensure all working environments and working practices are designed to ensure the safety and well-being of the workforce;

Minimise the consumption of energy and natural resources;

Minimise harmful effects on the natural environment;

Establish working systems, processes and methods that reduce inherent variability; 

Treat all members of the workforce with respect and, wherever possible, engage them in decision-making processes that affect their work roles or working conditions;

Ensure that all staff have the tools, equipment and skills necessary to maximize their performance;

Engage individuals and teams in productivity and performance enhancing reviews and investigations.

Posted on linked on 27 June 2018
https://www.linkedin.com/groups/2587524/2587524-6417631280732651523

Industrial Engineers, Display Industrial Engineering Principles

 in Your Department. 

Practice them and Provide Value to the organization.

Principles of Industrial Engineering

1. Productivity Science Principle of Industrial Engineering.

2. Productivity Engineering Principle of Industrial Engineering.

3. Ubiquity of Industrial Engineering Principle.

4. Machine Utilization Economy Principle of Industrial Engineering.

5. Optimization Principle of Industrial Engineering.

6. Return on Investment Principle of Industrial Engineering.

7. Implementation Principle of Industrial Engineering.

8. Human Effort Engineering Principle of Industrial Engineering.

9. Motion Economy Principle of Industrial Engineering.

10. Operator Comfort and Health Principle of Industrial Engineering.

11. Work Measurement Principle of Industrial Engineering

12. Operator Selection Principle of Industrial Engineering

13. Process Training Principle of Industrial Engineering

14. Productivity training Principle of Industrial Engineering

15. Employee Involvement Principle of Industrial Engineering

16. Productivity Incentives Principle of Industrial Engineering

17. Hearty Cooperation Principle of Industrial Engineering

18. Productivity Management Principle of Industrial Engineering

19. System Level Focus   Principle of Industrial Engineering

20. Productivity Measurement Principle of Industrial Engineering

21. Cost Measurement Principle of Industrial Engineering

Industrial Engineering - More Principles

Industrial Engineering Principle of Respect for People.

Taylor wrote, "The writer has a profound respect for the working men of this country. "

https://nraoiekc.blogspot.com/2013/08/personal-relations-between-employers.html

Quality Principle of Industrial Engineering

https://nraoiekc.blogspot.com/2020/06/taylor-on-quality-human-relations-and.html

Effectiveness First - Efficiency Next Principle of Industrial Engineering.

https://nraoiekc.blogspot.com/2023/04/effectiveness-first-efficiency-next.html

Industrial Engineering Principle of Incremental Engineering Modifications and Engineering Improvements for Productivity

Taylor - Narayana Rao Principles of Industrial Engineering - Productivity Improvement.

http://nraoiekc.blogspot.com/2017/06/taylor-narayana-rao-principles-of.html

Updated on  4.8.2024,  29.6.2024,  13.10.2023, 6.4.2023,  27.1.2023,  13 Jan 2023,  4 August 2021, 29 June 2021,  5 July 2017, 28 June 2017

Research and Development Papers on Toyota Production System - Bibliography

2022

Toyota Way - As Described by Toyota Officially

https://nraoiekc.blogspot.com/2022/09/toyota-way-as-described-by-toyota.html

2020

Toyota Way 2020 / Toyota Code of Conduct

Company Information - Vision & Philosophy

https://global.toyota/en/company/vision-and-philosophy/toyotaway_code-of-conduct/

2012

The Birth of Lean: Conversations with Taiichi Ohno, Eiji Toyoda, and Other Figures who Shaped Toyota Management


Koichi Shimokawa (Editor), Takahiro Fujimoto (Editor)

Lean Enterprise Institute, 04-Mar-2012 - Business & Economics - 300 pages
This is an honest look at the origins of lean, written in the words of the people who created the system. Through interviews and annotated talks, you will hear first-person accounts of what these innovators and problem-solvers did and why they did it. You'll read rare, personal commentaries that explain the interplay of (sometimes opposing) ideas that created a revolution in thinking.

Google Book Link with Preview Facility
http://books.google.co.in/books?id=SDYLbXoW_EcC

https://nraoiekc.blogspot.com/2013/08/the-birth-of-lean-koichi-shimokawa-2012.html

2012

Toyota Motors - History of Productivity

https://nraoiekc.blogspot.com/2013/11/toyota-motors-history-of-productivity.html

2007

Lessons from Toyota’s Long Drive

by Thomas A. Stewart and Anand P. Raman.

Two HBR editors interviewed Toyota’s president, Katsuaki Watanabe, and several top executives. 

From the HBR Magazine (July–August 2007)

https://hbr.org/2007/07/lessons-from-toyotas-long-drive

2001
Rationalizing the Design of the Toyota Production System:
A Comparison of Two Approaches
J. Won, , D. Cochran, , H. T. Johnson, , S. Bouzekouk, B. Masha

Production System Design Laboratory, Department of Mechanical Engineering
Massachusetts Institute of Technology, Cambridge, Massachusetts, USA, and
School of Business Administration, Portland State University, Portland, Oregon, USA


Abstract
This paper examines two recent attempts to develop frameworks to explain the Toyota Production System (TPS).
In Decoding the DNA of the Toyota Production System, Spear and Bowen assert that the design, operation and improvement of manufacturing systems can be captured in four basic rules.
In A Decomposition Approach for Manufacturing System Design, Cochran et. al. show how a Manufacturing System Design Decomposition (MSDD) can express the relationships between the design requirements and corresponding solutions within a manufacturing system.
This paper compares and contrasts how each of these approaches incorporates the requirements of successful manufacturing system design.
http://www.sysdesign.org/pdf/paper15.pdf


1999

Decoding the DNA of the Toyota Production System
Steven Spear and H. Kent Bowen
Harvard Business Review, THE SEPTEMBER 1999
https://hbr.org/1999/09/decoding-the-dna-of-the-toyota-production-system

 The Evolution of a Manufacturing System at Toyota

Takahiro Fujimoto

Oxford University Press, 12-Aug-1999 - Business & Economics - 400 pages

What is the true source of a firm's long-term competitive advantage in manufacturing? 

Through original field studies, historical research, and statistical analyses, this book shows how Toyota Motor Corporation, one of the world's largest automobile companies, built distinctive capabilities in production, product development, and supplier management. Fujimoto asserts that it is Toyota's evolutionary learning capability that gives the company its advantage and demonstrates how this learning is put to use in daily work.

https://books.google.co.in/books?id=KBm8F9cI8OYC

https://nraoiekc.blogspot.com/2021/07/the-evolution-of-manufacturing-system.html

Relations Between Safety and Productivity
Kazuaki Goto & Shingo Kato
*Assembly Dept., Tsutsumi Factory of Toyota Motor Co.*
1999

1. Outline of Tsutusmi Works
Established: 1970 (28-year operation as a passenger car factory) Capacity: 400,000 - 500,000 cars per year The Number of Employees: 5,600 employees in the factory, including 1,500 employees working for the assembly department. The factory has been functioning as a mother plant of Toyota Kentucky factory in the USA and Derby factory in England
http://www.jniosh.go.jp/icpro/jicosh-old/english/osh/jisha-nsc/toyota.html

1997

1997

Guiding Principles at Toyota

Company Information Vision & Philosophy

https://global.toyota/en/company/vision-and-philosophy/guiding-principles/?padid=ag478_from_right_side

ud. 10.10.2025

Pub. 27.2.2015

Productivity Management - Research

My Research paper - 2019

Proceedings of the 61st National Convention of Indian Institute of Industrial Engineering & 5th International Conference on Industrial Engineering (ICIE-2019), pp.240-244 

Evolution of Productivity Management- Present Scope, Opportunity and Challenges

K.V.S.S. Narayana Rao

Professor, National Institute of Industrial Engineering (NITIE), Mumbai

Abstract - Frederick Taylor started productivity management theory development with his 1895 paper on piece rate system, and developed it further in "shop management" and "scientific management" papers. His methods were adopted in industrial engineering and operations management disciplines. Productivity, efficiency improvement and cost reduction as objectives of industrial engineering were indicated by many authors and scholars. Scott Sink and David Sumanth came out with textbooks on productivity management. But a review of the curricula of industrial engineering and a survey reveal that productivity management is not yet an important area in teaching and practice. In this paper, an attempt is made to highlight the development of important productivity management theories and practices through literature review, curricula review, opinion of IE faculty and profit center managers. The current scope, opportunity and challenges for productivity management are brought out in the paper. 

Keywords - Productivity management, Productivity, Efficiency

2.4 Productivity Measurement and Productivity Management

Sumanth (1984) described productivity management as a formal process involving all levels of management and employees with ultimate objective of reducing the cost of the manufacturing, distributing, and selling of a product or service through an integration of the four phases of the productivity cycle, namely, productivity measurement, evaluation, planning and improvement. Productivity planning is based on productivity measurement and evaluation. Productivity evaluation determines the change in the total productivity between two successive periods and derives the possible ways in which the change has occurred. This is an after the fact analysis and provides the causes of productivity change. When an organization understands the productivity change methods and techniques that it had used for productivity improvement and inputs and outputs of productivity change projects, it can plan for productivity improvement in the future. The data base generated for potential of each productivity improvement way can be used as a framework for assessing new productivity improvement ways proposed by industrial engineers. Thus a productivity planning framework was presented by Sumanth. The productivity plan is part of productivity management and the other functions of management, namely organization, staffing/resourcing, directing and controlling have to be performed to achieve the productivity plans.  The execution part of productivity management is termed productivity improvement by Sumanth. He noted that managers are practising the management of productivity in an informal fashion. But, the need and scope for formal productivity management was highlighted. Productivity management can be assigned to a “director of productivity’ or “productivity coordinator” or “productivity manager.”

In regard to teaching of productivity management, Sumanth remarked that topics related to productivity improvement and management are covered in several schools around the world in informal fashion in courses on operations management, work study, motion and time study, etc. He gave the opinion that his book provides the basic support for formal productivity management education in industrial engineering programs and business administration programs. It seems that still in many curricula, formal productivity management is not covered and only some topics related to productivity improvement are being covered in various courses.

Scott Sink authored the book “Productivity Management: Planning, Measurement and Evaluation, Control and Improvement in 1985 (Sink, 1985). He also described the productivity management process with the starting point as productivity measurement. The steps in the productivity management process are given as: (1) measuring and evaluating productivity; (2) planning for control and improvement of productivity based on information provided by measurement and evaluation process; (3) making control and improvement interventions; and (4) measuring and evaluating the impact of these interventions. For productivity evaluation, standards are to be generated by one of the various methods as appropriate. The methods indicated include: 1. Estimation 2. Engineering approach 3. Historical information 4. Normative values. Both Sumanth (1984) and Sink (1985) indicated large number of productivity improvement methods and techniques which can be used for productivity improvement. Sink gave more focus on human aspects of productivity management. The issues discussed include delegation, decentralization, Theory Z, motivation, incentive systems, behaviour modification, goal setting, job design and redesign, employee involvement and performance/productivity action teams. 

3. IMPORTANCE OF PRODUCTIVITY AND PRODUCTIVITY MANAGEMENT IN INDUSTRIAL ENGINEERING

The question whether productivity is an important objective of industrial engineering was assessed by looking at web pages of 73 industrial engineering programs ranked as top 75 courses in the USA by https://www.collegefactual.com. In 63 program descriptions, productivity was mentioned as an objective of interest in industrial engineering. 

3.1 Teaching of Productivity Management in IE Programs and Practice of Productivity Management 

Sumanth (1984) noted in his book that productivity issues are discussed in industrial engineering programs in various subjects. But he proposed a formal subject in industrial engineering programs that starts with productivity measurement, evaluate its improvement bases and uses that information to plan productivity improvement for future years. More authors have described functions of productivity management including organization, resource acquisition, directing and controlling. A study of program brochures and course descriptions of 73 institutes showed that productivity management as a separate subject is not being taught in IE programs. An attempt is made to contact faculty of these institutes connected to program coordination for exploring the relevance of productivity management to industrial engineering by asking the following questions through emails.

• Is productivity management a relevant area of industrial engineering?

• Was it tried as a subject in your institute and is it being taught presently?

• Do you support the view that productivity management has not attained a significant position in IE curricula?

• What needs to be done to promote productivity management in IE curricula as well as in IE professional practice?

28 responses have come. 16 responses have answers to some questions. All 16 agree that productivity management is a relevant area of industrial engineering. This is the general opinion that emerges from the email based survey carried out in this research endeavour. Regarding the second question, most of the responses state that it is covered in number of subjects. In one program, productivity improvement course covers management aspects also. In one program there was a course on productivity management earlier, but it is now discontinued. Thus, majority of the programs are still covering only some productivity management issues in multiple courses as stated by Sumanth (1984). Third question is regarding the importance attained by the area of productivity management. 11 responses are available. Three responses are emphatic that it has attained significance. Three responses indicate that it has not attained the significance. Five responses indicate that some significant role is given to productivity management.

Fourth question is concerned with suggestions to promote productivity management. 10 responses are available. The suggestions include better marketing of the course to institutions, industry demand for such a course in IE programs, and a requirement from the accreditation/certifying agencies for a course on productivity management. 

Full Paper - Download from:

Evolution of Productivity Management-Present Scope, Opportunity and Challenges

By Narayana Kvss

https://www.academia.edu/143701454/Evolution_of_Productivity_Management_Present_Scope_Opportunity_and_Challenges

https://publications.waset.org/industrial-and-systems-engineering


Pradip K. Ray, S. Sahu, (1990) "Productivity Management in India: A Delphi Study", International Journal of Operations & Production Management, Vol. 10 Issue: 5, pp.25-51, https://doi.org/10.1108/01443579010005245
https://www.emeraldinsight.com/doi/abs/10.1108/01443579010005245

Productivity and Competitiveness:A Model for Developing Economies
C Bhaktavatsala Rao
Manager, (Corporate Planning), Ashok Leyland, Madras
ASCI Journal of Management
Volume 23, 1994
Paper with lot details about Indian Industry and some productivity improvement models.
https://asci.org.in/journal/Vol.23(1994)/v23_2_bha.htm

Evolution of Scientific Management Towards Performance Measurement and Managing Systems for Sustainable Performance in Industrial Assets: Philosophical Point of View
R.M. Chandima Ratnayake
Vol 4, No 1 (2009)
https://www.jotmi.org/index.php/GT/article/view/tre3/483


Improvement of Manpower and Equipment Productivity in Indian Construction Projects
Venkatesh M.P. & Saravana Natarajan
IJAER, 2019, Vol.14(2), pp. 404.409
Paper downloaded






2017 IISE Annual Conference Pittsburgh
Productivity management is an important function in industrial engineering. Productivity science and productivity engineering are the other two important functions. Productivity training and productivity measurement can also be indicated as important activities of industrial engineering.

Principles of Industrial Engineering

Prof. Narayana Rao published the paper "Principles of Industrial Engineering" in the proceedings of IISE 2017 Annual Conference and presented the paper in the conference on 23 May 2017.

You can download the full paper.
http://www.xcdsystem.com/iise/abstract/File7673/UploadFinalPaper_2569.pdf

Presentation Video on YouTube

Presentation in the IISE 2017 Pittsburgh, USA Annual Conference

IISE 2017 Annual Conference Papers


IISE 2017  conference papers are available in pre-publication format in the site
https://www.xcdsystem.com/iise/program/A20a5CK/

Productivity management sessions are indicated below. Go to the day and session and you can download the papers.

Productivity Management

...(22 May 11.00  - 12.20 am; 22 May  2 - 3.20 pm; 23 May 12.30 to 1.50; )


22 May 11.00  - 12.20 am

Chair: Mario Beruvides, Texas Tech University
Presentations

An analysis on the prevention, appraisal and failure model in COQ: Convergences and contradictions.
https://www.xcdsystem.com/iise/abstract/File7673/UploadFinalPaper_2511.pdf
Armando Elizondo-Noriega, Texas Tech University; David Güemes-Castorena, Tecnológico de Monterrey; Mario Beruvides, Texas Tech University

System dynamics modeling of cost of quality: An initial review of the literature
 Armando Elizondo-Noriega, Texas Tech University; David Güemes-Castorena, Tecnológico de Monterrey; Mario Beruvides, Texas Tech University

An Examination of Behavioral Economic Nudges in Technical Management
https://www.xcdsystem.com/iise/abstract/File7673/UploadFinalPaper_3130.pdf
Richard Burgess, Texas Tech University - Whitacre College of Engineering ; Mario Beruvides, Texas Tech University

The Dynamics between Working Capital Management and Total Productivity Management
https://www.xcdsystem.com/iise/abstract/File7673/UploadFinalPaper_2486.pdf
Naveen Tiruvengadam, Texas Tech University; Mario Beruvides, Texas Tech University


Productivity Management 2

Day:Monday, May 22, 2017
Time: 2:00 PM - 3:20 PM



Chair: Brian Mitchell, Szent Istvan University
Presentations

Labor Productivity and Optimal Team Size in an Inspection Process
https://www.xcdsystem.com/iise/abstract/File7673/FinalPaperFile_2556.pdf
Alireza Namdari, Western New England University ; Julie Drzymalski, Drexel University; Hamid Tohidi, South Tehran Branch, Azad University

Improving Fleet Readiness: A Case Study Utilizing the AirSpeed Methodology
https://www.xcdsystem.com/iise/abstract/File7673/UploadFinalPaper_3359.pdf
David Englehart, Navy; Daniel Zalewski, University of Dayton; Kellie Schneider, University of Dayton

ICTS’ Use in Customer-supplier Relationship on Collaborative NPD: Literature Review
https://www.xcdsystem.com/iise/abstract/File7673/UploadFinalPaper_2863.pdf
Daisy Valle, Federal University of Rio Grande do Sul; Mateus Lima, Federal University of Rio Grande do Sul; Alejandro Germán Frank, Federal University of Rio Grande do Sul

A new dispersion model for decision making under risk
https://www.xcdsystem.com/iise/abstract/File7673/UploadFinalPaper_2972.pdf
Behnam Malakooti, Case Western Reserve University


Productivity Management 3

Day:Tuesday, May 23, 2017
Time: 12:30 PM - 1:50 PM



Chair: Gary Gress, University of Calgary
Presentations

A Multi-Objective Stochastic Programming Model for Team Formation Problems under Uncertainty in Time Requirements
Fahimeh Rahmanniya, The University of Tennessee Knoxville; Andrew Yu, The University of Tennessee

Principles of Industrial Engineering
12:50 PM - 1:10 PM
http://www.xcdsystem.com/iise/abstract/File7673/UploadFinalPaper_2569.pdf
Venkata Satya Surya Narayana Rao Kambhampati, National Institute of Industrial Engineering

Presentation Video
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___________________

Program Performance Impact of Integrating Program Management and Systems Engineering
https://www.xcdsystem.com/iise/abstract/File7673/UploadFinalPaper_2794.pdf
Eric Rebentisch, Massachusetts Institute of Technology; Thomas Reiner, RWTH Aachen; Edivandro Conforto, Independent Consultant; Stefan Breunig, RWTH Aachen University


Updated on 30.8.2025,  30 May 2019, 17 June 2017

Total Efficiency Framework - Industrial Engineering Research and Development Project in Sustainability Movement

Total Efficiency Framework - Productivity Science, Productivity Engineering and Productivity Management 

The Total Efficiency Framework will be based on four main pillars to overcome the current barriers and promote sustainable improvements:

a) an effective management system targeted at process and continuous improvement;

b) efficiency assessment tools to define improvement and optimisation strategies and support decision-making processes;

c) integration with a toolkit for Industrial Symbiosis focusing on material and energy exchange;

d) a software Platform, based on the Internet of Things (IoT), to simplify the concept implementation and ensure an integrated control of improvement process.


Productivity

Over a period of 4 years, the project will deliver exploitable results clustered into technological outputs (including eco-innovative products, processes and services tailored to industrial end-users) and management solutions (involving  economical, legislative and policy solutions synergistically combined).

https://maestri-spire.eu/

https://maestri-spire.eu/downloads/communication/



Read Chapter 9 in the Book
Efficient and Sustainable Manufacturing - Total Efficiency Framework
Technological Solution in Industry 4.0 for Business Applications
2018
https://books.google.co.in/books?id=GYhoDwAAQBAJ&printsec=frontcover#v=onepage&q&f=false

2025

List of Industry 4.0 Light Houses - WEF - McKinsey - Have You Benchmarked with the Best in Industry 4.0 Implementation?

https://nraoiekc.blogspot.com/2024/01/list-of-industry-40-light-houses-wef.html

Ud. 21.7.2025

Pub. 2.12.2018

Richard Hedman - Productivity Management Research

Richard Hedman - Google Scholar Citations

https://scholar.google.se/citations?user=WXRt_dAAAAAJ&hl=sv

Richard Hedman - Doctoral thesis, 2016

Capturing the Operational Improvement Potential of Production Systems
Doctoral thesis, 2016

In operations, customer orders are fulfilled through transforming raw materials into finished goods. The research presented here examines the productivity and capacity of operational processes in manufacturing firms. Though both terms are well established in industry, overall, there is ambiguity in their measurement and interpretation across the hierarchal levels of organizations in actual shop floor operations, all the way to the national level.  As a result, many existing approaches to assessing the productivity and capacity of production systems either narrowly focus on certain functions of a production process or address them at such an aggregated level that there is insufficient detail to determine the root causes of production system losses. This leads to the risk that improvement potential at an operational level may be disregarded when investment or improvement investment decisions are made, making it difficult to improve economic efficiency and preventing the sustainable utilization of a firm’s current manufacturing resources. The purpose of this research is accordingly to increase the understanding of the improvement potential of real operational processes by developing a framework for identifying and objectively measuring the relevant characteristics of real-life operational processes related to the improvement of shop floor operations.

The research, which incorporates five empirical studies, builds on the theory of performance frontiers and on the body of industrial engineering knowledge. The research illustrates how the analytical logic and structure of the framework can be applied in determining the overall productivity and capacity of firm operations from the micro level and up, by relying on first-order time data measured at the operational level. This establishes a direct link between firm level capacity utilization and the causes of shop-floor productivity losses, constituting the foundation on which to build more knowledge of the effects on plant level capacity utilization that come from realizing operational improvement potentials. The results are also intended to provide guidance for decision-making in manufacturing companies.

https://research.chalmers.se/en/publication/239660

Reference. Prof. Guruprasad Murthy

Ud. 6.1.2025

Pub. 10.10.2019

Journals of Industrial Engineering

 

Ranking of Journals in Industrial and Manufacturing Engineering
http://www.scimagojr.com/journalrank.php?category=2209

Journal of Industrial Engineering International

Journal of Industrial Engineering International  is a peer-reviewed open access journal published under the brand SpringerOpen, covering all aspects of industrial engineering. It is fully supported by the Islamic Azad University, who provide funds to cover all costs of publication, including the Article Processing Charges (APC’s) for all authors. Therefore the journal is both free to read and free to publish in.
http://www.springer.com/engineering/production+engineering/journal/40092

Journal of Industrial and Production Engineering

Official Journal of the Chinese Institute of Industrial Engineers
http://www.scimagojr.com/journalsearch.php?q=21100241791&tip=sid&clean=0
Volume 32, Issue 2, 2015
http://www.tandfonline.com/toc/tjci21/current#.VScvHtyUd1Y

International Journal of Applied Industrial Engineering (IJAIE)

Editor-in-Chief: Lanndon Ocampo (University of the Philippines Cebu, Philippines)
Indexed In: INSPEC and 10 more indices
Published: Semi-Annually |Established: 2012

Topics Covered
Business and strategy
Case studies in industry and services
Decision analysis
Engineering economy and cost estimation
Enterprise resource planning and ERPII
Facility location, layout, design, and materials handling
Forecasting, production planning, and control
Human factors, ergonomics, and safety
Industrial engineering education
Information and communication technology and systems
Innovation, knowledge management, and organizational learning
Inventory, logistics, and transportation
Knowledge and technology transfers in a globalized network
Manufacturing, control, and automation
Operations management
Performance analysis
Product and process design and management
Project Management
Purchasing and procurement
Reliability and maintenance engineering
Scheduling in industry and service
Service systems and service management
Supply chain management
Systems and service modeling and simulation
Technology transfer and management
Third party/fourth party logistics
Total quality management and quality engineering

https://www.igi-global.com/journal/international-journal-applied-industrial-engineering/41034


European Journal of Industrial Engineering
http://www.scimagojr.com/journalsearch.php?q=11200153401&tip=sid&clean=0


International Journal of Industrial Engineering Computations
http://www.scimagojr.com/journalsearch.php?q=21100223326&tip=sid&clean=0

International Journal of Industrial and Systems Engineering
http://www.scimagojr.com/journalsearch.php?q=5800179616&tip=sid&clean=0

Journal of Industrial Engineering and Management
http://www.scimagojr.com/journalsearch.php?q=19700188349&tip=sid&clean=0

International Journal of Industrial Engineering : Theory Applications and Practice
http://www.scimagojr.com/journalsearch.php?q=19151&tip=sid&clean=0

South African Journal of Industrial Engineering
http://www.scimagojr.com/journalsearch.php?q=19700173182&tip=sid&clean=0

Jordan Journal of Mechanical and Industrial Engineering
http://www.scimagojr.com/journalsearch.php?q=20000195025&tip=sid&clean=0

International Journal of Industrial Engineering and Management
http://www.scimagojr.com/journalsearch.php?q=21100211751&tip=sid&clean=0

Journal of Japan Industrial Management Association
http://www.scimagojr.com/journalsearch.php?q=144786&tip=sid&clean=0

Engineering Optimization
http://www.scimagojr.com/journalsearch.php?q=29114&tip=sid&clean=0

International Journal of Industrial Engineering & Production Research

http://ijiepr.iust.ac.ir/

International Journal of Industrial and Manufacturing Systems Engineering

https://www.sciencepublishinggroup.com/journal/index?journalid=210

Part of 

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

https://nraoiekc.blogspot.com/2018/06/a-to-z-of-industrial-engineering.html

International Journal of Industrial and Manufacturing Systems Engineering - Information

International Journal of Industrial and Manufacturing Systems Engineering

https://www.sciencepublishinggroup.com/journal/index?journalid=210

IE Research - Machining of Titanium Alloy Analysis of Productivity and Machining Efficiency

 

Analysis of Productivity and Machining Efficiency in Sustainable 

Machining of Titanium Alloy

Procedia Manufacturing 43 (2020) 111–118

https://www.researchgate.net/publication/341060127_Analysis_of_Productivity_and_Machining_Efficiency_in_Sustainable_Machining_of_Titanium_Alloy

Analysis of Productivity and Machining Efficiency in Sustainable 

Machining of Titanium Alloy

Aqib Mashood Khan, Ning He, Liang Li, Wei Zhao, Muhammad Jamil 

Abstract

Recently, hybrid lubricooling is considered as the emerging sustainable cooling technique and is in a rudimentary stage. The application of hybrid CryoMQL (Cryogenic+MQL) method is getting repute in the industrial sectors due to its benefits, such as less resource consumption and improved productivity. 

In this study, turning experiments were performed to analyze the productivity and machining efficiency in  machining of Ti-6Al-4V alloy. All experiments were performed in different cooling techniques, and comparative results were obtained. Cutting time was fixed for each environment during a particular set of experiments and cutting speed was kept relatively higher in hybrid CryoMQL (HCM) method. Results showed that the HCM method performed best as compared to dry and MQL method. Longer tool life, higher productivity, lower energy consumption can be obtained using the proposed method.

https://www.sciencedirect.com/science/article/pii/S2351978920306995?ref=cra_js_challenge&fr=RR-1

Framework of Knowledge Worker Productivity

 Framework of Knowledge Worker Productivity

Excerpts from the paper

Towards a Holistic Framework of Knowledge Worker Productivity

by Helga Guðrún Óskarsdóttir 1,Guðmundur Valur Oddsson 1,Jón Þór Sturluson 2 andRögnvaldur Jóhann Sæmundsson 1

1 Department of Industrial Engineering, University of Iceland, 101 Reykjavik, Iceland

2 Department of Business Administration, Reykjavik University, 102 Reykjavik, Iceland

*

Academic Editor: Isabel-María Garcia-Sanchez

Adm. Sci. 2022, 12(2), 50; https://doi.org/10.3390/admsci12020050

Knowledge work is performed by knowledge workers (KWs) which “have high degrees of expertise, education, or experience and use this to acquire, create, share, or apply knowledge in their jobs” (Óskarsdóttir et al. 2021, p. 1).

Óskarsdóttir and Oddsson (2017) and Óskarsdóttir et al. (2021) suggest a holistic approach to KWP using soft systems methodology (SSM) to aid in descriptive theory building. According to Carlile and Christensen (2005), descriptive theory building consists of three steps: observation, categorization and association, which are iterated to formulate a theory that can be applied and improved in normative theory building.

SSM consists of four activities: (1) finding out about a problem situation, (2) formulating purposeful activity models (PAMs), (3) debating the situation and (4) taking action for improvement. Óskarsdóttir and Oddsson (2017) executed the first activity and analyzed the problem situation of managing and improving KWP using extensive literature reviews on KWP challenges from both the perspective of the organization and the individual KW. Based on the review, they identified four problems from the perspective of the organization: information needs and knowledge interdependence; motivation, work engagement and health; organizational structure and changes; the nature of knowledge work. They also found that individual KWs experience the following problems as influential to their productivity: too much demand and insufficient resources, choosing what to do and how to do it, self-development, self-awareness, achieving and/or setting goals, performing to full potential, making thinking more productive, successful relationships, collaborations and motivation. The results were abstracted into simple rich pictures and specific root definitions of relevant systems.

Building on the results of Óskarsdóttir and Oddsson (2017), Óskarsdóttir et al. (2021) executed the second activity in the SSM and formulated a PAM of the system from the perspective of the individual KW. A PAM is a conceptual model which is used to explore what activities need to be performed to achieve the purpose of the system by looking at it as a process (Checkland 2011). The PAM in Óskarsdóttir et al. (2021, p. 4) was built by assembling and linking the activities relevant to “the process in which the KW uses resources to execute actions to create tangible or intangible artifacts with the intention of generating value”.

In this paper the PAM presented in Óskarsdóttir and Oddsson (2017) is debated from the perspective of the problem solvers using insights from a systematic literature review. The focus is on factors that are directly relevant to individuals and their work according to the PAM presented in Óskarsdóttir et al. (2021) but limited to the perspective of the individual KW.

This paper takes us a step closer towards a holistic theory of KWP by describing some of the factors and measures that an operationalized model of KWP should include regarding individual KWs and their work (see Section 5). The draft of a descriptive theory of KWP is based on the results of the third SSM activity, debating the situation, where the insights from the systematic literature review are mapped to the activities in the PAM of the individual presented in Óskarsdóttir et al. (2021) (see Section 4).

https://www.mdpi.com/2076-3387/12/2/50/htm

Journal of Industrial Engineering International - JIEI

 

Industrial engineering is an engineering profession that is concerned with improving and optimizing complex processes, systems, or organizations by analyzing, monitoring, developing, improving and implementing integrated systems of people, money, knowledge, information, equipment, energy and materials. Over the years, computers and data engineering have become an integral part of industrial engineering. 

Journal of Industrial Engineering International; JIEI in 2003 is aimed at an audience of researchers, educators and practitioners of industrial engineering and associated fields. To accomplish its goals, the journal has been publishing quarterly about 10 full-text articles per each issue in English since 2005. JIEI rapidly obtained the scientific-research journal score by the Iranian Ministry of Science, Research and Technology. From 2012 to 2019, this journal was published by the Springer open access and also, ranked as a Q1 journal by Scopus in 2018.

https://jiei.stb.iau.ir/issue_1135759_1136383.html

Vol5, Issue 9

https://jiei.stb.iau.ir/issue_110206_110207.html

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A mathematical model for optimization of strength of concrete: A case study for shear modulus of Rice Husk Ash Concrete

Pages 76-84

O.S Ogah

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Vol. 4, Issue 6

https://jiei.stb.iau.ir/issue_110209_110211.html

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Technical Note: An opportunity cost maintenance scheduling framework for a fleet of ships: A case study

Pages 64-77

O.E Charles-Owaba; A.E Oluleye; F.A Oyawale; S.K Oke

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

Vol 2, Issue 2

https://jiei.stb.iau.ir/issue_110215_110218.html

Vol. 1, Issue 1

https://jiei.stb.iau.ir/issue_110219_110220.html

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 Team size effect on teamwork productivity using information technology

Pages 37-42

H Tohidi; M.J Tarokh

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Productivity Drivers - Productivity Analysis - Productivity Science - Productivity Engineering - Productivity Management

Productivity Drivers - Productivity Analysis - Productivity Science - Productivity Engineering - Productivity Management

Productivity drivers provide direction to productivity engineering and productivity management project activities.

Productivity analysis identifies productivity drivers or determinants of productivity.

Productivity science is the systematic collection of theories related to productivity improvement and productivity drivers.

Analyze - Identify - Engineer.

Analysis is comparing current conditions of a process with standards or best practices. Identifying the gaps and possibilities of improvement is the next step. Then detailed engineering to implement the concepts selected follows.

2019

Find new productivity drivers from welding operations data. 

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.

Every time a welder completes a pass, a trove of data is infused in the weld. That data has the power to inform future decisions, improve productivity, and provide you with the information necessary to understand how the weld was made.

Data for Productivity in Welding

As a productivity tracking platform using 3G combined with WI-FI and Ethernet, WeldCloud can plug into practically any in-house software system. WeldCloud is a secure, locked-down system that ensures your data is totally confidential.

WeldCloud offers:

Traceability. The platform can trace back to welds that have already been created and provided the details on how and when they were created.
Two-way communications. The platform can actually push settings to machines, such as a new weld parameter combo, while the machines can send data back to the platform.
Easy set up. Be up and running with WeldCloud in a couple of hours and trained in just a few days.
Alert management. WeldCloud automatically pushes out alerts when machines have issues, as needed and scheduled by the user.
Simple integration.  WeldCloud has solutions that can work with existing machines and get you up and running - quickly.
User-friendly interface. WeldCloud's responsive design makes using the platform easy from wherever you are, whether that's at home on a desktop, at work on a laptop, or on the move on mobile or tablet devices.
100% scalable. Put WeldCloud to the test by adding it on one or two machines and adding it onto others over time. You'll also benefit from new features and functionality that are constantly being developed and updated on the platform.

Operations Managers.
The people in this department are constantly looking for opportunities to increase productivity while maintaining a high-quality output. WeldCloud makes that possible by equipping them with data to find new productivity drivers.

Welding Engineers.
Welders' main focus is to determine the best possible welding process for a given application and test it. Quite a bit of data is required to make that initial decision, then it has to be tested on one machine. Once proven, WeldCloud can send the process to the cloud and - when it's ready - push it out to all the machines in the shop, saving a tremendous amount of time.

http://www.esabna.com/us/en/weldcloud/index.cfm

OECD MultiProd Project: The micro drivers of aggregate productivity

The MultiProd project studies productivity patterns and investes the extent to which different policy frameworks can shape firm productivity. It examines the way resources are allocated to more productive firms.
Drawing on the experience of the OECD ‘DynEmp’ (Dynamics of Employment) project, which provided harmonised micro-aggregated data to analyse employment dynamics, the MultiProd project provides cross-country harmonised micro-aggregated data of paramount importance for understanding productivity performances.
http://www.oecd.org/sti/ind/multiprod.htm

2015

Productivity Drivers  

Productivity Accounting, Emili Grifell-Tatjé, C. A. Knox Lovell

Cambridge University Press, 26-Jan-2015 - Business & Economics - 408 pages
(Pages 22 to 32)
https://books.google.co.in/books?id=Xz3WBQAAQBAJ

Productivity Drivers Internal

1. Quality of Management: General
2. Quality of Management: Human Effort Engineering and Human Resource Management.
3. Quality of Management: Allocation of Resources
4. Quality of Management: Adoption of New Technology
5. Quality of Management: Product Range and Diversification
6. Quality of Management: Cost Reduction and Waste Elimination
7. Quality of Labor
8. Quality of Capital ( and Intermediate Goods in a Gross Output Context)
9. Quality of Outputs of Services

Productivity Drivers External

1. Institutions
2. Ownership
3.The competitive environment
4. Regulation, Deregulation and Regulatory Structure
5. Structural Reform and Liberalization
6. Demographics
7. Geography
8. Public Infrastructure and Research & Development


The authors say the list is only indicative and not comprehensive. They are intended to emphasize that productivity is controllable either by firm management, or by the aggregate economy's helmsmen.

Process Parameters of Abrasive Waterjet Machining

Hydraulic Parameters: Waterjet nozzle diameter, supply pressure
Abrasive Parameters: Abrasive material, abrasive size, abrasive flow rate
Mixing Parameters: Mixing tube dimensions, nozzle material,
Cutting Parameters: Traverse rate, standoff distance,impingement angle, depth of cut, material to be cut

Expected performance criteria in AWJM: Cut quality, Material removal rate, Kerf topography, Kerf geometry, Cutting time

In a research paper the following parameters were condired as important to achieve above performance criteria.

1. Pressure inside pumping system
2. Abrasive material grain size
3. Angle of impact
4. Target material properties
5. Focusing tube diameter
6. Nozzle speed
7. Stand-off distance
8. Abrasive mass flow rate

Source: "Identification of Process Parameter of Abrasive Waterjet Machining for Performance Improvement - An AHP Approach," Industrial Engineering Journal, June, 2015, Vol. VII Issue No. 6
Authors: P.P. Badgujar, M.G.  Rathi, S.D. Kalpande




2014

Die Casting - Process Parameters

Processing factors that can affect process performance are:

1. Melting temperature of the cast metal
2. Injection pressure
3. Plunger speed
4. Cooling time

Source: Mahesh N. Adke and Shrikant V. Karanjkar, "Optimization of die-casting process parameters to identify optimized level for cycle time using Taguchi method," International Journal of Innovations in Engineering and Technology (IJIET), Vol 4, Issue 4, December 2014

2012

Productivity Drivers - Additive Manufacturing

Build time is sum of non-sintering operations and sintering operations.

Non-sintering operation time is the sum of the time for moving the platform elevator down one step and the time to deposit a new material layer. Total non-sintering operation time is time for each layer, multiplied by number layers used.

The time for sintering operation for one layer is a function of area of the layer, beam scan speed and  scan spacing .

Source: p. 144 of Phd Thesis "Multi Objective Optimization in Additive Manufacturing" by Giovanni Strano, May 2012
University of Exeter

2008

A Productivity Driver in Laser Cutting

Improvement in beam delivery optics is a productivity driver in laser cutting process since the 1970s. Improved coatings and thicker lenses etc. have facilitated  high-pressure applications and ,  have helped drive the quality and productivity of today's laser cutting machines. Fabricators enjoyed benefits  of the enhanced beam quality, reduced maintenance, increased power, and improved mode stability, all stemming from years of research and development invested in laser beam delivery optics.

https://www.thefabricator.com/article/lasercutting/laser-optics-special-delivery

Adaptive adjustable radius optics

2007

Susanna Mancinelli and Massimilliano Mazzanti, "SME Performance, Innovation and Networking," Fondazione Eni Enrico Mattei, Nova Di Lavoro 50.2007

Section 3.1 Main Productivity Drivers

Structural firm feature: Size
Expenditure per employee on R&D


2006
Prof. Dominique Foray, "Enriching the indicator base for the economics of knowledge," Blue Sky II Conference, Statistics Canada, Ottawa.

Knowledge management and other organizational complements play a crucial role in explaining the recent surge in productivity in OECD countries. (p.6)

ud. 8.4.2022

pub. 25.1.2019

Research, Discoveries and Innovations in Productivity Improvement and Management

1940 - 49

Domar, E. D. 1946. “Capital Expansion, Rate of Growth, and Employment.” Econometrica 14 (2): 137–147.

 

1950 -59

Solow, R. M. 1957. “Technical Change and the Aggregate Production Function.” The Review of Economics and Statistics 39 (3): 312–320.

1970 - 1979

Åberg, Y. 1973. “I. Regional Productivity Differences in Swedish Manufacturing.” Regional and Urban Economics 3 (2): 131–155.

Massey, D., and R. A. Meegan. 1979. “Labour Productivity and Regional Employment Change.” Area 11 (2): 137–145.

1980 - 89

Grippo, P., M. Gandhi, and B. Thompson. 1987. “The Computer-aided Design of Modular Fixturing Systems.” The International Journal of Advanced Manufacturing Technology 2 (2): 75–88.

Roach, S. S. 1988. “Technology and the Services Sector: The Hidden Competitive Challenge.” Technological Forecasting and Social Change 34 (4): 387–403.

Carlsson, B. 1989. “The Evolution of Manufacturing Technology and Its Impact on Industrial Structure: An International Study.” Small Business Economics 1 (1): 21–37.

1990 - 99

Chen, F. F., and E. E. Adam, Jr. 1991. “The Impact of Flexible Manufacturing Systems on Productivity and Quality.” IEEE Transactions on Engineering Management 38 (1): 33–45.

Zhang, H. C., T. C. Kuo, H. Lu, and S. H. Huang. 1997. “Environmentally Conscious Design and Manufacturing: A State-of-the-art Survey.” Journal of Manufacturing Systems 16 (5): 352–371.

Zhu, Z., P. H. Meredith, and S. Makboonprasith. 1995. “Defining Critical Elements in JIT Implementation: A Survey.” Industrial Management & Data Systems 95 (8): 21–28.

Oliver, N., R. Delbridge, and J. Lowe. 1996. “Lean Production Practices: International Comparisons in the Auto Components Industry.” British Journal of Management 7 (s1): S29–S44.

Small, M. H. 1999. “Assessing Manufacturing Performance: An Advanced Manufacturing Technology Portfolio Perspective.” Industrial Management & Data Systems 99 (6): 266–278.

Smith, T. M., and J. S. Reece. 1999. “The Relationship of Strategy, Fit, Productivity, and Business Performance in a Services Setting.” Journal of Operations Management 17 (2): 145–161.

2000- 2009

Darroch, J. 2005. “Knowledge Management, Innovation and Firm Performance.” Journal of Knowledge Management 9 (3): 101–115.

De Toni, A., and S. Tonchia. 2001. “Performance Measurement Systems-models, Characteristics and Measures.” International Journal of Operations & Production Management 21 (1/2): 46–71.

Pun, K., K. Chin, and R. Gill. 2001. “Determinants of Employee Involvement Practices in Manufacturing Enterprises.” Total Quality Management 12 (1): 95–109.

Deming, W. E. 2000. Out of the Crisis. Cambridge, MA: MIT Press.

Sohal, A. S., S. Moss, and L. Ng. 2001. “Comparing IT Success in Manufacturing and Service Industries.” International Journal of Operations & Production Management 21 (1/2): 30–45.

Guan, J., and N. Ma. 2003. “Innovative Capability and Export Performance of Chinese Firms.” Technovation 23 (9): 737–747.

Ross, A., and K. Ernstberger. 2006. “Benchmarking the IT Productivity Paradox: Recent Evidence from the Manufacturing Sector.” Mathematical and Computer Modelling 44 (1–2): 30–42.

Holweg, Matthias. March 2007. “The Genealogy of Lean Production.” Journal of Operations Management 25 (2): 420–437.

Kim, E. 2000. “Trade Liberalization and Productivity Growth in Korean Manufacturing Industries: Price Protection, Market Power, and Scale Efficiency.” Journal of Development Economics 62 (1): 55–83.

Kumar, M., J. Antony, R. Singh, M. Tiwari, and D. Perry. 2006. “Implementing the Lean Sigma Framework in an Indian SME: A Case Study.” Production Planning and Control 17 (4): 407–423.

Lanoie, P., M. Patry, and R. Lajeunesse. 2008. “Environmental Regulation and Productivity: Testing the Porter Hypothesis.” Journal of Productivity Analysis 30 (2): 121–128.

Muthiah, K., and S. Huang. 2007. “Overall Throughput Effectiveness (OTE) Metric for Factory-level Performance Monitoring and Bottleneck Detection.” International Journal of Production Research 45 (20): 4753–4769.

2010 - 2019

Jackson, D. 2012. “CO2 Clean Technology: A Business Sustainability Strategy.” Metal Finishing 110 (6: 22–27.

Wan Mahmood, W. H., M. N. Ab Rahman, B. Md Deros, S. Subramonian, and Z. Jano. 2013. “Manufacturing Performance in Green Supply Chain Management.” World Applied Sciences Journal 21 (2): 76–84.

Park, J. H., and H. Y. Jeong. 2013. “Cloud Computing-based Jam Management for a Manufacturing System in a Green IT Environment.” The Journal of Supercomputing 69 (3): 1054–1067.

Woo, C., Y. Chung, D. Chun, S. Han, and D. Lee. 2014. “Impact of Green Innovation on Labor Productivity and Its Determinants: An Analysis of the Korean Manufacturing Industry.” Business Strategy and the Environment 23 (8): 567–576.

Holitistic Productivity Management Using Digitalization.

Tim Jeske, Marc-Andre Weber et al.

2019 AHFE

https://books.google.co.in/books?id=CpebDwAAQBAJ&pg=PA104#v=onepage&q&f=false

2020 -

General Optimization Model of Modular Equipment Selection and Serialization for Shale Gas Field

ORIGINAL RESEARCH article

Front. Energy Res., 30 June 2021 | https://doi.org/10.3389/fenrg.2021.711974

https://www.frontiersin.org/articles/10.3389/fenrg.2021.711974/full

Data-Driven Productivity Management: AGCS (Insurer) Shows What Implementation Can Look Like

1. October 2021

Data analytics expert Dr. Annika Bergbauer and Nicoline Liebl, Head of Global Productivity Management at AGCS, Allianz’s specialty insurer, talk about the topic of productivity management.

https://www.q-perior.com/en/news/data-driven-productivity-management-agcs-shows-what-implementation-can-look-like/

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