Productivity Machine Tool Engineering

Productivity machine tool engineering has two aspects. One is improving the productivity of a manufacturing process by selecting more efficient machine tools. It also involves developing special purpose machine tools that increase productivity in a process.

The second aspect is redesign of machine tools to make them more productive in manufacturing processes. The role of industrial engineers in this activity is to identify the performance features of machine tools that give increased productivity in manufacturing processes.


Advances in Machine Tool Design and Research 1967: Proceedings
https://books.google.co.in/books?id=Gc8gBQAAQBAJ&pg=PA1#v=onepage&q&f=false


Design Optimization of Machine-Tool Structures Considering Manufacturing Cost, Accuracy, and Productivity
M. Yoshimura, Y. Takeuchi and K. Hitomi
J. Mech., Trans., and Automation 106(4), 531-537 (Dec 01, 1984)
http://mechanicaldesign.asmedigitalcollection.asme.org/article.aspx?articleid=1452435

Lean Management - Lean Industrial Engineering

The further development in lean has to provide more scientific insight into how product and service attributes contribute to customer value; what matters most for improving classic lean variables, such as lead time, cost, quality, responsiveness, flexibility, and reliability; and new opportunities for cross-functional problem solving to eliminate anything that strays from customer-defined value.
http://www.mckinsey.com/business-functions/operations/our-insights/next-frontiers-for-lean




The lean approach conceptualized by MIT team lead Jim Womack based on Toyota Production system has  management component and industrial engineering component. The above statement of McKinsey consultants brings out the point clearly.

The management component is the  scientific insight into how product and service attributes contribute to customer value.  Managers have to understand what provides the value to customers. Customers idea of value keeps changing. Managers have to track and find out the changes and redesign the systems accordingly.

Industrial engineers have to the productivity focus. Each redesign of the system by managers to improve effectiveness has to be followed by IE redesign to improve efficiency. Apart from this IE discoveries and inventions provide the scope for increasing efficiency of systems.

From industrial engineering point of view, the development of lean approach is focus on reduction of inventories.

September Industrial Engineering Knowledge Revision Plan with Promotion Links

September 1st Week

Industrial Engineering Optimization

Mathematical optimization was used by F.W. Taylor. As operations research was developed and more optimization techniques were developed, industrial engineers advocated the use of them in companies to improve productivity, reduce costs, and increase profits. All industrial engineering redesigns are to be optimized and industrial engineers use various optimization techniques to optimize their engineering redesigns to increase productivity.

1 September Industrial Engineering Knowledge Revision Plan

Operations Research - An Efficiency Improvement Tool for Industrial Engineers
http://nraomtr.blogspot.com/2011/12/operations-research-efficiency.html

PRINCIPLES AND APPLICATIONS OF OPERATIONS RESEARCH
(from the perspective of an industrial engineer)
(From Maynard's Industrial Engineering Handbook, 5th Edition, pp. 11.27-11.44)
Jayant Rajgopal (From Rajgopal's website)
http://www.pitt.edu/~jrclass/or/or-intro.html

2 September Industrial Engineering Knowledge Revision Plan

What is mathematical programming?
http://coral.ie.lehigh.edu/~ted/files/ie316/lectures/Lecture1.pdf
Examples of Mathematical Programming.
http://coral.ie.lehigh.edu/~ted/files/ie316/lectures/Lecture2.pdf

3 September Industrial Engineering Knowledge Revision Plan

Simplex Method
http://mat.gsia.cmu.edu/classes/QUANT/NOTES/chap7.pdf

4. September Industrial Engineering Knowledge Revision Plan

 Transportation Problem
http://orms.pef.czu.cz/text/transProblem.html

5. September Industrial Engineering Knowledge Revision Plan
Queing Models
http://orms.pef.czu.cz/text/QueTeory/QueuingModels.html

September 2nd  Week

8. September Industrial Engineering Knowledge Revision Plan
Simulation
http://orms.pef.czu.cz/text/NolinearProgramming/simulation.html


9. September Industrial Engineering Knowledge Revision Plan
An Overview of Optimization Techniques for CNC Milling Machine
https://www.alliedjournals.com/download_data/IJEMS_V1IS50005.pdf

10. September Industrial Engineering Knowledge Revision Plan
 New Technology and Optimization of Mobile Phone Battery
https://theseus.fi/bitstream/handle/10024/110646/Liu%20Jian_Zhang%20Yixian.pdf?sequence=1

11. September Industrial Engineering Knowledge Revision Plan
Combustion Optimization in PF Boilers
http://www.eecpowerindia.com/codelibrary/ckeditor/ckfinder/userfiles/files/Session%201%20Combustion%20and%20Optimisation%20in%20coal%20fired%20boilers_KBP_17_09_2013.pdf

12. September Industrial Engineering Knowledge Revision Plan
 Application of Optimization Techniques in the Power System Control
https://uni-obuda.hu/journal/Kadar_43.pdf

September Third Week

Industrial Engineering Statistics

F.W. Taylor himself advocated maintaining of records and data for decision making. The other industrial engineering pioneers also promoted record keeping and data analysis. As sampling based  decision making became more robust, industrial engineers promoted it as a productivity improvement initiative and imperative. One of the prominent areas of application is statistical quality control. Now six sigma, a statistics based technique is being promoted by the IE profession.

15  September Industrial Engineering Knowledge Revision Plan
Basics of Statistics
http://bobhall.tamu.edu/FiniteMath/Module8/Introduction.html



16 September Industrial Engineering Knowledge Revision Plan
Statistical Process Control
http://www.itl.nist.gov/div898/handbook/pmc/section1/pmc12.htm
http://www.itl.nist.gov/div898/handbook/pmc/section3/pmc3.htm

Evaluation Improvement of Production Productivity Performance using Statistical Process Control, Overall Equipment Efficiency, and Autonomous Maintenance,
Amir Azizi
Procedia Manufacturing
Volume 2, 2015, Pages 186-190
open access
http://www.sciencedirect.com/science/article/pii/S2351978915000335


17 September Industrial Engineering Knowledge Revision Plan
Statistical Quality Control
http://www.itl.nist.gov/div898/handbook/pmc/section2/pmc2.htm


18 September Industrial Engineering Knowledge Revision Plan
Calculation of Sample Sizes in Work Measurement and Work Sampling

http://www.measuringu.com/sample_continuous.htm
http://www.prenhall.com/divisions/bp/app/russellcd/PROTECT/CHAPTERS/CHAP08/HEAD06.HTM  (WorK measurement full chapter - Includes sample size calculation for time study and work sampling)


19 September Industrial Engineering Knowledge Revision Plan

Test of Hypothesis
http://www.math.uah.edu/stat/hypothesis/Introduction.html

Test of hypothesis is to be used by industrial engineers to confirm or validate that their redesign or a process has resulted in the increase of productivity. This becomes useful when there is variation in the output from various workstations or persons.  We can also visualize activities in different places. In such case we test the hypothesis that productivity has improved in the workstations where redesign is is implemented.



HYPOTHESIS TESTING FOR THE PROCESS CAPABILITY RATIO - 2002 MS Thesis
https://etd.ohiolink.edu/!etd.send_file%3Faccession%3Dohiou1040054409%26disposition%3Dinline

One More presentation
http://fac.ksu.edu.sa/sites/default/files/DOE_Lecture%204%20test%20of%20hypothesis.pdf

September Fourth Week

22 September Industrial Engineering Knowledge Revision Plan
Design of Experiments
http://asq.org/learn-about-quality/data-collection-analysis-tools/overview/design-of-experiments-tutorial.html

http://www.itl.nist.gov/div898/handbook/pmd/section3/pmd31.htm

23 September Industrial Engineering Knowledge Revision Plan
Six Sigma

http://www.intechopen.com/books/quality-management-and-six-sigma/six-sigma

http://nraomtr.blogspot.com/2014/05/six-sigma-introduction.html

24 September Industrial Engineering Knowledge Revision Plan
Application of Six Sigma
http://www.intechopen.com/books/six-sigma-projects-and-personal-experiences/5-successful-projects-from-the-application-of-six-sigma-methodology

25 September Industrial Engineering Knowledge Revision Plan
Application of Six Sigma
http://www.wseas.us/e-library/conferences/2013/Vouliagmeni/INMAT/INMAT-01.pdf

26  September Industrial Engineering Knowledge Revision Plan
Application of Six Sigma
http://www.journalamme.org/papers_amme05/1414.pdf


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One Year Industrial Engineering Knowledge Revision Plan

January - February - March - April - May - June

July - August - September - October - November - December

In months after June the articles prescribed have to be modified as a new scheme is started in 2015.

Updated  23 August 2017, 11 September 2016,  30 September 2014

Productivity Science - Some Hypothesis like Statements

"When large companies get Agile right, the results can be stunning. Productivity can improve by a factor of three. Employee engagement, measured in quantitative surveys, increases dramatically too. New product features can be released within weeks or months rather than quarters or years. Rates of innovation rise, while the number of defects and do-overs declines. In the first year after going Agile, one bank’s development team increased the value delivered per dollar spent by 50%, simultaneously cutting development time in half and improving employee engagement by one-third."


Five Secrets to Scaling Up Agile
FEBRUARY 19, 2016 by Kaj Burchardi, Peter Hildebrandt, Erik Lenhard, Jérôme Moreau, and Benjamin Rehberg
https://www.bcgperspectives.com/content/articles/technology-digital-people-organization-five-secrets-scaling-up-agile/

Behavioral Approach to Productivity - Behavioral Variables and Productivity

"Behavioral strategies to improve productivity"
Gary P.Latham, Larry L.Cummings, and Terence R.Mitchell,
Organizational Dynamics
Volume 9, Issue 3, Winter 1981, Pages 5-23


Hum Resour Manage. 1983 Jan-Feb;13(1):1-5.
"Behavioral science approaches to improving productivity."

Shortell SM.

Abstract
We have suggested that improving the productivity of an individual or of one group of workers is not the same thing as improving the productivity of the organization overall. Further, because work units in hospitals are interdependent, attempts to improve productivity ultimately involve a reexamination of the organization's values and culture. Until this fundamental realization occurs, little can be done to improve the organization's productivity. As such, productivity is not simply a day-to-day managerial issue as a long-term leadership issue. Thus, we ought to begin mapping out productivity strategies for the long run. Some of the elements of such a strategy have been highlighted.
https://www.ncbi.nlm.nih.gov/pubmed/10258195


Aubrey C. Daniels "Performance management: The behavioral approach to productivity improvement"
Global Business and Organizational Excellence
Volume 4, Issue 3, Summer 1985, Pages 225–236


ROLE OF MOTIVATION IN HIGHER PRODUCTIVITY
S.K. Srivastava & Kailash Chandra Barmola
SMS Varanasi,  Vol. VII, No. 1; June, 2011

Evidence-Based Productivity Improvement: A Practical Guide to the Productivity Measurement and Enhancement System (ProMES)
Robert D. Pritchard, Sallie J. Weaver, Elissa Ashwood
Routledge, 04-May-2012 - Psychology - 316 pages
This new book explains the Productivity Measurement and Enhancement system (ProMES) and how it meets the criteria for an optimal measurement and feedback system. It summarizes all the research that has been done on productivity, mentioning other measurement systems, and gives detailed information on how to implement this one in organizations. This book will be of interest to behavioral science researchers and professionals who wish to learn more about the practical methods of measuring and improving organizational productivity.

https://books.google.co.in/books?id=hzaia0WL_oQC


2014
Enhancing Strategies to Improve Workplace Performance
Thesis by Francine Williams Richardson
Walden University
http://scholarworks.waldenu.edu/cgi/viewcontent.cgi?article=1105&context=dissertations


2017
Nudge management: applying behavioural science to increase knowledge worker productivity
Philip Ebert and Wolfgang Freibichler
Journal of Organization Design2017  6:4
Published: 21 March 2017
https://jorgdesign.springeropen.com/articles/10.1186/s41469-017-0014-1

Culture

Culture and Productivity - Bibliography

UK Productivity

2017

The UK currently lags behind its G7 competitors’ average productivity levels by an average of 18% - and 35% behind Germany. Poor management is estimated to cost the UK £84bn in lost productivity a year.

http://www.managers.org.uk/insights/news/2017/june/a-management-manifesto-for-the-uk-fixing-the-84bn-productivity-gap

Chartered Management Institute (CMI) proposed a management manifesto to improve management in UK and reclaim the 84 billion pounds lost in productivity due to poor management.

You can download the manifesto document from:

http://www.managers.org.uk/~/media/Files/PDF/CMI-Management-Manifesto.pdf

Cloud Computing - Productivity Science

Ozdemir A, Asil H (2017)
The Optimization of Query Processing in Sea Base Cloud Databases Based on CCEVP Model.
Ind Eng Manage 6:208. doi:10.4172/2169-0316.1000208

The increase in data volume in many applications and the need for their calculations are the database challenges. Cloud computing and the use of Sea Base databases are a solution to integrate a variety of DBMSs and integrated access to tables in databases. The study tried to optimize query processing in the Sea Base cloud database and reduce query processing time. The method used adaptability for optimization. The purpose of this method is to make adaptive the execution plans of high-traffic queries sent to the Sea Base. For adaptability, the method uses three parts: separator, similarity detector and replacement policy.  The results show that the system optimizes query processing in the database and reduces response time by one percent. The response time can be further decreased by changing the replacement policy.

https://www.omicsgroup.org/journals/the-optimization-of-query-processing-in-sea-base-cloud-databases-basedon-ccevp-model-2169-0316-1000208.php?aid=86516