Replacement Analysis

- Hi Mom How To Make Cake, On this share , I have provide any thing about cake

you must see


Engineering Economics Revision Article Series

Replacement refers to a broad concept embracing the selection of similar but new assets to replace existing assets as well as selection of entirely different ways to perform the function supported by the earlier asset.

Replacement decisions are a choice between the present asset, called defender and currently available alternatives termed as challengers.

Varieties of Replacement Necessity

Replacement due to deterioration

Replacement due to obsolescence

Replacement due to inadequacy

Engineering Economics Replacement Analysis
Lecture Presentation
_________________________

_________________________
Industrial Engineering

References

Engineering Economics, 4th Edition, James L. Riggs, David D. Bedworth, and Sabah U. Randhawa, McGraw Hill, New York, 1996


Online Material

Replacement Analysis - I - Nptel course material
http://nptel.ac.in/courses/105103023/33

Replacement Analysis - II - Nptel course material
http://nptel.ac.in/courses/105103023/34


Automobile replacement case studies for engineering economy classes
Engineering Economist, The, Spring 1998 by Hartman, Joseph C
http://findarticles.com/p/articles/mi_qa3621/is_199804/ai_n8801745

http://www.ise.ufl.edu/ein4354/Downloads/ch13/Ch13.ppt


Updated  18 July 2016,  29 November 2011

Originally published in
http://knol.google.com/k/narayana-rao/replacement-analysis/2utb2lsm2k7a/ 254#

Engineering Economics Revision Article Series

Replacement refers to a broad concept embracing the selection of similar but new assets to replace existing assets as well as selection of entirely different ways to perform the function supported by the earlier asset.

Replacement decisions are a choice between the present asset, called defender and currently available alternatives termed as challengers.

Varieties of Replacement Necessity

Replacement due to deterioration

Replacement due to obsolescence

Replacement due to inadequacy

Engineering Economics Replacement Analysis
Lecture Presentation
_________________________

_________________________
Industrial Engineering

References

Engineering Economics, 4th Edition, James L. Riggs, David D. Bedworth, and Sabah U. Randhawa, McGraw Hill, New York, 1996


Online Material

Replacement Analysis - I - Nptel course material
http://nptel.ac.in/courses/105103023/33

Replacement Analysis - II - Nptel course material
http://nptel.ac.in/courses/105103023/34


Automobile replacement case studies for engineering economy classes
Engineering Economist, The, Spring 1998 by Hartman, Joseph C
http://findarticles.com/p/articles/mi_qa3621/is_199804/ai_n8801745

http://www.ise.ufl.edu/ein4354/Downloads/ch13/Ch13.ppt


Updated  18 July 2016,  29 November 2011

Originally published in
http://knol.google.com/k/narayana-rao/replacement-analysis/2utb2lsm2k7a/ 254#

Engineering Economics Revision Article Series

Replacement refers to a broad concept embracing the selection of similar but new assets to replace existing assets as well as selection of entirely different ways to perform the function supported by the earlier asset.

Replacement decisions are a choice between the present asset, called defender and currently available alternatives termed as challengers.

Varieties of Replacement Necessity

Replacement due to deterioration

Replacement due to obsolescence

Replacement due to inadequacy

Engineering Economics Replacement Analysis
Lecture Presentation
_________________________

_________________________
Industrial Engineering

References

Engineering Economics, 4th Edition, James L. Riggs, David D. Bedworth, and Sabah U. Randhawa, McGraw Hill, New York, 1996


Online Material

Replacement Analysis - I - Nptel course material
http://nptel.ac.in/courses/105103023/33

Replacement Analysis - II - Nptel course material
http://nptel.ac.in/courses/105103023/34


Automobile replacement case studies for engineering economy classes
Engineering Economist, The, Spring 1998 by Hartman, Joseph C
http://findarticles.com/p/articles/mi_qa3621/is_199804/ai_n8801745

http://www.ise.ufl.edu/ein4354/Downloads/ch13/Ch13.ppt


Updated  18 July 2016,  29 November 2011

Originally published in
http://knol.google.com/k/narayana-rao/replacement-analysis/2utb2lsm2k7a/ 254#

Operations Research - An Efficiency Improvement Tool for Industrial Engineers

- Hi Mom How To Make Cake, On this share , I have provide any thing about cake

you must see


In the IE journals and magazines we need to read articles and papers that point out how IEs are able to come out with solutions to data development challenges of OR models.


Operations Research methods are characterized as efficiency improvement techniques by many scholars.


1. From efficiency measurement to efficiency improvement: The choice of a relevant benchmark, Eduardo González, and Antonio Álvarez, European Journal of Operational Research, Volume 133, Issue 3, 16 September 2001, Pages 512-520


2. Measuring Efficiency in Primary Health Care Centres in Saudi Arabia, ASMA M. A. BAHURMOZ,
http://www.economics.kaau.edu.sa/Faculty_Mag/Magallat/A12A2_PDF/122-ASMA999.pdf


3. Improving Transportation Efficiency at the Nanzan Educational Complexeral Motors
http://www.scienceofbetter.org/can_do/success_stories/iteatnecm.htm


4. Operations Research: The Productivity Engine: How to create unassailable productivity gains in your business, Lew Pringle, OR/MS Today, June 2000
http://www.lionhrtpub.com/orms/orms-6-00/pringle.html


Lew Pringle wrote:

"Operations research, as a field, is all about the creation and management of Productivity Gain. In fact, in a very real sense, productivity gain is virtually the sole purpose of OR. It's what we do. To raise the question of improvement in an organization's productivity without taking full advantage of all that OR offers would be analogous to pursuing a required improvement in one's health while ignoring the entire medical community. The realm of operations research is Productivity Gain.

OR people, in turn, are identifiable by: 1. our focus on productivity, and 2. the way we find, identify and come to describe, understand, appreciate and represent a problem. Operations research people are problem-conceptualizers. Our "solutions," in this sense, can (and should) be seen as flowing naturally and easily from the unique way in which we have visualized the problems/opportunities in the first place. We operate on such traditional quantities as profit, cost, efficiency and other practical, measurable items. Our goal, ordinarily, is to achieve higher and higher levels of performance. We are the people whose job it is to create productivity. We are, in fact, the productivity engine of an organization."

5. Productivity Improvement through Operational Research
G. W. Sears
Journal of the Royal Statistical Society. Series A (General) Vol. 126, No. 2 (1963), pp. 267-269
https://www.jstor.org/stable/2982368?seq=1#page_scan_tab_contents

6. The Necessity of Implementation of Operations Research for Managers for Decision-Making and Productivity Increase in Production
M. K. Amoli, S. M. T. Hosseini, M. Salehi, "The Necessity of Implementation of Operations Research for Managers for Decision-Making and Productivity Increase in Production", Advanced Materials Research, Vols. 488-489, pp. 1651-1656, 2012
http://www.scientific.net/AMR.488-489.1651


________________________________________________________________________________

Synergy Between Industrial Engineering and Operations Research


Industrial engineering is developed by engineers working in engineering departments of business companies engaged in manufacture using machines and metals. No doubt construction which is the earliest engineering activity also contributed in the development of industrial engineering as Frank Gilbreth was from construction sector. Operations Research as a discipline is identified with persons from science background working in the area of military operations. Industrial engineering and Departments of Mathematics and Statistics embraced the discipline of operations research in a big way. What is the synergy between industrial engineering and operations research?


Industrial engineering is system efficiency improvement. It examines proposed ways of doing work and improve them. Operations research has number of efficiency improvement tools. Operations researchers developed various standard models and have the ability to develop custom models that improve the efficiency of operations. Linear programming models, transportation, and assignment can be cited as examples using which the operations of an organization can be evaluated for efficiency of resource use subject to the constraints and optimal or efficient solutions can be found. Hence industrial engineers have to be the first group among various corporate organizations to recognize and implement OR models in the business organizations. This opportunity was correctly identified by the industrial engineering profession and OR was adopted as an important technique in the arsenal of industrial engineering.


What is the Contribution of IE to OR?


Industrial engineers could have promoted the practical utilization of OR by proving data in the form the OR models require. In systems engineering, there is mention of this step. From the synthesized design for a system design problem, various models are to be developed to evaluate the proposed design. To use OR models, various types of data are required and industrial engineers have the advantage of developing the required data. Why IEs have the advantage? Industrial engineers have the advantage because they have a strong attachment to measurement in one of their core subjects work measurement. Industrial engineers also are given inputs in understanding the financial and cost accounting data. Thus they are in the unique position to develop and provide the data that OR models required and come out the most efficient solutions and help the operating managers in implementing the solutions. But this does not seem to have happened in big scale.


The reason for the lack of popularity for OR in many organizations is the lack of this viewpoint in IEs. IEs have to use OR models as efficiency improvement avenues. To use OR models they have to develop the required data from the operations of the organization. They have to interact with the accounting departments meaningfully and acquire the required accounting data and statements. They have to develop engineering data and then use appropriate OR models. In the IE journals and magazines we need to read articles and papers that point out how IEs are able to come out with solutions to data development challenges of OR models.


_________________________________________________________________________

Problem Areas for Applying Operations Research

Loading machine centers for maximum utilization of equipment.
Controlling raw materials nd in-process inventories.
Planning the minimum production costs schedules through the sequencing and allocation of men and machines.
Minimizing waiting times between opeartions
Determining the true incremental benefits of adding new production equipment.
Scheduling direct labour.
Determining the most favourable preventive maintenance plans.
Assigning individuals to specific jobs
Specifying least-cost shipment patterns in multiplant multivendor purchasing situations.
Locating warehouses so as to minimize freight and production costs.
Allocating advertising budget in the most efficient manner.

Source:
"Opeartions Research", Chaper 9-3 in Industrial Engineering Handbook, H.B.Maynard (Ed.) 2nd Edition

______________________________________________________________________


OR Case Studies Discussed in Chapter 11.2 of Maynard's Industrial Engineering Handbook, 5th Edition


REFERENCES

Leachman, R. C., R. F. Benson, C. Liu and D. J. Raar, "IMPReSS: An Automated Production-Planning and Delivery-Quotation System at Harris Corporation - Semiconductor Sector," Interfaces, 26:1, pp. 6-37, 1996.
Rigby, B., L. S. Lasdon and A. D. Waren, "The Evolution of Texaco's Blending Systems: From OMEGA to StarBlend," Interfaces, 25:5, pp. 64-83, 1995.
Flanders, S. W. and W. J. Davis, "Scheduling a Flexible Manufacturing System with Tooling Constraints: An Actual Case Study," Interfaces, 25:2, pp. 42-54, 1995.
Subramanian, R., R. P. Scheff, Jr., J. D. Quillinan, D. S. Wiper and R. E. Marsten, "Coldstart: Fleet Assignment at Delta Air Lines,", Interfaces, 24:1, pp. 104-120, 1994.
Kotha, S. K., M. P. Barnum and D. A. Bowen, "KeyCorp Service Excellence Management System," Interfaces, 26:1, pp. 54-74, 1996.

Recent Case Study Papers


Energy Cost Optimization in a Water Supply System Case Study
Daniel F. Moreira and Helena M. Ramos
Journal of Energy
Volume 2013 (2013), Article ID 620698, 9 pages
http://www.hindawi.com/journals/energy/2013/620698/



Cost Minimizing Coal Logistics for Power Plants Considering Transportation Constraints
Ahmet Yucekaya
Industrial Engineering Department, Kadir Has University, istanbul, Turkey
Journal of Traffic and Logistics Engineering, Vol, 1, No. 2 June 2013
http://www.jtle.net/uploadfile/2013/0514/20130514045651963.pdf

Related Knols


Knol Handbook of Industrial Engineering (Year 2010 Version)
By Narayana Rao K.V.S.S.




________________________________________________________________________________


Bibliography


OR Models - Good brief description of Linear programming, Network Flow Programming, Integer programming, Nonlinear programming, Dynamic programming, Stochastic programming and Simulation etc.
http://www.me.utexas.edu/~jensen/ORMM/models/index.html


The Periodic Vehicle Routing Problem: A Case Study
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1368749


A Review of Integrated Analysis of Production-Distribution Systems, 1995
http://www.eng.buffalo.edu/~nagi/papers/ana.pdf


Optimization of Container Process at Multimodal Container Terminals, Phd Thesis, 2008, Andy Wong
http://eprints.qut.edu.au/16626/1/Andy_Wong_Thesis.pdf


Operations Research Models for Railway Rolling Stock Planning, Ph.d thesis, (2006), Gabor Maroti
http://alexandria.tue.nl/extra2/200610302.pdf


IFORS 2002 Conference Proceedings - Abstracts only 285 page file
http://meetings2.informs.org/IFORS2002/working_files/program.pdf


_______________________________________________________________________________

Recent papers about OR models and applications

The costs of poor data quality
Summary of Anders Haug, Frederik Zachariassen, Dennis van Liempd, "The costs of poor data quality", Journal of Industrial Engineering and Management, 2011 – 4(2): 168-193 – Online ISSN: 2013-0953 Print ISSN: 2013-8423


Updated 23 July 2013
Posted in the blog 14 December 2011
Article originally posted at
http://knol.google.com/k/operations-research-an-efficiency-improvement-tool-for-industrial-engineers#


Industrial Engineering Knowledge Revision Plan - One Year Plan


January - February - March - April - May - June



July - August - September - October - November - December


Updated   15 July 2016, 13 July 2016,  23 July 2013

In the IE journals and magazines we need to read articles and papers that point out how IEs are able to come out with solutions to data development challenges of OR models.


Operations Research methods are characterized as efficiency improvement techniques by many scholars.


1. From efficiency measurement to efficiency improvement: The choice of a relevant benchmark, Eduardo González, and Antonio Álvarez, European Journal of Operational Research, Volume 133, Issue 3, 16 September 2001, Pages 512-520


2. Measuring Efficiency in Primary Health Care Centres in Saudi Arabia, ASMA M. A. BAHURMOZ,
http://www.economics.kaau.edu.sa/Faculty_Mag/Magallat/A12A2_PDF/122-ASMA999.pdf


3. Improving Transportation Efficiency at the Nanzan Educational Complexeral Motors
http://www.scienceofbetter.org/can_do/success_stories/iteatnecm.htm


4. Operations Research: The Productivity Engine: How to create unassailable productivity gains in your business, Lew Pringle, OR/MS Today, June 2000
http://www.lionhrtpub.com/orms/orms-6-00/pringle.html


Lew Pringle wrote:

"Operations research, as a field, is all about the creation and management of Productivity Gain. In fact, in a very real sense, productivity gain is virtually the sole purpose of OR. It's what we do. To raise the question of improvement in an organization's productivity without taking full advantage of all that OR offers would be analogous to pursuing a required improvement in one's health while ignoring the entire medical community. The realm of operations research is Productivity Gain.

OR people, in turn, are identifiable by: 1. our focus on productivity, and 2. the way we find, identify and come to describe, understand, appreciate and represent a problem. Operations research people are problem-conceptualizers. Our "solutions," in this sense, can (and should) be seen as flowing naturally and easily from the unique way in which we have visualized the problems/opportunities in the first place. We operate on such traditional quantities as profit, cost, efficiency and other practical, measurable items. Our goal, ordinarily, is to achieve higher and higher levels of performance. We are the people whose job it is to create productivity. We are, in fact, the productivity engine of an organization."

5. Productivity Improvement through Operational Research
G. W. Sears
Journal of the Royal Statistical Society. Series A (General) Vol. 126, No. 2 (1963), pp. 267-269
https://www.jstor.org/stable/2982368?seq=1#page_scan_tab_contents

6. The Necessity of Implementation of Operations Research for Managers for Decision-Making and Productivity Increase in Production
M. K. Amoli, S. M. T. Hosseini, M. Salehi, "The Necessity of Implementation of Operations Research for Managers for Decision-Making and Productivity Increase in Production", Advanced Materials Research, Vols. 488-489, pp. 1651-1656, 2012
http://www.scientific.net/AMR.488-489.1651


________________________________________________________________________________

Synergy Between Industrial Engineering and Operations Research


Industrial engineering is developed by engineers working in engineering departments of business companies engaged in manufacture using machines and metals. No doubt construction which is the earliest engineering activity also contributed in the development of industrial engineering as Frank Gilbreth was from construction sector. Operations Research as a discipline is identified with persons from science background working in the area of military operations. Industrial engineering and Departments of Mathematics and Statistics embraced the discipline of operations research in a big way. What is the synergy between industrial engineering and operations research?


Industrial engineering is system efficiency improvement. It examines proposed ways of doing work and improve them. Operations research has number of efficiency improvement tools. Operations researchers developed various standard models and have the ability to develop custom models that improve the efficiency of operations. Linear programming models, transportation, and assignment can be cited as examples using which the operations of an organization can be evaluated for efficiency of resource use subject to the constraints and optimal or efficient solutions can be found. Hence industrial engineers have to be the first group among various corporate organizations to recognize and implement OR models in the business organizations. This opportunity was correctly identified by the industrial engineering profession and OR was adopted as an important technique in the arsenal of industrial engineering.


What is the Contribution of IE to OR?


Industrial engineers could have promoted the practical utilization of OR by proving data in the form the OR models require. In systems engineering, there is mention of this step. From the synthesized design for a system design problem, various models are to be developed to evaluate the proposed design. To use OR models, various types of data are required and industrial engineers have the advantage of developing the required data. Why IEs have the advantage? Industrial engineers have the advantage because they have a strong attachment to measurement in one of their core subjects work measurement. Industrial engineers also are given inputs in understanding the financial and cost accounting data. Thus they are in the unique position to develop and provide the data that OR models required and come out the most efficient solutions and help the operating managers in implementing the solutions. But this does not seem to have happened in big scale.


The reason for the lack of popularity for OR in many organizations is the lack of this viewpoint in IEs. IEs have to use OR models as efficiency improvement avenues. To use OR models they have to develop the required data from the operations of the organization. They have to interact with the accounting departments meaningfully and acquire the required accounting data and statements. They have to develop engineering data and then use appropriate OR models. In the IE journals and magazines we need to read articles and papers that point out how IEs are able to come out with solutions to data development challenges of OR models.


_________________________________________________________________________

Problem Areas for Applying Operations Research

Loading machine centers for maximum utilization of equipment.
Controlling raw materials nd in-process inventories.
Planning the minimum production costs schedules through the sequencing and allocation of men and machines.
Minimizing waiting times between opeartions
Determining the true incremental benefits of adding new production equipment.
Scheduling direct labour.
Determining the most favourable preventive maintenance plans.
Assigning individuals to specific jobs
Specifying least-cost shipment patterns in multiplant multivendor purchasing situations.
Locating warehouses so as to minimize freight and production costs.
Allocating advertising budget in the most efficient manner.

Source:
"Opeartions Research", Chaper 9-3 in Industrial Engineering Handbook, H.B.Maynard (Ed.) 2nd Edition

______________________________________________________________________


OR Case Studies Discussed in Chapter 11.2 of Maynard's Industrial Engineering Handbook, 5th Edition


REFERENCES

Leachman, R. C., R. F. Benson, C. Liu and D. J. Raar, "IMPReSS: An Automated Production-Planning and Delivery-Quotation System at Harris Corporation - Semiconductor Sector," Interfaces, 26:1, pp. 6-37, 1996.
Rigby, B., L. S. Lasdon and A. D. Waren, "The Evolution of Texaco's Blending Systems: From OMEGA to StarBlend," Interfaces, 25:5, pp. 64-83, 1995.
Flanders, S. W. and W. J. Davis, "Scheduling a Flexible Manufacturing System with Tooling Constraints: An Actual Case Study," Interfaces, 25:2, pp. 42-54, 1995.
Subramanian, R., R. P. Scheff, Jr., J. D. Quillinan, D. S. Wiper and R. E. Marsten, "Coldstart: Fleet Assignment at Delta Air Lines,", Interfaces, 24:1, pp. 104-120, 1994.
Kotha, S. K., M. P. Barnum and D. A. Bowen, "KeyCorp Service Excellence Management System," Interfaces, 26:1, pp. 54-74, 1996.

Recent Case Study Papers


Energy Cost Optimization in a Water Supply System Case Study
Daniel F. Moreira and Helena M. Ramos
Journal of Energy
Volume 2013 (2013), Article ID 620698, 9 pages
http://www.hindawi.com/journals/energy/2013/620698/



Cost Minimizing Coal Logistics for Power Plants Considering Transportation Constraints
Ahmet Yucekaya
Industrial Engineering Department, Kadir Has University, istanbul, Turkey
Journal of Traffic and Logistics Engineering, Vol, 1, No. 2 June 2013
http://www.jtle.net/uploadfile/2013/0514/20130514045651963.pdf

Related Knols


Knol Handbook of Industrial Engineering (Year 2010 Version)
By Narayana Rao K.V.S.S.




________________________________________________________________________________


Bibliography


OR Models - Good brief description of Linear programming, Network Flow Programming, Integer programming, Nonlinear programming, Dynamic programming, Stochastic programming and Simulation etc.
http://www.me.utexas.edu/~jensen/ORMM/models/index.html


The Periodic Vehicle Routing Problem: A Case Study
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1368749


A Review of Integrated Analysis of Production-Distribution Systems, 1995
http://www.eng.buffalo.edu/~nagi/papers/ana.pdf


Optimization of Container Process at Multimodal Container Terminals, Phd Thesis, 2008, Andy Wong
http://eprints.qut.edu.au/16626/1/Andy_Wong_Thesis.pdf


Operations Research Models for Railway Rolling Stock Planning, Ph.d thesis, (2006), Gabor Maroti
http://alexandria.tue.nl/extra2/200610302.pdf


IFORS 2002 Conference Proceedings - Abstracts only 285 page file
http://meetings2.informs.org/IFORS2002/working_files/program.pdf


_______________________________________________________________________________

Recent papers about OR models and applications

The costs of poor data quality
Summary of Anders Haug, Frederik Zachariassen, Dennis van Liempd, "The costs of poor data quality", Journal of Industrial Engineering and Management, 2011 – 4(2): 168-193 – Online ISSN: 2013-0953 Print ISSN: 2013-8423


Updated 23 July 2013
Posted in the blog 14 December 2011
Article originally posted at
http://knol.google.com/k/operations-research-an-efficiency-improvement-tool-for-industrial-engineers#


Industrial Engineering Knowledge Revision Plan - One Year Plan


January - February - March - April - May - June



July - August - September - October - November - December


Updated   15 July 2016, 13 July 2016,  23 July 2013

In the IE journals and magazines we need to read articles and papers that point out how IEs are able to come out with solutions to data development challenges of OR models.


Operations Research methods are characterized as efficiency improvement techniques by many scholars.


1. From efficiency measurement to efficiency improvement: The choice of a relevant benchmark, Eduardo González, and Antonio Álvarez, European Journal of Operational Research, Volume 133, Issue 3, 16 September 2001, Pages 512-520


2. Measuring Efficiency in Primary Health Care Centres in Saudi Arabia, ASMA M. A. BAHURMOZ,
http://www.economics.kaau.edu.sa/Faculty_Mag/Magallat/A12A2_PDF/122-ASMA999.pdf


3. Improving Transportation Efficiency at the Nanzan Educational Complexeral Motors
http://www.scienceofbetter.org/can_do/success_stories/iteatnecm.htm


4. Operations Research: The Productivity Engine: How to create unassailable productivity gains in your business, Lew Pringle, OR/MS Today, June 2000
http://www.lionhrtpub.com/orms/orms-6-00/pringle.html


Lew Pringle wrote:

"Operations research, as a field, is all about the creation and management of Productivity Gain. In fact, in a very real sense, productivity gain is virtually the sole purpose of OR. It's what we do. To raise the question of improvement in an organization's productivity without taking full advantage of all that OR offers would be analogous to pursuing a required improvement in one's health while ignoring the entire medical community. The realm of operations research is Productivity Gain.

OR people, in turn, are identifiable by: 1. our focus on productivity, and 2. the way we find, identify and come to describe, understand, appreciate and represent a problem. Operations research people are problem-conceptualizers. Our "solutions," in this sense, can (and should) be seen as flowing naturally and easily from the unique way in which we have visualized the problems/opportunities in the first place. We operate on such traditional quantities as profit, cost, efficiency and other practical, measurable items. Our goal, ordinarily, is to achieve higher and higher levels of performance. We are the people whose job it is to create productivity. We are, in fact, the productivity engine of an organization."

5. Productivity Improvement through Operational Research
G. W. Sears
Journal of the Royal Statistical Society. Series A (General) Vol. 126, No. 2 (1963), pp. 267-269
https://www.jstor.org/stable/2982368?seq=1#page_scan_tab_contents

6. The Necessity of Implementation of Operations Research for Managers for Decision-Making and Productivity Increase in Production
M. K. Amoli, S. M. T. Hosseini, M. Salehi, "The Necessity of Implementation of Operations Research for Managers for Decision-Making and Productivity Increase in Production", Advanced Materials Research, Vols. 488-489, pp. 1651-1656, 2012
http://www.scientific.net/AMR.488-489.1651


________________________________________________________________________________

Synergy Between Industrial Engineering and Operations Research


Industrial engineering is developed by engineers working in engineering departments of business companies engaged in manufacture using machines and metals. No doubt construction which is the earliest engineering activity also contributed in the development of industrial engineering as Frank Gilbreth was from construction sector. Operations Research as a discipline is identified with persons from science background working in the area of military operations. Industrial engineering and Departments of Mathematics and Statistics embraced the discipline of operations research in a big way. What is the synergy between industrial engineering and operations research?


Industrial engineering is system efficiency improvement. It examines proposed ways of doing work and improve them. Operations research has number of efficiency improvement tools. Operations researchers developed various standard models and have the ability to develop custom models that improve the efficiency of operations. Linear programming models, transportation, and assignment can be cited as examples using which the operations of an organization can be evaluated for efficiency of resource use subject to the constraints and optimal or efficient solutions can be found. Hence industrial engineers have to be the first group among various corporate organizations to recognize and implement OR models in the business organizations. This opportunity was correctly identified by the industrial engineering profession and OR was adopted as an important technique in the arsenal of industrial engineering.


What is the Contribution of IE to OR?


Industrial engineers could have promoted the practical utilization of OR by proving data in the form the OR models require. In systems engineering, there is mention of this step. From the synthesized design for a system design problem, various models are to be developed to evaluate the proposed design. To use OR models, various types of data are required and industrial engineers have the advantage of developing the required data. Why IEs have the advantage? Industrial engineers have the advantage because they have a strong attachment to measurement in one of their core subjects work measurement. Industrial engineers also are given inputs in understanding the financial and cost accounting data. Thus they are in the unique position to develop and provide the data that OR models required and come out the most efficient solutions and help the operating managers in implementing the solutions. But this does not seem to have happened in big scale.


The reason for the lack of popularity for OR in many organizations is the lack of this viewpoint in IEs. IEs have to use OR models as efficiency improvement avenues. To use OR models they have to develop the required data from the operations of the organization. They have to interact with the accounting departments meaningfully and acquire the required accounting data and statements. They have to develop engineering data and then use appropriate OR models. In the IE journals and magazines we need to read articles and papers that point out how IEs are able to come out with solutions to data development challenges of OR models.


_________________________________________________________________________

Problem Areas for Applying Operations Research

Loading machine centers for maximum utilization of equipment.
Controlling raw materials nd in-process inventories.
Planning the minimum production costs schedules through the sequencing and allocation of men and machines.
Minimizing waiting times between opeartions
Determining the true incremental benefits of adding new production equipment.
Scheduling direct labour.
Determining the most favourable preventive maintenance plans.
Assigning individuals to specific jobs
Specifying least-cost shipment patterns in multiplant multivendor purchasing situations.
Locating warehouses so as to minimize freight and production costs.
Allocating advertising budget in the most efficient manner.

Source:
"Opeartions Research", Chaper 9-3 in Industrial Engineering Handbook, H.B.Maynard (Ed.) 2nd Edition

______________________________________________________________________


OR Case Studies Discussed in Chapter 11.2 of Maynard's Industrial Engineering Handbook, 5th Edition


REFERENCES

Leachman, R. C., R. F. Benson, C. Liu and D. J. Raar, "IMPReSS: An Automated Production-Planning and Delivery-Quotation System at Harris Corporation - Semiconductor Sector," Interfaces, 26:1, pp. 6-37, 1996.
Rigby, B., L. S. Lasdon and A. D. Waren, "The Evolution of Texaco's Blending Systems: From OMEGA to StarBlend," Interfaces, 25:5, pp. 64-83, 1995.
Flanders, S. W. and W. J. Davis, "Scheduling a Flexible Manufacturing System with Tooling Constraints: An Actual Case Study," Interfaces, 25:2, pp. 42-54, 1995.
Subramanian, R., R. P. Scheff, Jr., J. D. Quillinan, D. S. Wiper and R. E. Marsten, "Coldstart: Fleet Assignment at Delta Air Lines,", Interfaces, 24:1, pp. 104-120, 1994.
Kotha, S. K., M. P. Barnum and D. A. Bowen, "KeyCorp Service Excellence Management System," Interfaces, 26:1, pp. 54-74, 1996.

Recent Case Study Papers


Energy Cost Optimization in a Water Supply System Case Study
Daniel F. Moreira and Helena M. Ramos
Journal of Energy
Volume 2013 (2013), Article ID 620698, 9 pages
http://www.hindawi.com/journals/energy/2013/620698/



Cost Minimizing Coal Logistics for Power Plants Considering Transportation Constraints
Ahmet Yucekaya
Industrial Engineering Department, Kadir Has University, istanbul, Turkey
Journal of Traffic and Logistics Engineering, Vol, 1, No. 2 June 2013
http://www.jtle.net/uploadfile/2013/0514/20130514045651963.pdf

Related Knols


Knol Handbook of Industrial Engineering (Year 2010 Version)
By Narayana Rao K.V.S.S.




________________________________________________________________________________


Bibliography


OR Models - Good brief description of Linear programming, Network Flow Programming, Integer programming, Nonlinear programming, Dynamic programming, Stochastic programming and Simulation etc.
http://www.me.utexas.edu/~jensen/ORMM/models/index.html


The Periodic Vehicle Routing Problem: A Case Study
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1368749


A Review of Integrated Analysis of Production-Distribution Systems, 1995
http://www.eng.buffalo.edu/~nagi/papers/ana.pdf


Optimization of Container Process at Multimodal Container Terminals, Phd Thesis, 2008, Andy Wong
http://eprints.qut.edu.au/16626/1/Andy_Wong_Thesis.pdf


Operations Research Models for Railway Rolling Stock Planning, Ph.d thesis, (2006), Gabor Maroti
http://alexandria.tue.nl/extra2/200610302.pdf


IFORS 2002 Conference Proceedings - Abstracts only 285 page file
http://meetings2.informs.org/IFORS2002/working_files/program.pdf


_______________________________________________________________________________

Recent papers about OR models and applications

The costs of poor data quality
Summary of Anders Haug, Frederik Zachariassen, Dennis van Liempd, "The costs of poor data quality", Journal of Industrial Engineering and Management, 2011 – 4(2): 168-193 – Online ISSN: 2013-0953 Print ISSN: 2013-8423


Updated 23 July 2013
Posted in the blog 14 December 2011
Article originally posted at
http://knol.google.com/k/operations-research-an-efficiency-improvement-tool-for-industrial-engineers#


Industrial Engineering Knowledge Revision Plan - One Year Plan


January - February - March - April - May - June



July - August - September - October - November - December


Updated   15 July 2016, 13 July 2016,  23 July 2013

Flashback Friday Project

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you must see




I just signed up the new blogging event.

Flashback Friday Project

For Details visit the page

http://www.alifeexamined.com.au/2016/05/how-long-have-you-been-blogging.html


_________________________________



__________________________________



I just signed up the new blogging event.

Flashback Friday Project

For Details visit the page

http://www.alifeexamined.com.au/2016/05/how-long-have-you-been-blogging.html


_________________________________



__________________________________



I just signed up the new blogging event.

Flashback Friday Project

For Details visit the page

http://www.alifeexamined.com.au/2016/05/how-long-have-you-been-blogging.html


_________________________________



__________________________________

Cost Of Ignorance - Knowledge Has Value

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I saw a post in a Linkedin Community on Ignorance and wrote the comment that I developed the concept, Cost of Ignorance long back.  The search of my blog posts took me to a post in 2008.

6 September 2008

Marketing and Marketing Concept
http://nrao-mgmt-smi-handbook.blogspot.in/2008/09/marketing-and-marketing-concept.html

I am writing articles on Google Knol platform with an intention to develop a management knowledge revision encyclopedia on the platform. I am calling it a revision encyclopedia as I want it to be useful to people who have studied the related texts and for the purpose of revising and refreshing their knowledge they read these articles. Knowledge workers have to make efforts to make sure that they bring all the appropriate principles into play when they are solving a problem or deciding an issue. Unless they make efforts to frequently revise the principles in various subjects related to management, managers cannot assure themselves or assure others that they are using all the relevant knowledge and taking right decisions.

Performing artists spend hours every day practicing their art and perform for three or four hours on a day. Many knowledge worker in contrast work a minimum of 8 hours per day. So they cannot study their knowledge material for an appreciable amount of time. But expecting them to spend at least half hour to sharpen their knowledge base in the brain is reasonable. Cost of ignorance is quite a large figure in the world. Knowledge workers also incur it and incur it for the organizations they are serving despite having certificates that attest that they have studied knowledge bases in a satisfactory manner. Knowledge base needs to be revised to make it useful when needed.

I specifically highlight the idea:

Cost of ignorance is quite a large figure in the world. Knowledge workers also incur it and incur it for the organizations they are serving despite having certificates that attest that they have studied knowledge bases in a satisfactory manner. Knowledge base needs to be revised to make it useful when needed.

The idea was again mentioned in 
26 April 2009
Narayana Rao K.V.S.S.

My aim is to provide revision or review articles for graduates of industrial engineering and management so that they can refresh their knowledge periodically. My effort of writing articles in all the subjects of the curriculum is to demonstrate that by appropriate committed effort, we can retain our learning of multiple subjects over a long period of time. 

Only when we retain a large number of principles in our brain, we take decisions properly when the occasion comes. According to me we are all incurring a huge amount of cost of ignorance.

My advocacy is the that knowledge workers have to make special efforts to retain, revise, refresh and update their knowledge.


I am happy to see that the idea has now captured the attention of many persons in the world

_________________

_________________



I saw a post in a Linkedin Community on Ignorance and wrote the comment that I developed the concept, Cost of Ignorance long back.  The search of my blog posts took me to a post in 2008.

6 September 2008

Marketing and Marketing Concept
http://nrao-mgmt-smi-handbook.blogspot.in/2008/09/marketing-and-marketing-concept.html

I am writing articles on Google Knol platform with an intention to develop a management knowledge revision encyclopedia on the platform. I am calling it a revision encyclopedia as I want it to be useful to people who have studied the related texts and for the purpose of revising and refreshing their knowledge they read these articles. Knowledge workers have to make efforts to make sure that they bring all the appropriate principles into play when they are solving a problem or deciding an issue. Unless they make efforts to frequently revise the principles in various subjects related to management, managers cannot assure themselves or assure others that they are using all the relevant knowledge and taking right decisions.

Performing artists spend hours every day practicing their art and perform for three or four hours on a day. Many knowledge worker in contrast work a minimum of 8 hours per day. So they cannot study their knowledge material for an appreciable amount of time. But expecting them to spend at least half hour to sharpen their knowledge base in the brain is reasonable. Cost of ignorance is quite a large figure in the world. Knowledge workers also incur it and incur it for the organizations they are serving despite having certificates that attest that they have studied knowledge bases in a satisfactory manner. Knowledge base needs to be revised to make it useful when needed.

I specifically highlight the idea:

Cost of ignorance is quite a large figure in the world. Knowledge workers also incur it and incur it for the organizations they are serving despite having certificates that attest that they have studied knowledge bases in a satisfactory manner. Knowledge base needs to be revised to make it useful when needed.

The idea was again mentioned in 
26 April 2009
Narayana Rao K.V.S.S.

My aim is to provide revision or review articles for graduates of industrial engineering and management so that they can refresh their knowledge periodically. My effort of writing articles in all the subjects of the curriculum is to demonstrate that by appropriate committed effort, we can retain our learning of multiple subjects over a long period of time. 

Only when we retain a large number of principles in our brain, we take decisions properly when the occasion comes. According to me we are all incurring a huge amount of cost of ignorance.

My advocacy is the that knowledge workers have to make special efforts to retain, revise, refresh and update their knowledge.


I am happy to see that the idea has now captured the attention of many persons in the world

_________________

_________________



I saw a post in a Linkedin Community on Ignorance and wrote the comment that I developed the concept, Cost of Ignorance long back.  The search of my blog posts took me to a post in 2008.

6 September 2008

Marketing and Marketing Concept
http://nrao-mgmt-smi-handbook.blogspot.in/2008/09/marketing-and-marketing-concept.html

I am writing articles on Google Knol platform with an intention to develop a management knowledge revision encyclopedia on the platform. I am calling it a revision encyclopedia as I want it to be useful to people who have studied the related texts and for the purpose of revising and refreshing their knowledge they read these articles. Knowledge workers have to make efforts to make sure that they bring all the appropriate principles into play when they are solving a problem or deciding an issue. Unless they make efforts to frequently revise the principles in various subjects related to management, managers cannot assure themselves or assure others that they are using all the relevant knowledge and taking right decisions.

Performing artists spend hours every day practicing their art and perform for three or four hours on a day. Many knowledge worker in contrast work a minimum of 8 hours per day. So they cannot study their knowledge material for an appreciable amount of time. But expecting them to spend at least half hour to sharpen their knowledge base in the brain is reasonable. Cost of ignorance is quite a large figure in the world. Knowledge workers also incur it and incur it for the organizations they are serving despite having certificates that attest that they have studied knowledge bases in a satisfactory manner. Knowledge base needs to be revised to make it useful when needed.

I specifically highlight the idea:

Cost of ignorance is quite a large figure in the world. Knowledge workers also incur it and incur it for the organizations they are serving despite having certificates that attest that they have studied knowledge bases in a satisfactory manner. Knowledge base needs to be revised to make it useful when needed.

The idea was again mentioned in 
26 April 2009
Narayana Rao K.V.S.S.

My aim is to provide revision or review articles for graduates of industrial engineering and management so that they can refresh their knowledge periodically. My effort of writing articles in all the subjects of the curriculum is to demonstrate that by appropriate committed effort, we can retain our learning of multiple subjects over a long period of time. 

Only when we retain a large number of principles in our brain, we take decisions properly when the occasion comes. According to me we are all incurring a huge amount of cost of ignorance.

My advocacy is the that knowledge workers have to make special efforts to retain, revise, refresh and update their knowledge.


I am happy to see that the idea has now captured the attention of many persons in the world

_________________

_________________

July - Management Knowledge Revision

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July  (Economics, Engineering Economics, & Managerial Ethics)

1st Week  ( 1 to 5 July)

Economic Theory of Production and Production Cost
Economic Analysis of Different Competitive Conditions.

Wages and the Labor Market - Samuelson and Nordhaus 

Capital, Interest and Profits - Review Notes
Markets and Economic Efficiency - Review Notes

Economic Role of Government and Its Expenditure an...
Economic Analysis of Poverty and Equity

Alternative Economic Systems - Review Notes
Theory of Economic Growth

2nd week  ( 8 to 12 July)

International Trade Theory and Issues
Exchange Rates: Markets Regulation and International Financial System

Supply Behavior/Decisions of Firm in Competitive Markets
Introduction to Engineering Economics


Engineering Economy or Engineering Economics: Economic Decision Making by Engineers
Time Value of Money - Time Value of Money Calculations


Cash Flow Estimation for Expenditure Proposals
Required Rate of Return - Cost of Capital  - Required Rate of Return for Investment or Expenditure Proposal..


Depreciation and Other Related Issues
NPV - IRR and Other Summary Project Assessment Measures



3rd week  (15 to 19 July)


Income Expansion Projects
Cost Reduction Projects


Replacement Decisons
Expected Values and Risk of Project Revenues and Costs


Present-Worth Comparisons
Rate-of-Return Calculations

18 July

Equivalent Annual-Worth Comparisons
Replacement Analysis


Replacement Problem - Engineering Economy Analysis...
Machine Selection Problem for an Engineer - Engine...

4th week

Depreciation and Income Tax Considerations
Sensitivity Analysis

Structural Analysis of Alternatives
Engineering Economic Analysis - Subject Update - Recent Case Studies

Business Ethics Revision Starts


Business Ethics – Introduction
Moral Standards and Moral Judgments – Approaches



Business System - Free Markets - Ethics
Ethics in the Market Place and Distribution System

Ethics in the Factory
Ethics in the Supply Chain







To August - Management Knowledge Revision

One Year MBA Knowledge Revision Plan

January  - February  - March  - April  - May   -   June

July  - August     - September  - October  - November  - December



Updated 10 July 2016






July  (Economics, Engineering Economics, & Managerial Ethics)

1st Week  ( 1 to 5 July)

Economic Theory of Production and Production Cost
Economic Analysis of Different Competitive Conditions.

Wages and the Labor Market - Samuelson and Nordhaus 

Capital, Interest and Profits - Review Notes
Markets and Economic Efficiency - Review Notes

Economic Role of Government and Its Expenditure an...
Economic Analysis of Poverty and Equity

Alternative Economic Systems - Review Notes
Theory of Economic Growth

2nd week  ( 8 to 12 July)

International Trade Theory and Issues
Exchange Rates: Markets Regulation and International Financial System

Supply Behavior/Decisions of Firm in Competitive Markets
Introduction to Engineering Economics


Engineering Economy or Engineering Economics: Economic Decision Making by Engineers
Time Value of Money - Time Value of Money Calculations


Cash Flow Estimation for Expenditure Proposals
Required Rate of Return - Cost of Capital  - Required Rate of Return for Investment or Expenditure Proposal..


Depreciation and Other Related Issues
NPV - IRR and Other Summary Project Assessment Measures



3rd week  (15 to 19 July)


Income Expansion Projects
Cost Reduction Projects


Replacement Decisons
Expected Values and Risk of Project Revenues and Costs


Present-Worth Comparisons
Rate-of-Return Calculations

18 July

Equivalent Annual-Worth Comparisons
Replacement Analysis


Replacement Problem - Engineering Economy Analysis...
Machine Selection Problem for an Engineer - Engine...

4th week

Depreciation and Income Tax Considerations
Sensitivity Analysis

Structural Analysis of Alternatives
Engineering Economic Analysis - Subject Update - Recent Case Studies

Business Ethics Revision Starts


Business Ethics – Introduction
Moral Standards and Moral Judgments – Approaches



Business System - Free Markets - Ethics
Ethics in the Market Place and Distribution System

Ethics in the Factory
Ethics in the Supply Chain







To August - Management Knowledge Revision

One Year MBA Knowledge Revision Plan

January  - February  - March  - April  - May   -   June

July  - August     - September  - October  - November  - December



Updated 10 July 2016






July  (Economics, Engineering Economics, & Managerial Ethics)

1st Week  ( 1 to 5 July)

Economic Theory of Production and Production Cost
Economic Analysis of Different Competitive Conditions.

Wages and the Labor Market - Samuelson and Nordhaus 

Capital, Interest and Profits - Review Notes
Markets and Economic Efficiency - Review Notes

Economic Role of Government and Its Expenditure an...
Economic Analysis of Poverty and Equity

Alternative Economic Systems - Review Notes
Theory of Economic Growth

2nd week  ( 8 to 12 July)

International Trade Theory and Issues
Exchange Rates: Markets Regulation and International Financial System

Supply Behavior/Decisions of Firm in Competitive Markets
Introduction to Engineering Economics


Engineering Economy or Engineering Economics: Economic Decision Making by Engineers
Time Value of Money - Time Value of Money Calculations


Cash Flow Estimation for Expenditure Proposals
Required Rate of Return - Cost of Capital  - Required Rate of Return for Investment or Expenditure Proposal..


Depreciation and Other Related Issues
NPV - IRR and Other Summary Project Assessment Measures



3rd week  (15 to 19 July)


Income Expansion Projects
Cost Reduction Projects


Replacement Decisons
Expected Values and Risk of Project Revenues and Costs


Present-Worth Comparisons
Rate-of-Return Calculations

18 July

Equivalent Annual-Worth Comparisons
Replacement Analysis


Replacement Problem - Engineering Economy Analysis...
Machine Selection Problem for an Engineer - Engine...

4th week

Depreciation and Income Tax Considerations
Sensitivity Analysis

Structural Analysis of Alternatives
Engineering Economic Analysis - Subject Update - Recent Case Studies

Business Ethics Revision Starts


Business Ethics – Introduction
Moral Standards and Moral Judgments – Approaches



Business System - Free Markets - Ethics
Ethics in the Market Place and Distribution System

Ethics in the Factory
Ethics in the Supply Chain







To August - Management Knowledge Revision

One Year MBA Knowledge Revision Plan

January  - February  - March  - April  - May   -   June

July  - August     - September  - October  - November  - December



Updated 10 July 2016






Good to Great - Jim Collins - Chapter Summaries

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Never give up on your dreams. Always improve your competence. Do not stop because of failures. Keep trying in the direction you think will give result.


Good to Great Chapter 2. Level 5 Leadership



The leaders in the organization have to practice Level 5 leadership.


Level 5 leader - Level 4 leader with Professional achievement goal and personal humility behavior.

Level 4 leader- Level 3 leader with a clear and compelling vision who catalyzes commitment of people and vigorously pursues achievement of the vision.

Level 3 leader - A person who has the ability to organize people and resources toward the effective and efficient pursuit of predetermined objectives.

Level 2 individual - Contributing team member.

Level 1 individual - Highly capable individual.

Professional Activities - Personal humility behavior

Commitment to transform - Never boastful
good to great

Unwavering commitment to - Standard performance is demonstrated. Motivation
achieve the long term goal by personal example
despite set backs in between.

Standards are set for greatness - Subordinates are developed to higher performance

Revises implementation plans - Success attributed to the cooperation of all.
based on objective plans. Actual Failure attributed to himself and a personal resolution is stated
results are collected and analyzed to do better management in the future.


Good to Great Chapter 3. First Who... Then What

Organize People First - Involvement in Vision and Strategy Development


When a company has to transform from good to great, first reorganize to put the right people in right jobs. Then involve them in strategy development. Don't develop strategy without taking right people into dialogue and discussion mode.





Never give up on your dreams. Always improve your competence. Do not stop because of failures. Keep trying in the direction you think will give result.


Good to Great Chapter 2. Level 5 Leadership



The leaders in the organization have to practice Level 5 leadership.


Level 5 leader - Level 4 leader with Professional achievement goal and personal humility behavior.

Level 4 leader- Level 3 leader with a clear and compelling vision who catalyzes commitment of people and vigorously pursues achievement of the vision.

Level 3 leader - A person who has the ability to organize people and resources toward the effective and efficient pursuit of predetermined objectives.

Level 2 individual - Contributing team member.

Level 1 individual - Highly capable individual.

Professional Activities - Personal humility behavior

Commitment to transform - Never boastful
good to great

Unwavering commitment to - Standard performance is demonstrated. Motivation
achieve the long term goal by personal example
despite set backs in between.

Standards are set for greatness - Subordinates are developed to higher performance

Revises implementation plans - Success attributed to the cooperation of all.
based on objective plans. Actual Failure attributed to himself and a personal resolution is stated
results are collected and analyzed to do better management in the future.


Good to Great Chapter 3. First Who... Then What

Organize People First - Involvement in Vision and Strategy Development


When a company has to transform from good to great, first reorganize to put the right people in right jobs. Then involve them in strategy development. Don't develop strategy without taking right people into dialogue and discussion mode.





Never give up on your dreams. Always improve your competence. Do not stop because of failures. Keep trying in the direction you think will give result.


Good to Great Chapter 2. Level 5 Leadership



The leaders in the organization have to practice Level 5 leadership.


Level 5 leader - Level 4 leader with Professional achievement goal and personal humility behavior.

Level 4 leader- Level 3 leader with a clear and compelling vision who catalyzes commitment of people and vigorously pursues achievement of the vision.

Level 3 leader - A person who has the ability to organize people and resources toward the effective and efficient pursuit of predetermined objectives.

Level 2 individual - Contributing team member.

Level 1 individual - Highly capable individual.

Professional Activities - Personal humility behavior

Commitment to transform - Never boastful
good to great

Unwavering commitment to - Standard performance is demonstrated. Motivation
achieve the long term goal by personal example
despite set backs in between.

Standards are set for greatness - Subordinates are developed to higher performance

Revises implementation plans - Success attributed to the cooperation of all.
based on objective plans. Actual Failure attributed to himself and a personal resolution is stated
results are collected and analyzed to do better management in the future.


Good to Great Chapter 3. First Who... Then What

Organize People First - Involvement in Vision and Strategy Development


When a company has to transform from good to great, first reorganize to put the right people in right jobs. Then involve them in strategy development. Don't develop strategy without taking right people into dialogue and discussion mode.

Internet of Things (IoT) - Very Big Business/Value Opportunity 2025

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$11.1 Trillion Value Opportunity in IoT by 2025

2015 McKinsey Estimate



What is Internet of Things?


The Internet of Things refers to the networking of physical objects through the use of embedded sensors, actuators, and other devices that can collect or transmit information about the objects. The data amassed from these devices can then be analyzed to optimize products, services, and operations. Perhaps one of the earliest and best-known applications of such technology has been in the area of energy optimization: sensors deployed across the electricity grid can help utilities remotely monitor energy usage and adjust generation and distribution flows to account for peak times and downtimes. But applications are also being introduced in a number of other industries.

IDC defines IoT as a network of networks of uniquely identifiable end points (or things) that
communicate without human interaction using IP connectivity — be it locally or globally. It is not an
individual technology that can be implemented in isolation but it is an integral part of an "innovation
platform" tying together multiple IT systems and teams within and sometimes outside an organization.


The Internet of Things Is Part of the Third Wave of IT-Driven Competition


For hundreds of years, the types of products that were produced were mechanical, and
value-chain activities were performed manually. This has changed with successive waves of
information technology.

WAVE 1: VALUE CHAIN AUTOMATION. In the 1960s and 1970s, IT automated previously manual processes of information collection and processing in individual activities across the value chain, such as order processing and billing, which improved productivity.

WAVE 2: VALUE CHAIN DISPERSION AND INTEGRATION. In the 1980s and 1990s, the Internet enabled connectivity and integration across the value chain. Customer relationship management stitched together what had been separate processes; supply chains became more global, efficient, and optimized; and again, productivity improved. So value chains got extended nationally and globally but were connected through internet.

WAVE 3: SMART, CONNECTED PRODUCTS. In this wave, information technology is embedded in the products themselves.  A product becomes “smart” when technology, such as a sensor, is embedded in the product. A product becomes “connected” when one product is connected to another. With miniaturization and ubiquitous connectivity, it is possible to make all types of products smart
and connected.
http://info.tcs.com/rs/tcs/images/Report-HBR-ConnectedProducts.pdf



According to a report from McKinsey & Company's Global Institute released in 2015,  IoT has the potential to be worth between $3.9 and $11.1 trillion by 2025. The value includes productivity improvements, time savings, and improved asset utilization, as well as an approximate economic value for reduced disease, accidents, and deaths. So, it does not represent revenue of IoT equipment, software and services selling companies.


The report includes some estimated segment values:

Vehicles: Autonomous vehicles and condition-based maintenance, with an estimated value of $210 to $740 billion

Cities: Public health and transportation: $930 billion to $1.7 trillion

Outside Logistics and navigation: $560 billion to $850 billion

Health and fitness: $170 billion to $1.6 trillion

Construction operations optimization, as well as improve health and safety of operators: $160 billion to $930 billion

Retail environments: Automated checkout: $410 billion to $1.2 trillion

Factories: Operations and equipment optimization: $1.2 billion to $3.7 trillion

Offices: Security and energy: $70 billion to $150 billion

Home: Chore automation and security: $200 billion to $350 billion

The growth of IoT applicatgions  means  IT departments and CIOs  have to plan learn the skills related to IoT systems and applications and plan corporate strategies around the new technology that could be worth trillions in just 10 years' time.


Business Scope  2014 Estimate:
http://www.mckinsey.com/industries/high-tech/our-insights/the-internet-of-things-sizing-up-the-opportunity


2015 Estimate
http://www.mckinsey.com/business-functions/business-technology/our-insights/the-internet-of-things-the-value-of-digitizing-the-physical-world


http://www.informationweek.com/it-life/4-iot-skills-it-pros-need/a/d-id/1320912



Industrial Giants are making Massive Investments in Industrial IoT
Even predictions of $60 trillion by 2030 are being made.
https://arc.applause.com/2016/06/24/industrial-iot-investment-ge-digital-hitachi/


GE into IoT in a Big Way - Offering  Predix - IoT Platform

February 2016

GE committing $1 billion to dvelop IoT based business. IoT involves placing sensors on gas turbines, jet engines, and other machines; connect them to the cloud; and analyze the resulting flow of data to identify ways to improve machine productivity and reliability.



IoT can also be used to improve yields from operations. The average recovery rate of an oil well is 35%. The rest of crude is left in the earth because available technology makes it too expensive, If improvement in technology raises yield by 1%, the world’s output will increase by 80 billion barrels the equivalent of three years of global supply. The economic benefits are  huge and IoT offers a way to find protable solutions.



In September 2015, GE projected its revenue from software products would reach $15 billion by 2020, three times its 2015 bookings. GE expects that IoT platform and software  Predix, a cloud-based platform for creating Industrial Internet applications will contribute a big portion of that increased revenue.


GE  began developing the IoT solution in 2012.  Initially, it was developed for GE. Now it is offered on the market.


The driving force behind taking Predix to market was the scope of the opportunity: GE determined that the market for a platform and applications in the industrial segment could reach $225 billion by 2020.2  The company developed  the commercial version, Predix 2.0, and in October 2015 made the platform directly available to channel and technology partners as well as customers who could use the platform to build their own set of analytics. .


Platform Benefits

Predix was designed to be a software platform. The platform has open standards and protocols that allow customers to more easily and quickly connect their machines to the Industrial Internet. The platform can accommodate the size and scale of industrial data for every customer at current levels of use, but it also has been designed to scale up as demand grows. Apart from what GE offers, customers may develop their own custom applications for use on the Predix platform, GE executives are working to build a developer community and create a new market for apps that can be hosted on the Predix platform. Finally, data security, a concern for many companies considering IoT applications, is embedded at all platform application layers: services enablement, data orchestration, and infrastructure layers.

IoT Pilots

A pilot is often an essential step of the adoption process. In early 2015, GE executed a four-week engagement with one of the largest global energy companies, which wanted to reinvent how it manages corrosion related maintenance its “static” equipment — specifically, its storage tanks used during oil and gas processing.   During the four-week exercise, after discussions with various experts related to the design and maintenance,  GE team developed a software solution that helped engineers to “walk through” the equipment digitally. This provided reliability engineers new insights they could  to better manage those assets. The project was a success and offered GE a way to discuss future engagements.

GE hopes to have three more customers booked by early 2016 to run pilots of Predix offerings. GE executives see the pilots as a way to bring customers onto the selling team. Global spending on the Industrial Internet was $20 billion in 2012. Analysts were forecasting that number would reach $514 billion by 2020, creating nearly $1.3 trillion in value.

http://sloanreview.mit.edu/case-study/ge-big-bet-on-data-and-analytics/

Sam Ransbotham the author of the article in MIT Review is an associate professor of information systems at the Carroll School of Management at Boston College and the MIT Sloan Management Review guest editor for the Data and Analytics Big Idea Initiative. He can be reached at sam.ransbotham  at the rate bc.edu and on Twitter at @ransbotham.



Digital Reimagination


Five key technologies (Digital Five Forces)  are  maturing and  precipitating
the shift to the Digital Consumer Economy. These are Mobility and Pervasive Computing, Big Data,
Social Media, Cloud, and AI-Robotics. These forces are  being used in various permutations
and combinations to drive new applications. As a result, new Digital Composite Forces are emerging.

Foremost among them is the Internet of Things, which combines mobility and pervasive computing,
big data, cloud, and—increasingly—artificial intelligence.

Large number of  devices can be connected to the IoT  driving new economic opportunities. “Digital Reimagination.” involves leveraging a combination of the Digital Five Forces and Digital Composite Forces to reimagine the enterprise along one or more of six dimensions: business models, products and services, customer segments, channels, business processes, and workplaces.

Recently,  a major global engine manufacturer created a new services stream by using sensor-collected big data to predict failures on their engines. When such a failure is predicted, the vehicle operators are automatically sent notifications along with directions to the nearest service center. There is value creation in this initiative. It gave new maintenance revenue to the manufacturer, but, this has also helped the customer  by reducing inconvenient breakdowns.
http://info.tcs.com/rs/tcs/images/Report-HBR-ConnectedProducts.pdf

$14.4 trillion of value - IOE (Internet of Every Thing) by Cisco - 2013 Estimate for 2022


In other words, between 2013 and 2022, $14.4 trillion of value (net profit) will be
“up for grabs” for enterprises globally — driven by IoE.

Where is the value?


The five main areas:
1) asset utilization (reduced costs) of $2.5 trillion;
2) employee productivity (greater labor efficiencies) of $2.5 trillion;
3) supply chain and logistics (eliminating waste) of $2.7 trillion;
4) customer experience (addition of more customers) of $3.7 trillion; and
5) innovation (reducing time to market) of $3.0 trillion.
http://www.cisco.com/c/dam/en_us/about/ac79/docs/innov/IoE_Economy.pdf  - Cisco 2013 report.

Cisco report has value estimate for countries and regions.


Various reports on Market Size
http://postscapes.com/internet-of-things-market-size


Updated  24 June 2016,  24 Mar 2016, 23 Mar 2016


$11.1 Trillion Value Opportunity in IoT by 2025

2015 McKinsey Estimate



What is Internet of Things?


The Internet of Things refers to the networking of physical objects through the use of embedded sensors, actuators, and other devices that can collect or transmit information about the objects. The data amassed from these devices can then be analyzed to optimize products, services, and operations. Perhaps one of the earliest and best-known applications of such technology has been in the area of energy optimization: sensors deployed across the electricity grid can help utilities remotely monitor energy usage and adjust generation and distribution flows to account for peak times and downtimes. But applications are also being introduced in a number of other industries.

IDC defines IoT as a network of networks of uniquely identifiable end points (or things) that
communicate without human interaction using IP connectivity — be it locally or globally. It is not an
individual technology that can be implemented in isolation but it is an integral part of an "innovation
platform" tying together multiple IT systems and teams within and sometimes outside an organization.


The Internet of Things Is Part of the Third Wave of IT-Driven Competition


For hundreds of years, the types of products that were produced were mechanical, and
value-chain activities were performed manually. This has changed with successive waves of
information technology.

WAVE 1: VALUE CHAIN AUTOMATION. In the 1960s and 1970s, IT automated previously manual processes of information collection and processing in individual activities across the value chain, such as order processing and billing, which improved productivity.

WAVE 2: VALUE CHAIN DISPERSION AND INTEGRATION. In the 1980s and 1990s, the Internet enabled connectivity and integration across the value chain. Customer relationship management stitched together what had been separate processes; supply chains became more global, efficient, and optimized; and again, productivity improved. So value chains got extended nationally and globally but were connected through internet.

WAVE 3: SMART, CONNECTED PRODUCTS. In this wave, information technology is embedded in the products themselves.  A product becomes “smart” when technology, such as a sensor, is embedded in the product. A product becomes “connected” when one product is connected to another. With miniaturization and ubiquitous connectivity, it is possible to make all types of products smart
and connected.
http://info.tcs.com/rs/tcs/images/Report-HBR-ConnectedProducts.pdf



According to a report from McKinsey & Company's Global Institute released in 2015,  IoT has the potential to be worth between $3.9 and $11.1 trillion by 2025. The value includes productivity improvements, time savings, and improved asset utilization, as well as an approximate economic value for reduced disease, accidents, and deaths. So, it does not represent revenue of IoT equipment, software and services selling companies.


The report includes some estimated segment values:

Vehicles: Autonomous vehicles and condition-based maintenance, with an estimated value of $210 to $740 billion

Cities: Public health and transportation: $930 billion to $1.7 trillion

Outside Logistics and navigation: $560 billion to $850 billion

Health and fitness: $170 billion to $1.6 trillion

Construction operations optimization, as well as improve health and safety of operators: $160 billion to $930 billion

Retail environments: Automated checkout: $410 billion to $1.2 trillion

Factories: Operations and equipment optimization: $1.2 billion to $3.7 trillion

Offices: Security and energy: $70 billion to $150 billion

Home: Chore automation and security: $200 billion to $350 billion

The growth of IoT applicatgions  means  IT departments and CIOs  have to plan learn the skills related to IoT systems and applications and plan corporate strategies around the new technology that could be worth trillions in just 10 years' time.


Business Scope  2014 Estimate:
http://www.mckinsey.com/industries/high-tech/our-insights/the-internet-of-things-sizing-up-the-opportunity


2015 Estimate
http://www.mckinsey.com/business-functions/business-technology/our-insights/the-internet-of-things-the-value-of-digitizing-the-physical-world


http://www.informationweek.com/it-life/4-iot-skills-it-pros-need/a/d-id/1320912



Industrial Giants are making Massive Investments in Industrial IoT
Even predictions of $60 trillion by 2030 are being made.
https://arc.applause.com/2016/06/24/industrial-iot-investment-ge-digital-hitachi/


GE into IoT in a Big Way - Offering  Predix - IoT Platform

February 2016

GE committing $1 billion to dvelop IoT based business. IoT involves placing sensors on gas turbines, jet engines, and other machines; connect them to the cloud; and analyze the resulting flow of data to identify ways to improve machine productivity and reliability.



IoT can also be used to improve yields from operations. The average recovery rate of an oil well is 35%. The rest of crude is left in the earth because available technology makes it too expensive, If improvement in technology raises yield by 1%, the world’s output will increase by 80 billion barrels the equivalent of three years of global supply. The economic benefits are  huge and IoT offers a way to find protable solutions.



In September 2015, GE projected its revenue from software products would reach $15 billion by 2020, three times its 2015 bookings. GE expects that IoT platform and software  Predix, a cloud-based platform for creating Industrial Internet applications will contribute a big portion of that increased revenue.


GE  began developing the IoT solution in 2012.  Initially, it was developed for GE. Now it is offered on the market.


The driving force behind taking Predix to market was the scope of the opportunity: GE determined that the market for a platform and applications in the industrial segment could reach $225 billion by 2020.2  The company developed  the commercial version, Predix 2.0, and in October 2015 made the platform directly available to channel and technology partners as well as customers who could use the platform to build their own set of analytics. .


Platform Benefits

Predix was designed to be a software platform. The platform has open standards and protocols that allow customers to more easily and quickly connect their machines to the Industrial Internet. The platform can accommodate the size and scale of industrial data for every customer at current levels of use, but it also has been designed to scale up as demand grows. Apart from what GE offers, customers may develop their own custom applications for use on the Predix platform, GE executives are working to build a developer community and create a new market for apps that can be hosted on the Predix platform. Finally, data security, a concern for many companies considering IoT applications, is embedded at all platform application layers: services enablement, data orchestration, and infrastructure layers.

IoT Pilots

A pilot is often an essential step of the adoption process. In early 2015, GE executed a four-week engagement with one of the largest global energy companies, which wanted to reinvent how it manages corrosion related maintenance its “static” equipment — specifically, its storage tanks used during oil and gas processing.   During the four-week exercise, after discussions with various experts related to the design and maintenance,  GE team developed a software solution that helped engineers to “walk through” the equipment digitally. This provided reliability engineers new insights they could  to better manage those assets. The project was a success and offered GE a way to discuss future engagements.

GE hopes to have three more customers booked by early 2016 to run pilots of Predix offerings. GE executives see the pilots as a way to bring customers onto the selling team. Global spending on the Industrial Internet was $20 billion in 2012. Analysts were forecasting that number would reach $514 billion by 2020, creating nearly $1.3 trillion in value.

http://sloanreview.mit.edu/case-study/ge-big-bet-on-data-and-analytics/

Sam Ransbotham the author of the article in MIT Review is an associate professor of information systems at the Carroll School of Management at Boston College and the MIT Sloan Management Review guest editor for the Data and Analytics Big Idea Initiative. He can be reached at sam.ransbotham  at the rate bc.edu and on Twitter at @ransbotham.



Digital Reimagination


Five key technologies (Digital Five Forces)  are  maturing and  precipitating
the shift to the Digital Consumer Economy. These are Mobility and Pervasive Computing, Big Data,
Social Media, Cloud, and AI-Robotics. These forces are  being used in various permutations
and combinations to drive new applications. As a result, new Digital Composite Forces are emerging.

Foremost among them is the Internet of Things, which combines mobility and pervasive computing,
big data, cloud, and—increasingly—artificial intelligence.

Large number of  devices can be connected to the IoT  driving new economic opportunities. “Digital Reimagination.” involves leveraging a combination of the Digital Five Forces and Digital Composite Forces to reimagine the enterprise along one or more of six dimensions: business models, products and services, customer segments, channels, business processes, and workplaces.

Recently,  a major global engine manufacturer created a new services stream by using sensor-collected big data to predict failures on their engines. When such a failure is predicted, the vehicle operators are automatically sent notifications along with directions to the nearest service center. There is value creation in this initiative. It gave new maintenance revenue to the manufacturer, but, this has also helped the customer  by reducing inconvenient breakdowns.
http://info.tcs.com/rs/tcs/images/Report-HBR-ConnectedProducts.pdf

$14.4 trillion of value - IOE (Internet of Every Thing) by Cisco - 2013 Estimate for 2022


In other words, between 2013 and 2022, $14.4 trillion of value (net profit) will be
“up for grabs” for enterprises globally — driven by IoE.

Where is the value?


The five main areas:
1) asset utilization (reduced costs) of $2.5 trillion;
2) employee productivity (greater labor efficiencies) of $2.5 trillion;
3) supply chain and logistics (eliminating waste) of $2.7 trillion;
4) customer experience (addition of more customers) of $3.7 trillion; and
5) innovation (reducing time to market) of $3.0 trillion.
http://www.cisco.com/c/dam/en_us/about/ac79/docs/innov/IoE_Economy.pdf  - Cisco 2013 report.

Cisco report has value estimate for countries and regions.


Various reports on Market Size
http://postscapes.com/internet-of-things-market-size


Updated  24 June 2016,  24 Mar 2016, 23 Mar 2016


$11.1 Trillion Value Opportunity in IoT by 2025

2015 McKinsey Estimate



What is Internet of Things?


The Internet of Things refers to the networking of physical objects through the use of embedded sensors, actuators, and other devices that can collect or transmit information about the objects. The data amassed from these devices can then be analyzed to optimize products, services, and operations. Perhaps one of the earliest and best-known applications of such technology has been in the area of energy optimization: sensors deployed across the electricity grid can help utilities remotely monitor energy usage and adjust generation and distribution flows to account for peak times and downtimes. But applications are also being introduced in a number of other industries.

IDC defines IoT as a network of networks of uniquely identifiable end points (or things) that
communicate without human interaction using IP connectivity — be it locally or globally. It is not an
individual technology that can be implemented in isolation but it is an integral part of an "innovation
platform" tying together multiple IT systems and teams within and sometimes outside an organization.


The Internet of Things Is Part of the Third Wave of IT-Driven Competition


For hundreds of years, the types of products that were produced were mechanical, and
value-chain activities were performed manually. This has changed with successive waves of
information technology.

WAVE 1: VALUE CHAIN AUTOMATION. In the 1960s and 1970s, IT automated previously manual processes of information collection and processing in individual activities across the value chain, such as order processing and billing, which improved productivity.

WAVE 2: VALUE CHAIN DISPERSION AND INTEGRATION. In the 1980s and 1990s, the Internet enabled connectivity and integration across the value chain. Customer relationship management stitched together what had been separate processes; supply chains became more global, efficient, and optimized; and again, productivity improved. So value chains got extended nationally and globally but were connected through internet.

WAVE 3: SMART, CONNECTED PRODUCTS. In this wave, information technology is embedded in the products themselves.  A product becomes “smart” when technology, such as a sensor, is embedded in the product. A product becomes “connected” when one product is connected to another. With miniaturization and ubiquitous connectivity, it is possible to make all types of products smart
and connected.
http://info.tcs.com/rs/tcs/images/Report-HBR-ConnectedProducts.pdf



According to a report from McKinsey & Company's Global Institute released in 2015,  IoT has the potential to be worth between $3.9 and $11.1 trillion by 2025. The value includes productivity improvements, time savings, and improved asset utilization, as well as an approximate economic value for reduced disease, accidents, and deaths. So, it does not represent revenue of IoT equipment, software and services selling companies.


The report includes some estimated segment values:

Vehicles: Autonomous vehicles and condition-based maintenance, with an estimated value of $210 to $740 billion

Cities: Public health and transportation: $930 billion to $1.7 trillion

Outside Logistics and navigation: $560 billion to $850 billion

Health and fitness: $170 billion to $1.6 trillion

Construction operations optimization, as well as improve health and safety of operators: $160 billion to $930 billion

Retail environments: Automated checkout: $410 billion to $1.2 trillion

Factories: Operations and equipment optimization: $1.2 billion to $3.7 trillion

Offices: Security and energy: $70 billion to $150 billion

Home: Chore automation and security: $200 billion to $350 billion

The growth of IoT applicatgions  means  IT departments and CIOs  have to plan learn the skills related to IoT systems and applications and plan corporate strategies around the new technology that could be worth trillions in just 10 years' time.


Business Scope  2014 Estimate:
http://www.mckinsey.com/industries/high-tech/our-insights/the-internet-of-things-sizing-up-the-opportunity


2015 Estimate
http://www.mckinsey.com/business-functions/business-technology/our-insights/the-internet-of-things-the-value-of-digitizing-the-physical-world


http://www.informationweek.com/it-life/4-iot-skills-it-pros-need/a/d-id/1320912



Industrial Giants are making Massive Investments in Industrial IoT
Even predictions of $60 trillion by 2030 are being made.
https://arc.applause.com/2016/06/24/industrial-iot-investment-ge-digital-hitachi/


GE into IoT in a Big Way - Offering  Predix - IoT Platform

February 2016

GE committing $1 billion to dvelop IoT based business. IoT involves placing sensors on gas turbines, jet engines, and other machines; connect them to the cloud; and analyze the resulting flow of data to identify ways to improve machine productivity and reliability.



IoT can also be used to improve yields from operations. The average recovery rate of an oil well is 35%. The rest of crude is left in the earth because available technology makes it too expensive, If improvement in technology raises yield by 1%, the world’s output will increase by 80 billion barrels the equivalent of three years of global supply. The economic benefits are  huge and IoT offers a way to find protable solutions.



In September 2015, GE projected its revenue from software products would reach $15 billion by 2020, three times its 2015 bookings. GE expects that IoT platform and software  Predix, a cloud-based platform for creating Industrial Internet applications will contribute a big portion of that increased revenue.


GE  began developing the IoT solution in 2012.  Initially, it was developed for GE. Now it is offered on the market.


The driving force behind taking Predix to market was the scope of the opportunity: GE determined that the market for a platform and applications in the industrial segment could reach $225 billion by 2020.2  The company developed  the commercial version, Predix 2.0, and in October 2015 made the platform directly available to channel and technology partners as well as customers who could use the platform to build their own set of analytics. .


Platform Benefits

Predix was designed to be a software platform. The platform has open standards and protocols that allow customers to more easily and quickly connect their machines to the Industrial Internet. The platform can accommodate the size and scale of industrial data for every customer at current levels of use, but it also has been designed to scale up as demand grows. Apart from what GE offers, customers may develop their own custom applications for use on the Predix platform, GE executives are working to build a developer community and create a new market for apps that can be hosted on the Predix platform. Finally, data security, a concern for many companies considering IoT applications, is embedded at all platform application layers: services enablement, data orchestration, and infrastructure layers.

IoT Pilots

A pilot is often an essential step of the adoption process. In early 2015, GE executed a four-week engagement with one of the largest global energy companies, which wanted to reinvent how it manages corrosion related maintenance its “static” equipment — specifically, its storage tanks used during oil and gas processing.   During the four-week exercise, after discussions with various experts related to the design and maintenance,  GE team developed a software solution that helped engineers to “walk through” the equipment digitally. This provided reliability engineers new insights they could  to better manage those assets. The project was a success and offered GE a way to discuss future engagements.

GE hopes to have three more customers booked by early 2016 to run pilots of Predix offerings. GE executives see the pilots as a way to bring customers onto the selling team. Global spending on the Industrial Internet was $20 billion in 2012. Analysts were forecasting that number would reach $514 billion by 2020, creating nearly $1.3 trillion in value.

http://sloanreview.mit.edu/case-study/ge-big-bet-on-data-and-analytics/

Sam Ransbotham the author of the article in MIT Review is an associate professor of information systems at the Carroll School of Management at Boston College and the MIT Sloan Management Review guest editor for the Data and Analytics Big Idea Initiative. He can be reached at sam.ransbotham  at the rate bc.edu and on Twitter at @ransbotham.



Digital Reimagination


Five key technologies (Digital Five Forces)  are  maturing and  precipitating
the shift to the Digital Consumer Economy. These are Mobility and Pervasive Computing, Big Data,
Social Media, Cloud, and AI-Robotics. These forces are  being used in various permutations
and combinations to drive new applications. As a result, new Digital Composite Forces are emerging.

Foremost among them is the Internet of Things, which combines mobility and pervasive computing,
big data, cloud, and—increasingly—artificial intelligence.

Large number of  devices can be connected to the IoT  driving new economic opportunities. “Digital Reimagination.” involves leveraging a combination of the Digital Five Forces and Digital Composite Forces to reimagine the enterprise along one or more of six dimensions: business models, products and services, customer segments, channels, business processes, and workplaces.

Recently,  a major global engine manufacturer created a new services stream by using sensor-collected big data to predict failures on their engines. When such a failure is predicted, the vehicle operators are automatically sent notifications along with directions to the nearest service center. There is value creation in this initiative. It gave new maintenance revenue to the manufacturer, but, this has also helped the customer  by reducing inconvenient breakdowns.
http://info.tcs.com/rs/tcs/images/Report-HBR-ConnectedProducts.pdf

$14.4 trillion of value - IOE (Internet of Every Thing) by Cisco - 2013 Estimate for 2022


In other words, between 2013 and 2022, $14.4 trillion of value (net profit) will be
“up for grabs” for enterprises globally — driven by IoE.

Where is the value?


The five main areas:
1) asset utilization (reduced costs) of $2.5 trillion;
2) employee productivity (greater labor efficiencies) of $2.5 trillion;
3) supply chain and logistics (eliminating waste) of $2.7 trillion;
4) customer experience (addition of more customers) of $3.7 trillion; and
5) innovation (reducing time to market) of $3.0 trillion.
http://www.cisco.com/c/dam/en_us/about/ac79/docs/innov/IoE_Economy.pdf  - Cisco 2013 report.

Cisco report has value estimate for countries and regions.


Various reports on Market Size
http://postscapes.com/internet-of-things-market-size


Updated  24 June 2016,  24 Mar 2016, 23 Mar 2016

 

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