Showing posts with label IE Measurements. Show all posts
Showing posts with label IE Measurements. Show all posts

Monday, August 18, 2025

Total Factor Productivity & Total Productivity Measurement and Management


Lesson 307 of IEKC Industrial Engineering ONLINE Course Notes.

Industrial Engineering Measurements - Online Course Module


Sumanth's total productivity model


https://books.google.co.in/books?id=mLAv09ocvTsC&pg=PA5#v=onepage&q&f=false


‘Productivity’  is the standard that indicates measures how efficiently the material, the labor, the capital and the energy can be utilized. Analysis and measurement of ‘Productivity’ can help to know the areas for taking corrective actions towards planning of business firm. 

Productivity is known as the relationship between output and all employed inputs measured in real terms. It refers to a comparison between what comes out of production and what goes into production that is the arithmetical ratio between the amount produced and the amount of all resources used in terms of manufacture. 

It may be measured for manufacturing organizations or their departments for which separate records are maintained.

The success of an industrial organization is determined by the level of efficiency in reducing cost and providing consumer services. Analysis and Measurement of Productivity can help to find out the areas where the corrective steps will have been taken in the way of planning of business firm. 

TOTAL PRODUCTIVITY MODEL  

Total Productivity Model developed by David J. Sumanth in 1979 considered 5 items as inputs. 

These are Human, Material, Capital, Energy and other expenses. 

This model can be applied in any manufacturing or service organization. 

Total Productivity= Total Tangible Output÷ Total Tangible Input. 

 Total tangible output= Value of finished units produced + partial units produced + Dividends from securities + Interests from bonds +Other incomes. 

 Total tangible inputs= Value of human inputs+ capital inputs+ materials purchased+ energy inputs + other expenses (taxes, transport, office expenses etc.)

Sumanth’s provided a structure for finding productivity at product level and summing product level productivities to total firm level productivity. 

The model also has the structure for finding partial productivities at the product level and aggregating them to product level productivities. 

Total Productivity= Total Tangible Output÷ Total Tangible Input

 = O1+O2+O3+O4+O5 / H+M+FC+WC+E+X 

Where,

O1 is value of finished units of output.

O2 value of partially completed units of output ,

O3 dividend income, 

O4 interest income ,

O5 other income. 

H human input, M material input , FC fixed capital input , WC working capital input, E energy input , and x other expense.

https://www.slideshare.net/anilp264/sumanths-total-productivity-model-29348562


A Case Study

Adapted from Edosomwan, J. A and David J. Sumanth. (1996). Productivity Measurement Guide: A Practical Approach for Productivity Measurement in Organizations. New York: McGraw-Hill, Inc. (pp. 179-198)

Human partial productivity index

Employees       Measure                     January              October

Workers

Hourly paid      Units/$                       17.88                  24.14

                          P.P.I                             1.00                     1.35

Salaried             Units/$                         0.366                  0.354

                          P.P.I                             1.00                     0.967

Professionals

Hourly paid       Units/$                         2.438                   3.155     

                          P.P.I                             1.00                      1.294


The calculation procedure used:  Divide the units produced in the month by expenses paid to a category of human resource. This gives  Units/$. Then calculate index  with the first month as the base year.  

Comments made on various tables by the authors. (Tables for all resources will be added)

Human Productivity 
The human partial productivity index showed a trend that followed the output curve very closely. 
Two major areas of input in this category (salaried workers and salaried professionals) had not changed significantly during the periods.
The human partial productivity index for hourly  paid professionals did show very significant gains during the last several measurement periods due to decreases in input. 


Material Productivity 
The index showed a steady decline through the first seven measurement periods, and then, showed a dramatic improvement in productivity for the final periods. This was apparently caused by the way in 
which purchases of materials from source #1 was planned. These were planned at the beginning of the year, based on a then current forecast for total productivity demand. 

Through the year, as demand fell short of the forecast, the appropriate action would have been to curtail purchases of materials from all sources. Contracts that were in place between systems manufacturing and source #1, however contained a clause that froze the level of purchases for several periods. For this reason, material productivity declined until the orders could be reset to lower levels to more accurately 
reflect the lower demand for the product. 

Capital Productivity 
The working capital partial productivity was by far the major ingredient for capital productivity and represented a major input for total productivity. 

The index showed stable or improved productivity through the first six periods, but a dramatic drop in productivity was evident in the final periods. 

This, again, relates back to the problems with the controls on material inputs and the resulting increasing of material inventory until the inputs could be reduced. During the final four periods, a slight improvement was seen and this could be expected to continue, as this measurement will follow the trend of the material productivity index, lagging by several periods. The occupancy and depreciation productivity measurements followed the same basic trend as the output since they had a small degree of variance and output had a large variance. 

Other Expense Productivity 
This category of partial productivity included many diverse expense type inputs. It was apparent, that for certain items  partial productivity improved. For example, the travel and professional fees partial productivity improved during the last several periods primarily due to management attention. However, the stationery, telephone and education partial productivity measurements did not show any improvements.  

Total Productivity 
The total productivity index followed the trend of the capital partial productivity most closely. This is due to the large percentage of input the capital productivity represents, most of this input being in the form of working capital. The total productivity index followed very closely, the output level of the product. That is the productivity index showed decline when output is below the base period output and the index shows improvements when the output is above the base period level. 


Case Studies on Sumanth's Approach

See chapter 6 case studies in

Total Productivity Management (TPmgt): A Systemic and Quantitative Approach to Compete in Quality, Price and Time

David J. Sumanth
CRC Press, 27-Oct-1997 - Business & Economics - 424 pages

Poised to influence innovative management thinking into the 21st century, Total Productivity Management (TPmgt), written by one of the pioneers of productivity management, has been a decade in the making.

This landmark publication is the most extensive book available on the subject of total productivity management. At a time when downsizing and layoffs are the norm, this innovative and highly organized book shows you how to treat human resource situations with a caring, customer-oriented, yet competitive attitude through integration of technical and human dimensions. This book makes use of a set of proven models and provides a systematic framework and structure to link total productivity to an organization's profitability.

Total Productivity Management describes the tasks required of all constituents in an understandable format that they can relate to and by which regards can be realized for performance in all resource categories including direct labor, administrative staff, managers, professional personnel, materials, liquid assets, technologies, energy, and other areas.

Read from the preview

Page 94 - 70 Productivity Improvement Techniques

Implementation of TPMgmt.


Total Factor Productivity  



Multifactor productivity Total, Annual growth rate (%), 2005 – 2022
Source: GDP per capita and productivity growth

Data table for: Multifactor productivity, Total, Annual growth rate (%), 2005 – 2022
https://data.oecd.org/lprdty/multifactor-productivity.htm
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              ▾ 2005  ▾ 2006  ▾ 2007  ▾ 2008  ▾ 2009  ▾ 2010  ▾ 2011  ▾ 2012  ▾ 2013  ▾ 2014  ▾ 2015  ▾ 2016  ▾ 2017  ▾ 2018▾ 2019▾ 2020▾ 2021▾ 2022
Australia -0.54 -0.09 0.14   1.44 -1.44 0.31 0.22 0.80 0.69 -0.08 1.80 -0.19 0.91 -0.01 0.32 1.42 1.05 -0.59
Austria 1.59 2.10 1.98 -0.45 -2.16 0.98 0.67 0.27 -0.30 -0.18 0.62 -0.32 0.74 0.14 -0.65 -0.74 0.22 1.99
Belgium 0.51 -0.07 1.11 -1.46 -1.88 1.03 -0.93 -0.16 0.16 0.78 0.91 -0.37 -0.49 -0.13 0.38 0.20 0.11 1.11
Canada 1.15 0.31 -0.66 -0.99 -1.07 0.84 1.30 -0.37 0.93 2.09 -0.49 0.40 1.51 0.29 0.26 3.56 -2.67 -0.40
Denmark 0.71 0.83 -0.68 -2.24 -2.87 2.62 0.26 0.97 0.39 1.11 1.17 1.08 1.46 1.41 0.42 -0.39 1.58 -0.23
Finland 1.41 2.17 2.87 -1.49 -6.13 2.93 1.33 -1.86 -0.19 -0.11 0.57 2.25 2.34 -0.97 -0.09 -0.84 0.95 1.33
France 0.35 1.69 -0.64 -1.30 -2.00 0.90 0.75 -0.27 0.59 0.42 0.32 -0.11 1.47 0.07 0.03 -2.37 -0.08 -1.37
Germany 0.90 1.60 1.01 -0.34 -4.07 2.42 2.45 0.22 0.19 1.02 0.37 1.15 1.55 -0.06 0.35 -0.41 0.96 0.46
Greece -3.06 3.09 0.95 -2.68 -4.22 -2.99 -8.27 -5.47 -1.50 0.85 3.28 -2.37 1.74 -1.71 2.24 -0.40 1.08 1.39
Ireland 0.01 0.28 1.10 -4.14 1.20 3.26 0.66 -1.27 -2.90 4.25 -5.51 2.53 3.72 -5.32 4.92 8.63 6.75
Israel 0.94 2.56 0.86 -0.64 -1.63 2.57 2.45 -0.61 1.77 1.48 0.07 0.75 1.40 1.69 2.16 3.15 1.57 0.01
Italy -0.16 -0.39 -0.39 -1.27 -3.30 1.74 0.40 -1.40 -0.03 0.04 0.23 0.04 0.80 0.10 0.35 -0.60 1.09 0.49
Japan 0.92 -0.05 0.39 -1.04 -2.97 3.28 0.37 1.04 1.94 -0.05 1.51 0.05 0.89 0.38 0.22 -2.12 1.58 0.73
Korea 3.08 2.96 4.53 3.53 1.56 4.65 1.64 0.29 1.23 1.17 0.45 1.50 2.58 2.27 1.31 0.91 1.89 -0.25
Luxembourg 0.52 2.09 2.62 -5.24 -1.58 1.31 -2.21 -1.05 1.18 -0.61 -0.81 1.96 -1.68 -1.71 -0.75 2.19 -1.46 -2.06
Netherlands 1.58 1.14 0.25 0.12 -3.17 1.45 0.38 -0.80 -0.06 0.61 -0.26 0.05 0.82 0.07 -0.43 -2.22 1.99 1.07
New Zealand -1.05 0.08 2.35 -4.01 3.36 -1.38 0.99 2.33 -2.09 -0.22 1.76 -0.58 0.20 1.59 -1.02 0.33 1.58 -1.28
Norway 0.48 -1.07 -2.06 -3.69 -1.57 -0.33 -1.05 0.60 -0.09 0.43 0.90 0.16 1.42 -1.12 -0.97 -0.56 1.32 -0.42
Portugal -0.11 0.66 0.57 -0.73 -2.22 1.61 -0.15 -0.92 0.49 -0.69 0.24 0.33 1.05 0.08 1.12 -1.93 1.34 4.83
Spain -0.16 -0.01 0.17 -1.05 -0.31 0.86 -0.03 -0.29 0.01 0.16 1.12 0.67 0.94 -0.02 0.47 -3.27 -0.03 1.97
Sweden 1.59 2.14 -0.01 -2.51 -2.85 3.56 0.72 -1.23 0.25 0.88 2.27 -0.76 0.21 -0.17 1.32 -0.90 2.24 -0.38
Switzerland 1.36 1.72 1.07 0.20 -3.17 2.05 -0.60 -0.25 1.03 0.65 -0.71 0.37 0.76 1.82 0.15 -0.29 1.52 0.34
United Kingdom 0.65 1.16 0.97 -0.63 -3.44 2.02 -0.34 -0.77 0.22 0.30 1.29 -0.45 1.33 0.16 0.18 -2.22 -0.01 0.94
United States 1.39 0.30 0.49 0.08 1.07 1.99 -0.23 0.14 0.06 0.13 0.43 -0.02 0.52 0.77 0.68 1.07 1.57 -1.18

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3% #productivity increase every year will make #production double in 24 years from the same #resources.
Industrial Engineering increases prosperity of the society.

New Links





Ud  18.8.2025, 5.8.2025,  21.10, 15.10.2023, 13.2.2022
Pub: 26.1.2022














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Friday, August 1, 2025

Productivity Measurement

New. Popular E-Book on IE,

Introduction to Modern Industrial Engineering.  #FREE #Download.

In 0.1% on Academia.edu. 11600+ Downloads so far.

https://academia.edu/103626052/INTRODUCTION_TO_MODERN_INDUSTRIAL_ENGINEERING_Version_3_0

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

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


Supporting Information.

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

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

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

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

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


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










Productivity management is one of the important functions of industrial engineering departments in companies. Productivity measurement facilitates planning and controlling productivity levels in the companies.

Productivity can be calculated for each individual input into an operation, process or product. Such productivity measurements for individual inputs are called partial productivity measures. At this measurement level, potential productivity for recent developments in technology can be defined or determined.

Productivity can be defined as output/input. Both output and input can be in numbers or quantity. When multiple outputs come from an input and if one wants to calculated combined productivity, the output may be converted into monetary terms or equivalent product units.

Productivity is calculated for combinations of inputs. Such a calculation becomes necessary, when increase in one input leads to decrease in another input. So an optimal or maximum profit combination is to be found, specified and monitored.

When all inputs into a process are considered in the denominator, we come to total factor productivity. 

Productivity - Definitions


Productivity was mentioned probably for the first time in an article by Quesnay in 1766.
In 1883, Littre defined productivity as the "faculty to produce," that is, the desire to produce.

In 1950, the Organisation  for European Economic Cooperation (OEEC) provided the following  definition of productivity:

"Productivity is the quotient obtained by dividing output by one of the factors of production. In this way it is possible to speak of the productivity of the capital, investment, or raw materials according to whether output is being considered in relation to capital, investment, or raw materials, etc."


Productivity may be defined as follows:
Productivity = Output/Input  (Kanawaty, ILO Work Study)

The term productivity can be used to assess or measure the extent to which a certain output can be extracted from a given input. The inputs of an enterprise are land and buildings, materials, plant machines and equipment, energy and human resources. The outputs are saleable products and services.

Productivity Measures at Micro-level

Life of a cutting tool is a productivity measure. If the life can be extended through design of cutting process parameters and if this change gives benefit at the overall process level to reduce total time including the time spent for tool sharpening and tool change, it is productivity improvement that is driven by the tool life. For each part, one can calculated output per machine per hour. This can be used as a productivity measure to compare alternative processes or process plans.

Measurement of productivity for single input and single output is relatively used. But to calculate productivity for single machine used for multiple outputs is relatively more difficult. We have to use some common output measure to calculate weekly productivity to compare at week level. When multiple input and multiple parts/products are there, the calculation is much more complex and we have different methods proposed by different persons or organizations. 

Productivity Measures proposed and used by economists and accountants

I.  Kendrick – Creamer Model :[1]

Kendrick and Creamer (1965) introduced productivity indexes at the company level in their book, "Measuring Company Productivity". They proposed two types of  indices: total productivity and partial productivity.

Partial productivity  of  labour, capital or material productivity index can be calculated as: 

 Partial productivity index for a period = (Output in base period price) / (Any one Input in base period price)
Total productivity index for given period = (Measured period output in base period price) / (Measured period input in base period price)


Total factor productivity index = net output/total factor input
Net output   = Gross output – intermediate goods and services
Total factor input = manhour input and total capital

II. Craig –Harris Model :

Craig and Harris (1972, 1973) [2,3] defined total productivity measure: 
           

  PT=   OT / ( L+C+R+Q )
Where;
PT  = total productivity,
OT= total out put.
L = labor input factor,
C = capital input factor,
R = raw material input factor and
Q = other miscellaneous goods and services input factor
The output is defined as the summation of all units produced times their selling price, plus dividends from securities and interest from bonds and other such sources-all adjusted to base-period values.

III. Hines (1976)[4] proposed some measurement improvements to various individual items in productivity measurement models.

IV. American Productivity Centre Model :[5]

American Productivity Center has measure that expresses profitability as a product of   productivity and price factor. The way it is done is:  
Profitability   = Sales / Cost
                            = (Output quantity) *(Price) / (input quantity)* (unit cost)
             
                            = (Productivity)* (Price recovery factor)

                            =  [(Output quantity)/(input quantity)]*[(Price)/(unit cost)]

Where; productivity = output quantity / input quantity

V. Sumanth’s Total Productivity Model (1979) [6]

Total productivity (TPM) = total tangible output/total tangible input
Where
Total tangible output = (value of finished units produced + value of partial units produced
                             + dividends from securities + interest from bonds+ other income)
 and
Total tangible input = value of (human + material + capital + energy + other expenses) inputs used.

Sumanth provided a structure for finding productivity at product level and summing product level productivities to total firm level productivity. The model also has the structure for finding partial productivities at the product level and aggregating them to product level productivities.

Is productivity measurement in practice today?

The answer is yes.

In FY 2006 the study group on the Creation of a Productivity Database on Japanese, Chinese, and South Korean Companies at the Japan Center for Economic Research (JCER) created the East Asian Listed Companies Database 2007 ("EALC 2007") along with the Hitotsubashi University Center for Economic Institutions (CEI), the CENU Center for China and Asian Studies (CCAS; Professor Tomohiko Inui as project representative), and the Center for Corporate Competitiveness of Seoul National University (Professor Keun Lee as project representative). EALC 2007 in principle targets all listed firms in Japan, China, and South Korea (not including the financial sector). It includes data necessary to measure total factor productivity at the company level and the periods covered are 1985 through 2004 for Japanese firms, 1985 through 2005 for South Korean firms, and 1999 through 2004 for Chinese firms.

Based on direct comparison of the total factor productivity of listed firms in Japan, China, and South Korea, the researchers analyzed the following questions. 1) In which industries in particular are South Korean and Chinese companies catching up to Japanese ones? 2) Has productivity growth in Japanese firms stagnated since the 1990s? 3) If it has stagnated, in which industries is this most remarkable? 4) What are the characteristics of disparities in productivity among companies in the same industries in each country? The reports (in Japanese) cover the results of the research on these questions[6].

The Japan Center for Economic Research, the Hitotsubashi University Center for Economic Institutions, the CENU Center for China and Asian Studies, and the Center for Corporate Competitiveness of Seoul National University plan to continue joint research in FY 2007, including revising and updating the EALC database, expanding the countries targeted, and analyzing results.


 References

1. Kendrick, J.W., and D. Creamer, “Measuring Company Productivity: Handbook with Case Studies.” Studies in Business Economics, No. 89, National Industrial Conference Board, New York, 1965.
2. Craig, C.E., and C.R. Harris, “Productivity Concepts and Measurement- A Management Viewpoint,”Unpublished Master’s thesis, M.I.T., Cambridge, Massachusetts, 1972.
3. Craig, C.E., and C.R. Harris,“Total Productivity measurement at the firm level,” Sloan Management Review, Vol 14, No. 3, 1973, pp. 13-29.
4.  Ruch, W.A., “Your Key to Planning Profits”, The Productivity Brief 6, Oct.1981, by American Productivity Cente,r Houston. TX-77024.
5. Sumanth, David J., Productivity Engineering and Management, McGraw Hill Book Company, 1984.
______________________________________________

Productivity Measurement - Additional Bibliography




Productivity Measurement: Racing to Keep Up
Annual Review of Economics

 Vol. 11:591-614 (Volume publication date August 2019)
First published as a Review in Advance on May 17, 2019
https://doi.org/10.1146/annurev-economics-080218-030439

 Daniel E. Sichel

Department of Economics, Wellesley College, Wellesley, Massachusetts 02481, USA,
National Bureau of Economic Research, Cambridge, Massachusetts 02138, USA
https://www.annualreviews.org/doi/abs/10.1146/annurev-economics-080218-030439?journalCode=economics

Productivity measurement in an age of multinational companies and new technologies
Finn Schuele and David Wessel,  November 19, 2018

Productivity Measurement of Manufacturing System
 Govind SinghRawat, Ashutosh Gupta, Chandan Juneja
Materials Today: Proceedings

Volume 5, Issue 1, Part 1, 2018, Pages 1483-1489
https://www.sciencedirect.com/science/article/pii/S2214785317325051

Productivity Measurement with Natural Capital and Bad Outputs
https://www.oecd-ilibrary.org/economics/productivity-measurement-with-natural-capital-and-bad-outputs_5jz0wh5t0ztd-en

OECD: Measuring Productivity – OECD Manual
11 Sep 2017
https://www.un.org/development/desa/capacity-development/tools/tool/oecd-measuring-productivity-oecd-manual/

This Manual presents the theoretical foundations to productivity measurement, and discusses implementation and measurement issues. The text is accompanied by empirical examples from OECD countries and by numerical examples to enhance its readability. The Manual also offers a brief discussion of the interpretation and use of productivity measures.

 What is productivity, and how do you measure it?
https://www.weforum.org/agenda/2016/07/what-is-productivity-and-how-do-you-measure-it/

International Applications of Productivity and Efficiency Analysis: A Special Issue of the Journal of Productivity Analysis
Thomas R. Gulledge, C.A. Knox Lovell
Springer Science & Business Media, Nov 11, 2013 - 200 pages

International Applications of Productivity and Efficiency Analysis features a complete range of techniques utilized in frontier analysis, including extensions of existing techniques and the development of new techniques. Another feature is that most of the contributions use panel data in a variety of approaches. Finally, the range of empirical applications is at least as great as the range of techniques, and many of the applications are of considerable policy relevance. 
https://books.google.co.in/books?id=VaW-BwAAQBAJ

Managerial Issues in Productivity Analysis
Ali Dogramaci, Nabil R. Adam
Springer Science & Business Media, Dec 6, 2012 - 246 pages

A. Dogramaci and N.R. Adam Productivity of a firm is influenced both by economic forces which act at the macro level and impose themselves on the individual firm as well as internal factors that result from decisions and processes which take place within the boundaries of the firm. Efforts towards increasing the productivity level of firms need to be based on a sound understanding of how the above processes take place. Our objective in this volume is to present some of the recent research work in this field. The volume consists of three parts. In part I, two macro issues are addressed (taxation and inflation) and their relation to productivity is analyzed. The second part of the volume focuses on methods for productivity analysis within the firm. Finally, the third part of the book deals with two additional productivity analysis techniques and their applications to public utilities. The objective of the volume is not to present a unified point of view, but rather to cover a sample of different methodologies and perspectives through original, scholarly papers.
https://books.google.co.in/books?id=f_nxCAAAQBAJ
(Chapter 8 is an interesting paper with propositions on technical efficiency of technology in electric utility companies)


Construction Management and Economics
Volume 24, Issue 10, 2006
Construction equipment productivity estimation using artificial neural network model
Seung C. Oka & Sunil K. Sinhaa*

pages 1029-1044
http://www.tandfonline.com/doi/abs/10.1080/01446190600851033?journalCode=rcme20


An Introduction to Efficiency and Productivity Analysis
Tim Coelli
Springer Science & Business Media, Jul 22, 2005 - 349 pages

The second edition of this book has been written for the same audience as the first edition. It is designed to be a "first port of call" for people wishing to study efficiency and productivity analysis. The book provides an accessible introduction to the four principal methods involved: econometric estimation of average response models; index numbers; data envelopment analysis (DEA); and stochastic firontier analysis (SFA). For each method, we provide a detailed introduction to the basic concepts, give some simple numerical examples, discuss some of the more important extensions to the basic methods, and provide references for further reading. In addition, we provide a number of detailed empirical applications using real-world data. The book can be used as a textbook or as a reference text. As a textbook, it probably contains too much material to cover in a single semester, so most instructors will want to design a course around a subset of chapters. For example, Chapter 2 is devoted to a review of production economics and could probably be skipped in a course for graduate economics majors. However, it should prove useful to undergraduate students and those doing a major in another field, such as business management or health studies.
 https://books.google.co.in/books?id=NMYB0Mh8ljcC


Updated 25 Jan 2022,  1 Oct 2020.  13 April 2020, 1 November 2019,  20 Nov 2016,   12 June 2016,  19 June 2015
First published on 10 Feb 2012



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Thursday, January 9, 2025

Waste Measurement and Reporting Using MES - Manufacturing Execution Sytem

 

Lean Manufacturing and MES — Minimize Waste and Improve Productivity

Warren Andrade

January 13, 2021

https://www.pinpointinfo.com/blog/lean-and-mes-minimize-waste-improve-productivity


Defining the Right Manufacturing Metrics is the First Step to Proving the Benefits of MES

FEBRUARY 09, 2021

https://www.ibaset.com/defining-the-right-mes-metric-is-the-first-step-to-proving-the-benefits/

Expand Lean Manufacturing with MES

White Paper

https://discover.3ds.com/expand-lean-manufacturing-with-mes


A Novel Methodology to Integrate Manufacturing Execution Systems with the Lean Manufacturing Approach

Gianluca D’Antonio, Joel Sauza Bedolla, Paolo Chiabert

Procedia Manufacturing

Volume 11, 2017, Pages 2243-2251

https://doi.org/10.1016/j.promfg.2017.07.372

https://www.sciencedirect.com/science/article/pii/S2351978917305802


A recent research  showed that companies need to increase the degree of use of IT tools in order to implement lean practices. The importance of MES in this field has also been shown. Cottyn [10] developed a first framework for the alignment of MES to lean objectives. He defined an automatic Value Stream Mapping (aVSM) methodology: the aVSM benefits from the information provided by the MES, since it is a rich source of information and historical data useful to define continuous improvement actions. The methodology is validated through the case studies of a furniture firm and a food and beverage company. In, the support of MES to lean manufacturing has been discussed through the case study of a supplier of components for buses and coaches. Nevertheless, a methodology for fully integrating the MES capability in data analysis and dispatching with lean practices is still lacking.


The methodology for data analysis 

Data source. First, the data necessary to perform the analysis and their sources must be defined. On the shopfloor several kinds of devices can be deployed to collect data. First, the PLC of the machine involved in the process can provide helpful data concerning, for example, axes position and errors, axes and spindle movement, the deployed tool and the content of the stock, the applied power and torque, and some key performance indicator (e.g. cycle times, throughput, the incidence of failures). Furthermore, different kind of sensors can be integrated into the machine to collect data related to the quality process and the state of the tool. In machining processes, the most deployed sensors are dynamometers, accelerometers, thermometers, acoustic emission and current sensors. 

Sensors can be used both online - while the process is occurring - or offline, for example to evaluate the quality of a finished part (e.g. geometrical dimensions, mechanical strength, electrical properties); of course, sensors collecting different kind of data can be used and their information can be integrated to have a more exhaustive picture. 

Data processing and Feature generation. The second step consists in choosing the mathematical technique to analyze the collected data. The aim of data processing is to transform data, regardless of the source, into information through the generation of a finite set of features. 

Mainly, two classes of data processing techniques can be used. The first one consists in mathematical models, based on deterministic or statistic approaches. This technique is convenient when the analyzed system is not too complex and its behavior is fully known. In particular, the statistical approach is effective in dealing with a huge amount of data and is widely used, for example, with data acquired by a sensor set. 

The second class of data processing techniques consists of simulation tools: they are preferable when the analytical description of the system is too complex. Data provided in input to the simulation can provide from several sources: theoretical (or expected) data can be used to evaluate the behavior of the system in standard situations; real data, collected at the shop-floor are helpful to be aware of the reaction of the system in the current situation. 

Feature extraction and Decision making. The role of the data processing technique is to synthesize the collected data into a smaller set of information features; nevertheless, some of them may be not significant or reliable to take decisions and, thus, should be discarded. Furthermore, new significant features can be extracted by combining some parameters: overall indices can be obtained by averaging features, by generating response surfaces or by comparing the expected state with the real condition of a process or a product. 

Finally, a strategy for decision making must be defined, based on the results of the feature extraction. The decision can be automatically taken by an algorithm able to choose the values of a set of parameters in order to optimize a given metric. Alternatively, the algorithm may provide hints to an operator and leave him free to act on the process. Furthermore, the decision making algorithm should also provide an estimation of the state of the process after such intervention, to evaluate the impact on the performance of the process.

Case study 

Step 1. Process and wastes. A manufacturing process in the field of aeronautics is presented.

The process of gear grinding is considered. This is a critical process, because these workpieces must be manufactured with great accuracy.  Since grinding is a costly operation , it should be utilized under optimal conditions. The established manual operation consists of two steps. First, a pre-processing task is made to identify the workpiece axis that minimizes the geometrical distortions. This action is performed by finishing the two countersinks of the gear, which are used to place the part into the grinding machine. Then, gear grinding is performed. The operators highlighted an excessive rate of defective parts; this led to expensive reworking operations and to process variability resulting, in turn, in excessive waiting times and inventory parts accumulating through the process. The latter two waste sources were confirmed by the Value Stream Mapping analysis. Therefore, a novel system to perform gear centering prior to the grinding operation has been studied. 

Step 2. Process description. After having identified the wastes affecting the process, a thorough description of the grinding process has been made. The input components are the gears leaving the upstream heat treatment process; gears belong to a finite set of well-known part families, and are grinded one-by-one. The quality of the output parts is measured through functional tolerances: residual concentricity for the bearing seats and the gear, and total axial runout of the side surface; the range of such tolerances – defined in the ISO 1101 standard  – is in the rder of 0.05-0.1 mm. The performance of he process is measured through well-established indicators: cycle-time, work in process, throughput and rate of failures. In order to perform the process, a skilled operator is necessary to perform the correct positioning of the workpiece in the machine. 

In order to improve the performance of the grinding process and the quality of the machined gears, a novel system to support piece positioning has been developed, supported by a proper mathematical technique. Mainly, the gear is placed into a manufacturing machine to finish two surfaces – at the top and bottom extremities of the piece – with the aim of defining a new reference system for the part that minimizes the residual geometrical error. Such surfaces are used in the subsequent grinding operation to easily place the gear into the machine. 

Step 3. Data-analysis. First, the sources for data acquisition have been selected. Given the strict quality needs, displacement transducers are used to measure the profile of the gear where the tolerances are set, 

2c. To perform the measurement, a rotation of the gear about the axis of the machine is made. Since the tolerances are tight, sensors with high reproducibility (30 μm) have been used and a high acquisition rate is set (3600 points/revolution). After the acquisition, data are processed: to minimize the impact of measurement noise and errors, a least-squares interpolation is made for each of the gathered profiles. In particular, the three radial sections (i.e. the gear and the bearing seats) are interpolated through least-squares ellipses, and the coordinates of their centers are extracted. 

Given the cost of the manufactured parts, the manufacturer is interested in exploiting as much as possible the functional tolerances, in order to minimize the quantity of rejected parts. Hence, an objective function has been defined: it collects the current positioning errors, eventually weighted according to the tolerances values. This function is based on two independent variables, corresponding to the two part rotations that can be made to correct the position of the gear into the machine. Finally, the objective function is minimized to reduce as much as possible the residual positioning error; the calculated values for the two feasible rotations are provided to the machine to correct the position of the gear within the machining area. Then, the two reference surfaces are finished. 

The role of MES. The integration of this monitoring and control system with a MES enables to analyze and use the collected data at different time-scales with different purposes. On the short-medium term, MES allows to check whether the process is stable or not. Further, when instability symptoms appear, MES can predict when the process is going to be out of control and produce parts not matching the expected quality. Thus, setup or maintenance interventions can be planned in a preventive approach, also taking into account further constraints, such as the availability of operators or already planned downtime. This kind of prediction is helpful to avoid producing parts that will be rejected, thus reducing waste. On the long term, MES information can be further analyzed to extract historical trends, to synthesize criticalities and identify the sources of issues and wastes. The integration of a traceability system strongly supports this functionality: in this case study, each workpiece is identified by a unique ID. Information concerning each gear, such as the time at which the centering operation occurs and the expected results of the alignment, can be collected and stored into a database. This information can be useful to monitor the results of the centering process over time, and identify the reasons for possible decays or drifts; however, a careful analysis of these data is necessary, since issues identified on the centering machine can be due to inefficiencies in the upstream workstations. The results of this analysis can be shared with different departments of the company. For example, the business unit can benefit from this information to define new strategies, or to correct the previously defined ones; the design department can use this experience-driven knowledge to improve the design of a product or process. The feedback information provided by the MES supports the test and validation of new process or product releases. This, in turn, enables the implementation of kaizen practices for continuous improvement, such as the PDCA cycle.





Manufacturing execution systems driven process analytics: A case study from individual manufacturing

Lea Mayer, Nijat Mehdiyev, Peter Fettke

Procedia CIRP

Volume 97, 2021, Pages 284-289

https://doi.org/10.1016/j.procir.2020.05.239

https://www.sciencedirect.com/science/article/pii/S2212827120314608



Online Loss Capturing Using  MES


Various losses occurring in a manufacturing plant have direct bearing on reduced productivity and increased costs. Though some losses like asset failure can be measured using traditional methods, advance systems are required for root cause analysis. Then there are many miniscule losses which are very difficult to measure. Their frequency of occurrence can be high and hence their cumulative effect significant. These are referred to as ‘minor stoppages’. Yet other types of losses occur due to lack of coordination between various departments. This case study demonstrates how PlantConnect SFactory, a Smart Factory solution from Ascent Intellimation tracks and analyzes all types of losses and helps in eliminating some losses and reducing others. The installation is done in. Overall business requirements of customer are:


• Loss analysis

• OEE improvement

• Just-In-Time Maintenance


THE SOLUTION PlantConnect SFactory with integrated Loss Analysis Module was deployed. Losses are categorized as:


• availability losses

• performance losses

• quality losses





Ud. 9.1.2025, 31.1.2022

  Pub 29.12.2021





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Saturday, December 21, 2024

Machine Cost and Work Measurement - Time and Cost Estimates for Metal Forming Processes


Cost estimation is important activity for industrial engineers. For making cash flow estimates for the cost reduction projects or productivity improvement projects, industrial engineers have to prepare cost estimates.


Cost Estimates to Guide Pre-selection of Processes
PDF by AMK Esawi · 2003


Estimation of Forging Cost and Time



Material Estimation for the Forging

Expected Losses in Forging


The losses expected in forging are:

(i) Scale loss.
(ii) Flash loss.
(iii) Tonghold loss.
(iv) Sprue loss.
(v) Shear loss.


(i) Scale loss

When the material used in forging, iron is heated at a high temperature in atmospheric conditions a thin film of iron oxide is formed all round the surface of the heated metal.  The iron oxide film falls from the surface of the metal on being beaten up by the hammer. This is termed scale loss and it depends upon the surface area, heating time and the type of material. For forgings under 5 kg, the loss is 7.5 per cent of the net weight, and for forgings from 5 to 12.5 kg and over an addition of 6 per cent and 5 per cent of the net weight is expected as the scale loss.

(ii) Flash loss

This is a loss related to die forging or machine forging.

There is a certain quantity of metal which comes between the flat surfaces of the two dies after the die cavity has been filled in. This material equal to the area of the flat surface is a wastage. For finding the flash loss, the circumference is determined which multiplied by cross-sectional area of flash will give the volume of the flash. The volume multiplied by material density gives the flash loss. Generally, it is taken as 3 mm thick and 2 mm wide all round the circumference.


(iii) Tonghold loss

This is the loss of material due to a projection at one end of the forging to be used for holding it
with a pair of tongs and turning it round and round to give the required cross section in drop forging.
About 1.25 cm and 2.5 cm of the size of the bar is used for tonghold. The tonghold loss is equal to
the volume of the protections. For example, the tonghold volume loss for a bar of 2 cm diameter and tonghold length 2 cm will be  (Ï€/4)*2(cube) =   1.25 cm(cube)


(iv) Sprue loss

The connection between the forging and tonghold is called the sprue or runner. The material loss
due to this portion of the metal used as a contact is called sprue loss. The sprue must be heavy
enough to permit lifting the workpiece out of the impression die without bending. The sprue loss is
generally 7.5 per cent of the net weight.



(v) Shear loss

In forging, the long bars or billets are cut into required length by means of a sawing machine.
The material consumed in the form of saw-dust or pieces of smaller dimensions left as defective
pieces is called shear loss. This is usually taken as 5% of the net weight.


Thus nearly 15 to 20% of the net weight of metal is lost during forging. The expected loss of material has to  be added to the net weight to get the gross weight of the material.


Forging Cost


The cost of a forged component consists of following elements:
(i) Cost of direct materials.
(ii) Cost of direct labour.
(iii) Direct expenses such as due to cost of die and cost of press.
(iv) Overheads.


(I) Direct material cost

Cost of direct materials used in the manufacture of a forged component are calculated by first determing the net weight based on component drawing and then adding expected losses.


(i) The net weight of forging

Net weight of the forged component is calculated from the drawings by first calculating the
volume and then multiplying it by the density of the metal used.
Net weight = Volume of forging × Density of metal.


(ii) Gross weight
Gross weight is the weight of forging stone required to make the forged component. Gross
weight is calculated by adding expected losses.

Gross weight = Net weight + Material loss in the process.

In case of smith or hand forging, only scale loss and shear loss are to be added to net weight but
in case of die forging other machine related losses are also to be taken into account. 

(iii) Diameter and length of stock
The greatest section of forging gives the diameter of stock to be used and
Length of stock = (Gross weight)/[ Sectional area of stock× Density of material]

(iv) The cost of direct metal is calculated by multiplying the gross weight by price of
the raw material
Direct material cost = Gross weight × Price/kg.


(II) Direct labour cost

Direct labour cost = t × l
Where t = Time for forging per piece (in hrs)
l = Labour rate per hour

No general formula is given in books for forging. It has to be estimated internally using time study data of the past.


(III) Direct expenses
Direct expenses include the expenditure incurred on dies and other equipment, cost of using
machines and any other items, which can be directly identified with a particular product.

The method of apportioning die cost and machine cost:


Apportioning of die cost Let cost of die = Rs. x
No. of components than can be produced using this die be  y components
Cost of die/component = Rs. x/y

Apportioning of machine (press) cost

Let cost of press = Rs. A
 Life of press be n years

 Life of press in hours = B =  n × 12 × 4 × 5 × 8 = 1920 n hours
(Assuming  12 months in a year, 4 weeks in a month, 5 days a week, 8 hours of working per day, 
Hourly machine price cost of production = A/B
No. of components produced per hour = N
Cost of using press per component = A/ (BN) Rs.

This excludes cost of power consumed and other consumables.


(IV) Overheads expenses

The overheads include supervisory charges, depreciation of plant and machinery, consumables,
power and lighting charges, office expenses etc. The overheads can be  expressed as percentage
of direct labour cost or machine hours.

The total cost of forging is calculated by adding the direct material cost, direct labour cost, direct
expenses and overhead.

Three hundred pieces of the bolt are to be made from 25 mm diameter rod. The head has to be 40 mm dia.  The length of the head is 22mm and the length of the remaining bolt is 113 mm. Find the
length of material required for forging by upsetting. What length of the rod is required if 4% of the length goes as scrap?


Volume of head of the bolt = (Ï€/4)* D(square)* L

D = 40 mm
L = 22 mm
=  (Ï€/4)* 40(square)*22  =   27,646 mm(cube)

Length of material required for making the head
= Volume/area of the blank  being used
In the problem the dia. of the blank used is 25 mm

Area =  (Ï€/4)* 25(square)  =  490.6 mm

∴ Length of bar = 27,632/490.6 =  56.35 mm

Total length required for forming = 56.35 + 113 = 169.35 mm
Length of rod required for making 300 bolts = 169.35*300/1000     =   50.8 metre

Considering loss 4%,
Total length required = (50.8 + .4) × 50.8 = 71.12 metre



Productivity Science and Cost Drivers for Forging


https://www.forging.org/forging/design/331-materials-cost.html
https://www.forging.org/forging/design/332-tooling-costs.html

Manufacturing Cost


Manufacturing cost includes the cost of labor plus the cost of purchasing, maintaining and operating the required machinery and material handling equipment (Machine cost + Labor cost). A portion of these costs is charged to each forging produced. In most cases it also includes the cost of maintaining and replacing the forging tools. Machinery typically includes saws, shears, furnaces, preforming equipment, the forging press or hammer with its associated controls and trim presses. Material handling equipment typically includes cranes, lift trucks, conveyors, etc.

Manufacturing cost of a job is driven by the number of operations required to produce the forging.

Each forging cost center is assigned an hourly operating cost, which is divided by the number of pieces produced per hour to arrive at the cost charged to the forging. 

When forging microalloyed steels, which are used to eliminate heat treating, the cost of using special cooling conveyors will be included in the cost of forging. The total manufacturing cost is the sum of the costs of the individual operations used to produce the forging (We can interpret it as a process of producing the forged component and operations involved in the process - operation process chart).

Design simplifications that reduce the number of operations, or reduce the size or complexity of the required forging machines drive toward minimum processing cost. For example, an impression die forging may require several preforming operations, a blocker operation, a finish operation and a trimming operation. The total processing cost is the sum of the costs for each operation. If the design can be modified to reduce the number of operations, processing cost is  reduced. 

Processing cost can be reduced by designing the forging to facilitate metal flow in the die and reduce forging pressures. This usually involves modifying sharp details to provide larger radii. In some cases it may be possible to use a smaller forging press with a lower hourly operating cost. It is also possible to use machines that produce more parts per hour. Lower forging pressures also tend to reduce tool maintenance and replacement cost, which reduces cost per piece.


More in:


A review of automation in manufacturing illustrated by a case study on mixed-mode hot forging
Colin S. Harrison
Manufacturing Rev. 2014, 1, 15

The key advantages:

Increased Volume (capacity).

Improved Quality – via consistency of manufacturing and reduction in variability.

Reduced Costs.


Drop Forging Cost Analysis and Quotes
https://www.dropforging.net/cost-analysis.html

Forging Press Selection And Tonnage Calculation
Stamping / 10 minutes of reading


Automatic Optimization


Optimization applied to a forging process aims at reducing production costs and improving the quality of the manufactured part.   FORGE® and COLDFORM® and SIMHEAT® softwares help in numerical simulation. 

WHAT IS AUTOMATIC OPTIMIZATION IN PROCESS SIMULATION?

‘Optimization’ or ‘optimizing’ means running a series of simulations to identify the ideal process conditions giving the best final result.

Optimization follows a number of set parameters:

 Objective: billet weight, die wear or die stress, tonnage, difference with experimental plots, etc. The objective can be to minimize or to maximize.

 Process conditions: billet size or position, lubricant, temperature, die geometry, etc.

Constraints to respect (additional mandatory condition): complete filling of the die cavity, no folds or laps, prescribed scalar value, prescribed force or torque value, etc.).

HOW DOES IT WORK?

Automatic Optimization is based on MAES methods (Metamodel-Assisted Evolution Strategies) proposed by Emmerich et al. It has shown its efficiency and robustness in several complex metal forming applications.

Each simulation uses a set of process parameters (diameter and length) and is referred to as an ‘individual’. Each ‘generation’ includes several individuals. Good individuals  match the objective and respect the constraints. Poor individuals  do not respect the constraints. The next generation is automatically based on the best current individuals. The algorithm loops until the given number of generations has been reached. At each generation, a new population of individuals is created. A cost-function is used to rank each individual and designate the ‘best candidate’.

Defining a Design of Experiment (DOE), the user indicates to the system a selection of values (process conditions) to be tested. Combining Automatic Optimization based on Metal Model Design with the DOE is a good technique to find the solution. 

The Activity-based Costing Approach for Estimation of Cost of a Forged Part`s  in FMS with A(2)-Degree Automation: A Case Study in a Forging Industry
K. Rezaie and B. Ostadi
Information Technology Journal
Year: 2006 | Volume: 5 | Issue: 3 | Page No.: 546-550
DOI: 10.3923/itj.2006.546.550
https://scialert.net/abstract/?doi=itj.2006.546.550

COMPUTERIZED COST ESTIMATION FOR FORGING - PDF
In this study, an interactive cost estimation software named “Forge Cost. Estimator”, which performs the early cost estimation for forgings, has been developed.
https://etd.lib.metu.edu.tr/upload/4/1060193/index.pdf

Estimation of Forging Die Wear and Cost  - THESIS  PDF
Knight‟s Cost Model. Knight developed a cost model to estimate die costs for hammer forging
https://etd.ohiolink.edu/apexprod/rws_etd/send_file/send?accession=osu1277993083&disposition=inline


Open Access
Published: 29 August 2020
An analytical cost estimation model for the design of axisymmetric components with open-die forging technology
Federico Campi, Marco Mandolini, Claudio Favi, Emanuele Checcacci & Michele Germani 
The International Journal of Advanced Manufacturing Technology volume 110, pages1869–1892 (2020)

Abstract

Open-die forging is a manufacturing process commonly used for realising simple shaped components with high mechanical performances and limited capability in terms of production volume. To date, an analytical model for estimating the costs of components manufactured with this technology is still an open issue. The paper aims to define an analytical model for cost estimation of axisymmetric components manufactured by open-die forging technology. The model is grounded on the analysis of geometrical features available at the design stage providing a detailed cost breakdown in relation to all the process phases and the raw material. The model allows predicting product cost, linking geometrical features and cost items, to carry out design-to-cost actions oriented to the reduction of manufacturing cost.

Cost model and related schemas for collecting equations and data are presented, including the approach for sizing the raw material and a set of rules for modelling the related cost. Finally, analytic equations for modelling the cost of the whole forging process (i.e. billet cutting, heating, pre-smoothing, smoothing, upsetting, max-shoulder cogging, necking and shoulders cogging) are reported. The cost model has been tested on eight cylindrical parts such as discs and shafts with different shapes, dimensions and materials. Two forge masters have been involved in the testing phase. The absolute average deviation between the actual and estimated costs is approximately 4% for raw material and 21% for the process. The absolute average deviation on the total cost (raw material and manufacturing process) is approximately 5%.



Forging - Introduction Material

Action Item

Do cost estimates or standard cost calculation for all parts and assemblies. Remember the final goal of industrial engineering is cost reduction that benefits consumers, employees and shareholders/owners. 

Industrial Engineering - Bulletin Board - Industrial Engineering Knowledge Center.

Updated 20.12.2024,  12 Jan 2022, 21 May 2021
Pub 29 Nov 2019














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