What are Problem Structuring Methods?

What are Problem Structuring Methods?

Source: PROBLEM STRUCTURING IN PUBLIC POLICY ANALYSIS

Problem structuring methods provide a methodological complement to theories of policy design. Arguably, structuring a problem is a prerequisite of designing solutions for that problem.4 In this context, problem structuring methods are metamethods. They are “about” and “come before” processes of policy design and other forms of problem solving.

Source: Strategic Development: Methods and Models

Key Terms

  • PSM
  • Soft OR
  • Hard OR
  • Unstructured Problems
  • Systems
  • System Sciences
  • SODA Strategic Options Development and Analysis
  • SSM Soft Systems Methodology
  • SCA Strategic Choice Approach
  • Robustness Analysis
  • Drama Theory
  • Interactive Planning
  • Scenario Planning
  • Critical Systems Heuristics
  • SWOT
  • Strategic Assumption Surfacing and Testing
  • Viable Systems Model VSM
  • System Dynamics
  • Decision Conferencing
  • Multi-methodology
  • John Mingers
  • Jonathan Rosenhead
  • John Morecroft
  • MC Jackson
  • Operational Research
  • Problem Structuring Methods PSM
  • Stafford Beer
  • Robert Dyson
  • Jay Forrester
  • Russell Ackoff
  • Robert Flood
  • Peter Checkland
  • Group Model Building
  • Behaviour Operational Research
  • Community Operations Research
  • Ill-structured versus Well-structured Problems
  • Wicked Versus Tame Problems
  • Ill-Defined versus Well-Defined Problems
  • Nigel Howard
  • Metagames
  • Hypergames

Problem Structuring Methods

Source: Past, present and future of problem structuring methods

The problematic situations for which PSMs aim to provide analytic assistance are characterized by

  • Multiple actors,
  • Differing perspectives, 
  • Partially conflicting interests,  
  • Significant intangibles,
  • Perplexing uncertainties.

The relative salience of these factors will differ between situations (and different methods are selective in the emphasis given to them). However, in all cases there is a meta-characteristic, that of complexity, arising out of the need to comprehend a tangle of issues without being able to start from a presumed consensual formulation. For an introduction to PSMs, see Rosenhead and Mingers, 2001

Source: Problem structuring methods in action

Strategic options development and analysis (SODA) is a general problem identification method that uses cognitive mapping as a modelling device for eliciting and recording individuals’ views of a problem situation. The merged individual cognitive maps (or a joint map developed within a workshop session) provide the framework for group discussions, and a facilitator guides participants towards commitment to a portfolio of actions.

Soft systems methodology (SSM) is a general method for system redesign. Participants build ideal-type conceptual models (CMs), one for each relevant world view. They compare them with perceptions of the existing system in order to generate debate about what changes are culturally feasible and systemically desirable. 

Strategic choice approach (SCA) is a planning approach centered on managing uncertainty in strategic situations. Facilitators assist participants to model the interconnectedness of decision areas. Interactive comparison of alternative decision schemes helps them to bring key uncertainties to the surface. On this basis the group identifies priority areas for partial commitment, and designs explorations and contingency plans.

Robustness analysis is an approach that focuses on maintaining useful flexibility under uncertainty. In an interactive process, participants and analysts assess both the compatibility of alternative initial commitments with possible future configurations of the system being planned for, and the performance of each configuration in feasible future environments. This enables them to compare the flexibility maintained by alternative initial commitments. 

Drama theory draws on two earlier approaches, meta games and hyper games. It is an interactive method of analysing co-operation and conflict among multiple actors. A model is built from perceptions of the options available to the various actors, and how they are rated. Drama theory looks for the “dilemmas” presented to the actors within this model of the situation. Each dilemma is a change point, tending to cause an actor to feel specific emotions and to produce rational arguments by which the model itself is redefined. When and only when such successive redefinitions have eliminated all dilemmas is the actors’ joint problem fully resolved. Analysts commonly work with one of the parties, helping it to be more effective in the rational-emotional process of dramatic resolution. (Descriptions based substantially on Rosenhead, 1996.)

Given the ill-defined location of the PSM/non- PSM boundary, there are a number of other methods with some currency that have at least certain family resemblances. These include critical systems heuristics (CSH) (Ulrich, 2000), interactive planning (Ackoff, 1981), and strategic assumption surfacing and testing (Mason and Mitroff, 1981). Other related methods which feature in this special issue are SWOT (Weihrich, 1998), scenario planning (Schoemaker, 1998), and the socio-technical systems approach (Trist and Murray, 1993). Those which are particularly close to the spirit of PSMs in at least some of their modes of use, and therefore thought to merit inclusion in Rosenhead and Mingers (2001), are the following:

Viable systems model (VSM) is a generic model of a viable organization based on cybernetic principles. It specifies five notional systems that should exist within an organization in some form––operations, co-ordination, control, intelligence, and policy, together with the appropriate control and communicational relationships. Although it was developed with a prescriptive intent, it can also be used as part of a debate about problems of organizational design and redesign (Harnden, 1990). 

System dynamics(SD) is a way of modelling peoples’ perceptions of real-world systems based especially on causal relationships and feedback. It was developed as a traditional simulation tool but can be used, especially in combination with influence diagrams (causal–loop diagrams), as a way of facilitating group discussion (Lane, 2000; Vennix, 1996).

Decision conferencing is a variant of the more widely known “decision analysis”. Like the latter, it builds models to support choice between decision alternatives in cases where the consequences may be multidimensional; and where there may be uncertainty about future events which affect those consequences. What distinguishes decision conferencing is that it operates in workshop mode, with one or more facilitators eliciting from the group of participants both the structure of the model, and the probabilities and utilities to be included in it. The aim is cast, not as the identification of an objectively best solution, but as the achievement of shared understanding, the development of a sense of common purpose, and the generation of a commitment to action (Phillips, 1989; Watson and Buede, 1987).

There are a number of texts which present a different selection of “softer” methods than do Rosenhead and Mingers. These include Flood and Jackson (1991), who concentrate on systems-based methods, Dyson and O’Brien (1998) who consider a range of hard and soft approaches in the area of strategy formulation; and Sorensen and Vidal (1999) who make a wide range of methods accessible to a Scandinavian readership. There is clearly an extensive repertoire of methods available. In fact it is common to combine together a number of PSMs, or PSMs together with more traditional methods, in a single intervention––a practice known as multimethodology (Mingers and Gill, 1997). So the range of methodological choice is wider even than a simple listing of methods might suggest.

Source: Are project managers ready for the 21th challenges? A review of problem structuring methods for decision support

Benefits of Problem Structuring Methods

Source: Are project managers ready for the 21th challenges? A review of problem structuring methods for decision support

My Related Posts

Systems and Organizational Cybernetics

Micro Motives, Macro Behavior: Agent Based Modeling in Economics

Production and Distribution Planning : Strategic, Global, and Integrated

Drama Theory: Choices, Conflicts and Dilemmas

Drama Theory: Acting Strategically

Quantitative Models for Closed Loop Supply Chain and Reverse Logistics

Hierarchical Planning: Integration of Strategy, Planning, Scheduling, and Execution

Stock Flow Consistent Input Output Models (SFCIO)

Stock Flow Consistent Models for Ecological Economics

Gantt Chart Simulation for Stock Flow Consistent Production Schedules

Shell Oil’s Scenarios: Strategic Foresight and Scenario Planning for the Future

Water | Food | Energy | Nexus: Mega Trends and Scenarios for the Future

Global Trends, Scenarios, and Futures: For Foresight and Strategic Management

HP’s Megatrends

Global Flow of Funds: Statistical Data Matrix across National Boundaries

Credit Chains and Production Networks

Supply Chain Finance (SCF) / Financial Supply Chain Management (F-SCM)

Financial Social Accounting Matrix

Morris Copeland and Flow of Funds accounts

Systems Biology: Biological Networks, Network Motifs, Switches and Oscillators

Oscillations and Amplifications in Demand-Supply Network Chains

Portfolio Planning Models for Corporate Strategic Planning

Cyber-Semiotics: Why Information is not enough

Truth, Beauty, and Goodness: Integral Theory of Ken Wilber

Key Sources of Research

Understanding behaviour in problem structuring methods interventions with activity theory.

White, L., Burger, K., & Yearworth, M. (2016).

European Journal of Operational Research, 249(3), 983-1004. https://doi.org/10.1016/j.ejor.2015.07.044

https://research-information.bris.ac.uk/en/publications/understanding-behaviour-in-problem-structuring-methods-interventi

“Is Value Focused Thinking a Problem Structuring Method or Soft OR or what?”

Keisler, Jeffrey,

(2012). 

Management Science and Information Systems Faculty Publication Series. Paper 42.


http://scholarworks.umb.edu/msis_faculty_pubs/42

Rational Analysis for a Problematic World Revisited: Problem Structuring Methods for Complexity, Uncertainty and Conflict

John Mingers, Jonathan Rosenhead

2001 Book Second ed.

The characteristics of problem structuring methods: A literature review

https://www.research.manchester.ac.uk/portal/en/publications/the-characteristics-of-problem-structuring-methods-a-literature-review(e4bbf605-6df1-4a33-853c-2bc17dc18a8e).html

Problem structuring methods in action

John Mingers a,*, Jonathan Rosenhead b

a Warwick Business School, University of Warwick, Coventry CV4 7AL, UK 

b London School of Economics, Houghton Street, London WC2A 2AE, UK

European Journal of Operational Research 152 (2004) 530–554

Click to access Problem%20structuring%20methods%20in%20action.pdf

https://www.semanticscholar.org/paper/Problem-structuring-methods-in-action-Mingers-Rosenhead/752fdb5dfaddbc0a7946f281a9c454d6f4203542

Click to access Problem%20structuring%20methods%20in%20action.pdf

Introduction to the Special Issue: Teaching Soft O.R., Problem Structuring Methods, and Multimethodology.

John Mingers, Jonathan Rosenhead, (2011)

INFORMS Transactions on Education 12(1):1-3. http://dx.doi.org/10.1287/ited.1110.0073

Click to access Mingers-Rosenberg-PSM-SoftOR.pdf

https://pubsonline.informs.org/toc/ited/12/1

Problem Structuring Methods, 1950s-1989: An Atlas of the Journal Literature

Georgiou, Ion and Heck, Joaquim,

(June 26, 2017).

Available at SSRN: https://ssrn.com/abstract=3077648 or http://dx.doi.org/10.2139/ssrn.3077648

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3077648

“An Investigation on the Effectiveness of a Problem Structuring Method in a GroupDecision-Making Process”

Thaviphoke, Ying.

(2020). Doctor of Philosophy (PhD), Dissertation, Engineering Management, Old Dominion University,

DOI: 10.25777/cx7x-z403
https://digitalcommons.odu.edu/emse_etds/182

What’s the Problem? An Introduction to Problem Structuring Methods

Jonathan Rosenhead

Published Online:1 Dec 1996

https://doi.org/10.1287/inte.26.6.117

PROBLEM STRUCTURING IN PUBLIC POLICY ANALYSIS

William N. Dunn
Graduate School of Public and International Affairs University of Pittsburgh

Past, present and future of problem structuring methods

J Rosenhead

London School of Economics, London, UK

Journal of the Operational Research Society (2006), 1–7

Framing and Reframing as a Creative Problem Structuring Aid

Victoria J Mabin, and John Davies Management Group Victoria University of Wellington PO Box 600 Wellington
email: vicky.mabin@vuw.ac.nz

Tel +4-495 5140
email: john.davies@vuw.ac.nz Tel + 4-471 5382
Fax + 4-471 2200

Reassessing the scope of OR practice: the influences of problem structuring methods and the analytics movement

Ranyard, J.C., Fildes, R. and Hun, T-I (2014).

(LUMS Working Paper 2014:8).

Lancaster University: The Department of Management Science.

Reasoning maps for decision aid: an integrated approach for problem-structuring and multi-criteria evaluation


G Montibeller1∗, V Belton2, F Ackermann2 and L Ensslin3

1London School of Economics, London, UK; 2University of Strathclyde, Glasgow, UK; and 3Federal University of Santa Catarina (UFSC), Floriano ́polis, Brazil

Journal of the Operational Research Society (2008) 59, 575–589

Special issue on problem structuring research and practice

Fran Ackermann • L. Alberto Franco • Etie ̈nne Rouwette • Leroy White

EURO J Decis Process (2014) 2:165–172 DOI 10.1007/s40070-014-0037-6

Soft OR Comes of Age – But Not Everywhere!

Mingers, John (2011)

ISSN 0305-0483. https://doi.org/10.1016/j.omega.2011.01.005

Omega, 39 (6). pp. 729-741

An Investigation on the Effectiveness of a Problem Structuring Method in a Group Decision-Making Process

Ying Thaviphoke
Old Dominion University, ythav001@odu.edu

2020

OR competences: the demands of problem structuring methods

Richard John Ormerod

EURO J Decis Process (2014) 2:313–340

DOI 10.1007/s40070-013-0021-6

Hard OR, Soft OR, Problem Structuring Methods, Critical Systems Thinking: A Primer

Hans G. Daellenbach

Department of Management University of Canterbury Christchurch, NZ

h.daellenbach@mang.canterbury.ac.nz

Are project managers ready for the 21th challenges? A review of problem structuring methods for decision support

José Ramón San Cristóbal Mateo

Emma Diaz Ruiz de Navamuel

María Antonia González Villa

https://repositorio.unican.es/xmlui/bitstream/handle/10902/13669/ijispm-050203.pdf?sequence=1

Towards a new framework for evaluating systemic problem structuring methods

Gerald Midgley  Robert Y. Cavana  John Brocklesby , Jeff L. Foote  David R.R. Wood , Annabel Ahuriri-Driscoll 

European Journal of Operational Research 229 (2013) 143–154

https://www.sciencedirect.com/science/article/pii/S0377221713000945

Problem structuring methods

Jonathan Rosenhead1

Chapter in book

(1) The London School of Economics and Political Science, London, England

Kluwer Academic Publishers 2001

https://doi.org/10.1007/1-4020-0611-X_806

Encyclopedia of Operations Research and Management Science

2001 Edition | Editors: Saul I. Gass, Carl M. Harris

Beyond Problem Structuring Methods: Reinventing the Future of OR/MS

Author(s): M. C. Jackson

Source: The Journal of the Operational Research Society, Vol. 57, No. 7, Special Issue: Problem Structuring Methods (Jul., 2006), pp. 868-878

Published by: Palgrave Macmillan Journals on behalf of the Operational Research Society

Stable URL: https://www.jstor.org/stable/4102274

Strategic Development: Methods and Models

Robert G. Dyson (Editor)Frances A. O’Brien (Editor)

ISBN: 978-0-471-97495-6 

May 1998 346 Pages

https://www.wiley.com/en-al/Strategic+Development:+Methods+and+Models-p-9780471974956

Group Model Building:
Problem Structuring, Policy Simulation and Decision Support

David F. Andersen, University at Albany
Jac A.M. Vennix, Radboud University Nijmegen George P. Richardson, University at Albany Etiënne A.J.A. Rouwette, Radboud University Nijmegen

Reassessing the Scope of OR Practice: the Influences of Problem Structuring Methods and the Analytics Movement

J. C. Ranyard, R. Fildes* and Tun-I Hu

The Department of Management Science Lancaster University Management School Lancaster LA1 4YX
UK

Profiles in Operations Research

Profiles in Operations Research

 

There are several sources for profiles of luminaries of OR field.

  • Book – Profiles in Operations Research
  • INFORMS – History website
  • UK OR society
  • IFORS

 

From Profiles in Operations Research

orprofiles

From IFORS Hall of Fame

IFORS

 

 

Key Sources of Research:

Profiles in Operations Research

Pioneers and Innovators

Editors: Assad, Arjang A., Gass, Saul I. (Eds.)

https://www.springer.com/us/book/9781441962805

 

Profiles in Operations Research:Jay Wright Forrester

David C. Lane

John D. Sterman

Click to access jwf-profile-in-op.pdf

 

 

Profiles in Operations Research

John D. C. Little Profile

By Glen Urban and John Hauser

Click to access little.pdf

https://dspace.mit.edu/openaccess-disseminate/1721.1/58104

 

 

George B. Dantzig

https://www.informs.org/Explore/History-of-O.R.-Excellence/Biographical-Profiles/Dantzig-George-B

John D. C. Little

https://www.informs.org/Explore/History-of-O.R.-Excellence/INFORMS-Award-Namesakes/Little-John-D.-C

 

 

Anthony Stafford Beer

https://www.informs.org/Explore/History-of-O.R.-Excellence/Biographical-Profiles/Beer-Anthony-Stafford

 

Russell L. Ackoff

https://www.informs.org/Explore/History-of-O.R.-Excellence/Biographical-Profiles/Ackoff-Russell-L

Abraham Charnes

https://www.informs.org/Explore/History-of-O.R.-Excellence/Biographical-Profiles/Charnes-Abraham

William W. Cooper

https://www.informs.org/Explore/History-of-O.R.-Excellence/Biographical-Profiles/Cooper-William-W

C. West Churchman

https://www.informs.org/Explore/History-of-O.R.-Excellence/Biographical-Profiles/Churchman-C.-West

Leonid V. Kantorovich

https://www.informs.org/Explore/History-of-O.R.-Excellence/Biographical-Profiles/Kantorovich-Leonid-V

Tjalling C. Koopmans

https://www.informs.org/Explore/History-of-O.R.-Excellence/Biographical-Profiles/Koopmans-Tjalling-C

Harry Markowitz

https://www.informs.org/Explore/History-of-O.R.-Excellence/Biographical-Profiles/Markowitz-Harry

Oskar Morgenstern

https://www.informs.org/Explore/History-of-O.R.-Excellence/Biographical-Profiles/Morgenstern-Oskar

Philip M. Morse

https://www.informs.org/Explore/History-of-O.R.-Excellence/Biographical-Profiles/Morse-Philip-M

 

John F. Nash, Jr.

https://www.informs.org/Explore/History-of-O.R.-Excellence/Biographical-Profiles/Nash-Jr.-John-F

 

Herbert A. Simon

https://www.informs.org/Explore/History-of-O.R.-Excellence/Biographical-Profiles/Simon-Herbert-A

John von Neumann

https://www.informs.org/Explore/History-of-O.R.-Excellence/Biographical-Profiles/von-Neumann-John

 

 

History of OR Excellence

INFORMS

https://www.informs.org/Explore/History-of-O.R.-Excellence

https://www.informs.org/Explore/History-of-O.R.-Excellence/Biographical-Profiles

https://www.informs.org/ORMS-Today/Public-Articles/August-Volume-44-Number-4/Forum-Your-go-to-site-for-O.R.-history

 

 

 

IFORS Hall of Fame

http://ifors.org/ifors-hall-of-fame/

Operations Research: Opportunities and Challenges

Click to access u_pitt_2014_OR.pdf

Towards the Circular Economy

Towards the Circular Economy

 

 

Circular Economy in reuse of

  • Metals
  • Plastics
  • Paper and Paper Board
  • Glass
  • Rubber
  • Wood/Timber/Construction Composites
  • Textiles
  • Organic Waste/Food/Agricultural/Biological

 

 

CIRCULAR ECONOMY: THE NEW NORMAL?

Key points

  • Keeping materials longer in the economy through reuse, re-purposing or recycling could reduce 33 per cent of the carbon dioxide emissions embedded in products.
  • Circularity requires a significant bridge between trade in goods and trade in services.
  • Increased recycling could reduce demand for primary resources, leading to both risks and opportunities in developing countries dependent on the extraction of natural resources.

 

CIRCULAR ECONOMY: THE NEW NORMAL?

Linear production is a familiar cycle. Resources are extracted and transformed into goods and services, sold and used, after which they are scrapped. This model has underpinned the expansion of the global economy since the industrial revolution.

It has linked material prosperity to the extraction of resources, yet has often overlooked the undue pressures placed on the environment and has rarely considered the cost of handling, scrapping and disposing of used materials, some of which are hazardous to human health. As the global population increases, incomes rise and nations strive to eradicate poverty, demand for goods and services will necessarily grow. The aim of achieving Sustainable Development Goal 12 on responsible consumption and production requires changing the linear production model. The concept of a circular economy and practice therefore merits close attention, as it can open new opportunities for trade and job creation, contribute to climate change mitigation and help reduce the costs of cleaning and scrapping in both developed and developing countries.

A circular economy entails markets that give incentives to reusing products, rather than scrapping them and then extracting new resources. In such an economy, all forms of waste, such as clothes, scrap metal and obsolete electronics, are returned to the economy or used more efficiently. This can provide a way to not only protect the environment, but use natural resources more wisely, develop new sectors, create jobs and develop new capabilities.

Each year, 1.3 billion tons of garbage are produced by 3 billion urban residents.1 This is the end point of a linear economic flow that starts with manufacturing, which uses 54 per cent of the world’s delivered energy, especially in energy-intensive industries such as petrochemicals, cement, metals and paper.2 Each year, 322 million tons of plastic, 240 million tons of paper and 59 million tons of aluminium are produced in the world, much of which goes to export markets and is not recycled.3

A rusty container or an obsolete mobile telephone are only two examples of the many products that end up being discarded, along with their transistors, metal structures and complex plastics. Each component requires a great deal of energy, time, land and capital to be produced and, even as the products become obsolete, their components often do not. The potential value of metals and plastics currently lost in electronic waste may be €55 billion annually.4

As the supply of recycled, reused and re-manufactured products increases, such products are maintained for longer in the economy, avoiding their loss to landfills. Food losses could be halved through food- sharing and discounting models that reduce fresh food waste. Access to efficient home appliances could be increased through leasing instead of sales. Organic waste could be recovered or transformed into high-value protein through the production of insect larvae.

Benefits such as these could be gained by both developed and developing countries; the potential economic gains are estimated at over $1 trillion per year in material cost savings.5 Several economies are already exploring circular strategies, including Brazil, China, India, Kenya, the Lao People’s Democratic Republic, Morocco, South Africa, Turkey, Uruguay, VietNam and the European Union.6 India and the European Union stand to gain savings of $624 billion and €320 billion, respectively.7

The effects of increased recycling on global value chains are an important area for research. For example, a circular model for metals implies an increase in the re-purposing, reuse and recycling of such materials. This can transform end points of the value chain, such as junkyards and dumping sites for metals, into new reprocessing hubs that supply metals to markets. This growth trend in recycling markets may be desirable from an environmental perspective, yet could reduce demand for primary resources, requiring an adjustment in employment, logistics and scal structures in countries dependent on the extraction of natural resources.8 At the same time, growth in the recycling, re-purposing and reuse of materials could support the emergence of regional reprocessing and recycling hubs and open new opportunities for the commodities and manufacturing sectors. Greater circularity could reduce the depreciation of physical capital in the economy, increasing overall wealth in societies. The specific benefits that developing countries could obtain by adopting formal circular economy strategies is a new subject for research, and further studies and data are needed.

 

Circularity can change trade patterns and improve the utilization of idle capacity

Circular models could help countries grow with resources already available in their territories. This may imply a reduction in international trade, yet the 140 million people joining the middle class each year guarantee growth in overall trade.9 Such growth may occur not in goods but in services such as access-over-ownership models.10 In addition, increased circularity can change production patterns, improving asset utilization rates and producing value chains based on recycling and re-manufacturing centres close to where products are used. This could lead to fewer transport-related losses, quicker turnarounds between orders and deliveries, lower levels of carbon dioxide emissions and the creation of jobs that cannot be offshored.

Some countries have trade surpluses in physical goods and others in immaterial services. Trade therefore results in a net transfer of materials from one region to another as seen, for example, in trade patterns between China and the United States. The United States imports many goods from China but does not export nearly as many finished goods in return. However, nearly 3,700 containers of recyclables per day are exported to China; in 2016, such exports amounted to 16.2 million tons of scrap metal, paper and plastics worth $5.2 billion.11

 

Key Terms:

  • Circular Economy
  • Cradle to Cradle
  • Closed Supply Chains
  • Industrial Ecology
  • Reverse Ecology
  • Blue Economy
  • Regenerative Design
  • Performance Economy
  • Natural Capitalism
  • Bio-mimicry
  • Doughnut Economics

 

 

From Input to the European Commission from European EPAs about monitoring progress of the transition towards a circular economy in the European Union

circular3

 

 

Circular Economy System Diagram

https://www.ellenmacarthurfoundation.org/circular-economy/interactive-diagram

System_diagram_cropped

From INTRODUCTION TO THE CIRCULAR ECONOMY Booklet

circular2

From Towards the Circular Economy: Accelerating the scale-up across global supply chains

circular8

 

Comprehensive Concept of Circular Economy

http://bio-based.eu/graphics/

circular1

From Input to the European Commission from European EPAs about monitoring progress of the transition towards a circular economy in the European Union

Circular4

From Taking the Circular Economy to the City Level

cicular5

Please see my related post:

Resource Flows: Material Flow Accounting (MFA), Life Cycle Analysis (LCA), Input Output Networks and other methods

Stock Flow Consistent Input Output Models (SFCIO)

Stock Flow Consistent Models for Ecological Economics

Jay W. Forrester and System Dynamics

 

Key Sources of Research:

Ellen MacArthur Foundation

https://www.ellenmacarthurfoundation.org

 

 

 

 

INTRODUCTION TO THE CIRCULAR ECONOMY

Circular economy booklet

 Ellen MacArthur Foundation

Click to access 5f046f0a12854e0301e8139fce7cddc7f065.pdf

 

 

 

 

 

TOWARDS THE CIRCULAR ECONOMY

Economic and business rationale for an accelerated transition

Ellen MacArthur Foundation

2013

Volume 1

Click to access Ellen-MacArthur-Foundation-Towards-the-Circular-Economy-vol.1.pdf

 

 

TOWARDS THE CIRCULAR ECONOMY

Opportunities for the consumer goods sector

 

Ellen MacArthur Foundation

2013

Volume 2

https://www.mckinsey.com/~/media/mckinsey/dotcom/client_service/sustainability/pdfs/towards_the_circular_economy.ashx

https://www.ellenmacarthurfoundation.org/publications/towards-the-circular-economy-vol-2-opportunities-for-the-consumer-goods-sector

 

 

 

TOWARDS THE CIRCULAR ECONOMY

Accelerating the scale-up across global supply chains

Volume 3

2014

Ellen MacArthur Foundation

Click to access Towards-the-circular-economy-volume-3.pdf

 

 

 

 

 

Towards the Circular Economy: Accelerating the scale-up across global supply chains

World Economic Forum

Prepared in collaboration with the Ellen MacArthur Foundation and McKinsey & Company

January 2014

 

rethinking value chains to boost resource productivity

Click to access 2._zils_v03.pdf

 

 

 

Circular Economy in Cities

Evolving the model for a sustainable urban future

WEF

 

Click to access White_paper_Circular_Economy_in_Cities_report_2018.pdf

Towards a circular economy: A zero waste programme for Europe

DG Environment

Minsk, 8 October 2014

 

Click to access EC-Circular-econonomy.pdf

 

 

 

 

Transitioning IKEA Towards a Circular Economy: A Backcasting Approach

Claudia Szerakowski

Master’s Thesis in Industrial Ecology

 

Click to access 252505.pdf

 

 

 

 

Circular Economy Industry Roundtable:

Towards a Circular Singapore

1st June, 2017

Click to access 170925-ead-summary_(1)_(1).pdf

 

 

 

Sustainable Supply Chain Management and the transition towards a Circular Economy: Evidence and some Applications.

Genovese, Andrea and Acquaye, Adolf and Figueroa, Alejandro and Koh, S.C. Lenny

(2015)

Omega,

https://kar.kent.ac.uk/49202/

 

 

 

Are you ready for the circular economy? The necessity of an integrated approach.

EY

Click to access EY-brochure-cas-are-you-ready-for-the-circular-economy.pdf

 

 

 

Barriers & Drivers towards a Circular Economy

Literature Review A-140315-R-Final

March 2015

 

Click to access e00e8643951aef8adde612123e824493.pdf

 

The Circular Economy Powered by Cradle to Cradle®

 

Click to access The-Circular-Economy-powered-by-Cradle-to-Cradle.pdf

 

 

 

 Towards a Circular Economy

Venkatachalam Anbumozhi Jootae Kim

Click to access ERIA_RPR_FY2014_44.pdf

 

 

 

Circular Economy

European Commission

Click to access Presentation-circular-economy-EU-kommissionen.pdf

 

 

 

CIRCULAR ECONOMY IN CHINA

OPPORTUNITIES FOR COMPANIES

Business Sweden

 

Click to access circular-economy-in-china.-report-v.1.0_final.pdf

 

 

 

SUPPORTING THE CIRCULAR ECONOMY TRANSITION

THE ROLE OF THE FINANCIAL SECTOR IN THE NETHERLANDS

Oliver Wyman

 

Click to access CircularEconomy_print.pdf

 

 

 

Transition towards a circular economy: The case of the Metropole region Amsterdam

Jacqueline Cramer

Ambassador Circular Economy

 

Click to access jacqueline-cramer-lecture-2016.pdf

 

 

 

 

The Circular Economy – a new sustainability paradigm?

Geissdoerfer, Martin1,2†; Savaget, Paulo1; Bocken, Nancy M.P.1,2; Hultink, Erik Jan2

 

https://www.repository.cam.ac.uk/bitstream/handle/1810/261957/The%20Circular%20Economy%20-%20a%20new%20sustainability%20paradigm_accepted%20version.pdf?sequence=1&isAllowed=y

 

 

 

A REVIEW OF THE CIRCULAR ECONOMY AND ITS IMPLEMENTATION

 

Click to access CircularEconomy_webb.pdf

 

SS8: Circular economy and decoupling

https://www.wrforum.org/ss8-circular-economy-and-decoupling/

 

 

 

Input to the European Commission from European EPAs about monitoring progress of the transition towards a circular economy in the European Union

 

May 2017

Click to access PBL-2017-EPA-network-discussion-paper-monitoring-progress-of-the-circular-economy-in-the-EU_2772.pdf

 

 

 

The European Economy: From a Linear to a Circular Economy

Florin Bonciu

 

Click to access RJEA_2014_vol14_no4_art5.pdf

 

 

 

 

THE CIRCULAR ECONOMY AND DEVELOPING COUNTRIES

A DATA ANALYSIS OF THE IMPACT OF A CIRCULAR ECONOMY ON RESOURCE-DEPENDENT DEVELOPING NATIONS

 

Click to access CEO_The%20Circular%20Economy.pdf

 

 

 

The opportunities of a circular economy for Finland

October, 2015

 

Click to access Selvityksia100.pdf

 

 

 

Circular economy

A review of definitions, processes and impacts

 

Click to access 2809-circular-impacts_0.pdf

 

 

 

CIRCULAR ECONOMY IN INDIA: RETHINKING GROWTH FOR LONG-TERM PROSPERITY

Click to access circular-economy-in-india-2-dec-2016.pdf

 

 

 

 

GROWTH WITHIN: A CIRCULAR ECONOMY VISION FOR A COMPETITIVE EUROPE

 

Click to access Circular%20economy%203.pdf

 

 

 

 

Report on State-of-the-Art Research in the Area of the Circular Economy

 

Sylvie Geisendorf

Felicitas Pietrulla ESCP Europe Campus Berlin

 

Click to access report-on-state-of-the-art-research.pdf

 

 

 

A Wider Circle? The Circular Economy in Developing Countries

 

Click to access 2017-12-05-circular-economy-preston-lehne.pdf

 

 

 

 

A safe and just space for humanity

CAN WE LIVE WITHIN THE DOUGHNUT?

Kate Raworth

OXFAM

 

Click to access dp-a-safe-and-just-space-for-humanity-130212-en.pdf

https://www.kateraworth.com

 

 

Doughnut Economics: seven ways to think like a 21st century economist

Kate Raworth

http://www.lse.ac.uk/website-archive/newsAndMedia/videoAndAudio/channels/publicLecturesAndEvents/player.aspx?id=3938

 

Taking the Circular Economy to the City Level

Click to access ICLEI_Webinar_Circular_Economy_Intro.pdf

 

 

 

 

 

Mapping the Political Economy of Design

Dr. Joanna Boehnert

Research Fellow
Centre for the Evaluation of Complexity Across the Nexus University of Surrey

 

Click to access RSD6-Mapping-the-Political-Economy-of-Design-Boehnert-6.12.17-Final2.pdf

 

 

 

A circular Economy

SITRA

https://www.sitra.fi/en/topics/a-circular-economy/

 

 

 

Rethinking Sustainability in Light of the EU’s New Circular Economy Policy

JULY 03, 2018
HBR

https://hbr.org/2018/07/rethinking-sustainability-in-light-of-the-eus-new-circular-economy-policy

 

 

 

 RE-CIRCLE

Resource Efficiency & Circular Economy Project

OECD

 

Click to access brochure-recircle-resource-efficiency-and-circular-economy.pdf

 

 

ECONOMICS OF THE CIRCULAR ECONOMY TRANSITION: A CRITICAL REVIEW OF MODELLING APPROACHES –

ENVIRONMENT WORKING PAPER No. 130

by Andrew McCarthy, Rob Dellink, and Ruben Bibas

(OECD)

 

https://www.oecd-ilibrary.org/docserver/af983f9a-en.pdf?expires=1531674000&id=id&accname=guest&checksum=1108F9A93394F591184369F48C2F5D4C

 

 

 

 

COMMUNICATION FROM THE COMMISSION TO THE EUROPEAN PARLIAMENT, THE COUNCIL, THE EUROPEAN ECONOMIC AND SOCIAL COMMITTEE AND THE COMMITTEE OF THE REGIONS

A European Strategy for Plastics in a Circular Economy

EU

 

Click to access plastics-strategy.pdf

 

 

 

The New Plastics Economy

Rethinking the future of plastics

WEF

 

Click to access WEF_The_New_Plastics_Economy.pdf

 

https://www.mckinsey.com/~/media/mckinsey/business%20functions/sustainability%20and%20resource%20productivity/our%20insights/rethinking%20future%20of%20plastics/the%20new%20plastics%20economy.ashx

 

Click to access NPEC-Hybrid_English_22-11-17_Digital.pdf

 

 

 

SCALING RECYCLED PLASTICS ACROSS INDUSTRIES

MARCH 2017

RESEARCHED BY JOS VLUGTER,
MSC CANDIDATE, STRATEGIC PRODUCT DESIGN, DELFT UNIVERSITY OF TECHNOLOGY

 

Click to access Scaling-Recycled-Plastics-across-Industries.pdf

 

 

 

 

CIRCULAR ECONOMY: THE NEW NORMAL?

UNCTAD

May 2018

 

Click to access presspb2017d10_en.pdf

 

 

 

 

The circular economy: Moving from theory to practice

McKinsey Center for Business and Environment Special edition,

October 2016

https://www.mckinsey.com/~/media/McKinsey/Business%20Functions/Sustainability%20and%20Resource%20Productivity/Our%20Insights/The%20circular%20economy%20Moving%20from%20theory%20to%20practice/The%20circular%20economy%20Moving%20from%20theory%20to%20practice.ashx

 

 

 

 

Renewable materials in the Circular Economy

April 2018

 

Click to access C296.pdf

 

 

 

A Review of the Circular Economy and its Implementation

Almas Heshmati

Sogang University and IZA

 

Click to access dp9611.pdf

 

 

 

Rethinking finance in Rethinking nance in a circular economy

Financial implications of circular business models

ING

 

Click to access ing-rethinking-finance-in-a-circular-economy-may-2015.pdf

 

 

 

 

 

The Circular Economy in International Trade

UNCTAD

2016

http://unctad.org/en/pages/newsdetails.aspx?OriginalVersionID=1400

 

 

 

 

Circular economy:
a commentary from the perspectives of the natural and social sciences

Click to access EASAC_Circular_Economy_Web.pdf

 

 

 

 

Circular by design

Products in the circular economy

Click to access circular_by_design_-_products_in_the_circular_economy.pdf

 

 

 

GROWTH WITHIN:

A CIRCULAR ECONOMY VISION FOR A COMPETITIVE EUROPE

Ellen MacArthur Foundation

 

Click to access EllenMacArthurFoundation_Growth-Within_July15.pdf

Resource Flows: Material Flow Accounting (MFA), Life Cycle Analysis (LCA), Input Output Networks and other methods

Resource Flows: Material Flow Accounting (MFA), Life Cycle Analysis (LCA), Input Output Networks and other methods

 

 

 

From Materials Flow and Sustainability

mfa5mfa6

 

Key Terms:

  • MFA (Material Flow Analysis)
  • MFCA (Material Flow Cost Accounting)
  • LCA (Life Cycle Analysis)
  • SFA (Substance Flow Analysis)
  • MF WIO (Material Flow Waste Input Output)
  • IO LCA (Input Output Life Cycle Analysis)
  • KLEM (Capital, Labor, Energy, Materials)
  • PIOT ( Physical Input Output Tables)
  • MIOT (Monetary Input Output Tables)
  • IO MFN (Input Output Material Flow Network)
  • Social Ecology
  • Industrial Ecology
  • Urban Metabolism
  • Industrial Symbiosis
  • Industrial Metabolism
  • M-P Chains (Material Product Chains)
  • Global Value Chains
  • National Footprint Accounts
  • Inter Industry Analysis
  • Input Output Economics
  • End to End Supply Chains
  • Supply and Use Tables
  • Material Balance
  • Mass Balance
  • Biophysical Economics
  • Ecological Economics
  • Environmentally Extended Input Output Analysis (EE-IOA)
  • Stocks and Flows
  • MaTrace
  • Global MaTrace

 

Software for Data Analysis and Visualization:

 

This article lists several other software packages for MFA/SFA

https://www.azavea.com/blog/2017/08/09/six-sankey-diagram-tool/

 

 

Material Flow Analysis

From Practical Handbook of MATERIAL FLOW ANALYSIS

Material flow analysis (MFA) is a systematic assessment of the flows and stocks of materials within a system defined in space and time. It connects the sources, the pathways, and the intermediate and final sinks of a material. Because of the law of the conservation of matter, the results of an MFA can be controlled by a simple material balance comparing all inputs, stocks, and outputs of a process. It is this distinct characteristic of MFA that makes the method attractive as a decision-support tool in resource management, waste management, and environmental management.

An MFA delivers a complete and consistent set of information about all flows and stocks of a particular material within a system. Through balancing inputs and outputs, the flows of wastes and environmental loadings become visible, and their sources can be identified. The depletion or accumulation of material stocks is identified early enough either to take countermeasures or to promote further buildup and future utilization. Moreover, minor changes that are too small to be measured in short time scales but that could slowly lead to long-term damage also become evident.

Anthropogenic systems consist of more than material flows and stocks (Figure 1.1). Energy, space, information, and socioeconomic issues must also be included if the anthroposphere is to be managed in a responsible way. MFA can be performed without considering these aspects, but in most cases, these other factors are needed to interpret and make use of the MFA results. Thus, MFA is frequently coupled with the analysis of energy, economy, urban planning, and the like.

In the 20th century, MFA concepts have emerged in various fields of study at different times. Before the term MFA had been invented, and before its comprehensive methodology had been developed, many researchers used the law of conservation of matter to balance processes. In process and chemical engineering, it was common practice to analyze and balance inputs and outputs of chemical reactions. In the economics field, Leontief introduced input–output tables in the 1930s, thus laying the base for widespread application of input–output methods to solve economic problems. The first studies in the fields of resource conservation and environmental management appeared in the 1970s. The two original areas of application were (1) the metabolism of cities and (2) the analysis of pollutant pathways in regions such as watersheds or urban areas. In the following decades, MFA became a widespread tool in many fields, including process control, waste and wastewater treatment, agricultural nutrient management, water-quality management, resource conservation and recovery, product design, life cycle assessment (LCA), and others.

 

Substance Flow Analysis

From Feasibility assessment of using the substance flow analysis methodology for chemicals information at macro level

SFA is used for tracing the flow of a selected chemical (or group of substances) through a defined system. SFA is a specific type of MFA tool, dealing only with the analysis of flows of chemicals of special interest (Udo de Haes et al., 1997). SFA can be defined as a detailed level application of the basic MFA concept tracing the flow of selected chemical substances or compounds — e.g. heavy metals (mercury (Hg), lead (Pb), etc.), nitrogen (N), phosphorous (P), persistent organic substances, such as PCBs, etc. — through society.

An SFA identifies these entry points and quantifies how much of and where the selected substance is released. Policy measures may address these entry points, e.g. by end‐of‐pipe technologies. Its general aim is to identify the most effective intervention points for policies of pollution prevention. According to Femia and Moll (2005), SFA aims to answer the following questions:

• Where and how much of substance X flows through a given system?

• How much of substance X flows to wastes?
• Where do flows of substance X end up?
• How much of substance X is stored in durable goods?
• Where could substance X be more efficiently utilised in technical processes?
• What are the options for substituting the harmful substance?
• Where do substances end up once they are released into the natural environment?

When an SFA is to be carried out, it involves the identification and collection of data on the one hand, and modelling on the other. According to van der Voet et al. (OECD, 2000), there are three possible ways to ‘model’ the system:

Accounting (or bookkeeping) The input for such a system is the data that can be obtained from trade and production statistics. If necessary, further detailed data can be recovered on the contents of the specific substances in those recorded goods and materials. Emissions and environmental fluxes or concentration monitoring can be used for assessing the environmental flows. The accounting overview may also serve as an identification system for missing or inaccurate data.

Missing amounts can be estimated by applying the mass balance principle. In this way, inflows and outflows are balanced for every node, as well as for the system as a whole, unless accumulation within the system can be proven. This technique is most commonly used in material flow studies, and can be viewed as a form of descriptive statistics. There are, however, some examples of case studies that specifically address societal stocks, and use these as indicator for possible environmental problems in the future (OECD, 2000).

Static modelling is the process whereby the network of flow nodes is translated into a mathematical ‘language’, i.e. a set of linear equations, describing the flows and accumulations as inter‐dependent. Emission factors and distribution factors over the various outputs for the economic processes and partition coefficients for the environmental compartments can be used as variables in the equations. A limited amount of substance flow accounting data is also required for a solution of the linear equations. However, the modelling outcome is determined largely by the substance distribution patterns.

Static modelling can be extended by including a so‐called origin analysis in which the origins of one specific problematic flow can be traced on several levels. Three levels may be distinguished:

• direct causes derived directly from the nodes balance (e.g one of the direct causes of cadmium (Cd) load in soil is atmospheric deposition);

• economic sectors (or environmental policy target groups) directly responsible for the problem. This is identified by following the path back from node to node to the point of emission (e.g. waste incineration is one of the economic sectors responsible for the cadmium load in soil);

• ultimate origins found by following the path back to the system boundaries (e.g. the extraction, transport, processing and trade of zinc (Zn) ore is one of the ultimate origins of the cadmium load in soil).

Furthermore, the effectiveness of abatement measures can be assessed with static modelling by recording timelines on substances (OECD, 2000).

Dynamic modelling is different to the static SFA model, as it includes substance stocks accumulated in society as well as in various materials and products in households and across the built‐up environments.

For SFA, stocks play an important role in the prediction of future emissions and waste flows of products with a long life span. For example, in the case of societal stocks of PVC, policy makers need to be supplied with information about future PVC outflows. Today’s stocks become tomorrow’s emissions and waste flows. Studies have been carried out on the analysis of accumulated stocks of metals and other persistent toxics in the societal system. Such build‐ups can serve as an ‘early warning’ signal for future emissions and their potential effects, as one day these stocks may become obsolete and recognisably dangerous, e.g. as in the case of asbestos, CFCs, PCBs and mercury in chlor‐alkali cells. As the stocks are discarded, they end up as waste, emissions, factors of risks to environment and population. In some cases, this delay between inflow and outflow can be very long indeed.

Stocks of products no longer in use, but not yet discarded, are also important. These stocks could include: old radios, computers and/or other electronic equipment stored in basements or attics, out‐of‐use pipes still in the ground, obsolete stocks of chemicals no longer produced but still stored, such as lead paints and pesticides. These ‘hibernating stocks’ are likely to be very large, according to OECD estimates (2000). Estimating future emissions is a crucial issue if environmental policy makers are to anticipate problems and take timely, effective action. In order to do this, stocks cannot be ignored. Therefore, when using MFA or SFA models for forecasting, stocks should play a vital part. Flows and stocks interact with each other. Stocks grow when the inflows exceed the outflows of a (sub)‐system and certain outflows of a (sub)‐system are disproportional to the stocks.

For this dynamic model, additional information is needed for the time dimension of the variables, e.g. the life span of applications in the economy; the half life of compounds; the retention time in environmental compartments and so forth. Calculations can be made not only on the ‘intrinsic’ effectiveness of packages of measures, but also on their anticipated effects in a specific year in the future. They can also be made on the time
it takes for such measures to become effective. A dynamic model is therefore most suitable for scenario analysis, provided that the required data are available or can be estimated with adequate accuracy (OECD, 2000).

 

Life Cycle Analysis (LCA)

 

What is Life Cycle Assessment (LCA)?

As environmental awareness increases, industries and businesses are assessing how their activities affect the environment. Society has become concerned about the issues of natural resource depletion and environmental degradation. Many businesses have responded to this awareness by providing “greener” products and using “greener” processes. The environmental performance of products and processes has become a key issue, which is why some companies are investigating ways to minimize their effects on the environment. Many companies have found it advantageous to explore ways of moving beyond compliance using pollution prevention strategies and environmental management systems to improve their environmental performance. One such tool is LCA. This concept considers the entire life cycle of a product (Curran 1996).

Life cycle assessment is a “cradle-to-grave” approach for assessing industrial systems. “Cradle-to-grave” begins with the gathering of raw materials from the earth to create the product and ends at the point when all materials are returned to the earth. LCA evaluates all stages of a product’s life from the perspective that they are interdependent, meaning that one operation leads to the next. LCA enables the estimation of the cumulative environmental impacts resulting from all stages in the product life cycle, often including impacts not considered in more traditional analyses (e.g., raw material extraction, material transportation, ultimate product disposal, etc.). By including the impacts throughout the product life cycle, LCA provides a comprehensive view of the environmental aspects of the product or process and a more accurate picture of the true environmental trade-offs in product and process selection.

The term “life cycle” refers to the major activities in the course of the product’s life-span from its manufacture, use, and maintenance, to its final disposal, including the raw material acquisition required to manufacture the product. Exhibit 1-1 illustrates the possible life cycle stages that can be considered in an LCA and the typical inputs/outputs measured.

 

Methods of LCA

  • Process LCA
  • Economic Input Output LCA
  • Hybrid Approach

 

 

From Life cycle analysis (LCA) and sustainability assessment

 

mfa8

 

 

Material Input Output Network Analysis

  • PIOT (Physical Input Output Tables)
  • MIOT (Monetary Input Output Tables)
  • WIOT (Waste Input Output Tables
  • MRIO (Multi Regional Input Output)
  • SUT (Supply and Use Tables)

 

From Industrial ecology and input-output economics: An introduction

Although it was the pioneering contributions by Duchin (1990, 1992) that explicitly made the link between input–output economics and industrial ecology, developments in input– output economics had previously touched upon the core concept of industrial ecology.

Wassily Leontief himself incorporated key ideas of industrial ecology into an input– output framework. Leontief (1970) and Leontief and Ford (1972) proposed a model where the generation and the abatement of pollution are explicitly dealt with within an extended IO framework. This model, which combines both physical and monetary units in a single coefficient matrix, shows how pollutants generated by industries are treated by so-called ‘pollution abatement sectors.’ Although the model has been a subject of longstanding methodological discussions (Flick, 1974; Leontief, 1974; Lee, 1982), its structure captures the essence of industrial ecology concerns: abatement of environmental problems by exploiting inter-industry interactions. As a general framework, we believe that the model by Leontief (1970) and Leontief and Ford (1972) deserves credit as an archetype of the various models that have become widely referred to in the field of industrial ecology during the last decade, including mixed-unit IO, waste IO and hybrid Life Cycle Assessment (LCA) models (Duchin, 1990; Konijn et al., 1997; Joshi, 1999; Nakamura and Kondo, 2002; Kagawa et al., 2004; Suh, 2004b). Notably, Duchin (1990) deals with the conversion of wastes to useful products, which is precisely the aim of industrial ecology, and subsequently, as part of a study funded by the first AT&T industrial ecology fellowship program, with the recovery of plastic wastes in particular (Duchin and Lange, 1998). Duchin (1992) clarifies the quantity-price relationships in an input–output model (a theme to which she has repeatedly returned) and draws its implications for industrial ecology, which has traditionally been concerned exclusively with physical quantities.

Duchin and Lange (1994) evaluated the feasibility of the recommendations of the Brundtland Report for achieving sustainable development. For that, they developed an input–output model of the global economy with multiple regions and analyzed the consequences of the Brundtland assumptions about economic development and technological change for future material use and waste generation. Despite substantial improvements in material efficiency and pollution reduction, they found that these could not offset the impact of population growth and the improved standards of living endorsed by the authors of the Brundtland Report.

Another pioneering study that greatly influenced current industrial ecology research was described by Ayres and Kneese (1969) and Kneese et al. (1970), who applied the massbalance principle to the basic input–output structure, enabling a quantitative analysis of resource use and material flows of an economic system. The contribution by Ayres and Kneese is considered the first attempt to describe the metabolic structure of an economy in terms of mass flows (see Ayres, 1989; Haberl, 2001).

Since the 1990s, new work in the areas of economy-wide research about material flows, sometimes based on Physical Input–Output Tables (PIOTs), has propelled this line of research forward in at least four distinct directions: (1) systems conceptualization (Duchin, 1992; Duchin, 2005a); (2) development of methodology (Konijn et al., 1997; Nakamura and Kondo, 2002; Hoekstra, 2003; Suh, 2004c; Giljum et al., 2004; Giljum and Hubacek, 2004; Dietzenbacher, 2005; Dietzenbacher et al., 2005; Weisz and Duchin, 2005); (3) compilation of data (Kratterl and Kratena, 1990; Kratena et al., 1992; Pedersen, 1999; Ariyoshi and Moriguchi, 2003; Bringezu et al., 2003; Stahmer et al., 2003); and (4) applications (Duchin, 1990; Duchin and Lange, 1994, 1998; Hubacek and Giljum, 2003; Kagawa et al., 2004). PIOTs generally use a single unit of mass to describe physical flows among industrial sectors of a national economy. In principle, such PIOTs are capable of satisfying both column-wise and row-wise mass balances, providing a basis for locating materials within a national economy.3 A notable variation in this tradition, although it had long been used in input–output economic studies starting with the work of Leontief, is the mixed-unit IO table. Konijn et al. (1997) analyzed a number of metal flows in the Netherlands using a mixed-unit IO table, and Hoekstra (2003) further improved both the accounting framework and data. Unlike the original PIOTs, mixed-unit IOTs do not assure the existence of column-wise mass-balance, but they make it possible to address more complex questions. Lennox et al. (2004) present the Australian Stocks and Flows Framework (ASFF), where a dynamic IO model is implemented on the basis of a hybrid input–output table. These studies constitute an important pillar of industrial ecology that is generally referred to as Material Flow Analysis (MFA).4

Although the emphasis in industrial ecology has arguably been more on the materials side, energy issues are without doubt also among its major concerns. In this regard, energy input–output analysis must be considered another important pillar for the conceptual basis of ‘industrial energy metabolism.’ The oil shock in the 1970s stimulated extensive research on the structure of energy use, and various studies quantifying the energy associated with individual products were carried out (Berry and Fels, 1973; Chapman, 1974). Wright (1974) utilized Input–Output Analysis (IOA) for energy analysis, which previously had been dominated by process-based analysis (see also Hannon, 1974; Bullard and Herendeen, 1975; Bullard et al., 1978). The two schools of energy analysis, namely process analysis and IO energy analysis, were merged by Bullard and Pillarti (1976) into hybrid energy analysis (see also van Engelenburg et al., 1994; Wilting,1996). Another notable contribution to the area of energy analysis was made by Cleveland et al. (1984), who present a comprehensive analysis, using the US input–output tables, quantifying the interconnection of energy and economic activities from a biophysical standpoint (see Cleveland, 1999; Haberl, 2001; Kagawa and Inamura, 2004). These studies shed light on how an economy is structured by means of energy flows and informs certain approaches to studying climate change (see for example Proops et al., 1993; Wier et al., 2001).

What generally escapes attention in both input–output economics and industrial ecology, despite its relevance for both, is the field of Ecological Network Analysis (ENA). Since Lotka (1925) and Lindeman (1942), material flows and energy flows have been among the central issues in ecology. It was Hannon (1973) who first introduced concepts from input–output economics to analyze the structure of energy utilization in an ecosystem. Using an input–output framework, the complex interactions between trophic levels or ecosystem compartments can be modeled, taking all direct and indirect relationships between components into account. Hannon’s approach was adopted, modified and re-introduced by various ecologists. Finn (1976, 1977), among others, developed a set of analytical measures to characterize the structure of an ecosystem using a rather extensive reformulation of the approach proposed by Hannon (1973). Another important development in the tradition of ENA is so-called environ analysis. Patten (1982) proposed the term ‘environ’ to refer to the relative interdependency between ecosystem components in terms of nutrient or energy flows. Results of environ analysis are generally presented as a comprehensive network flow diagram, which shows the relative magnitudes of material or energy flows between the ecosystem components through direct and indirect relationships (Levine, 1980; Patten, 1982). Ulanowicz and colleagues have broadened the scope of materials and energy flow analysis both conceptually and empirically (Szyrmer and Ulanowicz, 1987). Recently Bailey et al. (2004a, b) made use of the ENA tradition to analyze the flows of several metals through the US economy. Suh (2005) discusses the relationship between ENA and IOA and shows that Patten’s environ analysis is similar to Structural Path Analysis (SPA), and that the ENA framework tends to converge toward the Ghoshian framework rather than the Leontief framework although using a different formalism (Defourny and Thorbecke, 1984; Ghosh, 1958).

 

 

From Materials and energy flows in industry and ecosystem netwoks : life cycle assessment, input-output analysis, material flow analysis, ecological network flow analysis, and their combinations for industrial ecology

 

MFA

 

From Regional distribution and losses of end-of-life steel throughout
multiple product life cycles—Insights from the global multiregional
MaTrace model

mfa4

From Feasibility assessment of using the substance flow analysis methodology for chemicals information at macro level

 

mfa2MFA3

 

Sankey Diagram

From Hybrid Sankey diagrams: Visual analysis of multidimensional data for understanding resource use

Sankey diagrams are used to visualise flows of energy, materials or other resources in a variety of applications. Schmidt (2008a) reviewed the history and uses of these diagrams. Originally, they were used to show flows of energy, first in steam engines, more recently for modern systems such as power plants (e.g. Giuffrida et al., 2011) and also to give a big-picture view of global energy use (Cullen and Allwood, 2010). As well as energy, Sankey diagrams are widely used to show flows of resources (Schmidt, 2008a). Recent examples in this journal include global flows of tungsten (Leal-Ayala et al., 2015), biomass in Austria (Kalt, 2015), and the life-cycle of car components (Diener and Tillman, 2016). More widely, they have been used to show global production and use of steel and aluminium (Cullen et al., 2012; Cullen and Allwood, 2013), and flows of natural resources such as water (Curmi et al., 2013). In all of these cases, the essential features are: (1) the diagram represents physical flows, related to a given functional unit or period of time; and (2) the magnitude of flows is shown by the link1 widths, which are proportional to an extensive property of the flow such as mass or energy (Schmidt, 2008b). Creating these diagrams is supported by software tools such as e!Sankey (ifu Hamburg, 2017), and several Life Cycle Assessment (LCA) and Material Flow Analysis (MFA) packages include features to create Sankey diagrams.

 

From Hybrid Sankey diagrams: Visual analysis of multidimensional data for understanding resource use

 

mfa7

 

Please see my related posts:

Wassily Leontief and Input Output Analysis in Economics

Shell Oil’s Scenarios: Strategic Foresight and Scenario Planning for the Future

Water | Food | Energy | Nexus: Mega Trends and Scenarios for the Future

Stock Flow Consistent Input Output Models (SFCIO)

Measuring Globalization: Global Multi Region Input Output Data Bases (G-MRIO)

Production and Distribution Planning : Strategic, Global, and Integrated

Intra Industry Trade and International Production and Distribution Networks

Trends in Intra Firm Trade of USA

Development of Global Trade and Production Accounts: UN SEIGA Initiative

Accounting For Global Carbon Emission Chains

Stock Flow Consistent Models for Ecological Economics

Jay W. Forrester and System Dynamics

Classical roots of Interdependence in Economics

Stock-Flow Consistent Modeling

 

 

 

 

Key Sources of Research:

SPECIAL SESSION ON MATERIAL FLOW ACCOUNTING

OECD

Paris, 24 October 2000

Click to access 4425421.pdf

An Innovative Accounting Framework for the Food-Energy-Water Nexus
Application of the MuSIASEM approach to three case studies

Click to access i3468e.pdf

Creating your own online data visualizations: SankeyMatic, OMAT, CartoDB

https://metabolismofcities.org/blog/4-creating-your-own-online-data-visualizations

Hybrid Sankey diagrams: Visual analysis of multidimensional data for understanding resource use

https://www.sciencedirect.com/science/article/pii/S0921344917301167

e!Sankey

Visualization of energy, cash and material flows with a Sankey diagram

https://www.ifu.com/en/e-sankey/sankey-diagrams/

UPIOM: A New Tool of MFA and Its Application to the Flow of Iron and Steel Associated with Car Production

https://pubs.acs.org/doi/pdf/10.1021/es1024299

Material flow analysis

WIKIPEDIA

https://en.wikipedia.org/wiki/Material_flow_analysis

Economy-wide Material Flow Accounting. Introduction and Guide.

Version 1.0

Article · January 2015

Fridolin Krausmann, Helga Weisz, Nina Eisenmenger, Helmut Schütz, Willi Haas
and Anke Schaffartzik

https://www.researchgate.net/publication/272885234_Economy-wide_Material_Flow_Accounting_Introduction_and_Guide_Version_10

Society’s Metabolism The Intellectual History of Materials Flow Analysis,

Part II, 1970-1998

Marina Fischer-Kowalski and Walter Huttler

Institute for Intenliscipiimny
Studies of Austrian Universities
University of Vienna
Vienna, Austria

Click to access Fischer-Kowalski_Huttler_1998.pdf

“Society’s Metabolism. The Intellectual History of Material Flow Analysis,

Part I, 1860 – 1970″.

Fischer-Kowalski, M.

1998.

Journal of Industrial Ecology 2(1): 61-78

https://www.researchgate.net/publication/249481665_Society%27s_Metabolism_The_Intellectual_History_of_Materials_Flow_Analysis_Part_I_1860-_1970

Analysis on energy–water nexus by Sankey diagram: the case of Beijing

https://www.tandfonline.com/doi/abs/10.1080/19443994.2013.768038

Unified Materials Information System (UMIS): An Integrated Material Stocks and Flows Data Structure

First published: 07 February 2018

 

https://onlinelibrary.wiley.com/doi/abs/10.1111/jiec.12730

Material Flow Cost Accounting with Umberto®

Schmidt, A. Hache, B.; Herold, F.; Götze, U.

Click to access 2-05_Material_Flow_Cost_Accounting.pdf

Click to access WEF_Richards.pdf

Study on Data for a Raw Material System Analysis: Roadmap and Test of the Fully Operational MSA for Raw Materials

Final Report

BIO by Deloitte

(2015)

Prepared for the European Commission, DG GROW.

https://www.certifico.com/component/attachments/download/2886

Integrated Analysis of Energy, Material and Time Flows in Manufacturing Systems

 

https://ac.els-cdn.com/S2212827116305479/1-s2.0-S2212827116305479-main.pdf?_tid=90701061-86fc-4c11-b078-cb577d8f8bdf&acdnat=1525719999_9dcee960cd6033d950a583cea379539f

e! Sankey

Visualization of energy, cash and material flows with a Sankey diagram

The most popular software for creating Sankey diagrams. Visualize the cash, material & energy flow or value streams in your company or along the supply chain. Share these appealing diagrams in reports or presentations.

 

https://www.ifu.com/en/e-sankey/?gclid=CjwKCAjw8r_XBRBkEiwAjWGLlIcWq2pRigMmJLKAXP4-ndFXR9ik41MUp9ahFZL2M9Ht5CKtwKIvTRoCdbsQAvD_BwE

MATERIAL FLOW ANALYSIS WITH SOFTWARE STAN

Oliver Cencic* and Helmut Rechberger
Institute for Water Quality Resources and Waste Management
Vienna University of Technology
Vienna A-1040, Austria

Click to access CENCIC%20and%20RECHBERGER%202008%20Material%20Flow%20Analysis%20with%20Software%20STAN.pdf

Recovery of Key Metals in the Electronics Industry in the
People’s Republic of China: An Opportunity in Circularity
(Initial Findings)

January 2018

Created as Part of the Platform for Accelerating the Circular Economy

Click to access 39777_Recovery_Key_Metals_Electronics_Industry_China_Opportunity_Circularity_report_2018.pdf

Sankey diagram

WIKIPEDIA

https://en.wikipedia.org/wiki/Sankey_diagram

MATERIAL FLOWS IN THE UNITED STATES
A PHYSICAL ACCOUNTING OF THE U.S. INDUSTRIAL ECONOMY

DONALD ROGICH
AMY CASSARA
IDDO WERNICK
MARTA MIRANDA

WRI

Click to access material_flows_in_the_united_states.pdf

Industrial ecology and input-output economics: An introduction

Sangwon Suh

2005

Click to access Industrial-ecology-and-input-output-economics-An-introduction.pdf

A Handbook of Industrial Ecology

Robert Ayres

Leslie Ayres

http://pustaka.unp.ac.id/file/abstrak_kki/EBOOKS/A%20Handbook%20of%20Industrial%20Ecology.pdf#page=100

Physical and Monetary Input-Output Analysis:
What Makes the Difference?

Helga Weisz
Klagenfurt University
Faye Duchin
Rensselaer Polytechnic Institute

Click to access ab5b067aacafe555acbc1e077b5b42e1fc92.pdf

Theory of materials and energy flow analysis in ecology and economics

Sangwon Suh

2005

Click to access Materials-and-energy-flows-in-industry-and-ecosystem-networks.pdf

Conceptual Foundations and Applications of Physical Input-Output Tables

Stefan Giljum

Hubacek Klaus

2009

Click to access Conceptual-Foundations-and-Applications-of-Physical-Input-Output-Tables.pdf

Alternative Approaches of Physical Input-Output Analysis to Estimate
Primary Material Inputs of Production and Consumption Activities

Stefan Giljum

Hubacek Klaus

2004

Click to access 00b7d51cc1257aba71000000.pdf

Industrial Ecology: A Critical Review

Click to access IE.pdf

EXIOPOL – development and illustrative analyses of a detailed global
multiregional environmentally-extended supply and use table/input output
table

Article in Economic Systems Research · May 2013

Click to access 561d652a08aecade1acb3bfc.pdf

Developing the Sectoral Environmental Database for Input- Output Analysis: Comprehensive Environmental Data Archive of the U.S.

Article in Economic Systems Research · December 2005

Click to access 0c960531f1d910cda1000000.pdf

The material basis of the global economy

Worldwide patterns of natural resource extraction and their
implications for sustainable resource use policies

Arno Behrens,⁎, Stefan Giljum, Jan Kovanda, Samuel Niza

Click to access Material_Basis.pdf

The Sankey Diagram in Energy and Material Flow Management

Part I: History

https://onlinelibrary.wiley.com/doi/full/10.1111/j.1530-9290.2008.00004.x

The Sankey Diagram in Energy and Material Flow Management

Part II: Methodology and Current Applications

First published: 28 April 2008

https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1530-9290.2008.00015.x

Material and Energy Flow Analysis

First published: 23 March 2010

https://onlinelibrary.wiley.com/doi/pdf/10.1002/ceat.201090015

8. Biophysical economics: from physiocracy to ecological economics and industrial
ecology

Cutler J Cleveland

Article · January 1999

Click to access 0deec51b7274ca0035000000.pdf

The Use of Input-Output Analysis in REAP to allocate Ecological Footprints and Material Flows to Final Consumption Categories

http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.457.7453&rep=rep1&type=pdf

Waste Input–Output Material Flow Analysis of Metals in the Japanese Economy

Shinichiro Nakamura1 and Kenichi Nakajima2

Click to access 2550.pdf

A multi-regional environmental input-output model to quantify embodied material flows

Stefan Giljum a, Christian Lutz b,Ariane Jungnitz

Click to access Giljum%20et%20al_IIOA.pdf

Click to access jungnitzgiljumlutz.pdf

Material Flow Accounting and Analysis (MFA)

A Valuable Tool for Analyses of Society-Nature Interrelationships

Entry prepared for the Internet Encyclopedia of Ecological Economics

Friedrich Hinterberger *, Stefan Giljum, Mark Hammer

Sustainable Europe Research Institute (SERI)

Click to access material.pdf

Human Ecology: Industrial Ecology

Faye Duchin
Rensselaer Polytechnic Institute

Stephen H. Levine
Tufts University

Click to access rpi0603.pdf

Development of the Physical Input Monetary Output Model for Understanding Material Flows within Ecological -Economic Systems

XU Ming

2010

Click to access 2010010204.pdf

Accounting for raw material equivalents of traded goods

A comparison of input-output approaches in physical, monetary, and mixed units

Click to access working-paper-87-web.pdf

Material Flow Accounts and Policy. Data for Sweden 2004

by: Annica Carlsson, Anders Wadeskog, Viveka Palm, Fredrik Kanlén Environmental Accounts, Statistics Sweden,

2006.

Click to access mi1301_2004a01_br_mift0701.pdf

Economy-wide Material Flow Accounts with Hidden Flows for Finland: 1945–2008

Jukka Hoffrén (ed.)

Click to access isbn_978-952-244-233-8.pdf

EXIOBASE
Analysing environmental impacts of the global, interlinked economy

Konstantin Stadler, Richard Wood

Industrial Ecology Programme, NTNU, Norway

2014

http://www.syke.fi/download/noname/%7B3C267869-D6AE-447F-A98D-547C1D2B5819%7D/105511

ESSAYS ON INTERNATIONAL TRADE AND ENVIRONMENT
An Input-Output Analysis

http://repositorio.conicyt.cl/bitstream/handle/10533/179687/MUNOZ_PABLO_2868D.pdf?sequence=1

Using Material Flow Analysis for Sustainable Materials Management: Part of the Equation for Priority Setting

Frederick W. Allen

Priscilla A. Halloran

Angela H. Leith

M. Clare Lindsay

https://digitalcommons.unl.edu/cgi/viewcontent.cgi?referer=&httpsredir=1&article=1113&context=usepapapers

“Supply-Extension versus Use-Extension in Environmentally Extended Input-Output Modelling: Analyzing Physical Flows within the Austrian Economy”

Hanspeter Wieland*1, Nina Eisenmenger2, Dominik Wiedenhofer2, Martin Bruckner1

Click to access IO-Workshop-2017_Wieland_abstract.pdf

A Material Flow Analysis of Phosphorus in Japan
The Iron and Steel Industry as a Major Phosphorus Source

Kazuyo Matsubae-Yokoyama, Hironari Kubo, Kenichi Nakajima,
and Tetsuya Nagasaka

Click to access gpa_101_wa.pdf

Material Flows and Economic Development
Material Flow Analysis of the Hungarian Economy

Click to access IR-02-057.pdf

The material footprint of nations

Thomas O. Wiedmanna,b,c,1, Heinz Schandlb,d, Manfred Lenzenc, Daniel Moranc,e, Sangwon Suhf, James Westb, and Keiichiro Kanemotoc

Click to access 6271.full.pdf

Calculation of direct and indirect material inputs by type of raw material and economic activities

Paper presented at the London Group Meeting
19 – 21 June 2006

Karl Schoer

Wiesbaden, July 2006

Click to access Raw_material_Germany.pdf

Waste Input-Output  (WIO) Table

Shinichiro NAKAMURA and Yasushi KONDO,

Waste Input-Output Analysis: Concepts and Application to Industrial Ecology.

In Series: Eco-Efficiency in Industry and Science,

Vol. 26, Springer, February 2009.

http://www.f.waseda.jp/nakashin/WIO.html

Economy Wide Material Flow Accounting (EW-MFA)

http://data.geus.dk/MICASheetsEditor/document/21e5c517-53b9-4b00-b7a3-55939829824b

Material flow analyses in technosphere and biosphere
– metals, natural resources and chemical products

Viveka Palm

Click to access FULLTEXT01.pdf

The UK waste input-output table: Linking waste generation to the UK economy.

Salemdeeb, R., Al-Tabbaa, A. and Reynolds, C.

Waste Management & Research, 34 (10). pp. 1089-1094.

Click to access Re_Main_Document.pdf

Multiregion input / output tables and material footprint accounts session

Discussion of aspects of of MRIO / material footprinting work, and considerations for developing and resource based economies.

James West | Senior experimental scientist
25 May 2016

Click to access 10_MFand_MRIO_CSIRO_English.pdf

Construction of hybrid Input-Output tables for E3 CGE model calibration and consequences on energy policy analysis

COMBET Emmanuel – CIRED
GHERSI Frédéric – CIRED
LEFEVRE Julien – CIRED
LE TREUT Gaëlle – CIRED

Click to access 6988.pdf

Prospects and Drivers of Future European Resource Requirements
Evidence from a Multi-National Macroeconomic Simulation Study*

Paper prepared for the final WIOD Conference
Groningen, April 2012
by
Martin Distelkamp, Mark Meyer** and Bernd Meyer

GWS mbH Osnabrueck

Click to access Paper_Distelkamp_et_al.pdf

Material Flow Analysis to Evaluate Sustainability in Supply Chains

Haroune Zaghdaoui, Anicia Jaegler, Natacha Gondran, Jairo Montoya-Torres

Click to access 4189.pdf

Physical and monetary input–output analysis: What makes the difference?

Helga Weisz , Faye Duchin

Click to access Physical%20and%20monetary%20input-output.pdf

Recycling and Remanufacturing in Input-Output Models

Randall W Jackson, West Virginia University
Taelim Choi, Georgia Institute of Technology
Nancey Green Leigh, Georgia Institute of Technology

Click to access WP2008-4.pdf

The Water Footprint Assessment Manual

Click to access TheWaterFootprintAssessmentManual_2.pdf

The New Plastics Economy
Rethinking the future of plastics

Click to access WEF_The_New_Plastics_Economy.pdf

A Comparison of Environmental Extended Input-Output (EEIO) and Process Data in Life Cycle Assessment

Click to access Comparing-Input-Output-and-Process-LCA-Data.CE-form2-LM-edits.pdf

Managing Logistics Flows Through Enterprise Input-Output Models

V. Albino1, A. Messeni Petruzzelli1 and O. G. Okogbaa2

Click to access InTech-Managing_logistics_flows_through_enterprise_input_output_models.pdf

Social Metabolism and Accounting Approaches

Module:ECOLECON

Ecological economics

https://proxy.eplanete.net/galleries/broceliande7/social-metabolism-and-accounting-approaches

Input-Output Analysis in Laptop Computer Manufacturing

https://waset.org/publications/9998422/input-output-analysis-in-laptop-computer-manufacturing

IRON, STEEL AND ALUMINIUM IN THE UK: MATERIAL FLOWS AND THEIR
ECONOMIC DIMENSIONS

Final Project Report, March 2004

Click to access 0304_WP_Biffaward_Steel_Al-Final.pdf

A Framework for Sustainable Materials Management

Joseph Fiksel

Click to access Framework_for_SMM.pdf

Energy and water conservation synergy in China: 2007–2012

Yi Jina, Xu Tanga,⁎, Cuiyang Fenga, Mikael Höökb

Click to access Energy-and-water-conservation-synergy-in-China-2007-2012.pdf

Contributions of Material and Energy Flow Accounting to Urban Ecosystems Analysis: Case Study Singapore

Niels B. Schulz

Click to access IAS-WP136.pdf

A review of recent multi-region input–output models used for consumption-based
emission and resource accounting

Thomas Wiedmann

http://wedocs.unep.org/bitstream/handle/20.500.11822/19433/a_review.pdf?sequence=1&isAllowed=y

Physical Input Output (PIOT) Tables:  Developments and Future

Click to access 35_20100427111_Hoekstra-PIOT.pdf

Materials and energy flows in industry and ecosystem netwoks : life cycle assessment, input-output analysis, material flow analysis, ecological network flow analysis, and their combinations for industrial ecology

Suh, S,

2004

https://openaccess.leidenuniv.nl/handle/1887/8399

Applying Ecological Input‐Output Flow Analysis to Material Flows in Industrial Systems: Part I: Tracing Flows

First published: 08 February 2008

Applying Ecological Input‐Output Flow Analysis to Material Flows in Industrial Systems: Part II: Flow Metrics

First published: 08 February 2008

Local systems, global impacts
Using life cycle assessment to analyse the
potential and constraints of industrial symbioses

rising to global challenges

25 Years of Industrial Ecology

 https://is4ie.org/resources/documents/4/download

Literature study on Industrial Ecology

Gerard Fernandez Gonzalez

 

https://upcommons.upc.edu/bitstream/handle/2117/77035/Final%20version%20-%20Document.pdf?sequence=1&isAllowed=y

 

 

 

 

 

Practical Handbook of MATERIAL FLOW ANALYSIS

Paul H. Brunner and Helmut Rechberger

Handbook of Input-Output Economics in Industrial Ecology


 
edited by Sangwon Suh

Taking Stock of Industrial Ecology

edited by Roland Clift, Angela Druckman

Ecological Input-Output Analysis-Based Sustainability Analysis of Industrial Systems

 

Cristina Piluso and Yinlun Huang*

 

Helen H. Lou

An Extended Model for Tracking Accumulation Pathways of Materials Using Input–Output Tables: Application to Copper Flows in Japan

Ryosuke Yokoi * ID , Jun Nakatani ID and Yuichi Moriguchi
2008

TRACING MATERIAL FLOWS ON INDUSTRIAL SITES

Kálmán KÓSI and András TORMA
2005

 

 

 

Metabolism of Cities

 

https://metabolismofcities.org

 

 

 

 

 

Feasibility assessment of using the substance flow analysis methodology for chemicals information at macro level

 

https://www.eea.europa.eu/publications/technical_report_2007_1/file

 

 

 

Structural Investigation of Aluminum in the US Economy using Network Analysis

Philip Nuss, Wei-Qiang Chen Hajime Ohno, and T.E. Graedel

 

Click to access 2016_SA_Network-Analysis-Aluminum_EST.pdf

 

 

 

 

Economy-wide Material Flow Analysis and Indicators

http://www.umweltgesamtrechnung.at/ms/ugr/ugr_en/ugr_physicalaccounts/ugr_materialflowaccounts/

 

 

 

 

Regional distribution and losses of end-of-life steel throughout
multiple product life cycles—Insights from the global multiregional
MaTrace model

 

Stefan Pauliuka,∗, Yasushi Kondob, Shinichiro Nakamurab, Kenichi Nakajimac

 

https://ac.els-cdn.com/S0921344916302774/1-s2.0-S0921344916302774-main.pdf?_tid=838ffb90-95a9-4f3a-a617-f619f32d4558&acdnat=1531385937_3f722f2c2f71337c47a1024b0c841d16

 

 

 

 

MaTrace: Tracing the Fate of Materials over Time and Across Products in Open-Loop Recycling

Shinichiro Nakamura,*,† Yasushi Kondo,† Shigemi Kagawa,‡ Kazuyo Matsubae,§ Kenichi Nakajima,⊥ and Tetsuya Nagasaka§

https://pubs.acs.org/doi/pdf/10.1021/es500820h

 

 

 

 

 

Tracing China’s energy flow and carbon dioxide flow based on Sankey diagrams

 

Feiyin Wang1,2 • Pengtao Wang1,2 • Xiaomeng Xu1,2 • Lihui Dong1,2 • Honglai Xue1,2 • Shuai Fu1,2 • Yingxu Ji

 

Click to access Tracing-Chinas-energy-flow-and-carbon-dioxide-flow-based-on-Sankey-diagrams.pdf

 

 

 

 

Materials Flow and Sustainability

USGS

 

 

 

Life-cycle assessment

https://en.wikipedia.org/wiki/Life-cycle_assessment

 

 

 

 

LIFE CYCLE ASSESSMENT: PRINCIPLES AND PRACTICE

Scientific Applications International Corporation (SAIC) 11251 Roger Bacon Drive
Reston, VA 20190

 

Click to access chapter1_frontmatter_lca101.pdf

 

 

 

 

Life cycle analysis (LCA) and sustainability assessment

 

Click to access IntroductiontoLCAAU32013.pdf

Oscillations and Amplifications in Demand-Supply Network Chains

Oscillations and Amplifications in Demand-Supply Network Chains

 

From Modeling and Measuring the Bullwhip Effect

Demand variability and uncertainty is a driver of supply chain inventory. Managing supply chains can be a challenge when demand variability and uncertainty is high. For a company in a supply chain consisting of multiple stages, each of which is run by a separate organization (or company), the variability of demand faced by this company can be much higher than the variability faced by downstream stages (where “downstream stages” refers to the stages closer to the final consumption of the product). The bullwhip effect refers to the phenomenon where demand variability amplifies as one moves upstream in a supply chain (Lee et al, 1997a, or LPW). LPW described this as a form of demand information distortion. Lee et al (1997b) further discussed the managerial and practical aspects of the bullwhip effect, giving more industry examples. The bullwhip effect phenomenon is closely related to studies in systems dynamics (Forrester, 1961; Sterman, 1989; Senge, 1990). Sterman (1989) observed a systematic pattern of demand variation amplification in the Beer Game, and attributed it to behavioral causes (i.e., misperceptions of feedback). Macroeconomists have also studied the phenomenon (Holt et al, 1968; Blinder, 1981; Blanchard, 1983).

 

From Operational and Behavioral Causes of Supply Chain Instability

 

Supply chain instability is often described as the bullwhip effect, the tendency for variability to increase at each level of a supply chain as one moves from customer sales to production (Lee et al. 1997, Chen et al. 2000). While amplification from stage to stage is important, supply chain instability is a richer and more subtle phenomenon. The economy, and the networks of supply chains embedded within it, is a complex dynamic system and generates multiple modes of behavior. These include business cycles (oscillation), amplification of orders and production from consumption to raw materials (the bullwhip), and phase lag (shifts in the timing of the cycles from consumption to materials). High product returns and spoilage are common in industries from consumer electronics to hybrid seed corn (Gonçalves 2003). Many firms experience pronounced hockey-stick patterns in which orders and output rise sharply just prior to the end of a month or quarter as the sales force and managers rush to hit revenue goals. Boom and bust dynamics in supply chains are often worsened by phantom orders—orders customers place in response to perceived shortages in an attempt to gain a greater share of a shrinking pie (T. Mitchell 1923, Sterman 2000, ch. 18.3, Gonçalves 2002, Gonçalves and Sterman 2005).

What are the causes of supply chain instability? Why does supply chain instability persist, despite the lean revolution and tremendous innovations in technology? What can be done to stabilize supply chains and improve their efficiency?

Here I describe the origins of supply chain instability from a complex systems perspective. The dynamics of supply chain networks arise endogenously from their structure. That structure includes both operational and behavioral elements.

 

From Operational and Behavioral Causes of Supply Chain Instability

Oscillation, Amplification, and Phase Lag

Exhibit 1 shows industrial production in the US. The data exhibit several modes of behavior. First, the long-run growth rate of manufacturing output is about 3.4%/year. Second, as seen in the bottom panel, production fluctuates significantly around the growth trend. The dominant periodicity is the business cycle, a cycle of prosperity and recession of about 3–5 years in duration, but exhibiting considerable variability.

The amplitude of business cycle fluctuations in materials production is significantly greater than that in consumer goods production (exhibit 2). The peaks and troughs of the cycle in materials production also tend to lag behind those in production of consumer goods. Typically, the amplitude of fluctuations increases as they propagate from the customer to the supplier, with each upstream stage tending to lag behind its customer. These three features, oscillation, amplification, and phase lag, are pervasive in supply chains.

 

From Booms, Busts, and Beer: Understanding the Dynamics of Supply Chains

A central question in operations management is whether the oscillations, amplification and phase lag observed in supply chains arise as the result of operational or behavioral causes.

Operational theories assume that decision makers are rational agents who make optimal decisions given their local incentives and information.  Supply chain instability must then result from the interaction of rational actors with the physical and institutional structure of the system.

  • Physical structure includes the network linking customers and suppliers and the placement of inventories and buffers within it, along with capacity constraints and time delays in production, order fulfillment, transportation, and so on.
  • Institutional structure includes the degree of horizontal and vertical coordination and competition among firms, the availability of information to decision makers in each organization, and the incentives faced by each decision maker.

Behavioral explanations also capture the physical and institutional structure of supply chains, but view decision makers as boundedly rational actors with imperfect mental models, actors who use heuristics to make ordering, production, capacity acquisition, pricing and other decisions (Morecroft 1985, Sterman 2000, Boudreau et al. 2003, Gino & Pisano 2008, Bendoly et al. 2010, Croson et al. 2013).

Amplifications and Phase Lag

Amplification and phase lag arise from the presence of basic physical structures including stocks of inventory and delays in adjusting production or deliveries to changes in incoming orders.

Oscillations

Oscillations, however, are not inevitable. They arise from boundedly rational, behavioral decision processes

The difference matters: if supply chain instability arises from operational factors and rational behavior, then policies must be directed at changing the physical and institutional structure of the system, including incentives.

If, however, instability arises from bounded rationality and emotional arousal such policies may not be sufficient.

 

Key People:

  • Jay W Forrester
  • John Sterman
  • Rogelio Oliva
  • Hau L Lee

 

Key Terms:

  • Bullwhip Effect
  • Oscillations
  • Amplifications
  • Negative Feedback Loop
  • Positive Feedback Loop
  • Phase Lag
  • Supply Chain Networks
  • Inventory Management
  • Production Smoothening
  • Beer Distribution Game
  • Industrial Dynamics
  • Operational and Institutional Structures
  • Behavioral causes
  • Instability
  • Variability
  • Uncertainty

 

Key Sources of Research:

 

Behavioral Causes of Demand Amplification in Supply Chains: “Satisficing” Policies with Limited Information Cues

Rogelio Oliva

Paulo Gonçalves

 

Click to access 1889_087eb4f1-0532-4a7d-a206-3d565efc02af_2005-OLIVA133.pdf

 

 

REDUCING THE IMPACT OF DEMAND PROCESS VARIABILITY WITHIN A MULTI-ECHELON SUPPLY CHAIN

Francisco Campuzano Bolarín1,Lorenzo Ros Mcdonnell1, Juan Martín García

2008

 

Click to access CAMPU215.pdf

 

 

The impact of order variance amplification/dampening on supply chain performance

 

Robert N. Boute, Stephen M. Disney, Marc R. Lambrecht and Benny Van Houdt

 

Click to access KBI_0603.pdf

 

 

Coping with Uncertainty: Reducing ”Bullwhip” Behaviour in Global Supply Chains

 

Rachel Mason-Jones, and Denis R. Towill

Click to access 24557527da7aa9da7de238fe7f4a463b2af6.pdf

 

 

Bullwhip in Supply Chains ~ Past, Present and Future

Steve Geary Stephen M Disney and Denis R Towill

 

Click to access 492a6e6ae1d0f186fe2570b7477428e8e467.pdf

 

 

Shrinking the Supply Chain Uncertainty Circle

R Mason-Jones

Click to access 19980901d.pdf

 

 

THE BULLWHIP EFFECT IN SUPPLY CHAIN Reflections after a Decade

Gürdal Ertek, Emre Eryılmaz

 

Click to access ertek_eryilmaz_cels2008.pdf

 

 

Information distortion in a supply chain: The bullwhip effect

Hau L Lee; V Padmanabhan; Seugjin Whang

Management Science; Apr 1997; 43, 4;

Click to access f26117d56ab96aabe2d6cee4c390ab20ee18.pdf

 

 

 

THE SUPPLY CHAIN COMPLEXITY TRIANGE: UNCERTAINTY GENERATION IN THE SUPPLY CHAIN

 

Click to access 140687.pdf

 

 

The Bullwhip Effect in Supply Chains

Hau L. Lee, V. Padmanabhan and Seungjin Whang

1997

http://sloanreview.mit.edu/article/the-bullwhip-effect-in-supply-chains/

 

 

The Bullwhip Effect: Analysis of the Causes and Remedies

 

Jonathan Moll

Rene Bekker

 

Click to access werkstuk-moll_tcm243-354834.pdf

 

 

‘BULLWHIP’ AND ‘BACKLASH’ IN SUPPLY PIPELINES

Vinaya Shukla, Mohamed M Naim, Ehab A Yaseen

 

https://hal.archives-ouvertes.fr/hal-00525857/document

 

 

How human behaviour amplifies the bullwhip effect – a study based on the beer distribution game online

Joerg Nienhaus, Arne Ziegenbein*, Christoph Duijts

 

Click to access Bullwhip_Effect_Article.pdf

 

 

The Bullwhip Effect in Different Manufacturing Paradigm: An Analysis

Shamila Nabi KHAN1 Mohammad Ajmal KHAN2 Ramsha SOHAIL

 

Click to access 11.pdf.pdf

 

 

On replenishment rules, forecasting and the bullwhip effect in supply chains

Stephen M. Disney1 and Marc R. Lambrecht

 

Click to access Disney%20-%20On%20replenishment%20rules%20forecasting%20and%20the%20bullwhip%20effect%20in%20supply%20chains%20pre%20print.pdf

 

 

Causes and Remedies of Bullwhip Effect in Supply Chain

Sivakumar Balasubramanian Larry Whitman Kartik Ramachandran Ravindra Sheelavant

 

Click to access 2001IERCBullwhip.pdf

 

 

Booms, Busts, and Beer: Understanding the Dynamics of Supply Chains

John Sterman

 

Click to access Sterman%20Beh%20Ops%20Handbook%20Chapter%20140210.pdf

 

 

Modeling and Measuring the Bullwhip Effect

Li Chen and Hau L. Lee

2015

Click to access Chen_Lee_Bullwhip_2015.pdf

 

 

Operational and Behavioral Causes of Supply Chain Instability

John D. Sterman

Click to access 2a3118c5c7d2bd475335549b0b943009d66c.pdf

 

 

Order Stability in Supply Chains: Coordination Risk and the Role of Coordination Stock

Rachel Croson, Karen Donohue, Elena Katok, and John Sterman

Click to access Order%20Stability%20in%20SCs050212.pdf

Click to access Order_Stability_070505.pdf

 

 

SUPPLY CHAIN DYNAMICS, THE “BEER DISTRIBUTION GAME” AND MISPERCEPTIONS IN DYNAMIC DECISION MAKING

John D. Sterman

Click to access E6-63-01-02.pdf

 

 

When Do Minor Shortages Inflate To Great Bubbles?

Paulo Gonçalves

2002

 

Click to access Gonca1.pdf

 

 

A new technology paradigm for collaboration in the supply chain

Branko Pecar and Barry Davies

Click to access c522d454d1dc036a22db29b2dee005dbc44e.pdf

 

 

MANAGERIAL INSIGHTS ON THE IMPACT OF FORECASTING AND INFORMATION ON VARIABILITY IN A SUPPLY CHAIN

Frank Chent, Zvi Drezner2 , Jennifer K. Ryan3 and David Simchi-Levi

 

Click to access 4%20chen.pdf

 

 

Supply and Production Networks: From the Bullwhip Effect to Business Cycles

Dirk Helbing Stefan Laemmer

2004

 

Click to access 04-12-033.pdf

 

 

Inventory dynamics and the bullwhip effect : studies in supply chain performance

Udenio, M.

2014

 

Click to access 776508.pdf

 

 

RECENT WORK ON BUSINES CYCLES IN HISTORICAL PERSPECTIVE: REVIEW OF THEORIES AND EVIDENCE

VictorZarnowitz

1984

 

Click to access w1503.pdf

 

 

THEORY AND HISTORY BEHIND BUSINESS CYCLES:ARE THE 1990S

THE ONSET OF A GOLDEN AGE?

 

Victor Zarnowitz

WorkingPaper7010 htp:/w.nber.org/papers/w7010

1999

 

Click to access w7010.pdf

 

 

The Beginning of System Dynamics

Jay W. Forrester

 

Click to access D-4165-1.pdf

 

 

Profiles in Operations Research: Jay Wright Forrester

David C. Lane John D. Sterman

 

Click to access jwf-profile-in-op.pdf

 

 

SYSTEM DYNAMICS MODELLING IN SUPPLY CHAIN MANAGEMENT: RESEARCH REVIEW

2000

 

Click to access 54fe11ea0aaa47f4c8e08959be2ef52d50a6.pdf

 

 

INDUSTRIAL DYNAMICS-AFTER THE FIRST DECADE

JAY W. FORRESTER

 

Click to access Forrester68.pdf

 

 

Industrial Dynamics

Jay W Forrester

1961

 

 

Business Dynamics

John Sterman

2000

 

Jay W. Forrester and System Dynamics

Jay W. Forrester and System Dynamics

 

 

Jay Forrester passed away at the age of 98 on November 16, 2016

The link below will take you to JWF memorial webpage.

Jay W Forrester Memorial Web Page at the System Dynamics Society

I admire Jay W Forrester greatly.  I was introduced to Operational Research and System dynamics back in early 1980s after I graduated from IIT Roorkee Engineering undergraduate degree in India.  I had bought a book on Operations Research at a road side book seller in Dariya Ganj, Old Delhi, India.

I met Jay on three occasions.  I attended Business Dynamics Executive Education program at MIT Sloan School of Management back in 2002.  Jay was one of the Instructor.  Then I again met Jay at 2003 SDS International Conference at New York City.  Last time I met Jay was in Washington DC at the Club of Rome Symposium celebrating 40 yrs anniversary of publication of The Limits to Growth book.

Jay will be missed greatly.

– Mayank Chaturvedi

 

Jay Forrester’s vision of future of Economics and System Dynamics.

Traditional mainstream academic economics, by trying to be a science, has failed to answer major questions about real- life economic behavior. Economics should become a systems profession, such as management, engineering, and medicine. By closely observing the structures and policies in business and government, simulation models can be constructed to answer questions about business cycles, causes of major depressions, inflation, monetary policy, and the validity of descriptive economic theories. A system dynamics model, as a general theory of economic behavior, now endogenously generates business cycles, Kuznets cycles, the economic long wave, and growth. A model is a theory of the behavior that it generates. The economic model provides the theory, thus far missing from economics, for the Great Depression of the 1930s and how such episodes can recur 50–70 years apart. Simpler system dynamics models can become the vehicle for a relevant and exciting pre-college economics education.

 

From PHD thesis of I David Wheat

Within the interdisciplinary system dynamics (SD) community, the motivation to improve understanding of economic systems came nearly fifty years ago with Jay W. Forrester’s seminal call for a new kind of economics education, a call that he has renewed in the K-12 education setting in recent years. John Sterman’s encyclopedic Business Dynamics is a symbol not only of the breadth of his own economic policy and management research and teaching but also the range of work done by others in this field.

Teaching the economics of resource management with system dynamics tools has been the devotion of Andrew Ford and Erling Moxnes. James Lyneis took his management consultant’s expertise into the university classroom and developed an SD-based microeconomics course. Economists Michael Radzicki and Kaoru Yamaguchi have developed complete graduate-level economics courses on a system dynamics foundation. An informal survey produced this list of others who have used SD as a teaching tool in economics courses: Glen Atkinson, Scott Fullwiler, John Harvey, Steve Keen, Ali Mashayekhi, Jairo Parada, Oleg Pavlov, Khalid Saeed, Jim Sturgeon, Linwood Tauheed, Pavlina Tcherneva, Scott Trees, Eric Tymoigne, Lars Weber, and Agnieszka Ziomek, and that is surely just a fraction.

 

Key Sources of Research:

 

Economic theory for the new millennium

Jay W. Forrester

2003

 

System Dynamics Review vol 29, No 1 (January-March 2013): 26–41

 

 

 

Three slices of Jay Forrester’s general theory of economic behavior: An interpretation

 

Khalid Saeed

Worcester Polytechnic Institute Worcester, MA, USA

February 13, 2013

 

Click to access P1018.pdf

 

 

System Dynamics: A disruptive science

A conversation with Jay W. Forrester, founder of the field

Khalid Saeed Worcester Polytechnic Institute Sept. 2013

 

Click to access ISDC2013_forresterchat.pdf

http://digitalcommons.wpi.edu/cgi/viewcontent.cgi?article=1000&context=ssps-papers&sei-redir=1&referer=https%3A%2F%2Fscholar.google.com%2Fscholar%3Fstart%3D40%26q%3Djay%2Bw%2Bforrester%2Bsystem%2Bdynamics%26hl%3Den%26as_sdt%3D0%2C47%26as_ylo%3D2013#search=%22jay%20w%20forrester%20system%20dynamics%22

 

 

Unintended Consequences

Jay Forrester

Click to access SB_Forrester.pdf

 

 

A dynamic synthesis of basic macroeconomic theory : implications for stabilization policy analysis

Nathan Forrester

PHD THESIS

https://dspace.mit.edu/handle/1721.1/15739

 

 

SYSTEM DYNAMICS: PORTRAYING BOUNDED RATIONALITY

 

John D.W. Morecroft

1982

 

https://dspace.mit.edu/bitstream/handle/1721.1/49181/systemdynamicspo00more.pdf?sequence=1

 

 

THE SYSTEM DYNAMICS NATIONAL MODEL:  MACRO BEHAVIOR FROM MICRO STRUCTURE

JAY W FORRESTER

 

Click to access 1470_0a924c5b-b909-42fa-be9b-932588278f36_forre004.pdf

 

 

1976 Economic Forecast Report including studies by Jay W Forrester and Nathial Mass

US congress Joint Economic Review of US Economy

Click to access 76603310f_1.pdf

 

 

Backround Material for a Meeting on Long Waves, Depression and Innovation –

IMPLICATIONS FOR NATIONAL AND REGIONAL ECONOMIC POLICY

Jay W. Forrester, Alan K.Oraham, Peter M.Senge, John D Sterman

 

Siena/Florence, October 26-29, 1983

Bianchi, G., Bruckmann, G. and Vasko, T.

 

Click to access CP-83-044.pdf

 

 

 

Industrial Dynamics-After the First Decade

Author(s): Jay W. Forrester

Management Science, Vol. 14, No. 7, Theory Series (Mar., 1968), pp. 398-415

 

Click to access Forrester68.pdf

 

 

Systems Analysis as a Tool for Urban Planning

JAY W. FORRESTER, FELLOW, IEEE

1970

 

Click to access forrester.pdf

 

 

IS ECONOMETRIC MODELING OBSOLETE?

AUTHOR: Mr. Oakley E. Van Slyke

 

Click to access 80dpp650.pdf

 

 

Money and Macroeconomic Dynamics : Accounting System Dynamics Approach

 

Kaoru Yamaguchi

Ph.D. Japan Futures Research Center

Awaji Island, Japan

November 11, 2016

 

Click to access Macro%20Dynamics.pdf

 

 

The Feedback Method : A System Dynamics Approach to Teaching Macroeconomics

I. David Wheat, Jr.

Dissertation for the degree philosophiae doctor (PhD)

System Dynamics Group, Social Science Faculty University of Bergen

 

http://bora.uib.no/bitstream/handle/1956/2239/Introduction_David_Wheat.pdf?sequence=46

 

 

Disequilibrium Systems Representation of Growth Models—Harrod-Domar, Solow, Leontief, Minsky, and Why the U.S. Fed Opened the Discount Window to Money-Market Funds

Frederick Betz

2015

 

Click to access ME_2015120814432915.pdf

 

 

Cyclical dynamics of airline industry earnings

Kawika Piersona and John D. Sterman

System Dynamics Review vol 29, No 3 (July-September 2013): 129–156

Click to access 5550ead108ae739bdb9202a9.pdf

 

 

 

Modeling Financial Instability

Steve Keen

Click to access Keen2014ModelingFinancialInstability.pdf

 

 

Harvey, J.T.,

2013.

Keynes’s trade cycle: a system dynamics model.

Journal of Post Keynesian Economics, 36(1), pp.105-130.

 

 

ECONOMICS, TECHNOLOGY, AND THE ENVIRONMENT

Jay Forrester

 

https://dspace.mit.edu/bitstream/handle/1721.1/2197/SWP-1983-18213738.pdf?sequence=1

 

 

Forrester, J. W. (1968). Market Growth as Influenced by Capital Investment. Industrial Management Review (now Sloan Management Review), 9(2), 83-105.

 

 

Forrester, J. W 1971). Counterintuitive Behavior of Social Systems. Collected Papers of J.W. Forrester. Cambridge, MA: Wright-Allen Press.

 

 

Forrester, J. W (1976). Business Structure, Economic Cycles, and National Policy. Futures, June.

 

 

Forrester, J. W (1979). An Alternative Approach to Economic Policy: Macrobehavior from Microstructure. In Kamrany & Day (Eds.), Economic Issues of the Eighties. Baltimore: The Johns Hopkins University Press.

 

 

Forrester, J. W., Mass, N. J., & Ryan, C. (1980). The System Dynamics National Model: Understanding Socio-economic Behavior and Policy Alternatives. Technology Forecasting and Social Change, 9, 51-68.

 

 

Forrester, N. B. (1982). A Dynamic Synthesis of Basic Macroeconomic Theory: Implications for Stabilization Policy Analysis. Unpublished PhD dissertation, Massachusetts Institute of Technology, Cambridge, MA.

 

 

Low, G. (1980). The Multiplier-Accelerator Model of Business Cycles Interpreted from a System Dynamics Perspective. In J. Randers (Ed.), Elements of the System Dynamics Method. Cambridge, MA: MIT Press.

 

 

Mass, N. J. (1975). Economic Cycles: An Analysis of Underlying Causes. Cambridge, MA: Wright-Allen Press, Inc.

 

 

Mass, N. J.(1980). Stock and Flow Variables and the Dynamics of Supply and Demand. In J. Randers (Ed.), Elements of the System Dynamics Method. (pp. 95-112). Cambridge, MA: MIT Press.

 

 

Meadows, D. L., Behrens III, W. W., Meadows, D. H., Naill, R. F., & Zahn, E. (1974). Dynamics of Growth in a Finite World. Cambridge, MA: Wright-Allen Press.

 

 

Morecroft, J. D. W. & Sterman, J. D. (Eds.). (1994). Modeling for Learning Organizations. Portland, OR: Productivity Press.

 

 

Radzicki, M. (1993). A System Dynamics Approach to Macroeconomics (Guest lecture at the Department of Information Science, University of Bergen.).

 

 

Richardson, G. P. (1991). Feedback Thought in Social Science and Systems Theory. Waltham, MA: Pegasus Communications, Inc.

 

 

Senge, P. M. (1990). The Fifth Discipline: The Art and Practice of the Learning Organization. New York: Doubleday.

 

 

Sterman, J. D. (1985). A Behavioral Model of the Economic Long Wave. Journal of Economic Behavior and Organization, 6, 17-53.

 

 

Sterman, J. D. (2000). Business Dynamics: Systems Thinking and Modeling for a Complex World. Boston, MA: McGraw-Hill Companies.

Feedback Thought in Economics and Finance

Feedback Thought in Economics and Finance

  • Negative Feedbacks
  • Positive Feedbacks
  • Stocks and Flows
  • Limiting Factors

 

Key People:

  • Jay Forrester
  • George Richardson
  • John Sterman
  • Michael Radzicki
  • Mikhail Oet
  • Oleg Pavlov
  • Eric D. Beinhocker
  • Stuart A. Umpleby
  • Khalid Saeed
  • Kaoru Yamaguchi

 

Reflexivity and Second order economics are closely related concepts.

 

From System Dynamics and Its Contribution to Economics and Economic Modeling

 

System dynamics is a computer simulation modeling methodology that is used to analyze complex nonlinear dynamic feedback systems for the purposes of generating insight and designing policies that will improve system performance. It was originally created in 1957 by Jay W. Forrester of the Massachusetts Institute of Technology as a methodology for building computer simulation models of problematic behavior within corporations. The models were used to design and test policies aimed at altering a corporation’s structure so that its behavior would improve and become more robust.

Today, system dynamics is applied to a large variety of problems in a multitude of academic disciplines, including economics. System dynamics models are created by identifying and linking the relevant pieces of a system’s structure and simulating the behavior generated by that structure. Through an iterative process of structure identification, mapping, and simulation a model emerges that can explain (mimic) a system’s problematic behavior and serve as a vehicle for policy design and testing. From a system dynamics perspective a system’s structure consists of stocks, flows, feedback loops, and limiting factors.

Stocks can be thought of as bathtubs that accumulate/de-cumulate a system’s flows over time. Flow can be thought of as pipe and faucet assemblies that fill or drain the stocks. Mathematically, the process of flows accumulating/de-cumulating in stocks is called integration. The integration process creates all dynamic behavior in the world be it in a physical system, a biological system, or a socioeconomic system. Examples of stocks and flows in economic systems include a stock of inventory and its inflow of production and its outflow of sales, a stock of the book value of a firm’s capital and its inflow of investment  spending and its outflow of depreciation, and a stock of employed labor and its inflow of hiring and its outflow of labor separations.

Feedback is the transmission and return of information about the amount of information or material that has accumulated in a system’s stocks. Information travels from a stock back to its flow(s) either directly or indirectly, and this movement of information causes the system’s faucets to open more, close a bit, close all the way, or stay in the same place. Every feedback loop has to contain at least one stock so that a simultaneous equation situation can be avoided and a model’s behavior can be revealed recursively. Loops with a single stock are termed minor, while loops containing more than one stock are termed major. 

Two types of feedback loops exist in system dynamics modeling: positive loops and negative loops. Generally speaking, positive loops generate self-reinforcing behavior and are responsible for the growth or decline of a system. Any relationship that can be termed a virtuous or vicious circle is thus a positive feedback loop. Examples of positive loops in economic systems include path dependent processes, increasing returns, speculative bubbles, learning by-doing, and many of the relationships found in macroeconomic growth theory. Forrester [12], Radzicki and Sterman [46],Moxnes [32], Sterman (Chap. 10 in [55]), Radzicki [44], Ryzhenkov [49], and Weber [58] describe system dynamics models of economic systems that possess dominant positive feedback processes.

Negative feedback loops generate goal-seeking behavior and are responsible for both stabilizing systems and causing them to oscillate. When a negative loop detects a gap between a stock and its goal it initiates corrective action aimed at closing the gap. When this is accomplished without a significant time delay, a system will adjust smoothly to its goal. On the other hand, if there are significant time lags in the corrective actions of a negative loop, it can overshoot or undershoot its goal and cause the system to oscillate. Examples of negative feedback processes in economic systems include equilibrating mechanisms (“auto-pilots”) such as simple supply and demand relationships, stock adjustment models for invetory control, any purposeful behavior, and many of the relationships found in macroeconomic business cycle theory. Meadows [27], Mass [26], Low [23], Forrester [12], and Sterman [54] provide examples of system dynamics models that generate cyclical behavior at the macro-economic and micro-economic levels.

From a system dynamics point of view, positive and negative feedback loops fight for control of a system’s behavior. The loops that are dominant at any given time determine a system’s time path and, if the system is nonlinear, the dominance of the loops can change over time as the system’s stocks fill and drain. From this perspective, the dynamic behavior of any economy that is, the interactions between the trend and the cycle in an economy over time can be explained as a fight for dominance between the economy’s most significant positive and negative feedback loops.

 

Key Sources of Research:

 

Systemic Financial Feedbacks – Conceptual Framework and Modeling Implications

Dieter Gramlich1 and Mikhail V. Oet

Click to access 54992d5c0cf2519f5a1df20b.pdf

 

 

FEEDBACK MECHANISMS IN THE FINANCIAL SYSTEM: A MODERN VIEW

Mikhail V. Oet

Oleg V. Pavlov

Click to access P1441.pdf

 

 

Mr. Hamilton, Mr. Forrester, and a Foundation for Evolutionary Economics

Michael J. Radzicki

 

Click to access 0a85e52e41951a468c000000.pdf

 

European Contributions to Evolutionary Institutional Economics: The Cases of ‘Cumulative Circular Causation’ (CCC) and ‘Open Systems Approach’ (OSA).
Some Methodological and Policy Implications

 

Sebastian Berger and Wolfram Elsner

http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.519.7169&rep=rep1&type=pdf

 

System Dynamicsand Its Contribution to Economics and Economic Modeling

MICHAEL J. RADZICKI

 

Click to access 02e7e53331fe5f394b000000.pdf

 

 

Institutional Economics, Post Keynesian Economics, and System Dynamics: Three Strands of a Heterodox Economics Braid

Michael J. Radzicki, Ph.D.

Click to access 02e7e53331eeea388c000000.pdf

 

 

Was Alfred Eichner a System Dynamicist?

by

Michael J. Radzicki

Click to access 0f317536d3f41a13fb000000.pdf

 

Second-Order Economics as an Example of Second-Order Cybernetics

Stuart A. Umpleby

 

Click to access 890.pdf

 

Reflexivity, complexity, and the nature of social science

Eric D. Beinhocker

 

Click to access Beinhocker%20(JEM%202013).pdf

 

Path dependence, its critics and the quest for ‘historical economics’

By

Paul A. David

Click to access 0deec53b482217c114000000.pdf

 

Endogenous Feedback Perspective on Money in a Stock-Flow Consistent Model

I. David Wheat
University of Bergen

 

Click to access Wheat%20Endogenous%20Feedback%20Perspective%20on%20Money%20WP.pdf

 

Classical Economics on Limits to Growth

Khalid Saeed

 

Click to access Classical%20Economics%20on%20Limits%20to%20Growth.pdf

 

 

Misperceptions of Feedback in Dynamic Decisionmaking

John D. Sterman

 

Click to access 54359e4e0cf2bf1f1f2b3520.pdf

 

Learning in and about complex systems

John D. Sterman

 

Click to access sterman-learning-in-and-about-complex-systems.pdf

 

Micro-worlds and Evolutionary Economics

Michael J. Radzicki

Click to access radzi533.pdf

 

Feedback Thought in Social Science and Systems Theory

George Richardson

Pegasus Communications, Inc. ©1999
ISBN:1883823463

 

The Feedback concept in American Social Sciences 

George Richardson

1983

Click to access richa001.pdf

 

Evolutionary Economics and System Dynamics

Radzicki and Sterman

 

Effects of Feedback Complexity on Dynamic Decision Making
Ernst Diehl, John D. Sterman

Organizational Behavior and Human Decision Processes

Volume 62, Issue 2, May 1995, Pages 198-215

 

Old Wine in a New Bottle:
Towards a Common Language for Post-Keynesian Macroeconomics Model

Ginanjar Utama

2014

Click to access P1307.pdf

 

On Component Based Modeling Approach using System Dynamics for The Financial System (With a Case Study of Keen-Minsky Model)

Ginanjar Utama

2013

Click to access P1209.pdf

 

On the Monetary and Financial Stability under A Public Money System

– Modeling the American Monetary Act Simplified –

Kaoru Yamaguchi

 

Click to access P1065.pdf

 

Integration of Real and Monetary Sectors with Labor Market
– SD Macroeconomic Modeling (3) –

Kaoru Yamaguchi

http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.436.1085&rep=rep1&type=pdf

 

Balance of Payments and Foreign Exchange Dynamics

– SD Macroeconomic Modeling (4) –

Kaoru Yamaguchi, Ph.D

2007

Click to access YAMAG211.pdf

 

 

Money and Macroeconomic Dynamics

Accounting System Dynamics Approach

Kaoru Yamaguchi, Ph.D

 

Click to access Macro%20Dynamics.pdf

 

Does Money Matter on the Formation of Business Cycles and Economic Recessions ?
– SD Simulations of A Monetary Goodwin Model –

 

Kaoru Yamaguchi

Click to access DBS12-01.pdf

 

Head and Tail of Money Creation and its System Design Failures

– Toward the Alternative System Design –

JFRC Working Paper No. 01-2016

Kaoru Yamaguchi, Ph.D.

Yokei Yamaguchi

Click to access Head-and-Tail-2016_WP__-_Japan_Futures_Research_Center.pdf

 

Modelling the Great Transition

 

Emanuele Campiglio

New Economics Foundation

Click to access Emanuel-SD-conference-9-2-12.pdf

 

The role of System Dynamics modelling to understand food chain complexity and address challenges for sustainability policies

Irene Monasterolo1, Roberto Pasqualino, Edoardo Mollona

 

Click to access CFP3-06_Full_Paper.pdf

 

Dynamic regional economic modeling: a systems approach

I. David Wheat

2014

 

Click to access 1.17_wheat_pawluczuk.pdf

 

Expectation Formation and Parameter Estimation in Uncertain Dynamical Systems: The System Dynamics Approach to Post Keynesian-Institutional Economics

Introduction

 

Michael J. Radzicki

 

Click to access 0deec536d3da974962000000.pdf

 

The Circular and Cumulative Structure of Administered Pricing

Mark Nichols, Oleg Pavlov, and Michael J. Radzicki

2006

Click to access 02e7e5282d33c933df000000.pdf

 

A System Dynamics Approach to the Bhaduri‐Marglin Model

Klaus D. John

Click to access P1306.pdf

 

An Institutional Dynamics Model of the Euro zone crisis: Greece as an Illustrative Example

Domen Zavrl

Miroljub Kljajić

Click to access P1144.pdf

 

Is system dynamics modelling of relevance to neoclassical economists? 

Douglas J. Crookes Martin P. De Wit

Click to access 00b7d53861d6b14d9f000000.pdf

 

System dynamics modelling and simulating the effects of intellectual capital on economic growth

Ivona Milić Beran

http://hrcak.srce.hr/ojs/index.php/crorr/article/viewFile/2803/2121