I have studied complex systems science from following teachers:
Yaneer Bar Yam of NECSI: Physical, Biological, and Social Systems
Scott Page of University of Michigan: Model Thinking: Complex Systems in Economics and Social/Political Sciences.
Melanie Mitchell of Santa Fe Institute: Introduction to Complex Systems
Michael Kearns of University of Pennsylvania: Networked Life: Science of Networks
Ravi Iyenger of Mount Sinai Icahn School of Medicine: Introduction to Systems Biology
Perry Mehrling of Columbia University/INET: Economics of Money and Banking
Bottom Up Modeling
Agent Based Modeling
Santa Fe Institute
Yaneer Bar Yam
Artifical Life (A-Life)
Albert Laszlo Barabasi
Small World Networks
Scale Free Networks
J Doyne Farmer
W. Brian Arthur
Six Degrees of Separation
Non Linear Dynamics
Competition and Cooperation
What are Complex Systems?
Source: A Brief History of Systems Science, Chaos and Complexity
Key Topics in Complex Systems
Sources: Complex Systems: A Survey
Lattices and Networks
Discrete Systems and Cellular Automata
Scaling and Criticality
Adaptation and Game Theory
Agent based Modeling
Chaos and Fractals
Spontaneous Order and Synchronization
The original journal devoted to the science, mathematics and engineering of systems with simple components but complex overall behavior; publishes high-quality articles that focus on, but are not limited to, the following areas:
Dynamic, topological and algebraic aspects of cellular automata and discrete dynamical systems
Complex systems and complexity theory
Algorithmic complexity and information theory
Emergent properties of dynamical systems
Formal languages, grammars and automata
Algorithmic information dynamics
Symbolic dynamics and connections to continuous systems
Tilings, rewriting and substitution systems
Synchronous versus asynchronous models
Applications of automata to areas such as machine and deep learning, physics, biology, social sciences and others
Source: A Brief History of Systems Science, Chaos and Complexity
History of Systems and Complex Systems
Source: A Brief History of Systems Science, Chaos and Complexity
Yang-YuLiu1,2 andAlbert-L ́aszl ́oBarab ́asi3,2,4,5 1Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School,
Boston, Massachusetts 02115, USA 2Center for Cancer Systems Biology, Dana-Farber Cancer Institute, Boston, Massachusetts 02115,
USA 3Center for Complex Network Research and Departments of Physics, Computer Science and Biology, Northeastern University, Boston, Massachusetts 02115, USA 4Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts 02115, USA 5Center for Network Science, Central European University, Budapest 1052, Hungary
(Dated: March 15, 2016)
Physical approach to complex systems
Jarosław Kwapień a,∗, Stanisław Drożdż a,b
a Complex Systems Theory Department, Institute of Nuclear Physics, Polish Academy of Sciences, PL–31-342 Kraków, Poland b Institute of Computer Science, Faculty of Physics, Mathematics and Computer Science, Cracow University of Technology, PL–31-155 Kraków, Poland
Physics Reports 2011
Dynamics of Complex Systems (Studies in Nonlinearity)
Addison-Wesley, New York, 1997; ISBN 0-201-55748-7; 800 pp.,
A complex systems approach to constructing better models for managing financial markets and the economy
J. Doyne Farmer1, M. Gallegati2, C. Hommes3, A. Kirman4, P. Ormerod5, S. Cincotti6, A. Sanchez7, and D. Helbing8
1 Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, NM 87501, USA
2 DiSES, Universit Politecnica delle Marche, Ancona, Italy
3 CeNDEF, University of Amsterdam, The Netherlands
4 GREQAM, Aix Marseille Universit ́e, EHESS, France
5 Volterra Partners, London and University of Durham, UK
6 DIME-DOGE.I, University of Genoa, Italy
7 GISC, Universidad Carlos III de Madrid, Spain
8 ETH, Zu ̈rich
Received 1 August 2012 / Received in final form 9 October 2012 Published online 5 December 2012
Eur. Phys. J. Special Topics 214, 295–324 (2012)
THE ONTOLOGY OF COMPLEX SYSTEMS: Levels of Organization, Perspectives, and Causal Thickets*
(Canadian Journal of Philosophy, supp. vol #20, 1994, ed. Mohan Matthen and Robert Ware, University of Calgary Press, 207-274). by William C. Wimsatt Department of Philosophy University of Chicago January 4, 1994 firstname.lastname@example.org
The Universal Key to the Stability of Networks and Complex Systems
P ́eter Csermely
February 25, 2009
Weak Links: Stabilizers of Complex Systems from Proteins to Social Networks,
by Peter Csermely.
2006 XX, 408 p. 37 illus. 3-540-31151-3. Berlin: Springer, 2006.
An Introduction to Agent-Based Modeling: Modeling Natural, Social, and Engineered Complex Systems with NETLogo
Wilensky, Uri and Rand, William MIT Press: London, 2015 ISBN 978-0262731898 (pb)
Dynamics of Complex Systems: Scaling Laws for the Period of Boolean Networks
Réka Albert and Albert-László Barabási*
Department of Physics, University of Notre Dame, Notre Dame, Indiana 46556
(Received 28 April 1999)
PHYSICAL REVIEW LETTERS
VOLUME 84, NUMBER 24
12 JUNE 2000
Cities as Complex Systems: Scaling, Interactions, Networks, Dynamics and Urban Morphologies
Centre for Advanced Spatial Analysis, University College London, 1-19 Torrington Place, London WC1E 6BT, UK Email: email@example.com, Web: www.casa.ucl.ac.uk
The Encyclopedia of Complexity & System Science, Springer, Berlin, DE, forthcoming 2008. Date of this paper: February 25, 2008.
Scale invariance and universality: organizing principles in complex systems
H.E. Stanleya;∗, L.A.N. Amarala , P. Gopikrishnana , P.Ch. Ivanova , T.H. Keittb , V. Pleroua
aCenter for Polymer Studies and Department of Physics, Boston University, Boston, MA 02215, USA bNational Center for Ecological Analysis and Synthesis, 735 State Street, Suite 300, Santa Barbara, CA 93101, USA
Physica A 281 (2000) 60–68
A pragmatist approach to transdisciplinarity in sustainability research: From complex systems theory to reflexive science
Department of Philosophy, University of Bristol, U.K.
Department of Mathematics and Centre for Complexity Sciences, University of Bristol, U.K.
(Dated: March 8, 2012)
Modelling and prediction in a complex world
Michael Battya, Paul M. Torrensb,*
aCentre for Advanced Spatial Analysis, University College London, 1 to 19 Torrington Place, London WC1E 6BT, UK bDepartment of Geography, University of Utah, 260 S. Central Campus Dr., Rm. 270, Salt Lake City, UT 84112-9155, USA
Available online 19 March 2005
Complex networks Augmenting the framework for the study of complex systems
THE EUROPEAN PHYSICAL JOURNAL B
L.A.N. Amarala and J.M. Ottino Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL 60208, USA
Received 12 November 2003 Published online 14 May 2004
Eur. Phys. J. B 38, 147–162 (2004) DOI: 10.1140/epjb/e2004-00110-5
Learning from Evidence in a Complex World
John D. Sterman Jay W. Forrester Professor of Management and Professor of Engineering Systems Sloan School of Management Massachusetts Institute of Technology 30 Wadsworth Street, E53-351 Cambridge MA 02142 617.253.1951 firstname.lastname@example.org web.mit.edu/jsterman/www
Revision of May 2005
Forthcoming, American Journal of Public Health
INTERDISCIPLINARYDESCRIPTION OFCOMPLEX SYSTEMS
7(2), pp. 22-116, 2009 ISSN 1334-4684
Error and attack tolerance of complex networks
R ́eka Albert, Hawoong Jeong, Albert-L ́aszl ́o Barab ́asi
Department of Physics, University of Notre Dame, Notre Dame, IN 46556
The Kuramoto model in complex networks
Francisco A. Rodriguesa, Thomas K. DM. Peronb,c,∗, Peng Jic,d,∗, Jürgen Kurthsc,d,e,f
aDepartamento de Matemática Aplicada e Estatística, Instituto de Ciências Matemáticas e de Computação, Universidade de São Paulo, Caixa Postal 668, 13560-970 São Carlos, São Paulo, Brazil bInstituto de Física de São Carlos, Universidade de São Paulo, Caixa Postal 369, 13560-970, São Carlos, São Paulo, Brazil cPotsdam Institute for Climate Impact Research (PIK), 14473 Potsdam, Germany dDepartment of Physics, Humboldt University, 12489 Berlin, Germany eInstitute for Complex Systems and Mathematical Biology, University of Aberdeen, Aberdeen AB24 3UE, United Kingdom fDepartment of Control Theory, Nizhny Novgorod State University, Gagarin Avenue 23, Nizhny Novgorod 606950, Russia
Uncovering the overlapping community structure of complex networks in nature and society
Gergely Palla†‡, Imre Dere ́nyi‡, Ille ́s Farkas†, and Tama ́s Vicsek†‡ †Biological Physics Research Group of HAS, Pa ́zma ́ny P. stny. 1A, H-1117 Budapest, Hungary,
‡Dept. of Biological Physics, Eo ̈tvo ̈s University, Pa ́zma ́ny P. stny. 1A, H-1117 Budapest, Hungary.
System Dynamics: Systems Thinking and Modeling for a Complex World
John D. Sterman MIT Sloan School of Management Cambridge MA 02421
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
Cradle to Cradle
Closed Supply Chains
From Input to the European Commission from European EPAs about monitoring progress of the transition towards a circular economy in the European Union
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
Economic Input Output LCA
From Life cycle analysis (LCA) and sustainability assessment
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
From Regional distribution and losses of end-of-life steel throughout multiple product life cycles—Insights from the global multiregional MaTrace model
From Feasibility assessment of using the substance flow analysis methodology for chemicals information at macro level
From Hybrid Sankey diagrams: Visual analysis of multidimensional data forunderstanding 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
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.
Physical Input Output (PIOT) Tables: Developments and Future
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
Wassily Leontief and Input Output Analysis in Economics
Wassily Leontief: The Concise Encyclopedia of Economics | Library of Economics and Liberty
From the time he was a young man growing up in Saint Petersburg, Wassily Leontief devoted his studies to input-output analysis. When he left Russia at the age of nineteen to begin the Ph.D. program at the University of Berlin, he had already shown how leon walras’s abstract equilibrium theory could be quantified. But it was not until many years later, in 1941, while a professor at Harvard, that Leontief calculated an input-output table for the American economy. It was this work, and later refinements of it, that earned Leontief the Nobel Prize in 1973.
Input-output analysis shows the extensive process by which inputs in one industry produce outputs for consumption or for input into another industry. The matrix devised by Leontief is often used to show the effect of a change in production of a final good on the demand for inputs. Take, for example, a 10 percent increase in the production of shoes. With the input-output table, one can estimate how much additional leather, labor, machinery, and other inputs will be required to increase shoe production.
Most economists are cautious in using the table because it assumes, to use the shoe example, that shoe production requires the inputs in the proportion they were used during the time period used to estimate the table. There’s the rub. Although the table is useful as a rough approximation of the inputs required, economists know from mountains of evidence that proportions are not fixed. Specifically, when the cost of one input rises, producers reduce their use of this input and substitute other inputs whose prices have not risen. If wage rates rise, for example, producers can substitute capital for labor and, by accepting more wasted materials, can even substitute raw materials for labor. That the input-output table is inflexible means that, if used literally to make predictions, it will necessarily give wrong answers.
At the time of Leontief’s first work with input-output analysis, all the required matrix algebra was done using hand-held calculators and sheer tenacity. Since then, computers have greatly simplified the process, and input-output analysis, now called “interindustry analysis,” is widely used. Leontief’s tables are commonly used by the World Bank, the United Nations, and the U.S. Department of Commerce.
Early on, input-output analysis was used to estimate the economy-wide impact of converting from war production to civilian production after World War II. It has also been used to understand the flow of trade between countries. Indeed, a 1954 article by Leontief shows, using input-output analysis, that U.S. exports were relatively labor intensive compared with U.S. imports. This was the opposite of what economists expected at the time, given the high level of U.S. wages and the relatively high amount of capital per worker in the United States. Leontief’s finding was termed the Leontief paradox. Since then, the paradox has been resolved. Economists have shown that in a country that produces more than two goods, the abundance of capital relative to labor does not imply that the capital intensity of its exports should exceed that of its imports.
Throughout his life Leontief campaigned against “theoretical assumptions and nonobserved facts” (the title of a speech he delivered while president of the American Economic Association, 1970–1971). According to Leontief too many economists were reluctant to “get their hands dirty” by working with raw empirical facts. To that end Wassily Leontief did much to make quantitative data more accessible, and more indispensable, to the study of economics.
1941. The Structure of American Economy, 1919–1929. Cambridge: Harvard University Press.
1966. Essays in Economics: Theories and Theorizing. New York: Oxford University Press.
From NY Times
Wassily Leontief, Economist Who Won a Nobel, Dies at 93
By HOLCOMB B. NOBLE
Wassily Leontief, who won the Nobel prize in economics in 1973 for his analyses of America’s production machinery, showing how changes in one sector of the economy can exact changes all along the line, affecting everything from the price of oil to the price of peanut butter, died Friday night at the New York University Medical Center. He was 93.
His analytic methods, as the Nobel committee observed, were adopted and became a permanent part of production planning and forecasting in scores of industrialized nations and in private corporations all over the world.
Following the model of his so-called input-output analysis, General Electric, for example, was able to load data from 184 sectors of the economy — such as energy, home construction and transportation — into a mammoth computer to help it predict how the energy crisis brought on by the Arab oil boycott in 1973 would affect public demand for its products and services, from light bulbs to turbines.
A well-known academic figure, Mr. Leontief was the director of the Institute for Economic Analysis of New York University from 1975 until 1991; even after his retirement he still taught at the university into his 90’s. Before coming to N.Y.U. he taught economics at Harvard for 44 years and directed large research projects there as well.
Mr. Leontief was a thinker who often complained that too many of his academic colleagues spent too much time staring out their office windows instead of being out in the field, as any good economist ought to be, counting things. ”Facts,” he said. ”You have to have facts. Theories aren’t good unless you have facts to back them.”
When asked how he developed the input-output analysis recognized by his Nobel memorial prize, he would invariably begin, ”Oh, it’s really very simple — what I wanted to do was collect facts.” The facts he sought were those that explained how segments of production were interconnected.
He showed that if you carefully studied changes in the cost and components of one type of product, you could determine the resulting changes in cost and components of others along the production chain.
Suppose you have a sudden rise the price of oil or steel? Mr. Leontief taught government officials and corporate executives to track how this influenced the costs of production in other segments of a local or national economy, both within an industry or more broadly across many industries and many nations.
Wassily Leontief was born Aug. 5, 1905, in St. Petersburg, the son of Wassily W. Leontief, an economist, and the former Eugenia Bekker. A brilliant student, he was allowed to enroll when he was only 15 at the newly renamed University of Leningrad. But he got in trouble by expressing vehement opposition to the lack of intellectual and personal freedom under the country’s Communist regime, which had taken power three years earlier. He was arrested as he was nailing up anti-Communist posters on the wall of a military barracks and placed in solitary confinement. Released after several days, he promptly resumed his anti-Communist activities and was arrested several more times.
Finally, in 1925, he was allowed to leave the country, a turn of fate he attributed to a growth on his neck. He said the authorities believed that the growth was cancerous and that he would die and be of no use to the state. He left Russia to resume his studies in economics at the University of Berlin, and his parents soon followed. The growth was benign and he completed his doctorate in 1929. He spent a year as an economist advising the Government of China, particularly on the planning of a new railroad network.
Then he came to the United States and worked briefly in New York at the National Bureau of Economic Research, where his published work quickly attracted attention, and Harvard invited him to join its economics faculty. He agreed, provided the university help him develop his ideas about production. Harvard gave him a research assistant and a $2,000 grant to develop the system of input-output analysis that the world was to adopt. He and his assistant began constructing a table covering 42 American industries, taking months to compile figures and perform calculations that computers would latter handle in fractions of seconds.
During the war, he helped the United States Government with planning for industrial production, worked as a consultant to the Office of Strategic Services and supervised compilation of a 92-economic-sector table for the Department of Labor. In 1948, Mr. Leontief set up the Harvard Research Project on the Structure of the American Economy with the aid of large grants from the Ford and Rockefeller Foundations and the Air Force to expand and refine his input-output models. Soon he had a staff of 20 — and a 650-punch-card computer from I.B.M., then the state-of-the art.
He did not, however, keep the Air Force grant long once the Eisenhower Administration came to power; some of its officials were critical of his input-output theory as smacking too much of a planned economy. That was precisely what he thought it should smack of.
One of his goals in studying the nature of changes in industrial production was to enable nations to plan in ways that would be economically beneficial and help them avoid periods of economic hardship. But to some economists the idea of national economic planning was ill advised: not only would it not work, they said, but it might make matters worse and also might open the door to excessive Government control. They maintained it would be better to let the private sector and the free market determine the course of future economic events.
To Mr. Leontief, it seemed short-sighted for nations to devote little or no thought to the analysis of the future of the overall economy, especially after what he regarded as the effective work of modern economists in devising projections that are mathematically and statistically sound. He spoke out often on the subject in the 1970’s and 80’s.
He and Leonard Woodcock, then president of the United Auto Workers, proposed that the Federal Government establish an Office of National Economic Planning to help coordinate economic projects and make recommendations on policies they said could avert unnecessary unemployment, inflation, failures in health care, shortages in affordable housing, energy, public transportation and other requirements of a civilized society.
The idea never materialized. If anything, the generation of younger economists who followed him, many of whom he taught, developed less respect for the abilities of national Governments to plan for the long term. It bothered him greatly that toward the end of the century many Americans seemed to have lost broad faith in their Government’s ability to improve the lot of its citizens, particularly through economic programs.
In an Op-Ed article in The New York Times in 1992, he said there was little doubt that the United States Government had played an important role in a generally prosperous economy for more than half the century, from ending the Great Depression in the 30’s to guiding the nation through most of the rest of the century in generally sounder economic health than most of the rest of the world.
Mr. Leontief was always fearful that employment problems would accompany widespread use of the high-speed computers that he himself relied on almost from the moment they first became applicable for nonmilitary purposes after World War II. He warned that computers would be for many workers what the tractor was to the horse — great for the farmer but not great for the horse.
In an interview in 1996, when he was 90, Mr. Leontief, noting the trend toward corporate downsizing, said: ”Individual entrepreneurs will continue to do better and better and better, but significant segments of the work force will do worse and worse. Ultimately, Governments will have to play a role in arbitrating and correcting this.”
Mr. Leontief seemed to grow more liberal with age. During the student protests on the Harvard campus in 1969, he split with most senior faculty members and joined with a younger group more sympathetic to the protesting students. In 1975, he resigned from Harvard, where he was the Henry Lee Professor of Economics and chairman of the university’s Society of Fellows, its most distinguished group of scholars. He left a year ahead of schedule, complaining that too often teachers at the graduate level did not teach and researchers did not do research.
Shortly before he resigned, he joined an internal report criticizing Harvard’s economics department, which had long been regarded as among the world’s best. The report said that the department had failed to adequately recruit minority faculty members, that it took an overly narrow approach in scholarship and that a ”deterioration in attitudes and relationships” had occurred.
At N.Y.U., he continued to expand his work on input-output analysis and helped foreign nations adopt it. China was among the last to do so, as it intensified its industrialization in the late 1980’s.
Wassily Leontief, a balletomane and connoisseur of fine wines, said he also thought of himself as a squire of Willoughby Brook in northern Vermont, where he and his family had a summer home. It was all very well to be an internationally regarded scholar, but landing a beautiful brook trout, he would say with his sly smile, was his passion.
He is survived by his wife, Estelle Helena Marks, a writer, whom he married in 1932, his daughter, Svetlana Alpers, the art historian, author, and professor of fine arts at the University of California at Berkeley, and two grandsons.
USA and China: What are Trade in Value Added (TiVA) Balances
Changes in Global Trade
Global Value Chains
Value added content of Trade
FROM INTERCONNECTED ECONOMIES : BENEFITING FROM INDUSTRY GLOBALISATION
From Domestic Value Added in Chinese Exports
From Measurement and Determinants of Trade in Value Added
From OECD WTO TIVA
Ongoing TiVA Projects
OECD TIVA Initiative
EU FIGARO Initiative
NA TIVA Initiative
APEC TiVA Initiative
There is also OECD TiVA – MNE Project which incorporates Intra Firm trade of MNEs.
From An Overview on the Construction of North American Regional Supply-Use and Input-Output Tables and their Applications in Policy Analysis
Trade-in-Value Added (TiVA) is a statistical approach used to measure the interconnectivity and marginal contribution in production of participating economies in global value chains (GVCs) (Degain and Maurer, 2015). The advantage of TiVA over traditional trade statistics is that TiVA measures trade flows consistent with internationally, vertically integrated global production networks, often called GVCs. TiVA statistics allow us to better analyze three aspects of international trade: measuring the contribution of domestic versus foreign intermediates in the exports, tracing production across countries to their final destination, and finally quantifying how individual industries contribute to producing exports (Lewis, 2013).
TiVA statistics allow us to map and quantify the interdependencies between industries and economies, and help us develop better estimates of the contribution from each country in the production processes and, consequently, better measure the impact from GVC engagement for domestic economies. However, it is necessary to highlight the underlying compilation methodology of TiVA in order to better understand the characteristics, scope and interpretation of TiVA. Hence, it is important to remember that TiVA statistics are estimated statistics that are derived, in part, from official statistics. TiVA statistics are meant to complement but not to replace official statistics.
Measuring trade flows in value added as opposed to gross value of trade flows has become increasingly important as the influence that GVCs has on international trade continues to rise. (Johnson, 2014; Ahmad and Ribarsky, 2014). The proliferation of GVCs means that production has become increasingly fragmented and vertically integrated across countries (Jones and Kierzkowski, 1988; Hummels, Ishii, and Yi, 2001; OECD, 2013). At the micro level, this means that many firms in disparate countries are interconnected. Across international borders, these firms take part in particular stages of the production process, together forming a global supply chain. As a result, intermediate inputs may cross international borders several times before being used to produce final consumable goods. This matters for several reasons. First, when goods cross multiple borders multiple times, they are exposed to more trade costs, which accumulate and compound before the goods are sold for final consumption. Additionally, traditional gross trade flows are overstated because gross trade flows may count intermediates multiple times. Relatedly, gross trade flows obscure the marginal contributions of countries along GVCs. TiVA measures the flows related to the value that is added at each stage of production by each country and maps from where value is created, where it is exported, and how it is used, as final consumption or as an input for future exports. How we understand gains from trade from trade flows is fundamental, and value-added approaches lead to better understanding of GVCs and their role in international trade.
There are two ways to capture TiVA. The first method is a direct approach, which decomposes existing data on trade statistics. Johnson (2012) introduce a TiVA indicator using value-added to output ratios from the source country to compute the value-added associated with the implicit output transfer to each destination. Koopman, Wang, and Wei (2014) build on the literature in vertical specialization (e.g. Hummels, Ishii, and Yi 2001) and the literature on TiVA (e.g. Johnson and Noguera, 2012; Daudin, Rifflart, and Schweisguth, 2011) to implement a complete decomposition of a country’s gross exports by value added components. This work has evolved into a second, indirect method of capturing TiVA. The indirect method is employed in the regional North American supply-use table (NASUT) and the regional North American inter-country input-output table (NAIOT). Estimating TiVA this way relies on national and international input-output tables as well as bilateral trade statistics to derive the international intermediate and final supply-demand matrices. These matrices reveal the origin and use of goods and services produced and exchanged among the countries and industries within the table domain. Other major international input-output tables include the Asian International Input-Output (AIO) Tables published by the Institute of Developing Economies Japan External Trade Organization (IDE-JETRO), the Inter-Country Input-Output (ICIO) Tables published by the OECD, the World Input-Output Tables (WIOT) published by the World Input-Output Database (WIOD) project, and the Eora Multi-region Input-Output Database (Eora MRIO).
The studies based on the above two approaches have revealed a trend of rising foreign value-added content in international trade flows and the resulting implications for trade policies. Johnson and Noguera (2016) find that value-added exports are falling relative to gross exports, which means that double-counting is increasingly more common in trade flows. This is consistent with increased GVC activity. Hummels, Ishii, and Yi (2001) show that vertical specialization has grown about 30 percent and accounts for about one-third of the growth in trade from about 1970 to 1990.
In recent years, more than half of global manufacturing imports are intermediate goods and more than 70 percent of global services imports are intermediate services (OECD, 2013). This is relevant because tariffs (and other trade costs) have a higher impact on the cost of GVC activity. Each time an intermediate input crosses an international border as part of the production process, the input incurs trade costs. As first observed by Yi (2003), trade costs are compounded when intermediate goods cross borders multiple times to complete the production process. Rouzet and Miroudot (2013) demonstrate that small tariffs can add up to a significant sum by the time a finished product reaches its consumers. Other trade costs such as non-tariff measures also have such accumulative effect on downstream products.
What the literature indicates the trends in GVCs mean for trade flows, generally, are two-fold. First, with the growth of GVC activity, gross value of trade flows will continue to be larger than the value of final goods that cross borders. Second, trade policy designed with respect to gross trade flows could have the potential to be overly restrictive or even impose costs indirectly on domestic production. Trade-in-Value Added thus provides a supplementary, relevant reference for evaluating the economic effect of trade policies.
In this paper, we introduce the North American Trade-in-Value Added (NA-TiVA) project, a trilateral, multiyear initiative that aims to produce a regional TiVA database that maps the value chains connecting Canada, the United States, and Mexico. Furthermore, we introduce and discuss the project’s deliverables, the agencies involved, how the NA-TiVA project complements other ongoing TiVA initiatives around the world, the technical framework for producing a regional inter-country input-output table for the NA region, and the value of this work to resolving open policy questions within international trade.
Ongoing TiVA Initiatives
Currently there are three major ongoing global and regional TiVA projects that are related to the North America TiVA project. They are the World Input-Output database (WIOD), OECD-WTO TiVA, and APEC TiVA initiatives.
The World Input-Output database (WIOD): The official WIOD project ran from May 1, 2009 to May 1, 2012, as a joint effort of eleven European research institutions. It was funded by the European Commission. Under the official WIOD project, the accounting framework and methodologies of constructing the TiVA databases, as well as the first version of the World Input-Output database were developed. The database was officially launched in April 2012. Since then, two additional versions of WIOD databases, namely the 2013 and 2016 Releases, were published. The 2016 Released database covers 28 EU countries and 15 other major economies in the world for years 2000-2014 with 56 industries.
The OECD-WTO TiVA database: The Organization for Economic Cooperate and Development (OECD) and World Trade Organization (WTO) undertook a joint initiative on TiVA in 2013. Since then, two versions of TiVA databases have been released (2013 and 2015 release). The 2015 release of OECD-WTO TiVA database covers 61 countries and 13 regions, with 34 industries, for years 1995, 2000, 2005, 2008-2011.
APEC TiVA initiative: In 2014, APEC economic leaders endorsed the APEC TiVA database initiative, a four-year project co-led by China and the United States. Under this project, an APEC TiVA database would be constructed by the end of 2018, covering 21 APEC economies.
Each of these three major global and regional TiVA initiatives include Canada, Mexico, and the United States. In the light of this, why is there still a need for constructing the NA TiVA database? What kind of additional value can the NA TiVA project bring to this global and regional network of TiVA initiatives?
The NA-TiVA project was motivated by regional statistical developments and continuous improvements in compiling TiVA databases. The 2003 Mexican input-output table distinguishes trade flows by domestic producers and production undertaken in Maquiladoras, a tax-free, tariff-free special processing zone, which allowed the estimates of separate production coefficients and thus TiVA measures for these two distinctive zones in Mexico (Koopman, Powers, Wang, and Wei, 2010; De la Cruz, Koopman, Wang, and Wei, 2011). The government of Canada further highlighted the importance and relevance of global value chains in the publication of a book assessing the impact and implication of GVCs (Foreign Affairs and International Trade Canada, 2011); and as of the 2015 edition of the OECD’s ICIO tables, Mexico is broken out as Mexico Global Manufacturers and Mexico Non-Global Manufacturers. This NA TiVA project builds off of these developments.
Constructing inter-country input-output tables, or so called TiVA databases, requires the harmonization of national supply-use tables (SUTs) or input-output tables (IOTs) as well as bilateral trade statistics from different countries. However, the data produced by countries often vary greatly in the level of detail and differ in industry and product classifications. Thus, the more countries are included in a global or regional TiVA project, the higher level of aggregation would be required for the purpose of harmonization. With only three countries involved, it is feasible for the NA TiVA database to include more products and sectors than other global and regional TiVA projects.
Moreover, other factors, such as all three countries adopt the same industry and product classifications (e.g. using the North American Industry Classification System (NAICS)), and produce SUTS at similarly detailed levels, would ensure the compatibility of data components, and thus lead to better quality of the resulting NA TiVA database.
Finally, the NA TiVA project could synthesize the ongoing trilateral trade statistics reconciliation effort and produce better-quality balanced bilateral trade data to feed into other global and regional TiVA initiatives. One of the key inputs for constructing TiVA databases is balanced bilateral trade statistics. However, countries rarely report symmetric bilateral trade statisticsone country’s reported exports rarely equals its trading partner’s reported imports, and vice versa. To reconcile such asymmetries to produce balanced bilateral trade statistics, joint effort by both trading countries is warranted, including investigating the causes of asymmetries at detailed product level and making corresponding adjustment mechanically. However, global and regional TiVA initiatives often have to consider an incredible number of country pairs, making such an elaborate reconciliation practice rather infeasible. Thus, global and regional TiVA initiatives often turn to economic modelling to balance bilateral trade statistics which could be applied in a systematic way to all countries. Although such approach can be mathematically sound, the resulting data often require additional scrutiny, validation, and adjustment, as they do not always reflect the reality accurately. Canada, Mexico, and the United States have ongoing bilateral trade reconciliation. This NA TiVA project provides additional motivation and framework for this effort.
The History, Scope, and Major Objectives of the NA TiVA Initiative
In October 2014, the representatives from the United States, Canada, and Mexico met and kicked off the idea of constructing the NA TiVA database at a UN conference in Mexico. The main objective of this project is to construct the NA TiVA database by 2021 covering three NA countries with more detailed industry and firm information, and to improve the quality of TiVA measures for the value chains in the NA region.
The NA-TiVA project involves eight government agencies across the three NA countries: for Canada, Statistics Canada (STATCAN) and Global Affairs Canada; for Mexico, Instituto Nacional de Estadística y Geografía (INEGI) and Banco de Mexico; and for the United States, the Bureau of Economic Analysis (BEA), the U.S. Census Bureau (CENSUS), the U.S. International Trade Commission (USITC), and the Office of the U.S. Trade Representative (USTR).
In addition, because the resulting NA-TiVA database would be eventually integrated into the OECD-WTO TiVA database to improve the quality of information on the North American region, participants of the NA-TiVA project regularly meet with OECD representatives to harmonize TiVA database compilation methodologies, exchange data to synthesize the effort and ensure consistency across countries, and discuss best practices. Other international organizations, such as United Nations Statistics Division (UNSD), and WTO, are often consulted as well for national account and trade statistics related issues.
Under the NA-TiVA initiative, three parallel work streams have been established: The trade in goods and services reconciliation team, which is tasked to produce balanced bilateral trade statistics for goods and services; the SUT team, whose goal is to harmonize the national SUTs and compile the regional NASUTs and NAIOTs; and the White Paper team, the goal of which is to produce documentation that outlines the conceptual methodology, identifies major technical issues, describes policy applications of a NA-TiVA initiative, and details project outputs as well as future work.
FROM INTERCONNECTED ECONOMIES :BENEFITING FROM INDUSTRY GLOBALISATION
From Supply-Use Tables, Trade-in-Value-Added Initiatives, and their Applications
US Trade Wars with Emerging Countries in the 21st Century: Make America and Its Partners Lose Again
Antoine Bouët (International Food Policy Research Institute, Washington, D.C., and Groupe de Recherche en Économie Théorique et Appliquée [GREThA], University of Bordeaux, France)
David Laborde (International Food Policy Research Institute)
Measuring Value Added in the People’s Republic of China’s Exports: A Direct Approach.
ADBI Working Paper 493. Tokyo: Asian Development Bank Institute
International Trade Costs, Global Supply Chains and Value-added Trade in
Gerard Kelly and Gianni La Cava
Trade in Value Added Revisited: A Comment on R. Johnson and G. Noguera,
Accounting for Intermediates: Production Sharing and Trade in Value Added
Credit Terms in a Supplier Buyer contracts determine payment delays which accumulate in current accounts of a Firm.
Bank to Bank
Bank to Firm
Firm to Firm
Dyad of Credit Relations
Supplier – Buyer
Triad of Credit Relations
Supplier – Bank – Buyer
Sources of Systemic Risk
Failure of a Firm and its impact on Suppliers and Customers (Flow of Goods)
Failure of a Bank and its impact on Trade Credit
Credit Contraction due to de-risking by the Banks
Decline in Correspondent Banking relations and its impact on Trade Finance
From Credit Chains and Sectoral Co-movement: Does the Use of Trade Credit Amplify Sectoral Shocks?
Trade credit is an important source of short-term financing for firms, not only in the U.S., as documented by Petersen and Rajan (1997), but also around the World. For instance, accounts payables are larger than short-term debt in 60 percent of the countries covered by Worldscope. Also, across the world most firms simultaneously receive credit from their suppliers and grant it to their customers, which tend to be concentrated on specific sectors. These characteristics of trade credit financing have led some authors to propose it as a mechanism for the propagation and amplification of idiosyncratic shocks. The intuition behind the mechanism is straightforward; a firm that faces a default by its customers may run into liquidity problems that force it to default to its own suppliers. Therefore, in a network of firms that borrow from each other, a temporary shock to the liquidity of some firms may cause a chain reaction in which other firms also get in financial difficulties, thus resulting in a large and persistent decline in aggregate activity. This idea was first formalized by Kiyotaki and Moore (1997) in a partial equilibrium setting, and has been recently extended to a general equilibrium environment by Cardoso-Lecourtois (2004), and Boissay (2006) who have also provided evidence of the potential quantitative importance of the mechanism by calibrating their models to the cases of Mexico and the U.S., respectively.
From Ontology of Bankruptcy Diffusion through Trade Credit Channel
A supply network is a network of entities interacting to transform raw material into finished product for customers. Since interdependencies among supply network members on material, information, and finance are becoming increasingly intensive, financial status of one firm not only depends on its own management, but also on the performance and behaviours of other members. Therefore, understanding the financial flows variability and the material interactions is a key to quantify the risk of a firm. Due to the complex structure and dynamic interactions of modern supply networks, there are some difficulties faced by pure analysis approaches in analyzing financial status of the supply network members and the high degree of nonlinear interactions between them. Mathematical and operation research models usually do not function very well for this kind of financial decision making. These models always start with many assumptions and have difficulties modeling such complex systems that include many entities, relationships, features, parameters, and constraints. In addition, traditional modeling and analysis tools lack the ability to predict the impact of a specific event on the performance of the entire supply network. Current financial data analysis with large volumes of structure data cannot offer the full picture and intrinsic insights into the risk nature of a company. Motivated by the literature gap in risk monitoring in investment background and limitations of analysis approaches for handling bankruptcy contagion phenomenon, we propose an ontological approach to present a formal, shared conceptualization of this domain knowledge.
From Inter-Firm Trade Finance in Times of Crisis
The severe recession that is hitting the global economy, with very low or even negative growth rates, has caused widespread contractions in international trade, both in developed and developing countries. World Trade Organization (WTO) has forecast that exports will decline by roughly 9% in volume terms in 2009 due to the collapse in global demand brought on by the biggest economic downturn in decades. The contraction in developed countries will be particularly severe with exports falling by 10%. In developing countries, which account for one-third of world trade, exports will shrink by some 2% to 3% in 2009.
The contraction in international trade has been accompanied by a sharp decline in the availability of trade finance. This decline is only partly explained by the contraction in demand: according to a BAFT (Banker’s Association for Trade and Finance) and International Monetary Fund (IMF) joint survey (2009), flows of trade finance to developed countries have fallen by 6% relative to the previous year, more than the reduction in trade flows, suggesting that part of the fall reflects a disruption of financial intermediation. The contraction in value of trade finance has also been accompanied by a sharp increase in its price. Fear that the decline in trade finance and the increase in its cost would accelerate the slowdown of world trade has triggered a number of government initiatives in support of trade finance (Chauffour and Farole,2009).
The situation is especially worrisome for firms operating in developing countries which rely heavily on trade finance to support both their exports and imports.1 With a restricted access to financing and an increased cost of financing, these firms may find difficulties in maintaining their production and trade activities.
There have been several developments in economics in last 20 years. Although these have been developed by different groups of economists, there are common relations among all of them. Because of Institutional silos, many of these developments are not shared. My attempt is to compile them here in this post and other previous related posts.
IMF Balance Sheet Approach (BSA) – From-Who-To-Whom
Balance Sheet Economics/Asset Liability Matrices (ALM) of Tsujimura
Financial Input-Output Analysis (F-IO Tables)
F-SAM ( Financial Social Accounting Matrix)
Interrelated Balance Sheet Approach of Perry Mehrling and Zoltan Pozsar
Stock Flow Consistent Modeling – Marc Lavoie, G Zezza, W Godley
Extended Supply and Use Tables (E-SUT)/UN SEIGA Initiative
Supply Chain Finance/Financial Supply Chain Management/Operations and Finance
Trade Finance/Global Value Chains/Accounting for Global Value Chains
Integrated Macroeconomic Accounts – NIPAs and Financial Accounts
From Balance Sheets, Transaction Matrices and the Monetary Circuit
Chapter 2 of book Monetary Economics by M Lavoie and W Godley 2007
The lack of integration between the flows of the real economy and its financial side greatly annoyed a few economists, such as Denizet and Copeland. For Denizet, J.M. Keynes’s major contribution was his questioning of the classical dichotomy between the real and the monetary sides of the economy. The post-Keynesian approach, which prolongs Keynes’s contribution on this, underlines the need for integration between financial and income accounting, and thus constitutes a radical departure from the mainstream. 1
Denizet found paradoxical that standard national accounting, as was initially developed by Richard Stone, reproduced the very dichotomy that Keynes had himself attempted to destroy. This was surprising because Stone was a good friend of Keynes, having provided him with the national accounts data that Keynes needed to make his forecasts and recommendations to the British Treasury during the Second World War, but of course it reflected the initial difficulties in gathering enough good financial data, as Stone himself later got involved in setting up a proper framework for financial flows and balance sheet data (Stone 1966).2
In trade relations, if goods flow from X to Y, then Money (Payments) flows from Y to X.
Intra Firm amd Inter Firm relations between Accounts Receivables and Accounts Payable
UNITED NATIONS STATISTICS DIVISION SEIGA Initiative
Under System of Extended International and Global Accounts (SEIGA) Initiative
United Nations is developing:
UN Handbook on Accounting for Global Value Chains
Presentation from 2017 Seminar on Accounting for Global Value Chains
From Financial input-output multipliers
From Sectoral interlinkages in balance sheet approach
From STANDARD DEFINITIONS FOR TECHNIQUES OF SUPPLY CHAIN FINANCE
There are two Areas where FSCM/SCF names are used but in different contexts.
Inter firm FSCM
Intra firm FSCM
Inter firm F-SCM
Supply Chain Finance (SCF)
Value Chain Finance
Inter firm Finance
Collaborative Cash to Cash Cycles Management
During 2008 global financial crisis, the trade financing dried up resulting in decline in trade of goods and services.
Since the crisis, Financial De-globalization and Decline of Correspondent Banking has also made availability of financial credit harder.
Cash flow and working capital management is helped by inter firm collaboration among Suppliers and Buyers.
Financial Institutions which provide trade credit also benefit from inter firm collaboration.
From SUPPLY CHAIN FINANCE FUNDAMENTALS: What It Is, What It’s Not and How it Works
What Supply Chain Finance is Not
The world of trade finance is complex and varied. There are numerous ways to increase business capital on hand and, in many cases, the differences are slightly nuanced. Given this landscape, it’s not just important to understand what supply chain finance is; it’s also important to understand what it is not.
It is not a loan. Supply chain finance is an extension of the buyer’s accounts payable and is not considered financial debt. For the supplier, it represents a non-recourse, true sale of receivables. There is no lending on either side of the buyer/supplier equation, which means there is no impact to balance sheets.
It is not dynamic discounting or an early payment program. Early payment programs, such as dynamic discounting, are buyer-initiated programs where buyers offer suppliers earlier payments in return for discounts on their invoices. Unlike supply chain finance, buyers are seeking to lower their cost of goods, not to improve their cash flow. Dynamic discounting and early payment programs often turn out to be expensive for both suppliers (who are getting paid less than agreed upon) and buyers who tie up their own cash to fund the programs.
It is not factoring. Factoring enables a supplier to sell its invoices to a factoring agent (in most cases, a financial institution) in return for earlier, but partial, payment. Suppliers initiate the arrangement without the buyer’s involvement. Thus factoring is typically much more expensive than buyer-initiated supply chain finance. Also, suppliers trade “all or nothing” meaning they have no choice to participate from month-to-month to the degree that their cash flow needs dictate. Finally, most factoring programs are recourse loans, meaning if a supplier has received payment against an invoice that the buyer subsequently does not pay, the lender has recourse to claw back the funds.
From Mckinsey on Payments
From Financial Supply Chain Management
From Best Practices in Cash Flow Management and Reporting
From STANDARD DEFINITIONS FOR TECHNIQUES OF SUPPLY CHAIN FINANCE
From Financing GPNs through inter-firm collaboration? Insights from the automotive industry in Germany and Brazil
Intra Firm F-SCM
Working Capital Management
Cash Flow Management
Cash to Cash Conversion Cycle Management (C2C Cycle/CCC)
Financial Supply Chain Management (F-SCM) in Manufacturing companies
Financial Supply chain management in financial institutions
Supply Chain Finance
Accounts Payable Optimization
Accounts Receivable Optimization
Operations and Finance Interfaces
Current Asset Management (Current Ratio Analysis)
This is not a new subject. Corporate Finance, Financial Controls, and working capital management have been active business issues. Benefits of Supply chain management include increase in inventory turnover and decline in current assets.
There are many world class companies who manage their supply chains well and work with minimal working capital. Lean Manufacturing, Agile Manufacturing, JIT manufacturing are related concepts. Just-In-Time manufacturing developed in Toyota Corp. reduces inventory portion of C2C cycle. Other examples include
Currently, most of the Supply Chain analytics efforts unfortunately do not integrate analysis of financial benefits of operating decisions.
There are many studies recently which suggest that Cash to Cash Conversion Cycle is a better determinant of corporate liquidity. C2C Cycle is a dynamic liquidity indicator and Current Assets is a static indicator of liquidity. I would like to point out that none of the studies relate C2C cycle with Current Ratio. Current Ratio is based on balance sheet positions of current assets and current liabilities. C2C cycle is based on flows in supply chains. Accumulation of flow results in Current assets (Stock). To make it Stock-Flow Consistent, more work is required.
From Supply Chain Finance: some conceptual insights.
From Financial Supply Chain Management
From The Interface of Operations and Finance in Global Supply Chains
From SUPPLY CHAIN-ORIENTED APPROACH OF WORKING CAPITAL MANAGEMENT
From IMPROVING FIRM PERFORMANCE THROUGH VALUE-DRIVEN SUPPLY CHAIN MANAGEMENT: A CASH CONVERSION CYCLE APPROACH
From IMPROVING FIRM PERFORMANCE THROUGH VALUE-DRIVEN SUPPLY CHAIN MANAGEMENT: A CASH CONVERSION CYCLE APPROACH
From THE CYCLE TIMES OF WORKING CAPITAL: FINANCIAL VALUE CHAIN ANALYSIS METHOD
Call for papers: Supply Chain Finance
Call for papers for Special Topic Forum in Journal of Purchasing and Supply Management (Manuscript Submission: March 31, 2017)
Supply chain finance is a concept that lacks definition and conceptual foundation. However, the recent economic downturn forced corporates to face a series of financial and economic difficulties that strongly increased supply chain financial risk, including bankruptcy or over-leveraging of debt. The mitigation and management of supply chain financial risk is becoming an increasingly important topic for both practitioners and academics leading to a developing area of study known as supply chain finance. There are two major perspectives related to the idea of managing finance across the supply chain. The first is a relatively short-term solution that serves as more of a “bridge” and that is provided by financial institutions, focused on accounts payables and receivables. The second is more of a supply chain oriented perspective – which may or may not involve a financial institution, focused on working capital optimization in terms of accounts payable, receivable, inventory, and asset management. These longer-term solutions focus on strategically managing financial implications across the supply chain.
Recent years have seen a considerable reduction in the granting of new loans, with a significant increase in the cost of corporate borrowing (Ivashina and Scharfstein, 2010). Such collapse of the asset and mortgage-backed markets dried up liquidity from industries (Cornett et al., 2011). In such difficult times, firms (especially those with stronger bargaining power) forced suppliers to extend trade credit in order to supplement the reduction in other forms of financing (Coulibaly et al., 2013; Garcia-Appendini and Montoriol-Garriga, 2013). The general lack of liquidity, in particular for SMEs, has directly affected companies’ ability to stay in the market, reflecting on the stability of entire supply chains. There are many other factors influencing liquidity and financial health that are critical to assess.
These trends and the continued growth of outsourced spend have contributed considerably to the need for and spread of solutions and programs that help to mitigate and better manage financial risk within and across the supply chain. One of the most important approaches is what is being termed Supply Chain Finance (SCF) (Gelsomino et al., 2016; Pfohl and Gomm, 2009; Wuttke et al., 2013a). SCF is an approach for two or more organizations in a supply chain, including external service provides, to jointly create value through means of planning, steering, and controlling the flow of financial resources on an inter-organizational level (Hofmann, 2005; Wuttke et al., 2013b). It involves the inter-company optimization of financial flows with customers, suppliers and service providers to increase the value of the supply chain members (Pfohl and Gomm, 2009). According to Lamoureux and Evans (2011) supply chain financial solutions, processes, methods are designed to improve the effectiveness of financial supply chains by preventing detrimental cost shifting and improving the visibility, availability, delivery and cost of cash for all global value chain partners. The benefits of the SCF approach include reduction of working capital, access to more funding at lower costs, risk reduction, as well as increase of trust, commitment, and profitability through the chain (Randall and Farris II, 2009).
Literature on SCF is still underdeveloped and a multidisciplinary approach to research is needed in this area. In order to better harmonize contributions of a more financial nature with ones coming from the perspective of purchasing & supply chain, there is a need of developing theory on SCF, starting with a comprehensive definition of those instruments or solutions that constitute the SCF landscape. SCF has been neglected in the Purchasing & Supply Management (PSM) literature, although PSM plays a critical role in managing finance within the supply chain. PSM uses many of the processes and tools that are part of a comprehensive supply chain financial program to better manage the supply base, in terms of relationships, total cost of ownership, cost strategies and pricing volatility (see for example Shank and Govindarajan 1992). Reverse factoring is a technique which is also widely used to manage the supply base (Wuttke et al, 2013a) as is supplier development and investment in suppliers.
Research on SCF from a PSM perspective needs further development. In particular, empirical evidence would prove useful for testing existing models and hypotheses, addressing the more innovative schemes and investigating the adoption level and the state of the art of different solutions. Research is also needed for the development of a general theory of supply chain finance. There is also limited research that focuses on the link between supply chain financial tools and supply chain financial performance. Finally, considering the plurality of solutions that shape the SCF landscape, literature should move towards the definition of holistic instruments to choose the best SCF strategy for a supply chain, considering its financial performance and the contextual variables (e.g. structure, bargaining power) that characterize it.
The purpose of this special topic forum is to publish high-quality, theoretical and empirical papers addressing advances on Supply Chain Finance. Original, high quality contributions that are neither published nor currently under review by any other journals are sought. Potential topics include, but are not limited to:
Theory development, concept and definition of SCF
Taxonomy of SCF solutions
Strategic cost management across the supply chain
Total cost of ownership
Life cycle assessment and analysis
Commodity risk and pricing volatility
Supply chain financial metrics and measures
Relationship implications of supply chain finance
Tax and transfer pricing in the supply chain
Foreign exchange and global currency and financing risk
Financial network design and financial supply chain flows
The organizational perspective on SCF and the implementation process
Role of innovative technologies to support SCF ( (e.g. block chain, internet of things)
Supply chain collaboration for improved supply chain financial solutions
SCF adoption models, enablers and barriers
SCF from different party perspectives (especially suppliers and providers)
SCF and risk mitigation and management
Manuscript preparation and submission
Before submission, authors should carefully read the Journal’s “Instructions for Authors”. The review process will follow the Journal’s normal practice. Prospective authors should submit an electronic copy of their complete manuscript via Elsevier’s manuscript submission system (https://ees.elsevier.com/jpsm) selecting “STF Supply Chain Finance” as submission category and specifying the Supply Chain Finance topic in the accompanying letter. Manuscripts are due March 31, 2017 with expected publication in June of 2018.
FOR COMMENTS OR QUESTIONS PLEASE CONTACT THE GUEST EDITORS:
Federico Caniato, Politecnico di Milano, School of Management, email@example.com
Michael Henke, TU Dortmund and Fraunhofer IML, Michael.Henke@iml.fraunhofer.de
George A. Zsidisin, Virginia Commonwealth University, firstname.lastname@example.org
Cornett, M.M., McNutt, J.J., Strahan, P.E., Tehranian, H., 2011. Liquidity risk management and credit supply in the financial crisis. J. financ. econ. 101, 297–312.
Coulibaly, B., Sapriza, H., Zlate, A., 2013. Financial frictions, trade credit, and the 2008–09 global financial crisis. Int. Rev. Econ. Financ. 26, 25–38.
Garcia-Appendini, E., Montoriol-Garriga, J., 2013. Firms as liquidity providers: Evidence from the 2007–2008 financial crisis. J. financ. econ. 109, 272–291.
Gelsomino, L.M., Mangiaracina, R., Perego, A., Tumino, A., 2016. Supply Chain Finance: a literature review. Int. J. Phys. Distrib. Logist. Manag. 46, 1–19.
Govindarajan, Vijay, and John K. Shank. “Strategic cost management: tailoring controls to strategies.” Journal of Cost Management 6.3 (1992): 14-25.
Wuttke, D. A., Blome, C., Foerstl, K., & Henke, M. (2013a). Managing the innovation adoption of supply chain finance—Empirical evidence from six European case studies. Journal of Business Logistics, 34(2), 148-166.
Wuttke, D. A., Blome, C., & Henke, M. (2013b). Focusing the financial flow of supply chains: An empirical investigation of financial supply chain management. International journal of production economics, 145(2), 773-789.
Hofmann, E., 2005. Supply Chain Finance: some conceptual insights. Logistik Manag. Innov. Logistikkonzepte. Wiesbad. Dtsch. Univ. 203–214.
Ivashina, V., Scharfstein, D., 2010. Bank lending during the financial crisis of 2008. J. financ. econ. 97, 319–338.
Lamoureux, J.-F., Evans, T.A., 2011. Supply Chain Finance: A New Means to Support the Competitiveness and Resilience of Global Value Chains. Social Science Research Network, Rochester, NY.
Lekkakos, S.D., Serrano, A., 2016. Supply chain finance for small and medium sized enterprises: the case of reverse factoring. Int. J. Phys. Distrib. Logist. Manag.
Pfohl, H.C., Gomm, M., 2009. Supply chain finance: optimizing financial flows in supply chains. Logist. Res. 1, 149–161.
Randall, W., Farris II, T., 2009. Supply chain financing: using cash-to-cash variables to strengthen the supply chain. Int. J. Phys. Distrib. Logist. Manag. 39, 669–689.