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
SODA Strategic Options Development and Analysis
SSM Soft Systems Methodology
SCA Strategic Choice Approach
Critical Systems Heuristics
Strategic Assumption Surfacing and Testing
Viable Systems Model VSM
Problem Structuring Methods PSM
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
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
Partially conflicting interests,
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
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
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, 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.
Jay W Forrester
Hau L Lee
Negative Feedback Loop
Positive Feedback Loop
Supply Chain Networks
Beer Distribution Game
Operational and Institutional Structures
Key Sources of Research:
Behavioral Causes of Demand Amplification in Supply Chains: “Satisficing” Policies with Limited Information Cues
REDUCING THE IMPACT OF DEMAND PROCESS VARIABILITY WITHIN A MULTI-ECHELON SUPPLY CHAIN
Francisco Campuzano Bolarín1,Lorenzo Ros Mcdonnell1, Juan Martín García
The impact of order variance amplification/dampening on supply chain performance
Robert N. Boute, Stephen M. Disney, Marc R. Lambrecht and Benny Van Houdt
Coping with Uncertainty: Reducing ”Bullwhip” Behaviour in Global Supply Chains
Rachel Mason-Jones, and Denis R. Towill
Bullwhip in Supply Chains ~ Past, Present and Future
Steve Geary Stephen M Disney and Denis R Towill
Shrinking the Supply Chain Uncertainty Circle
THE BULLWHIP EFFECT IN SUPPLY CHAIN Reflections after a Decade
Gürdal Ertek, Emre Eryılmaz
Information distortion in a supply chain: The bullwhip effect
Hau L Lee; V Padmanabhan; Seugjin Whang
Management Science; Apr 1997; 43, 4;
THE SUPPLY CHAIN COMPLEXITY TRIANGE: UNCERTAINTY GENERATION IN THE SUPPLY CHAIN
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
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
Worcester Polytechnic Institute Worcester, MA, USA
February 13, 2013
System Dynamics: A disruptive science
A conversation with Jay W. Forrester, founder of the field
Khalid Saeed Worcester Polytechnic Institute Sept. 2013
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.
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 , Radzicki and Sterman ,Moxnes , Sterman (Chap. 10 in ), Radzicki , Ryzhenkov , and Weber  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 , Mass , Low , Forrester , and Sterman  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
FEEDBACK MECHANISMS IN THE FINANCIAL SYSTEM: A MODERN VIEW
Mikhail V. Oet
Oleg V. Pavlov
Mr. Hamilton, Mr. Forrester, and a Foundation for Evolutionary Economics
Michael J. Radzicki
European Contributions to Evolutionary Institutional Economics: The Cases of ‘Cumulative Circular Causation’ (CCC) and ‘Open Systems Approach’ (OSA). Some Methodological and Policy Implications