Increasing Market Concentration in USA: Update April 2019
In this post, I have compiled recent articles and papers on the issues of:
Increased Market Power
Increased Market Concentration
Increased Corporate Profits
Anti Trust Laws and Competition policy
Interest rates and Business Investments
Interest rates and Mergers and Acquisitions
Stock Buybacks, Dividends, and Business Investments
Outsourcing, and Global Value Chains
Corporate Savings Glut
Slower Economic Growth
From Low Interest Rates, Market Power, and Productivity Growth
How does the production side of the economy respond to a low interest rate environment? This study provides a new theoretical result that low interest rates encourage market concentration by giving industry leaders a strategic advantage over followers, and this effect strengthens as the interest rate approaches zero. The model provides a unified explanation for why the fall in long-term interest rates has been associated with rising market concentration, reduced dynamism, a widening productivity-gap between industry leaders and followers, and slower productivity growth. Support for the model’s key mechanism is established by showing that a decline in the ten year Treasury yield generates positive excess returns for industry leaders, and the magnitude of the excess returns rises as the Treasury yield approaches zero.
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
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.
Gantt Chart Simulation for Stock Flow Consistent Production Schedules
I have knowledge of two software which do Gantt chart simulation for production scheduling. These are used by top most companies in the world for production planning and scheduling now a days known as Supply Chain Management (SCM).
Production Schedules are stock flow consistent which means that starting inventories, and unused production of products result in cumulative inventory which is plotted for each of the product.
Production and Shipments (arrivals and dispatched) create Flows and Inventory levels indicate Stock level positions.
Gantt Chart simulators are excellent tools for operations management in plants.
The first Gantt chart was actually developed by Karol Adamiecki in Poland. He called it a Harmonogram. Henry Gantt in 1910 published first gantt chart which was later than publication by Karol Adamiecki.
These two charts below show Simulator window in which Gantt chart and inventory level plots are displayed.
Gantt Chart Simulator in Aspen Tech Plant Scheduler for Production Scheduling
Gantt Chart Simulator in Atlantic Decision Sciences Scheduler
Key Sources for Research:
A Presentation by Chris Jones on Evolution of Graphical Production Scheduling Software
Intra Industry Trade and International Production and Distribution Networks
Inter Industry Trade is known as One way Trade.
Intra Industry Trade is known as Two way Trade.
Intra Industry Trade (IIT)
Can be Intra Firm or Inter Firm (Arms’ Length)
Can be Vertical or Horizontal (VIIT and HIIT)
Intra Industry Trade is measured using G-L Index among other indices.
Import and Export of Parts and Components (Intermediate Goods) causes measurement issues of IIT.
From Structure and Determinants of Intra-Industry Trade in the U.S. Auto-Industry
Intra-industry trade is defined as the simultaneous export and import of products, which belong to the same statistical product category. According to Kol and Rayment (1989), three types of bilateral trade flows may occur between countries: inter-industry trade, horizontal IIT and vertical IIT. Historically, the international trade between countries has been inter-industry form, which is described as the exchange of products belonging to different industries. Traditional trade models, such as Heckscher-Ohlin model or Ricardian model, have tried to explain this type of trade based on comparative advantage in relative technology and factor endowments. However, a significant portion of the world trade over the last three decades took the form of the intra-industry trade rather than inter-industry trade. As a result, the traditional trade models has been considered to be inadequate in explaining this new trade pattern because in these models there is no reason for developed countries to trade in similar but slightly differentiated goods.
From Structure and Determinants of Intra-Industry Trade in the U.S. Auto-Industry
Horizontal IIT has been defined as the exchange of similar goods that are similar in terms of quality but have different characteristics or attributes. The models developed by Dixit and Stiglitz (1977), Lancaster (1980), Krugman (1980, 1981), Helpman (1981), and Helpman and Krugman (1985) explain horizontal IIT by emphasizing the importance of economies of scale, product differentiation, and demand for variety within the setting of monopolistic competition type markets. In these models, IIT in horizontally differentiated goods should be greater, the greater the difference in income differences and relative factor endowments between the trading partners.
From Structure and Determinants of Intra-Industry Trade in the U.S. Auto-Industry
In contrast, vertical IIT represents trade in similar products of different qualities but they are no longer the same in terms unit production costs and factor intensities.5 Falvey (1981) and Falvey and Kierzkowski (1987) have shown that the IIT in vertically differentiated goods occurs because of factor endowment differences across countries. In particular, Falvey and Kierzkowski (1987) suggest that the amount of capital relative to labor used in the production of vertically differentiated good indicates the quality of good. As a consequence, in an open economy, higher- quality products are produced in capital abundant countries whereas lower-quality products are produced in labor abundant countries. This will give rise to intra-industry trade in vertically differentiated goods: the capital abundant country exports higher-quality varieties and labor abundant country exports lower-quality products. The models of vertical IIT predict that the share of vertical IIT will increase as countries’ income and factor endowments diverge.
From Structure and Determinants of Intra-Industry Trade in the U.S. Auto-Industry
Various ways of calculating intra-industry trade have been proposed in the empirical literature, including the Balassa Index, the Grubel-Lloyd (G-L) index, the Aquino index. The most widely used method for computing the IIT is developed by Grubel and Lloyd (1971). However, beside aggregation bias, the traditional G-L index has one major problem often cited in the empirical literature. The unadjusted G-L index is negatively correlated with a large overall trade imbalance. With national trade balances, the level of IIT in a country will be clearly underestimated. To avoid this problem, Grubel and Lloyd (1975) proposed another method to adjust the index by using the relative size of exports and imports of a particular good within an industry as weights.
From Structure and Determinants of Intra-Industry Trade in the U.S. Auto-Industry
From Structure and Determinants of Intra-Industry Trade in the U.S. Auto-Industry
From: World Trade Flows Characterization: Unit Values, Trade Types and Price Ranges
Normally, production and distribution planning are handled separately in firms. Integrated planning of production and distribution can add significant value to a company, particularly, in strategic decisions.
From Facility Location and Supply Chain Management – A comprehensive review
Since, in the literature, model objectives change as a function of the planning horizon length, we consider it opportune to define the features of each horizon in order to contextualize the parameters chosen for the models’ comparison. According to , the planning horizons of the supply chain can be clustered as follows:
• Strategic planning: this level refers to a long-term horizon (3-5 years) and has the objective of identifying strategic decisions for a production network and defining the optimal configuration of a supply chain. The decisions involved in this kind of
planning include vertical integration policies, capacity sizing, technology selection, sourcing, facility location, production allocation and transfer pricing policies.
• Tactical planning: this level refers to a mid-term horizon (1-2 years) and has the objective of fulfilling demand and managing material flows, with a strong focus on the trade-off between the service level and cost reduction. The main aspects considered in tactical planning include production allocation, supply chain coordination, transportation policies, inventory policies, safety stock sizing and supply chain lead time reduction.
• Operational planning: this level refers to a short term period (1 day to 1 year) and has the objective of determining material/logistic requirement planning. The decisions involved in programming include the allocation of customer demands, vehicle routing, and plant and warehouse scheduling.
From Integrated Location-Production-Distribution Planning in a Multi products Supply Chain Network Design Model
‘supply chain strategic design’,
‘supply chain planning’,
‘supply chain optimization’,
‘supply chain network design’,
‘supply chain production planning’,
‘supply chain delocalization’,
‘logistic network design’,
‘distribution network design’,
‘supply chain linear programming’
‘supply chain mixed-integer programming’.
From From Manufacturing to Distribution: The Evolution of ERP in Our New Global Economy
Over the past fifty years, manufacturing has changed from individual companies producing and distributing their own products, to a global network of suppliers, manufacturers, and distributors. Efficiency, price, and quality are being scrutinized in the production of each product. Because of this global network, manufacturers are competing on a worldwide scale, and they have moved their production to countries where the costs of labor and capital are low in order to gain the advantages they need to compete.
Today, the complex manufacturing environment faces many challenges. Many products are manufactured in environments where supplies come from different parts of the world. The components to be used in supply chain manufacturing are transported across the globe to different manufacturers, distributors, and third party logistics (3PL) providers. The challenges for many manufacturers have become how to track supply chain costs and how to deal with manufacturing costs throughout the production of goods. Software vendors, however, are now addressing these manufacturing challenges by developing new applications.
Global competition has played a key role in industrialized countries shifting from being production-oriented economies to service-based economies. Manufacturers in North America, Western Europe, and other industrialized nations have adapted to the shift by redesigning their manufacturing production into a distribution and logistics industry, and the skills of the labor force have changed to reflect this transition. Developing countries have similarly changed their manufacturing production environments to reflect current demands; they are accommodating the production of goods in industries where manufacturers have chosen to move their production offshore–the textile industry being a prime example of this move.
A report from the US Census Bureau titled Statistics for Industry Groups and Industries: 2005 and another from Statistics Canada titled Wholesale Trade: The Year 2006 in Review indicate that wholesalers are changing their business models to become distributors as opposed to manufacturers. Between 2002 and 2005, overall labor and capital in the manufacturing sectors decreased substantially. US industry data (from about 10 years ago) indicates that the North American manufacturing industry was engaged in 80 percent manufacturing processes and only 20 percent distribution activities. Today, however, these percentages have changed dramatically; the current trend is in the opposite direction. Manufacturing processes account for around 30 percent of the industry processes, and wholesale and distribution activities, approximately 70 percent.
In addition, a report from the National Association of Manufacturers indicates that the US economy imports $1.3 trillion (USD) worth of manufactured goods, but exports only $806 billion (USD) worth of goods manufactured in the US. This negative trade balance is a clear indication of the changing economic trend toward the manufacturing of goods in low-cost labor nations.
The main reason for this huge manufacturing shift is the increasing operating costs of production in industrialized countries. These rising costs are forcing manufacturers to move their production to developing nations because of the low cost of labor in these countries. This includes Asian countries (such as China and Indonesia) as well as Eastern European countries (such as the Czech Republic and Slovakia).
The number of workers (in percentages) in specified industries in G7 countries, and uses 1980 as the base year with 100 percent full employment in each industry. The industries with relatively constant rates of employment are the food and drink and the tobacco industries. Since 1995, all other industries have been maintaining less and less manufacturing employees, as indicated by the declining slopes in the graph. The shift in the textiles and leather, metals, and other manufacturing industries is moving toward production of goods in low-wage, developing countries.
Manufacturing is a global industry, and although a manufacturing company may be based in an industrialized country, it may have the bulk of its manufacturing facilities in a developing country. Producing goods in such a country reduces wage and capital costs for the manufacturer; however, some manufacturing control is lost in offshore production. Shipping, distribution, and rental costs, for example, are often difficult to track and manage, and quality control can be compromised in a production environment that is not local.
Two main outcomes can be seen within the manufacturing industry because of this manufacturing shift: manufacturers have a sense of having relinquished control of their production to low-cost labor nations, and supply chain management (SCM) has now become the answer to manufacturing within industrialized nations.
Suppliers that provide components to manufacturers often have issues with quality. Being part of a large network of suppliers, each supplier tries to offer the lowest prices for its products when bidding to manufacturers. Although a supplier may win the bid, its products may not be up to standard, and this can lead to the production of faulty goods. Therefore, when using offshore suppliers, quality issues, product auditing, and supplier auditing become extremely important.
Because the manufacturing model is changing, manufacturing has become more of a service-based industry than a pure manufacturing industry. Even though the physical process of manufacturing hasn’t changed, the actual locations of where the goods are being produced have. This fact is now compelling industrialized countries to engage in more assembly driven activities–a service-based model. The manufacturing process has transformed into obtaining parts and reassembling them into the final product. The final product is then redistributed throughout the appropriate channel or to the consumer. SCM methods are now reacting to this change as well; they are taking into account final assembly needs, and they are distributing particular products to consumers or manufacturers.
SCM is becoming the norm for manufacturers in the industrialized world. Offshoring is now standard practice, and methods such as SCM have been set up to deal with these economic and logistical business realities.
The economic shift happening in both industrialized and developing countries is dramatic. As the level of management knowledge increases, better methods of constructing offshore products are available in SCM solutions. In both types of economies, the changes in the labor force skill sets and manufacturing environments have consequently led to new software solutions being developed in order to manage this dramatic change.
Within the software industry, many SCM and enterprise resource-planning (ERP) vendors are following the economic shift. They are developing new functionality–ERP-distribution software–to meet the recent demands and needs of the changing manufacturing and distribution industries.
SCM and ERP software are converging to better address these new demands in the manufacturing industry. In the enterprise software market, ERP software vendors have reached a point of saturation; their installs are slowing down and they are seeing a reduction in sales. Therefore, ERP providers are developing new functionality in order to remain competitive with other ERP vendors, in addition to looking for new opportunities. ERP vendors are trying to adapt to the changing market in order to increase their revenues. They are integrating SCM functionality into their ERP offerings, creating ERP-distribution software that can span the entire production process across many continents (if necessary), and that is able to track final goods, components, and materials.
Traditional ERP solutions included some SCM functionality, which was needed to distribute the companies’ produced goods. These systems also allowed components and parts to be imported in order to assemble these goods. But offshore manufacturing and expansion into new markets has required SCM functionality in ERP software to be extended. Some larger vendors have acquired other companies in order to meet these changing demands. For example, Oracle acquired G-Log, a transportation management systems (TMS) vendor, and Agile, a product lifecycle management (PLM) vendor; and Activant acquired Intuit Eclipse.
SCM software vendors, in contrast, have felt encroached upon by ERP vendors. The situation has posed a real threat to SCM providers in the market, forcing them to extend their ERP functionality to compete with ERP vendors and to try to gain new clients in the distribution and logistics industry.
ERP-distribution software has integrated SCM functionality into its existing functionality to navigate through the complex global manufacturing environment. SCM software maps five processes into one solution: planning, sourcing (obtaining materials), producing, delivering, and returning final products if defective. These processes help to track and manage the goods throughout their entire life cycles. In addition, ERP solutions are used to manage the entire operations of an organization, not only a product’s life cycle. This gives users the broad capability to manage operations and use the SCM functionality to manage the movement of goods, whether components or finished product.
With the ability to gain accurate inventory visibility and SCM production, ERP-distribution software is able to see the whole chain of manufacturing and distribution events, from supplier to manufacturer, all the way to the final consumer.
There are three business models.
The first is the SCM model, which includes the manufacturing process.
The second is the retail model, which is the distribution of final products to the consumer, business, or retailer.
The third model is a combination of the first two business models, joined by the ERP-distribution software solution into one seamless process.
Within the SCM process, goods can either be brought in (imported) through foreign manufacturers, or acquired locally. The goods are then given to a distributor, 3PL provider, or wholesaler in order to reach the final client.
Within the retail model, the products are taken from a distributor, 3PL provider, or wholesaler, and are distributed to the appropriate person. Note that there is a “shift” for the consumer. This is to indicate that through the Internet or other forms of technology, consumers are now able to buy directly from distributors. The power of the consumer has changed; where manufacturers once provided products to consumers, consumers are now creating demand, and manufacturers have to meet that demand.
SCM solutions focus on the relationship between the supplier and manufacturer. However, ERP- distribution software has taken functionality from SCM software and combined it with retail software (such as point-of-sale and e-commerce solutions); it is now able to span across the entire supply chain and to track goods along the complete manufacturing process.
This is a simplified view of the complexities of today’s manufacturing processes. These complexities have made it crucial for trading partners to unite with manufacturers in order to help alleviate the frustrations that can occur within this global network. Specifically, trading partners are coming together with manufacturers to unite services, products, and customer experience so that business processes (such as manufacturing and distribution) become more efficient and that goods can move through these processes with minimal problems.
SCM can be thought of as the management of “warehousing processes,” in which the movement of goods occurs through multiple warehouses or manufacturing facilities. Tracking the costs of moving products and components through the maze of warehousing and manufacturing facilities is a tricky process, and many organizations lose money at each warehousing step.
Within the flow of goods in the manufacturing sector, the warehouse is a crucial part of the supply chain. Traditionally, the warehouse has been a source of frustration because the manufacturer or supplier pays for the use of the warehouse (whether owned or rented by the company). This leads to two possible scenarios: 1) the costs of the warehouse are incurred by a 3PL or manufacturing company, or 2) the costs are passed from one warehouse to another warehouse, and the original warehouse charges for these costs.
The typical warehouse process includes the following steps: receiving, put away, picking, kitting, packing, repacking, cross-docking, and shipping. ERP-distribution software is able to track costs across the entire organization and to aid companies in reducing costs that were previously tough to track.
ERP-distribution system encompasses the entire production of the final good. The ERP- distribution system is able to include inventory visibility from points “A to Z” (start to finish) and to track each warehouse cost from supplier to manufacturer to user, whether consumer, business, or retailer.
The Final Word: ERP-distribution software has been developed to meet the growing needs of the manufacturing and distribution industries. The capabilities incorporated into the software work across entire organizations, and even across continents.
Because of the economic shift in the manufacturing industry, the emergence of new software has been vital for businesses to stay competitive, meet the industry demands and emerging shift, and to keep business processes efficient to gain better profit margins.
ERP-distribution software is able to track the processes of manufacturing goods and distributing components, even if the manufacturer has facilities in North America and the Far East. With the SCM component in ERP software, manufacturing and tracking goods becomes manageable. Distributors and manufacturers can now work together in order to better meet customer requirements.
In addition of factors for domestic location selection analysis, other factors in international location selection are:
Taxes and Tariffs
How do companies in Computers, Automotive, Apparel, Electronics, Consumer Goods, Machinery manage their supply chain planning functions? What software do they use for forecasting, planning, and scheduling?
I know of these software solutions for Network Design and Optimization:
Trade Finance is the lubricant in Global Trade. The concentration of banks providing Trade Finance is very high. So are the risks if a bank fails or withdraws credit due to regulations.
How many Banks provide Trade Finance?
What happens when Banks withdraw credit due to Financial Crisis?
What other alternatives are there for Trade Finance ? GTLP?
What is the role of increased regulations on Trade Finance? BASEL III
From Trade finance around the world
Decline in Trade Finance as a cause of Global Trade Collapse
Concentration of Banks providing Trade Finance
De-risking by EU Banks to EMEs due to BASEL III requirement
Backlash against Trade
From DE-RISKING BY BANKS IN EMERGING MARKETS – EFFECTS AND RESPONSES FOR TRADE / IFC EMCOMPASS
Emerging evidence suggests that de-risking is a reality. Increased capital requirements, coupled with rising Know-Your-Customer, Anti-Money-Laundering, and Combating-the-Financing-of-Terrorism compliance costs have resulted in the exit of several global banks from cross-border relationships with many emerging market clients and markets, particularly in the correspondent banking business. A subset of this business, trade finance, is also at risk, with potential consequences for segments of emerging market trade. The emerging market trade finance gap was significant before the crisis and has since likely expanded. Those involved in addressing the de-risking challenge must focus on compliance consistency and effective adaptation of technological innovations.
From ADB 2016 Trade Finance Gaps, Growth, and Jobs Survey
The estimated global trade finance gap is $1.6 trillion.
$692 billion of the gap is in developing Asia (including India and the People’s Republic of China).
56% of SME trade finance proposals are rejected, while large corporates face rejection rates of 34% and multinational corporations are rejected only 10% of the time.
Firms report that 25% more trade finance would enable them to hire 20% more people.
Woman-owned firms face higher than average rejection rates.
70% of surveyed firms are unfamiliar with digital finance, uptake rates highest in peer-to-peer lending.
From ADDRESSING THE GLOBAL SHORTAGE OF TRADE FINANCE
The International Chamber of Commerce (ICC) 2016 Global Survey on Trade Finance reveals that 61 percent of respondents cited a global shortage of trade finance—a figure that is particularly concerning as we continue to observe a period of prolonged sluggishness when it comes to global trade growth. But hope is not lost. Doina Buruiana, Project Manager at ICC Banking Commission, explains the various ways that the trade-finance gap can be filled.
For the fifth consecutive year, trade growth has been reported at below 3 percent and has not recovered to pre-crisis levels—with a global trade-finance shortage estimated to have reached US$1.6 trillion in 2016, according to the Asian Development Bank (ADB). Such figures certainly make for grim reading. And what’s more, the findings from the International Chamber of Commerce’s (ICC) 2016 Global Survey on Trade Finance—an annual report reflecting the issues and trends on the trade-finance landscape—are also providing cause for concern. Sixty-one percent of respondents—national, regional and global banks providing trade finance—reported a global shortage of trade finance.
There are various reasons for this. Ninety percent cited the cost or complexity of compliance requirements relating to anti-money laundering (AML), know your customer (KYC) and sanctions as a chief barrier to the provision of trade finance. Furthermore, 77 percent of respondents to the Global Survey cited Basel III regulatory requirements as a significant impediment to trade finance. Many global banks are withdrawing from several emerging-market regions dependent on trade and trade finance, partly due to pressures to favour domestic clients following some banks’ bailouts by taxpayers.
And the fallout can be severe. A shortage of trade finance impacts the growth of businesses worldwide. In particular, small to medium-sized enterprises (SMEs) are being affected by the shortage of bank liquidity. According to the Global Survey, 58 percent of rejected trade-finance proposals were SME applications, despite the sector submitting 44 percent of all trade-finance proposals.
Yet hope is not lost. There are various ways in which the industry can adapt to not only bridge the gap in unmet demand for finance and help revive global growth, but also to evolve the industry, to drive healthy competition and to remove the focus from being global-bank dependent.
Backlash against trade
Improving understanding and attitudes toward trade, and awareness around trade finance, would be a good place to start. Across the world, many have attacked trade and globalisation for threatening jobs and benefitting only big businesses—sentiments that have been evident across the European Union (EU) during Transatlantic Trade and Investment Partnership (TTIP) negotiations, and also during the recent US presidential election campaigns.
Indeed, we’ve seen a clear rise in protectionist and populist policies—a recent World Trade Organization (WTO) report cited that between mid-October 2015 and mid-May 2016, G20 economies had introduced new protectionist trade measures at the fastest pace since 2008. To address this, we need to first make the case for trade itself in order to highlight the importance of trade finance. It is therefore crucial that businesses and trade-finance industry stakeholders reinvigorate the narrative around global trade, relaying its significance to the public and ensuring that trade is on the agenda of policymakers worldwide.
Understanding trade finance.
Next, enhancing awareness around trade finance should also remain a top priority. While there has already been significant progress in the dialogue between trade-finance practitioners and regulators, and a noticeable shift towards a more suitable risk-aligned treatment of trade finance, it is crucial that we continue to emphasise the low risk nature of trade-finance instruments.
Indeed, ICC’s 2015 Trade Register report highlights the low risk nature of trade-finance products—with favourable credit and default-risk experience. For instance, the Trade Register shows that there is a low default rate across all short-term trade-finance products, with the average expected loss for short-term trade finance lower than typical corporate exposures. In particular, traditional documentary trade-finance products such as letters of credit (LC) are low risk. Remarkably, the transaction default rate for export LCs between 2008 and 2014 was 0.01 percent. Medium- to long-term products also fare well, with a low loss nature due to the export credit agency’s (ECA) guarantee—normally with investment-grade ratings and backed by high-income Organisation for Economic Co-operation and Development (OECD) governments.
The need for increased awareness around trade finance extends well beyond traditional trade finance and also includes newer techniques and instruments under the supply-chain finance umbrella. We also need to raise industry understanding around compliance measures—differentiating between client KYC and non-client KYC, for instance, in order to ease processes. In addition, enhanced awareness and understanding in relatively unsettled areas in trade finance, such as trade-based money laundering, would help direct compliance measures. Despite common belief, for instance, only a small proportion of trade-based money laundering actually occurs in trade-finance transactions.
Yet while progress has certainly been made with regulation and compliance proposals, the Global Survey suggests that the costs associated with such measures are still, and will perhaps continue to be, prohibitive. As such, if we want to close the trade-finance gap, we need to move slightly away from a global bank-dominated financial landscape and embrace collaboration.
Financial-technology firms (fintechs) are increasingly shaping the future of trade finance, and make an obvious banking partner, with both parties bringing strengths and expertise to such arrangements. Indeed, many fintechs are looking to partner with—rather than compete with—banks due to balance-sheet requirements, the regulatory framework to navigate, and the industry expertise required to bring new concepts to fruition. Certainly, partnerships between the two players could drive additional efficiencies and the capacity of banks to conduct business—perhaps eventually reducing the trade-finance shortage.
Fintechs aren’t the only players that could potentially collaborate with banks—or even fill the trade-finance gap independently. The Global Survey found that export credit agencies (ECAs) are increasingly supporting export finance, with alternative liquidity flowing into the ECA space. Thirty-seven percent of respondents reported that they had successfully concluded business with institutional investors in ECA finance, up from 30 percent in the previous survey in 2015, reflective of the growing role of alternative investors.
The Global Survey also highlighted the important role of multilateral development banks (MDBs), with 75 percent of respondents agreeing that MDBs (and ECAs) help reduce trade-finance gaps. In particular, MDBs provide financial assistance to emerging markets for investment projects and policy-based loans. This can prove crucial for enabling access to trade finance in general, and for SMEs.
The ADB’s Trade Finance Program (TFP), for instance, fills market gaps for trade finance by providing guarantees and loans through more than 200 banks. The TFP has supported more than 12,000 transactions across Asia, valued at over US$23.1 billion—of which more than 7,700 involved SMEs. What’s more, the TFP focuses on markets in which the private sector has less capacity to provide trade finance, and where there are large trade-finance gaps.
However, the Global Survey also indicated that MDB and ECA support varies by region—with respondents deeming it most effective in advanced Asia, Russia and sub-Saharan Africa, and less effective in Commonwealth of Independent States (CIS) countries, India and Central America and the Caribbean. Clearly, an increase in the envelope and effectiveness of MDB trade-finance provision in these regions will help further reduce the gap. In order to counter geographical disparities, the next step for MDBs is to consider any structural limitations in existing trade-finance programmes—or contextual difficulties in particular markets.
Finally, non-bank capital provides another useful source of trade finance, particularly from private-sector sources of finance—such as specialist financiers or alternative-finance providers. Since the financial crisis, these players have played an increasingly crucial role in meeting unmet demand, and have experienced considerable growth. What’s more, specialist financing is growing increasingly popular among companies in emerging markets, in which trade-finance demand is most acute.
Revamping trade finance.
Of course, one way to possibly boost the provision of trade finance is to make it more efficient and attractive. Certainly, the digitisation of trade finance holds huge potential. Automating trade finance can make overall processes more effective and reliable, increasing capacity for banks, corporates and other stakeholders along the supply chain. For instance, eDocs (paperless documents) streamline processes, with the ability for multiple parties to access, review and collaborate at any one time. The resulting operational improvements in turn reduce errors, maintain data integrity and accelerate the completion of agreements.
Despite the clear benefits, the Global Survey shows that there has been a slow uptake of digitisation. In fact, one-fifth of respondents reported that there is no evident digitisation at all, two-thirds saw very little impact of technology on trade finance, and just over 7 percent saw digitisation as being widespread. The slow uptake is likely due to the challenges of digitising trade—including the considerable scale and complexity of the task at hand, for instance. Banks should play a key role in advocating the benefits of digitisation and help their corporate clients adapt to new systems.
We cannot let the trade-finance gap incapacitate trade. Clearly, there are steps that the trade-finance industry can take to help meet unmet demand. Looking ahead, improving attitudes and raising understanding, encouraging collaboration and making progress towards innovation in the industry will support the growth of businesses of all sizes—and the economy—worldwide.
From Global Trade Liquidity Program /IFC
The Global Trade Liquidity Program (GTLP) is a unique, coordinated global initiative that brings together governments, development finance institutions (DFIs), and private sector banks to support trade in developing markets and address the shortage of trade finance resulting from the global financial crisis.
With targeted commitments of $4 billion from public sector sources, the program has supported nearly $20 billion of trade since its inception. It raises funds from international finance and development institutions, governments, and banks, and it works through global and regional banks to extend trade finance to importers and exporters in developing countries. IFC’s commitment to the program is $1 billion.
GTLP began its operations in May 2009, channeling much-needed funds to back trade in developing countries. Phase 2 was launched in January 2010 with an unfunded solution, based on the existing GTLP platform, to support trade finance directed at the food and agribusiness sectors. The program was extended in January 2012 to continue to stabilize and foster trade and commodity finance to emerging markets.
Since its launch, GTLP has been acknowledged in the financial industry as an innovative structure to help infuse much needed liquidity into the trade finance market, thereby catalyzing global trade growth. The solution also represents a win-win proposition: for the banks it provides an opportunity to continue supporting clients through these difficult times; for IFC and its partners, it affords the ability to channel liquidity and credit into markets to help revitalize trade flows by leveraging on the banks’ vast networks across emerging markets in Asia, Africa, Middle East, Europe, and Latin America.
The program is already benefiting thousands of importers and exporters and small- and medium-sized enterprises.
From ADB Trade Finance Program
ADB’s Trade Finance Program (TFP) fills market gaps for trade finance by providing guarantees and loans to banks to support trade.
Backed by its AAA credit rating, ADB’s TFP works with over 200 partner banks to provide companies with the financial support they need to engage in import and export activities in Asia’s most challenging markets. With dedicated trade finance specialists and a response time of 24 hours, the TFP has established itself as a key player in the international trade community, providing fast, reliable, and responsive trade finance support to fill market gaps.
A substantial portion of TFP’s portfolio supports small and medium-sized enterprises (SMEs), and many transactions occur either intra-regionally or between ADB’s developing member countries. The program supports a wide range of transactions, from commodities and capital goods to medical supplies and consumer goods.
The TFP continues to grow, supporting billions of dollars of trade throughout the region, which in turn helps create sustainable jobs and economic growth in Asia’s developing countries.
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