Changes In Demand Conditions Research Articles

SME Failures Under Large Liquidity Shocks: an Application to the Covid-19 Crisis

Abstract We study the effects of financial frictions on firm exit when firms face large liquidity shocks. We develop a simple model of firm cost-minimization, where firms’ borrowing capacity to smooth temporary shocks to liquidity is limited. In this framework, firm exit arises from the interaction between this financial friction and fluctuations in cash flow due to aggregate and sectoral changes in demand conditions, as well as more traditional shocks to productivity. To evaluate the implications of our model, we use firm level data on small and medium sized enterprises (SMEs) in 11 European countries. We confirm that our framework is consistent with official failure rates in 2017–2019, a period characterized by standard business cycle fluctuations. To capture a large liquidity shock, we apply our framework to the COVID-19 crisis. We find that, absent government support, SME failure rates would have increased by 6.01 percentage points, putting 3.1% of employment at risk. Our results also show that in the presence of financial frictions and in the absence of government support, the firms failing due to COVID have similar productivity and growth to firms that survive COVID.

Multi-objective evolutionary scheduling based on collaborative virtual workflow model and adaptive rules for flexible production process with operation reworking

Dynamic event disturbances during production such as reworking of defective operations more challenge scheduling optimization. Efficient response to operation reworking is crucial for collaborative optimization of production quality and scheduling performance. Given the complex constraints within production processes of jobs involving key operation reworking and uncertainty of dynamic disturbances, a two-level virtual workflow modeling method and a multi-objective evolutionary scheduling algorithm with adaptive rules (MOESA-AR) based on collaborative virtual workflow framework are proposed. Virtual workflow modeling fulfills linear abstraction of tasks to be scheduled and priority constraints. Through decision arrangement, task state tracking and information feedback, collaborative virtual workflow model constitutes framework for dynamic update of task workflows and real-time response to rescheduling triggered by event disturbances. MOESA-AR adopts two-dimensional chromosome evolution and heuristic rule training to adapt to the complexity of scheduling optimization. Adaptive strategy of dispatching rules enhances the dynamic adaptability of decision-making to variable scheduling scenarios, thereby improving the dominance level of multi-objective trade-off optimization under changing demand conditions. Experiments confirm the superiority of MOESA-AR in improving scheduling performance and the effectiveness of collaborative virtual workflow scheduling framework in responding to dynamic disturbances.

Supply‐side inducements and resource redeployment in multiunit firms

AbstractResearch SummaryWe examine to what extent and when multiunit firms internally redeploy managers between units. While theory has emphasized how changes in demand conditions affect redeployment, we argue that optimal internal resource allocation involves consideration of both demand and each unit's resource supply. We formalize this argument, showing how redeployment arises from “supply‐side inducements”—return advantages in new over existing resource uses resulting from changes in resource supply. Empirical tests using manager deaths as an exogenous, supply‐side shock to firms' resource stocks support our arguments, showing that firms frequently redeploy resources away from better‐endowed and toward negatively affected units. Incorporating supply‐side inducements into redeployment theory implies additional value‐creation opportunities from redeployment and carries novel predictions for the direction of intra‐firm resource flows.Managerial SummaryFirms continuously decide how to allocate valuable resources—such as their most productive workers, unique inputs, or machinery—among different units. We argue that to optimally allocate such resources, managers must respond to changes in both the demand environment and in the resource stock of different units. When some units successfully accumulate resources while others suffer adverse shocks, opportunity exists to improve efficiency by internally redeploying resources. Counterintuitively, optimal redeployment frequently involves shifting resources away from successful, well‐endowed units. New business units, being resource‐poor, are often the most important recipients of resources. Empirical analyses studying how firms allocate managers in a large sample of Brazilian firms offer support for these arguments.

Small and medium-sized businesses in agricultural sector on the example of the Almaty region of the Republic of Kazakhstan

In a market economy, tough competition, the urgent task is to ensure the effective functioning of the agro-industrial business, which creates a social and economic basis that allows to maintain a rural lifestyle, develop rural areas, responding flexibly and in a timely manner to changing supply and demand conditions. The goal is to study the current state of entrepreneurship in the agro-industrial complex of the Almaty region, the pace of development and the effectiveness of support measures. Methods – economic-statistical, monographic, abstract-logical, comparative analysis and synthesis. Results – analyzed the number of operating small and medium-sized agribusiness entities in this region over the past 5 years, the number of employees employed in this area, the gross agricultural output in the country, as well as in the context of districts. An analysis was made of the share of small and medium-sized enterprises (SMEs) in agricultural production in general in Kazakhstan and the Almaty region, in particular. The main problems and ways to solve them are outlined. The indicators of objects of entrepreneurial activity, the volume of output of goods in agricultural sector and their structure in the region according to statistical data for 2018-2022 are presented. Shows the amount of state financial assistance at the regional level in recent years. Conclusions – on the GDP of agricultural sector over a five-year period, on average, SME accounts for 28.5%, individual entrepreneurs 5.3%, PPF 66.2%. The issues of irrational use of agricultural land, expansion of irrigated areas, regulatory support for the effective use of allocated public funds, insufficient institutional support for rural entrepreneurs are considered.

A Reinforcement Learning approach for bus network design and frequency setting optimisation

This paper proposes a new approach to solve the problem of bus network design and frequency setting (BNDFS). Transit network design must satisfy the needs of both service users and transit operators. Numerous optimisation techniques have been proposed for BNDFS in the literature. Previous approaches tend to adopt a sequential optimisation strategy that conducts network routing and service frequency setting in two separate steps. To address the limitation of sequential optimisation, our new algorithm uses Reinforcement Learning for a simultaneous optimisation of three key components of BNDFS: the number of bus routes, the route design and service frequencies. The algorithm can design the best set of bus routes without defining the total number of bus routes in advance, which can reduce the overall computational time. The proposed algorithm was tested on the benchmark Mandl Swiss network. The algorithm is further extended to the routing of express services. The validation includes additional test scenarios which modify the transit demand level on the Mandl network. The new algorithm can be useful to assist transit agencies and planners in improving existing routing and service frequency to cope with changing demand conditions.

Navigating windows of opportunity: The role of international experience

AbstractResearch SummaryScholars have noted that pronounced changes in consumer demand and technology often offer firms temporary opportunities to strengthen their performance vis‐à‐vis rivals. This article contributes to the literature on windows of opportunity from an organizational learning perspective. It investigates whether the depth and breadth of a firm's international experience with pronounced changes in demand conditions (demand windows) and technologies (technological windows) affect its ability to take advantage of such changes within a country to increase its market share. The results, based on a sample of 615 telecommunication companies competing in 124 countries, suggest that mainly two out of four dimensions of international experience help firms to exploit windows of opportunity in a country.Managerial SummaryWhat can help multinational companies (MNCs) to navigate periods of marked changes in demand and technology? When an MNC encounters a marked change in demand or technology in a country, it may have already experienced in the past many or just a few of these events, depending on its international footprint, and this serves to assess the MNC's international experience with such changes. Using data on telecommunication companies, we show that both (a) an MNC's repeated exposure to a certain type of change over time (depth of international experience) and (b) the variety of changes an MNC has been exposed to (breadth of international experience) in international markets may help the MNC to obtain market share advantages when such changes occur in a country.

Resolving-power domination number of probabilistic neural networks

Power utilities must track their power networks to respond to changing demand and availability conditions to ensure effective and efficient operation. As a result, several power companies continuously employ phase measuring units (PMUs) to continuously check their power networks. Supervising an electric power system with the fewest possible measurement equipment is precisely the vertex covering graph-theoretic problems otherwise a variation of the dominating set problem, in which a set D is defined as a power dominating set (PDS) of a graph if it supervises every vertex and edge in the system with a couple of rules. If the distance vector eccentrically characterizes each node in G with respect to the nodes in R, then the subset R of V (G) is a resolving set of G. The problem of finding power dominating set and resolving set problems are proven to be NP-complete in general. The finite subset R of V (G) is said to be resolving-power dominating set (RPDS) if it is both resolving and power dominating set, which is another NP-complete problem. The ηp (G) is the minimal cardinality of an RPDS of a graph G. A neural network is a collection of algorithms that tries to figure out the underlying correlations in a set of data by employing a method that replicates how the human brain functions. Various neural networks have seen rapid progress in multiple fields of study during the last few decades, including neurochemistry, artificial intelligence, automatic control, and informational sciences. Probabilistic neural networks (PNNs) offer a scalable alternative to traditional back-propagation neural networks in classification and pattern recognition applications. They do not necessitate the massive forward and backward calculations that ordinary neural networks entail. This paper investigates the resolving-power domination number of probabilistic neural networks.

Optimal PMU placement problem in octahedral networks

Power utilities must track their power networks to respond to changing demand and availability conditions to ensure effective and efficient operation. As a result, several power companies employ phase measuring units (PMUs) to check their power networks continuously. Supervising an electric power system with the fewest possible measurement equipment is precisely the vertex covering graph-theoretic problem, in which a set D is defined as a power dominating set (PDS) of a graph if it supervises every components (vertices and edges) in the system (with a couple of rules). The γp(G) is the minimal cardinality of a PDS of a graph G. In this present study, the PDS is identified for octahedral networks.

Dynamic Price Control Using Pole Placement Method in Smart Grids

Energy has been one of the most important concepts that we cannot remove from our lives with its many uses, from transportation, warming, and enlightenment to the tourism sector, being at the beginning of the most basic requirements for humanity for centuries. Renewable energy is a resource that does not decrease according to the increase of the population, can be obtained naturally on earth, can create an alternative solution to energy shortages and several problems. The concept of a smart grid, which provides an advantage for the use of renewable energy sources, ensures that the energy network can be controlled in the future under conditions of intermittent energy production and instantaneous demand. With the increase in distributed production in smart grids, production and consumption conditions have paved the way for energy pricing to be dynamic. In this study, price balancing was achieved by dynamic electricity pricing, which is one of the methods of achieving autonomous control in smart grids and obtaining energy safely. For this purpose, the coefficients of the Proportional-Integral-Derivative (PID) controller obtained using the pole placement method are modeled in the MATLAB/SIMULINK program with the energy supply-demand scenario. In order to examine the effect of change in energy demand on price, a temporary peak and fall are given to energy demand. According to changing demand conditions, the PID controller has been shown to provide price balance, regulate energy price online and maintain energy balance.

Intelligent coordinated control for improved voltage and frequency regulation with smooth switchover operation in LV microgrid

Abstract During demand changes and incidence of grid faults, the microgrid system is subjected to an imbalance between the generation and demand. The maintenance of power balance under such dynamic conditions is a major concern for the proper functioning of the microgrid network. During those dynamic periods, achieving voltage and frequency regulation, in particular, during islanded/standalone mode of operation is a challenging task. For reliable operation, in addition to power balance, voltage and frequency regulation, reduced switchover transients and fast switchover operations are necessary. Conventional coordinated controllers comprising of droop, voltage and current control loops, aim at achieving stability of the microgrid network but fails to ensure a quick and smooth response. This work proposes an Adaptive Neuro-Fuzzy Inference System based intelligent coordinated control strategy by combining control techniques namely, inverse droop control, virtual impedance control and current control. The proposed coordinated control scheme provides improved voltage and frequency regulation, reduced switching transients and quick adaptation of the system during demand changes and fault events. The proposed system uses an inverse droop control technique to achieve power decoupling. Additionally, a virtual impedance-based voltage control loop is implemented which ensures voltage regulation of the microgrid and a feed-forward current control loop is developed to minimize the transients during load switching and switchover operations. The reverse droop based PLL strategy is implemented for grid synchronization and smooth switchover operations. The proposed system is simulated in a 20-kVA grid-tied microgrid system in MATLAB/SIMULINK R2018b and tested in real-time of 5-kVA grid-tied solar PV system which demonstrated that the proposed approach is effective under varying irradiance, changing demand conditions and switchover operations. The experimental results prove that the proposed scheme is performing better under different system conditions with fast switching response when compared with other control algorithms.

Enterprise’s Strategies to Deal with Epidemic Crisis Based on Super-Dynamic Capability Theory

In this paper, the supply chain management risks arising from 2019-novel coronavirus (hereinafter referred to as “COVID-19”) outbreak was proposed, and they were further analyzed from three main aspects such as change in demand conditions of domestic customers, change in domestic supply market, impact on domestic logistics industry. Besides, multiple feasible strategies for coping with such epidemic situation were proposed for enterprises based on the super-dynamic capability theory. The research in this paper has powerful theoretical value and practical significance for the current development of enterprises, especially the reorganization of enterprises under the current epidemic crisis in China.

Efficiency measurement of Ontario's sawmills using bootstrap data envelopment analysis

AbstractSawmills in Ontario are an important forest products industry, contributing to the economic prosperity of the entire province. However, these sawmills have been facing extreme competitive pressures, impacting their operational efficiency. This study uses a nonparametric technique, the bootstrap data envelopment analysis, to analyse the relative efficiencies of 125 Ontario sawmills over a period of 17 years (1999 to 2015). The results indicate low levels of overall technical and managerial efficiencies in Ontario sawmills, which have been further impacted by economic downturns. Further analysis reveals that the size of the sawmills has had a statistically significant impact on their relative technical efficiencies. The main source of inefficiency was the management of operations, particularly when these sawmills were not able to adjust their inputs with changing and uncertain market demand conditions. These results provide policymakers and sawmill managers with comprehensive details so that future resources can be reallocated to improve the performance of the Ontario forest products industry.

Currency Mispricing and Dealer Balance Sheets

We relate currency mispricing originating from the breakdown of covered interest rate parity to the dealer balance-sheet constraints resulting from the post-crisis financial regulation. Using a unique data set on contract-level foreign exchange derivatives with disclosed counterparty identities, we find that dealers with a higher leverage ratio demand an additional premium from their clients for synthetic dollar funding. We handle endogeneity using two exogenous variations associated with the public disclosure of the leverage ratio, and the introduction of the UK leverage ratio framework while controlling for changes in demand conditions at the client level.

Long-run variation in capacity utilization in the presence of a fixed normal rate

A generic Kalecki-Robinson model of growth is developed that, subject to different closures, illustrates the different channels through which the economy can adjust to a change in demand conditions in the long run. The closures are shown to have different implications for the behaviour of the rate of capacity utilization and hence whether and how the economy achieves a ‘fully-adjusted position’ (equalization of the actual and normal rates of capacity utilization). Assuming that the normal rate of capacity utilization is exogenously fixed, it is then shown that variation in the actual capacity utilization rate can nevertheless occur—at least within limits—without triggering ‘Harrodian instability’. This result emanates from a discontinuity in the investment function that is grounded in Harrod’s own macrodynamics, so that it is ultimately the combination of Harrodian and Kaleckian dynamics that gives rise to long-run variations in the actual rate of capacity utilization in the presence of a fixed normal rate. Aggregate and industry-level US capacity utilization data are then used to calculate possible bands within which the rate of capacity utilization may vary without triggering Harrodian instability. A key finding is that the conditions necessary for the latter appear to be relatively rare.

Price Setting in Online Markets: Does IT Click?

Using a unique dataset of daily U.S. and U.K. price listings and the associated number of clicks for precisely defined goods from a major shopping platform, we shed new light on how prices are set in online markets, which have a number of special properties such as low search costs, low costs of monitoring competitors’ prices, and low costs of nominal price adjustment. We document that although online prices are more flexible than offline prices, they nevertheless exhibit relatively long spells of fixed prices, large size, and low synchronization of price changes, considerable crosssectional dispersion, and low sensitivity to predictable or unanticipated changes in demand conditions. Qualitatively, these patterns are similar to those observed for offline prices, a finding that suggests a need for more research on the sources of price rigidities and dispersion.

Exchange Rate Uncertainty and R&D Investment: Evidence from Japanese Firms

The Japanese yen is one of the most volatile among developed country currencies. In this paper, we investigate how real effective exchange rate (REER) uncertainty affects firms’ research and development (R&D) investment, using firm‐level panel data for Japanese manufacturing firms for the period 1994–2011. Our results show that firms that are more exposed to REER uncertainty are less responsive to changes in demand conditions. Uncertainty makes firms more cautious when investing, since high uncertainty increases the chances of making a costly mistake. Our finding thus provides evidence of the caution effect of uncertainty. The caution effect also increases the persistence of R&D, implying that R&D investment does not increase much even if firms face favorable demand conditions. Reducing REER uncertainty is important to stimulate R&D investment, especially for firms that are more exposed to international competition and REER uncertainty.

The Effects of Product Line Breadth: Evidence from the Automotive Industry

Using a detailed data set from the U.S. automotive industry, we enrich the existing literature on product line breadth with new results that highlight previously unexplored operational aspects of its benefits and costs. We find that expanding product line breadth has a significant effect on increasing mismatch costs arising from the increased demand uncertainty associated with product proliferation. These mismatch costs are manifested through additional discounts and inventories. The effect of product line breadth on mismatch costs is comparable in magnitude to the effect on production costs, suggesting that the operational benefits of inventory pooling achievable by rationalizing product lines can be very substantial. Furthermore, we quantify the benefit of using a platform strategy to mitigate the effects of a broad product line on production costs. Finally, we propose an additional, attribute-based measure of product line breadth and find that product line breadth can work as a hedge against changes in demand conditions. For example, automakers that offer a broader range of fuel economy levels increase their market share and reduce their average discounts as gas prices become more volatile.

Long-Run Variation in Capacity Utilization in the Presence of a Fixed Normal Rate

We develop a generic Kalecki-Robinson model of growth that, subject to different closures, illustrates the different channels through which the economy can adjust to a change in demand conditions in the long run. The closures are shown to have different implications for the behaviour of the rate of capacity utilization and hence whether and how the economy achieves a “fully-adjusted position” (equalization of the actual and normal rates of capacity utilization). Assuming that the normal rate of capacity utilization is exogenously fixed, it is then shown that variation in the actual capacity utilization rate can nevertheless occur – at least within limits – without triggering “Harrodian instability”. This result emanates from a discontinuity in the investment function that is grounded in Harrod’s own macrodynamics, so that it is ultimately the combination of Harrodian and Kaleckian dynamics that gives rise to long-run variations in the actual rate of capacity utilization in the presence of a fixed normal rate. Aggregate and industry-level US capacity utilization data are then used to calculate possible bands within which the rate of capacity utilization may vary without triggering Harrodian instability. A key finding is that the conditions necessary for the latter appear to be relatively rare.

Diagnostics and Long Term Prognostics for Investment Decision Support in Smart Grids

Abstract: Proliferation of distributed renewable generation and other low carbon technologies create new challenges for the electricity distribution networks. In particular, distribution network operators are increasingly exploring alternatives to conventional reinforcement in order to reduce network operation costs, increase security of supply and allow more renewable generation to be connected to the network. Selecting the most optimal combinations of interventions in regards to long-term cost and performance of the network is something that the current tools and approaches used by the industry cannot adequately do. This paper describes a novel techno-economic planning and investment modelling tool for electricity distribution networks termed as Scenario Investment Model (SIM). The SIM is able to find optimal network evolution scenarios in the presence of changing demand conditions by applying multiple electricity and storage technologies to resolve network issues.

Innovation system of the seafood industry in Thailand

This paper explores convergence innovations in resource-based industries by conducting a case study of the Thai seafood industry. It adopts the concept of sectoral innovation system as its framework. Through in-depth interviews with representatives from firms and other actors in the system, as well as a technology foresight workshop, the study reveals that there were several product innovations, namely, branded ready-to-eat, ready-to-cook, and frozen products based on Thai, Oriental, Western, and fusion recipes. These innovations were the results of changing demand conditions as well as an integration of knowledge and expertise from science, social science, art, and the services industry. These innovations were achieved through R&D departments having personnel from various disciplines, in addition to interaction between R&D and other departments within firms. In several cases, firms leveraged external knowledge through collaboration with local universities and public research institutes. The study finds that in order for convergence innovation to occur, firms must be proactive in their approach to acquiring and creating new knowledge in their industry. Convergence innovation also requires a strong, coherent sectoral innovation system to be in place.