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The thought and rationale of sustainable competitive advantage in strategy are significantly influenced by the Schumpeterian models of dynamic competition in IO and evolutionary economics. Yet, most analytical accounts of sustainable competitive advantage fail to explain how firms' investment choices influence, and are simultaneously influenced by, the co‐evolution of “external” industry competition and “internal” firm competences. This paper aims to contribute to the development of a theory of endogenous market structure in strategy.

Two alternative assumptions are developed – concerning temporally heterogeneous firm investment strategy – that lie central to a proposed behavioral theory of endogenous market structure. Additionally, a theoretical description is provided of the endogeneity of the demand‐side determinants of firm investment strategy and industrial market structure. Finally, guidelines are provided for empirical application of [incorporating] the alternative assumptions and theoretical arguments.

It is expected that the theoretical arguments in the paper will influence strategy scholars to develop dynamic models of firm performance that render themselves amenable to sound empirical analyses.

The paper contributes towards developing a theory of endogenous market structure in strategy.

This paper aims to explain how the dynamic demand environment influences strategic firm behavior along an industry’s evolutionary path. A conceptual gap concerning the influence of demand-side environmental factors (vis-à-vis changes in technology and policy) on firms’ strategic choices motivates the theory developed herein. The paper’s contribution to the literature on “evolutionary perspective in strategy” also addresses an important gap in the emerging literature on “strategy dynamics”.

The conceptual framework in this paper features a dynamic demand environment that provides the structural context for firms’ strategic choices. It conceptualizes demand-side competence as a mediating firm-specific construct to explain the endogenous relationship between the characteristics of the demand environment and firms’ path dependent demand-side investments.

A review of the literature on evolutionary perspective in strategy reveals an important conceptual gap concerning the structural determinants of dynamic firm behavior. There is no explanation of the endogenous relationship between dynamic demand structure, firms’ dynamic demand-side competence, and temporally heterogeneous strategic choices.

The demand-side explanation of how idiosyncratic firm behavior is endogenously determined, with both structural characteristics (demand structure) and firm competences (demand-side competence), addresses an important conceptual gap. The novelty of the theory developed herein lies in its explication of the effect of dynamic demand environment on the evolution of idiosyncratic strategic firm behavior – entry, investment and exit – along the evolutionary path of an industry. The theory developed herein not only explains the effect of both determinants of idiosyncratic strategic firm behavior – the external industry environment (dynamic market structure) and internal firm environment (dynamic firm competences) – but also explains how the determinants evolve along the industry’s lifecycle.

The extant “supply‐side” frameworks of industry evolution fail to predict the evolutionary patterns in industries based on systemic technologies. This paper aims to describe the complex demand environment in industries based on systemic technologies and to explain how the continuously evolving demand structure influences the choice and level of firm investments in the above context.

The paper identifies a conceptual gap in the “technology‐centric” literature on industry evolution by conducting a detailed interpretive survey of the literature that focuses on the demand‐side determinants of firm‐ and industry‐level technological processes underlying industry evolution, and co‐evolution of the technological system underlying an industry and the consumer applications based on the same.

The paper provides a set of empirically verifiable mechanisms to explain competing firms' choice and level of investment under conditions of technological and demand uncertainty in industries based on systemic technologies. On one hand, firms' investments influence the evolution of both the technological system(s) and their constituent components that underlie such industries and, on the other, firms' investments influence the consumption of the array of consumer applications that are generated in these industries.

The theoretical explanation provided herein not only enhances the understanding of the role of demand‐side factors as determinants of rate and direction of technological advances but also lies central to the understanding of the evolution of industries based on systemic technologies. More specifically, the paper explains how the interaction between continuously evolving demand structure in the downstream market(s) for consumer applications and the technological components comprising the technological system influences competing firms' choice and level of investments.

This paper examines the different features of China's economic development in different stages of economic globalization. The study finds that the investment- and export-based growth model drove China's high-speed economic growth between 2000 and 2007, which came into existence around 2000 when China plugged into the global production network.

This paper also finds that China slowed down to the New Normal because of the disruption to the socio-economic underpinnings of this growth model. As China adapts to and steers the New Normal, supply-side structural reforms can channel excess capacity to the construction of underground pipe networks in rural areas of central China and fix capital while advance rural revitalization.

At the same time, enterprises must strive to build a key component development platform for key component innovation and the standard-setting power in global manufacturing.

The establishment of a domestic production network integrating the integrated innovation-driven core enterprises and modular producers at different levels can satisfy the dynamic demand structure of China in which standardized demands and personalized demands coexist.

This paper analyzes the factors that potentially affect grain security in China and reviews the techniques used for prediction. It reviews and compares forecasts for grain security in 2020 and 2030 with the aim of making some judgments, from the perspective of grain supply and demand, about the future grain security situation in China.

In this article, the paper will introduce the methods and results of the forecast and also focus on the predictions for grain security in 2020 and 2030 in order to give a clear review of previous researches in this regard.

The results indicate that the traditional threats to food supply and demand in China still exist, while demand for biomass energy continues to rise. With regard to grain aggregate, the grain supply-demand balance will still be relatively tight for both 2020 and 2030. In terms of structure, grain for feed will experience increase – mainly driven by the supply of corn – adding to the unabated structural issues confronting regional grain supply and demand. In the future, therefore, China should try to preserve favorable factors that increase grain production, optimize grain structure and production, maintain the proper scale and make-up of grain imports and exports, and work for a sound global trading environment for grain.

This article contributes to existing literature by analyzing the factors affecting grain security in China and reviewing prediction techniques and forecasts for grain security in China in 2020 and 2030. The findings suggest that China needs to take appropriate measures to ensure future grain security.

The purpose of this paper is to propose and demonstrate criteria for omnibearing imbalance of macroeconomic system structure (regions, industries, urban and rural areas, departments, ownership, etc.) and their strategic optimization, for structural optimization of the global regional macroeconomic system, providing universal application of the formula for calculating, and quantitative basis.

Relevant statistical data will compare receipts (GBC) model calculation to quantitatively determine structural imbalance and the formation of structural optimization and direction and intensity of the strategic program.

Contemporary system science believes that all things take the form of systems, and that the output function depends on the internal structure of systems. Therefore, the system structure must be judged by the output benefit. Although the output benefit of a society's macroeconomic system is enslaved to the system's external environment (the peripheral environment, international economy, or even global economy), the state of the system's internal structure directly determines its output benefit. In reverse, the output benefit of the macroeconomic system is also the reference to evaluate its internal structure.

Research on the macroeconomic system structural imbalances and optimization with universal applicability.

The paper is of great significance with regard to the theory, methodology, and practice for structural optimization of the global regional macroeconomic system.

Three simple, single pass multi‐stage lot‐sizing heuristics are examined using simulation. The heuristics are based on using different cost policies in single stage lot‐sizing procedures when applied to a multi‐stage setting. The focus is on the echelon holding cost policy and its performance relative to using “full value” holding costs and McLaren's adjusted setup costs. It is shown that echelon holding costs can lead to an extremely poor overall cost performance. A simple measure that will detect situations for which the echelon holding cost policy is potentially not suitable is suggested and evaluated. Application of the proposed measure results in substantial cost improvements for the echelon holding cost policy; despite this, the policy was outperformed by the MLSA policy in most cases. More research is needed, however, before any conclusive evidence can be presented on the effectiveness of echelon holding costs in multi‐stage lot‐sizing.

This paper aims to explore the impact of inventory system inaccuracies on the benefits of collaborative supply chain practices under various supply chain scenarios. To achieve this purpose, two popular collaboration initiatives are considered in a four‐stage supply chain. The first practice is a vendor managed inventory program where the distributor takes the full responsibility of managing the retailer's inventory. The second practice is a collaborative planning, forecasting, and replenishment program where all members work together to plan, forecast, and replenish the product.

The study utilizes Monte Carlo computer simulation in an experimental design.

The analysis suggests that while the inaccurate inventory records result in significant performance reductions for all supply chain configurations, their impact is substantially greater for the supply chains where members collaborate more closely on key supply chain management activities. In addition, the author also realize that the adverse impact of inaccurate inventory information is stronger under the conditions where lead times are shorter and/or where demand uncertainty in market place is lower.

This analysis provides a means for practitioners to realize the importance of inventory accuracy to successful adaptation of collaborative supply chain practices. Moreover, this research also helps in understanding the supply chain conditions where the attempts of eliminating or reducing errors in inventory information are more crucial and more beneficial.

Although there is a range of research focusing on collaborative practices, none of these studies considered the errors in inventory information. This is the first study to investigate the impact of inaccurate inventory information on the benefits of collaborative practices.

The objective of this paper is to propose a methodology based on random demand inventory models and dependence structures for a set of raw materials, referred to as “components”, used by food services that produce food rations referred to as “menus”.

The contribution margins of food services that produce menus are optimised using random dependent demand inventory models. The statistical dependence between the demand for components and/or menus is incorporated into the model through the multivariate Gaussian (or normal) distribution. The contribution margins are optimised by using probabilistic inventory models for each component and stochastic programming with a differential evolution algorithm.

When compared to the non-optimised system previously used by the company, the (average) expected contribution margin increases by 18.32 per cent when using a continuous review inventory model for groceries and uniperiodic models for perishable components (optimised system).

The multivariate modeling can be improved by using (a) other non-Gaussian (marginal) univariate probability distributions, by means of the copula method that considers more complex statistical dependence structures; (b) time-dependence, through autoregressive time-series structures and moving average; (c) random modelling of lead-time; and (d) demands for components with values equal to zero using zero-inflated or adjusted probability distribution.

Professional management of the supply chain allows the users to register data concerning component identification, demand, and stock levels to subsequently be used with the proposed methodology, which must be implemented computationally.

The proposed multivariate methodology allows it to describe demand dependence structures through inventory models applicable to components used to produce menus in food services.

Este trabajo propone una metodología basada en modelos de inventarios con demanda aleatoria y estructura de dependencia para un conjunto de materias primas, denominadas “componentes”, usadas por servicios de alimentación que producen raciones alimenticias denominadas “menús”.

Los margen de contribución de servicios de alimentación que producen menús son optimizados empleando modelos de inventarios con demandas aleatorias dependientes. La dependencia estadística entre demandas de componentes y/o menús es incorporada en el modelado mediante la distribución gaussiana (o normal) multivariada. La optimización de los márgenes de contribución se logra usando modelos de inventarios probabilísticos para cada componente y programación estocástica mediante el algoritmo de evolución diferencial.

El margen de contribución esperado (promedio) aumenta en un 18,32% usando modelos de inventario de revisión continua para abarrotes y modelos uniperiódicos para componentes perecederos (sistema optimizado), en relación al sistema no optimizado usado anteriormente por la compañía.

La metodología multivariada propuesta permite describir estructuras de dependencia de la demanda mediante modelos de inventario aplicables a componentes usados para producir menús en servicios de alimentación.

Una administración profesional de la gestión de la cadena de suministros permite registrar datos de la identificación del componente, su demanda y sus niveles de stock para ser usados posteriormente con la metodología propuesta, la que debe estar implementada computacionalmente.

El modelado multivariado puede ser mejorado (a) utilizando distribuciones probabilísticas univariadas (marginales) distintas a la gaussiana, mediante métodos de cópulas que recojan estructuras de dependencia estadística más complejas; (b) considerando demandas de componentes con valores iguales a cero, mediante distribuciones probabilísticas infladas en cero; (c) usando dependencia temporal, mediante estructuras de series de tiempo autorregresivas y de media móvil, y (d) modelando el lead-time en forma aleatoria.