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One strategic design parameter in capacity management is the setting of a planned level of capacity utilization at which the manufacturing operation will operate long term. Seeks to examine systematically the implications of varying levels of capacity utilization within an assemble‐to‐order firm through experiments with a simulation model. Four performance measures and a total weekly cost measure are analysed under nine capacity utilization levels, two demand patterns, and 11 ratios of the costs of idle capacity to the costs of late orders. The prescribed capacity utilization level is a function of the firm′s competitive goals, demand pattern, and cost structures.

The objective of the paper is to get a better understanding of capacity utilisation (CU) in Indian public bus companies. More specifically, this paper would be measuring CU and identifying the drivers of the same. Finally, the influence of CU on the financial performance of Indian bus companies is examined.

The study adopted data envelopment analysis (DEA) to measure the CU in Indian public bus companies. Truncated regression was used to identify the drivers of CU. Subsequently, the ordinary least squares (OLS) regression was used to analyse the influence of CU on Indian bus companies' financial performance. The period of study was from 2013 to 17.

The significant drivers of CU were fleet age, passenger lead and fleet utilisation. Additionally, it was found that CU had a significant positive influence on the financial performance of Indian public bus companies and a unit increase in unused capacity has led to an increase of 7% in the operating ratio of the bus companies.

Getting insights into CU, apart from technical efficiency, is of immense use to both public transport researchers and practitioners. Managers of public bus companies should be mindful of CU as it has a significant bearing on their financial performance.

This is the first study in public transport, which establishes the linkage between CU and financial performance. Besides, a modified measure of cost-efficiency has also been conceptualised in this study.

Job‐shop operations, the emerging norm of industry, require reform of the management methods employed to achieve cost control and efficiency. Maximisation of capacity utilisation, the standard of efficiency for more than a century, does not work in the job shop. Queueless work flow and the flexible labour policies that support it offer potential for reform which can substantially enhance customer service and quality. Simulation of work flow and capacity to identify bottlenecks along with scheduling of set‐ups on a leadtime basis are potentially important supporting methodologies.

Though production rates for construction materials are generally available, potential capacity on a global scale is poorly understood. Commencement of infrastructure projects to address climate change, such as dikes and levees, could increase demand making critical resources scarce. Since increasing production capacity of scarce products can be a challenge, understanding current potential capacity is an imperative. The purpose of this paper is to develop a new method to assess capacity and to create one such global estimate for cement.

The proposed global hybrid method creates a global estimate of cement production capacity in four steps: collect capacity and production data from existing reports; select top regional capacity holders; compute regional utilisation; back‐calculate capacity from production.

The method overcomes shortfalls of other methods, but – like all estimating methods – is inherently limited by the amount of data available. It nonetheless provides economists, climate change scientists, government officials, investors, and other researchers a better understanding of current maximum global cement capacity.

Most studies only focus on industry demand and actual production, because these forces drive commodity pricing. Capacity is generally estimated either through surveying goods‐producing industries at the plant level or examining economic data. Methods that employ these types of analysis are useful for regional estimates of production, but are ineffective at the global scale.

Performance improvement for requirements planning systems is an issue which receives wide interest. Many programming approaches have been proposed to improve material requirements planning procedures. However, most of them appear to be too complex for large manufacturing problems. Alternatively it might be promising to explore the integration of materials requirements planning and capacity requirements planning. Several simple heuristics for integrated requirements planning systems are suggested. Several heuristics are offered to balance the load in the systems and several procedures presented to adjust the planned requirements so that the system will execute more smoothly. An industrial example supports the adequacy of the general concepts provided in this research. Results are presented which demonstrate the adequacy of these heuristics, and illustrate the ease of implementing the procedures into any MRP system.

This paper aims to uncover the significance of capacity, capacity utilization (CU) and its role in the quality and productivity improvement in an industrial environment. Besides, the current study is also aiming to explore the various ways to estimate CU and its status across the world.

In the present study, a comprehensive literature review on capacity and CU is carried out to expose the research direction in the field of CU. This work is primarily focused on capacity, CU and their estimation methods based on the research in various industries of different countries and current status in present scenario across the world.

The literature reveals that CU estimation is carried out by some government/central agencies at the national or sector level rather than the industry level in most of the productive nations. As far as industrial growth is concerned, capacity management should be carried out at a particular industry level so that engineering managers can be able to find out loopholes for huge capacity waste within the plant. It is observed that CU in the industrial sectors mainly computed by time series method, survey method, economic approach and engineering approach worldwide.

This paper tries to cover almost all research work in the field of CU in various industrial sectors. However, the organizations which are producing the product with limited demand may get benefit inadequately.

This paper provides a vision to management toward productivity improvement through optimal utilization of available resources. As in most organizations, CU issues are much neglected areas.

This paper provides valuable insights on capacity and CU in the industrial sector across the world. Besides, it focused on comprehensive literature of capacity and various methods to estimate CU in industrial sectors.

The few management accounting pricing methods in the management accounting literature are ineffective in helping small firms use their idle capacity during lingering economic recessions, and some of these methods may even worsen this problem.

Extending the traditional break-even-cost-volume-profit model, we derive a more effective pricing method, the break-even-full-capacity-utilization (BEFCU) model, to handle this problem. Seeking full capacity utilization, the BEFCU model has two characteristics: (a) highlighting the importance of the exigent fixed cost category for utilizing idle capacity and (b) using a functional cost structure that focuses on a hierarchy of value drivers in the firm's value creation process. Accordingly, under the BEFCU model, management has an instrumental pricing continuum extending from the minimum acceptable BEFCU sale price to the regular sale price.

To demonstrate its practicality, the authors apply the BEFCU model to an actual job shop. This model integrates certain strategies based on built-in flexibility in commitments with suppliers and customers and maintaining a mode of conservatism in accounting for plant assets. The model can also help small tooling companies currently seeking entrance into China; it may take a while for these companies to gain a foothold in this new market because copyrights and other legalities are rarely enforced (Bunkley, 2004).

The purpose of this paper is to introduce a novel measure of access to credit suited to estimate the relative change in credit reserves.

A debt possibility frontier is estimated using data envelopment analysis and the Malmquist index is calculated. The Malmquist index is redubbed the Debt Development index and decomposed into “change in debt capacity” and “change in debt capacity utilization”. Bootstrapping is applied for statistical inference. The method is applied to an unbalanced panel of 92,000 Danish farm accounts from 1996 to 2009.

The paper finds that credit capacity roughly doubled for Danish farmers over the period, and that utilization of credit capacity generally was proportional to capacity change, utilization being higher for dairy and pig farms, than for crop farms.

Changes in credit reserves may have important implications for risk management practice, investment and technology adoption and related policy issues. The method is limited by the possibility of strategic behavior of lenders during credit cycle busts. In credit cycle booms, the method gives a good basis for the estimates of change in credit reserves.

In a period of increasing credit reserves, risk management institutions are unlikely to develop. Like agricultural policy, access to credit may crowd out market-based risk management.

The study represents a novel application and interpretation of a well-known method.

This paper aims to investigate the efficiency implications of belonging to a strategic hospital alliance (SHA) and measuring the effects over capacity utilization of such agreements in a Mexican healthcare context.

Data Envelopment Analysis (DEA) is the nonparametric methodology used, which supports both objectives. Technological gaps ratios are calculated by using DEA-metafrontier approach to compare efficiency between SHA members and a hospital’s control group. Also, hospital capacity utilization ratios are used as the maximum rate of output possible from fixed inputs in a frontier setting using directional distance functions. Data were collected from an alliance called Consorcio Mexicano de Hospitales in México, which has 29 general private hospitals and a group of 47 hospitals with same characteristics from a database made by the Instituto Nacional de Estadística y Geografía for year 2014.

The results indicate that efficiency is better at hospitals that belong to an alliance; it also shows an improvement of installed capacity management for hospital alliances in México.

The results can be useful for both private health organization managers and regulators themselves to adopt management practices that may end up having a favorable impact on cost and prices containment. Additionally, there are no previous studies neither in Mexico nor in Latin America that analyze the impact of strategic hospitality alliances on the efficiency and utilization of the capacity of private hospitals.

Este documento tiene como objetivo investigar las implicaciones de pertenecer a una alianza hospitalaria estratégica (AHE) en la eficiencia, así como cuantificar los efectos sobre la utilización de la capacidad de dichos acuerdos en el contexto mexicano de atención médica.

El Análisis Envolvente de Datos (DEA) es la metodología no paramétrica utilizada para lograr ambos objetivos. Las brechas tecnológicas se estiman empleando meta-fronteras calculadas mediante modelos DEA, comparando la eficiencia entre los miembros de la AHE y un grupo de control de hospitales. El nivel de utilización de la capacidad hospitalaria se calcula, utilizando funciones direccionales de distancia, a partir del máximo output alcanzable a partir de la dotación de inputs fijos. Los datos fueron obtenidos de la alianza Consorcio Mexicano de Hospitales en México, integrada por 29 hospitales privados generales, y de un grupo de 47 hospitales con las mismas características obtenidos de una base de datos del Instituto Nacional de Estadística y Geografía para el año 2014.

adosLos resultados indican que los niveles de eficiencia son superiores en los hospitales pertenecientes a la alianza, así como una mejor gestión de la capacidad instalada en la alianza hospitalaria en México.

Los resultados pueden ser útiles tanto para los administradores de las organizaciones de salud privadas como para los reguladores, de forma que puedan adoptar prácticas de gestión con un impacto favorable en la contención de costos y precios. Asimismo, no existen estudios previos ni en México ni en América Latina que analicen el impacto de las alianzas estratégicas hospitalarias en la eficiencia y la utilización de la capacidad de los hospitales privados.