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An economic statistical design approach takes statistical properties into account while designing control charts economically. It improves both statistical design and economic design. In this paper, we present a statistically constrained economic model for the optimal design of S control chart for controlling process variability. In the model, the process quality can be affected by an assignable cause resulting in a shift of the variance of the distribution of output when it is operating according to its capability. The parameters are obtained by minimizing a total cost function proposed by Lorenzen and Vance, which is embellished with Taguchi loss function, subject to additional statistical constraints on average run length or average time‐to‐signal (ATS). Sensitivity analysis of the minimum cost will be performed to depict the effect of the choice of ATS bounds.

The purpose of this paper is to develop a cost model by the variable sampling interval and optimization of the average cost per unit of time. The paper considers an economic–statistical design of the X̅ control charts under the Burr shock model and multiple assignable causes were considered and compared with three types of prior distribution for the mean shift parameter.

The design of the modified X̅ chart is based on the two new concepts of adjusted average time to signal and average number of false alarms for X̅ control chart under Burr XII shock model with multiple assignable causes.

The cost model was examined through a numerical example, with the same cost and time parameters, so the optimal of design parameters were obtained under uniform and non-uniform sampling schemes. Furthermore, a sensitivity analysis was conducted in a way that the variability of loss cost and design parameters was evaluated supporting the changes of cost, time and Burr XII distribution parameters.

The economic–statistical model scheme of X̅ chart was developed for the Burr XII distributed with multiple assignable causes. The correlated data are among the assumptions to be examined. Moreover, the optimal schemes for the economic-statistic chart can be expanded for correlated observation and continuous process.

The economic–statistical design of control charts depends on the process shock model distribution and due to difficulties from both theoretical and practical aspects; one of the proper alternatives may be the Burr XII distribution which is quite flexible. Yet, in Burr distribution context, only one assignable cause model was considered where more realistic approach may be to consider multiple assignable causes.

This study presents an advanced theoretical model for cost model that improved the shock model that presented in the literature. The study obviously indicates important evidence to justify the implementation of cost models in a real-life industry.

The purpose of this study is to analyze the effect of low carbon product design on firm's competitiveness and economic performance. It also examines the mediating role of the proactive environmental strategy and eco-innovation on low carbon product design, competitiveness and economic performance.

Through a questionnaire-based survey, the data were collected from 69 Indian manufacturing firms and analyzed using a variance-based structural equation modeling (SEM) technique to test the proposed hypotheses.

The results show that the low carbon product design significantly improves firm's competitiveness and economic performance, and proactive environmental strategy significantly mediates the relationship between low carbon product design and firm's competitiveness.

This study provides a framework for the adoption of low carbon product design. It demonstrates how manufacturing firms can implement environmental friendly product design. It also analyses the contextual factors that ensure a successful low carbon product design.

This article investigates the economic benefit of low carbon product design, thus filling lacuna in existing research.

The purpose of this paper is to investigate the economic‐statistical design of EWMA charts with variable sampling intervals (VSIs) under non‐normality to reduce the process production cycle cost and improve the statistical performance of control charts. The objective is to minimize the cost function by adjusting the control chart parameters which suffice for the statistical restriction.

First, using the Burr distribution to approximate various non‐normal distributions, the economic‐statistical model of the VSI EWMA charts under non‐normality can be developed. Further, the genetic algorithms will be used to search for the optimal values of parameters of the VSI EWMA charts under non‐normality. Finally, a sensitivity analysis is carried out to investigate the effect of model parameters and statistical restriction on the solution of the economic‐statistical design.

The result of sensitivity analysis shows that a large lower bound of average time to signal when the process is in control increases the control limit coefficient, no model parameter significantly affects the short sampling intervals, and so on.

The economic‐statistical design method proposed in this paper can improve the statistical performance of economic design of control charts and the general idea can be applied to other VSI control charts.

The use of control charts to monitor a product quality characteristic requires the selection of their design parameters. To select feasible design parameters, the constraints related to the inspection resources available at the workstation, its configuration and the process operating parameters should be taken into account. The aim of this research is to discuss the design of Shewhart and EWMA control charts monitoring dispersion in the presence of these constraints and for processes characterized by a random shift size.

In this paper the design of the investigated control charts has been intended in a broader sense as the design of a local inspection procedure constrained by a set of resource/environmental process factors characterizing the workstation configuration. An economic objective pursuing the minimization of the total inspection cost has been considered.

Constraining the design of a control chart is an essential issue to find chart design parameters that can be effectively implemented by quality practitioners. The influence of the process‐operating parameters has been modelled and some guidelines have been suggested through the use of contour plots.

The economic design of control charts has been scarcely implemented by quality practitioners due to the difficulty of adapting them to the multifaceted complexity and constraints present within manufacturing environments. This paper tries to contribute to the existing literature by improving the available mathematical models through modelling the actual workstation configuration and resource allocation; furthermore, for random shift processes it investigates the performance of the Shewhart and EWMA control charts monitoring process dispersion.

The increasing interest in economic, social, and ecological sustainability has important implications for the traditional views on organization effectiveness, organization design, and organization development. Managers need to design organizations to achieve a “triple bottom line.” A review of the organization effectiveness literature suggests that no single model seems to provide the necessary guidance, and there is a clear need for creation, revision, and integration. Organization effectiveness criteria in the future require a clearer modeling of the multistakeholder demands so that organization designers can specify appropriate strategies, structures, systems, and processes as well as the changes necessary to develop them. We propose an integration called “responsible progress” and suggest that it represents an important new stream of organization development theory. The relationships between this new criterion of organization effectiveness and the design features necessary to pursue them must be tested.

The purpose of this paper is to assess the relationship between five green practices and firm performance. In addition, this paper investigates the influence of each green practice on environmental performance, economic benefits, and economic costs.

Data were collected based on a cross-sectional survey of owner/managers of 200 manufacturing SME firms in Uganda, Africa. SPSS was used to find descriptive means and test relationships between green practices and performance outcomes. Structural equation modelling was used to test for the influence of each practice on performance outcomes. The structural equation modelling results were obtained using the Covariance-Based Structural Equation Modelling software. Results were compared with similar studies conducted in developing countries.

Different green practices affect different performance dimensions in different ways across different industries. For example, eco-design and internal environmental management practices significantly influence environmental performance; green purchasing and internal environmental management practices significantly influence economic benefits; and internal environmental management practices affect economic costs. Overall internal environmental management is the key to positive outcomes across the three performance criteria. The authors show how the results obtained vary from similar studies conducted in developing countries and explain possible reasons for the difference.

Africa is a rapidly industrialising nation faced with difficult choices between economic growth and increased pollution. Because SMEs represent the majority of manufacturing firms, they are the main polluters. Hence, better understanding of the costs and benefits, both environmental and economic, is important to encourage green practice adoption for the betterment of community health and prosperity.

Despite numerous studies on the relationships between green practice adoption and performance outcomes, only a few studies include both economic costs and benefits in addition to environmental performance. The study covers five green supply chain practices, whereas most similar studies are limited in the number of practices examined. The African context is unique and important because industrial development and environmental protection goals are in conflict. Similar studies are predominant in an Asian context which is more developed than Africa. The findings and comparisons raise important questions for further research in relation to the roles of national regulations, geographical markets and industry types in furthering green practices in manufacturing.

The purpose of this paper is to develop an economic statistical design based on the concepts of adjusted average time to signal (AATS) and ANF for X ¯ control chart under a Weibull shock model with multiple assignable causes.

The design used in this study is based on a multiple assignable causes cost model. The new proposed cost model is compared with the same cost and time parameters and optimal design parameters under uniform and non-uniform sampling schemes.

Numerical results indicate that the cost model with non-uniform sampling cost has a lower cost than that with uniform sampling. By using sensitivity analysis, the effect of changing fixed and variable parameters of time, cost and Weibull distribution parameters on the optimum values of design parameters and loss cost is examined and discussed.

This research adds to the body of knowledge relating to the quality control of process monitoring systems. This paper may be of particular interest to practitioners of quality systems in factories where multiple assignable causes affect the production process.

The cost functions for uniform and non-uniform sampling schemes are presented based on multiple assignable causes with AATS and ANF concepts for the first time.

The purpose of this paper is to design a modified version of the double sampling plan to handle the inspection processes requiring a minimum sample size to assure the median life for the products under the new Weibull–Pareto distribution. The economic design of the proposed plan is also considered to assure the product's lifetime with minimum cost.

The authors have developed an optimization model for obtaining the required plan parameters by solving simultaneously two non-linear inequalities and such inequalities have been formed based on the two points on the operating characteristic curve approach.

The results show that the average sample number, average total inspection and total inspection cost under the proposed plan are smaller than the same of a single sampling plan. This means that the proposed plan will be more efficient than a single sampling plan in reducing inspection effort and cost while providing the desired protection.

The proposed modified double sampling plan designed to assure the median life of the products under the new Weibull–Pareto distribution is not available in the literature. The proposed plan will be very useful in assuring the product median lifetime with minimum sample size as well as minimum cost in all the manufacturing industries.