Green supply chain performance measurement using the analytic hierarchy process: a comparative analysis of manufacturing organisations

The main purpose of this research is to develop and deploy an analytical framework for measuring the environmental performance of manufacturing supply chains. This work's theoretical bases combine and reconcile three major areas: supply chain management, environmental management and performance measurement. Researchers have suggested many empirical criteria for green supply chain (GSC) performance measurement and proposed both qualitative and quantitative frameworks. However, these are mainly operational in nature and specific to the focal company. This research develops an innovative GSC performance measurement framework by integrating supply chain processes (supplier relationship management, internal supply chain management and customer relationship management) with organisational decision levels (both strategic and operational). Environmental planning, environmental auditing, management commitment, environmental performance, economic performance and operational performance are the key level constructs. The proposed framework is then applied to three selected manufacturing organisations in the UK. Their GSC performance is measured and benchmarked by using the analytic hierarchy process (AHP), a multiple-attribute decision-making technique. The AHP-based framework offers an effective way to measure and benchmark organisations’ GSC performance. This study has both theoretical and practical implications. Theoretically it contributes holistic constructs for designing a GSC and managing it for sustainability; and practically it helps industry practitioners to measure and improve the environmental performance of their supply chain.     

Performance management of suppliers in outsourcing project: case analysis from the financial services industry

This study investigates the characteristics of an effective performance management framework for outsourcing projects in a UK-based financial services organisation and how this may contribute to the success of the outsourcing arrangement. The analysis draws on outsourcing and performance management theory, and uses both primary and secondary data. Valuable information was found on objective setting, performance measurement and performance improvement in the outsourcing project. An adapted version of the Balanced Scorecard, termed a Logic Scorecard, is suggested as a measurement tool; a service credit system and a continuous improvement schedule used to enhance supplier performance. The performance management framework, which is one of the pillars of the supply chain operations reference (SCOR) model, was developed, and provides practitioners with step-by-step guidance for the implementation of performance management in outsourcing projects. This combines both suggestions for performance management before and after the outsourcing decision, thus considering the entire outsourcing lifecycle. The proposed 10-step framework for outsourcing not only incorporates strategic propositions but also shows its implementation at an operational level.     

The Retail Planning Problem Under Demand Uncertainty

We consider the retail planning problem in which the retailer chooses suppliers and determines the production, distribution, and inventory planning for products with uncertain demand to minimize total expected costs. This problem is often faced by large retail chains that carry private‐label products. We formulate this problem as a convex‐mixed integer program and show that it is strongly NP‐hard. We determine a lower bound by applying a Lagrangian relaxation and show that this bound outperforms the standard convex programming relaxation while being computationally efficient. We also establish a worst‐case error bound for the Lagrangian relaxation. We then develop heuristics to generate feasible solutions. Our computational results indicate that our convex programming heuristic yields feasible solutions that are close to optimal with an average suboptimality gap at 3.4%. We also develop managerial insights for practitioners who choose suppliers and make production, distribution, and inventory decisions in the supply chain.     

Global supplier development considering risk factors using fuzzy extended AHP-based approach

Global supplier development is a multi-criterion decision problem which includes both qualitative and quantitative factors. The global supplier selection problem is more complex than domestic one and it needs more critical analysis. The aim of this paper is to identify and discuss some of the important and critical decision criteria including risk factors for the development of an efficient system for global supplier selection. Fuzzy extended analytic hierarchy process (FEAHP) based methodology will be discussed to tackle the different decision criteria like cost, quality, service performance and supplier's profile including the risk factors involved in the selection of global supplier in the current business scenario. FEAHP is an efficient tool to handle the fuzziness of the data involved in deciding the preferences of different decision variables. The linguistic level of comparisons produced by the customers and experts for each comparison are tapped in the form triangular fuzzy numbers to construct fuzzy pair-wise comparison matrices. The implementation of the system is demonstrated by a problem having four stages of hierarchy which contains different criteria and attributes at wider perspective. The proposed model can provide not only a framework for the organization to select the global supplier but also has the capability to deploy the organization's strategy to its supplier.     

Determinants and barriers to lean implementation in food-processing SMEs – a multiple case analysis

This study explored contextual or determining factors and their impacts on lean manufacturing in small- and medium-sized enterprises (SMEs) operating in food-processing industries. In order to get an in-depth insight into the real situation at the work floor, this study adopted a multiple-case-study research approach. The inherent characteristics of food industries, such as mandatory quality assurance requirements, low shelf life of food products, and the extremely volatile demand and supply presented barriers to lean manufacturing adoption. In addition, the challenges of ‘change’ in an SME environment are distinct from those faced by large organizations. The small size of the plant, the traditional setup, and inflexible layout make it difficult to implement lean manufacturing in food-processing SMEs. The knowledge of contextual factors influencing lean manufacturing adoption in food processing SMEs will be a contribution to current knowledge. Many studies have explored lean constructs and tools, while far fewer have explored the crucial element of actually implementing these. The study will also help practitioners to anticipate potential obstacles and take proper measures to deal with them during lean implementation.     

Effect of detection methods on risk estimates of exposure to biosolids-associated human enteric viruses

This study illustrates the effect of virus detection methods on estimates of risks of infection of biosolids-associated viruses for occupational workers and residential population during a hypothetical exposure of biosolids. Five gastroenteritis-associated human enteric viruses--enteroviruses (echovirus-12, enteroviruse types 68-71), adenoviruses, rotaviruses, and noroviruses genotype--I-were considered to represent human enteric viruses for risk estimation purposes. Ingested viral doses were calculated using literature-reported total infectious virus concentrations (based on BGM and A549 cell lines) and genome copies (GCs) in Michigan dewatered and class B biosolids. Cell-line-based infectivity parameters (i.e., ratio of total infectious virus concentration to GCs) were developed for different viruses in biosolids to use GCs for calculating ingested viral dose, addressing the issue of integration of molecular methods with biosolids-based virus risk assessment. Use of virus concentrations from molecular methods (with and without using cell-line-based infectivity parameter) resulted in higher risk estimates than culture methods, indicating the effect of the virus detection method on risk estimates. Further, use of virus concentrations from A549 cell lines resulted in higher risk estimates compared to those from BGM cell lines, suggesting the need for a proper choice of cell lines in determining infectious viral dose. The Monte Carlo uncertainty analyses of estimates for risk of infection due to enteroviruses showed that enteroviruses concentration was the most important parameter influencing risk estimates, indicating the need for reducing associated uncertainty. More work is required to develop cell-line-based infectivity parameters for different virus concentration levels and sample matrix types using a cut-off-based approach.     

Reliability estimation in generalized inverted exponential distribution with progressively type II censored sample

In this paper, a generalization of inverted exponential distribution is considered as a lifetime model [A.M. Abouammoh and A.M. Alshingiti, Reliability estimation of generalized inverted exponential distribution, J. Statist. Comput. Simul. 79(11) (2009), pp. 1301–1315]. Its reliability characteristics and important distributional properties are discussed. Maximum likelihood estimation of the two parameters involved along with reliability and failure rate functions are derived. The method of least square estimation of parameters is also studied here. In view of cost and time constraints, type II progressively right censored sampling scheme has been used. For illustration of the performance of the estimates, a Monte Carlo simulation study is carried out. Finally, a real data example is given to show the practical applications of the paper.     

Selecting the Best Exponential Population When the Scale Parameters are Bounded

Consider k (≥ 2) independent exponential populations with different location and scale parameters. Call a population associated with largest of unknown location parameters as the best population. For the goal of selecting the best population, it is established that if the scale parameters are completely unknown, then the indifference-zone probability requirement can not be guaranteed by any single sample decision rule which is just and translation invariant. Under the assumption that the scale parameters are bounded above by a known constant, a single sample selection procedure is proposed for which the indifference-zone probability requirement can be guaranteed. Under the same assumption, 100P*% simultaneous upper confidence intervals for all distances from the largest location parameter are also obtained.     

Robust R-Estimation of a Consensus Value from Multi-Center Studies

We consider multi-center experiments (for determining a consensus value) conducted in possibly heterogeneous set-ups leading to unbalanced heteroscedastic one-way random effects models. When normality of both the random components and their homoscedasticity are in doubt, standard statistical methods may not be valid. Two robust R-estimators (for the common location parameter), based on signed-rank statistics, are proposed and their properties studied. When large heteroscedasticity is present or the distribution of random effect is abnormal, the proposed estimators perform better than the classical weighted least squares and selected estimators. This feature is illustrated with an arsenic in oyster tissue problem, along with some other simulation studies.     

Discriminating Between the Log-Normal and Log-Logistic Distributions

Log-normal and log-logistic distributions are often used to analyze lifetime data. For certain ranges of the parameters, the shape of the probability density functions or the hazard functions can be very similar in nature. It might be very difficult to discriminate between the two distribution functions. In this article, we consider the discrimination procedure between the two distribution functions. We use the ratio of maximized likelihood for discrimination purposes. The asymptotic properties of the proposed criterion are investigated. It is observed that the asymptotic distributions are independent of the unknown parameters. The asymptotic distributions are used to determine the minimum sample size needed to discriminate between these two distribution functions for a user specified probability of correct selection. We perform some simulation experiments to see how the asymptotic results work for small sizes. For illustrative purpose, two data sets are analyzed.