A Lean Six Sigma framework for the reduction of ship loading commercial time in the iron ore pelletising industry

Evidence suggests that specifically designed frameworks to implement Lean Six Sigma (LSS) projects to tackle particular problems are more effective than ‘generic’ versions. This paper proposes an implementation framework to effectively deploy LSS to improve a key operation and performance indicator, i.e. ship loading commercial time, of one of the largest world producer of iron ore. This article therefore contributes with a refined framework to effectively implement LSS, and documents its successful application and effectiveness within the context of the case organisation. The LSS framework and project contributed in helping the studied organisation to improve both the capability of its ship loading process and commercial time by more than 30%, resulting in operational savings in the range of $300,000 USD per year. The systematic nature of the framework proposed also helped the organisation to establish a standardised routine to improve its operations. Managerial implications exposing the challenges faced during the implementation of LSS are also discussed to serve as lessons learnt to be considered in other LSS projects. Managers and engineers incharge of improving operations and processes can benefit from this paper as it can be used as a guide to direct the conduction of LSS projects and the empirical application of its principles and tools.     

Lean and green in the transport and logistics sector – a case study of simultaneous deployment

The transport and logistics sector is of vital importance for the stimulation of trade and hence the economic development of nations. However, over the last few years, this sector has taken central stage in the green agenda due to the negative environmental effects derived from its operations. Several disciplines including operations research and sub-areas of supply chain management such as green supply chains, green logistics and reverse logistics have tried to address this problem. However, despite the work undertaken through these disciplines, theoretical or empirical research into the sequential or simultaneous deployment of the lean and green paradigms, particularly, in the road transport and logistics sector is limited. This paper presents a case study where both paradigms have been combined to improve the transport operations of a world leader logistics organisation in the metropolitan area of Monterrey, Mexico. To do this, a systematic methodology and a novel tool called Sustainable Transportation Value Stream Map (STVSM) were proposed. The results obtained from the case study indicate that the concurrent deployment of the green and lean paradigms through such methodology and the STVSM tool is an effective approach to improve both operational efficiency and environmental performance of road transport operations. The paper can be used as a guiding reference for transport and logistics organisations to undertake improvement projects similar to the one presented in this paper. Additionally, this research also intends to stimulate scholarly research into the application of lean and green paradigms in the transport and logistics sector to expand the limited research pursued in this area.     

Inverse Lindley power series distributions: a new compounding family and regression model with censored data

This paper introduces a new class of distributions by compounding the inverse Lindley distribution and power series distributions which is called compound inverse Lindley power series (CILPS) distributions. An important feature of this distribution is that the lifetime of the component associated with a particular risk is not observable, rather only the minimum lifetime value among all risks is observable. Further, these distributions exhibit an unimodal failure rate. Various properties of the distribution are derived. Besides, two special models of the new family are investigated. The model parameters of the two sub-models of the new family are obtained by the methods of maximum likelihood, least square, weighted least square and maximum product of spacing and compared them using the Monte Carlo simulation study. Besides, the log compound inverse Lindley regression model for censored data is proposed. Three real data sets are analyzed to illustrate the flexibility and importance of the proposed models.     

A Class of Two-Stage Selection Procedures Using L-Statistics

Summary: A class of selection procedures for selecting the least dispersive distribution from k available distributions has been proposed. This problem finds applications in reliability and engineering. In engineering, for example, the goal of the experimenter is to select a firm whose components have least dispersive distribution from the available set of competing firms manufacturing the components of the desired specifications meant for the same purpose. The proposed procedures can be used even when the underlying distributions belong to different families. Applications of the proposed selection procedures are discussed by taking exponential, gamma and Lehmann type distributions. Performance of the proposed selection procedures is assessed through simulation study. Implementation of the proposed selection procedure is illustrated through an example. * The authors are very grateful to the editor and referees for their valuable comments.     

Some New Residual Treatment Effects Designs for Comparing Test Treatments with a Control

Pigeon & Raghavarao (1987) introduced control balanced residual treatment effects designs for the situation where one treatment is a control or standard and is to be compared with the v test treatments, and they have also given methods of construction of control balanced residual treatment effects designs and have investigated their efficiencies. In this paper we have developed some new families of control balanced residual treatment effects designs, which are Schur-optimal.     

The Irregular Migrant as Homo Sacer: Migration and Detention in Australia, Malaysia, and Thailand

Abstract This paper looks at aspects of the detention of irregular migrants in Australia, Malaysia, and Thailand. The principle intention of the paper is to study detention of irregular migrants as a means of understanding politics and how notions of political participation and of sovereignty are affected by the detention of certain sorts of individual. What does the identification of certain “forms of life” to be detained say about the political norms of different societies? The conduit for this examination will be the Italian philosopher Giorgio Agamben's concept of homo sacer. Homo sacer is a term Agamben extrapolates from “ancient Roman law”. It denotes a naked or bare life that is depoliticized. Homo sacer is the excess of processes of political constitution that create a governable form of life. Homo sacer is thus exempt or excluded from the normal limits of the state. At the same time, however, homo sacer is not simply cast out but is held in particular relation to the norm: it is through the exclusion of the depoliticized form of life that the politicized norm exists. This essay seeks to contextualize aspects of Agamben's argument by looking at detention as a form of exclusion in three different contexts.     

The Determinants of Immigration from Fiji to New Zealand: An Empirical Reassessment using the Bounds Testing Approach

This article re‐examines Gani's (1998) findings on the determinants of migrant flows from Fiji to New Zealand by employing the bounds testing procedure to cointegration, within an autoregressive distributive lag framework. The main findings are that in the long run all variables are statistically insignificant, although correctly signed with the exception of the unemployment differential. In the short run, in sharp contrast to Gani's (1998) findings, political instability is consistently the most important determinant of migration flows while the standard of living and real wage differentials are statistically insignificant across all specifications.     

Replenishment Policies for Multi‐Product Stochastic Inventory Systems with Correlated Demand and Joint‐Replenishment Costs

This study analyzes optimal replenishment policies that minimize expected discounted cost of multi‐product stochastic inventory systems. The distinguishing feature of the multi‐product inventory system that we analyze is the existence of correlated demand and joint‐replenishment costs across multiple products. Our objective is to understand the structure of the optimal policy and use this structure to construct a heuristic method that can solve problems set in real‐world sizes/dimensions. Using an MDP formulation we first compute the optimal policy. The optimal policy can only be computed for problems with a small number of product types due to the curse of dimensionality. Hence, using the insight gained from the optimal policy, we propose a class of policies that captures the impact of demand correlation on the structure of the optimal policy. We call this class (s, c, d, S)‐policies, and also develop an algorithm to compute good policies in this class, for large multi‐product problems. Finally using an exhaustive set of computational examples we show that policies in this class very closely approximate the optimal policy and can outperform policies analyzed in prior literature which assume independent demand. We have also included examples that illustrate performance under the average cost objective.