A methodology for effective implementation of lean strategies and its performance evaluation in manufacturing organizations

Purpose – The purpose of this paper is to develop an effective methodology for implementing lean manufacturing strategies and a leanness evaluation metric using continuous performance measurement (CPM). Design/methodology/approach – Based on five lean principles, a systematic lean implementation methodology for manufacturing organizations has been proposed. A simplified leanness evaluation metric consisting of both efficiency and effectiveness attributes of manufacturing performance has been developed for continuous evaluation of lean implementation. A case study to validate the proposed methodology has been conducted and proposed CPM metric has been used to assess the manufacturing leanness. Findings – Proposed methodology is able to systematically identify manufacturing wastes, select appropriate lean tools, identify relevant performance indicators, achieve significant performance improvement and establish lean culture in the organization. Continuous performance measurement matrices in terms of efficiency and effectiveness are proved to be appropriate methods for continuous evaluation of lean performance. Research limitations/implications – Effectiveness of the method developed has been demonstrated by applying it in a real life assembly process. However, more tests/applications will be necessary to generalize the findings. Practical implications – Results show that applying the methods developed, managers can successfully identify and remove manufacturing wastes from their production processes. By improving process efficiency, they can optimize their resource allocations. Manufacturers now have a validated step by step methodology for successfully implementing lean strategies. Originality/value – According to the authors’ best knowledge, this is the first known study that proposed a systematic lean implementation methodology based on lean principles and continuous improvement techniques. Evaluation of performance improvement by lean strategies is a critical issue. This study develops a simplified leanness evaluation metric considering both efficiency and effectiveness attributes and integrates it with the lean implementation methodology.

A methodology for estimating exposure-controlled crash risk using traffic Police crash data

Exposure control or case-control methodologies are common techniques for estimating crash risks, however they require either observational data on control cases or exogenous exposure data, such as vehicle-kilometres travelled. This study proposes an alternative methodology for estimating crash risk of road user groups, whilst controlling for exposure under a variety of roadway, traffic and environmental factors by using readily available police-reported crash data. In particular, the proposed method employs a combination of a log-linear model and quasi-induced exposure technique to identify significant interactions among a range of roadway, environmental and traffic conditions to estimate associated crash risks. The proposed methodology is illustrated using a set of police-reported crash data from January 2004 to June 2009 on roadways in Queensland, Australia. Exposure-controlled crash risks of motorcyclists—involved in multi-vehicle crashes at intersections—were estimated under various combinations of variables like posted speed limit, intersection control type, intersection configuration, and lighting condition. Results show that the crash risk of motorcycles at three-legged intersections is high if the posted speed limits along the approaches are greater than 60 km/h. The crash risk at three-legged intersections is also high when they are unsignalized. Dark lighting conditions appear to increase the crash risk of motorcycles at signalized intersections, but the problem of night time conspicuity of motorcyclists at intersections is lessened on approaches with lower speed limits. This study demonstrates that this combined methodology is a promising tool for gaining new insights into the crash risks of road user groups, and is transferrable to other road users.

A novel protecting group methodology for syntheses using nitroxides

The methoxyamine group represents an ideal protecting group for the nitroxide moiety. It can be easily and selectively introduced in high yield (typically >90%) to a range of functionalised nitroxides using FeSO4.7H2O and H2O2 in DMSO. Its removal is readily achieved under mild conditions in high yield (70-90%) using mCPBA in a Cope-type elimination process.

A short-term production scheduling methodology for open-pit mines

A multi-resource multi-stage scheduling methodology is developed to solve short-term open-pit mine production scheduling problems as a generic multi-resource multi-stage scheduling problem. It is modelled using essential characteristics of short-term mining production operations such as drilling, sampling, blasting and excavating under the capacity constraints of mining equipment at each processing stage. Based on an extended disjunctive graph model, a shifting-bottleneck-procedure algorithm is enhanced and applied to obtain feasible short-term open-pit mine production schedules and near-optimal solutions. The proposed methodology and its solution quality are verified and validated using a real mining case study.

Towards a methodology for examining Twitter follower accession

The analysis of content and meta–data has long been the subject of most Twitter studies, however such research only tells part of the story of the development of Twitter as a platform. In this work, we introduce a methodology to determine the growth patterns of individual users of the platform, a technique we refer to as follower accession, and through a number of case studies consider the factors which lead to follower growth, and the identification of non–authentic followers. Finally, we consider what such an approach tells us about the history of the platform itself, and the way in which changes to the new user signup process have impacted upon users.

Automated classification of limb fractures from free-text radiology reports using a clinician-informed gazetteer methodology

Background Timely diagnosis and reporting of patient symptoms in hospital emergency departments (ED) is a critical component of health services delivery. However, due to dispersed information resources and a vast amount of manual processing of unstructured information, accurate point-of-care diagnosis is often difficult. Aims The aim of this research is to report initial experimental evaluation of a clinician-informed automated method for the issue of initial misdiagnoses associated with delayed receipt of unstructured radiology reports. Method A method was developed that resembles clinical reasoning for identifying limb abnormalities. The method consists of a gazetteer of keywords related to radiological findings; the method classifies an X-ray report as abnormal if it contains evidence contained in the gazetteer. A set of 99 narrative reports of radiological findings was sourced from a tertiary hospital. Reports were manually assessed by two clinicians and discrepancies were validated by a third expert ED clinician; the final manual classification generated by the expert ED clinician was used as ground truth to empirically evaluate the approach. Results The automated method that attempts to individuate limb abnormalities by searching for keywords expressed by clinicians achieved an F-measure of 0.80 and an accuracy of 0.80. Conclusion While the automated clinician-driven method achieved promising performances, a number of avenues for improvement were identified using advanced natural language processing (NLP) and machine learning techniques.

Methodology for developing real-time motorway traffic risk identification models using individual-vehicle data

Most of existing motorway traffic safety studies using disaggregate traffic flow data aim at developing models for identifying real-time traffic risks by comparing pre-crash and non-crash conditions. One of serious shortcomings in those studies is that non-crash conditions are arbitrarily selected and hence, not representative, i.e. selected non-crash data might not be the right data comparable with pre-crash data; the non-crash/pre-crash ratio is arbitrarily decided and neglects the abundance of non-crash over pre-crash conditions; etc. Here, we present a methodology for developing a real-time MotorwaY Traffic Risk Identification Model (MyTRIM) using individual vehicle data, meteorological data, and crash data. Non-crash data are clustered into groups called traffic regimes. Thereafter, pre-crash data are classified into regimes to match with relevant non-crash data. Among totally eight traffic regimes obtained, four highly risky regimes were identified; three regime-based Risk Identification Models (RIM) with sufficient pre-crash data were developed. MyTRIM memorizes the latest risk evolution identified by RIM to predict near future risks. Traffic practitioners can decide MyTRIM’s memory size based on the trade-off between detection and false alarm rates. Decreasing the memory size from 5 to 1 precipitates the increase of detection rate from 65.0% to 100.0% and of false alarm rate from 0.21% to 3.68%. Moreover, critical factors in differentiating pre-crash and non-crash conditions are recognized and usable for developing preventive measures. MyTRIM can be used by practitioners in real-time as an independent tool to make online decision or integrated with existing traffic management systems.

Development of gas sensing technology for ground and airborne applications powered by solar energy : methodology and experimental results

Monitoring gases for environmental, industrial and agricultural fields is a demanding task that requires long periods of observation, large quantity of sensors, data management, high temporal and spatial resolution, long term stability, recalibration procedures, computational resources, and energy availability. Wireless Sensor Networks (WSNs) and Unmanned Aerial Vehicles (UAVs) are currently representing the best alternative to monitor large, remote, and difficult access areas, as these technologies have the possibility of carrying specialised gas sensing systems, and offer the possibility of geo-located and time stamp samples. However, these technologies are not fully functional for scientific and commercial applications as their development and availability is limited by a number of factors: the cost of sensors required to cover large areas, their stability over long periods, their power consumption, and the weight of the system to be used on small UAVs. Energy availability is a serious challenge when WSN are deployed in remote areas with difficult access to the grid, while small UAVs are limited by the energy in their reservoir tank or batteries. Another important challenge is the management of data produced by the sensor nodes, requiring large amount of resources to be stored, analysed and displayed after long periods of operation. In response to these challenges, this research proposes the following solutions aiming to improve the availability and development of these technologies for gas sensing monitoring: first, the integration of WSNs and UAVs for environmental gas sensing in order to monitor large volumes at ground and aerial levels with a minimum of sensor nodes for an effective 3D monitoring; second, the use of solar energy as a main power source to allow continuous monitoring; and lastly, the creation of a data management platform to store, analyse and share the information with operators and external users. The principal outcomes of this research are the creation of a gas sensing system suitable for monitoring any kind of gas, which has been installed and tested on CH4 and CO2 in a sensor network (WSN) and on a UAV. The use of the same gas sensing system in a WSN and a UAV reduces significantly the complexity and cost of the application as it allows: a) the standardisation of the signal acquisition and data processing, thereby reducing the required computational resources; b) the standardisation of calibration and operational procedures, reducing systematic errors and complexity; c) the reduction of the weight and energy consumption, leading to an improved power management and weight balance in the case of UAVs; d) the simplification of the sensor node architecture, which is easily replicated in all the nodes. I evaluated two different sensor modules by laboratory, bench, and field tests: a non-dispersive infrared module (NDIR) and a metal-oxide resistive nano-sensor module (MOX nano-sensor). The tests revealed advantages and disadvantages of the two modules when used for static nodes at the ground level and mobile nodes on-board a UAV. Commercial NDIR modules for CO2 have been successfully tested and evaluated in the WSN and on board of the UAV. Their advantage is the precision and stability, but their application is limited to a few gases. The advantages of the MOX nano-sensors are the small size, low weight, low power consumption and their sensitivity to a broad range of gases. However, selectivity is still a concern that needs to be addressed with further studies. An electronic board to interface sensors in a large range of resistivity was successfully designed, created and adapted to operate on ground nodes and on-board UAV. The WSN and UAV created were powered with solar energy in order to facilitate outdoor deployment, data collection and continuous monitoring over large and remote volumes. The gas sensing, solar power, transmission and data management systems of the WSN and UAV were fully evaluated by laboratory, bench and field testing. The methodology created to design, developed, integrate and test these systems was extensively described and experimentally validated. The sampling and transmission capabilities of the WSN and UAV were successfully tested in an emulated mission involving the detection and measurement of CO2 concentrations in a field coming from a contaminant source; the data collected during the mission was transmitted in real time to a central node for data analysis and 3D mapping of the target gas. The major outcome of this research is the accomplishment of the first flight mission, never reported before in the literature, of a solar powered UAV equipped with a CO2 sensing system in conjunction with a network of ground sensor nodes for an effective 3D monitoring of the target gas. A data management platform was created using an external internet server, which manages, stores, and shares the data collected in two web pages, showing statistics and static graph images for internal and external users as requested. The system was bench tested with real data produced by the sensor nodes and the architecture of the platform was widely described and illustrated in order to provide guidance and support on how to replicate the system. In conclusion, the overall results of the project provide guidance on how to create a gas sensing system integrating WSNs and UAVs, how to power the system with solar energy and manage the data produced by the sensor nodes. This system can be used in a wide range of outdoor applications, especially in agriculture, bushfires, mining studies, zoology, and botanical studies opening the way to an ubiquitous low cost environmental monitoring, which may help to decrease our carbon footprint and to improve the health of the planet.

1000 voices: reflective online multimodal narrative inquiry as a research methodology for disability research

This article outlines the research approach used in the international 1000 Voices Project. The 1000 Voices project is an interdisciplinary research and public awareness project that uses a customised online multimodal storytelling platform to explore the lives of people with disability internationally. Through the project, researchers and partners have encouraged diverse participants to select the modes of storytelling (e.g. images, text, videos and combinations thereof) that suit them best and to self-define what both ‘disability’ and ‘life story’ mean to them. The online reflective component of the approach encourages participants to organically and reflectively develop story events and revisions over time in ways that suit them and their emerging lives. This article provides a detailed summary of the project's theoretical and methodological development alongside suggestions for future development in social work and qualitative research.

Biodiesel production from non-edible beauty leaf (Calophyllum Inophyllum) oil : process optimization using response surface methodology (RSM)

In recent years, the beauty leaf plant (Calophyllum Inophyllum) is being considered as a potential 2nd generation biodiesel source due to high seed oil content, high fruit production rate, simple cultivation and ability to grow in a wide range of climate conditions. However, however, due to the high free fatty acid (FFA) content in this oil, the potential of this biodiesel feedstock is still unrealized, and little research has been undertaken on it. In this study, transesterification of beauty leaf oil to produce biodiesel has been investigated. A two-step biodiesel conversion method consisting of acid catalysed pre-esterification and alkali catalysed transesterification has been utilized. The three main factors that drive the biodiesel (fatty acid methyl ester (FAME)) conversion from vegetable oil (triglycerides) were studied using response surface methodology (RSM) based on a Box-Behnken experimental design. The factors considered in this study were catalyst concentration, methanol to oil molar ratio and reaction temperature. Linear and full quadratic regression models were developed to predict FFA and FAME concentration and to optimize the reaction conditions. The significance of these factors and their interaction in both stages was determined using analysis of variance (ANOVA). The reaction conditions for the largest reduction in FFA concentration for acid catalysed pre-esterification was 30:1 methanol to oil molar ratio, 10% (w/w) sulfuric acid catalyst loading and 75 °C reaction temperature. In the alkali catalysed transesterification process 7.5:1 methanol to oil molar ratio, 1% (w/w) sodium methoxide catalyst loading and 55 °C reaction temperature were found to result in the highest FAME conversion. The good agreement between model outputs and experimental results demonstrated that this methodology may be useful for industrial process optimization for biodiesel production from beauty leaf oil and possibly other industrial processes as well.

A robust methodology for developing an operational systems view of mine water interactions

The Minerals Council of Australia’s (MCA) Water Accounting Framework (WAF) is an industry lead initiative to enable cross company communication and comparisons of water management performance. The WAF consists of two models, the Input-Output Model that represents water interactions between an operation and its surrounding environment and the Operational Model that represents water interactions within an operation. Recently, MCA member companies have agreed to use the Input-Output Model to report on their external water interactions in Australian operations, with some adopting it globally. The next step will be to adopt the Operational Model. This will expand the functionality of the WAF from corporate reporting to allowing widespread identification of inefficiencies and to connect internal and external interactions. Implementing the WAF, particularly the Operational Model, is non-trivial. It can be particularly difficult for operations that are unfamiliar with the WAF definitions and methodology, lack information pertaining to flow volumes or contain unusual configurations. Therefore, there is a need to help industry with its implementation. This work presents a step-by-step guide to producing the Operational Model. It begins by describing a methodology for implementing the Operational Model by describing the identification of pertinent objects (stores, tasks and treatments), quantification of flows, aggregation of objects and production of reports. It then discusses how the Operational Model can represent a series of challenging scenarios and how it can be connected with Input-Output Model to improve water management.

Estrogen deficiency-associated bone loss in the maxilla: A methodology to quantify the changes in the maxillary intra-radicular alveolar bone in an ovariectomized rat osteoporosis model

The effects of estrogen deficiency on bone characteristics are site-dependent, with the most commonly studied sites being appendicular long bones (proximal femur and tibia) and axial bones (vertebra). The effect on the maxillary and mandibular bones is still inconsistent and requires further investigation. This study was designed to evaluate bone quality in the posterior maxilla of ovariectomized rats in order to validate this site as an appropriate model to study the effect of osteoporotic changes. Methods: Forty-eight 3-month-old female Sprague-Dawley rats were randomly divided into two groups: an ovariectomized group (OVX, n=24) and Sham-operated group (SHAM, n=24). Six rats were randomly sacrificed from both groups at time points 8, 12, 16 and 20 weeks. The samples from tibia and maxilla were collected for Micro CT and histological analysis. For the maxilla, the volume of interest (VOI) area focused on the furcation areas of the first and second molar. Trabecular bone volume fraction (BV/TV, %), trabecular thickness (Tb.Th.), trabecular number (Tb.N.), trabecular separation (Tb.Sp.), and connectivity density (Conn.Dens) were analysed after Micro CT scanning. Results: At 8 weeks the indices BV/TV, Tb.Sp, Tb.N and Conn.Dens showed significant differences (P<0.05) between the OVX and SHAM groups in the tibia. Compared with the tibia, the maxilla developed osteoporosis at a later stage, with significant changes in maxillary bone density only occurring after 12 weeks. Compared with the SHAM group, both the first and second molars of the OVX group showed significantly decreased BV/TV values from 12 weeks, and these changes were sustained through 16 and 20 weeks. For Tb.Sp, there were significant increases in bone values for the OVX group compared with the SHAM group at 12, 16 and 20 weeks. Histological changes were highly consistent with Micro CT results. Conclusion: This study established a method to quantify the changes of intra-radicular alveolar bone in the posterior maxilla in an accepted rat osteoporosis model. The degree of the osteoporotic changes to trabecular bone architecture is site-dependent and at least 3 months are required for the osteoporotic effects to be apparent in the posterior maxilla following rat OVX.

A methodology for mapping Instagram hashtags

While social media research has provided detailed cumulative analyses of selected social media platforms and content, especially Twitter, newer platforms, apps, and visual content have been less extensively studied so far. This paper proposes a methodology for studying Instagram activity, building on established methods for Twitter research by initially examining hashtags, as common structural features to both platforms. In doing so, we outline methodological challenges to studying Instagram, especially in comparison to Twitter. Finally, we address critical questions around ethics and privacy for social media users and researchers alike, setting out key considerations for future social media research.