A knowledge creation perspective on ‘LEAN’ approaches to policing in England and Wales

The police service in England and Wales continue to face intense pressures to manage and reduce budgets while simultaneously maintaining and improving levels of service. In achieving reform, attention has been directed towards the implementation of proven operational improvement frameworks, such as ‘lean thinking’ taken from the automotive industry. However, the qualitatively different contexts have resulted in ‘lean’ interventions making only limited contribution to police reform. This research draws on contemporary views of ‘lean’ as a knowledge creation process to assess how such a reconceptualization may contribute to more successful police transformation

A stakeholder perspective on process improvement behaviours: delivering the triple bottom line in SMEs

The primary focus of operations management is to add value through operational processes. Considerable attention has been given to using process improvement (PI) techniques to reduce costs and time, in order to develop a competitive advantage for the wider organisation. However, this narrow definition of value at times overlooks the Triple Bottom Line (TBL) which can result in a number of unintended consequences, specifically issues related to environmental and social measures of performance. To address this, a stakeholder theory lens will be used to analyze PI activities within the context of small and medium sized enterprises (SMEs). The TBL will be used to complement the stakeholder perspective, to interpret the benefits that are realised from PI activities. This paper highlights both the direct benefits from PI as well as more indirect benefits realised by involving a selection of salient stakeholders in PI. It will show how a developed view of PI can provide an important mechanism for delivering improvements to a firm's TBL. The work concludes by highlighting the contributions made to both PI practice and stakeholder theory, while acknowledging the need for more research on PI, both from a stakeholder perspective, and how it impacts a firm's TBL

Unlocking supply chain disruption risk within the Thai beverage industry [forthcoming]

Purpose: A growing need for global sourcing of business has subjected firms to higher levels of uncertainty and increased risk of supply disruption. Differences in industry and infrastructure make it more difficult for firms to manage supply disruption risks effectively. This study aims to extend developing research in this area by addressing gaps within existing literature related to environmental turbulence and uncertainties. Design/Methodology/Approach: We test our model using data collected from 253 senior managers and directors in the Thai beverage industry using advanced statistical techniques to explore the relationship between representations of supply disruption risk and uncertainty. Findings: The results show that both magnitude and probability of risk impact on the disruption risk, but the probability of loss is a dominant determinant. We also find that demand uncertainty and quality uncertainty affect the risk perception of purchasing managers, and are related to the magnitude of disruption risk, rather than the frequency of occurrence. Interestingly, our results show that quality uncertainty negatively impacts on the severity of disruption risk. Research limitations/implication: The construct validity of demand uncertainty was under the required threshold, intimating the need for further construct development. Practical Implications: The framework provides managers with direction on how to formulate and target their disruption risk management strategies. The work also allows practitioners to critically reflect on implicit risk management strategies they may already employ and their effectiveness. Originality/Value: The paper identifies key antecedents of supply disruption risk and tests them within a novel industrial context of the beverage industry and a novel national context of Thailand

Homogenisation and analysis of an expanded long-term monthly rainfall network for the Island of Ireland (1850-2010)

Long-term precipitation series are critical for understanding emerging changes to the hydrological cycle. To this end we construct a homogenised Island of Ireland Precipitation (IIP) network comprising 25 stations and a composite series covering the period 1850-2010, providing the second- longest regional precipitation archive in the British-Irish Isles. We expand the existing catalogue of long-term precipitation records for the island by recovering archived data for an additional eight stations. Following bridging and updating of stations HOMER homogenisation software is used to detect breaks using pairwise and joint detection. Twenty-five breakpoints are detected across 14 stations, and the majority (20) are corroborated by metadata. Assessment of variability and change in homogenised and extended precipitation records reveal positive (winter) and negative (summer) trends. Trends in records covering the typical period of digitisation (1941 onwards) are not always representative of longer records. Furthermore, trends in post-homogenisation series change magnitude and even direction at some stations. While cautionary flags are raised for some series, confidence in the derived network is high given attention paid to metadata, coherence of behaviour across the network and consistency of findings with other long-term climatic series such as England and Wales precipitation. As far as we are aware, this work represents the first application of HOMER to a long- term precipitation network and bodes well for use in other regions. It is expected that the homogenised IIP network will find wider utility in benchmarking and supporting climate services across the Island of Ireland, a sentinel location in the North Atlantic

An evaluation of persistent meteorological drought using a homogeneous Island of Ireland precipitation network

This paper investigates the spatial and temporal properties of persistent meteorological droughts using the homogeneous Island of Ireland Precipitation (IIP) network. Relative to a 1961-1990 baseline period it is shown that the longest observed run of below average precipitation since the 1850s lasted up to 5 years (10 half-year seasons) at sites in southeast and east Ireland, or 3 years across the network as a whole. Dry- and wet-spell length distributions were represented by a first-order Markov model which yields realistic runs of below average rainfall for individual sites and IIP series. This model shows that there is relatively high likelihood (p=0.125) of a 5 year dry-spell at Dublin, and that near unbroken dry runs of 10 years or more are conceivable. We suggest that the IIP network and attendant rainfall deficit modelling provide credible data for stress testing water supply and drought plans under extreme conditions

Closing the sea surface mixed layer temperature budget from in situ observations alone: Operation Advection during BoBBLE

Sea surface temperature (SST) is a fundamental driver of tropical weather systems such as monsoon rainfall and tropical cyclones. However, understanding of the factors that control SST variability is lacking, especially during the monsoons when in situ observations are sparse. Here we use a ground-breaking observational approach to determine the controls on the SST variability in the southern Bay of Bengal. We achieve this through the first full closure of the ocean mixed layer energy budget derived entirely from in situ observations during the Bay of Bengal Boundary Layer Experiment (BoBBLE). Locally measured horizontal advection and entrainment contribute more significantly than expected to SST evolution and thus oceanic variability during the observation period. These processes are poorly resolved by state-of-the-art climate models, which may contribute to poor representation of monsoon rainfall variability. The novel techniques presented here provide a blueprint for future observational experiments to quantify the mixed layer heat budget on longer time scales and to evaluate these processes in models

Transferability of hydrological models and ensemble averaging methods between contrasting climatic periods

Understanding hydrological model predictive capabilities under contrasting climate conditions enables more robust decision making. Using Differential Split Sample Testing (DSST) we analyse the performance of six hydrological models for 37 Irish catchments under climate conditions unlike those used for model training. Additionally, we consider four ensemble averaging techniques when examining inter-period transferability. DSST is conducted using two/three-year non-continuous blocks of (i) the wettest/driest years on record based on precipitation totals, and (ii) years with a more/less pronounced seasonal precipitation regime. Model transferability between contrasting regimes was found to vary depending on the testing scenario, catchment and evaluation criteria considered. As expected, the ensemble average outperformed most individual ensemble members. However, averaging techniques differed considerably in the number of times they surpassed the best individual model-member. Bayesian Model Averaging (BMA) and the Granger-Ramanathan (GRA) method were found to outperform the simple arithmetic mean (SAM) and Akaike Information Criteria Averaging (AICA). Here, GRA performed better than the best individual model in 51% to 86% of cases (according to the Nash-Sutcliffe criterion). When assessing model predictive skill under climate change conditions we recommend (i) setting up DSST to select the best available analogues of expected annual mean and seasonal climate conditions; (ii) applying multiple performance criteria; (iii) testing transferability using a diverse set of catchments and; (iv) using a multi-model ensemble in conjunction with an appropriate averaging technique. Given the computational efficiency and performance of GRA relative to BMA, the former is recommended as the preferred ensemble averaging technique for climate assessment

Role Stress, Role Reward, and Mental Health in a Multiethnic Sample of Midlife Women: Results from the Study of Women's Health Across the Nation (SWAN)

Abstract Background: Little is known about the independent associations of reward and stress within specific roles with multiple measures of mental health in an ethnically diverse community sample of midlife women. The objective of this study is to examine if (1) role reward (within each role and across roles) contributes directly to mental health and buffers the negative impact of role stress and (2) associations among role occupancy, role stress, and role reward and mental health vary by race/ethnicity. Methods: With separate logistic regression analysis, we investigated cross-sectional relationships between role stress and role reward with presence/absence of high depressive symptoms (Center for Epidemiologic Studies Depression Scale [CES-D≥16]), anxiety symptoms (feeling tense or nervous, irritable or grouchy, fearful for no reason, and heart pounding or racing total score≥4), or low social functioning (bottom 25th percentile of the Short-Form-36 [SF-36] social functioning subscale) in 2549 women participating in the third visit of the Study of Women's Health Across the Nation (SWAN), a longitudinal population-based study of menopause. Results: High reward across roles attenuated the negative impact of role stress on social functioning but not on anxiety or depression. High reward marriage buffered the impact of marital stress on depression, and high reward mothering buffered the effect of maternal stress on depression and social functioning. Compared to Caucasians, Hispanics and Chinese with high stress across roles had better social functioning, and African American mothers had lower odds of high depressive symptoms. Conclusions: Role reward buffers the negative impact of stress on social functioning and depression, but not on anxiety. Minorities may respond to role stress by seeking social support.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/98462/1/jwh%2E2011%2E3180.pd