Involving individuals in the manufacturing strategy formation : Strategic consensus among workers and managers

Decisions made and actions taken by individuals in the operations function impact the formation of a company’s manufacturing strategy (MS). Therefore, it is important that the MS is understood and agreed on by all employees, that is, strategic consensus among the individuals in the operations function is essential. This research contributes to the current body of knowledge by including a workers’ perspective on MS formation. It is the workers on the shop floor who bring the MS to life in the actual operations through their daily decisions and actions. The MS falls short if the priorities outlined do not materialise in practice as intended. The purpose of this research is to investigate how the individuals in the operations function perceive the MS in order to understand how these individuals are involved in the MS formation. The research is based on five studies, differing by evidence, as follows: one theoretical, three qualitative in the setting of small and medium-sized enterprises (SMEs), and one quantitative at a large company. Based on the findings presented in the six appended papers, the results show that empirically and conceptually, workers have been overlooked or given a passive role in the MS formation. Empirically, it is seen that workers and managers do not have a shared understanding of the underlying reasons for strategic priorities; hence, the level of strategic consensus is low. Furthermore, the level of strategic consensus varies among the different MS dimensions depending on their organisational level. Moreover, the empirical findings reveal that internal contextual factors influence the individuals’ perceptions of the MS and the possibilities for strategic consensus. Regarding the external context, the results show that major customers’ strategies influence the subcontractor SMEs’ MS formation. The usage of means of communication in the operations function has also shown to be of importance for how the MS is perceived. Conceptually, the findings indicate that the MS literature tends to treat individuals in the operations function in a deterministic manner; individuals on the shop floor are regarded as manufacturing resources. To ensure a successful MS formation process, where the patterns of the decisions made by the individuals in the operations function forms the MS, the view on human nature within the MS requires a more voluntaristic approach. This research suggests to view the MS formation as an iterative “patterning process” which builds on a reciprocal relationship between workers and managers. The introduction of the patterning process contributes to the research on MS formation by explaining the perception range within the hierarchical levels, by re-defining the hierarchical levels included in the MS formation and by detailing the activities in the MS formation.

Volatile Blood Pressure Due To Baroreflex and Autonomic Failure

An 85-year-old male was hospitalized because of deterioration of his general condition and infection of the tracheostoma. He had had laryngectomy, bilateral neck dissection and radiation therapy for a laryngeal carcinoma 5 years earlier. Despite a good recovery, he could not get up because of a new onset of postural symptoms (dizziness, lightheadedness, collapse). Late onset of baroreflex failure and autonomic nervous system failure were diagnosed. Volatility of blood pressure (supine hypertension, upright hypotension) was treated with NaCl supplement during the day and a short-acting antihypertensive (clonidine) at night. With this regimen, the patient could walk without support.

Agent based simulation as a novel decision support tool for retail managers

Intelligent agents offer a new and exciting way of understanding the world of work. We apply agent-based simulation to investigate a set of problems in a retail context. Specifically, we are working to understand the relationship between human resource management practices and retail productivity. Our multi-disciplinary research team draws upon expertise from work psychologists and computer scientists. Our research so far has led us to conduct case study work with a top ten UK retailer. Based on our case study experience and data we are developing a simulator that can be used to investigate the impact of management practices (e.g. training, empowerment, teamwork) on customer satisfaction and retail productivity.

CHARACTERISTICS OF PRIVATE HIGH SCHOOLS FOR STUDENTS WITH EMOTIONAL DISABILITIES AND PERCEPTIONS OF THEIR PUBLIC SCHOOL CASE MANAGERS

Placement of students with disabilities in private special-education schools remains costly and controversial. This is particularly concerning, given the lack of research on the characteristics and quality of these restrictive settings. The purpose of this study was to identify the academic and vocational course offerings and behavioral supports provided in private special-education schools the serve high school students with emotional disabilities (ED). Second, the research examined the perceptions of the quality of services in these setting from the perspectives of public school case managers. Using a mixed-method design to collect data, 9 administrative heads of private special-education schools were surveyed, and 7 public school case managers were interviewed. Results indicated that (a) private special-education schools offer the basic academic core courses needed to meet graduation requirements, (b) vocational options for students enrolled in these schools are quite limited, (c) these schools provide a variety of behavioral interventions and supports, and (d) case managers are concerned with the lack of academic rigor and inconsistent programming at these schools but applauded the notion that students with ED are exiting with a high school diploma. Findings from this study may have policy implications for improving and developing programming options for high school students with ED.

Too Busy to Be Fair? The Effect of Managers’ Perceived Workload on Their Core Technical Performance and Justice Rule Adherence

Despite the organizational benefits of treating employees fairly, both anecdotal and empirical evidence suggest that managers do not behave fairly towards their employees in a consistent manner. As treating employees fairly takes up personal resources such as time, effort, and attention, I argue that when managers face high workloads (i.e., high amounts of work and time pressure), they are unable to devote such personal resources to effectively meet both core technical task requirements and treat employees fairly. I propose that in general, managers tend to view their core technical task performance as more important than being fair in their dealings with employees; as a result, when faced with high workloads, they tend to prioritize the former at the expense of the latter. I also propose that managerial fairness will suffer more as a result of heightened workloads than will core technical task performance, unless managers perceive their organization to explicitly reward fair treatment of employees. I find support for my hypotheses across three studies: two experimental studies (with online participants and students respectively) and one field study of managers from a variety of organizations. I discuss the implications of studying fairness in the wider context of managers’ complex role in organizations to the fairness and managerial work demands literatures.

Autonomic disorders in Parkinson's disease: Estudio del sistema nervioso vegetativo como marcador de sinucleinpatías

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Analyzing systolic-diastolic interval interaction characteristics in diabetic cardiac autonomic neuropathy progression

Cardiac autonomic neuropathy (CAN), one of the major complications in diabetes, if detected at the subclinical stage allows for effective treatment and avoiding further complication including cardiovascular pathology. Surface ECG (Electrocardiogram)-based diagnosis of CAN is useful to overcome the limitation of existing cardiovascular autonomic reflex tests traditionally used for CAN identification in clinical settings. The aim of this paper is to analyze the changes in the mechanical function of the ventricles in terms of systolic-diastolic interval interaction (SDI) from a surface ECG to assess the severity of CAN progression [no CAN, early CAN (ECAN) or subclinical CAN, and definite CAN (DCAN) or clinical CAN]. ECG signals recorded in supine resting condition from 72 diabetic subjects without CAN (CAN-) and 70 diabetic subjects with CAN were analyzed in this paper. The severity of CAN was determined by Ewing's Cardiovascular autonomic reflex tests. Fifty-five subjects of the CAN group had ECAN and 15 subjects had DCAN. In this paper, we propose an improved version of the SDI parameter (i.e., TQ/RR interval ratio) measured from the electrical diastolic interval (i.e., TQ interval) and the heart rate interval (i.e., RR interval). The performance of the proposed SDI measure was compared with the performance of the existing SDI measure (i.e., QT/TQ interval ratio). The proposed SDI parameter showed significant differences among three groups (no CAN, ECAN, and DCAN). In addition, the proposed SDI parameter was found to be more sensitive in detecting CAN progression than other ECG interval-based features traditionally used for CAN diagnosis. The modified SDI parameter might be used as an alternative measure for the Ewing autonomic reflex tests to identify CAN progression for those subjects who are unable to perform the traditional tests. These findings could also complement the echocardiographic findings of the left ventricular diastolic dysfunction by providing additional information about alteration in systolic and diastolic intervals in heart failure.

Detecting subclinical diabetic cardiac autonomic neuropathy by analyzing ventricular repolarization dynamics

In this study, a linear parametric modeling technique was applied to model ventricular repolarization (VR) dynamics. Three features were selected from the surface ECG recordings to investigate the changes in VR dynamics in healthy and cardiac autonomic neuropathy (CAN) participants with diabetes including heart rate variability (calculated from RR intervals), repolarization variability (calculated from QT intervals), and respiration [calculated by ECG-derived respiration (EDR)]. Surface ECGs were recorded in a supine resting position from 80 age-matched participants (40 with no cardiac autonomic neuropathy (NCAN) and 40 with CAN). In the CAN group, 25 participants had early/subclinical CAN (ECAN) and 15 participants were identified with definite/clinical CAN (DCAN). Detecting subclinical CAN is crucial for designing an effective treatment plan to prevent further cardiovascular complications. For CAN diagnosis, VR dynamics was analyzed using linear parametric autoregressive bivariate (ARXAR) and trivariate (ARXXAR) models, which were estimated using 250 beats of derived QT, RR, and EDR time series extracted from the first 5 min of the recorded ECG signal. Results showed that the EDR-based models gave a significantly higher fitting value (p < 0.0001) than models without EDR, which indicates that QT-RR dynamics is better explained by respiratory-information-based models. Moreover, the QT-RR-EDR model fitting values gradually decreased from the NCAN group to ECAN and DCAN groups, which indicate a decoupling of QT from RR and the respiration signal with the increase in severity of CAN. In this study, only the EDR-based model significantly distinguished ECAN and DCAN groups from the NCAN group (p < 0.05) with large effect sizes (Cohen's d > 0.75) showing the effectiveness of this modeling technique in detecting subclinical CAN. In conclusion, the EDR-based trivariate QT-RR-EDR model was found to be better in detecting the presence and severity of CAN than the bivariate QT-RR model. This finding also establishes the importance of adding respiratory information for analyzing the gradual deterioration of normal VR dynamics in pathological conditions, such as diabetic CAN.

Multi-layer attribute selection and classification algorithm for the diagnosis of cardiac autonomic neuropathy based on HRV attributes

Cardiac autonomic neuropathy (CAN) poses an important clinical problem, which often remains undetected due difficulty of conducting the current tests and their lack of sensitivity. CAN has been associated with growth in the risk of unexpected death in cardiac patients with diabetes mellitus. Heart rate variability (HRV) attributes have been actively investigated, since they are important for diagnostics in diabetes, Parkinson's disease, cardiac and renal disease. Due to the adverse effects of CAN it is important to obtain a robust and highly accurate diagnostic tool for identification of early CAN, when treatment has the best outcome. Use of HRV attributes to enhance the effectiveness of diagnosis of CAN progression may provide such a tool. In the present paper we propose a new machine learning algorithm, the Multi-Layer Attribute Selection and Classification (MLASC), for the diagnosis of CAN progression based on HRV attributes. It incorporates our new automated attribute selection procedure, Double Wrapper Subset Evaluator with Particle Swarm Optimization (DWSE-PSO). We present the results of experiments, which compare MLASC with other simpler versions and counterpart methods. The experiments used our large and well-known diabetes complications database. The results of experiments demonstrate that MLASC has significantly outperformed other simpler techniques.

Waste management practices in construction projects: perceptions of project managers

The construction industry has been found to be a major generator of waste and there are many challenges associated with finding the most sustainable way to manage construction waste. As the construction industry is a project based industry, it is essential to look at cultural issues related to waste management at the project level. Therefore, this research aims to identify the current status of waste management practices in construction projects by analysing project managers’ views on waste management performance in construction projects; project managers’ attitudes towards waste management; and project managers’ views on waste management culture in construction projects. A questionnaire survey was carried out and project managers were selected as a target group to distribute questionnaires, as project managers have a vital involvement in promoting and maintaining project culture in the construction project environment. Data was analysed using descriptive statistics and the Kruskal-Wallis test. The findings reveal that project managers believe that even though the operational cost of waste minimisation is high in construction projects, overall waste management is profitable. At the same time it was interesting that even though project managers believe most project participants are satisfied with existing waste management systems, overall waste management efforts are not perceived as being at a satisfactory level in construction projects. Project managers consider waste as an inevitable by-product, but they do not believe that waste management is beyond the control of project members or that waste has no value. At the same time, it was found that project managers infer that project participants are cost and time conscious in waste management despite the roles, responsibilities and duties of each party in waste management not being well-coordinated or fully understood. Taken together, these findings highlight the misconceptions related to waste management in construction projects and emphasise the necessity of collective responsibility on the part of project participants to enhance the performance of waste management in construction projects.

Geospatial Data Indexing Analysis and Visualization via Web Services with Autonomic Resource Management

With the exponential growth of the usage of web-based map services, the web GIS application has become more and more popular. Spatial data index, search, analysis, visualization and the resource management of such services are becoming increasingly important to deliver user-desired Quality of Service. First, spatial indexing is typically time-consuming and is not available to end-users. To address this, we introduce TerraFly sksOpen, an open-sourced an Online Indexing and Querying System for Big Geospatial Data. Integrated with the TerraFly Geospatial database [1-9], sksOpen is an efficient indexing and query engine for processing Top-k Spatial Boolean Queries. Further, we provide ergonomic visualization of query results on interactive maps to facilitate the user’s data analysis. Second, due to the highly complex and dynamic nature of GIS systems, it is quite challenging for the end users to quickly understand and analyze the spatial data, and to efficiently share their own data and analysis results with others. Built on the TerraFly Geo spatial database, TerraFly GeoCloud is an extra layer running upon the TerraFly map and can efficiently support many different visualization functions and spatial data analysis models. Furthermore, users can create unique URLs to visualize and share the analysis results. TerraFly GeoCloud also enables the MapQL technology to customize map visualization using SQL-like statements [10]. Third, map systems often serve dynamic web workloads and involve multiple CPU and I/O intensive tiers, which make it challenging to meet the response time targets of map requests while using the resources efficiently. Virtualization facilitates the deployment of web map services and improves their resource utilization through encapsulation and consolidation. Autonomic resource management allows resources to be automatically provisioned to a map service and its internal tiers on demand. v-TerraFly are techniques to predict the demand of map workloads online and optimize resource allocations, considering both response time and data freshness as the QoS target. The proposed v-TerraFly system is prototyped on TerraFly, a production web map service, and evaluated using real TerraFly workloads. The results show that v-TerraFly can accurately predict the workload demands: 18.91% more accurate; and efficiently allocate resources to meet the QoS target: improves the QoS by 26.19% and saves resource usages by 20.83% compared to traditional peak load-based resource allocation.

Why resilience managers aren’t resilient, and what human resource management can do about it

Prior resilience research typically focuses on either the individual or the organisational level of analysis, emphasises resilience in relation to day-to-day stressors rather than extreme events and is empirically under-developed. In response, our study inductively theorises about the relationships between individual and organisational resilience, drawing upon a large-scale study of resilience work in UK and French organisations. Our first-hand accounts of resilience work reveal the micro-processes involved in producing resilient organisations, and highlight the challenges experienced in doing resilience work in large organisations. We show that these micro-processes have significant implications for resilience at both individual and organisational levels, and draw implications for how HRM interventions can help to promote individual, and thus organisational, resilience.

Risk stratification of cardiac autonomic neuropathy based on multi-lag tone-entropy

Cardiac autonomic neuropathy (CAN) is an irreversible condition affecting the autonomic nervous system, which leads to abnormal functioning of the visceral organs and affects critical body functions such as blood pressure, heart rate and kidney filtration. This study presents multi-lag Tone-Entropy (T-E) analysis of heart rate variability (HRV) at multiple lags as a screening tool for CAN. A total of 41 ECG recordings were acquired from diabetic subjects with definite CAN (CAN+) and without CAN (CAN-) and analyzed. Tone and entropy values of each patient were calculated for different beat sequence lengths (len: 50-900) and lags (m: 1-8). The CAN- group was found to have a lower mean tone value compared to that of CAN+ group for all m and len, whereas the mean entropy value was higher in CAN- than that in CAN+ group. Leave-one-out (LOO) cross-validation tests using a quadratic discriminant (QD) classifier were applied to investigate the performance of multi-lag T-E features. We obtained 100 % accuracy for tone and entropy with len = 250 and m = {2, 3} settings, which is better than the performance of T-E technique based on lag m = 1. The results demonstrate the usefulness of multi-lag T-E analysis over single lag analysis in CAN diagnosis for risk stratification and highlight the change in autonomic nervous system modulation of the heart rate associated with cardiac autonomic neuropathy.