Alignment of operations strategy: Exploring the marketing interface

Purpose - The purpose of this paper is to identify factors that can help managers to overcome barriers to alignment of operations strategy at the interface with marketing. Design/methodology/approach - This objective required the application of a procedure based on strategic consensus and a deeper analysis, such that the delimitation of the study in a single case was mandatory. The strategic processes of interfacing involve managerial attributes that are subject to the influence of human aspects and, therefore, the research method used a qualitative approach. The protocol design included the following data sources: interviews, document reviews and researcher observations. The categorisation was made based on the theoretical references, the frequency of observations, common responses and information from documents. Findings - The balance between intra-functional trade-offs, joint research on the competitive context, reflections on the understanding of customer needs and operational performance, and understanding of inter-functional trade-offs were the main factors verified. They effectively support decisions associated with interface processes and promotes the integration of these processes. They can generate inputs that enable managers to achieve an appropriate balance among alternatives in light of various trade-offs. Practical implications - These factors make possible new connections between strategic processes in the context of operations and marketing functions. The formations of these strategies are aligned through a better understanding of both threats and opportunities by means of a joint analysis of the competitive context. The presented findings can be used to develop a clear definition of strategic objectives of operations and a more appropriate treatment of market needs. Originality/value - The findings from the research can be considered as new elements for promoting alignment in the formation process of the operations strategy. Little research to date has examined the operations-marketing strategic interface of companies in the context of strategic consensus. © Emerald Group Publishing Limited.

Research on policies and institutions to support small and medium scale enterprise in the Caribbean: Trinidad adn Tobago report

The Government of Trinidad and Tobago continues to provide support to SMEs in order to enhance their international competitiveness. The increasing effects of globalization and the reality of several trade agreements require that local businesses attain and maintain a level of competitiveness which ensures their continued survival and growth. This report examines in detail the policy environment within which these enterprises operate. It also examines the role of the key implementing agencies such as the BDC and NEDCO for government’s policy on the sector and also the role of the respective line ministries. These organizations strive to deliver value added technical, financial and export promotion services to its clients on a subsidised basis. The services offered reflect five key business areas such as financing, training, technical assistance, trade assistance, business re-engineering, project management and export promotion. In the case of the BDC its services target six sectors: food and beverage, metal processing, leisure marine, including yachting, information and communication technology/electronics, printing and packaging and entertainment. These said sectors are identified by the government, on the basis of a study which was done by TIDCO, for the promotion of a cluster development strategy. In the case of NEDCO it targets the following sectors: art and craft, food and beverages, fashion and fashion accessories, culture and ecotourism, bed and breakfast operations, indigenous entertainment and light manufacturing.

Comparing support vector machines and artificial neural networks in the recognition of steering angle for driving of mobile robots through paths in plantations

The use of mobile robots turns out to be interesting in activities where the action of human specialist is difficult or dangerous. Mobile robots are often used for the exploration in areas of difficult access, such as rescue operations and space missions, to avoid human experts exposition to risky situations. Mobile robots are also used in agriculture for planting tasks as well as for keeping the application of pesticides within minimal amounts to mitigate environmental pollution. In this paper we present the development of a system to control the navigation of an autonomous mobile robot through tracks in plantations. Track images are used to control robot direction by pre-processing them to extract image features. Such features are then submitted to a support vector machine and an artificial neural network in order to find out the most appropriate route. A comparison of the two approaches was performed to ascertain the one presenting the best outcome. The overall goal of the project to which this work is connected is to develop a real time robot control system to be embedded into a hardware platform. In this paper we report the software implementation of a support vector machine and of an artificial neural network, which so far presented respectively around 93% and 90% accuracy in predicting the appropriate route. (C) 2013 The Authors. Published by Elsevier B.V. Selection and peer review under responsibility of the organizers of the 2013 International Conference on Computational Science

Low-carbon operations and production: putting training in perspective

The purpose of this paper is mainly to show how training may support low-carbon operations and production management in a more sustainable organizational context. Design/methodology/approach – A conceptual framework to facilitate the integration between training and low-carbon operations and production is presented. Findings – To accomplish better training in a low-carbon organization, some steps should be followed. Challenges may occur, including the necessity of collaboration across the supply chain. Research limitations/implications – The proposed framework should be applied and improved based on the actual conditions in organizations.

Managing Large Data Sets Using Support Vector Machines

Hundreds of Terabytes of CMS (Compact Muon Solenoid) data are being accumulated for storage day by day at the University of Nebraska-Lincoln, which is one of the eight US CMS Tier-2 sites. Managing this data includes retaining useful CMS data sets and clearing storage space for newly arriving data by deleting less useful data sets. This is an important task that is currently being done manually and it requires a large amount of time. The overall objective of this study was to develop a methodology to help identify the data sets to be deleted when there is a requirement for storage space. CMS data is stored using HDFS (Hadoop Distributed File System). HDFS logs give information regarding file access operations. Hadoop MapReduce was used to feed information in these logs to Support Vector Machines (SVMs), a machine learning algorithm applicable to classification and regression which is used in this Thesis to develop a classifier. Time elapsed in data set classification by this method is dependent on the size of the input HDFS log file since the algorithmic complexities of Hadoop MapReduce algorithms here are O(n). The SVM methodology produces a list of data sets for deletion along with their respective sizes. This methodology was also compared with a heuristic called Retention Cost which was calculated using size of the data set and the time since its last access to help decide how useful a data set is. Accuracies of both were compared by calculating the percentage of data sets predicted for deletion which were accessed at a later instance of time. Our methodology using SVMs proved to be more accurate than using the Retention Cost heuristic. This methodology could be used to solve similar problems involving other large data sets.

Engineering geological data in support of municipal land use planning-a case study in Analandia, southeast Brazil

This paper presents a method for transforming the information of an engineering geological map into useful information for non-specialists involved in land-use planning. The method consists of classifying the engineering geological units in terms of land use capability and identifying the legal and the geologic restrictions that apply in the study area. Both informations are then superimposed over the land use and a conflict areas map is created. The analysis of these data leads to the identification of existing and forthcoming land use conflicts and enables the proposal of planning measures on a regional and local scale. The map for the regional planning was compiled at a 1:50,000 scale and encompasses the whole municipal land area where uses are mainly rural. The map for the local planning was compiled at a 1:10,000 scale and encompasses the urban area. Most of the classification and operations on maps used spatial analyst tools available in the Geographical Information System. The regional studies showed that the greater part of Analandia's territory presents appropriate land uses. The local-scale studies indicate that the majority of the densely occupied urban areas are in suitable land. Although the situation is in general positive, municipal policies should address the identified and expected land use conflicts, so that it can be further improved.

Estimating greenhouse gas emissions of highway construction and rehabilitation to support pavement life cycle assessment

Highway infrastructure plays a significant role in society. The building and upkeep of America’s highways provide society the necessary means of transportation for goods and services needed to develop as a nation. However, as a result of economic and social development, vast amounts of greenhouse gas emissions (GHG) are emitted into the atmosphere contributing to global climate change. In recognizing this, future policies may mandate the monitoring of GHG emissions from public agencies and private industries in order to reduce the effects of global climate change. To effectively reduce these emissions, there must be methods that agencies can use to quantify the GHG emissions associated with constructing and maintaining the nation’s highway infrastructure. Current methods for assessing the impacts of highway infrastructure include methodologies that look at the economic impacts (costs) of constructing and maintaining highway infrastructure over its life cycle. This is known as Life Cycle Cost Analysis (LCCA). With the recognition of global climate change, transportation agencies and contractors are also investigating the environmental impacts that are associated with highway infrastructure construction and rehabilitation. A common tool in doing so is the use of Life Cycle Assessment (LCA). Traditionally, LCA is used to assess the environmental impacts of products or processes. LCA is an emerging concept in highway infrastructure assessment and is now being implemented and applied to transportation systems. This research focuses on life cycle GHG emissions associated with the construction and rehabilitation of highway infrastructure using a LCA approach. Life cycle phases of the highway section include; the material acquisition and extraction, construction and rehabilitation, and service phases. Departing from traditional approaches that tend to use LCA as a way to compare alternative pavement materials or designs based on estimated inventories, this research proposes a shift to a context sensitive process-based approach that uses actual observed construction and performance data to calculate greenhouse gas emissions associated with highway construction and rehabilitation. The goal is to support strategies that reduce long-term environmental impacts. Ultimately, this thesis outlines techniques that can be used to assess GHG emissions associated with construction and rehabilitation operations to support the overall pavement LCA.

Corporate decision-making: A case study of decision support systems and financial performance in health care organizations

The purpose of this study was twofold: (1) To describe the relation of the intensity of DSS implementation to financial performance as an empirical exploration of improved performance at the organizational level. (2) To describe the relation of the intensity of DSS implementation to the type of organizational decision culture. A multiple case study design was utilized to compare three groups of paired cases. A pattern matching strategy was applied in this study. Four predictions were specified and compared to the empirical data. A progressively upward trend in the scores was predicted for the following theoretical relationships. (1) The greater the number of DSSs, the higher the sophistication index. (2) The greater the number of DSSs, the higher the financial ratios. (3) The greater the number of DSSs, the higher the culture score. (4) The higher the culture score, the higher the financial ratios. The data did not support any of the predicted trends except the relation between the number of DSSs and the financial ratios. The Income/Revenue ratio indicates the efficiency of a company's operations. One would expect that this ratio would be most affected by the operational and financial decision support systems. The majority of the systems measured in the study supported decisions tangential to the patient service areas. The evidence suggested that the type and number of decision support systems affects the bottom line. ^

An assessment of post-disaster psychological stress in hazardous waste operations and emergency response (HAZWOPER) workers

Personnel involved in natural or man-made disaster response and recovery efforts may be exposed to a wide variety of physical and mental stressors that can exhibit long-lasting and detrimental psychopathological outcomes. In a disaster situation, huge numbers of "secondary" responders can be involved in contaminant clean-up and debris removal and can be at risk of developing stress-related mental health outcomes. The Occupational Safety and Health Administration (OSHA) worker training hierarchy typically required for response workers, known as "Hazardous Waste Operations and Emergency Response" (HAZWOPER), does not address the mental health and safety concerns of workers. This study focused on the prevalence of traumatic stress experienced by secondary responders that had received or expressed interest in receiving HAZWOPER training through the National Institute of Environmental Health Sciences Worker Education and Training Program (NIEHS WETP). ^ The study involved the modification of two preexisting and validated survey tools to assess secondary responder awareness of physical, mental, and traumatic stressors on mental health and sought to determine if a need existed to include traumatic stress-related mental health education in the current HAZWOPER training regimen. The study evaluated post-traumatic stress disorder (PTSD), resiliency, mental distress, and negative effects within a secondary responder population of 176 respondents. Elevated PTSD levels were seen in the study population as compared to a general responder population (32.9% positive vs. 8%-22.5% positive). Results indicated that HAZWOPER-trained disaster responders were likely to test positive for PTSD, whereas, untrained responders with no disaster experience and responders who possessed either training or disaster experience only were likely to test PTSD negative. A majority (68.75%) of the population tested below the mean resiliency to cope score (80.4) of the average worker population. Results indicated that those who were trained only or who possessed both training and disaster work experience were more likely to have lower resiliency scores than those with no training or experience. There were direct correlations between being PTSD positive and having worked at a disaster site and experiencing mental distress and negative effects. However, HAZWOPER training status does not significantly correlate with mental distress or negative effect. ^ The survey indicated clear support (91% of respondents) for mental health education. The development of a pre- and post-deployment training module is recommended. Such training could provide responders with the necessary knowledge and skills to recognize the symptomology of PTSD, mental stressors, and physical and traumatic stressors, thus empowering them to employ protective strategies or seek professional help if needed. It is further recommended that pre-deployment mental health education be included in the current HAZWOPER 24- and 40-hour course curriculums, as well as, consideration be given towards integrating a stand-alone post-deployment mental health education training course into the current HAZWOPER hierarchy.^

Desing and Validation of a Light Inference System to Support Embedded Context Reasoning

Embedded context management in resource-constrained devices (e.g. mobile phones, autonomous sensors or smart objects) imposes special requirements in terms of lightness for data modelling and reasoning. In this paper, we explore the state-of-the-art on data representation and reasoning tools for embedded mobile reasoning and propose a light inference system (LIS) aiming at simplifying embedded inference processes offering a set of functionalities to avoid redundancy in context management operations. The system is part of a service-oriented mobile software framework, conceived to facilitate the creation of context-aware applications—it decouples sensor data acquisition and context processing from the application logic. LIS, composed of several modules, encapsulates existing lightweight tools for ontology data management and rule-based reasoning, and it is ready to run on Java-enabled handheld devices. Data management and reasoning processes are designed to handle a general ontology that enables communication among framework components. Both the applications running on top of the framework and the framework components themselves can configure the rule and query sets in order to retrieve the information they need from LIS. In order to test LIS features in a real application scenario, an ‘Activity Monitor’ has been designed and implemented: a personal health-persuasive application that provides feedback on the user’s lifestyle, combining data from physical and virtual sensors. In this case of use, LIS is used to timely evaluate the user’s activity level, to decide on the convenience of triggering notifications and to determine the best interface or channel to deliver these context-aware alerts.d

Design and Validation of a Light Inference System to Support Embedded Context Reasoning

Embedded context management in resource-constrained devices (e.g. mobile phones, autonomous sensors or smart objects) imposes special requirements in terms of lightness for data modelling and reasoning. In this paper, we explore the state-of-the-art on data representation and reasoning tools for embedded mobile reasoning and propose a light inference system (LIS) aiming at simplifying embedded inference processes offering a set of functionalities to avoid redundancy in context management operations. The system is part of a service-oriented mobile software framework, conceived to facilitate the creation of context-aware applications?it decouples sensor data acquisition and context processing from the application logic. LIS, composed of several modules, encapsulates existing lightweight tools for ontology data management and rule-based reasoning, and it is ready to run on Java-enabled handheld devices. Data management and reasoning processes are designed to handle a general ontology that enables communication among framework components. Both the applications running on top of the framework and the framework components themselves can configure the rule and query sets in order to retrieve the information they need from LIS. In order to test LIS features in a real application scenario, an ?Activity Monitor? has been designed and implemented: a personal health-persuasive application that provides feedback on the user?s lifestyle, combining data from physical and virtual sensors. In this case of use, LIS is used to timely evaluate the user?s activity level, to decide on the convenience of triggering notifications and to determine the best interface or channel to deliver these context-aware alerts.

Analysis of the geometric altimetry to support aircraft optimal profiles within future 4D trajectory management

The use of barometric altimetry is to some extent a limiting factor on safety, predictability and efficiency of aircraft operations, and reduces the potential of the trajectory based operations capabilities. However, geometric altimetry could be used to improve all of these aspects. Nowadays aircraft altitude is estimated by applying the International Standard Atmosphere which differs from real altitude. At different temperatures for an assigned barometric altitude, aerodynamic forces are different and this has a direct relationship with time, fuel consumption and range of the flight. The study explores the feasibility of using sensors providing geometric reference altitude, in particular, to supply capabilities for the optimization of vertical profiles and also, their impact on the vertical Air Traffic Management separation assurance processes. One of the aims of the thesis is to assess if geometric altitude fulfils the aeronautical requirements through existing sensors. Also the thesis will elaborate on the advantages of geometric altitude over the barometric altitude in terms of efficiency for vertical navigation. The evidence that geometric altitude is the best choice to improve the efficiency in vertical profile and aircraft capacity by reducing vertical uncertainties will also be shown. In this paper, an atmospheric study is presented, as well as the impact of temperature deviation from International Standard Atmosphere model is analyzed in order to obtain relationship between geometric and barometric altitude. Furthermore, an aircraft model to study aircraft vertical profile is provided to analyse trajectories based on geometric altitudes.

Novel Negotiation protocol to support CDM process in a layered ATM System

In the SESAR Step 2 concept of operations a RBT is available and seen by all making it possible to conceive a different operating method than the current ATM system based on Collaborative Decisions Making processes. Currently there is a need to describe in more detail the mechanisms by which actors (ATC, Network Management, Flight Crew, airports and Airline Operation Centre) will negotiate revisions to the RBT. This paper introduces a negotiation model, which uses constraint based programing applied to a mediator to facilitate negotiation process in a SWIM enabled environment. Three processes for modelling the negotiation process are explained as well a preliminary reasoning agent algorithm modelled with constraint satisfaction problem is presented. Computational capability of the model is evaluated in the conclusion.

Analysis of the geometric altimetry to support aircraft optimal trajectories within the future 4d trajectory management

Over the past few years, the common practice within air traffic management has been that commercial aircraft fly by following a set of predefined routes to reach their destination. Currently, aircraft operators are requesting more flexibility to fly according to their prefer- ences, in order to achieve their business objectives. Due to this reason, much research effort is being invested in developing different techniques which evaluate aircraft optimal trajectory and traffic synchronisation. Also, the inefficient use of the airspace using barometric altitude overall in the landing and takeoff phases or in Continuous Descent Approach (CDA) trajectories where currently it is necessary introduce the necessary reference setting (QNH or QFE). To solve this problem and to permit a better airspace management born the interest of this research. Where the main goals will be to evaluate the impact, weakness and strength of the use of geometrical altitude instead of the use of barometric altitude. Moreover, this dissertation propose the design a simplified trajectory simulator which is able to predict aircraft trajectories. The model is based on a three degrees of freedom aircraft point mass model that can adapt aircraft performance data from Base of Aircraft Data, and meteorological information. A feature of this trajectory simulator is to support the improvement of the strategic and pre-tactical trajectory planning in the future Air Traffic Management. To this end, the error of the tool (aircraft Trajectory Simulator) is measured by comparing its performance variables with actual flown trajectories obtained from Flight Data Recorder information. The trajectory simulator is validated by analysing the performance of different type of aircraft and considering different routes. A fuel consumption estimation error was identified and a correction is proposed for each type of aircraft model. In the future Air Traffic Management (ATM) system, the trajectory becomes the fundamental element of a new set of operating procedures collectively referred to as Trajectory-Based Operations (TBO). Thus, governmental institutions, academia, and industry have shown a renewed interest for the application of trajectory optimisation techniques in com- mercial aviation. The trajectory optimisation problem can be solved using optimal control methods. In this research we present and discuss the existing methods for solving optimal control problems focusing on direct collocation, which has received recent attention by the scientific community. In particular, two families of collocation methods are analysed, i.e., Hermite-Legendre-Gauss-Lobatto collocation and the pseudospectral collocation. They are first compared based on a benchmark case study: the minimum fuel trajectory problem with fixed arrival time. For the sake of scalability to more realistic problems, the different meth- ods are also tested based on a real Airbus 319 El Cairo-Madrid flight. Results show that pseudospectral collocation, which has shown to be numerically more accurate and computa- tionally much faster, is suitable for the type of problems arising in trajectory optimisation with application to ATM. Fast and accurate optimal trajectory can contribute properly to achieve the new challenges of the future ATM. As atmosphere uncertainties are one of the most important issues in the trajectory plan- ning, the final objective of this dissertation is to have a magnitude order of how different is the fuel consumption under different atmosphere condition. Is important to note that in the strategic phase planning the optimal trajectories are determined by meteorological predictions which differ from the moment of the flight. The optimal trajectories have shown savings of at least 500 [kg] in the majority of the atmosphere condition (different pressure, and temperature at Mean Sea Level, and different lapse rate temperature) with respect to the conventional procedure simulated at the same atmosphere condition.This results show that the implementation of optimal profiles are beneficial under the current Air traffic Management (ATM).

Performance evaluation of respiratory protective equipment used in paint spraying operations /

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