Dissertations / Theses on the topic 'Time management Measurement'

This thesis shows the results of an experiment in order to test the validity of Methods Time Measurement (MTM) data. MTM, developed in 1948, is one of the most commonly used Predetermined Motion Time Systems to calculate standard time for a task. However, there is limited research on the validity of the MTM data in the literature. Today&rsquo
s technology provides new computerized tools to perform time studies. One of such tools is Real Time Method Study (RTMS). RTMS is a computerized method study tool being developed in METU Technopolis, which uses Image Processing and Machine Learning to conduct time studies automatically. RTMS uses MTM data as a benchmark data to compare observed performance results
therefore validity of MTM data is an important issue for it. In order to test the validity of MTM data an experiment conducted in the Ergonomics Laboratory of the METU Industrial Engineering Department. In this experiment 40 undergraduate students performed four different tasks. These tasks were recorded by a video camera and analyzed frame by frame to calculate normal times for basic motions of MTM. Results are compared with the original MTM data and it is shown that MTM times do not fit the observed data. This study in the end suggests updating MTM data or constructing a new standard time database by using a tool like RTMS.

The purpose of this research is to investigate current company business process from sales forecasting perspective and provide potential improvements of how to deal with unstable market demand and increase overall precision of forecasting. The problem which company face is an unstable market demand and not enough precision in sales forecasting process. Therefore the research questions are:  How current forecasting process can be improved?  What methods, can be implemented in order to increase the precision of forecasting? Study can be described as an action research using an abductive approach supported by combination of quantitative and qualitative analysis practices. In order to achieve high degree of reliability the study was based on verified scientific literature and data collected from the case company while collaborating with company’s COO. Research exposed the current forecasting process of the case company. Different forecasting methods were chosen according to the existing circumstances and analyzed in order to figure out which could be implemented in order to increase forecasting precision and forecasting as a whole. Simple exponential smoothing showed the most promising accuracy results, which were measured by applying MAD, MSE and MAPE measurement techniques. Moreover, trend line analysis was applied as well, as a supplementary method. For the reason that the case company presents new products to the market limited amount of historical data was available. Therefore simple exponential smoothing technique did not show accurate results as desired. However, suggested methods can be applied for testing and learning purposes, supported by currently applied qualitative methods.

The measurement of sales force performance is an issue of the upmost importance. Research in this area has primarily focused on cross-sectional studies establishing a link between various types of predictors and sales performance at a specific moment in time, despite the well accepted idea that performance is dynamic over time. Moreover, the most frequent way to measure performance has been through subjective measures. Yet, little is actually known empirically about trends (growth trajectories) of objective performance over time and their determinants. The empirical research study presented in this dissertation is designed to fill this gap. First, we conducted an extensive survey of the literature in order to identify empirical work referred to objective measures of performance at the individual level in the sales domain, yielding 133 published studies and 148 samples. Then, we analyzed in detail, on one side, all studies using two or more objective measures of performance and, on the other, studies conducting a longitudinal research. Building on job stages theory, we argue specifically that measurements of objective performance taken at different times are not related when salespeople are involved in changing contexts. Furthermore, we hypothesize that growth trajectories measured with different indicators of objective performance are not related. Random coefficient modeling in the form of Hierarchical Linear Modeling is then used to analyze objective performance over time. The individual performance growth trajectories of 230 salespeople that joined a Spanish direct selling firm were modeled using SPSS and R software. To the best of our knowledge, this thesis represents the first longitudinal study to explicitly analyze and compare the trends (growth trajectories) of various measures of objective performance (sales, units and compensation) of salespeople during their first months at av company. This analysis yielded three important results at the individual salesperson level. First, time matters when measuring individual objective performance. Our findings confirm that performance is dynamic over time and that there is a rank-order effect when measuring salespeople. Second, different objective measures of performance quantify different things. We found no evidence that the growth trajectories of objective measures of performance taken during the same period are related, thus, building on the idea that objective measures of performance are not interchangeable. Third, these findings help understand the specificities of new salespeople in direct selling, facing a transitional job stage. This thesis, thus, contributes to the longitudinal analysis of sales performance confirming (a) that future esearch studies have to consider the relationship over time of objective performance with any set of predictors, and (b) that objective indicators of sales performance are not interchangeable and have to be chosen carefully by scholars according to the objectives of each investigation. Additionally, it has important implications for practitioners referred to selection, promotion, retention, evaluation, training and compensation of salesforces.
La medición de la performance de los vendedores de una empresa es una asunto de gran importancia. La investigación en esta área se ha centrado especialmente en análisis estáticos, determinando la relación entre varios tipos de predictores y la performance en un momento en el tiempo, pese a la idea aceptada en el mundo académicos de que la performance es dinámica en el tiempo. Además, la forma más habitual de medir esa performance ha sido mediante métricas subjetivas. Finalmente, se sabe muy poco sobre las tendencias (trayectorias de crecimiento) de la performance objetiva o sobre sus determinantes. El presente estudio empírico pretende abordar estos asuntos. Primero, realizamos un análisis detallado de las principales revistas académicas para identificar estudios empíricos referidos a medidas objetivas de performance a nivel individual en el ámbito de las ventas, obteniendo 133 artículos y 148 muestras. Posteriormente, analizamos exhaustivamente, por una parte, los estudios utilizando dos o más medidas objetivas de performance y, por otra, aquellos estudios con análisis longitudinales. Basándonos en la teoría de las etapas laborales ("job stages"), establecimos las hipótesis de que medidas de performance objetivas tomadas en momentos diferentes no están relacionadas entre sí cuando los vendedores están implicados en situaciones de cambio; además, consideramos que las trayectorias de crecimiento medidas con distintos indicadores no están relacionadas entre sí. Se utilizó "random coefficient modeling" en la forma de Modelos Jerárquicos Lineales ("HLM") para analizar la performance objetiva a lo largo del tiempo. Se modelizaron las trayectorias de crecimiento de la performance de 230 vendedores de una compañía española de venta directa utilizando los software "SPSS" y "R". Hasta donde hemos podido identificar, este es el primer estudio longitudinal que analiza y compara las trayectorias de varias medidas de performance objetiva (ventas en euros, unidades y retribución en euros) de vendedores durante sus primeros meses en la empresa. El análisis produjo tres resultados a nivel individual de los vendedores. Primero, el tiempo es un factor relevante cuando se mide la performance individual objetiva; nuestros resultados confirman que la performance es dinámica en el tiempo. Segundo, distintas métricas de performance objetiva miden cosas diferentes. No encontramos evidencia de que las trayectorias de crecimiento de medidas objetivas tomadas en el mismo periodo estén relacionadas, soportando la idea de que dichas métricas no son intercambiables entre sí. Tercero, estas conclusiones ayudan a entender los aspectos específicos de nuevos vendedores en una empresa. Por tanto, esta Tesis contribuye al análisis longitudinal de la performance de vendedores confirmando (a) que futuros estudios académicos deben considerar la relación en el tiempo de la performance objetiva con los predictores que se estén analizando, y (b) que las métricas de performance objetiva no son intercambiables entre sí y que, por tanto, deben escogerse cuidadosamente en función de los objetivos de la investigación. Adicionalmente, tiene importantes implicaciones para el mundo de la empresa referidas a la selección, promoción, retención, evaluación, formación y retribución de sus vendedores.

Thesis (Ph. D.)--Ohio State University, 2004.
Title from first page of PDF file. Document formatted into pages; contains xvii, 220 p. : ill. (some col.). Advisor: Peter T. Ward, Dept. of Business Administration. Includes bibliographical references (p. 197-203).

Inventories totaling 1.7 trillion U.S. dollars represent an opportunity for U.S. manufacturers. This exploratory case study researched supply chain strategies used to manage inventory in manufacturing operations of a U.S. manufacturing company. A mature value chain contained within a single organization using the value chain framework was the basis for this study. Individual interviews conducted with 16 managers responsible for defining and implementing inventory control strategies, and 4 internal users provided primary information for the study. Other sources of information included a value chain map created through the observation of operations, various inventory measurements, and policies and guidelines related to managing inventory levels. An inductive content analysis employing zero-level coding of the interview transcripts identified 4 themes that describe inventory control strategies as economic order quantity, kanban, vendor managed inventory, and process integration. Physical observation of the value chain, review of supporting documents, and analysis of inventory data ensured the trustworthiness of interpreted themes. Findings identified no single inventory control strategy that fit all applications. Findings also revealed that the financial governing bodies' measurements were not the best tools for operational managers' improvement activities related to inventory control. Included are measures providing alternative means to gauge inventory efficiency. With the results of this study, managers may develop effective strategies to optimize inventory and improve material flow. Manufacturing managers improving material flow may promote sustainability of raw materials and business efficiencies through reduced waste, improved environmental conditions, and increased employment opportunities in associated communities.

Thesis (MScEng)--University of Stellenbosch, 2000.
ENGLISH ABSTRACT: Air pollution is a major concern III the Cape Metropole. A major contributor to the air pollution problem is road transport. For this reason, a national vehicle emissions study is in progress with the aim of developing a national policy regarding motor vehicle emissions and control. Such a policy could bring about vehicle emission control and regulatory measures, which may have far-reaching social and economic effects. Air pollution models are important tools 10 predicting the effectiveness and the possible secondary effects of such policies. It is therefore essential that these models are fundamentally sound to maintain a high level of prediction accuracy. Complex air pollution models are available, but they require spatial, time-resolved information of emission sources and a vast amount of processing power. It is unlikely that South African cities will have the necessary spatial, time-resolved emission information in the near future. An alternative air pollution model is one that is based on the Gaussian Plume Model. This model, however, relies on gross simplifying assumptions that affect model accuracy. It is proposed that statistical and mathematical analysis techniques will be the most viable approach to modelling air pollution in the Cape Metropole. These techniques make it possible to establish statistical relationships between pollutant emissions, meteorological conditions and pollutant concentrations without gross simplifying assumptions or excessive information requirements. This study investigates two analysis techniques that fall into the aforementioned category, namely, Non-linear Time Series Analysis (specifically, the method of delay co-ordinates) and Singular Spectrum Analysis (SSA). During the past two decades, important progress has been made in the field of Non-linear Time Series Analysis. An entire "toolbox" of methods is available to assist in identifying non-linear determinism and to enable the construction of predictive models. It is argued that the dynamics that govern a pollution system are inherently non-linear due to the strong correlation with weather patterns and the complexity of the chemical reactions and physical transport of the pollutants. In addition to this, a statistical technique (the method of surrogate data) showed that a pollution data set, the oxides of Nitrogen (NOx), displayed a degree of non-linearity, albeit that there was a high degree of noise contamination. This suggested that a pollution data set will be amenable to non-linear analysis and, hence, Non-linear Time Series Analysis was applied to the data set. SSA, on the other hand, is a linear data analysis technique that decomposes the time series into statistically independent components. The basis functions, in terms of which the data is decomposed, are data-adaptive which makes it well suited to the analysis of non-linear systems exhibiting anharmonic oscillations. The statistically independent components, into which the data has been decomposed, have limited harmonic content. Consequently, these components are more amenable to prediction than the time series itself. The fact that SSA's ability has been proven in the analysis of short, noisy non-linear signals prompted the use of this technique. The aim of the study was to establish which of these two techniques is best suited to the modelling of air pollution data. To this end, a univariate model to predict NOx concentrations was constructed using each of the techniques. The prediction ability of the respective model was assumed indicative of the accuracy of the model. It was therefore used as the basis against which the two techniques were evaluated. The procedure used to construct the model and to quantify the model accuracy, for both the Non-linear Time Series Analysis model and the SSA model, was consistent so as to allow for unbiased comparison. In both cases, no noise reduction schemes were applied to the data prior to the construction of the model. The accuracy of a 48-hour step-ahead prediction scheme and a lOO-hour step-ahead prediction scheme was used to compare the two techniques. The accuracy of the SSA model was markedly superior to the Non-linear Time Series model. The paramount reason for the superior accuracy of the SSA model is its adept ability to analyse and cope with noisy data sets such as the NOx data set. This observation provides evidence to suggest that Singular Spectrum Analysis is better suited to the modelling of air pollution data. It should therefore be the analysis technique of choice when more advanced, multivariate modelling of air pollution data is carried out. It is recommended that noise reduction schemes, which decontaminate the data without destroying important higher order dynamics, should be researched. The application of an effective noise reduction scheme could lead to an improvement in model accuracy. In addition to this, the univariate SSA model should be extended to a more complex multivariate model that explicitly encompasses variables such as traffic flow and weather patterns. This will explicitly expose the inter-relationships between the variables and will enable sensitivity studies and the evaluation of a multitude of scenarios.
AFRIKAANSE OPSOMMING: Die hoë vlak van lugbesoedeling in die Kaapse Metropool is kommerwekkend. Voertuie is een van die hoofoorsake, en as gevolg hiervan word 'n landswye ondersoek na voertuigemissie tans onderneem sodat 'n nasionale beleid opgestel kan word ten opsigte van voertuigemissie beheer. Beheermaatreëls van so 'n aard kan verreikende sosiale en ekonomiese uitwerkings tot gevolg hê. Lugbesoedelingsmodelle is van uiterste belang in die voorspelling van die effektiwiteit van moontlike wetgewing. Daarom is dit noodsaaklik dat hierdie modelle akkuraat is om 'n hoë vlak van voorspellingsakkuraatheid te handhaaf. Komplekse modelle is beskikbaar, maar hulle verg tyd-ruimtelike opgeloste inligting van emmissiebronne en baie berekeningsvermoë. Dit is onwaarskynlik dat Suid-Afrika in die nabye toekoms hierdie tydruimtelike inligting van emissiebronne gaan hê. 'n Alternatiewe lugbesoedelingsmodel is dié wat gebaseer is op die "Guassian Plume". Hierdie model berus egter op oorvereenvoudigde veronderstellings wat die akkuraatheid van die model beïnvloed. Daar word voorgestel dat statistiese en wiskundige analises die mees lewensvatbare benadering tot die modellering van lugbesoedeling in die Kaapse Metropool sal wees. Hierdie tegnieke maak dit moontlik om 'n statistiese verwantskap tussen besoedelingsbronne, meteorologiese toestande en besoedeling konsentrasies te bepaal sonder oorvereenvoudigde veronderstellings of oormatige informasie vereistes. Hierdie studie ondersoek twee analise tegnieke wat in die bogenoemde kategorie val, naamlik, Nie-lineêre Tydreeks Analise en Enkelvoudige Spektrale Analise (ESA). Daar is in die afgelope twee dekades belangrike vooruitgang gemaak in die studieveld van Nie-lineêre Tydreeks Analise. 'n Volledige stel metodes is beskikbaar om nie-lineêriteit te identifiseer en voorspellingsmodelle op te stel. Dit word geredeneer dat die dinamika wat 'n besoedelingsisteem beheer nie-lineêr is as gevolg van die sterk verwantskap wat dit toon met weerpatrone asook die kompleksiteit van die chemiese reaksies en die fisiese verplasing van die besoedelingstowwe. Bykomend verskaf 'n statistiese tegniek (die metode van surrogaatdata) bewyse dat 'n lugbesoedelingsdatastel, die okside van Stikstof (NOx), melineêre gedrag toon, alhoewel daar 'n hoë geraasvlak is. Om hierdie rede is die besluit geneem om Nie-lineêre Tydreeks Analise aan te wend tot die datastel. ESA daarenteen, is 'n lineêre data analise tegniek. Dit vereenvoudig die tydreeks tot statistiese onafhanklike komponente. Die basisfunksies, in terme waarvan die data vereenvoudig is, is data-aanpasbaar en dit maak hierdie tegniek gepas vir die analise van nielineêre sisteme. Die statisties onafhanklike komponente het beperkte harmoniese inhoud, met die gevolg dat die komponente aansienlik makliker is om te voorspel as die tydreeks self. ESA se effektiwitiet is ook al bewys in die analise van kort, hoë-graas nie-lineêre seine. Om hierdie redes, is ESA toegepas op die lugbesoedelings data. Die doel van die ondersoek was om vas te stel watter een van die twee tegnieke meer gepas is om lugbesoedelings data te analiseer. Met hierdie doelwit in sig, is 'n enkelvariaat model opgestel om NOx konsentrasies te voorspel met die gebruik van elk van die tegnieke. Die voorspellingsvermoë van die betreklike model is veronderstelom as 'n maatstaf van die model se akkuraatheid te kan dien en dus is dit gebruik om die twee modelle te vergelyk. 'n Konsekwente prosedure is gevolg om beide die modelle te skep om sodoende invloedlose vergelyking te verseker. In albei gevalle was daar geen geraasverminderings-tegnieke toegepas op die data nie. Die akuraatheid van 'n 48-uur voorspellingsmodel en 'n 100-uur voorspellingsmodel was gebruik vir die vergelyking van die twee tegnieke. Daar is bepaal dat die akkuraatheid van die ESA model veel beter as die Nie-lineêre Tydsreeks Analise is. Die hoofrede vir die ESA se hoër akkuraatheid is die model se vermoë om data met hoë geraasvlakke te analiseer. Hierdie ondersoek verskaf oortuigende bewyse dat Enkelvoudige Spektrale Analiese beter gepas is om lugbesoedelingsdata te analiseer en gevolglik moet hierdie tegniek gebruik word as meer gevorderde, multivariaat analises uitgevoer word. Daar word aanbeveel dat geraasverminderings-tegnieke, wat die data kan suiwer sonder om belangrike hoë-orde dinamika uit te wis, ondersoek moet word. Hierdie toepassing van effektiewe geraasverminderings-tegniek sal tot 'n verbetering in model-akkuraatheid lei. Aanvullend hiertoe, moet die enkele ESA model uitgebrei word tot 'n meer komplekse multivariaat model wat veranderlikes soos verkeersvloei en weerpatrone insluit. Dit sal die verhoudings tussen veranderlikes ten toon stel en sal sensitiwiteit-analises en die evaluering van menigte scenarios moontlik maak.

Youth sport is acknowledged as an ideal setting for promoting positive youth development. In particular, youth sport participation has been linked to life skills development and psychological well-being. The coaching climate has been proposed to play a role in facilitating such positive outcomes. Nonetheless, few measures exist to examine life skills development through sport and it is unclear how positive youth development may be facilitated by the coach. Using existing and newly developed measures, this thesis examined how the coaching climate is related to life skills development and psychological well-being in youth sport participants. Phase 1 of this programme of research investigated Benson and Saito’s (2001) conceptual framework for youth development theory and research within sport. Study 1 examined a model whereby the coaching climate is related to life skills development (personal and social skills, cognitive skills, goal setting, and initiative); which, in turn, is related to participants’ psychological well-being (self-esteem, positive affect, and satisfaction with life). Data from 202 youth sport participants suggested that an autonomy supportive coaching climate was positively related to all four life skills. Further analysis revealed that the development of personal and social skills mediated the relationships between coach autonomy support and all three indices of psychological well-being. However, the validity of the scale used to measure life skills was brought into question during this study. Therefore, the studies which follow developed and validated a new scale which could accurately assess eight key life skills young people learn through sport. Phase 2 of this programme of research involved developing and validating a scale which measures life skills development through sport. Study 2 outlines the initial development of a scale which would assess whether young people learn the following life skills through sport: teamwork, goal setting, time management, emotional skills, interpersonal communication, social skills, leadership, and problem solving and decision making. This study involved defining each of the eight life skills, deciding what components made up each life skill and developing items which could assess each life skill. The initial item pool was reviewed by 39 academics, with between two and seven experts assessing the items for each of the eight life skills. Using the ratings and comments provided by experts, the first version of the Life Skills Scale for Sport (LSSS) was developed. Study 3 reduced the number of items contained within the LSSS from 144 to 47 items using both exploratory factor analysis (EFA) and descriptive statistics. For this task, 338 youth sport participants completed the LSSS. EFA results supported the unidimensional factor structure of each of the eight subscales. Each subscale also displayed adequate internal consistency reliability. Study 4 examined the factor structure of the LSSS using confirmatory factor analysis (CFA) with an independent sample of 223 youth sport participants. After the removal of four emotional skills items, seven of the eight subscales and the revised 43-item scale displayed adequate model fit. Results supported both the convergent and discriminant validity of the LSSS and each of the eight subscales displayed adequate internal consistency reliability. Study 5 assessed the test-retest reliability of the LSSS with an independent sample of 37 youth sport participants. Each participant completed the scale on two occasions which were two weeks apart. Results revealed that time 1 and time 2 scores were relatively unchanged over this two-week period, providing evidence of test-retest reliability. Phase 3 of this programme of research involved re-testing Benson and Saito’s (2001) framework. Study 6 retested the coaching climate – life skills development – psychological well-being model from Study 1 using the LSSS. Data from 326 youth sport participants suggested that an autonomy supportive coaching climate was positively related to young people learning teamwork, goal setting, time management, emotional skills, interpersonal communication, social skills, leadership, and problem solving and decision making. The total amount of life skills a young person developed through sport was positively related to their self-esteem, positive affect and satisfaction with life. Again, the factor structure and reliability of the scale was supported. The findings from this PhD research suggest that the coaching climate plays an important role in young peoples’ development through sport. Specifically, an autonomy supportive coaching climate was positively related to life skills development and psychological well-being in youth sport participants. This thesis also provides researchers with a valid and reliable measure of life skills development through sport. Future research using the LSSS should examine other factors (e.g., peer relationships) which may promote positive youth development through sport. Additionally, future studies can use the LSSS to examine the efficacy of existing programmes (e.g., the SUPER programme) which teach life skills through sport. Such research will help guide coaches and sports programmes efforts to promote positive youth development through sport.

Doutoramento em Engenharia Eletrotécnica
This PhD Thesis falls within the domain of spectrum engineering and spectrum management, and intends to address current and concrete problems, with which, regulators have to deal. Particularly, the definition of technical conditions to be met by radio systems, which will operate in specific bands, selected to introduce novel concepts such as flexibility and technological neutrality. The Block Edge Mask approach was adopted to define technical conditions of operation, in those bands. However, this model, based on spectral masks, which are defined in the frequency domain, do not take into account the transient behavior or time-varying characteristics of signals used by emerging radio communication systems. Furthermore, measurement methodologies developed for validation of technical parameters associated to these models, which are recommended by international bodies, potentially lead to practical issues that must be scrutinized. Thus, alternative time-frequency mixed domain signal processing techniques are explored, in this thesis, to be used for assessing the compliance of radio systems operating under such constraints.
Esta Tese de Doutoramento insere-se nos domínios da engenharia do espectro e da gestão do espectro radioelétrico, e pretende abordar problemas atuais e concretos com que os reguladores se deparam. Em particular, a definição de condições técnicas a serem cumpridas pelos sistemas rádio que irão operar em determinadas faixas de frequências, selecionadas para a introdução de abordagens de gestão do espectro mais flexíveis e tecnologicamente neutras. O modelo de Máscara Delimitadora de Bloco (Block Edge Mask) foi adotado, a nível europeu, como estratégia de definição de condições técnicas de operação, nessas faixas. Contudo, este modelo, que recorre a restrições que são apenas estabelecidas no domínio da frequência, não entra em linha de conta com comportamentos transitórios ou com a variabilidade temporal de sinais inerentes aos sistemas de radiocomunicações atuais. Para além disso, a medição e validação de parâmetros técnicos associados a estes modelos, conforme definidas nas recomendações internacionais aplicáveis, levantam problemas práticos que importa escalpelizar. Nesse sentido, são exploradas, nesta tese, técnicas alternativas de processamento de sinal no domínio misto tempo-frequência, tendo em vista a sua utilização na avaliação de conformidade dos sistemas rádio em face das restrições aplicáveis.

The contributions of the research are (a) an infrastructure of data acquisition systems that provides the necessary information for an automated EMS system enabling autonomous distributed state estimation, model validation, simplified protection, and seamless integration of other EMS applications, (b) an object-oriented, interoperable, and unified component model that can be seamlessly integrated with a variety of applications of the EMS, (c) a distributed dynamic state estimator (DDSE) based on the proposed data acquisition system and the object-oriented, interoperable, and unified component model, (d) a physically-based synchronous machine model, which is expressed in terms of the actual self and mutual inductances of the synchronous machine windings as a function of rotor position, for the purpose of synchronous machine parameters identification, and (e) a robust and highly efficient algorithm for the optimal power flow (OPF) problem, one of the most important applications of the EMS, based on the validated states and models of the power system provided by the proposed DDSE.

This thesis is concerned with developer project Rezidence Austerlitz in Slavkov u Brna realized by KALÁB company. It describes managing this project from buying the grounds to selling the real estate to clients. At the same time the thesis deals with the project's financing, time schedule and assesses market and construction risks. Part of the work is also analyzing the estate market which adverts to evolution of housing construction across the whole Czech Republic in the past ten years. Progress of real estate construction in Jihomoravský kraj is described in detail, including the progress of estate valuation.

Title: Humanitarian Supply Chain: Improvement of lead time effectiveness and cost-efficiency. A multiple case study on the preparedness stage of humanitarian organizations with their partners. Authors: Ester Lisnati Jayadi, Hugo Richit, Najmus Sadat. Background: 315 natural disasters events were reported, causing 11,804 deaths, affecting 68 million people, and costing US$131.7 billion in economic losses worldwide. This fact emerges the importance of humanitarian organizations (HOs) to act in reducing suffering and improving peoples’ life. However, the greater donations and support to HOs still do not solve this enormous issue at all which forces HOs to pursue greater accountability by improving their effectiveness and efficiency in terms of time and cost in disaster activities, especially in preparedness activities. No single actors like HOs have sufficient resources to solve the disaster problem alone; thus, they need partners to work hand in hand to relieve the suffering. Performance measurement through integration called performance management process is the key to enabling HOs and their partners to achieve the lead time effectiveness and cost-efficiency. Purpose: The purposes of the study are to explore which performance measurements are needed between humanitarian organizations with their partners and to explore how to integrate their relationship to improve lead time effectiveness and cost-efficiency. Then, the suggestions can be made by fulfilling the purposes. Method: A multiple case study by utilizing qualitative data through semi-structured interviews. Findings and Conclusions: RQ 1. What HSC (humanitarian supply chain) performance measurements are needed in the HSC’s preparedness stage in order to achieve the lead time effectiveness and cost efficiency? The performance required measurements in HSC’s preparedness stage to achieve lead-time effectiveness, and cost-efficiency are organizational procedures, learning and evaluation, HO’s mission, feedback, budgeting, fund management, sourcing, human and resource management, IT utilization, infrastructure utilization, human resources utilization, delivery time, knowledge management, information sharing, and employee management. RQ 2. How to integrate the HSC performance management process in the HSC’s preparedness stage to improve the lead time effectiveness and cost efficiency? By implementing a proposed performance management process, aligning vision and mission, trusting each other, utilizing IT technologies, improving the language, and applying standardization in HSC. Keywords: Humanitarian Supply Chain Management. Humanitarian Supply Chain. Preparedness Stage. Natural Disasters. Humanitarian Organizations. Partners. Dyads. Multiple Case Studies. Performance Measurements. Performance Management Process. Supply Chain Process Integration

The Order-to-Delivery (OTD) process covers the entire flow from an initiated order until the productor service is delivered to the customer. OTD is considered a core flow in order to successfully deliverproducts according to the customer’s needs. Therefore, is it of importance to track the performanceof this flow and all intermediary processes through performance measurements. The key to successfulperformance measurement is to identify critical factors that can be measured towards previous states,future goals and industry best-practices. However, there are no standardized solutions that can beadopted. Instead performance measurements need to be adapted to each organization’s capabilities,limitations and needs. Effective performance measures and indicators can help steer a company in theright direction.In this thesis, Scania’s industrial OTD process and the connecting indicators has been studied. Thecompany would like to know possible indicators to measure the performance of this process so that itwill contribute to find deviations, continuous improvement and challenge the current state. There isalso a need to increase the knowledge of how to interpret and manage indicators.The project has been conducted through both empirical and theoretical studies. An extensive literaturereview has been performed. The study concluded in methods and best practices for performancemeasurements, as suggested by literature. It has also resulted in an identification of relevant OTDmeasures. Empirical studies have been conducted through a current state analysis of Scania and acompany comparison. Five companies were interviewed on the topic of performance measurements.The aim was to identify the measurements used in each of the companies OTD flow and to investigateif any best practices could be identified. The empirical findings have been analyzed towards thetheoretical findings in order to find gaps and improvement suggestions. It was found that lead timeand delivery performance are two strategic elements to evaluate a company’s OTD flow.Lastly, a concept has been developed as an additional approach to measure the part of a lead timethat has the largest variance - transport time. The concept can, with further work, be extended toinclude the entire lead time. The aim with the concept was to fairly compare different transport timesagainst each. It is believed that the approach can be used as a tool to visualize the efficiency of thetransport flow to different delivery countries and therefore determine if the country behavesaccording to a normal situation.The thesis has resulted in recommendations to implement new measures that could be of value toScania. Process wise it is suggested to better differentiate between indicators and to improve the waythey are presented and reported. It is also suggested that that quality of the input data and thereporting process for this should be reviewed.
Order till leverans-processen täcker hela flödet från orderläggning tills att slutprodukten eller servicenlevereras till kund. Processen anses vara en kärnprocess och är kritisk för att kunna leverera produkterenligt kundens behov. Det är därför viktigt att följa upp prestandan av detta processflöde genom olikaprestationsmått. För att lyckas med prestationsmätning är det viktigt att identifiera kritiska faktoreroch mäta dessa mot tidigare lägen, framtida mål och best practices inom industrin. Det finns dock ingastandardiserade lösningar som kan implementeras på företag. Prestationsmått måste anpassas eftervarje organisations kapacitet, begränsningar och behov. Genom att använda effektiva prestationsmåttoch indikatorer kan ett företag styras i önskvärd riktning.I detta examensarbete har Scanias industriella order till leverans-process och dess tillhörandeprestationsmått studerats. Företaget har en önskan om att identifiera möjliga indikatorer för att mätaprestandan av order till leverans-processen, vilket kan bidra till att hitta avvikelser, ständigaförbättringar och att utmana den nuvarande situationen. Det finns även ett behov av att ökakunskapen om hur prestationsmått ska tolkas och hanteras.Detta projekt har utförts genom empiriska och teoretiska studier. En omfattande litteraturstudie harresulterat i lämpliga metoder och utföranden gällande prestationsmått. Studien har även resulterat irelevanta order till leverans-mått enligt akademisk forskning. De empiriska studierna har bestått av ennulägesanalys av Scania och en jämförelsestudie där fem olika företag har blivit intervjuade inomämnet prestationsmätning. Målet med jämförelsestudien var att undersöka de olika mått somföretagen använder i sin order till leverans-process och för att ta reda på hur de arbetar medprestationsmätning. De empiriska studierna har analyserats i förhållande till teori, för att hittakunskapsluckor och förbättringsförslag. Det kan konstateras att ledtid och leveransprestanda är tvåstrategiskt viktiga faktorer för att kunna utvärdera ett företags order till leverans-process.Slutligen har ett nytt ledtidskoncept blivit utvecklat. Konceptet mäter transporttiden, vilket är den delav ledtiden där störst variation uppstår. Konceptet har potential att utvecklas och förlängas till att mätaden totala ledtiden. Målet med konceptet var att jämföra olika transporttider på ett rättvist sätt motvarandra. Konceptet kan användas som ett verktyg för att visualisera effektiviteten av transportflödettill olika leveransländer och därav avgöra om landets processer presterar enligt ett normalläge.Examensarbetet har resulterat i rekommendationer om att implementera prestationsmått som kanskapa värde för Scania. Det rekommenderas också att särskilja olika typer av prestationsmått samtförbättra hur dessa presenteras och rapporteras. Även kvalitén på indata och hur data rapporters börses över.

Today, the main research field for the automotive industry is to find solutions for active safety. In order to perceive the surrounding environment, tracking nearby traffic objects plays an important role. Validation of the tracking performance is often done in staged traffic scenarios, where additional sensors, mounted on the vehicles, are used to obtain their true positions and velocities. The difficulty of evaluating the tracking performance complicates its development. An alternative approach studied in this thesis, is to record sequences and use non-causal algorithms, such as smoothing, instead of filtering to estimate the true target states. With this method, validation data for online, causal, target tracking algorithms can be obtained for all traffic scenarios without the need of extra sensors. We investigate how non-causal algorithms affects the target tracking performance using multiple sensors and dynamic models of different complexity. This is done to evaluate real-time methods against estimates obtained from non-causal filtering. Two different measurement units, a monocular camera and a LIDAR sensor, and two dynamic models are evaluated and compared using both causal and non-causal methods. The system is tested in two single object scenarios where ground truth is available and in three multi object scenarios without ground truth. Results from the two single object scenarios shows that tracking using only a monocular camera performs poorly since it is unable to measure the distance to objects. Here, a complementary LIDAR sensor improves the tracking performance significantly. The dynamic models are shown to have a small impact on the tracking performance, while the non-causal application gives a distinct improvement when tracking objects at large distances. Since the sequence can be reversed, the non-causal estimates are propagated from more certain states when the target is closer to the ego vehicle. For multiple object tracking, we find that correct associations between measurements and tracks are crucial for improving the tracking performance with non-causal algorithms.

No
Abstract: In periodic manner the analysts teams are in the process of designing, updating and verifying the situational awareness SWA system. Initially, at the designing stage the risk assessment model has little information about the dynamic environment. Hence, any missing information can directly impact the situational assessment capabilities. With this in mind, researchers relied on various performance metrics in order to verify how well they were doing in assessing different situations. In fact, before measuring the ranking capabilities of the SWA system, the underlying performance metrics should be examined against its intended purpose. In this paper, we have conducted quality based evaluations for the performance metrics, namely "The Ranking Capability Score". The results obtained showed that the proposed performance metrics have scaled well over a number of scenarios. Indeed, from the data fusion perspectives the underlying metrics have adequately satisfied different SWA system needs and configurations.

A thesis submitted in partial fulfillment of the requirements for the degree of Doctor in Information Management, specialization in Statistics and Econometrics
The modelling, measurement, and management of systemic financial stability remains a critical issue in most countries. Policymakers, regulators, and managers depend on complex models for financial stability and risk management. The models are compelled to be robust, realistic, and consistent with all relevant available data. This requires great data disclosure, which is deemed to have the highest quality standards. However, stressed situations, financial crises, and pandemics are the source of many new risks with new requirements such as new data sources and different models. This dissertation aims to show the data quality challenges of high-risk situations such as pandemics or economic crisis and it try to theorize the new machine learning models for predictive and longitudes time series models. In the first study (Chapter Two) we analyzed and compared the quality of official datasets available for COVID-19 as a best practice for a recent high-risk situation with dramatic effects on financial stability. We used comparative statistical analysis to evaluate the accuracy of data collection by a national (Chinese Center for Disease Control and Prevention) and two international (World Health Organization; European Centre for Disease Prevention and Control) organizations based on the value of systematic measurement errors. We combined excel files, text mining techniques, and manual data entries to extract the COVID-19 data from official reports and to generate an accurate profile for comparisons. The findings show noticeable and increasing measurement errors in the three datasets as the pandemic outbreak expanded and more countries contributed data for the official repositories, raising data comparability concerns and pointing to the need for better coordination and harmonized statistical methods. The study offers a COVID-19 combined dataset and dashboard with minimum systematic measurement errors and valuable insights into the potential problems in using databanks without carefully examining the metadata and additional documentation that describe the overall context of data. In the second study (Chapter Three) we discussed credit risk as the most significant source of risk in banking as one of the most important sectors of financial institutions. We proposed a new machine learning approach for online credit scoring which is enough conservative and robust for unstable and high-risk situations. This Chapter is aimed at the case of credit scoring in risk management and presents a novel method to be used for the default prediction of high-risk branches or customers. This study uses the Kruskal-Wallis non-parametric statistic to form a conservative credit-scoring model and to study its impact on modeling performance on the benefit of the credit provider. The findings show that the new credit scoring methodology represents a reasonable coefficient of determination and a very low false-negative rate. It is computationally less expensive with high accuracy with around 18% improvement in Recall/Sensitivity. Because of the recent perspective of continued credit/behavior scoring, our study suggests using this credit score for non-traditional data sources for online loan providers to allow them to study and reveal changes in client behavior over time and choose the reliable unbanked customers, based on their application data. This is the first study that develops an online non-parametric credit scoring system, which can reselect effective features automatically for continued credit evaluation and weigh them out by their level of contribution with a good diagnostic ability. In the third study (Chapter Four) we focus on the financial stability challenges faced by insurance companies and pension schemes when managing systematic (undiversifiable) mortality and longevity risk. For this purpose, we first developed a new ensemble learning strategy for panel time-series forecasting and studied its applications to tracking respiratory disease excess mortality during the COVID-19 pandemic. The layered learning approach is a solution related to ensemble learning to address a given predictive task by different predictive models when direct mapping from inputs to outputs is not accurate. We adopt a layered learning approach to an ensemble learning strategy to solve the predictive tasks with improved predictive performance and take advantage of multiple learning processes into an ensemble model. In this proposed strategy, the appropriate holdout for each model is specified individually. Additionally, the models in the ensemble are selected by a proposed selection approach to be combined dynamically based on their predictive performance. It provides a high-performance ensemble model to automatically cope with the different kinds of time series for each panel member. For the experimental section, we studied more than twelve thousand observations in a portfolio of 61-time series (countries) of reported respiratory disease deaths with monthly sampling frequency to show the amount of improvement in predictive performance. We then compare each country’s forecasts of respiratory disease deaths generated by our model with the corresponding COVID-19 deaths in 2020. The results of this large set of experiments show that the accuracy of the ensemble model is improved noticeably by using different holdouts for different contributed time series methods based on the proposed model selection method. These improved time series models provide us proper forecasting of respiratory disease deaths for each country, exhibiting high correlation (0.94) with Covid-19 deaths in 2020. In the fourth study (Chapter Five) we used the new ensemble learning approach for time series modeling, discussed in the previous Chapter, accompany by K-means clustering for forecasting life tables in COVID-19 times. Stochastic mortality modeling plays a critical role in public pension design, population and public health projections, and in the design, pricing, and risk management of life insurance contracts and longevity-linked securities. There is no general method to forecast the mortality rate applicable to all situations especially for unusual years such as the COVID-19 pandemic. In this Chapter, we investigate the feasibility of using an ensemble of traditional and machine learning time series methods to empower forecasts of age-specific mortality rates for groups of countries that share common longevity trends. We use Generalized Age-Period-Cohort stochastic mortality models to capture age and period effects, apply K-means clustering to time series to group countries following common longevity trends, and use ensemble learning to forecast life expectancy and annuity prices by age and sex. To calibrate models, we use data for 14 European countries from 1960 to 2018. The results show that the ensemble method presents the best robust results overall with minimum RMSE in the presence of structural changes in the shape of time series at the time of COVID-19. In this dissertation’s conclusions (Chapter Six), we provide more detailed insights about the overall contributions of this dissertation on the financial stability and risk management by data science, opportunities, limitations, and avenues for future research about the application of data science in finance and economy.

No
The analysts need timely and accurate information to conduct proactive action over complex situations. Typically, there are thousands of reported activities in real time operation, although, to direct the analysts attentions to the most important one, researchers have designed multiple levels of situational awareness (SWA). Each process lends itself to ranking the most important activities into a predetermined order. According to our best knowledge, less attention has been given to the performance evaluation with regards to the prioritisation stage. Specifically, the performance metric, "The Activity of Interest Scores" has not considered corner cases of different situational assessments needs and configurations. Originally, it had not been designed for measuring the capability of the SWA system. In this paper, we have proposed a new performance metric, as well as a guidance case study for measuring the ranking capability of SWA systems. Our initial result shows that, The Ranking Capability Score has provided an appropriate scoring scheme for different ranking capabilities of SWA systems.

碩士
輔仁大學
電子工程學系
94
Low power consumption is an important factor in designing PC systems. Dynamic power management (DPM) can be an effective approach to reduce power consumption without significantly degrading performance. When a system shuts down or wakes up, it requires extra power consumption. In this paper, we focus on the transition penalty of the power consumption of typical PCs. We built a dynamic power transition model for the transition penalty. According to our model, the estimation of the transition penalty of a PC system can result in more efficient DPM policies.