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1
Alsalous, Osama. "Global Demand Forecast Model." Thesis, Virginia Tech, 2016. http://hdl.handle.net/10919/78331.
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Air transportation demand forecasting is a core element in aviation planning and policy decision making. NASA Langley Research Center addressed the need of a global forecast model to be integrated into the Transportation Systems Analysis Model (TSAM) to fulfil the vision of the Aeronautics Research Mission Directorate (ARMD) at NASA Headquarters to develop a picture of future demand worldwide. Future forecasts can be performed using a range of techniques depending on the data available and the scope of the forecast. Causal models are widely used as a forecasting tool by looking for relationships between historical demand and variables such as economic and population growth. The Global Demand Model is an econometric regression model that predicts the number of air passenger seats worldwide using the Gross Domestic Product (GDP), population, and airlines market share as the explanatory variables. GDP and Population are converted to 2.5 arc minute individual cell resolution and calculated at the airport level in the geographic area 60 nautical miles around the airport. The global demand model consists of a family of models, each airport is assigned the model that best fits the historical data. The assignment of the model is conducted through an algorithm that uses the R2 as the measure of Goodness-of-Fit in addition to a sanity check for the generated forecasts. The output of the model is the projection of the number of seats offered at each airport for every year up to the year 2040.Master of Science
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Ozkaya, Evren. "Demand management in global supply chains." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2008. http://hdl.handle.net/1853/26617.
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Thesis (Ph.D)--Industrial and Systems Engineering, Georgia Institute of Technology, 2009.Committee Chair: Keskinocak, Pinar; Committee Co-Chair: Vande Vate, John; Committee Member: Ferguson, Mark; Committee Member: Griffin, Paul; Committee Member: Swann, Julie. Part of the SMARTech Electronic Thesis and Dissertation Collection.
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Lee, Esther S. M. Massachusetts Institute of Technology. "Global demand transparency in the ABB supply chain." Thesis, Massachusetts Institute of Technology, 2012. http://hdl.handle.net/1721.1/75661.
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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science; and, (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; in conjunction with the Leaders for Global Operations Program at MIT, 2012.Cataloged from PDF version of thesis.
Includes bibliographical references (p. 80).
This paper attempts to provide a solution to a problem facing many multinational firms: the lack of an accessible and comprehensive database for up-to-date component part forecasts. We consider this problem in the context of ABB BU DMPE. After considering various requirements and constraints regarding the consolidation of forecasting information, we propose a novel combination of standardized process and the use of certain IT tools as a first step. After a test run, we discovered that consolidation of forecasting information increases transparency within the supply chain. As a corollary result of our pilot program, we propose that prior to any attempt at consolidation, enforcement of a standardized form and method of forecasting at the local level.
by Esther Lee.
M.B.A.
S.M.
4
Freire, Burgos Edwin R. "Aviation Global Demand Forecast Model Development: Air Transportation Demand Distribution and Aircraft Fleet Evolution." Thesis, Virginia Tech, 2017. http://hdl.handle.net/10919/81313.
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The Portfolio Analysis Management Office (PAMO) for the Aeronautics Research Mission Directorate (ARMD) at NASA Headquarters tasked the Systems Analysis and Concepts Directorate at NASA Langley to combine efforts with Virginia Tech to develop a global demand model with the capability to predict future demand in the air transportation field. A previous study (Alsalous, 2015) started the development of the Global Demand Mode (GDM) to predict air travel demand based on Gross Domestic Product (GDP) and population trends for 3,974 airports worldwide. The study was done from year 2016 to year 2040.
This research project intends to enhance the GDM capabilities. A Fratar model is implemented for the distribution of the forecast demand during each year. The Fratar model uses a 3,974 by 3,974 origin-destination matrix to distribute the demand among 55,612 unique routes in the network. Moreover, the GDM is capable to estimate the aircraft fleet mix per route and the number of flights per aircraft that are needed to satisfy the forecast demand. The model adopts the aircraft fleet mix from the Official Airline Guide data for the year 2015. Once the aircraft types are distributed and flights are assigned, the GDM runs an aircraft retirement and replacement analysis to remove older generation aircraft from the network and replace them with existing or newer aircraft. The GDM continues to evolve worldwide aircraft fleet by introducing 14 new
generation aircraft from Airbus, Boeing, Bombardier, and Embraer and 5 Advanced Technology Aircraft from NASA.Master of Science
The Portfolio Analysis Management Office (PAMO) for the Aeronautics Research Mission Directorate (ARMD) at NASA Headquarters tasked the Systems Analysis and Concepts Directorate at NASA Langley to combine efforts with Virginia Tech to develop a global demand model with the capability to predict future demand in the air transportation field. A previous study (Alsalous, 2015) started the development of the Global Demand Mode (GDM) to predict air travel demand based on Gross Domestic Product (GDP) and population trends for 3,974 airports worldwide. The study was done from year 2016 to year 2040. The previous study done by Alsaous, predicts how many seats will be departing out of the 3,974 airports worldwide. This project intends to use the outputs of the GDM and distribute the seats predicted among the airports. The objective is to predict how many seats will be offered that will be departing from airport “A” and arriving at airport “B”. For this, a Fratar model was implemented. The second objective of this project is to estimate what will the aircraft fleet be in the future and how many flights will be needed to satisfy the predicted air travel demand. If the number of seats going from airport A to airport B is known, then, by analyzing real data it can be estimated what type of aircraft will be flying from airport “A” to airport “B” v and how many flights each aircraft will have to perform in order to satisfy the forecasted demand. Besides of estimating the type of aircraft that will be used in the future, the modeled created is capable of introducing new aircraft that are not part of the network yet. Fourteen new generation aircraft from Airbus, Boeing, Bombardier, and Embraer and 5 Advanced Technology Aircraft from NASA.
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Radcliffe, Nicholas Ryan. "Adjusting Process Count on Demand for Petascale Global Optimization." Thesis, Virginia Tech, 2011. http://hdl.handle.net/10919/36349.
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There are many challenges that need to be met before efficient and reliable computation at the petascale is possible. Many scientific and engineering codes running at the petascale are likely to be memory intensive, which makes thrashing a serious problem for many petascale applications. One way to overcome this challenge is to use a dynamic number of processes, so that the total amount of memory available for the computation can be increased on demand. This thesis describes modifications made to the massively parallel global optimization code pVTdirect in order to allow for a dynamic number of processes. In particular, the modified version of the code monitors memory use and spawns new processes if the amount of available memory is determined to be insufficient. The primary design challenges are discussed, and performance results are presented and analyzed.Master of Science
6
Cao, Yu. "Long-distance procurement planning in global sourcing." Thesis, Châtenay-Malabry, Ecole centrale de Paris, 2015. http://www.theses.fr/2015ECAP0015/document.
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Cette thèse porte sur l’optimisation de l’approvisionnement dans les zones géographiquement lointaines. Au moment de planifier des approvisionnements de matières premières ou de composants dans des pays lointains, la longue distance géographique entre l’acheteur et le fournisseur devient un enjeu essentiel à prendre en compte. Puisque le transport se fait souvent par la voie maritime, le délai d’approvisionnement est si long que les besoins peuvent évoluer pendant la longue période de livraison, ce qui peut engendrer un risque de rupture élevé. Cette thèse présente des approches adaptatives afin d’élaborer des plans d’approvisionnements lointains d’une manière rentable. Tout d’abord, nous proposons un cadre d’adaptation de la planification des approvisionnements lointains. Il déploie des techniques de prévision de la demande et des méthodes d’optimisation d’approvisionnements à horizon glissant. En utilisant ce cadre, nous transformons le problème de la planification sur l’horizon globale en plusieurs problèmes standards de lotissement avec demandes stochastiques sur des sous-horizons. Ce cadre permet aussi d’évaluer la performance sur une longue période des méthodes utilisées. Nous considérons ensuite la planification optimale d’approvisionnement sur les sous-horizons. Deux hypothèses de ruptures de stocks sont considérées: livraison tardive et vente perdue (ou sous-traitance). Nous développons des approches optimales ou quasi-optimales pour faire des plans d’approvisionnement tout en minimisant les coûts totaux prévus de commande, de stockage et de rupture sur les sous-horizons. Les méthodes proposées peuvent servir de repères pour évaluer d’autres méthodes. Pour chaque hypothèse, nous menons des expériences numériques pour évaluer les algorithmes développés et les approches adaptatives de planification globales. Les résultats expérimentaux montrent bien leur efficacitéThis research discusses procurement planning problems engaged in global sourcing. The main difficulty is caused by the geographically long distance between buyer and supplier, which results in long lead times when maritime transport is used. Customer demands of finished products usually evolve during the shipment, thus extra costs will be produced due to unpredictable overstocks or stockouts. This thesis presents adaptive planning approaches to make adequate long-distance procurement plans in a cost-efficient manner. Firstly, an adaptive procurement planning framework is presented. The framework deploys demand forecasting and optimal planning in a rolling horizon scheme. In each subhorizon, demands are assumed to follow some known distribution patterns, while the distribution parameters will be estimated based on up-to-date demand forecasts and forecast accuracy. Then a portable processing module is presented to transform the sub-horizon planning problem into an equivalent standard lot-sizing problem with stochastic demands.Secondly, optimal or near-optimal procurement planning methods are developed to minimize expected total costs including setup, inventory holding and stockout penalty in subhorizons. Two extreme stockout assumptions are considered: backorder and lost sale (or outsourcing). The proposed methods can serve as benchmarks to evaluate other methods. Numerical tests have validated the high efficiency and effectiveness of both sub-horizon planning methods and the overall adaptive planning approaches
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McElroy, Wade Allen. "Demand prediction modeling for utility vegetation management." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/117973.
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Thesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, in conjunction with the Leaders for Global Operations Program at MIT, 2018.Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, in conjunction with the Leaders for Global Operations Program at MIT, 2018.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 63-64).
This thesis proposes a demand prediction model for utility vegetation management (VM) organizations. The primary uses of the model is to aid in the technology adoption process of Light Detection and Ranging (LiDAR) inspections, and overall system planning efforts. Utility asset management ensures vegetation clearance of electrical overhead powerlines to meet state and federal regulations, all in an effort to create the safest and most reliable electrical system for their customers. To meet compliance, the utility inspects and then prunes and/or removes trees within their entire service area on an annual basis. In recent years LiDAR technology has become more widely implemented in utilities to quickly and accurately inspect their service territory. VM programs encounter the dilemma of wanting to pursue LiDAR as a technology to improve their operations, but find it prudent, especially in the high risk and critical regulatory environment, to test the technology. The biggest problem during, and after, the testing is having a baseline of the expected number of tree units worked each year due to the intrinsic variability of tree growth. As such, double inspection and/or long pilot projects are conducted before there is full adoption of the technology. This thesis will address the prediction of circuit-level tree work forecasting through the development a model using statistical methods. The outcome of this model will be a reduced timeframe for complete adoption of LiDAR technology for utility vegetation programs. Additionally, the modeling effort provides the utility with insight into annual planning improvements. Lastly for later usage, the model will be a baseline for future individual tree growth models that include and leverage LiDAR data to provide a superior level of safety and reliability for utility customers.
by Wade Allen McElroy.
M.B.A.
S.M.
8
Cao, Z. "Modelling economic interdependencies of international tourism demand : the global vector autoregressive approach." Thesis, University of Surrey, 2016. http://epubs.surrey.ac.uk/810483/.
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Tourism demand is one of the major areas of tourism economics research. The current research studies the interdependencies of international tourism demand across 24 major countries around the world. To this end, it proposes to develop a tourism demand model using an innovative approach, called the global vector autoregressive (GVAR) model. While existing tourism demand models are successful in measuring the causal effects of economic variables on tourism demand for a single origin-destination pair, they tend to miss the spillover effects onto other countries. In the era of globalisation, tourism destinations become interdependent on each other. Impacts of a distant event can be transmitted across borders and be felt globally. Hence, modelling international tourism demand requires one to go beyond a particular origin-destination pair, and take into account the interdependencies across multiple countries. The proposed approach overcomes the ‘curse of dimensionality’ when modelling a large set of endogenous variables. The empirical results show that, to different extents, co-movements of international tourism demand and of macroeconomic variables are observed across all the 24 countries. In the event of a negative shock to China’s real income level and that to China’s own price level, it is found that in the short run, almost all countries will face fluctuations in their international tourism demand and their own price. But in the long run the shocks will impact on developing countries and China’s neighbouring countries more deeply than on developed countries in the West. The current research contributes to the knowledge on tourism demand. It models tourism demand in the setting of globalisation and quantifies the interdependencies across major countries. On the practical front, tourism policy makers and business practitioners can make use of the model and the results to gauge the scale of impacts of unexpected events on the international tourism demand of their native markets.
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Holbrook, Blair Sato. "Point-of-sale demand forecasting." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/104397.
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Thesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, 2016. In conjunction with the Leaders for Global Operations Program at MIT.Thesis: S.M. in Engineering Systems, Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, 2016. In conjunction with the Leaders for Global Operations Program at MIT.
Cataloged from PDF version of thesis.
Includes bibliographical references (page 38).
Nike Always Available (AA) is a significant global business unit within Nike that allows retail customers to purchase athletic essentials at weekly replenishment intervals and 95% availability. However, demand fluctuations and current forecasting processes have resulted in frequent stock-outs and inventory surpluses, which in turn affect revenue, profitability, and brand trust. Potential root causes for demand fluctuations have included: -- Erratic customer behavior, including unplanned promotional events, allocation of open-to- buy dollars for futures (i.e., contract) versus replenishment (i.e., AA), and product inventory loading to protect from anticipated stock-outs; -- Lack of incentives and accountability to encourage accurate forecasting by customers. Current forecasting processes, which utilize historical sell-in data (i.e., product sold to retail customers) were found to be significantly inaccurate - 100% MAPE. The goal of this project was to develop a more accurate forecast based on historical sell-through data (i.e., product sold to consumers), which were recently made available. Forecast error was drastically reduced using the new forecasting method - 35% MAPE. A pilot was initiated with a major retail customer in order to test the new forecast model and determine the effects of a more transparent ordering partnership. The pilot is ongoing at the time of thesis completion.
by Blair Sato Holbrook.
M.B.A.
S.M. in Engineering Systems
10
Bornelind, Patrik. "Challenges in forecasting management for global companies." Thesis, KTH, Skolan för industriell teknik och management (ITM), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-264218.
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In today’s fast-moving world, a company´s ability to align with changes in the market is becoming a major competitive factor. Demand forecasting form the basis of all supply chain planning and is a process that companies often fail to recognize as a key contributor to corporate success. Different contexts and market dynamics creates different challenges for companies to overcome in order to have an efficient forecasting process, matching demand with supply. This master thesis looks at the whole forecasting process, also called forecasting management, at a decentralized global company to identify the main challenges within the process and propose recommendations on how to overcome them. The research is based on a single case study where the forecasting process is investigated using four different dimensions: Functional Integration, Approach, Systems and Performance Measurements. The study identified twelve challenges in the forecasting process where a majority can be connected to issues within information sharing and lack of support in the process. Based on the identified challenges, eight improvement suggestions where developed to target the challenges and improving the process for a decentralized global company.I dagens snabbt utvecklande och växande landskap så är ett företags förmåga att anpassa sig till marknadens behov en betydande konkurrensfaktor. Säljprognoser utgör grunden för all planering inom försörjningskedjan och är en process som företag ofta inte erkänner som en viktig bidragsgivare till företagets framgång. Olika marknadslandskap och förutsättningar skapar olika utmaningar för företag att bemästra för att kunna bedriva ett effektivt prognosarbete och matcha efterfrågan med utbud. Detta examensarbete tittar på hela prognosprocessen, även kallad prognoshantering, hos ett decentraliserat globalt företag för att identifiera de viktigaste utmaningarna i processen och föreslå rekommendationer om hur man kan övervinna dem. Forskningen bygger på en enda fallstudie där prognosprocessen undersöks utifrån fyra olika dimensioner: Funktionell integration, strategi, system och prestandamätningar. Studien identifierade tolv utmaningar i prognosprocessen där en majoritet kan kopplas till utmaningar inom informationsdelning och brist på stöd i processen. Baserat på de identifierade utmaningarna utvecklades åtta förbättringsåtgärder för att övervinna utmaningarna och förbättra processen för ett decentraliserat globalt företag.
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Zehavi, Limor (Limor Hadas). "Evaluating demand planning strategy in the retail channel." Thesis, Massachusetts Institute of Technology, 2012. http://hdl.handle.net/1721.1/73401.
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Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Engineering Systems Division; in conjunction with the Leaders for Global Operations Program at MIT, 2012.Cataloged from PDF version of thesis.
Includes bibliographical references (p. 77-78).
In 2007 Dell began selling through the retail channel. Five years later, the retail channel is still in the early stages relative to competitors and is growing rapidly. Short product lifecycles, long lead times and a high mix of configurations create a complex supply chain. Dell had decided to introduce a Build to Plan fulfillment strategy. Dell has been known for its direct consumer strategy and the retail channel adds an additional stage in the supply chain between Dell and the end consumer. This change impacts the visibility of true demand, resulting in the bullwhip effect. Better collaborative processes such as S&OP and CPFR can improve the channel forecast accuracy, inventory levels, on time delivery, and sales revenue. The personal computer industry has become commoditized; promotions and price have a high impact on the end consumer's purchase decision. Long lead times and high price fluctuation increase uncertainty. Forecasting at a SKU level is challenging and inaccurate. An aggregated approach can reduce the variability and postpone the customization. Forecast accuracy is a key metric that can be used to improve the supply chain. To improve that metric, the appropriate forecast must be tracked and compared to the actual sales. A significant portion of any new planning process will need to account for updated software tools. These tools can help Dell's demand planners facilitate weekly conversations with retailers and ensure more accurate tracking of appropriate demand signals for forecasting. The current product portfolio that allows high flexibility to retailers does not fit the low margins of the product. Demand segmentation can identify which SKU have high volatility and offer a different supply chain strategy for those with low volumes, or spotlight high costs in offering those SKU.
by Limor Zehavi.
S.M.
M.B.A.
12
Koul, Ashish 1979. "Device-oriented telecommunications customer call center demand forecasting." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/90787.
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Thesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, 2014. In conjunction with the Leaders for Global Operations Program at MIT.Thesis: S.M., Massachusetts Institute of Technology, Engineering Systems Division, 2014. In conjunction with the Leaders for Global Operations Program at MIT.
Cataloged from PDF version of thesis.
Includes bibliographical references (page 53).
Verizon Wireless maintains a call center infrastructure employing more than 15,000 customer care representatives across the United States. The current resource management process requires a lead time of several months to hire and train new staff for the customer rep position. To ensure that call center resources are balanced with customer demand, an accurate forecast of incoming call volume is required months in advance. The standard forecasting method used at Verizon relies on an analysis of aggregate call volume. By analyzing the growth trend of the customer base and the month-upon-month seasonal fluctuations within each year, the total incoming call volume is predicted several months in advance. While this method can yield solid results, it essentially groups all customers into a single category and assumes uniform customer behavior. Given the size of the Verizon customer base, forecast inaccuracy in the current process can lead to resource allocation errors on the order of tens of thousands of labor hours per month. This thesis proposes a call forecasting model which segments customers according to wireless device type. By taking into consideration customer behavior on a per device basis and accounting for the continuous churn in mobile devices, there is the potential to create a forecasting tool with better accuracy. For each device model, future call volumes are estimated based upon projected device sales and observed customer behavior. Aggregate call volume is determined as the sum across all device models. Linear regression methods are employed to construct forecast models for each of the top 20 mobile devices (those which generate the most customer service calls) using historical device data. The aggregate call volume forecast for these top 20 devices is benchmarked against the standard forecast currently in use at Verizon to validate the reliability of the new approach. Furthermore, device-oriented analytics processes developed for this project will enable Verizon to build a rich library of device data without additional staff or resource investments. By incorporating device-oriented data analysis into the call volume forecasting process, Verizon Wireless hopes to improve forecast accuracy and staff planning, effectively maintaining service levels while reducing overall staffing costs at call centers.
by Ashish Koul.
M.B.A.
S.M.
13
Hrabovsky, Ellen E. "Global demand for certified hardwood products as determined from a survey of hardwood exporters." Morgantown, W. Va. : [West Virginia University Libraries], 2003. http://etd.wvu.edu/templates/showETD.cfm?recnum=3074.
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Thesis (M.S.)--West Virginia University, 2003.Title from document title page. Document formatted into pages; contains ix, 61 p. : ill. (some col.), col. map. Vita. Includes abstract. Includes bibliographical references (p. 28-30).
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Bonnefoi, Tatiana (Bonnefoi Monroy). "Demand forecast for short life cycle products : Zara case study." Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/74454.
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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering; and, (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; in conjunction with the Leaders for Global Operations Program at MIT, 2010.Cataloged from PDF version of thesis.
Includes bibliographical references (p. 79-80).
The problem of optimally purchasing new products is common to many companies and industries. This thesis describes how this challenge was addressed at Zara, a leading retailer in the "fast fashion" industry. This thesis discusses the development of a methodology to optimize the purchasing process for seasonal, short life-cycle articles. The methodology includes a process to develop a point forecast of demand of new articles, the top-down forecast at the color and size level and an optimization module to produce recommendations to define the optimal quantity to purchase and the optimal origin to source from. This thesis is the first phase of a two phases purchasing optimization process. The focus of this thesis is: a) the outline of an enhanced purchasing methodology b) the development of the most important input in the system: a point forecast of demand at the article, color, and size level, and c) the development of an IT prototype to automatically manage the purchasing methodology. The second phase of the purchasing optimization process focuses on the optimization module. The optimization module is beyond the reach of this thesis.
by Tatiana Bonnefoi.
M.B.A.
S.M.
15
Rodrigues, Erlana Castro. "Global Branding Roadmap: identificação dos fatores determinantes e proposição de um modelo conceitual para a gestão estratégica de marcas em âmbito global." Escola Superior de Propaganda e Marketing, 2015. http://tede2.espm.br/handle/tede/279.
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Decisions on the international branding strategy are among the most researched themes in global branding, but there is no predominant theoretical framework that guides research in this academic field, nor in its management practice. The present study aims to propose a conceptual model for global brand management based on the identification of the critical factors that influence this activity. To reach the proposed goal the research is divided into two stages. In the deductive stage a systematic review of the most cited articles in the field of global branding is carried out to identify the determining factors 'a priori'. At the inductive stage, 14 experienced global brand managers of different nationalities are interviewed in order to identify critical factors that emerge from their perspective and practice. Confronting literature with management practice, it is also possible to conclude that main theoretical principles of global branding are still valid and under discussion; even though globalization‘s current phase is presenting a much more complex business environment than it was predicted 30 years ago, when the theoretical foundations of this field were created. The main contribution of this study is the establishment of a relationship between internal and external determinant factors and its influence on the global management of brands. The aim is to contribute to the formulation of a theory on Global Branding.
As decisões sobre a estratégia internacional de marcas figuram entre as questões mais pesquisadas no âmbito do Global Branding, porém não há um framework teórico predominante que oriente as pesquisas no campo acadêmico, tampouco a prática gerencial. O presente estudo tem objetivo propor um modelo conceitual para gestão global de marcas a partir da identificação dos fatores que influenciam esta atividade de maneira crítica. Para alcance do objetivo proposto a pesquisa é dividida em duas etapas. Na etapa dedutiva realiza-se uma revisão sistemática de literatura dos artigos mais citados no campo de Global Branding para identificação dos fatores determinantes ‗a priori‘. Na etapa indutiva, são entrevistados 14 gestores de diferentes nacionalidades, com atuação e experiência na gestão global de marcas a fim de identificar fatores críticos emergentes. Por meio do método de análise de conteúdo qualitativa dirigida é proposto o modelo conceitual. Confrontando a literatura com a prática gerencial conclui-se que os princípios teóricos do Global Branding são válidos e continuam em discussão; não obstante a atual fase da globalização apresente um ambiente de negócios mais complexo do que era possível prever há 30 anos, quando as bases teóricas do campo foram criadas. A principal contribuição alcançada foi o estabelecimento da relação entre fatores determinantes internos e externos à empresa e sua influência sobre a gestão global de marcas. Busca-se assim contribuir com a formulação de uma teoria sobre Global Branding.
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Kong, Evelyne L. "Cannibalization effects of products in Zara's stores and demand forecasting." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/98728.
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Thesis: S.M., Massachusetts Institute of Technology, Engineering Systems Division, 2015. In conjunction with the Leaders for Global Operations Program at MIT.Thesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, 2015. In conjunction with the Leaders for Global Operations Program at MIT.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 60-61).
Because of short product life cycles, large product offerings and fickle consumer tastes, product demand in the apparel retail industry is volatile and difficult to predict. It is also thought to be impacted significantly by the presence of other products in the store, which may cannibalize or complement sales. This thesis explores the interactions among products in Zara's stores, and uses this information to improve the existing demand forecast. It focuses on attribute-based and price-based cannibalization effects for the trousers product category. A necessary preliminary step to study cannibalization is to obtain an accurate classification of all products according to distinct features. We present such a classification for the trousers product category according to three main characteristics-fit, color and color lightness. The text mining and picture color detection techniques used here are of independent interest and can be used for the classification of other product categories. We then use Zara's RFID system and sales databases to estimate cannibalization effects among products in stores. To this purpose, we introduce similarity factors and define a linear regression model with fixed and time effects. We obtain statistically significant cannibalization effects. However, these effects tend to account for only a small portion of demand variations. Finally, we propose various improvements to the demand forecast method, incorporating display, availability and cannibalization effects. The resulting demand forecast represents a significant improvement over the base forecasting model. It reduces the forecasting error by over 10% for the sample stores in Madrid, Barcelona and London and by over 7% with only partial display information. Thus the implementation of the present work will reduce overstock and lost sales. It also constitutes the first step towards product assortment optimization at Zara.
by Evelyne L. Kong.
S.M.
M.B.A.
17
Niles, Augusta (Augusta L. ). "Stochastic capacity modeling to support demand/capacity gap planning." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/90770.
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Thesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, 2014. In conjunction with the Leaders for Global Operations Program at MIT.Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2014. In conjunction with the Leaders for Global Operations Program at MIT.
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Cataloged from PDF version of thesis.
Includes bibliographical references (pages 62-63).
Capacity strategy has established methods of dealing with uncertainty in future demand. This project advances the concept of capacity strategy under conditions of uncertainty in cases where capacity is the primary source of uncertainty. Novartis Vaccines, one of five divisions of Novartis AG, produces nearly two dozen vaccines which are offered in syringes, vials, multi or single pack, and multi or single dose and delivered in language-specific packaging to countries all over the world. Bexsero is a new product in 2013. As demand for Bexsero and other products increases over the next ten years, the production lines used to package them will need to accommodate more and more volume. Capacity planning compares capacity gaps between future demand and current estimated capacity. Because of recurring shortfalls in production relative to planned capacity, current estimates of capacity are not trusted for long-term planning. Understanding how international product demand will be allocated to each production line and what drives current capacity limitations will help Novartis Vaccines prioritize investment to optimally develop this capacity over time. Thus, the purpose of this model is to establish baseline capacity estimates using historical data and allow for the simulation of new production scenarios in order to demonstrate the impact of production policy on mean and variance of capacity over a specified time horizon. Incorporating simulated results produces a mean and standard deviation of capacity we are likely to see. Long-term demand was assessed, capacity versus peak demand views were created, and production scenarios were simulated on a single line/product/format basis over the time horizon to determine expected capacity. Recommendations were made for each of the pre-filled syringe, multi-format, and vial format lines and these results were used to shape an overall packaging capacity development plan.
by Augusta Niles.
M.B.A.
S.M.
18
Wright, Meghan Savage. "An analysis and mitigation of demand variability on external supply chains." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/122267.
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Thesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, 2019, In conjunction with the Leaders for Global Operations Program at MITThesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2019, In conjunction with the Leaders for Global Operations Program at MIT
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 115-117).
In the pharmaceutical industry, increasing product complexity, shifts towards specialty medicine and growth in emerging markets have resulted in increased forecast variation and manufacturing complexity for new products. In the past six years, AstraZeneca has outperformed its peers in research and development productivity, increasing the number and speed of product launches. The resulting demand variability and shifting operational environment have led to financial and non-financial impacts, such as poor inventory performance and strained supplier relationships. The objective of this research is to identify processes and procedures that amplify the impact of demand variability and the areas in the end-to-end operation that are significantly impacted. The secondary objective is to identify process improvements in the existing system and develop strategies to mitigate the risk of demand variability.
This thesis presents an analysis of the impact of demand variability on the external manufacturing and supply chain operations for new products. A case study approach is used to assess its impact on the forecast processes, manufacturing systems and supplier relationships. A simulation tool was developed as a method to measure financial impact based on inventory performance. The simulation was expanded for use as a decision assist tool to evaluate test cases developed from the current state analysis. The research illustrates that the end-to-end manufacturing and supply chain operation is experiencing significant bullwhip effects for new products. The primary sources of financial impacts are the policy stock requirements tied to monthly demand and segmentation of the supply chain causing different forecasts to be used for certain stages. Non-financial impacts include loss of trust with suppliers, manually managed complexity and limited communication resulting in the bullwhip effect.
The short-term and long-term recommendations focus on increased operational transparency and scenario-based forecast planning to mitigate the impact of demand variability on the system. Pilot programs for statistical process control implementation in drug substance manufacturing and development of a future state commercial partnership model were defined as follow-up work to this research
by Meghan Savage Wright.
M.B.A.
S.M.
M.B.A. Massachusetts Institute of Technology, Sloan School of Management
S.M. Massachusetts Institute of Technology, Department of Mechanical Engineering
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Arthur, Joseph E. "Global Broadcast Service reach back via ultra high frequency demand assigned multiple access satellite communications." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1998. http://handle.dtic.mil/100.2/ADA349710.
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Thesis (M.S. in Systems Technology) Naval Postgraduate School, June 1998."June 1998." Thesis advisor(s): Paul H. Moose, Roy A. Axford. Includes bibliographical references (p. 57-58). Also available online.
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Alhawari, Omar Ibrahim Salem. "Global Supply Chain Design Under Stochastic Demand Considering Manufacturing Operations and the Impact of Tariffs." Ohio University / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1565388377821285.
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Garro, Andres. "New product demand forecasting and distribution optimization : a case study at Zara." Thesis, Massachusetts Institute of Technology, 2011. http://hdl.handle.net/1721.1/66072.
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Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering; in conjunction with the Leaders for Global Operations Program at MIT, 2011.Cataloged from PDF version of thesis.
Includes bibliographical references (p. 191-194).
The problem of optimally distributing new products is common to many companies and industries. This thesis describes how this challenge was addressed at Zara, a leading retailer in the "fast fashion" industry. The thesis discusses the development and evaluation of a modular system including distributional demand forecasting and dynamic programming distribution optimization. The demand forecasting module combined the practice of using similar products to predict the demand of a new product with a new store or customer cluster data aggregation scheme. Moreover, distributional forecasts were generated using a generic distribution of the expected relative forecast error constructed based on historical forecast performance. Finally, an empirical study of expert or qualitative forecasting within Zara was performed to evaluate the potential for forecast improvement. The distribution optimization module leveraged the distributional forecasts and dynamic programming to determine the optimal initial shipment quantities. The dynamic program directly accounted for the inventory constraints as well as the information dynamics that result from the improvement in forecast accuracy after initial sales are observed. The complete system was validated using extensive simulation. Overall, the new demand forecast reduced forecasting error by over 30% and the final simulation results showed that the overall system would be expected to improve initial sales by over 12%. Given Zara's scale, these results would translate to hundreds of millions in additional profit. Thus, a live pilot was approved and initiated by Zara with the goal of confirming the simulated impact of the system under real conditions. Assuming a successful pilot, full system implementation is expected in 2011.
by Andres Garro.
S.M.
M.B.A.
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Wei, Mengdi, and Yang Liu. "Key Factors and Key Obstacles in Global Supply Chain Management : A Study in Demand Planning Process." Thesis, Högskolan i Gävle, Avdelningen för Industriell utveckling, IT och Samhällsbyggnad, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-15964.
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In Recent years, global supply chain management has been a popular study area due to the economic globalization. This study mainly focus on the demand planning process of demand management in global supply chain management. The purpose of this thesis is to find the key factors and obstacles in demand planning process both in theory and practice, and solutions for the obstacles. Based on many scholar researches, a brief introduction of demand management and demand planning has been made at the beginning of the theoretical framework. Key factors, key obstacles and solutions are collected and clarified from empirical study and scholar researches in the theoretical framework. Qualitative approach is adopted as basic approach. We use case study to do the research and interviews to collect data. A server manufacturer of IBM named ISTC (International System Technology Corporation) is chosen as a case for this research. The key factors, obstacles and improvements of empirical study are compared with the facts of ISTC. New factors, obstacles and their new descriptions are figured out through the comparison between theory and the fact of the case. A suggestion for improvement and solution for the demand planning process is also put forward based on the empirical study and the facts of ISTC by this method.
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Liu, Junxian. "Molecular Design of High-Performance Materials for Electrocatalysis." Thesis, Griffith University, 2022. http://hdl.handle.net/10072/416315.
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The development of green and efficient electrocatalysis, which targets the generation and storage of renewable energy by transforming electrical energy to chemical energy, is strongly driven by the challenges we face in increasing energy demand. Consequently, great efforts have been made in exploring efficient electrocatalysts. The conventional trial-and-error approach for electrocatalysts is timeconsuming due to the lack of direct information regarding the atomic-scale properties of electrocatalysts and the underlying elementary reaction mechanisms. To date, the rational molecular design of high-performance electrocatalysts has been extensively used. However, most of these computational studies are still in their infancy and more reliable modelling of electrochemical processes is needed to bridge the gap between experiments and theory. This thesis aims to utilize structural engineering at the atomic scale to develop high-performance electrocatalysts for hydrogen evolution reaction (HER) and chlorine evolution reaction (CER), and model the external factors of the operating environment to provide a better description of electrocatalytic processes.
The general background and objectives of this PhD project are presented in Chapter 1. The recent progress in numerical modelling of electrochemical reactions and processes is discussed in Chapter 2. The importance of theoretical identification and understanding of catalytic active sites is highlighted in this chapter. The computational method employed in this project is the density functional theory (DFT), which has been demonstrated to achieve increasing success in the description and understanding of the II complexity of electrocatalysis. Chapter 3 provides a short introduction of the DFT method, including its origin, development, and implementation.
Chapters 4-7 present all the research work completed for this project. As metalorganic frameworks (MOFs) are considered a large family of low-dimensional materials, a comprehensive computational study was conducted to investigate the structural properties and electronic properties of one-dimensional (1D) transition metalbased dithiolene MOFs. Their high electrical conductivities offer the potential for electrocatalytic hydrogen evolution, which is examined with the consideration of electrolyte effects in Chapter 4. As the one of main industrial reactions, CER electrolysis is challenging due to the selectivity of Cl2. This can be ascribed to the unavoidable oxygen evolution from the noble metal-based dimensionally stable anodes (DSAs) used in industry. To this end, six TMN4 complex embedded graphene (TMN4@G) single-atom catalysts (SACs) were systematically investigated in Chapter 5. The DFT results predicted that NiN4@G is a promising candidate for efficiently and selectively catalyzing chlorine evolution in acidic solution. Chapter 6 theoretically studied the performance of CER for eight two-dimensional (2D) semiconducting group- VA monolayers with α and β phases. It is suggested that β-arsenene monolayer exhibits high activity and selectivity of gaseous Cl2 generation by virtue of the expected Cl* precursor. In Chapter 7, three low-dimensional Fe/Co/Ni−dithiolene MOFs were purposely selected due to their acid resistance and comprehensively investigated for electrocatalytic CER. The calculated results demonstrate that Ni-based dithiolene MOF can efficiently catalyze the CER via the Cl* pathway.
This thesis makes significant contributions to the theoretical understanding of electrochemical processes, materials science, and electrochemical energy conversion and storage through: (i) demonstrating the importance of electronic configurations of metal cations in the electrical conductivities of transition metal-dithiolene MOFs; (ii) proposing a novel strategy for optimizing the electronic structure of materials on the basis of the resonant charge transfer mechanism; (iii) predicting efficient lowdimensional electrocatalysts for Cl2 evolution with the Cl* intermediate rather than the ClO* intermediate; and (iv) investigating the interactions between adsorbates and catalysts to provide a new descriptor for the discovery of high-performance CER electrocatalysts. It is worth noting that the studies on the electrocatalytic properties of low-dimensional materials are still in the early stage. As such, more accurate models and approaches combined with multiscale simulation are needed in future studies, such as the modelling of the electrode-electrolyte interface, dynamic solvent, and electrical double layer.Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Environment and Sc
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García, José M. (José Manuel). "Demand forecasting at Zara : a look at seasonality, product lifecycle and cannibalization." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/90163.
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Thesis: S.M., Massachusetts Institute of Technology, Engineering Systems Division, 2014. In conjunction with the Leaders for Global Operations Program at MIT.Thesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, 2014. In conjunction with the Leaders for Global Operations Program at MIT.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 78-79).
Zara introduces 10,000 new designs every year and distributes 5.2 million clothing articles per week to a network of over 1925 stores in more than 86 countries. Their high product mix and vast global network makes demand forecasting for Zara a challenging endeavor. This thesis sets out to incorporate the effects from seasonality, product lifecycle, and cannibalization into a long term aggregate demand forecast and a short term SKU replenishment forecast. For seasonality, there are two categories of events that are explored in detail: 1) Macro patterns, which are the year to year sales patterns that remain fairly consistent, such as rising sales in spring; and, 2) Specific Events, which refers to events that have an impact on demand but shift dates from one year to the next, such as Easter or Ramadan. These two factors are used to forecast short and long term aggregated store demand by using regression that leverages historical demand with dummy variables for specific events. Product lifecycle and cannibalization are incorporated in the SKU demand forecast. Products at Zara experience a majority of their sales in the first few weeks in the store. For this reason, when forecasting demand for replenishment purposes, it is of paramount importance to understand: 1) How long the item has been in a store; and, 2) how many new items are being displayed for the first time at the store on the week in question. This thesis details a methodology that successfully uses regression to incorporate both of those components. In addition to detailing the methods for-forecasting demand this thesis also covers: an overview of the current forecasting methodology and the special characteristics of Zara's demand; a results section which detail reductions in forecast error from 21% to 17%. This has the potential to reduce lost sales by 24%; lastly, it details implementation efforts at Zara.
by Jose M. Garcia.
S.M.
M.B.A.
25
Zhang, Jingshu Ph D. Massachusetts Institute of Technology. "A bottom-up prospective dynamic materials flow assessment for platinum group metals (PGM) global demand forecast." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/93048.
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Thesis: S.M., Massachusetts Institute of Technology, Department of Materials Science and Engineering, 2014.Cataloged from PDF version of thesis.
Includes bibliographical references (pages 71-77).
by Jingshu Zhang.
S.M.
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Pradhan, Prajal [Verfasser], Jürgen [Akademischer Betreuer] Kropp, Günther Akademischer Betreuer] Fischer, and Ariane [Akademischer Betreuer] [Walz. "Food demand and supply under global change / Prajal Pradhan ; Jürgen Peter Kropp, Günther Fischer, Ariane Walz." Potsdam : Universität Potsdam, 2015. http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-77849.
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Pradhan, Prajal [Verfasser], Jürgen Peter [Akademischer Betreuer] Kropp, Günther [Akademischer Betreuer] Fischer, and Ariane [Akademischer Betreuer] Walz. "Food demand and supply under global change / Prajal Pradhan ; Jürgen Peter Kropp, Günther Fischer, Ariane Walz." Potsdam : Universität Potsdam, 2015. http://d-nb.info/1218399244/34.
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Ryan, Tim. "In Harmony : Virtual Power Plants: Predicting, Optimising and Leveraging Residential Electrical Flexibility for Local and Global Benefit." Thesis, KTH, Energiteknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-285482.
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Electrical demand flexibility is a key component to enabling a low cost, low carbon grid. In this study, residential electricity demand and flexibility is explored from the lens of a virtual power plant operator. Individual and aggregate asset consumption is analysed using a pool of >10,000 household assets over 6 years. Key safety, comfort and availability limitations are identified per asset type. Pool flexibility is analysed using a combination of past data and principled calculations, with flexibility quantified for different products and methods of control. A machine learning model is built for a small pool of 200 assets, predicting consumption 24 hours in advance. Calculated flexibility and asset limitations are then used within an optimisation model, leveraging flexibility and combining the value of self consumption and day ahead price optimisation for a residential home.Flexibilitet i efterfrågan av elektricitet är essentiellt för att möjliggöra ett elnät med låga kostnader och utsläpp. I denna studie undersöks elanvändning av en bostad samt flexibilitet i perspektiv från en virtuell kraftverksoperatör. Individuell och sammanlagd konsumtion analyseras genom tillgång av data från >10 000 bostäder över 6 år. Begränsningar av säkerhet, komfort och tillgänglighet identifieras per tillgångstyp. Sammanlagda flexibiliteten analyseras genom en kombination av tidigare data och principiella beräkningar, med flexibilitet kvantifierad för diverse produkter och kontrollmetoder. En modell för maskininlärning utvecklades för 200 bostäder och förutser konsumtion 24 timmar i förväg. Den beräknade flexibiliteten och tillgångsbegränsningar används sedan i en optimeringsmodell som utnyttjar flexibilitet och kombinerar värdet av självkonsumtion och optimerat pris för nästkommande dag för ett bostadshus.
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Uriarte, Daniel Antonio. "Developing a framework for dependable demand forecasts in the consumer packaged goods industry." Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/59172.
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Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Engineering Systems Division; in conjunction with the Leaders for Global Operations Program at MIT, 2010.Cataloged from PDF version of thesis.
Includes bibliographical references (p. 104-106).
As a consumer packaged goods company, "Company X" manufactures products "make-to-stock"; therefore, having reliable demand forecasts is fundamental for successful planning and execution. Not isolated to "Company X" or to the CPG industry, current global economic conditions have forced companies to seek increased cash flows as a method for weathering this financially difficult period. As a result, many organizations are pursuing improvements in demand forecasting and planning methodologies as a precursor to inventory optimization and to further liquidity positions. This thesis attempts to improve forecasting and planning processes by developing a framework that focuses on four general components identified as key for success by experts and practitioners. In addition, this thesis explores these components while utilizing "Company X" as the case study for improvement. The four forecasting and planning components explored at "Company X" include Data Treatment, Forecast Models, Planning Process, and Organizational Behavior. In the Data Treatment section, we present implications of data aggregation in forecasting and planning activities, as well as provide a methodology to segment SKU's for prioritization during forecasting and planning. While in the Forecast Models section, we explore various forecasting models applied during different stages of the product lifecycle, and utilize these models under "least error" selection with sales data at different levels of aggregation to determine which combination results in the most accurate forecast. Moreover, in the Planning Process section, we explore the Sales and Operations Planning methodology, and provide a set of best practices to design a planning process that meets the requirements of "Company X". Lastly, in the Organizational Behavior section, we depict the "Company X's" planning process and organization, and highlight some of the behaviors typically observed during forecasting and planning activities. Although most of the proposed components provided forecasting and planning improvements over the legacy method, not all of these were implemented at "Company X". Nevertheless, the implemented improvements provided a forecast error reduction from 17% to 10% over the life of the project. However, these improvements were not equal for all SKU segments, as B segment SKU's, or medium criticality products, benefited the most from the execution of this project.
by Daniel Antonio Uriarte.
S.M.
M.B.A.
30
Zhang, Zhuang. "Optimal foreign reserves, the dollar trap and demand for global safe assets : a DSGE analysis for China." Thesis, Durham University, 2015. http://etheses.dur.ac.uk/11148/.
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The recent surge of foreign reserves in emerging markets has sparked fierce debate about what level of reserves is the optimal amount for a country. Conventional models have achieved important advances in understanding the behaviour of central banks’ reserve policy, but fail to find convincing solutions to the puzzle of why emerging economies, and China in particular, would continue to accumulate massive reserves. With reference to China’s massive hoarding of foreign reserves, this thesis develops a representative agent model with elements of dynamic stochastic general equilibrium (DSGE) modelling. The model constructed in this thesis explicitly considers the risky steady state as the equilibrium point when agents take into account future uncertainty but when the shock realizations are zero. In this risky steady state we derive the optimal reserves for emerging markets, with particular reference to the Chinese case. The precautionary savings motivation for holding reserves is then analysed within this framework. This thesis derives the optimality of Chinese reserve accumulation, and provides a plausible explanation for reserve build-up in China and its underlying driving forces. In order to better understand the foreign reserves accumulation, this thesis further attempts to analyse current external wealth allocation in a portfolio perspective within a DSGE framework. A two-country model is employed, and a Value at Risk (VaR) constraint is introduced to reproduce the risk averse behaviour of investors. After accounting for risk diversification, our findings imply that an investor would shift their portfolio holding to bond related assets. Finally, China has accumulated a huge amount of foreign reserves. The majority of these assets are denominated in the US dollar. Furthermore, in terms of asset type, the US T-bill is the dominant investment instrument in China’s international portfolio choice. This raises questions as to why the central bank of China chooses to make such an investment decision, and what the global repercussions might be. Therefore, China’s role in the growing demand for global safe assets deserves exploration. Given the world-wide shortage of global safe assets, to what extent China will continue the current international investment decision, and the driving forces behind such policy inertia, are major concerns. In order to gain a better understanding, this thesis applies a global solving method, as well as a standard local solving method.
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Smith, Emily (Emily C. ). "Reducing the demand forecast error due to the bullwhip effect in the computer processor industry." Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/59176.
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Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering; in conjunction with the Leaders for Global Operations Program at MIT, 2010.Cataloged from PDF version of thesis.
Includes bibliographical references (p. 70-71).
Intel's current demand-forecasting processes rely on customers' demand forecasts. Customers do not revise demand forecasts as demand decreases until the last minute. Intel's current demand models provide little guidance for judging customer orders when the market changes. As a result, during the economic downturn of Q3 and Q4 '08, Intel's model could not predict how much billings would decrease. The demand forecast had large amounts of error caused by the bullwhip effect (order amplification in a supply chain). This project creates a new demand forecast model in two phases. The first phase investigated the supply chain of OEMs and Retailers. The second phase of the project used the supply chain information discovered in phase one to create a new demand forecast that reduces the error caused by the bullwhip effect. The first phase determined that the average time it takes a CPU to go from Intel to end customer purchase is seventeen weeks. The first phase also indentified ownership of products throughout the supply chain and parties making purchase decisions. The supply chain information was then used in the second phase of the project to create a demand forecast model. The new model is a heuristic model that simulates quarterly purchase decisions of retailers and OEMs including lead times and inventory. The resulting model allows Intel to monitor and react to consumption changes faster than waiting for customers to change their demand forecasts. The model also provides a better forecast during times of change. The model reduces the error due to the bullwhip effect and indentifies early when a downturn or upturn is going to happen in ordering behavior.
by Emily Smith.
S.M.
M.B.A.
32
Fridley, Lila (Lila J. ). "Improving online demand forecast using novel features in website data : a case study at Zara." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/117976.
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Thesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, in conjunction with the Leaders for Global Operations Program at MIT, 2018.Thesis: S.M., Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, in conjunction with the Leaders for Global Operations Program at MIT, 2018.
Cataloged from PDF version of thesis.
Includes bibliographical references (page 77).
The challenge of improving retail inventory customer service level while reducing costs is common across many retailers. This problem is typically addressed through efficient supply chain operations. This thesis discusses the development of new methodologies to predict e-commerce consumer demand for seasonal, short life-cycle articles. The new methodology incorporates novel data to predict demand of existing products through a bottom-up point forecast at the color and location level. It addresses the widely observed challenge of forecasting censored demand during a stock out. Zara introduces thousands of new items each season across over 2100 stores in 93 markets worldwide [1]. The Zara Distribution team is responsible for allocating inventory to each physical and e-commerce store. In line with Zara's quick to retail strategy, Distribution is flexible and responsive in forecasting store demand, with new styles arriving in stores twice per week [1]. The company is interested in improving the demand forecast by leveraging the novel e-commerce data that has become available since the launch of Zara.com in 2010 [2]. The results of this thesis demonstrate that the addition of new data to a linear regression model reduces prediction error by an average of 16% for e-commerce articles experiencing censored demand during a stock out, in comparison to traditional methods. Expanding the scope to all e-commerce articles, this thesis demonstrates that incorporating easily accessible web data yields an additional 2% error reduction on average for all articles on a color and location basis. Traditional methods to improve demand prediction have not before leveraged the expansive availability of e-commerce data, and this research presents a novel solution to the fashion forecasting challenge. This thesis project may additionally be used as a case-study for companies using subscriptions or an analogous tracking tool, as well as novel data features, in a user-friendly and implementable demand forecast model.
by Lila Fridley.
M.B.A.
S.M.
33
Chytilová, Petra. "Inovace a konkurenceschopnost podniku." Master's thesis, Vysoká škola ekonomická v Praze, 2013. http://www.nusl.cz/ntk/nusl-162352.
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This thesis deals with issues related to innovation in the Czech company and their influence on their competitiveness. The aim of the work in the first part is the definition of terms: innovation, competitiveness and demand and their relationships. Attention is paid to the impacts of global competitive and turbulent world and the competitiveness of Czech companies. They show the expected basic trends of the world. The second part describes Czech industrial company (GZ Digital Media, Inc.) and its trends over the past year. It analyzes the development of the basic indicators (profitability, liquidity and business activity) and quantifies the prediction possibilities of bankrupt (Altman index, IN 05). Finally there are definitions of factors of the current success of the business, including its persistent and intense innovation and strong influence of leading figures in business management.
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Goh, Nigel(Nigel Goh Min Feng). "Applications of risk pooling for the optimization of spare parts with stochastic demand within large scale networks." Thesis, Massachusetts Institute of Technology, 2020. https://hdl.handle.net/1721.1/126955.
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Thesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, in conjunction with the Leaders for Global Operations Program at MIT, May, 2020Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, in conjunction with the Leaders for Global Operations Program at MIT, May, 2020
Cataloged from the official PDF of thesis.
Includes bibliographical references (pages 85-86).
Amazon is able to deliver millions of packages to customers every day through its Fulfillment Center (FC) network that is powered by miles of material handling equipment (MHE) such as conveyor belts. Unfortunately, this reliance on MHE means that failures could cripple an entire FC. The exceptionally high stock-out cost associated with equipment failure means spare parts must always available when required. This is made difficult as Amazon does not hold any central repository of inventory at present - all inventory is held at a site-level. Unfortunately, FCs have to stock more inventory than required due to unpredictable failures, long lead times from suppliers, and no standard work processes for site-to-site transfers. However, if Amazon is able to pool its spares across multiple FCs, it has an opportunity to reduce the spares kept across the entire FC network, position itself to better respond to catastrophic failures, and consolidate interfaces with suppliers. The goal of this thesis is to identify the inventory model and network design that would maximize parts availability while minimizing cost. Additionally, an implementation roadmap will be developed to outline how such a system (e.g. hub locations, logistic channels etc.) can be developed. This thesis concludes by proposing potential extensions of the work conducted in this thesis to improve the practicality and financial impact of the proposed network and inventory model.
by Nigel Goh Min Feng.
M.B.A.
S.M.
M.B.A. Massachusetts Institute of Technology, Sloan School of Management
S.M. Massachusetts Institute of Technology, Department of Mechanical Engineering
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Dekel, Shai S. M. Massachusetts Institute of Technology. "Separating signal from noise : material demand forecasting and network simulation in a multi-echelon supply chain." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/120643.
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Thesis: S.M., Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, in conjunction with the Leaders for Global Operations Program at MIT, 2018.Thesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, in conjunction with the Leaders for Global Operations Program at MIT, 2018.
Cataloged from PDF version of thesis. Page 111 blank.
Includes bibliographical references (pages 105-110).
Mismatches between forecasted and actual demand, for construction, repair and maintenance work in a regulated utility, is a growing risk for the performance of the supply chain. High target service levels and high levels of demand uncertainties necessitate Inventory Management to maintain a significant amount of safety stock to buffer against uncertainties. Furthermore, the increasing complex of supply chains make it difficult to anticipate possible effects changes, such as improved forecasting or policy changes. In this thesis we propose an innovative approach for demand forecasting by creating a predictive model based on identifed patterns of repair and maintenance projects underlying the demand data. We further present a unique approach to simulate an overall supply chain, using locally available data, giving the supply chain the ability to evaluate the implications of improved forecasting on the overall network. Through the improved methodology the supply chain can reduce the amount of noise in the data and create a forecast based on data that better represents the real demand. The proposed method improves on current forecasting methods by reducing forecasting noise, such as bullwhip and human error, by tying the forecast for material demand to the forecast of the source of the demand. To do so, we use unsupervised clustering K-means to identify similar consumption behaviors in the data. We further propose the use of a time-series analysis and hierarchical forecast aggregation for the creation of the final forecast, although this will not be the focus of this thesis. Although the results of the clustering process were inconclusive, we present data that supports the validity of out premise and propose alternative algorithms that could produce superior results. In addition we propose a supply chain network simulation to validate the model and valuate its affects. We use the model to emulate the possible effects of forecast improvements on the overall supply chain.
by Shai Dekel.
S.M.
M.B.A.
36
Silva, Sarah Feilman Gentil Quina. "Enoturismo no Alentejo: visão global e perspectivas de desenvolvimento." Master's thesis, Escola Superior de Hotelaria e Turismo do Estoril, 2012. http://hdl.handle.net/10400.26/4772.
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A crescente concorrência internacional entre destinos turísticos acentua a necessidade de
diferenciação da oferta através de factores como a identidade e autenticidade. Neste âmbito, o enoturismo ou “turismo do vinho” têm vindo a ser objeto de crescente interesse por parte de agentes económicos e académicos, em praticamente todas as zonas produtoras de vinho. Tal facto, deve-se ao seu potencial em termos de desenvolvimento socioeconómico, de preservação de recursos naturais, culturais e sociais nas regiões vitivinícolas.
Também em Portugal assistimos, nos últimos anos, sobretudo nas regiões do Alentejo e Douro, à concretização de projetos que têm como objectivo, criar uma oferta turística diversificada capaz de atrair mais e melhores turistas, com base nos seus atributos naturais e culturais e na riqueza e qualidade dos seus vinhos. É neste contexto que se insere a presente proposta de investigação. Concretamente, pretende-se aprofundar o conhecimento do enoturismo na região do Alentejo, nomeadamente as suas limitações atuais e perspectivas de desenvolvimento. Para o efeito, apresenta-se um estudo de carácter exploratório, com incidência no distrito de Évora, que partindo da análise da realidade atual, em termos das características do destino propriamente dito, da oferta e da procura, identificar pistas para o seu desenvolvimento futuro.
Este estudo, obedeceu a um conjunto de passos, a saber: 1) revisão da literatura de referência sobre o tema, incluindo as diferentes ópticas de abordagem; 2) caracterização das indústrias do vinho e do turismo, nomeadamente a nível regional e nacional; 3) estudo qualitativo; e 4) estudo quantitativo.
Do presente trabalho, resulta evidente o impacto positivo que o enoturismo pode ter para um destino turístico e concretamente para a região Alentejo. A partir dos resultados obtidos, elaborou-se um modelo de consolidação e desenvolvimento deste produto na região. Apresentamos ainda um conjunto de conclusões e sugestões que nos parecem pertinentes, embora passíveis de confirmação e/ou correção por futuras investigações que importa incentivar e apoiar.The increasing competition among international tourism destinations, highlights the need for their differentiation and strategic (re)positioning, based on identity and authenticity factors. In this context, wine tourism has been subject of increasing interest from academics and from economic players, in almost every wine-producing regions given its potential as a socio-economic development tool, and for natural, cultural and social resource preservation. Likewise in Portugal, and in particular in the Alentejo and Douro regions, we have witnessed in recent years, the implementation of projects that aim, benefiting from the natural and cultural attributes of the regions and the richness and quality of its wines, to develop a diversified tourism supply capable of attracting more and better tourists. The current research proposal emerges in this framework. More specifically, it aims to deepen the knowledge of wine tourism in the Alentejo region, identify its current limitations and potentialities, through an exploratory study focused on the Évora district, based on an analysis of the current state-of-the-art of the destinations supply and demand characteristics and identify clues for its future development. With this purpose, following the 1) review of the published literature on the topic and its different angles of approach, we proceeded to 2) characterize the wine and tourism industries in the region, undertook the 3) qualitative and 4) quantitative studies on the current supply and demand, identifying a set of structural dimensions of wine tourism in the Alentejo. From the current study, it results clear the positive impact that wine tourism can have in the region both as a tourism and socioeconomic development factor. The obtained results enabled us to propose a consolidation and development framework for this tourism product in the region. Furthermore a set of conclusions and suggestions, which seem relevant, but liable to confirmation and/or correction by future investigations, that should be encouraged and supported, are presented.
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Hare, Jeremy (Jeremy B. ). "Disaggregation of residential home energy via non-intrusive load monitoring for energy savings and targeted demand response." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/117983.
Full textAbstract:
Thesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, in conjunction with the Leaders for Global Operations Program at MIT, 2018.Thesis: S.M., Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, in conjunction with the Leaders for Global Operations Program at MIT, 2018.
"June 2018." Cataloged from PDF version of thesis.
Includes bibliographical references (pages 59-61).
Residential energy disaggregation is a process by which the power usage of a home is broken down into the consumption of individual appliances. There are a number of different methods to perform energy disaggregation, from simulation models to installing "smart-plugs" at every outlet where an appliance is connected to the wall. Non-Intrusive Load Monitoring (NILM) is one such disaggregation option. NILM is widely recognized as one of the most cost-effective methods for gathering disaggregated energy data while maintaining a high level of accuracy. Although the technology has existed for many years, the adoption rate of NILM, and other devices that disaggregate energy, has been minimal. This thesis provides details on the potential benefits, both for the customer and utility provider, associated with furthering the adoption of NILM devices and obtaining the disaggregated appliance level energy-use. A broad overview of potential benefits is presented; however, the primary goal of this thesis will be to investigate two benefits of NILM in detail: overall household energy reduction and targeted demand response. First, installation of a NILM device can provide electricity customers information that allows them to become more aware of their energy consumption, and thereby, more energy efficient. A study was conducted that looked at the electricity consumption of 174 homes that were using a passive NILM device in their home. This NILM device provided immediate feedback on the power consumption for a portion of the home's appliances via smart-phone application. The homes reduced their monthly energy consumption by an average of 2.6 - 3.1% after the NILM installation. This was validated by a number of analysis methods returning similar results. Aligned with this benefit comes a recommendation for an incentive structure that can reduce the price paid by the consumer and develop a higher adoption rate of NILM devices. Second, the wide-spread adoption of NILM devices can provide electric utilities information to reduce carbon intensity via targeted demand response. There is a significant opportunity for utilities to engage their customers based on the time of use of detailed appliances. Multiple metrics are presented in this thesis to quantify the deferrable load opportunity of specific appliances and individual households. Utility operational cost savings and greater customer incentives can be linked to the use of these metrics.
by Jeremy Hare.
M.B.A.
S.M.
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Boulin, Juan Manuel. "Call center demand forecasting : improving sales calls prediction accuracy through the combination of statistical methods and judgmental forecast." Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/59159.
Full textAbstract:
Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Engineering Systems Division; in conjunction with the Leaders for Global Operations Program at MIT, 2010.Cataloged from PDF version of thesis.
Includes bibliographical references (p. 79-81).
Call centers are important for developing and maintaining healthy relationships with customers. At Dell, call centers are also at the core of the company's renowned direct model. For sales call centers in particular, the impact of proper operations is reflected not only in long-term relationships with customers, but directly on sales and revenue. Adequate staffing and proper scheduling are key factors for providing an acceptable service level to customers. In order to staff call centers appropriately to satisfy demand while minimizing operating expenses, an accurate forecast of this demand (sales calls) is required. During fiscal year 2009, inaccuracies in consumer sales call volume forecasts translated into approximately $1.1M in unnecessary overtime expenses and $34.5M in lost revenue for Dell. This work evaluates different forecasting techniques and proposes a comprehensive model to predict sales call volume based on the combination of ARIMA models and judgmental forecasting. The proposed methodology improves the accuracy of weekly forecasted call volume from 23% to 46% and of daily volume from 27% to 41%. Further improvements are easily achievable through the adjustment and projection processes introduced herein that rely on contextual information and the expertise of the forecasting team.
by Juan Manuel Boulin.
S.M.
M.B.A.
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Moreno, Cherry. "Urban water demand model: the case study of Emilia Romagna (Italy)." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2013. http://amslaurea.unibo.it/5938/.
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Water is the driving force in nature. We use water for washing cars, doing laundry, cooking, taking a shower, but also to generate energy and electricity. Therefore water is a necessary product in our daily lives (USGS. Howard Perlman, 2013).
The model that we created is based on the urban water demand computer model from the Pacific Institute (California). With this model we will forecast the future urban water use of Emilia Romagna up to the year of 2030. We will analyze the urban water demand in Emilia Romagna that includes the 9 provinces: Bologna, Ferrara, Forli-Cesena, Modena, Parma, Piacenza, Ravenna, Reggio Emilia and Rimini. The term urban water refers to the water used in cities and suburbs and in homes in the rural areas. This will include the residential, commercial, institutional and the industrial use.
In this research, we will cover the water saving technologies that can help to save water for daily use. We will project what influence these technologies have to the urban water demand, and what it can mean for future urban water demands. The ongoing climate change can reduce the snowpack, and extreme floods or droughts in Italy. The changing climate and development patterns are expected to have a significant impact on water demand in the future. We will do this by conducting different scenario analyses, by combining different population projections, climate influence and water saving technologies. In addition, we will also conduct a sensitivity analyses. The several analyses will show us how future urban water demand is likely respond to changes in water conservation technologies, population, climate, water price and consumption.
I hope the research can contribute to the insight of the reader’s thoughts and opinion.
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Dantas, Guilherme de Azevedo. "O impacto dos créditos de carbono na rentabilidade da co-geração sucroalcooleira brasileira." Master's thesis, Instituto Superior de Economia e Gestão, 2008. http://hdl.handle.net/10400.5/680.
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Mestrado em Economia e Política da Energia e do AmbienteA compatibilização entre a expansão da oferta mundial de energia e a necessidade de mitigar o aquecimento global é complexa. O aumento da eficiência energética e uma maior utilização de fontes renováveis de energia são os dois principais meios de se atenuar o conflito supracitado. Embora a matriz energética brasileira tenha uma participação de fontes renováveis de energia muito superior à média mundial, o potencial de utilização de fontes renováveis no Brasil é muito promissor dada as condições de oferta de insumos energéticos renováveis. Entre estes, a electricidade gerada a partir da biomassa da cana-de-açúcar é um grande exemplo deste potencial promissor ainda não utilizado. A bioelectricidade sucroalcooleira é compatível com os objectivos de segurança energética e mitigação do aquecimento global. Entretanto, o sector sucroalcooleiro tradicionalmente tem optado por preterir investimentos em tecnologias capazes de maximizarem a geração de energia eléctrica. Os principais entraves tradicionais a bioelectricidade sucroalcooleira vem sendo minimizados e a estrutura do sector eléctrico e a conjuntura energética actual são propícias à inserção da bioelectricidade na matriz eléctrica brasileira. Porém, muitos agentes do sector permanecem receosos em efectuarem investimentos em plantas capazes de gerarem elevados montantes de energia eléctrica devido à remuneração da bioelectricidade sucroalcooleira não incluir suas externalidades positivas. Esta dissertação tem o objectivo de analisar como os créditos de carbono podem funcionar como uma remuneração prémio à bioelectricidade pelas suas externalidades ambientais positivas incentivando a muitos agentes do sector sucroalcooleiro vencerem a inércia em que se encontram e realizarem investimentos na geração de excedentes comercializáveis de electricidade
It is complex the dilemma of compatibility between the expansion of global energy supply and the necessity of reducing the global warming. The increasing of energetic efficiency and the higher utilization of renewable sources of energy are the two main ways to solve the dilemma. Although the Brazilian energy matrix has a much higher contribution of renewable sources than the global average, the potential of further increasing is still very promising giving the supply of renewable energy inputs. Among these, the electricity generated throw the biomass from the sugar cane is a great example of an input with a promissory and underused potential. The sucroalcooleira bioelectricity is compatible with the goals of energetic security and of reducing global warming. However, the sucroalcooleiro sector is choosing to invest in technologies that maximize electric generation. The main traditional obstacles to the sucroalcooleira bioelectricity are being minimized and the structure of the energetic sector and the current energetic reality are favorable for the insertion of bioelectricity in Brazilian matrix of electricity. However many players in the sector remain afraid to make investments in plants capable of generating large amounts of electricity due to the fact that the remuneration of sucroalcooleira bioelectricity does not include its positive externalities. This thesis has the objective of analyze how the carbon credits can function as a pay award on bioelectricity for their positive environmental externalities encouraging many players in the sector to overcome the inertia in which they are conducting investment in his generation of marketable surplus of electricity
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Costa, Luzia Bouzan Oliveira. "Avaliação do ciclo de vida da produção de biogás via estação de tratamento de esgoto e uso em célula a combustível de óxido sólido." Universidade de São Paulo, 2012. http://www.teses.usp.br/teses/disponiveis/85/85134/tde-24102012-080923/.
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A busca pelo uso de energia renovável, bem como a mitigação dos impactos antropogênicos, desempenha importante papel no desenvolvimento da sociedade contemporânea. O uso de energia de origem renovável é uma possível solução para os problemas relacionados aos impactos ambientais, em especial, às alterações climáticas. Uma importante fonte de energia renovável é a biomassa oriunda de resíduos orgânicos que, após a digestão anaeróbia, resulta em um gás rico em metano, conhecido como biogás. Sob o ponto de vista de qualidade ambiental, o aproveitamento energético dos resíduos produzidos a partir do tratamento das águas residuárias domésticas pode minimizar os impactos ambientais à medida que permite a diminuição da carga orgânica descartada na água e no solo. Adicionalmente, também é possível mitigar os efeitos negativos de emissões de metano na atmosfera quando o biogás é utilizado na produção de energia por meio das células a combustível (CaC) do tipo óxido sólido (SOFC). Neste sentido, o presente trabalho objetivou avaliar o ciclo de vida da ETE, da unidade geradora de biogás, sua purificação e uso em CaCs, identificando o potencial de mitigação dos gases do efeito estufa e de aproveitamento energético do biogás. Dentre os principais resultados obtidos, a etapa construtiva, é a principal contribuinte da demanda acumulada de energia, participando com 55% da CED, enquanto a etapa de tratamento do esgoto, fase líquida, destaca-se na produção de emissões atmosféricas, cerca de 23.500 Kg CO2 eq por dia. O potencial de emissões dos gases de efeito estufa podem ser evitados, durante todo o ciclo, em cerca de 3.000 kg CO2 eq por dia. A energia total que pode ser aproveitada com o biogás gerado na ETE e usado em CaC, do tipo SOFC, é de cerca de 14.000 kWh/dia, o que pode suprir em 100% a demanda de eletricidade da fase de tratamento. Os resultados apresentados lançam um desafio e geram oportunidades para pesquisadores e planejadores de sistemas energéticos desenvolverem estratégias ambientalmente saudáveis para a utilização dos recursos renováveis.The search for renewable energy use and mitigation of anthropogenic impacts play an important role in the development of contemporary society. The use of energy from renewable sources is a possible solution to the problems related to environmental impacts, in particular, climate change. An important renewable energy source is biomass deriving from organic waste, after the anaerobic digestion, resulting in a gas rich in methane, known as biogas. From the point of view environmental quality, energy recovery of waste generated from the treatment of domestic wastewater can minimize environmental impacts as it allows the reduction of organic load dropped in water and soil. Additionally, it is also possible to mitigate the negative effects of methane emissions in the atmosphere when the biogas is used in the production of energy through solid oxide fuel cells (SOFC). In this sense, this study aimed at assessing the life cycle of a Wastewater Treatment Plant (WWTP), the biogas-generating unit, its purification and fuel cells use by identifying the potential mitigation of greenhouse gases and energy use of biogas. Among the main results obtained, the constructive phase is the main contributor to the cumulative energy demand, accounting for 55% of the CED, while in the step of sewage treating its particularly important the production of atmospheric emissions, about 23,500 kg CO2eq per day. The potential for emissions of greenhouse gases can be avoided throughout the cycle, at around 3,000 kg CO2eq per day. The total energy that can be produced with the biogas generated in WWTP and burned in the SOFC is approximately 14,000 kWh/day, which can provide 100% of the power demand of the treatment phase. The results presented launch challenges and generate opportunities for researchers and energy systems planners to develop strategies for environmentally healthy use of renewable resources.
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Gurriaran, Léna. "Quantifying the impact of climate change on power demand and associated CO2 emissions with machine learning methods." Electronic Thesis or Diss., université Paris-Saclay, 2023. http://www.theses.fr/2023UPASJ031.
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Les activités humaines, à travers l'émission de gaz à effet de serre (GES), en particulier le dioxyde de carbone (CO2), sont la principale cause du réchauffement climatique. Ce réchauffement a des répercussions significatives sur les activités humaines, notamment dans le secteur de l'énergie et de la production d'électricité. L'augmentation des températures, dépassant déjà 1°C et atteignant localement des valeurs plus élevées, influence directement la demande énergétique. Elle favorise le recours à la climatisation tout en réduisant le besoin de chauffage. Comprendre ces impacts est essentiel pour les décideurs politiques et les acteurs de l'énergie afin d'anticiper les défis liés à la distribution et à la capacité de production. De plus, la production d'électricité à partir de sources fossiles contribue aux émissions de CO2, créant ainsi une boucle de rétroaction entre production d'énergie et réchauffement climatique.Pour répondre à ces enjeux, cette thèse vise à développer des modèles de simulation de la demande journalière en électricité à l'échelle nationale en utilisant des modèles de Machine Learning (ML) entraînés sur des données climatiques (réanalyses ERA5) et socio-économiques. Deux cas d'études, le Qatar et le Japon, ont permis de développer la méthode ensuite appliquée à l'ensemble du globe. Au Qatar, un modèle simulant la demande basée sur une régression polynomiale du second ordre de la température journalière a été développé. Au Japon plusieurs modèles utilisant différents régresseurs d'apprentissage automatique, Random Forest, Gradient Boosting et Multivariate Adaptive Regression Spline, ont été testés pour simuler la demande (et l'intensité carbone) journalière, avec un plus grand nombre de variables climatiques. À partir de ces modèles, les variables clés influençant la demande ont été identifiées grâce à des méthodes d'interprétation (Partial Dependence, Local Accumulated Effect, Shapley Values). Ces modèles ont ensuite été utilisés pour projeter la demande en électricité jusqu'en 2100, en utilisant des projections de variables climatiques (CMIP6, ISIMIP3b) et socio-économiques pour différents scénarios futurs. Les émissions de CO2 associées ont été calculées en faisant des hypothèses sur l'évolution des mix énergétiques des pays.Cette méthodologie a ensuite été appliquée à une dizaine de pays (Australie, Brésil, Union Européenne, Inde, Chine, Afrique du Sud, Russie, Chili, Mexique, Norvège et Etat Unis) pour lesquels des données de demande en électricité sont disponibles grâce au projet Carbon Monitor, en ajoutant les Modèle Additif Généralisé à la liste des modèles de ML testés. Pour les pays sans données énergétiques, un pays (et modèle) de référence parmi ceux précédemment cités leur a été attribué en se basant sur leurs similitudes climatiques et socio-économiques. Pour ces pays, le modèle de référence a été appliqué avec leur propre projections climatiques et socio-économiques pour estimer l'évolution de leur demande en électricité en réponse au changement climatique. Les émissions de CO2 issue de la production d'électricité globale ont été calculées en utilisant des projections d'intensité carbone disponibles à l'échelle de grandes régions issues du modèle d'évaluation intégré IMAGE3.2. Enfin, ces émissions de CO2 ont été ajoutées à un modèle climatique simplifié pour évaluer leur impact sur la température globale.Les résultats indiquent que dans les hautes latitudes, la baisse de la demande en chauffage peut parfois surpasser l'augmentation liée à la climatisation, tandis que sous les tropiques, l'augmentation de la demande en climatisation est plus marquée. Globalement, les émissions supplémentaires de CO2 ont un impact faible sur la température globale, bien que localement et ponctuellement pendant certains mois de l'année, des augmentations significatives de la demande en électricité aient été observéesHuman activities are the main driver of anthropogenic global warming through the emission of greenhouse gases (GHGs), in particular carbon dioxide (CO2). The global warming has significant impacts on human activities, particularly in the energy and power generation sectors. Rising temperatures, already +1°C compared to the pre-industrial era and locally higher, have a direct impact on energy demand. It encourages the use of air conditioning while reducing the need for heating. Understanding these impacts is essential for policymakers and energy system planners to prepare for challenges related to distribution and production capacity. In addition, power generation from fossil fuels contributes to CO2 emissions, creating a feedback loop: human activities --> global warming --> human activities.To address these issues, this thesis aims to develop national simulation models of daily power demand using machine learning methods trained on climatic (ERA5 reanalyses) and socioeconomic data. Two case studies, Qatar and Japan, were used to develop the methodology, which was then applied globally. A demand simulation model based on second-order polynomial regression was developed in Qatar. In Japan, several models using different machine learning regressors, including Random Forest, Gradient Boosting, and Multivariate Adaptive Regression Spline, were tested to simulate daily demand and carbon intensity. From these models, the key variables influencing demand were identified using interpretation methods (Partial Dependence, Local Accumulated Effect, Shapley Values, and Feature Importance). These models were then used to project power demand over the course of the century, using daily climate (CMIP6, ISIMIP3b) and socioeconomic projections for different future scenarios. CO2 emissions from power generation were calculated by assuming fixed energy mixes.This methodology was then applied to more than ten countries (Australia, Brazil, the European Union, India, China, South Africa, Russia, Chile, Mexico, Norway, and the USA) for which power demand data are available through the Carbon Monitor project. Generalized Additive Models were added to the list of machine learning models tested. For the other countries, a reference country was selected from the above countries using the k-means method based on climatic and socioeconomic similarities. For these countries, the reference model was applied with their own climatic and socioeconomic projections to estimate the evolution of their power demand in response to climate change. CO2 emissions from global power generation were calculated using regionally available carbon intensity projections from the IMAGE3.2 integrated assessment model.Finally, these CO2 emissions were added to a simple climate model ACC2 to assess their impact on global temperature. The results show that in high latitudes, the decrease in power demand due to global warming can sometimes outweigh the increase due to air conditioning, while in the tropics, the increase in air conditioning demand is more pronounced. Overall, these additional CO2 emissions have very little impact on global temperatures, although locally and temporarily significant increases in power demand are observed
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Coetzee, Philip Barry. "Best practice in transporting uranium oxide from a Namibian perspective in response to an increased demand for clean energy in the global arena." Thesis, Stellenbosch : Stellenbosch University, 2006. http://hdl.handle.net/10019.1/50581.
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Thesis (MBA)--Stellenbosch University, 2006.ENGLISH ABSTRACT: The International Energy Agency will for the first time in its 32 years history urge governments to speed up the construction of nuclear power plants. According to Fatih Birol, lEA chief economist, "the decision almost needs to be made tomorrow if we are going to act before we reach a point of no return in climate and security of supply." Nuclear energy, through the conversion and enrichment of uranium oxide, is one of the energy sources with the potential to make an immediate and substantial difference in the emission of greenhouse gasses. Uranium Oxide currently provides a cost effective and sustainable source of clean energy through nuclear power generation, directly comparable in price to gas and coal. With the growth of the world economy, comes a requirement for more energy. This can only be sustained through a number of sources, of which uranium and renewable sources of energy i.e. wind power generation, is part of. The supply of uranium oxide from production to conversion is key to the success of the uranium industry. The high energy potential of relative small quantities of uranium makes rt ideal to transport as the cost and handling is reduced. The transportation of uranium oxide is continuously increasing in complexity. This combined with a high price scenario, increased demand and supply shortages increases the risk associated with denial and delays of shipments.
AFRIKAANSE OPSOMMING: Die Internasionale Energie Agentskap gaan vir die eerste keer in die organisasie se 32-jaar geskiedenis' regerings aanspoor om die konstruksie van kemkragsentrales te bespoedig. Volgens Fatih Birol, IEA se hoof-ekonoom, "moet die besluit amper more geneem word as ons gaan reageer voor ons 'n punt van geen omdraai bereik het ten opsigte van die klimaat en sekuriteit van aanbod." Kernkrag, deur die omskakeling en verryking van uraanoksied, is een van die energie bronne met die potensiaal om 'n onmiddelike en substansiele verskil in die vrylating van groenhuis-gasse te maak. Uraanoksied verskaf huidiglik 'n koste effektiewe en volhoudbare bron van skoon energie deur kernkrag opwekking, direk vergelykbaar met steenkool en gas. Met die groei in die wereld ekonomie, kom die vraag na meer energie. Dit kan slegs volhoudbaar wees uit 'n aantal bronne waarvan uraan en hernubare bronne soos windkrag-opwekking, deel is. Die verskaffing van uraanoksied van produksie tot verryking is die sleutel tot die sukses van die uraan industrie. Die hoe energie potensiaal van relatiewe klein hoeveelhede uraan, maak dit geskik vir vervoer omdat die koste van vervoer en hantering verminder word. Die transportering van uraan oksied is voortdurend besig om in kompleksiteit toe te neem. Gekombineerd met 'n hoe prys senario, verhoogde vraag na en aanbodtekorte, verhoog die risiko verbind met wyering en vertragings in verskeping.
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Younes, Sinaki Roohollah. "Financial Analysis and Global Supply Chain Design : A Case Study of Blood Sugar Monitoring Industry." Ohio University / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1509119632628001.
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Du, Guangli. "Life cycle assessment of bridges, model development and case studies." Doctoral thesis, KTH, Bro- och stålbyggnad, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-161196.
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In recent decades, the environmental issues from the construction sector have attracted increasing attention from both the public and authorities. Notably, the bridge construction is responsible for considerable amount of energy and raw material consumptions. However, the current bridges are still mainly designed from the economic, technical, and safety perspective, while considerations of their environmental performance are rarely integrated into the decision making process. Life Cycle Assessment (LCA) is a comprehensive, standardized and internationally recognized approach for quantifying all emissions, resource consumption and related environmental and health impacts linked to a service, asset or product. LCA has the potential to provide reliable environmental profiles of the bridges, and thus help the decision-makers to select the most environmentally optimal designs. However, due to the complexity of the environmental problems and the diversity of bridge structures, robust environmental evaluation of bridges is far from straightforward. The LCA has rarely been studied on bridges till now. The overall aim of this research is to implement LCA on bridge, thus eventually integrate it into the decision-making process to mitigate the environmental burden at an early stage. Specific objectives are to: i) provide up-to-date knowledge to practitioners; ii) identify associated obstacles and clarify key operational issues; iii) establish a holistic framework and develop computational tool for bridge LCA; and iv) explore the feasibility of combining LCA with life cycle cost (LCC). The developed tool (called GreenBridge) enables the simultaneous comparison and analysis of 10 feasible bridges at any detail level, and the framework has been utilized on real cases in Sweden. The studied bridge types include: railway bridge with ballast or fix-slab track, road bridges of steel box-girder composite bridge, steel I-girder composite bridge, post tensioned concrete box-girder bridge, balanced cantilever concrete box-girder bridge, steel-soil composite bridge and concrete slab-frame bridge. The assessments are detailed from cradle to grave phases, covering thousands of types of substances in the output, diverse mid-point environmental indicators, the Cumulative Energy Demand (CED) and monetary value weighting. Some analyses also investigated the impact from on-site construction scenarios, which have been overlooked in the current state-of-the-art. The study identifies the major structural and life-cycle scenario contributors to the selected impact categories, and reveals the effects of varying the monetary weighting system, the steel recycling rate and the material types. The result shows that the environmental performance can be highly influenced by the choice of bridge design. The optimal solution is found to be governed by several variables. The analyses also imply that the selected indicators, structural components and life-cycle scenarios must be clearly specified to be applicable in a transparent procurement. This work may provide important references for evaluating similar bridge cases, and identification of the main sources of environmental burden. The outcome of this research may serve as recommendation for decision-makers to select the most LCA-feasible proposal and minimize environmental burdens.QC 20150311
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Лопаткина, И. В., and Ю. В. Поготовка. "Потребительский спрос как фактор конкурентоспособности национальных экономик в условиях глобального кризиса." Thesis, Украинская академия банковского дела Национального банка Украины, 2010. http://essuir.sumdu.edu.ua/handle/123456789/63034.
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Позитивные сдвиги начала 2010 года в мировой экономике
незначительно улучшили показатели потребительского доверия и деловой
активности в разных странах. В США малозаметное оживление
потребительского спроса обусловлено все еще тяжелым состоянием рынка
труда. За последние месяцы уровень безработицы уменьшился до 9,7%, что
все еще является очень высоким показателем по историческим меркам. При
этом следует отметить, что период высокой безработицы не прошел для
американской экономики (в частности, производства) даром. Предприятия
значительно повысили (на 4%) производительность труда, стремясь
обеспечить стабильные темпы производства при ограниченных трудовых
ресурсах. Экономисты склоняются к мысли, что рост производительности
сыграл свою роль и в сдерживании потенциальной инфляции, обусловленной
массивными государственными вливаниями. При этом, очевидно, что рост
производительности должен скоро прекратиться (если еще не достиг своего
пика) и, как следствие, ослабится противодействие инфляции со стороны
этого фактора.
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Moghadam, Hamid Amini. "Quantified Characterization of Active Defects in 4H–SiC MOS Devices." Thesis, Griffith University, 2016. http://hdl.handle.net/10072/366432.
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According to the U.S. Energy Information Administration (EIA), global energy demand is expected to increase considerably in the coming years as the result of population growth and economic development [1]. The vast majority of the world’s energy is generated from non-renewable sources, specifically oil, coal and natural gas. The increased volumes of carbon dioxide and other greenhouse gases released by burning these fossil fuels are believed to be the primary sources of global warming and the long term climate changes [2]. Furthermore, global warming is considered to be the greatest humanitarian crisis of our time, responsible for raising sea levels, raging storms, ferocious fires, and severe drought [3]. Electricity is mainly considered to be an environmentally friendly source of energy. However, electricity is largely generated by burning fossil fuels in power plants. This process releases substantial amounts of carbon dioxide in the earth’s atmosphere. Therefore, it is very important to utilize the generated electricity in an efficient way in order to reduce greenhouse gas emissions and ultimately the climate change effects.Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
Griffith School of Engineering
Science, Environment, Engineering and Technology
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Hansson, Andrea. "Krav, kontroll och stöd bland vårdgivare i en kommun i Mellansvergie." Thesis, Högskolan i Gävle, Folkhälsovetenskap, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-25578.
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Abstract Hansson, A. (2017). The experience of demands, control and social support among a group of health care providers in Central Sweden - a cross sectional study. Bachelor thesis in Public Health Science. Department of Occupational and Public Health Science. Faculty of Health and Occupational Studies. University of Gävle, Sweden. Background: In Sweden, employees who work within health and social care is the largest occupational group and is also the group which accounts for the highest number of illness cases. Work in health care often means a variety of psychosocial workloads such as high demands, low control and lack of social support. Aim: The aim of the study was to examine the experiences and/or presence of demands, control and social support among nurses and home care workers in their daily work in health care in a municipality in central Sweden. Method: The study included 44 participants, 12 nurses and 32 home care workers. In a quantitative cross-sectional study, data was collected by use of a questionnaire designed by the author for the aim of the study. Participants reported their age, gender, working years and working hours, and answered questions about the experience and/or presence of demands, control and social support in their daily work. Results: The majority of the respondents experienced social support at their workplace. However, respondents 44 years or younger, did rarely/did not experience high work demands from managers or colleagues. Employees who worked more irregular hours experienced more often that they achieved expectations of what should be done from the care takers and their relatives. Respondents who had worked as caregivers 10 years or more, felt that their working hours were governed by the needs of care taker. Conclusion: The results show that an inadequate psychosocial work environment, with high demands, low control and lack of social support, was not experienced and/or a present problem among the majority of the respondents. Further research and knowledge is needed on how the perception of demands, control and social support at work has an impact on the self-perceived health of municipal employees within the health care sector since the work leave due to illness in this sector in Sweden continues to rise. Keywords: Demand, control and support, Public Health, Psychosocial Working Environment.
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Qi, Cheng. "Systems Analysis for Urban Water Infrastructure Expansion with Global Change Impact under Uncertainties." Doctoral diss., University of Central Florida, 2012. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/5441.
Full textAbstract:
Over the past decades, cost-effectiveness principle or cost-benefit analysis has been employed oftentimes as a typical assessment tool for the expansion of drinking water utility. With changing public awareness of the inherent linkages between climate change, population growth and economic development, the addition of global change impact in the assessment regime has altered the landscape of traditional evaluation matrix. Nowadays, urban drinking water infrastructure requires careful long-term expansion planning to reduce the risk from global change impact with respect to greenhouse gas (GHG) emissions, economic boom and recession, as well as water demand variation associated with population growth and migration. Meanwhile, accurate prediction of municipal water demand is critically important to water utility in a fast growing urban region for the purpose of drinking water system planning, design and water utility asset management. A system analysis under global change impact due to the population dynamics, water resources conservation, and environmental management policies should be carried out to search for sustainable solutions temporally and spatially with different scales under uncertainties. This study is aimed to develop an innovative, interdisciplinary, and insightful modeling framework to deal with global change issues as a whole based on a real-world drinking water infrastructure system expansion program in Manatee County, Florida. Four intertwined components within the drinking water infrastructure system planning were investigated and integrated, which consists of water demand analysis, GHG emission potential, system optimization for infrastructure expansion, and nested minimax-regret (NMMR) decision analysis under uncertainties. In the water demand analysis, a new system dynamics model was developed to reflect the intrinsic relationship between water demand and changing socioeconomic environment. This system dynamics model is based on a coupled modeling structure that takes the interactions among economic and social dimensions into account offering a satisfactory platform. In the evaluation of GHG emission potential, a life cycle assessment (LCA) is conducted to estimate the carbon footprint for all expansion alternatives for water supply. The result of this LCA study provides an extra dimension for decision makers to extract more effective adaptation strategies. Both water demand forecasting and GHG emission potential were deemed as the input information for system optimization when all alternatives are taken into account simultaneously. In the system optimization for infrastructure expansion, a multiobjective optimization model was formulated for providing the multitemporal optimal facility expansion strategies. With the aid of a multi-stage planning methodology over the partitioned time horizon, such a systems analysis has resulted in a full-scale screening and sequencing with respect to multiple competing objectives across a suite of management strategies. In the decision analysis under uncertainty, such a system optimization model was further developed as a unique NMMR programming model due to the uncertainties imposed by the real-world problem. The proposed NMMR algorithm was successfully applied for solving the real-world problem with a limited scale for the purpose of demonstration.ID: 031001428; System requirements: World Wide Web browser and PDF reader.; Mode of access: World Wide Web.; Adviser: Ni-Bin Chang.; Title from PDF title page (viewed June 24, 2013).; Thesis (Ph.D.)--University of Central Florida, 2012.; Includes bibliographical references (p. 120-131).
Ph.D.
Doctorate
Industrial Engineering and Management Systems
Engineering and Computer Science
Industrial Engineering
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Ates, Ozan K. "Global Supply Chain and Competitive Business Strategies: A Case Study of Blood Sugar Monitoring Industry." Ohio University / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1364987292.
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