Dans le cadre du protocole de Kyoto, la France s’est engagée à diviser par quatre ses émissions de gaz à effet de serre (GES) de 1990 à l'horizon 2050. Le Mexique a pour objectif d’atteindre une réduction de 50% en 2050 par rapport à l’année 2000. En vue du poids croissant du secteur des transport dans les villes d’environ 1 million d’habitants dans le total des émissions de CO2, nous souhaitons vérifier dans quelles mesures les expériences observées dans le Nord (plafonnement de la mobilité, diffusion de nouvelles technologies sur les véhicules) peuvent se répéter dans le Sud. Nous nous sommes pour cela appuyé sur des études de cas en France (Paris et Lille) et au Mexique (Juarez et Puebla). Le premier objectif de cette thèse a été d’identifier les déterminants de la mobilité urbaine. Le deuxième objectif a été d’appliquer le modèle âge-cohorte pour la prévision de la demande de transport, afin de prendre en compte l’évolution de la structure de la population (vieillissement) et les changements de comportement. Finalement, nous avons développé des diagnostics des émissions de GES. En France, nous avons observé des tendances vers une réduction des émissions de GES due à la baisse de la mobilité et aux nouvelles technologies, mais qui est encore loin d'être suffisante pour atteindre les objectifs fixés. Les études de cas du Mexique montrent l’incapacité à inverser la tendance à l'augmentation des émissions de GES ; par conséquent les objectifs de réduction seront difficilement atteints. Le cas du Mexique peut nous donner un aperçu des tendances dans les pays émergents, qui sont très loin d'atteindre un développement durable et resteront face à un grand défi dans le futur. / In the context of the Kyoto Protocol, France has set Greenhouse Gas (GHG) emission reduction targets of 75% below 1990 levels by 2050. More recently, Mexico has set the objective to achieve a 50% reduction by 2050 with respect to the base year 2000. Since the transport sector in urban areas with approximately 1 million inhabitants accounts for most CO2 emissions and will continue to increase its share, we wanted to determine to what extent the experiences observed in cities from developed countries (peak travel, dissemination of new vehicle technologies) may be repeated in urban areas from developing nations. For this purpose, we focus on case studies in France (Paris and Lille) and Mexico (Juarez and Puebla). The first objective of this thesis was to identify the determinants of mobility on each urban region. The second objective was to apply the age-cohort model for the development of long-term travel demand forecasts in order to take into account changes in the population structure (ageing) and in travel behaviour. The last objective was to develop GHG emissions assessments from observed travel demand. The decline in mobility and the dissemination of new vehicle technologies in France led to a reduction in GHG emissions. However, these changes are not sufficient to achieve the GHG reduction targets. The case studies in Mexico show the inability to reverse the trend towards the increase of GHG emissions; therefore the reduction targets will be hardly achieved. The case of Mexico give us an overview of trends in emerging countries, which are very far from achieving sustainable development and will face a major challenge in the coming decades.

This diploma thesis deals with a demand forecasting in electronic shop focused on electronics Alza.cz. The aim of the thesis is to evaluate several forecasting methods for different groups of products and to determine which of them provides the most accurate forecasts. The theoretical part is focused on electronic business, logistics cost, demand forecast, demand forecasting methods and forecast accuracy measuring methods. In practical part, selected methods are applied on data of past demand to calculate the forecasts. Afterwards the forecast accuracy is measured. At the end the thesis provides evaluation of forecast accuracy of the methods.

El presente trabajo abarca el análisis de las operaciones de una empresa de producción industrial que abastece al sector de minería con productos como bandas de ventilación para sus operaciones. Para este tipo de empresas el manejo del inventario es vital pues de él depende si la operación es rentable o no, debido al alto costo unitario de sus productos. El análisis de la empresa identificó altos niveles de stock de productos en las instalaciones de la empresa lo cual en los últimos dos años aumento el costo logístico de la empresa reduciendo su rentabilidad. Esta situación implica la necesidad de optar por un sistema que permita mejorar la disposición de los inventarios y las actividades generadas en su manipulación. Para ello se propone un modelo basado en la implementación del VMI (Vendor Inventory Management) para lograr la optimización de la gestión del almacén e inventarios de la empresa CIDELSA. El motivo de esta propuesta nace de la necesidad de controlar el sobre stock generada en el producto mangas de ventilación para lo cual se propone un nuevo sistema de gestión de inventarios en la empresa, basado en el VMI, apoyado en el kanban y en la gestión de información de pronóstico de la demanda. Los resultados de la aplicación del modelo demuestran a través de la simulación del proceso que se logran mejoras en los principales indicadores de inventario como el aumento del índice de rotación, reducción del stock sin movimiento y la reducción de almacenamiento en las instalaciones de la empresa. / This paper covers the analysis of the operations of an industrial production company that supplies the mining sector with products such as ventilation bands for its operations. For these types of companies, inventory management is vital because it depends on whether the operation is profitable or not, due to the high unit cost of its products. The analysis of the company identified high levels of product stock in the company's facilities, which in the last two years increased the logistics cost of the company, reducing its profitability. This situation implies the need to opt for a system that allows improving the disposition of inventories and the activities generated in their handling. For this, a model based on the implementation of the VMI (Vendor Inventory Management) is proposed to achieve the optimization of the management of the warehouse and inventories of the company CIDELSA. The reason for this proposal arises from the need to control the excess stock generated in the product ventilation sleeves, for which a new inventory management system is proposed in the company, based on the VMI, supported by kanban and management demand forecast information. The results of the application of the model show through the simulation of the process that improvements are achieved in the main inventory indicators such as the increase in the turnover rate, reduction of the stock without movement and the reduction of storage in the company's facilities. / Trabajo de Suficiencia Profesional

This thesis deals with the optimization of the supply process of a small business, especially with determining the optimal inventory level and demand forecasting, using tools of time series analysis. The final part gives a description of the creation of the program that is used to calculate individual indicators and forecasts. The aim is to increase the efficiency of the supply process, thereby reducing operating costs of the company.

To handle uncertainties and variabilities in production demands, many manufacturing companies have adopted different strategies, such as varying quoted lead time, rejecting orders, increasing stock or inventory levels, and implementing volume flexibility. Make-to-stock (MTS) systems are designed to offer zero lead time by providing an inventory buffer for the organizations, but they are costly and involve risks such as obsolescence and wasted expenditures. The main concern of make-to-order (MTO) systems is eliminating inventories and reducing the non-value-added processes and wastes; however, these systems are based on the assumption that the manufacturing environments and customers' demand are deterministic. Research shows that in MTO systems variability and uncertainty in the demand levels causes instability in the production flow, resulting in congestion in the production flow, long lead times, and low throughput. Neither strategy is wholly satisfactory. A new alternative approach, multi-channel manufacturing (MCM) systems are designed to manage uncertainties and variabilities in demands by first focusing on customers' response time. The products are divided into different product families, each with its own manufacturing stream or sub-factory. MCM also allocates the production capacity needed in each sub-factory to produce each product family. In this research, the performance of an MCM system is studied by implementing MCM in a real case scenario from textile industry modeled via discrete event simulation. MTS and MTO systems are implemented for the same case scenario and the results are studied and compared. The variables of interest for this research are the throughput of products, the level of on-time deliveries, and the inventory level. The results conducted from the simulation experiments favor the simulated MCM system for all mentioned criteria. Further research activities, such as applying MCM to different manufacturing contexts, is highly recommended.

Indian pharmaceutical companies spend one-third of their revenue from supply chain management (SCM) activities due to inherently poor transportation infrastructure. SCM is a vital function for many companies, as it is usually employed to lower expenses and increase sales for the company. SCM costs are higher in India than they are in other areas of the world, amounting to 13% of India's GDP. The purpose of this study was to explore SCM strategies Indian business leaders in the pharmaceutical industry have used to reduce the high costs associated with SCM. This study used a single case study research design and semistructured interviews to collect data from 3 SCM business leaders working in Indian pharmaceutical organizations and possessing successful experience in using SCM strategies to reduce high costs. Goldratt's (1990) theory of constraints was used as the conceptual framework for this study to identify challenges associated with SCM strategies. Data from semistructured interviews, observations, and company documents were processed and analyzed using data source triangulation, grouping the raw data into key themes. The following 3 themes emerged: distribution and logistics challenges, impact of SCM processes, and best practices and solutions. The implications for positive social change include the potential to reduce supply chain risk, which could lead to lower product prices for consumers, increased stakeholder satisfaction, and a higher standard of living.

[pt] Esta dissertação apresenta o estudo de um método para o planejamento operacional da distribuição de granéis líquidos, que visa à otimização dos recursos corporativos e a redução de custos operacionais. Iniciando pelo conceito da gestão do conhecimento, procura-se extrair as melhores práticas da empresa através da experiência dos seus profissionais. Todas as informações obtidas são estruturadas e organizadas em um sistema de apoio a decisão (SAD), a fim de montar uma base de conhecimento para suportar e assistir os processos de negócio relacionados à distribuição: previsão de demanda, programação de abastecimentos e roteirização de veículos. Baseado em pesquisas bibliográficas fundamentadas em disciplinas relacionadas à análise dos processos supra descritos e, principalmente, no conhecimento adquirido na própria empresa, são sugeridas soluções heurísticas para os problemas de planejamento da distribuição. Com o objetivo de validar a utilização deste método, é apresentado um estudo de caso realizado em uma empresa distribuidora de GLP (Gás Liquefeito de Petróleo), comparando os resultados obtidos antes e depois da sua aplicação. Os indicadores de desempenho adotados pela mesma apresentam os benefícios e valores agregados, ratificando a eficiência do referido método. / [en] This dissertation presents the study of a method for the operational distribution planning of liquid bulks, that aims the optimization of the corporate resources and the operational cost reduction. Starting by the concept of the knowledge management, it pursuits to extract the company`s best practices through the experience of its professionals. All the information obtained are structured and organized in a support decision system (SAD), in order to build a knowledge base to support and to assist the business processes related with the distribution: demand forecast, supply programming and vehicle routing. Based on bibliography researches well-founded in disciplines related with the analysis of the processes described above and, specially, from the knowledge gained in the company, heuristics solutions are proposed for the distribution planning problems. With the purpose of validating this method utilization, it is presented a study case in a LPG distribution company (liquid petroleum gas), comparing the results achieved before and after its application. The key performance indicators adopted present benefits and add values, ratifying the efficiency of the method above mentioned.

<div>Accurate prediction of electricity demand is a critical step in balancing the grid. Many factors influence electricity demand. Among these factors, climate variability has been the most pressing one in recent times, challenging the resilient operation of the grid, especially during climatic extremes. In this dissertation, fundamental challenges related to accurate characterization of the climate-energy nexus are presented in Chapters 2--4, as described below. </div><div><br></div><div>Chapter 2 explores the cost of neglecting the role of humidity in predicting summer-time residential electricity consumption. Analysis of electricity demand in the CONUS region demonstrates that even though surface temperature---the most widely used metric for characterising heat stress---is an important factor, it is not sufficient for accurately characterizing cooling demand. The chapter proceeds to show significant underestimations of the climate sensitivity of demand, both in the observational space as well as under climate change. Specifically, the analysis reveals underestimations as high as 10-15% across CONUS, especially in high energy consuming states such as California and Texas. </div><div><br></div><div>Chapter 3 takes a critical look at one of the most widely used metrics, namely, the Cooling Degree Days (CDD), often calculated with an arbitrary set point temperature of 65F or 18.3C, ignoring possible variations due to different patterns of electricity consumption across different regions and climate zones. In this chapter, updated values are derived based on historical electricity consumption data across the country at the state level. Chapter 3 analysis demonstrates significant variation, as high as +-25%, between derived set point variables and the conventional value of 65F. Moreover, the CDD calculation is extended to account for the role of humidity, in the light of lessons learnt in the previous chapter. Our results reveal that under climate change scenarios, the air-temperature based CDD underestimates thermal comfort by as much as ~22%.</div><div><br></div><div>The predictive analytics conducted in Chapter 2 and Chapter 3 revealed a significant challenge in characterizing the climate-demand nexuses: the ability to capture the variability at the upper tails. Chapter 4 explores this specific challenge, with the specific goal of developing an algorithm to increase prediction accuracy at the higher quantiles of the demand distributions. Specifically, Chapter 4 presents a data-centric approach at the utility level (as opposed to the state-level analyses in the previous chapters), focusing on high-energy consuming states of California and Texas. The developed algorithm shows a general improvement of 7% in the mean prediction accuracy and an improvement of 15% for the 90th quantile predictions.</div>

This thesis explores the potential use and implementation of a revenuemanagement model for coworking operators. With a critical realism philosophyand abductive approach, a quantitative study using primary data from a coworkingoperator has been conducted. Based on a comprehensive literature review, wehave found that much research is done on revenue management in the airline andhotel industries. However, we found no research on revenue management modelsthat intend to optimize revenue for coworking operations. Hence, this thesis aimsto fill this gap in existing academic research. Features from revenue managementmodels used in the hotel and airline industries are identified and analyzed with theobjective of implementing these in the coworking industry to efficiently maximizerevenue. The paper proposes the use of multinomial logit (MNL) model in theprocess of market segmentation; this method allows one to determine whichfactors influence the different segments. Moreover, the MNL model is used todefine the demand function from which a probability (probable?) distributionof total demand can be separated into demands representing each product class.Furthermore, the demand is used to calculate protection limits according to theExpected Marginal Seat Revenue (EMSR) model, with the objective of allocatingcapacity to the highest-yielding customers.Results indicate that the MNL regression is an effective tool to analyze themarket segmentation and demand allocation for coworking operators. Afterour successful analysis, we are prepared to argue with confidence that revenuemanagement models are applicable to coworking operations. / I denna uppsats undersöker vi den potentiella användningen och genomförandetav intäktsoptimeringsmodeller för coworking operatörer. En kvantitativ studiemed primär data från en coworking operatör har genomförts med en kritiskrealismfilosofi och ett abduktivt tillvägagångssätt. Det finns mycket forskningkring intäktsoptimering, framför allt inom flyg- och hotellbranschen, men ingensom behandlar intäktsoptimeringsmodeller med avseendepå coworking operatörer. Denna masteruppsats syftar till att bidra med kunskapför att fylla gapet kring revenue management för coworkingkontor, som saknas ibefintlig, svensk akademisk forskning idag.Vitala funktioner som utgör intäktoptimeringsmodeller ämnade för hotellochflygindustrin har identifierats och analyserats med målet att utforskamöjlig implementering för coworkingoperatörer. I uppsatsen genomförsen marknadssegmentering med hjälp av en multinomial regressionsanalys.Vidare görs en multinomial regressionsanalys med samtliga produktklasser somberoende variabler, för att få ut sannolikhetsfördelningen för vilka produkter somefterfrågas av den totala efterfrågan. Resultatet kan användas för att optimeratotala intäkterna genom att beräkna hur många platser som bör reserveras åthögt avkastande kunder, och hur många som kan hyras ut i tidigt skede. Förändamålet har vi tillämpat den så kallade Expected Marginal Seat Revenuemetoden, EMSR.Resultatet indikerar att multinomial logistisk regression är ett effektivt sätt attanalysera marknadssegment och styra efterfrågan till önskad produktklass. Samtatt användandet av rekommenderad revenue management modell är applicerbarpå coworking verksamheter. Alternativt: Resultatet indikerar att: i. multinomiallogistisk regression är ett effektivt sätt att analysera marknadssegment och styraefterfrågan till önskad produktklass. ii. användandet av rekommenderad revenuemanagement modell är applicerbar på coworking verksamheter.

[pt] O mundo vive hoje uma era de transformação digital resultante da chamada indústria 4.0 ou quarta revolução industrial. Nesta fase, a tecnologia tem exercido um papel cada vez mais estratégico no desempenho das organizações. Estes avanços tecnológicos têm revolucionado o processo de tomada de decisão na gestão e operação de cadeias de suprimentos. Neste contexto, esta dissertação apresenta uma metodologia de apoio à decisão na gestão de estoques, que combina multi-criteria decision making (MCDM) e machine learning (ML). A princípio, é realizada uma revisão sistemática da literatura para analisar como estas duas abordagens são aplicadas na gestão de estoques. Os resultados são complementados com um scoping review abrangendo a previsão de demanda. Inicia-se então um estudo de caso, aplicado em uma ferrovia de transporte de cargas. É aplicado, inicialmente, o método MCDM combinado Fuzzy AHP Vikor para ranquear os stock keeping units (SKUs) em ordem de criticidade. O passo seguinte é a aplicação do método de ML combinado GA-ANN, artificial neural network com genetic algorithm, com o objetivo de realizar a previsão de demanda em um piloto com alguns dos itens mais críticos. A etapa final consiste em estruturar um dashboard gerencial, integrando os resultados das etapas anteriores. Dentre os resultados alcançados, a partir do modelo proposto, observa-se considerável melhora na performance da previsão de demanda dos SKUs selecionados. Além disso, a integração entre as abordagens e implementação em um dashboard gerencial permitiu o desenvolvimento de um modelo semiautomático de tomada de decisão na gestão de estoques. / [en] The world is experiencing an era of digital transformation resulting from the industry 4.0 or fourth industrial revolution. In this period, technology has played an increasingly strategic role in the performance of organizations. These technological advances have revolutionized the decision-making process in the management and operation of supply chains. In this context, this dissertation presents a methodology to support decision-making in inventory management, which combines multi-criteria decision-making (MCDM) and machine learning (ML). At first, there is a systematic literature review in order to analyze how these two approaches are applied in inventory management. The results are complemented with a scoping review that includes the demand forecasting. A case study is then applied to a freight transport railway. Initially, the MCDM combined Fuzzy AHP Vikor method is applied to rank stock keeping units (SKUs) in degrees of criticality. The next step is the application of the ML combined GA-ANN method, artificial neural network with genetic algorithm, for the purpose of demand forecasting in a pilot with some of the most critical items. The final step is to structure a management dashboard, integrating the results of the previous steps. Among the results achieved from the proposed model, there is a considerable improvement in the performance of the demand forecasting for the selected SKUs. In addition, the integration between approaches and implementation in a management dashboard allowed the development of a semiautomatic model for decision-making in inventory management.