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.

[pt] No Brasil, devido aos incentivos governamentais ministrados na área de energia renovável, é postulada uma perspectiva crescente no número de instalações de micro e minigeração distribuída (MMGD), sendo a fonte solar destaque no país. Dessa forma, o aumento na inserção de fontes intermitentes promove alterações significativas no comportamento da curva de carga horária, podendo atingir de maneira direta a operação e o planejamento da rede elétrica. Para atender aos novos panoramas dispostos pelo sistema elétrico brasileiro, esta dissertação propõe uma nova metodologia para contabilizar a geração distribuída fotovoltaica para as horas que compõem o dia. Usando o modelo Holt-Winters Sazonal Duplo são feitas previsões de carga e demanda para o Sistema Interligado Nacional e os subsistemas que o integram, considerando, em particular, o impacto causado pela conexão destes sistemas de MMGD solar fotovoltaica na rede de distribuição. Para as previsões são utilizados o horizonte de tempo de 24 horas, em intervalos horários, efetuadas para a primeira semana de 2020. Os resultados indicam que a metodologia proposta para a criação das séries de geração distribuída fotovoltaica é válida, pois é observada uma diminuição dos erros de previsão para a série de demanda, constituída pelo montante da geração distribuída adicionado a carga. Os valores de MAPE analisados neste trabalho não ultrapassam 10 porcento para dias típicos, exceto feriados, indicando que o método apresentado é um recurso eficiente. / [en] In Brazil, due to government incentives given in the area of renewable energy, a growing perspective in the number of micro and mini distributed generation (MMGD) installations is postulated, being the solar source highlighted in the country. Thus, the increase in the insertion of intermittent sources promotes significant changes in the behavior of the hourly load curve, which can directly affect the operation and planning of the electrical network. To meet the new panoramas provided by the Brazilian electricity system, this dissertation proposes a new methodology to account for distributed photovoltaic generation for the hours that make up the day. Using the Double Seasonal Holt-Winters model, load and demand forecasts are made for the National Interconnected System and the subsystems that integrate it, considering, in particular, the impact caused by the connection of these solar photovoltaic MMGD systems in the distribution network. For the forecasts, the 24-hour time horizon is used, in hourly intervals, carried out for the first week of 2020. The results indicate that the proposed methodology for the creation of distributed photovoltaic generation series is valid, as it is observed a decrease in the forecast errors for the demand series, constituted by the amount of the distributed generation added to the load. The MAPE values analyzed in this work do not exceed 10 percent for typical days, except holidays, indicating that the presented method is an efficient resource.

As urban population across the globe increases, the demand for adequate transportation grows. Several strategies have been suggested as a solution to the congestion which results from this high demand outpacing the existing supply of transportation facilities. High –Occupancy Toll (HOT) lanes have become increasingly more popular as a feature on today’s highway system. The I-95 Express HOT lane in Miami Florida, which is currently being expanded from a single Phase (Phase I) into two Phases, is one such HOT facility. With the growing abundance of such facilities comes the need for indepth study of demand patterns and development of an appropriate pricing scheme which reduces congestion. This research develops a method for dynamic pricing on the I-95 HOT facility such as to minimize total travel time and reduce congestion. We apply non-linear programming (NLP) techniques and the finite difference stochastic approximation (FDSA), genetic algorithm (GA) and simulated annealing (SA) stochastic algorithms to formulate and solve the problem within a cell transmission framework. The solution produced is the optimal flow and optimal toll required to minimize total travel time and thus is the system-optimal solution. We perform a comparative evaluation of FDSA, GA and SA non-linear programming algorithms used to solve the NLP and the ANOVA results show that there are differences in the performance of the NLP algorithms in solving this problem and reducing travel time. We then conclude by demonstrating that econometric iv forecasting methods utilizing vector autoregressive (VAR) techniques can be applied to successfully forecast demand for Phase 2 of the 95 Express which is planned for 2014

The Swedish healthcare system is considered to have good healthcare productivity and efficiency with moderate cost but seems to have some future challenges. Sweden is moving towards the aging population as it requires development in medical care techniques and technologies to provide care to elderly patients. This increases the pressure on the healthcare system. Hence, the number of patients in the hospital increase, as a result, the flow of patients within the wards are increased. Furthermore, the pandemic has increased the number of people admitted to hospitals. As a consequence, even for high-priority cases, the wait times are rising. The Skaraborg Hospital Group, SHG, and other general hospitals, in particular, are focusing on how to handle patient flow at various levels within departments and clinics by improving patient flow quality. Production and capacity preparation (PCP) is a commonly used industry tool for resolving bottlenecks. Hence, this method needs to be adopted within the hospital and by the healthcare sector to a larger extent. Since many patients from different specialty departments use the Radiology department's facilities, it is often a "bottleneck" in inpatient traffic at hospitals. Furthermore, the influx of patients with covid-19 has increased the department's workload. This master's thesis aims to assist the Radiology department in improving their production and capacity planning to increase unit flow performance. The project involves supporting key staff in the department in estimating demand to align different patient movements with equipment and personnel services. Improving radiology department flow efficiency can lead to more even and healthy patient flows around the hospital, reducing "buffers" of patients and longer stays at different specialist clinics. / Det svenska sjukvården anses ha god hälsovårdsproduktivitet och effektivitet till måttliga kostnader men verkar ha några framtida utmaningar. Sverige går mot den åldrande befolkningen eftersom det kräver utveckling av tekniker och tekniker för medicinsk vård för att ge äldre patienter vård. Detta ökar trycket på sjukvården. Därför ökar antalet patienter på sjukhuset, vilket leder till att patientflödet inom avdelningarna ökar. Dessutom har pandemin ökat antalet personer som läggs in på sjukhus. Som en konsekvens ökar väntetiderna även för fall med hög prioritet. Skaraborg sjukhusgrupp, SHG och andra allmänna sjukhus fokuserar särskilt på hur man hanterar patientflöde på olika nivåer inom avdelningar och kliniker genom att förbättra patientflödeskvaliteten. Produktion och kapacitetsberedning (PCP) är ett vanligt branschverktyg för att lösa flaskhalsar. Därför måste denna metod i större utsträckning antas inom sjukhuset och inom sjukvården. Eftersom många patienter från olika specialavdelningar använder Radiologiavdelningens anläggningar är det ofta en "flaskhals" i slutenvården på sjukhus. Dessutom har inflödet av patienter med covid-19 ökat avdelningens arbetsbelastning. Detta examensarbete syftar till att hjälpa Radiologiavdelningen att förbättra sin produktionsoch kapacitetsplanering för att öka enhetsflödesprestanda. Projektet innebär att stödja nyckelpersoner på avdelningen för att uppskatta efterfrågan för att anpassa olika patientrörelser till utrustning och personal. Förbättrad radiologisk avdelnings flödeseffektivitet kan leda till jämnare och hälsosammare patientflöden runt sjukhuset, vilket minskar "buffertar" hos patienter och längre vistelser på olika specialistkliniker.

The forecasting of Eskom transmission electrical network demands is a complex task. The lack of historical data on some of the network components complicates this task even further. In this dissertation a model is suggested which will address all the requirements of the transmission system expansion engineers in terms of future loads and market trends. Suggestions are made with respect to ways of overcoming the lack of historical data, especially on the point loads, which is a key factor in modelling the electrical networks. A brief overview of the transmission electrical network layout is included to provide a better understanding of what is required from such a forecast. Lastly, some theory on multiple regression, neural networks and qualitative forecasting techniques is included, which will be of value for further model developments. / Computing / M. Sc. (Operations Research)

[pt] Esta dissertação aborda a concepção do uso de auditoria interna no contexto da gestão de riscos da cadeia de suprimentos, como mecanismo que agrega valor à organização. O gerenciamento de riscos da cadeia de suprimentos consiste na identificação e na gestão dos riscos da cadeia de suprimentos com a finalidade de se reduzir vulnerabilidades na cadeia de suprimentos. O objetivo da auditoria interna é realizar recomendações de modo que se promovam a eficiência e a eficácia das operações e auxiliar a organização a atingir os objetivos do negócio e, dessa forma, corrobora com a gestão de riscos. Esta dissertação tem em vista o problema de gestão de clientes de uma empresa do ramo de gases industriais e medicinais. A presente pesquisa tem como objetivo analisar a gestão de suprimentos desta empresa mostrando que, por meio da segmentação de clientes aplicando técnica Holt-Winters (HW) de previsão de demanda, é possível obterem-se reduções de custos logísticos ao se utilizar da gestão por Vendor Managed Inventory (VMI). Os resultados da pesquisa apresentam uma visão geral de como e o que pode ser feito pela auditoria interna, a fim de que pesquisadores e gestores possam se beneficiar deste trabalho ao utilizar os conceitos e o conteúdo compilados no documento como subsídio a estudos de maior alcance, à realização de auditorias e à gestão de riscos no tema. Ganhos relacionados à redução de custos foram obtidos pela empresa estudada, reforçando assim a contribuição dos resultados deste trabalho para a indústria. / [en] This Master s Thesis deals with the use of internal audit within the context of Supply Chain Risk Management, as an adding value mechanism to the organization. Supply Chain Risk Management relies on the identification and management of supply chain risks in order to reduce vulnerabilities along the supply chain. The purpose of internal auditing is to make recommendations that promote the efficiency and effectiveness of operations that can help the organization to achieve business goals and thereby corroborate with risk management. The purpose of this research is to analyze the supply management being adequately provisioned to customers showing that with the use of Holt-Winters (HW) demand-forecasting it is possible to highlight the customers with the implementation of the Vendor Managed Inventory (VMI) method in order to reduce logistics costs. The results present an overview of how and what can be done by internal audit, in order that academic researchers and managers can benefit from this work by using the concepts and content brought together in the document as a subsidy to further studies, audit practices and overall risk management. Gains related to the cost reduction were also obtained within the studied corporation, reinforcing the contribution of this study s results to the industry.

The forecasting of Eskom transmission electrical network demands is a complex task. The lack of historical data on some of the network components complicates this task even further. In this dissertation a model is suggested which will address all the requirements of the transmission system expansion engineers in terms of future loads and market trends. Suggestions are made with respect to ways of overcoming the lack of historical data, especially on the point loads, which is a key factor in modelling the electrical networks. A brief overview of the transmission electrical network layout is included to provide a better understanding of what is required from such a forecast. Lastly, some theory on multiple regression, neural networks and qualitative forecasting techniques is included, which will be of value for further model developments. / Computing / M. Sc. (Operations Research)