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Dissertations / Theses on the topic 'Demand Forecasting'
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Martin, C. A. "International tourism demand forecasting." Thesis, University of Bradford, 1987. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.379816.
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Syntetos, Argyrios. "Forecasting of intermittent demand." Thesis, Online version, 2001. http://bibpurl.oclc.org/web/26215.
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Gato, Shirley, and s3024038@rmit edu au. "Forecasting Urban Residential Water Demand." RMIT University. Civil, Environmental and Chemical Engineering, 2006. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20070202.113452.
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The city of Melbourne in Victoria, Australia has been recognised as having high quality drinking water, but like other urban cities in the world, its growing population means increasing water demand. Melbourne is also already on its eight year of dry climatic conditions and is currently experiencing a drought that forced water authorities to impose water restrictions after 20 years of unrestricted supply. The current drought, dwindling supplies and possible impact of climate change highlight the importance of making better use of this precious resource. The Water Resources Strategy has been developed for Melbourne, which serve as the basis for the Victorian Government to set per capita consumption reduction targets of 15%, 25% and 30% by 2010, 2015 and 2020 respectively. The strategy was developed to ensure a continuation of a safe, reliable and cost effective water supply that is environmentally sustainable in the long term. This is in recognition that population growth and water consumption will eventually require additional supplies of water (Water Resources Strategy Committee for the Melbourne Area 2002). One of the key findings of the National Land and Water Resources Audit's Australian Water Resources Assessment 2000 is the lack of detailed knowledge about the end use (Australian Water Association 2001). The
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Rostami, Tabar Bahman. "ARIMA demand forecasting by aggregation." Phd thesis, Université Sciences et Technologies - Bordeaux I, 2013. http://tel.archives-ouvertes.fr/tel-00980614.
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Demand forecasting performance is subject to the uncertainty underlying the time series an organisation is dealing with. There are many approaches that may be used to reduce demand uncertainty and consequently improve the forecasting (and inventory control) performance. An intuitively appealing such approach that is known to be effective is demand aggregation. One approach is to aggregate demand in lower-frequency 'time buckets'. Such an approach is often referred to, in the academic literature, as temporal aggregation. Another approach discussed in the literature is that associated with cross-sectional aggregation, which involves aggregating different time series to obtain higher level forecasts.This research discusses whether it is appropriate to use the original (not aggregated) data to generate a forecast or one should rather aggregate data first and then generate a forecast. This Ph.D. thesis reveals the conditions under which each approach leads to a superior performance as judged based on forecast accuracy. Throughout this work, it is assumed that the underlying structure of the demand time series follows an AutoRegressive Integrated Moving Average (ARIMA) process.In the first part of our1 research, the effect of temporal aggregation on demand forecasting is analysed. It is assumed that the non-aggregate demand follows an autoregressive moving average process of order one, ARMA(1,1). Additionally, the associated special cases of a first-order autoregressive process, AR(1) and a moving average process of order one, MA(1) are also considered, and a Single Exponential Smoothing (SES) procedure is used to forecast demand. These demand processes are often encountered in practice and SES is one of the standard estimators used in industry. Theoretical Mean Squared Error expressions are derived for the aggregate and the non-aggregate demand in order to contrast the relevant forecasting performances. The theoretical analysis is validated by an extensive numerical investigation and experimentation with an empirical dataset. The results indicate that performance improvements achieved through the aggregation approach are a function of the aggregation level, the smoothing constant value used for SES and the process parameters.In the second part of our research, the effect of cross-sectional aggregation on demand forecasting is evaluated. More specifically, the relative effectiveness of top-down (TD) and bottom-up (BU) approaches are compared for forecasting the aggregate and sub-aggregate demands. It is assumed that that the sub-aggregate demand follows either a ARMA(1,1) or a non-stationary Integrated Moving Average process of order one, IMA(1,1) and a SES procedure is used to extrapolate future requirements. Such demand processes are often encountered in practice and, as discussed above, SES is one of the standard estimators used in industry (in addition to being the optimal estimator for an IMA(1) process). Theoretical Mean Squared Errors are derived for the BU and TD approach in order to contrast the relevant forecasting performances. The theoretical analysis is supported by an extensive numerical investigation at both the aggregate and sub-aggregate levels in addition to empirically validating our findings on a real dataset from a European superstore. The results show that the superiority of each approach is a function of the series autocorrelation, the cross-correlation between series and the comparison level.Finally, for both parts of the research, valuable insights are offered to practitioners and an agenda for further research in this area is provided.
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GOMES, RENATA MIRANDA. "BIAS DETECTION IN DEMAND FORECASTING." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2011. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=18477@1.
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COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIORPROGRAMA DE SUPORTE À PÓS-GRADUAÇÃO DE INSTS. DE ENSINO
Essa dissertação teve como objetivo propor dois novos métodos para detecção de viés na previsão de demanda. Os métodos consistem numa adaptação de duas técnicas de controle estatístico de processos, o gráfico de controle de EWMA e o algoritmo CUSUM, ao contexto de detecção de viés na previsão de demanda. O desempenho dos métodos foi analisado por simulação, para diversos casos de mudança na inclinação (tendência) da série de dados (mudança de modelo constante para modelo com tendência; alteração na tendência de série crescente; estabilização de série crescente em um patamar constante), e com diferentes parâmetros para os métodos. O estudo limitou-se a séries sem sazonalidade e aos métodos de previsão de amortecimento exponencial simples e de Holt. Os resultados mostraram a grande superioridade do gráfico de EWMA proposto e apontam questões para pesquisas futuras.
The purpose of this dissertation is to propose two new methods for detection of biases in demand forecasting. These methods are adaptations of two statistical process control techniques, the EWMA control chart and the CUSUM control chart (or CUSUM algorithm), to the context of the detection of biases in demand forecasting. The performance of the proposed methods was analyzed by simulation, for several magnitudes of changes in the trend of the series (change from a level series to a series with a trend, changes in the trend parameter, and stabilization of a series with a trend in a constant average level) and with different parameters for all methods. The study was limited to non-seasonal models and to the methods of simple exponential smoothing and Holt’s Exponential Smoothing. The results have shown the superiority of the EWMA method proposed and indicate issues for future research.
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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
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Al-Madfai, Hasan. "Weather corrected electricity demand forecasting." Thesis, University of South Wales, 2002. https://pure.southwales.ac.uk/en/studentthesis/weather-corrected-electricity-demand-forecasting(2e066cc4-58b1-4694-9937-ee8f57fbed02).html.
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Electricity load forecasts now form an essential part of the routine operations of electricity companies. The complexity of the short-term load forecasting (STLF) problem arises from the multiple seasonal components, the change in consumer behaviour during holiday seasons and other social and religious events that affect electricity consumption. The aim of this research is to produce models for electricity demand that can be used to further the understanding of the dynamics of electricity consumption in South Wales. These models can also be used to produce weather corrected forecasts, and to provide short-term load forecasts. Two novel time series modelling approaches were introduced and developed. Profiles ARIMA (PARIMA) and the Variability Decomposition Method (VDM). PARIMA is a univariate modelling approach that is based on the hierarchical modelling of the different components of the electricity demand series as deterministic profiles, and modelling the remainder stochastic component as ARIMA, serving as a simple yet versatile signal extraction procedure and as a powerful prewhitening technique. The VDM is a robust transfer function modelling approach that is based on decomposing the variability in time series data to that of inherent and external. It focuses the transfer function model building on explaining the external variability of the data and produces models with parameters that are pertinent to the components of the series. Several candidate input variables for the VDM models for electricity demand were investigated, and a novel collective measure of temperature the Fair Temperature Value (FTV) was introduced. The FTV takes into account the changes in variance of the daily maximum and minimum temperatures with time, making it a more suitable explanatory variable for the VDM model. The novel PARIMA and VDM approaches were used to model the quarterly, monthly, weekly, and daily demand series. Both approaches succeeded where existing approaches were unsuccessful and, where comparisons are possible, produced models that were superior in performance. The VDM model with the FTV as its explanatory variable was the best performing model in the analysis and was used for weather correction. Here, weather corrected forecasts were produced using the weather sensitive components of the PARIMA models and the FTV transfer function component of the VDM model.
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Fernandes, Filho Roberto Braga. "Integração de modelos de previsão de demanda qualitativos e quantitativos e comparação com seus desempenhos individuais." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2015. http://hdl.handle.net/10183/117896.
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Um bom sistema de previsão de demanda é um dos passos para o sucesso de uma empresa. Previsões com baixos erros permitem a manutenção de um estoque reduzido, uma ocupação de fábrica e uma gestão financeira mais eficiente, em conjunto com outros benefícios trazidos por um sistema confiável. Há diversas formas de realizar uma previsão, mas há anos a que vem sendo considerada mais promissora é a que integra métodos quantitativos e qualitativos. Ambos os métodos possuem vantagens exclusivas, o que torna a integração particularmente interessante. Este trabalho visa o desenvolvimento e teste de um sistema de previsão em uma empresa de grande porte, a fim de disponibilizar uma forma confiável de integração de métodos. Busca ainda validar o auxilio de especialistas nos ajustes de previsão de forma que os problemas provenientes do julgamento humano possam ser evitados. Uma comparação entre as várias previsões realizadas é apresentada, de forma que o leitor possa interpretá-las e julgar quais possam ser as mais adequadas à situação em que se encontra.A good forecasting system is one of the steps to the success of a company. Forecasts with small errors enable the maintenance of a reduced inventory, a more efficient factory occupation and financial management, together with other benefits provided by a reliable system. There are several ways to make a forecast, but for years the quantitative and qualitative methods integrated has been considered more promising. Both methods have unique advantages which makes it particularly interesting integration. This paper aims to develop and test a forecasting system in a large company in order to provide a reliable form of integration methods. It also seeks to validate the aid of experts in the predictive adjustments so that problems derived from the human judgment can be avoided. A comparison of the various forecasts made is provided in a way that the reader can interpret them and judge which may be the most appropriate to the situation you are in.
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Ho, Kien K. (Kine Kit). "Demand forecasting for aircraft engine aftermarket." Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/43833.
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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 Civil and Environmental Engineering; in conjunction with the Leaders for Manufacturing Program at MIT, 2008.Includes bibliographical references.
In 2006, Pratt and Whitney launched the Global Material Solutions business model aiming to supply spare parts to non-OEM engines with minimum 95% on-time delivery and fill-rate. Lacking essential technical knowledge of the target engines, predictability and associated confidence of the parts demands are very limited. This thesis focuses on exploring alternative and innovative approaches to providing more accurate demand forecasts based on limited information. Approaches including application of fundamental sampling theorems, random walk simulations based on Markov Chain simplification, and sensitivity analysis on incremental scrap rates were introduced. A software tool, based on the sensitivity analysis was introduced for all gas path parts. The methodology could potentially be applicable to industries other than Aerospace.
by Kien K. Ho.
S.M.
M.B.A.
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Obbiso, Pietro. "Forecasting intermittent demand: a comparative approach." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/24792/.
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In this thesis, a comparative approach between forecasting intermittent demand by using machine learning and by using statistical models is carried out. Models implementations are done with the support of different Python libraries aimed at discovering which model would provide better results. For what concerns the data, an electricity demand dataset is used for building the
models, where their generated predictions are compared with the real ones. Moreover, performances are investigated against specifically selected scenarios, where different forecast horizons and different times of the day are considered. It gave us the possibility of analysing how these models would perform over distinct settings, including the ones during an anomalous period. The final results showed the KNeighbors Regressor being the best model, especially in scenarios that consider moments in time of very low demand in a normal week, with an accuracy value of 93%. However, despite being the best result, it is not the most intriguing to consider, as instead discovering how some forecasters
perform surprisingly well in scenarios where the anomaly is present is the main interest.
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Ahmed, Shadman. "Phase-Out Demand Forecasting : Predictive modeling on forecasting product life cycle." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-287446.
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The phase-out stage in a product life cycle can face unpredictable demand. Accurate forecast of the phase-out demand can help supply chain managers to control the number of obsolete inventories. Consequently, having a positive effect in terms of resources and lower scrap costs. In this thesis, we investigated if data-driven forecasting models could improve the accuracy of forecasting the phase-out stage when compared with domain experts. Since the space of available models is vast, a set of 11 best performing models according to literature were investigated. Furthermore, a thorough model selection based on performance suggested that the following three models were best suited to our dataset: Autoregressive Integrated Moving Average (ARIMA), Support Vector Regression (SVR), and Gaussian Process Regression (GPR). The final results showed that none of the models were able to improve the forecast accuracy overall. However, SVR displayed good performance close to the domain experts’ estimates across 14 unique products through variation of analysis. In addition to the comparative study, this study showed that using less data improved the models’ performances. Only 60% of the training data seemed optimal for ARIMA and GPR, while SVR had a good performance with only 80% of data. We present the results along with further research questions to be explored in this domain.Utfasningen i en produktlivscykel kan kännetecknas vara oförutsägbart. En noggrann prognos av stadiet kan ge värdefull insikt såsom att begränsa antalet utgångna inventeringar och om produktens efterfrågan. Detta kan ge positiv ekonomisk effekt samt spara resurser. I denna studie jämförde vi med domän experter om data drivna prognosmodeller kunde förbättra estimeringen av efterfrågan inom utfasningen i en produktlivscykel. På grund av att tillgängligheten av prognosmodeller är omfattande, ett antal modeller studerades som visat bäst resultat i olika studier. Efter en nogrann urval av 11 olika modeller som visade bäst prestanda, användes följande 3 modeller för den senare delen av studien: Autoregressiv Integrerad Glidande Medelvärde (ARIMA), Stödvektor Regression (SVR) och Gaussisk Process Regression (GPR). Resultat visade att ingen av modellerna kunde generellt förbättra prognoserna, dock visade SVR signifikant liknande prognosfel som planestimeringarna från domän experter för 14 unika produkter. Dessutom visades sig att en minskning av data förbättrade prestandan hos modellerna. Där endast 60% av träningsdatat tycktes vara optimalt för ARIMA och GPR medan SVR med 80%. Vi presenterar resultaten ihop med ytterligare frågor som undersöktes inom detta område.
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Norvell, Joakim. "Statistical forecasting and product portfolio management." Thesis, Umeå universitet, Institutionen för matematik och matematisk statistik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-116866.
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For a company to stay profitable and be competitive, the customer satisfaction must be very high. This means that the company must provide the right item at the right place at the right time, or the customer may bring its business to the competitor. But these factors bring uncertainty for the company in the supply chain of when, what and how much of the item to produce and distribute. For reducing this uncertainty and for making better plans for future demand, some sort of forecasting method must be provided. A forecast can however be statistically based and also completed with a judgmental knowledge if the statistics are not sufficient. This thesis has been done in cooperation with the Sales and Operations (S&OP) department at Sandvik Mining Rock Tools in Sandviken, where a statistical forecast is currently used in combination with manual changes from sales. The forecasts are used as base for planning inventory levels and making production plans and are created by looking at the history of sales. This is done in order to meet market expectations and continuously be in sync with market fluctuations. The purpose with this thesis has been to study the item- customer combination demand and the statistical forecasting process that is currently used at the S&OP department. One problem when creating forecast is how to forecast irregular demand accurately. This thesis has therefore been examining the history of sales too see in what extent irregular demand exists and how it can be treated. The result is a basic tool for mapping customers' demand behavior, where the behavior is decomposed into average monthly demand and volatility. Another result is that history of sales can get decomposed into Volatility, Volume, Value, Number of sales and Sales interval for better analysis. These variables can also be considered whenever analyzing and forecasting irregular demand. A third result is a classification of time series working as a guideline if demand should be statistically or judgmentally forecasted or being event based. The study analyzed 36 months history of sales for 56 850 time series of item- customer specific demand. The findings were that customers should have at least one year of continuous sales before the demand can be entirely statistically forecasted. The limits for demand to even be forecasted, the history of sales should at least occur every third month in average and contain at least six sales. Then the demand is defined as irregular and the forecast method is set to judgmental forecasting, which can be forecasted using statistical methods with manual adjustments. The results showed that the class of irregular demand represents approximately 70 percent in the aspect of revenue and therefore requires attention.För att ett företag ska kunna vara lönsamt och konkurrenskraftigt måste kundnöjdheten vara mycket hög. Detta betyder att ett företag måste kunna förse rätt produkt i rätt tid på rätt plats, annars kommer kunden troligtvis att vända sig till konkurrenten. Men dessa faktorer kommer med osäkerhet för företaget i försörjningskedjan i när, vad och hur mycket av produkten de ska producera och distribuera. För att minska osäkerheten och för att planera bättre för framtida efterfrågan, måste någon typ av prognos upprättas. En prognos kan vara baserad på statistiska metoder men också kompletterad med subjektiv marknadsinformation om statistiken inte är tillräcklig. Studien som denna rapport beskriver är gjord i samarbete med Sales och Operations- avdelning (S&OP) på Sandvik Mining Rock Tools i Sandviken. Där används statistiska prognoser i kombination med manuella förändringar av säljare samt regionala planerare som bas för planering av lagernivåer och produktion. Detta gör man för att möta marknadens efterfråga och för att kontinuerligt vara uppdaterad med marknadens variationer. Syftet med detta arbete har varit att studera kunders efterfrågan av produkt- kund kombination och den metod som används vid statistiska prognoser hos S&OP- avdelningen. Ett problem som finns när man vill skapa prognoser är hur man ska prognostisera oregelbunden försäljning korrekt. Detta arbete har därför analyserat historisk försäljning för att se i vilken utsträckning oregelbunden efterfrågan finns och hur den kan hanteras. Resultatet är ett enkelt verktyg för att kunna kartlägga kunders köpbeteende. Ett till resultat är att historisk försäljning kan bli uppdelat i Volatilitet, Volym, Värde, Antalet köptillfällen och Tidsintervallet mellan köptillfällena. Dessa variabler kan även tas till hänsyn när man analyserar och prognostiserar oregelbunden försäljning. Ett tredje resultat är en klassificering av tidsserier som kan fungera som riktmärken om efterfrågan ska vara statistisk eller manuellt prognostiserade eller inte bör ha en prognos över huvud taget. Denna studie analyserade 36 månaders historik för 56 850 tidsserier av försäljning per produkt- kund kombination. Resultaten var att en kund bör ha åtminstone ett år av kontinuerlig efterfrågan innan man kan ha en prognos med statistiska modeller. Gränsen för att ens ha en prognos är att efterfrågan bör återkomma var tredje månad i genomsnitt och ha en historik av åtminstone sex försäljningstillfällen. Då klassificeras efterfrågan som oregelbunden och prognosen kan vara baserad på statistiska metoder men med manuella ändringar. I resultatet framkom det att oregelbunden efterfrågan representerar cirka 70 procent i avseende på intäkter och kräver således mycket uppmärksamhet.
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Lawson, Richard. "Adaptive state-space forecasting of gas demand." Thesis, Lancaster University, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.358799.
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Xu, Runmin S. M. Massachusetts Institute of Technology. "Machine learning for real-time demand forecasting." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/99565.
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Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015.Thesis: S.M. in Transportation, Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, 2015.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 89-92).
For a taxi company, the capability to forecast taxi demand distribution in advance provides valuable decision supports. This thesis studies real-time forecasting system of spatiotemporal taxi demand based on machine learning approaches. Traditional researches usually examine a couple of candidate models by setting up an evaluation metric and testing the overall forecasting performance of each model, finally the best model is selected. However, the best model might be changing from time to time, since the taxi demand patterns are sensitive to the dynamic factors such as date, time, weather, events and so on. In this thesis, we first study range searching techniques and their applications to taxi data modeling as a foundation for further research. Then we discuss machine learning approaches to forecast taxi demand, in which the pros and cons of each proposed candidate model are analyzed. Beyond single models, we build a five-phase ensemble estimator that makes several single models work together in order to improve the forecasting accuracy. Finally, all the forecasting approaches are evaluated in a case study over rich taxi records of New York City. Experiments are conducted to simulate the operation of real-time forecasting system. Results prove that multi-model ensemble estimators do produce better forecasting performances than single models.
by Runmin Xu.
S.M.
S.M. in Transportation
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Riedel, Silvia. "Forecast combination in revenue management demand forecasting." Thesis, Bournemouth University, 2008. http://eprints.bournemouth.ac.uk/9640/.
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The domain of multi level forecast combination is a challenging new domain containing a large potential for forecast improvements. This thesis presents a theoretical and experimental analysis of different types of forecast diversification on forecast error covariances and resulting combined forecast quality. Three types of diversification are used: (a) diversification concerning the level of learning (b) diversification of predefined parameter values and (c) the use of different forecast models. The diversification is carried out on forecasts of seasonal factor predictions in Revenue Management for Airlines. After decomposing the data and generating diversified forecasts a (multi step) combination procedure is applied. We provide theoretical evidence of why and under which conditions multi step multi level forecast combination can be a powerful approach in order to build a high quality and adaptive forecast system. We theoretically and experimentally compare models differing with respect to the used decomposition, diversification as well as the applied combination models and structures. After an introduction into the application of forecasting seasonal behaviour in Revenue Management, a literature review of the theory of forecast combination is provided. In order to get a clearer idea of under which condition combination works, we then investigate aspects of forecast diversity and forecast diversification. The diversity of forecast errors in terms of error covariances can be expressed in a decomposed manner in relation to different independent error components. This type of decomposed analysis has the advantage that it allows conclusions concerning the potential of the diversified forecasts for future combination. We carry out such an analysis of effects of different types of diversification on error components corresponding to the bias-variance-Bayes decomposition proposed by James and Hastie. Different approaches of how to include information from different levels into forecasting are also discussed in the thesis. The improvements achieved with multi level forecast combination prove that theoretical analysis is extremely important in this relatively new field. The bias-variance-Bayes decomposition is extended to the multi level case. An analysis of the effects of including forecasts with parameters learned at different levels on the bias and variance error components show that forecast combination is the best choice in comparison to some other discussed alternatives. The proposed approach represents a completely automatic procedure. It realises changes in the error components which are not only advantageous at the low level, but have also a stabilising effect on aggregates of low level forecasts to the higher level. We also identify cases in which multi level forecast combination should ideally be connected with the use of different function spaces and/or thick modelling related to certain parameter values or preprocessing procedures. In order to avoid problems occurring for large sets of highly correlated forecasts when considering covariance information, we investigated the potential of pooling and trimming for our case. We estimate the expected behaviour of our diversified forecasts in purely error variance based pooling represented by a common approach of Aiolfi and Timmermann and analyse effects of different kinds of covariances on the accuracy of the combined forecast. We show that a significant loss in the expected forecast accuracy may ensue because of typical inhomogeneities in the covariance matrix for the analysed case. If covariance information is available in a sufficiently high quality, it is possible to run a clustering directly based on covariance information. We discuss how to carry out a clustering in that case. We also consider a case (quite common in our application) when covariance information may not be available and propose a novel simplified representation of the covariance matrix which represents the distance in the forecast generation space and is only based on knowledge about the forecast generation process. A new pooling approach is proposed that avoids inhomogeneities in the covariance matrix by considering the information contained in the simplified covariance representation. One of the main advantages of the proposed approach is that the covariance matrix does not have to be calculated. We compared the results of our approach with the approach of Aiolfi and Timmermann and explained the reasons for significant improvement. Another advantage of our approach is that it leads to the generation of novel multi step, multi level forecast generation structures that carry out the combination in different steps of pooling. Finally, we describe different evolutionary approaches in order to generate combination structures automatically. We investigate very flexible approaches as well as approaches that avoid the expected inhomogeneities in the error covariance matrix based on our theoretical findings. The theoretical analysis is supported by experimental results. We could achieve an improvement of forecast quality up to 11 percent for the practical application of demand forecasting in Revenue Management compared to the current optimised forecasting system.
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Sani, Babangida. "Periodic inventory control systems and demand forecasting methods for low demand items." Thesis, Lancaster University, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.309040.
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ARVIDSSON, JENS. "Forecasting on-demand video viewership ratingsusing neural networks." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-156382.
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Forecasting short-term viewership ratings for on-demand video is crucial for the online advertisement market because advertisement sales is done ahead of time, and errors in forecasting means either loss of profit opportunities or having to compensate advertisers for not upholding agreements. These forecasts can be made using an uncomplicated Seasonal Averaging method, which produces forecasts for the coming weeks using averaged hourly values from previous weeks (where the forecast for next Sunday is the average of the actual value from the last three Sundays). In this thesis, an alternative approach using a neural network is implemented and benchmarked against the Seasonal Averaging method, using data from December–February from a major online videosite. The network utilizes a Multilayer Perceptron design with inputs corresponding to the seasonal patterns of the ratings data. It finds that while good forecasting performance can be reached even over very long horizons, weekly averages wins out when comparing standard forecasting error metrics, likely owing to the strong seasonal pattern.Att förutsäga tittarsiffror för strömmande video är viktigt för reklamindustrin då försäljning av reklam sker innan den visats. Alltför stora fel i dessa förutsägelser leder till att annonsörer måste kompenseras för ej visade reklamsnuttar, alternativt att möjligheter till att sälja mer reklam går förlorade. Dessa förutsägelser kan göras genom att ta genomsnittet av tidigare veckors tittarsiffror och använda detta som förutsägelse för påföljande veckor (där tittarsiffran för söndag nästa vecka är lika med genomsnittet av de senaste tre söndagarna). I det här exjobbet undersöks möjligheten att använda ett neuronnätverk för att göra dessa förutsägelser istället, genom att jämföra resultaten från detta mot den nuvarande metoden på data från December till Februari. Neuronnätetär av typen Multilayer Perceptron och använder en design som är anpassat till de veckovisa mönster som data uppvisar. Undersökningen finner att trots goda förutsägelser från neuronnätverket når det inte samma träffsäkerhet (mätt med standardmått på förutsägelser) som den nu använda metoden, troligtvis på grund av det starka veckovisa mönstret som data uppvisar.
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Louw, Riëtte. "Forecasting tourism demand for South Africa / Louw R." Thesis, North-West University, 2011. http://hdl.handle.net/10394/7607.
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Tourism is currently the third largest industry within South Africa. Many African countries, including
South Africa, have the potential to achieve increased economic growth and development with the aid of
the tourism sector. As tourism is a great earner of foreign exchange and also creates employment
opportunities, especially low–skilled employment, it is identified as a sector that can aid developing
countries to increase economic growth and development. Accurate forecasting of tourism demand is
important due to the perishable nature of tourism products and services. Little research on forecasting
tourism demand in South Africa can be found. The aim of this study is to forecast tourism demand
(international tourist arrivals) to South Africa by making use of different causal models and to compare
the forecasting accuracy of the causal models used. Accurate forecasts of tourism demand may assist
policy–makers and business concerns with decisions regarding future investment and employment.
An overview of South African tourism trends indicates that although domestic arrivals surpass foreign
arrivals in terms of volume, foreign arrivals spend more in South Africa than domestic tourists. It was
also established that tourist arrivals from Africa (including the Middle East), form the largest market of
international tourist arrivals to South Africa. Africa is, however, not included in the empirical analysis
mainly due to data limitations. All the other markets namely Asia, Australasia, Europe, North America,
South America and the United Kingdom are included as origin markets for the empirical analysis and
this study therefore focuses on intercontinental tourism demand for South Africa.
A review of the literature identified several determinants of tourist arrivals, including income, relative
prices, transport cost, climate, supply–side factors, health risks, political stability as well as terrorism
and crime. Most researchers used tourist arrivals/departures or tourist spending/receipts as dependent
variables in empirical tourism demand studies.
The first approach used to forecast tourism demand is a single equation approach, more specifically an
Autoregressive Distributed Lag Model. This relationship between the explanatory variables and the
dependent variable was then used to ex post forecast tourism demand for South Africa from the six
markets identified earlier. Secondly, a system of equation approach, more specifically a Vector
Autoregressive Model and Vector Error Correction Model were estimated for each of the identified six
markets. An impulse response analysis was undertaken to determine the effect of shocks in the
explanatory variables on tourism demand using the Vector Error Correction Model. It was established that it takes on average three years for the effect on tourism demand to disappear. A variance
decomposition analysis was also done using the Vector Error Correction Model to determine how each
variable affects the percentage forecast variance of a certain variable. It was found that income plays an
important role in explaining the percentage forecast variance of almost every variable. The Vector
Autoregressive Model was used to estimate the short–run relationship between the variables and to ex
post forecast tourism demand to South Africa from the six identified markets.
The results showed that enhanced marketing can be done in origin markets with a growing GDP in
order to attract more arrivals from those areas due to the high elasticity of the real GDP per capita in the
long run and its positive impact on tourist arrivals. It is mainly up to the origin countries to increase
their income per capita. Focussing on infrastructure development and maintenance could contribute to
an increase in future tourist arrivals. It is evident that arrivals from Europe might have a negative
relationship with the number of hotel rooms available since tourists from this region might prefer
accommodation with a safari atmosphere such as bush lodges. Investment in such accommodation
facilities and the marketing of such facilities to Europeans may contribute to an increase in arrivals from
Europe. The real exchange rate also plays a role in the price competitiveness of the destination country.
Therefore, in order for South Africa to be more price competitive, inflation rate control can be a way to
increase price competitiveness rather than to have a fixed exchange rate.
Forecasting accuracy was tested by estimating the Mean Absolute Percentage Error, Root Mean Square
Error and Theil’s U of each model. A Seasonal Autoregressive Integrated Moving Average (SARIMA)
model was estimated for each origin market as a benchmark model to determine forecasting accuracy
against this univariate time series approach. The results showed that the Seasonal Autoregressive
Integrated Moving Average model achieved more accurate predictions whereas the Vector
Autoregressive model forecasts were more accurate than the Autoregressive Distributed Lag Model
forecasts. Policy–makers can use both the SARIMA and VAR model, which may generate more
accurate forecast results in order to provide better policy recommendations.Thesis (M.Com. (Economics))--North-West University, Potchefstroom Campus, 2011.
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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.
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Lelo, Nzita Alain. "Forecasting spare parts demand using condition monitoring information." Diss., University of Pretoria, 2009. http://hdl.handle.net/2263/67760.
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The control of an inventory where spare parts demand is infrequent has always been complex to manage because of the randomness of the demand, as well as the existence of a large proportion of zero values in the demand pattern. However, considering the importance of spare parts demand forecasting in production manufacturing and inventory management, several forecasting methods have been developed over the years to allow decision makers in industry to optimize the management of inventory where the demand pattern is infrequent. The Croston method is one of the traditional forecasting method, known because of its ability to take into consideration periods with zero demands. Yet, despite the Croston method’s advantage over other traditional methods, there are still shortcomings in the method because it does not consider the condition of the components to be replaced.
This dissertation proposes an alternative forecasting method to the traditional methods, by means of condition monitoring. This method overcomes the Croston method’s shortcomings by considering the condition information of the component under operation. A statistical model, the so-called proportional hazards model (PHM), which is a regression model, blending event and condition monitoring data, is used to estimate the risk of failure for the component under analysis, while subjected to condition monitoring. To obtain optimal decision making on spare parts demand, a blending of the hazard or risk with the economics is performed, and an optimal risk point is specified. The optimal risk point guides optimal decision making on spare parts policy for the component under analysis.
To generate the data needed to construct the proportional hazards model, a numerical investigation was performed on a fan axial bade where a crack was inserted and propagated to estimate the fatigue crack life and corresponding natural frequencies. The simulation was run using MSC.MARC/MENTAT 2016 software. To validate the finite element model, an experiment was run by using a 50kN Spectral Dynamics electrodynamics shaker to apply base excitation to the fan axial blade specimens. The treatment and computation of data generated from experimental and numerical approaches allowed the construction of the proportional hazards model, with the fatigue lifetime as event data and the blade natural frequencies as covariates or condition monitoring information. The baseline Weibull parameters were estimated by maximizing the likelihood function using the Newton Raphson method and the MATLAB package. This allowed the computation of an objective function to determine the shape, scale and location parameters. Instead of defining the covariate behaviour needed to build the cost function by means of the Markov process, a simulation procedure was utilized to define the cost function and determine the optimal risk which minimizes the cost. Furthermore, as the proportional hazards model depends on both, time and covariates, it was also shown how the PHM behaves when time or covariates carry more weight.
The added value of the proportional hazard model as forecasting spare parts method lies in the fact that it allows one to proactively gather failure information which enables a ‘just in time’ supply of spare parts as well as an optimal maintenance plan.
Forecasting spare parts demand, using condition information, performs better than traditional methods because it reduces an overly large spare parts stock pile. By allowing a ‘just in time’ part availability, the spare parts management becomes more related to the condition of the asset. Additionally, the supply chain management and maintenance cost are optimized, and the preventive replacement of components is optimized compared to the time-based method where a component can be replaced while still having a useful life.Dissertation (MSc)--University of Pretoria, 2018.
Mechanical and Aeronautical Engineering
MSc
Unrestricted
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Silva, Jesús, Naveda Alexa Senior, Guliany Jesús García, Núẽz William Niebles, and Palma Hugo Hernández. "Forecasting Electric Load Demand through Advanced Statistical Techniques." Institute of Physics Publishing, 2020. http://hdl.handle.net/10757/652142.
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Traditional forecasting models have been widely used for decision-making in production, finance and energy. Such is the case of the ARIMA models, developed in the 1970s by George Box and Gwilym Jenkins [1], which incorporate characteristics of the past models of the same series, according to their autocorrelation. This work compares advanced statistical methods for determining the demand for electricity in Colombia, including the SARIMA, econometric and Bayesian methods.
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Tsivras, Sotirios-Ilias. "Load Demand Forecasting : A case study for Greece." Thesis, Högskolan i Gävle, Energisystem och byggnadsteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-29841.
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It is more than a fact that electrical energy is a main production factor of every economic activity. Since electrical power is not easy to store, it needs to be consumed as it is generated in order to keep a constant balance between supply and demand. As a result, for developing an efficient energy market it is significant to create a method for accurately forecasting the electricity consumption. This thesis describes a method for analyzing data provided by the ENTSO-E transparency platform. The ENTSO-E (European Network of Transmission System Operators) is a network of electricity operators from 36 countries across Europe. Its main objective is to provide transparency concerning data of electricity generation and consumption in Europe in order to promote the development of efficient and competitive electricity markets. By using the method described in this thesis, one may use historical data provided by ENTSO-E to forecast the electricity consumption of an EU country for the years to come. As an example, data of electricity consumption in Greece during the years 2015-2018 have been used in order to calculate the average load demand of a weekday during the year 2030. On the other hand, in order to correctly predict the electricity demand of a specific region over the next decade, one should take into account some crucial parameters that may influence not only the evolution of the load demand, but also the fuel mix that will be used in order to cover our future electricity needs. Advances in power generation technologies, evolution of fuel prices, expansion of electricity grid and economic growth are a subset of parameters that should be taken into account for an accurate forecast of the electricity consumption in the long run. Particularly for Greece, a set of parameters that may affect the electricity consumption are being computationally analyzed in order to evaluate their contribution to the load demand curve by the year 2030. These include the interconnection of Greek islands to the mainland, the development of Hellinikon Project and the increase of the share of electric vehicles. The author of this thesis has developed code in Python programming language that can be found in the Appendix. These scripts and functions that implement most of the calculations described in the following chapters can also be used for forecasting the load demand of other EU countries that are included in the ENTSO-E catalogue. The datasets used as input to these algorithms may also be used from the readers to identify more patterns for predicting the load demand for a specific region and time. A sustainable energy system is based on consumers with environmental awareness. As a result, citizens living inside the European Union should become a member of a community that promotes energy saving measures, investments in renewable energy sources and smart metering applications.
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Talupula, Ashik. "Demand Forecasting Of Outbound Logistics Using Machine learning." Thesis, Blekinge Tekniska Högskola, Institutionen för datavetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-18834.
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Background: long term volume forecasting is important for logistics service providers for planning their capacity and taking the strategic decisions. At present demand is estimated by using traditional methods of averaging techniques or with their own experiences which often contain some error. This study is focused on filling these gaps by using machine learning approaches. The sample data set is provided by the organization, which is the leading manufacturer of trucks, buses and construction equipment, the organization has customers from more than 190 markets and has production facilities in 18 countries. Objectives: This study is to investigate a suitable machine learning algorithm that can be used for forecasting demand of outbound distributed products and then evaluating the performance of the selected algorithms by experimenting to articulate the possibility of using long-term forecasting in transportation. Methods: primarily, a literature review was initiated to find a suitable machine learn- ing algorithm and then based on the results of the literature review an experiment is performed to evaluate the performance of the selected algorithms Results: Selected CNN, ANN and LSTM models are performing quite well But based on the type and amount of historical data that models were given to learn, models have a very slight difference in performance measures in terms of forecasting performance. Comparisons are made with different measures that are selected by the literature review Conclusions. This study examines the efficacy of using Convolutional Neural Networks (CNN) for performing demand forecasting of outbound distributed products at the country level. The methodology provided uses convolutions on historical loads. The output from the convolutional operation is supplied to fully connected layers together with other relevant data. The presented methodology was implemented on an organization data set of outbound distributed products per month. Results obtained from the CNN were compared to results obtained by Long Short Term Memories LSTM sequence-to-sequence (LSTM S2S) and Artificial Neural Networks (ANN) for the same dataset. Experimental results showed that the CNN outperformed LSTM while producing comparable results to the ANN. Further testing is needed to compare the performances of different deep learning architectures in outbound forecasting.
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Guerrero, Gomez Gricel Celenne. "Lumpy demand characterization and forecasting performance using self-adaptive forecasting models and Kalman filter." To access this resource online via ProQuest Dissertations and Theses @ UTEP, 2008. http://0-proquest.umi.com.lib.utep.edu/login?COPT=REJTPTU0YmImSU5UPTAmVkVSPTI=&clientId=2515.
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Lee, Wing Yee. "Improving the role of judgment in demand forecasting through enhanced forecasting support system design." Thesis, University of Bath, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.436773.
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Nyulu, Thandekile. "Weather neutral models for short-term electricity demand forecasting." Thesis, Nelson Mandela Metropolitan University, 2013. http://hdl.handle.net/10948/d1018751.
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Energy demand forecasting, and specifically electricity demand forecasting, is a fun-damental feature in both industry and research. Forecasting techniques assist all electricity market participants in accurate planning, selling and purchasing decisions and strategies. Generation and distribution of electricity require appropriate, precise and accurate forecasting methods. Also accurate forecasting models assist producers, researchers and economists to make proper and beneficial future decisions. There are several research papers, which investigate this fundamental aspect and attempt var-ious statistical techniques. Although weather and economic effects have significant influences on electricity demand, in this study they are purposely eliminated from investigation. This research considers calendar-related effects such as months of the year, weekdays and holidays (that is, public holidays, the day before a public holiday, the day after a public holiday, school holidays, university holidays, Easter holidays and major religious holidays) and includes university exams, general election days, day after elections, and municipal elections in the analysis. Regression analysis, cate-gorical regression and auto-regression are used to illustrate the relationships between response variable and explanatory variables. The main objective of the investigation was to build forecasting models based on this calendar data only and to observe how accurate the models can be without taking into account weather effects and economic effects, hence weather neutral models. Weather and economic factors have to be forecasted, and these forecasts are not so accurate and calendar events are known for sure (error-free). Collecting data for weather and economic factors is costly and time consuming, while obtaining calendar data is relatively easy.
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Zhao, Zezheng. "Residential Side Load Forecasting and Optimisation." Thesis, The University of Sydney, 2021. https://hdl.handle.net/2123/27396.
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With the continuous growth in population and energy demands, more attention is being paid to energy consumption issues in residential environments. From the perspective of energy providers, high-accuracy short-term load forecasting plays a significant role in the energy generation plan, efficient energy distribution process and electricity price strategy optimisation. At the user-end, the home energy management system (HEMS) has been proposed as a cost-effective solution to reduce the electricity cost in households, while maintaining users' comfort and reducing the pressure on energy providers.
To handle these challenges, in this work, we introduce a novel model based on the Transformer network to provide an accurate day-ahead load forecasting service. Our model combines a similar day selection approach involving the LightGBM and k-means algorithms. As compared to the traditional RNN-based approach, our proposed model can avoid to falling into local minimum and outperform in the global search. To evaluate the performance of our proposed model, we set up a series of simulation experiments based on the energy consumption data in Australia from 2006 to 2010. The results reveal that the average MAPE our proposed model can achieve is 1.09, the RNN is 2.37 and the LSTM is 1.69.
Furthermore, it is a challenge to design cost-effective scheduling strategies for HEMS, which take many objectives into consideration while potentially benefiting both users and providers. In our work, we propose a new approach named adaptive multi-objective salp swarm algorithm (AMSSA), based on the traditional multi-objective salp swarm algorithm (MSSA), to realise a multi-objective optimisation approach for the power scheduling problem. AMSSA not only fulfils the trade-off among users' comfort, electricity cost and peak to average ratio (PAR), but also enhances the convergence speed for the overall optimisation process. Moreover, we also set up a testbed by using smart appliances and implemented our design on an edge-based energy management system. The experiment results demonstrated a reduction in both electricity cost (47.55%) and PAR (45.73%) as compared with the case without a scheduling scheme.
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Crudelini, Miriam. "Demand Forecasting mediante algoritmi di boosting: una valutazione sperimentale." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/19136/.
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Nell'epoca in cui viviamo, grazie ai dispositivi a nostra disposizione, ognuno di noi è produttore di una grande mole di dati, all'interno dei quali sono racchiuse importanti informazioni.
Il processo di analisi ed estrazione della conoscenza permette di ottenere importanti informazioni.
Un'utilizzo di tali informazioni si ha nel demand forecasting, ossia il processo di previsione della domanda.
In questa tesi verranno analizzate alcune metodologie per effettuare previsioni sulla domanda di un prodotto, concentrandosi su una tipologia di algoritmi spesso utilizzati in questo ambito. Sono stati proposti e valutati tre algoritmi di machine learning basati sul boosting. Per migliorare le prestazioni è stata implementata un fase iniziale di ottimizzazione dei modelli. Infine, i modelli costruiti sono stati testati ed è stata effettuata un'analisi delle relative prestazioni.
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Lobban, Stacey, and Hana Klimsova. "Demand Forecasting : A study at Alfa Laval in Lund." Thesis, Växjö University, School of Management and Economics, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:vxu:diva-2127.
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Accurate forecasting is a real problem at many companies and that includes Alfa Laval in Lund. Alfa Laval experiences problems forecasting for future raw material demand. Management is aware that the forecasting methods used today can be improved or replaced by others. A change could lead to better forecasting accuracy and lower errors which means less inventory, shorter cycle times and better customer service at lower costs.
The purpose of this study is to analyze Alfa Laval’s current forecasting models for demand of raw material used for pressed plates, and then determine if other models are better suited for taking into consideration trends and seasonal variation.
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Sanders, Nada R. "Forecasting short term demand in the physical distribution environment /." Connect to resource, 1986. http://rave.ohiolink.edu/etdc/view.cgi?acc%5Fnum=osu1262198594.
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Mohammadipour, Maryam. "Intermittent demand forecasting with integer autoregressive moving average models." Thesis, Bucks New University, 2009. http://bucks.collections.crest.ac.uk/9586/.
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This PhD thesis focuses on using time series models for counts in modelling and forecasting a special type of count series called intermittent series. An intermittent series is a series of non-negative integer values with some zero values. Such series occur in many areas including inventory control of spare parts. Various methods have been developed for intermittent demand forecasting with Croston’s method being the most widely used. Some studies focus on finding a model underlying Croston’s method. With none of these studies being successful in demonstrating an underlying model for which Croston’s method is optimal, the focus should now shift towards stationary models for intermittent demand forecasting. This thesis explores the application of a class of models for count data called the Integer Autoregressive Moving Average (INARMA) models. INARMA models have had applications in different areas such as medical science and economics, but this is the first attempt to use such a model-based method to forecast intermittent demand. In this PhD research, we first fill some gaps in the INARMA literature by finding the unconditional variance and the autocorrelation function of the general INARMA(p,q) model. The conditional expected value of the aggregated process over lead time is also obtained to be used as a lead time forecast. The accuracy of h-step-ahead and lead time INARMA forecasts are then compared to those obtained by benchmark methods of Croston, Syntetos-Boylan Approximation (SBA) and Shale-Boylan-Johnston (SBJ). The results of the simulation suggest that in the presence of a high autocorrelation in data, INARMA yields much more accurate one-step ahead forecasts than benchmark methods. The degree of improvement increases for longer data histories. It has been shown that instead of identification of the autoregressive and moving average order of the INARMA model, the most general model among the possible models can be used for forecasting. This is especially useful for short history and high autocorrelation in data. The findings of the thesis have been tested on two real data sets: (i) Royal Air Force (RAF) demand history of 16,000 SKUs and (ii) 3,000 series of intermittent demand from the automotive industry. The results show that for sparse data with long history, there is a substantial improvement in using INARMA over the benchmarks in terms of Mean Square Error (MSE) and Mean Absolute Scaled Error (MASE) for the one-step ahead forecasts. However, for series with short history the improvement is narrower. The improvement is greater for h-step ahead forecasts. The results also confirm the superiority of INARMA over the benchmark methods for lead time forecasts.
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Stefancik, John. "Demand forecasting using Monte Carlo Multi-Attribute Utility Theory." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/104825.
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Thesis: S.M. in Technology and Policy, Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, Technology and Policy Program, 2016.Cataloged from PDF version of thesis.
Includes bibliographical references (pages 173-176).
Volatile commodity prices over the past decade, environmentally-focused policy initiatives and new technology developments have forced manufacturers to consider the idea of substituting towards alternative materials in order meet both consumer and societal needs. The threat of substitution has created the need for manufacturing firms and other members of the supply chain to have the ability to understand the implications of substitution on future product market shares and overall raw material demand. This thesis demonstrates how Multi-Attribute Utility Theory (MAUT) can be extended to the group level to forecast future market shares by applying a distribution to the attribute weights and using a Monte Carlo simulation to capture the choices made by a heterogeneous set of decision makers. Unlike established demand forecasting techniques, such as discrete choice models, this methodology requires only a few data points from a handful of expert interviews and allows for systematic changes of preferences over time. Furthermore, the Monte Carlo MAUT methodology utilizes both revealed preference and stated preference data by integrating the two data types through a response surface methodology. Two case studies on underground distribution and overhead distribution power cables are explored in order to illustrate how the Monte Carlo MAUT methodology can be successfully applied in cases where there are diverse product types, limited numbers of decisions makers and historical market share data is sparse. Each case study illustrates how Monte Carlo MAUT can, on a regional basis, provide key insights into the impacts of changing commodity prices, changing product attribute levels, varying new technology learning rates and changing consumer preferences over time. Furthermore, an example of how Monte Carlo MAUT can be utilized to help policymakers evaluate the advantages, disadvantages and overall impact of different policy schemes within an environmental context is provided. Private firms and public governments alike can utilize Monte Carlo MAUT to improve their understanding of how market shares are likely to change over time, and more importantly, the key decisions needed on each party's behalf in order to maximize societal well-being.
by John Stefancik.
S.M. in Technology and Policy
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Kaftan, David. "Design Day Analysis - Forecasting Extreme Daily Natural Gas Demand." Thesis, Marquette University, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10825062.
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This work provides a framework for Design Day analysis. First, we estimate the temperature conditions which are expected to be colder than all but one day in N years. This temperature is known as the Design Day condition. Then, we forecast an upper bound on natural gas demand when temperature is at the Design Day condition.
Natural gas distribution companies (LDCs) need to meet demand during extreme cold days. Just as bridge builders design for a nominal load, natural gas distribution companies need to design for a nominal temperature. This nominal temperature is the Design Day condition. The Design Day condition is the temperature that is expected to be colder than every day except one in N years. Once Design Day conditions are estimated, LDCs need to prepare for the Design Day demand. We provide an upper bound on Design Day demand to ensure LDCs will be able to meet demand.
Design Day conditions are determined in a variety of ways. First, we fit a kernel density function to surrogate temperatures - this method is referred to as the Surrogate Kernel Density Fit. Second, we apply Extreme Value Theory - a field dedicated to finding the maxima or minima of a distribution. In particular, we apply Block-Maxima and Peak-Over-Threshold (POT) techniques. The upper bound of Design Day demand is determined using a modified version of quantile regression.
Similar Design Day conditions are estimated by both the Surrogate Kernel Density Fit and Peaks-Over-Threshold methods. Both methods perform well. The theory supporting the POT method and the empirical performance of the SKDF method lends confidence in the Design Day conditions estimates. The upper bound of demand on these conditions is well modeled by the modified quantile regression technique.
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Sanders, Nada Rankovic. "Forecasting short term demand in the physical distribution environment." The Ohio State University, 1986. http://rave.ohiolink.edu/etdc/view?acc_num=osu1262198594.
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Alsup, Meia(Meia L. ). "Forecasting electricity demand in the data-poor Indian context." Thesis, Massachusetts Institute of Technology, 2020. https://hdl.handle.net/1721.1/129084.
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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, September, 2020Cataloged from student-submitted PDF of thesis.
Includes bibliographical references (pages 51-53).
Electricity demand at the grid level is steadily growing in India. More areas are getting interconnected to the grid; and with rising incomes, electricity is highly affected by adoption of air conditioning systems and electric vehicles. Compared with the developed world context where electricity demand is approximately flat if not decreasing year to year, demand in India is growing. In this paper, we aim to examine forecasting methods and determine an optimal method for forecasts in India. Despite limited historical data, we improve forecasts of electricity demand in India out to the year 2050. The forecasts are in five year increments across three different GDP growth scenarios (not accounting for Covid-19). In addition, a layer of natural variation is added to the forecasts for the purpose of modeling the role of various energy technologies on the grid. The methodology to generate more realistic sample loads from predicted average scenarios is a key contribution.
by Meia Alsup.
M. Eng.
M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
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Bellfield, Sherryl. "Short-term domestic water demand : estimation, forecasting and management." Thesis, University of Leeds, 2001. http://etheses.whiterose.ac.uk/2576/.
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In the UK, the water resource problems during the droughts of 1988-1992 and the well- publicized problems of 1995-1996, serve to highlight the finite nature of the potable water resource. Demand management is increasingly considered a fundamental tool in promoting a sustainable water resource strategy. However, of equal importance is the development of accurate water demand forecasts that work in parallel with demand management measures. These forecasts should predict all components ot water use on different planning horizons. Presently, water plcs have a very limited understanding of factors influencing short-term domestic water demands and rely upon crude methods of forecasting. However, no precise definition of short-term exists within the water industry. Previous research defined ‘short- term to be between twenty-four hours and seven days ahead. It was argued that effective weather prediction and operationally useful forecast times were the determining factor in the definition. The domestic water consumption data used in this research is derived from two different methods, (i) zonal metering and (ii) individual household metering. Welsh Water and Yorkshire Water provided zonal metering data, which refers to the flow to a zone that has many households. Essex and Suffolk Water, Thames Water and Yorkshire Water provided individual household metering data, which is a measure of consumption in single households. These data were used in the determination of: (i) underlying factors that influence the demand for water in both the short- and the medium- to long-term and (ii) factors that influence short-term demands. The influential factors aided in the exploration of modelling strategies to forecast short-term domestic water demands. Approaches explored included a pragmatic approach, based on a form of accounting using a series of 'lookup tables’, and advanced approaches, including stepwise regression, both with and without k-means cluster analysis, and univariate and multivariate ARMA time-series modelling. The most successful approach was then used to determine how future scenarios such as changes in the population base, climate, culture and technology might influence the characteristics of short-term domestic water demands. Household size and property type appears to exert the greatest underlying influence on medium- to long-term domestic water demands. In the short-term, domestic water demand appears to be influenced by the two days antecedent and the prevailing day’s weather conditions, day of the week, calendar effects, school holidays and demand management measures. No single approach provided the best overall prediction of short-term domestic water demands. However, the pragmatic approach emerged as one of the most promising techniques. The pragmatic approach, used to determine how future scenarios might change the characteristics of short-term domestic water demand, suggests that increases in demands are associated with changes in the population base, climate and culture. However, changes in technology associated with the widespread implementation of demand management measures have the potential to suppress the increases and indeed reduce demands to less than those of the present day.
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Haaf, Christine Grace. "Vehicle Demand Forecasting with Discrete Choice Models: 2 Logit 2 Quit." Research Showcase @ CMU, 2014. http://repository.cmu.edu/dissertations/491.
Full textAbstract:
Discrete choice models (DCMs) are used to forecast demand in a variety of engineering, marketing, and policy contexts, and understanding the uncertainty associated with model forecasts is crucial to inform decision-making. This thesis evaluates the suitability of DCMs for forecasting automotive demand. The entire scope of this investigation is too broad to be covered here, but I explore several elements with a focus on three themes: defining how to measure forecast accuracy, comparing model specifications and forecasting methods in terms of prediction accuracy, and comparing the implications of model specifications and forecasting methods on vehicle design. Specifically I address several questions regarding the accuracy and uncertainty of market share predictions resulting from choice of utility function and structural specification, estimation method, and data structure assumptions. I1 compare more than 9,000 models based on those used in peer-reviewed literature and academic and government studies. Firstly, I find that including more model covariates generally improves predictive accuracy, but that the form those covariates take in the utility function is less important. Secondly, better model fit correlates well with better predictive accuracy; however, the models I construct— representative of those in extant literature— exhibit substantial prediction error stemming largely from limited model fit due to unobserved attributes. Lastly, accuracy of predictions in existing markets is neither a necessary nor sufficient condition for use in design. Much of the econometrics literature on vehicle market modeling has presumed that biased coefficients make for bad models. For purely predictive purposes, the drawbacks of potentially mitigating bias using generalized method of moments estimation coupled with instrumental variables outweigh the expected benefits in the experiments conducted in this dissertation. The risk of specifying invalid instruments is high, and my results suggest that the instruments frequently used in the automotive demand literature are likely invalid. Furthermore, biased coefficients are not necessarily bad for maximizing the predictive power of the model. Bias can even aid predictions by implicitly capturing persistent unobserved effects in some circumstances. Including alternative specific constants (ASCs) in DCM utility functions improves model fit but not necessarily forecast accuracy. For frequentist estimated models all tested methods of forecasting ASCs improved share predictions of the whole midsize sedan market over excluding ASC in predictions, but only one method results in improved long term new vehicle, or entrant, forecasts. As seen in a synthetic data study, assuming an incorrect relationship between observed attributes and the ASC for forecasting risks making worse forecasts than would be made by a model that excludes ASCs entirely. Treating the ASCs as model parameters with full distributions of uncertainty via Bayesian estimation is more robust to selection of ASC forecasting method and less reliant on persistent market structures, however it comes at increased computational cost. Additionally, the best long term forecasts are made by the frequentist model that treats ASCs as calibration constants fit to the model post estimation of other parameters.
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Jeon, Gyoo Jeong. "Innovative methods for long-term mineral forecasting." Diss., The University of Arizona, 1989. http://hdl.handle.net/10150/184653.
Full textAbstract:
This study presents improved methods for long-term forecasting of mineral demands. Intensity of use, both in its simple, original form and as described by richer economic relations is one such method, particularly when intensity of use is estimated using rigorous statistical methods. Additionally, this dissertation explores the implications of the learning curve for long term forecasting of mineral demands. This study begins by considering the empirical evidence which applies when a learning curve is present. Then, if a learning pattern is present, the learning model is used to examine an economic measure for specified levels of economic activity. This dissertation also provides some empirical results on the learning curve in mineral industries and demonstrates how the learning model can be used as an economic forecasting tool. As an alternative to the intensity of use and learning models, there is a vector model, either using time varying coefficients or expressed as a transcendental function, to capture dynamics. This model estimates the time varying parameters from the vector space instead of the variable space. The major advantage of this model is that it honors correlations between variables. This is especially important in ex ante forecasting in which explanatory variables themselves must be forecast to obtain a forecast of the dependent variable.
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Schwann, Gregory Michael. "Housing demand : an empirical intertemporal model." Thesis, University of British Columbia, 1987. http://hdl.handle.net/2429/27526.
Full textAbstract:
I develop an empirical model of housing demand which is based as closely as possible on a theoretical
intertemporal model of consumer demand. In the empirical model, intertemporal behavior by households is incorporated in two ways. First, a household's expected length of occupancy in a dwelling is a parameter in the model; thus, households are able to choose when to move. Second, a household's decision to move and its choice of dwelling are based on the same intertemporal utility function. The parameters of the utility function are estimated using a switching regresion model in which the decision to move and the choice of housing quantity are jointly determined. The model has four other features: (1) a characteristics approach to housing demand is taken, (2) the transaction costs of changing dwellings are incorporated in the model, (3) sample data on household
mortgages are employed in computing the user cost of owned dwellings, and (4) demographic variables are incorporated systematically into the household utility function.
Rosen's two step proceedure is used to estimate the model. Cragg's technique for estimating regressions in the presence of heteroscedasticity of unknown form is used to estimate the hedonic regressions in step one of the proceedure. In the second step, the switching regression model, is estimated by maximum likelihood.
A micro data set of 2,513 Canadian households is used in the estimations.
The stage one hedonic regressions indicate that urban housing markets are not in long run equilibrium,
that the errors of the hedonic regressions are heteroscedastic, and that simple functional forms for hedonic regressions may perform as well as more complex forms. The stage two estimates
establish that a tight link between the theoretical and empirical models of housing demand produces a better model. My results show that conventional static models of housing demand are misspecified. They indicate that households have vastly different planned lengths of dwelling occupancy.
They also indicate that housing demand is determined to a great extent by demographic factors.Arts, Faculty of
Vancouver School of Economics
Graduate
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Al-Aoudah, Ahmed A. "Long term load forecasting for the Central Region of Saudi Arabia." Thesis, Cranfield University, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.250633.
Full text
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Hartley, Joseph Alan. "A neural network and rule based system application in water demand forecasting." Thesis, Brunel University, 1995. http://bura.brunel.ac.uk/handle/2438/7867.
Full textAbstract:
This thesis describes a short term water demand forecasting application that is based upon a combination of a neural network forecast generator and a rule based system that modifies the resulting forecasts. Conventionally, short term forecasting of both water consumption and electrical load demand has been based upon mathematical models that aim to either extract the mathematical properties displayed by a time series of historical data, or represent the causal relationships between the level of demand and the key factors that determine that demand. These conventional approaches have been able to achieve acceptable levels of prediction accuracy for those days where distorting, non cyclic influences are not present to a significant degree. However, when such distortions are present, then the resultant decrease in prediction accuracy has a detrimental effect upon the controlling systems that are attempting to optimise the operation of the water or electricity supply network. The abnormal, non cyclic factors can be divided into those which are related to changes in the supply network itself, those that are related to particular dates or times of the year and those which are related to the prevailing meteorological conditions. If a prediction system is to provide consistently accurate forecasts then it has to be able to incorporate the effects of each of the factor types outlined above. The prediction system proposed in this thesis achieves this by the use of a neural network that by the application of appropriately classified example sets, can track the varying relationship between the level of demand and key meteorological variables. The influence of supply network changes and calendar related events are accounted for by the use of a rule base of prediction adjusting rules that are built up with reference to past occurrences of similar events. The resulting system is capable of eliminating a significant proportion of the large prediction errors that can lead to non optimal supply network operation.
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Monahan, Kayla M. "Aircraft Demand Forecasting." 2016. https://scholarworks.umass.edu/masters_theses_2/329.
Full textAbstract:
This thesis aims to forecast aircraft demand in the aerospace and defense industry, specifically aircraft orders and deliveries. Orders are often placed by airline companies with aircraft manufacturers, and then suddenly canceled due to changes in plans. Therefore, at some point during the three-year lead time, the number of orders placed and realized deliveries may be quite different. As a result, orders and deliveries are very difficult to predict and are influenced by many different factors. Among these factors are past trends, macroeconomic indicators as well as aircraft sales measures. These predictor variables were analyzed thoroughly, then used with time series and multiple regression forecasting methods to develop different forecasts for quarterly and annual orders and deliveries. The relative accuracies of forecasts were measured and compared through the use of Theil’s U statistic. Finally, a linear program was used to aggregate multiple forecasts to develop an optimal combination of all forecasts. In conclusion, the methods employed in this thesis are quite effective and produce a wholesome aggregate forecast with an error that is generally quite low for a forecasting task as challenging as this one.
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Weinberg, David. "Electrical power demand forecasting." Thesis, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-385594.
Full textAbstract:
The electrical load, sampled every hour, at Salagatan 18 in Uppsala was used to form models and for forecasting the load. It was investigated whether Multi Seasonal ARIMA models and Support Vector Regression were suitable. The models were compared to a naive persistence benchmark in periods of high and low volatility. Both short range 24h and long range 168h forecasts were made. It was concluded that both model proposals could be used to forecast the electrical load series. ARIMA and Support Vector Regression model proposals outperformed the benchmark for long and short range forecasts in both volatile and non volatile settings. The mean absolute percentage errors of the best ARIMA model for a one week forecast were 15.1% and 21.6% for non volatile and volatile settings, respectively.
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Chang, Ku-kuang, and 張谷光. "Improved Methods in Collaborative Forecasting and Demand Forecasting." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/95773804895773020937.
Full textAbstract:
博士國立臺灣科技大學
工業管理系
96
Forecasting is an important part of supply chain management. The forecasting accuracy will influence the material planning and the scheduling; even affect the replenishment plan of the retailer of the supply chain downstream. In this study, we focus on two issues which are the collaborative forecasting between enterprises and the demand forecasting. The collaborative forecasting concept, shares the information and risk between the supply chain partners, combines both advantage the seller and buyer. It no longer only focuses on one side of supply chain, but contains the coordination, planning, execution and monitoring among all members until collaboratively producing the final forecasting with of both agreement. The final forecasting could drive the replenishment plan successfully. Demand forecasting emphasized on how to promote forecasting accuracy of the products. We could obtain a more suitable demand plan and a better collaborative forecasting due to the higher degree of accuracy. If we promote the accuracy of collaborative forecasting and demand forecasting, we can monitor the forecasting accuracy and avoid the shortage or too many stocks in the whole supply chain. To improve the accuracy of the forecasting, we consider three forecasting problems which include collaborative forecasting, multi-product forecasting and combining forecasting under the demand forecasting. First, we applied Six Sigma methodology and proposed a continuous improvement model on different phases of collaborative planning, forecasting and replenishment (CPFR). A real case is used to demonstrate how to improve the performance of collaborative forecasts. Second, in a multi-product framework, the traditional estimation methods could not get the satisfied results. We have conducted research using the hybrid genetic algorithm (GA) for an efficient parameter estimation method for multi-product forecasting. Finally, we developed a demand forecasting methodology that combines market and shipment forecasts. We used the LCD monitor sales data to test and verify our methods. In the past studies, the linear ways were usually used to estimate the parameters of combining forecasts. Fuzzy neural network (FNN) is a nonlinear model and often used to find the best combination in past references. The applications of FNN to combining forecasting problems are extremely few. We developed an integrated fuzzy neural network model to find the best combining forecasting and compare with other traditional methods such as k method, adaptive set of weights and linear composite. Results show that the proposed model using integrated FNN can gain the superior forecasting efficiency and performance in the whole.
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Pan, Shiau-Wet, and 潘曉葦. "Constructing the Demand Forecasting Model." Thesis, 2000. http://ndltd.ncl.edu.tw/handle/33268185814063481892.
Full textAbstract:
碩士國立臺灣科技大學
資訊管理系
88
Demand forecasting is one of the most crucial issues of inventory management since production planning and inventory management are based on demand forecasts. To satisfy the random demands of customers, managers need to decide appropriate production planning and inventory levels. Reducing the demand forecasting error can free up capital and inventory space, which leads to reduction in the cost of inventory management. There exists no unique best forecasting method for different demand models. Everyone has already known that there is no best forecasting method fitting all situations in the world. Combining forecasting method use historical data to select the most promising method in each of the three types of forecasting method. Then a quadratic program is solved for the appropriate weights on each method such that the minimum mean square errors is achieved. Using mean squared errors as a comparison criterion, this study compares three common types of forecasting methods with the combining-method in sixteen different situations. Empirical show that the combining-method is outperforms the existing methods by more than ten percent in the mean squared errors..
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Liu, Chia-Hsin, and 劉佳欣. "Consuming Emergency Relief Demand Forecasting." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/13063804109913340843.
Full textAbstract:
碩士國立交通大學
交通運輸研究所
95
Because of rescue time, quantity and quality of resources and information of affected areas are limited. After the large-scale earthquake disaster happened, how to distribute urgent relief effectively, efficiently and precisely is vital to the alleviation of disaster impact in the affected areas, which remains challenging in the field of logistics and related study areas. In this study, we present a data-fusion approach to the operations of emergency relief distribution system responding to deal with the disorder information in the initial stage of disaster. Based on a proposed 2-layer consuming emergency relief demand forecasting conceptual framework, the proposed methodology involves two recursive mechanisms: (1) consuming relief demand forecasting, and (2) disaster-affected area grouping. Numerical studies with a simulated data sets are conducted, and the corresponding results indicate the applicability of the proposed method and its potential advantages. We hope that this study can not only make the proposed emergency logistics system available with more benefits to the development of emergency logistics systems for the urgent needs of disaster areas around the world but also stimulate more excellent researches concerning emergency logistics management.
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RAJESH, PAILI. "FORECASTING ON A INTERMITTENT DEMAND." Thesis, 2020. http://dspace.dtu.ac.in:8080/jspui/handle/repository/18208.
Full textAbstract:
Forecasting aamethods aare aaoften aavalued aaby aameans aaof aasimulation aastudies. aaFor
aaintermittent aademandaaitems aathereaareaaoftenaaveryaafewaanon–zeroaaobservations, aasoaait aais aahardaato
aacheckaanyaassumptions, aabecauseaastatistical aainformationaais aaoftenaatooaaweakaatoaadetermine, aafor
aaexample, aadistributionaaof aa aavariable. aaTherefore, aait aaseems aaimportant aatoaaverifyaa theaaforecasting
aamethods aaonaatheaabasis aaof aareal aadata. aaTheaamainaaimaaof aatheaarticleaais aanaaempirical aaverification
aaof aaseveral aaforecastingaamethods aapplicableaainaacaseaaof aaintermittent aademand. aaSomeaaitems aare
aasold aaonly aain aaspecific aasubperiods aa(in aagiven aamonth aain aaeach aayear, aafor aaexample), aabut aamost
aaforecasting aamethods aa(such aas aaCroston's aamethod) aagive aanon–zero aaforecasts aafor aall aaperiods.
aaFor aaexample, aasummer aawork aaclothes aashould aahave aanon–zero aaforecasts aaonly aafor aasummer
aamonths aand aamany aamethods aawill aausually aaprovide aanon–zero aaforecasts aafor aall aamonths aaunder
aaconsideration. aaThis aawas aathe aamotivation aafor aaproposing aand aatesting aa aanew aaforecasting
aatechnique aawhich aacan aabe aapplicable aato aaseasonal aaitems. aa In aathe aarticle aaeight aamethods aawere
aappliedaatoaaconstruct aaseparateaaforecastingaasystems aasuchaas,
▪ CROSTON
▪ TSB_CROSTON
▪ HyperbolicaaExponential aaSmootheningaaModel
▪ SBA
▪ Holt's aaLinear aa
▪ Error aatrendaaSeasonality
▪ L-STARaa(logisticaasmoothaatransitionaaAutoaaregressive)
▪ ARMAaa(AutoaaRegressiveaaMovingaaAverage)
The aapresented aanalysis aamight aabe aahelpful aafor aaenterprises aafacing aathe aaproblem aaof
aaforecastingaaintermittent aaitems aa(andaaseasonal aaintermittent aaitems aas aawell).
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Chen, Meng-Yin, and 陳孟吟. "Intermittent Demand Classification and Demand Forecasting for Medical Materials." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/45671883790010094435.
Full textAbstract:
碩士東海大學
工業工程與經營資訊學系
103
The accuracy forecasting is the basic of inventory management. Intermittent demand is random demand with a lot of zero values. Materials have different types and frequency of usage in healthcare., Usage is affect by patients’ wound size and age. Due to the uncertainty of materials usage, it is difficult to predict materials. Therefore, this paper classify demand by square coefficient of variation (CV2) and the average inter-demand interval (ADI) and use Simple Moving Average(SMA), Single Exponential Smoothing(SES) , Autoregressive Integrated Moving Average model(ARIMA) and Croston’s method to forecasting. By a hospital’s datasets, it shows that there are optimal forecasting method in each classification. In lumpy, erratic and intermittent demand, Croston single exponential smoothing produces more accurate forecast. Simple moving average has better performance in smooth demand. Based on the classification and forecasting method, it can decrease inventory , shortage and the cost of inventory management . Finally, the proposed model could be applied on other hospital replenishment case.
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Ahmed, Naveed Ahmed Nasar. "Demand Forecasting Model for Emergent Manufacturing." 2008. http://trace.tennessee.edu/utk_gradthes/414.
Full textAbstract:
Emergence of outsourcing and global partnerships has driven the need for emergent manufacturing. Emergent manufacturing is a concept and mechanism that allows manufacturing based organizations to mitigate the risk of outsourcing their manufacturing functions. The implementation of emergent manufacturing is on a rise and yet many industrial facilities have to decide when to switch to emergent manufacturing. To achieve a strategic fit of emergent manufacturing with the existing manufacturing facilities is the current need of the Industry. There is a strong need to develop a body of literature and models specifically for this task. This thesis aims to develop a model to better forecast the demand of emergent manufacturing. This is achieved by designing mathematical, simulation and statistical models to predict the demand of emergent manufacturing. This new proposed model would develop a guide line to implement, manage and sustain emergent manufacturing in today‟s aggressively outsourcing world, where manufacturing facilities are rapidly being downsized to cut down operational costs.
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Liou, Vincent, and 劉文祺. "Trend Seasonal Smoothing Demand Forecasting Models." Thesis, 1995. http://ndltd.ncl.edu.tw/handle/11889280232213944305.
Full text