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.

Dissertation (PhD)--University of Stellenbosch, 2002 / ENGLISH ABSTRACT: Singular spectrum analysis (SSA) originated in the field of Physics. The technique is non-parametric by nature and inter alia finds application in atmospheric sciences, signal processing and recently in financial markets. The technique can handle a very broad class of time series that can contain combinations of complex periodicities, polynomial or exponential trend. Forecasting techniques are reviewed in this study, and a new coordinate free joint-horizon k-period-ahead forecasting formulation is derived. The study also considers model selection in SSA, from which it become apparent that forward validation results in more stable model selection. The roots of SSA are outlined and distributional assumptions of signal senes are considered ab initio. Pitfalls that arise in the multivariate statistical theory are identified. Different approaches of recurrent one-period-ahead forecasting are then reviewed. The forecasting approaches are all supplied in algorithmic form to ensure effortless adaptation to computer programs. Theoretical considerations, underlying the forecasting algorithms, are also considered. A new coordinate free joint-horizon kperiod- ahead forecasting formulation is derived and also adapted for the multichannel SSA case. Different model selection techniques are then considered. The use of scree-diagrams, phase space portraits, percentage variation explained by eigenvectors, cross and forward validation are considered in detail. The non-parametric nature of SSA essentially results in the use of non-parametric model selection techniques. Finally, the study also considers a commercial software package that is available and compares it with Fortran code, which was developed as part of the study. / AFRIKAANSE OPSOMMING: Singulier spektraalanalise (SSA) het sy oorsprong in die Fisika. Die tegniek is nieparametries van aard en vind toepassing in velde soos atmosferiese wetenskappe, seinprossesering en onlangs in finansiële markte. Die tegniek kan 'n wye verskeidenheid tydreekse hanteer wat kombinasies van komplekse periodisiteite, polinomiese- en eksponensiële tendense insluit. Vooruitskattingstegnieke word ook in hierdie studie beskou, en 'n nuwe koërdinaatvrye gesamentlike horison k-periodevooruitskattingformulering word afgelei. Die studie beskou ook model seleksie in SSA, waaruit duidelik blyk dat voorwaartse validasie meer stabiele model seleksie tot gevolg het. Die agtergrond van SSA word ab initio geskets en verdelingsaannames van seinreekse beskou. Probleemgevalle wat voorkom in die meervoudige statistiese teorie word duidelik geïdentifiseer. Verskeie tegnieke van herhalende toepassing van een-periode-vooruitskatting word daarna beskou. Die benaderings tot vooruitskatting word in algororitmiese formaat verskaf wat die aanpassing na rekenaarprogrammering vergemaklik. Teoretiese vraagstukke, onderliggend aan die vooruitskattings-algortimes, word ook beskou. 'n Nuwe koërdinaatvrye gesamentlike horison k-periode-vooruitskattingsformulering word afgelei en aangepas vir die multikanaal SSA geval. Verskillende model seleksie tegnieke is ook beskou. Die gebruik van "scree"- diagramme, fase ruimte diagramme, persentasie variasie verklaar deur eievektore, kruis- en voorwaartse validasie word ook aangespreek. Die nie-parametriese aard van SSA noop die gebruik van nie-parametriese model seleksie tegnieke. Die studie vergelyk laastens 'n kommersiële sagtewarepakket met die Fortran bronkode wat as deel van hierdie studie ontwikkel is.

published_or_final_version / Civil and Structural Engineering / Doctoral / Doctor of Philosophy

published_or_final_version / Civil Engineering / Doctoral / Doctor of Philosophy

published_or_final_version / Statistics and Actuarial Science / Doctoral / Doctor of Philosophy

published_or_final_version / Environmental Management / Master / Master of Science in Environmental Management

published_or_final_version / abstract / Statistics and Actuarial Science / Master / Master of Philosophy

published_or_final_version / Environmental Management / Master / Master of Science in Environmental Management

Atmospheric artifacts superimposed on interferometric synthetic aperture radar (InSAR) measurements have the potential to greatly impede the accurate estimation of deformation signals. The research presented in this dissertation demonstrates a novel InSAR time series algorithm, called HiRAPS algorithm, for effectively estimating high resolution atmospheric phase screens (APS) from differential InSAR measurements. In summary, the HiRAPS algorithm utilizes short time span differential interferograms and rearranges components of existing advanced InSAR techniques to identify a higher density of scatterers used to create the APS. The improved scatterer density allows one to estimate high spatial frequency atmospheric signals not recovered from existing InSAR time series techniques. The HiRAPS algorithm was tested with simulated and actual data, which contain phase contributions from linear and nonlinear deformation, topographic height errors, and atmospheric artifacts. Simulated differential interferograms were generated to have the same spatial and temporal baselines as the actual differential interferograms formed from RADARSAT-1 data over Phoenix, Arizona. The APS superimposed on simulated differential interferograms were then estimated and compared to simulated APS. The root mean square error (RMSE) between the estimated and simulated APS was calculated to qualitatively assess the different values obtained. The RMSE was 0.26 radians when utilizing the HiRAPS algorithm, compared to an RMSE value of 0.39 radians using an implementation of the permanent scatterer (PS) algorithm. The HiRAPS algorithm also showed its applicability for estimating high spatial frequency atmospheric signals for actual data. Sixty-six SAR images, starting from October 5, 2002 and spanning 5 years, were processed for this research. The APS pixel density obtained using the HiRAPS algorithm was 253 pixels per square kilometer, compared to 14 pixels per square kilometer utilizing the PS algorithm. The APS superimposed on the differential interferograms were estimated with both the proposed and PS algorithms. High resolution APS were estimated with the HiRAPS algorithm, whereas only low resolution APS were obtained with the PS algorithm. After estimating and removing estimated APS, the phase stability of APS-free differential interferograms was examined by identifying the permanent scatterers (PS). The final density of identified PS obtained with the HiRAPS algorithm was 453 PS per square kilometer, whereas the density of detected PS using the generic PS algorithm was 381 PS per square kilometer. The maximum difference in the deformation time series between the HiRAPS algorithm and the PS algorithm was less than 6 mm. However, the HiRAPS algorithm resulted in less apparent noise in the time series than the PS algorithm due to the precise estimation of APS. / text

This dissertation is composed of three essays, each examining a unique question relating to the role of intergovernmental transfers in fiscal federalism. Using a panel of the 48 contiguous U.S. states along with recent advances in nonstationary panel and spatial econometric methods this dissertation offers a number of important insights into the workings of intergovernmental transfers and therefore a clearer understanding of the interactions among the different layers of government. The third chapter examines the relationship between intergovernmental revenues from the federal government and intergovernmental expenditures to local governments. As observed by Wildasin (2010), there remains remarkable stability in the ratio of state-tolocal transfers to federal-to-state transfers despite the disparate programs being financed by each. Therefore, the purpose of this essay is to examine the extent to which states serve as a conduit for funds from the federal government to local governments. In particular, the research question asks to what degree do federal transfers stimulate transfers to local governments. The fourth chapter explores the direction of causality between tax revenues and expenditures in answering the four hypotheses set forth in the literature: tax-spend, spend-tax, fiscal synchronization, and institutional separation. Furthermore, along with exploring the role served by intergovernmental transfers within the revenue-expenditure nexus, this essay also examines differences relating to the revenue-expenditure nexus between states with relatively higher debt levels and states with low debt levels, in order to better understand the fiscal causal links favorable for debt accumulation. The purpose of the fifth chapter is to ascertain the effect interstate fiscal interactions on the stimulative effect of grants on state level expenditures. The vast literature on fiscal competition suggests that states do not make decisions in isolation, therefore, spatial econometrics are used to capture spillovers and mimicking behavior across states. Following Boarnet and Glazer (2002), the effect of informational externalities arising from grants awarded to neighboring states are examined as well as the effect of spending spillovers from neighboring states. The results show that the flypaper anomaly (i.e. the stimulative effect of grants greater than a pure income effect) can be explained by interstate fiscal interactions.