A Spatial Decision Support System for Oil Spill Response and Recovery

abstract: Coastal areas are susceptible to man-made disasters, such as oil spills, which not only have a dreadful impact on the lives of coastal communities and businesses but also have lasting and hazardous consequences. The United States coastal areas, especially the Gulf of Mexico, have witnessed devastating oil spills of varied sizes and durations that resulted in major economic and ecological losses. These disasters affected the oil, housing, forestry, tourism, and fishing industries with overall costs exceeding billions of dollars (Baade et al. (2007); Smith et al. (2011)). Extensive research has been done with respect to oil spill simulation techniques, spatial optimization models, and innovative strategies to deal with spill response and planning efforts. However, most of the research done in those areas is done independently of each other, leaving a conceptual void between them. In the following work, this thesis presents a Spatial Decision Support System (SDSS), which efficiently integrates the independent facets of spill modeling techniques and spatial optimization to enable officials to investigate and explore the various options to clean up an offshore oil spill to make a more informed decision. This thesis utilizes Blowout and Spill Occurrence Model (BLOSOM) developed by Sim et al. (2015) to simulate hypothetical oil spill scenarios, followed by the Oil Spill Cleanup and Operational Model (OSCOM) developed by Grubesic et al. (2017) to spatially optimize the response efforts. The results of this combination are visualized in the SDSS, featuring geographical maps, so the boat ramps from which the response should be launched can be easily identified along with the amount of oil that hits the shore thereby visualizing the intensity of the impact of the spill in the coastal areas for various cleanup targets.Dissertation/ThesisMasters Thesis Computer Science 201

Developing A Spatial Interface For Information Visualization And Management In A Crisis Response Scenario

The focus of this study was to investigate how a spatial interface can be effectively utilized to support information presentation and information integration via human-centric data visualization, leading to decreased cognitive load, more accurate situation awareness, and subsequently, improved task performance. In high tempo, information intensive environments like those managed by an emergency operations center (EOC), information organization tools are essential. Though users can be trained to use conventional email software applications efficiently, the constraints of the information management paradigms inherent to conventional systems may limit a user\u27s ability to gather context and create an accurate picture of the situation. It is possible that new data visualization techniques and information management paradigms may improve a user\u27s performance far beyond these limits. To address these issues, theories regarding information management, cognitive workload and data visualization paradigms were explored and applied to create a software prototype spatial interface. This study focused on how an individual member of an EOC would need to collect and organize incoming incident reports (e.g., emails) for the purpose of quick analysis and integration. The operator then used this information to build a picture of the event or events taking place in their sphere of influence. Performance metrics were applied to determine whether or not an individual could perform faster and more accurately with the Incident Report Visual Organizer (IRVO) prototype software interface as opposed to a conventional interface (Microsoft Outlook). The findings from this exploratory evaluation are discussed, as well as the potential implications of utilizing spatial interfaces to manage information in dynamic environments

Cognitive Fit in Visualizing Big Data

This dissertation examines the consequences of cognitive fit in visualizing big data. Specifically, it focuses on the interplay between different types of business data analysis tasks and visualization methods, and how the defining characteristics of big data (i.e., volume and variety) moderate the outcomes concerning data analysis performance (i.e., solution time and solution accuracy). A 12-cell repeated-measures laboratory experiment (n=145) using eye trackers is conducted to test the hypotheses. Data analysis performance is observed to improve when the information emphasized by a visualization method matches the specific information requirements for a data analysis task. Such improvements in data analysis performance are further amplified when the visualized information has high volume and variety. This dissertation contributes to the literature in at least three ways. First, it improves our understanding of cognitive fit and how it manifests in analysts’ problem solving behaviors when using visualization tools. This is done by analyzing participants’ eye movement and gaze fixation patterns while they work with different types of data analysis tasks and visualization methods. Based on this analysis, this study proposes an objective method for assessing and measuring cognitive fit. Second, this study maps visualization characteristics to business data analysis task types, and informs the choice of visualization tools among an ever-increasing number of alternatives for supporting the complex problems faced by big data analysts. Third, this dissertation extends the cognitive fit theory to the big data context and highlights the relative importance of cognitive fit in this setting by demonstrating that increases in volume and variety amplify the task performance consequences of cognitive fit. The limitations of the experiment conducted for this dissertation and the future research opportunities they present are discussed. The findings of this dissertation also can inform the development of new visualization tools and techniques based on task and data characteristics

Supply management and procurement at a South African FMCG company : a practical example of developing a decision support tool for managing direct material cost

Thesis (MComm)—Stellenbosch University, 2016.ENGLISH ABSTRACT: One of the functions within the supply chain of manufacturing companies is the procurement of direct materials that are needed for the production of products sold to consumers. Even though the process of purchasing materials was always required within the manufacturing process, it only recently gained attention on management level. Today, organisations set up centralised procurement functions that develop global sourcing strategies in order to align procurement processes, people and technology. The goal is to reduce total cost while maintaining high quality, availability and service levels. However, procurement functions are challenged by a number of risks during its global sourcing activities that can have major impacts on direct material cost. Johnson & Johnson is a major global player within the Fast Moving Consumer Goods (FMCG) industry and acknowledged a significant sense of uncertainty relating to the identification and measurement of direct material cost drivers within their global procurement function. Even though Johnson & Johnson is aware that the economic environment has an impact on its procurement spend, it has a limited ability to measure and quantify these effects. Based on the case of Johnson & Johnson, this study’s objective was to develop a decision support tool that measures and analyses the impact of cost drivers on direct material cost. The aim was to develop a model that can be used by procurement professionals in industry in order to provide insight into the procurement cost structure and to identify opportunities that can lead to risk and cost reduction. A case study research design was followed, which included secondary and primary research to collect qualitative and quantitative data. The research methods included observations and input discussions at the company, as well as a comprehensive model development process, which was used in order to create the decision support tool. As the decision support tool was developed on the case of Johnson & Johnson, data were collected from the company in order to test the model and generate outputs. Following the individual process steps of the development process resulted in a highly structured and documented approach to develop the decision support tool. Two major cost drivers of procurement spend when conducting global sourcing were identified: fluctuating exchange rates and volatile commodity markets. Both of these cost drivers were analysed and included during the decision-support tool development process. As a result, a decision support tool is presented that provides functionality to measure the exposure and the potential impact value of the first-tier currency impact, second-tier currency impact as well as the inflation impact. Furthermore, “what-if” and scenario analyses provide a predictive view based on actual forecasts. As an additional output, the decision support tool provides detailed insight and transparency of the total procurement spend, providing important information for decision makers.AFRIKAANSE OPSOMMING: Een van die funksies in die voorsieningsketting van vervaardigingsmaatskappye is die verkryging van direkte materiaal wat benodig word vir die vervaardiging van produkte wat aan verbruikers verkoop word. Hoewel die proses van materiaalaankope nog altyd binne die vervaardigingsproses nodig was, het dit eers onlangs op bestuursvlak aandag getrek. Vandag stel organisasies gesentraliseerde verkrygingsfunksies saam wat globale strategieë ontwikkel vir die verkryging van hulpbronne om sodoende verkrygingsprosesse, mense en tegnologie met mekaar in ooreenstemming te bring. Die doel is om totale koste te verminder terwyl hoë gehalte, beskikbaarheid en diensvlakke gehandhaaf word. Tydens die globale verkrygingsaktiwiteite word daardie funksies egter deur ’n aantal risiko-uitdagings in die gesig gestaar wat ’n groot impak op direkte materiaalkoste kan hê. Johnson & Johnson is ’n vername wêreldspeler binne die industrie vir vinnig bewegende verbruikersgoedere (VBVG) en herken ’n beduidende gevoel van onsekerheid wat verband hou met die identifisering en meting van direkte materiaalkostedrywers binne hulle globale verkrygingsfunksie. Hoewel Johnson & Johnson daarvan bewus is dat die ekonomiese omgewing ’n impak op hulle verkrygingsbesteding het, beskik hulle oor beperkte vermoë om hierdie effekte te meet en te kwantifiseer. Gegrond op die geval van Johnson & Johnson, was hierdie studie se doelwit om ’n besluitsteunhulpmiddel te ontwikkel vir die identifisering en meting van direkte materiaalkostedrywers. Die oorhoofse doel was om ’n model te ontwikkel wat deur verkrygingsberoepslui in die bedryf gebruik kan word om insig in die verkrygingkostestruktuur te voorsien, en om geleenthede te identifiseer wat na risiko en kostevermindering kan lei. ’n Gevallestudienavorsingsontwerp is gevolg wat primêre en sekondêre navorsing ingesluit het vir die versameling van kwalitatiewe en kwantitatiewe data. Die navorsingsmetodes het waarnemings en besprekings by die maatskappy sowel as die omvattende modelontwikkelingsproses ingesluit om hierdie besluitsteunhulpmiddel te skep. Aangesien die besluitsteunhulpmiddel op die geval van Johnson & Johnson berus, is data van die maatskappy versamel om sodoende die model te toets, en uitset te genereer. Die volg van die individuele stappe van die ontwikkelingsproses het gelei tot ’n hoogs gestruktureerde en gedokumenteerde benadering in die ontwikkeling van die besluitsteunhulpmiddel. Twee belangrike kostedrywers van verkrygingbesteding in die uitvoer van globale verkryging is geïdentifiseer: wisselkoersfluktuering en onbestendige kommoditeitsmarkte. Albei hierdie kostedrywers is ontleed en ingesluit tydens die ontwikkelingsproses van die besluitsteunhulpmiddel. Gevolglik word ’n besluitsteunhulpmiddel gebied wat funksionaliteit verskaf om die blootstelling en potensiële impakwaarde van die eerstevlakvaluta-impak, tweedevlakvaluta-impak, sowel as die inflasie-impak te meet. Verder verskaf “wat as”- en scenario-ontledings voorspellende beskouings wat op werklike vooruitskattings gegrond is. As bykomende uitset verskaf die besluitsteunhulpmiddel gedetailleerde insig en deursigtigheid van die totale verkrygingbesteding, en verskaf sodoende belangrike inligting vir besluitnemers