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Current industrial companies are highly pressured by growing competitiveness and globalization, while striving for increased production effectiveness. Meanwhile, flustered markets and amplified customer demands are causing manufacturers to shift strategy. Hence, international companies are challenged to pursue changes, in order to continue being competitive on global markets. Consequently, a new industrial revolution has taken place, introduced as Industry 4.0. This new concept incorporates organizational improvement and digitalization of current information and data flows. Accomplished by data from embedded systems through connected machines, devices and humans into a combined interface. Thus, companies are given possibilities to improve current production systems, simultaneously saving operational costs and minimizing insufficient production development. Smart Factories, being the foundation of Industry 4.0 results in making more accurate and precise operational decisions from abilities to test industrial changes in a virtual world before real-life implementation. However, in order to assure these functions as intended, enormous amount of data must be collected, analysed and evaluated. The indicated data will aid companies to make more self-aware and automated decisions, resulting in increased effectiveness in production. Thus, the concept will clearly change how operational decisions are made today. Nowadays, Discrete Event Simulation is a commonly applied tool founded on specific data requirements as operational changes can be tested in virtual settings. Accordingly, it is believed that simulation can aid companies that are striving for implementing Industry 4.0. As a result, data requirements between Discrete Event Simulation and Industry 4.0 needs to be established, while detecting the current data gap in operational context. Hence, the purpose of this thesis is to analyse the data requirements of Discrete Event Simulation and Industry 4.0 for improving operational decisions of production systems. In order to justify the purpose, the following research questions has been stated:   RQ1: What are the data challenges in existing production systems? RQ2: What data is required for implementing Industry 4.0 in production systems? RQ3: How can data requirements from Discrete Event Simulation benefit operational decisions when implementing Industry 4.0?   The research questions were answered by conducting a case study, in collaboration with Scania CV AB. The case study performed observations, interviews and other relevant data collection to accomplish the purpose. In parallel, a literature review focusing on data requirements for operational decisions was compared to the empirical findings. The analysis identified the current data gap in existing production systems, in correlation to Industry 4.0, affecting the accuracy of operational decisions. In addition, it was shown that simulation can undoubtedly give positive outcome for adaptation of Industry 4.0, and a clear insight on data requirements.

The modeling of freight travel demand has gained increasing attention in the recent past due to the importance of efficient and safe freight transportation to regional economic growth. Despite the attention paid to the modeling of freight travel demand, advances in modeling methods and the development of practical tools for forecasting freight flows have been limited. The development of freight demand models that incorporate the behavioral aspects of freight demand face significant hurdles, partially due to the data requirements, which are a consequence of the inherent complexity of the mechanisms driving freight demand. This research attempts to make a contribution in this context by proposing a relatively data simple, but behaviorally robust statewide modeling framework for the state of Florida, in the spirit of an aggregate level four-step planning process. The modeling framework that is developed in this research can be applied to the modeling of freight travel demand using data contained in readily available commercial databases such as the Reebie TRANSEARCH database and the InfoUSA employer database. The modeling methodology consists of a structural equations modeling framework that can accommodate multiple dependent variables simultaneously. This framework predicts freight flows on various modes between two zipcodes based on the socio-economic characteristics and the modal level of service characteristics. Separate models have been developed for various commodity groups. The estimated models for various commodity groups are found to offer statistically valid indications and plausible interpretations suggesting that these models may be suitable for application in freight transportation demand forecasting applications. The sensitivity analysis conducted on these models clearly added evidence to the fact that employment is the key factor influencing freight flows between two regions.

Thesis (MSc)--Stellenbosch University, 2011. / ENGLISH ABSTRACT: Floods have been identified as one of the major natural hazards occurring in South Africa. A disaster risk assessment forms the first phase in planning for effective disaster risk management through identifying and assessing all hazards that occur within a geographical area, as required by the Disaster Management Act (Act No. 57 of 2002). The National Water Act (Act No. 36 of 1998) requires that flood lines be determined for areas where high risk dams exist and where new town developments occur. However, very few flood hazard maps exist in South Africa for rural areas. The data required for flood modelling analysis is very limited, particularly in rural areas. This study investigated whether flood hazard maps can be created using the existing data sources. A literature review of flood modelling methodologies, data requirements and flood hazard mapping was carried out and an assessment of all available flood-related data sources in South Africa was made. The most appropriate data sources were identified and used to assess an evaluation site. Through combining GIS and hydraulic modelling, results were obtained that indicate the likely extent, frequency and depth of predicted flood events. The results indicate that hydraulic modelling can be performed using the existing data sources but that not enough data is available for calibrating and validating the model. The limitations of the available data are discussed and recommendations for the collection of better data are provided. / AFRIKAANSE OPSOMMING: Vloede is van die vernaamste natuurlike gevare wat in Suid-Afrika voorkom. 'n Ramprisiko-analise is die eerste stap in die proses van suksesvolle ramprisiko-beplanning deur middel van die identifisering en analise van alle gevare wat voorkom in 'n geografiese gebied, soos vereis deur die Rampbestuurwet (Wet 57 van 2002). Die Nasionale Waterwet (Wet 36 van 1998) bepaal dat vloedlyne slegs vir gebiede waar hoë-risiko damme voorkom en vir nuwe uitbreidingsplanne in dorpe vasgestel moet word. Egter is die data wat vir vloedmodelleringsanalises benodig word baie skaars in Suid-Afrikaanse landelike gebiede. Hierdie studie het ondersoek of vloedgevaar-kartering met die beskikbare data moontlik is. 'n Literatuurstudie oor vloedmodelleringsmetodologieë, data-vereistes en vloedgevaarkartering is voltooi en alle beskikbare vloed-verwante data in Suid-Afrika is geëvalueer. Geskikte data-bronne is gekies en gebruik om 'n toetsgebied te assesseer. Deur GIS en hidrouliese modellering te kombineer, is die omvang, waarskynlikheid en diepte van die voorspelde vloedgebeurtenisse gemodelleer. Die studie het bevind dat, alhoewel vloedgevaarkartering met die beskikbare data moontlik is, daar nie genoeg data beskikbaar is om die model te kalibreer en te valideer nie. Tekortkominge van die bestaande data word bespreek en aanbevelings oor die verbetering van die bestaande data vir toepassings in vloedgevaarkartering word gemaak.

The interlinking of enriched BIM data to Building Energy Performance Simulation (BEPS) models facilitates the data flow throughout the building life cycle. This seamless data transfer from BIM to BEPS models increases design efficiency. To investigate the interoperability between these models, this paper analyses different data transfer methodologies along with input data requirements for the simulation process. Based on the analysed knowledge, a methodology is adopted and demonstrated to identify the quality of the data transfer process. Furthermore, discussions are provided on identified efficiency gaps and future work.:Abstract Introduction and background Methodology Methodology demonstration Creation and export of BIM data Verification of OpenBIM meta-data BEPS model generation and validation Import statics Model Geometry and Orientation Construction details Thermal Profile Results and discussion Summary and future work References