Surface treatments are often used in the manufacturing industry to change the surface of a product, including its related properties and functions. The occurrence of degradation and corrosion in surface treatment processes can lead to critical breakdowns over time. Critical breakdowns may impair the properties of the products and shorten their service life, which causes increased lead times or additional costs in the form of rework or scrapping.  Prevention of critical breakdowns due to machine component failure requires a carefully selected maintenance policy. Predictive maintenance is used to anticipate equipment failures to allow for maintenance scheduling before component failure. Developing predictive maintenance policies for surface treatment processes is problematic due to the vast number of attributes to consider in modern surface treatment processes. The emergence of smart sensors and big data has led companies to pursue predictive maintenance. A company that strives for predictive maintenance of its surface treatment processes is Seco Tools in Fagersta. The purpose of this master's thesis has been to investigate the occurrence of critical breakdowns and failures in the machine components of the chemical vapor deposition and post-treatment wet blasting processes by mapping the interaction between its respective process variables and their impact on critical breakdowns. The work has been conducted as a Six Sigma project utilizing the problem-solving methodology DMAIC.  Critical breakdowns were investigated combining principal component analysis (PCA), computational fluid dynamics (CFD), and statistical process control (SPC) to create an understanding of the failures in both processes. For both processes, two predictive solutions were created: one short-term solution utilizing existing dashboards and one long-term solution utilizing a PCA model and an Orthogonal Partial Least Squares (OPLS) regression model for batch statistical process control (BSPC). The short-term solutions were verified and implemented during the master's thesis at Seco Tools. Recommendations were given for future implementation of the long-term solutions. In this thesis, insights are shared regarding the applicability of OPLS and Partial Least Squares (PLS) regression models for batch monitoring of the CVD process. We also demonstrate that the prediction of a certain critical breakdown, clogging of the aluminum generator in the CVD process, can be accomplished through the use of SPC. For the wet blasting process, a PCA methodology is suggested to be effective for visualizing breakdowns.

This thesis formulates and solves an economic decision problem of the acquisi- tion of costly information in data-driven decision making. The thesis assumes an agent predicting a random variable utilizing several costly explanatory vari- ables. Prior to the decision making, the agent learns about the relationship between the random variables utilizing its past realizations. During the deci- sion making, the agent decides what costly variables to acquire and predicts using the acquired variables. The agent's utility consists of the correctness of the prediction and the costs of the acquired variables. To solve the decision problem, the thesis divides the decision process into two parts: acquisition of variables and prediction using the acquired variables. For the prediction, the thesis presents a novel approach for training a single predictive model accepting any combination of acquired variables. For the acquisition, the thesis presents two novel methods using supervised machine learning models: a backward es- timation of the expected utility of each variable and a greedy acquisition of variables based on a myopic increase in the expected utility of variables. Next, the thesis formulates the decision problem as a Markov decision process which allows approximating the optimal acquisition via deep...

I takt med digitaliseringen blir datadrivet beslutsfattande det nya normala i många branscher. Konkurrensfördelarna är allmänt kända eftersom det hjälper företag att utvecklas. Denna fallstudie syftar till att belysa de möjligheter som datadriven optimering bidrar med för skidorter när det kommer till att förbättra tjänster och anpassa skidanläggningar för framtiden. Huvudfokuset är att studera rörelsemönster hos skidåkare med hjälp av processutvinningsverktyg och andra metoder för visualisering. Detta har lett till följande forskningsfrågor: Vilken information går att utvinna ur data från liftsystem? Hur skulle denna typ av information kunna skapa värde i en organisation? Tidigare studier inom detta forskningsområde visar på stora möjligheter med användning av datautvinning och uppmanar till fortsatt forskning. Studien bidrar till forskningen genom att studera specifika åldersgrupper vilket tidigare inte genomförts. Studien visar att det finns skillnader i rörelsemönster hos olika åldersgrupper av skidåkare, vilket i sin tur visar på potentiella optimeringsområden hos skidanläggningarna. Utöver att belysa potentiella förbättringsområden med hjälp av datadrivna beslut visar studien även på en markant förändring hos typen av skidåkare som besöker svenska skidorter 2021, vilket troligtvis berodde på att Alperna höll stängt under skidsäsongen. I framtiden kan studien spela en viktig roll för forskning gällande hur Covid-19 påverkade svenska skidorter. / Given the digitalization, data-driven decision making is becoming the new normal in many industries. The competitive advantages are widely known as it helps companies to evolve. This case study aims to highlight the possibilities data-driven optimization provides when it comes to improving services and adapting to the future for ski resorts. Our focus is skier movement patterns which we generated by analyzing ski lift transportation data with a process mining tool and other methods for visualizations. Hence, our research questions: What information can be extracted from lift usage data? In what way can this information create value in an organization? Previous studies done in the field demonstrate many possibilities with data mining and urges for continued research. The research provided by this study is a contribution to the field through the research done on specific age-groups as this has not previously been done. This study introduces findings based on differences in the movement patterns based on skier age groups which lead to possible areas of optimization. In addition to highlighting possible ways to improve decision making using data, this study shows a significant shift in the type of skier visiting the Swedish ski-resorts 2021, possibly due to The Alps being closed this season. In the future, this study could play an essential role in studying how Covid-19 impacted Swedish ski-resorts.

Dry powder inhalers (DPI), used as a means for pulmonary drug delivery, typically contain a combination of active pharmaceutical ingredients (API) and significantly larger carrier particles. The microsized drug particles-which have a strong propensity to aggregate and poor aerosolization performance-are mixed with significantly large carrier particles that cannot penetrate the mouth-throat region to deagglomerate and entrain the smaller API particles in the inhaled airflow. Therefore, a DPI's performance depends on the carrier-API combination particles' entrainment and the time and thoroughness of the individual API particles' deagglomeration from the carrier particles. Since DPI particle transport is significantly affected by particle-particle interactions, particle sizes and shapes present significant challenges to computational fluid dynamics (CFD) modelers to model regional lung deposition from a DPI. We employed the Particle-In-Cell method for studying the transport/deposition and the agglomeration and deagglomeration for DPI carrier and API particles in the present work. The proposed development will leverage CFD-PIC and sensitivity analysis capabilities from the Department of Energy laboratories: Multiphase Flow Interface Flow Exchange and Dakota UQ software. A data-driven framework is used to obtain the reliable low order statics of the particle's residence time in the inhaler. The framework is further used to study the effect of drug particle density, carrier particle density and size, fluidizing agent density and velocity, and some numerical parameters on the particles' residence time in the inhaler.

The competition in the manufacturing industry has never been higher. Owing to the technological changes and advancements in the market, readily available data is no longer a thing of the past. Numerous studies have discussed the impact of industry 4.0, digital transformation as well as better production planning methods in the manufacturing industry.  The Mass-Manufacturing industry, in specific, has gained efficiency levels in production that were previously unimaginable. Industry 4.0 has been discussed as the ‘next big thing’ in the manufacturing context. In fact, it is seen as a necessity for manufacturing companies to stay competitive. However, efficient production planning methodologies are a preliminary requirement in order to successfully adopt the new manufacturing paradigms. The Engineering-to-order (ETO) industry is still widely unexplored by the academia ETO industries, barely have any production planning methodologies to rely on owing to their complex production processes and high reliance on manual-labour. Regression techniques have repeatedly been used in the production planning context. Considering its statistical prowess, it is no surprise that even the newer machine-learning techniques are based on regression. Considering its success in the mass-manufacturing industry for production planning, is it possible that its usage in the ETO industry might lead to the same results? This thesis involves a case study that was performed at an electrical transformer manufacturing plant in Sweden. After understanding the several operations that are performed in the production process, regression techniques are employed to estimate man-hours. The results from the study reconfirm the statistical prowess of regression and show the possibility of using regression in order to estimate man-hours in the ETO industry. In addition, several factors that can affect successful adoption of this tool in the production planning context are discussed. It is hoped that this study will lay the foundation for better production planning methodologies for the ETO industries in the future which might subsequently result in more data-driven decision making rather than instincts.

Reading competency is related to individuals' success at school and in their careers. Students who experience significant problems with reading may be at risk of long-term academic and social problems. High-quality measures that determine student progress toward curricular goals are needed for early identification and interventions to improve reading abilities and ultimately prevent subsequent failure in reading. The purpose of this quantitative nonexperimental ex post facto research study was to determine whether a correlation existed amongst student achievement scores on the Fountas and Pinnell Reading Benchmark Assessment and reading comprehension scores on the Criterion Reference Competency Test (CRCT). The item response theory served as the conceptual framework for examining whether a relationship exists between Fountas and Pinnell Benchmark Instructional Reading Levels and the reading comprehension scores on the CRCT of students in Grades 3, 4, and 5 in the year 2013-2014. Archival data for 329 students in Grades 3-5 were collected and analyzed through Spearman's rank-order correlation. The results showed positive relationships between the scores. The findings promote positive social change by supporting the use of benchmark assessment data to identify at-risk reading students early.

No description available.

In an air defence scenario decisions need to be taken with extreme precision and under high pressure. These decisions becomes even more challenging when the aircraft in question need to function as a team and coordinate their effort. Because of the difficulty of the task, and the amount of information that needs to be rapidly processed, fighter pilots can benefit greatly from computer-assisted decision making.  In this thesis this kind of decentralised multi-agent coordination problem is studied and mission assignment models, based on robust and stochastic optimisation, are evaluated. Since the information obtained by aircraft sensors often suffer from a notable amount of noise and the scenario state therefore is uncertain, a Poisson multi-Bernoulli mixture filter is implemented in order to model these noisy measurements and keep track of potential adversaries. The study finds that the filter used was more than capable of handling the scenario uncertainties and provided valuable task information to the mission assignment models. However, the preliminary robust optimisation models based entirely on the positional uncertainty of the adversaries were not sophisticated enough for such a complex coordination problem, indicating that further research is needed in this area.

As data availability increases so do the opportunities within businesses. Companies need to explore technologies that are able to exploit and capitalise on this vast amount of data in order to stay relevant in today’s competitive market. Artificial intelligence and simulation are two promising technologies that are able to manage and utilise these large amounts of data. This paper explores the opportunities and challenges that exist of combining artificial intelligence with simulation in order to achieve data-driven decisions within industries. Although these two technologies are well researched in isolation, their combination and synergetic effects remain largely unexplored. The aim of this study is to survey this existing vacuum by performing a literature review and producing a digitised AI and simulation ecosystem that encapsulates the opportunities and challenges enabled by these two technologies. This research explored this ecosystem by applying and developing it on a real case study of an automotive parts supplier’s production process. It was concluded that this modularised digitised ecosystem could act as an alternative to expensive and generic software solutions due to its high customisation, simple integration and cost-efficiency, especially for SMEs. The study also concluded that adding additional AI and simulation models to the ecosystem reduces the modules’ unit costs since they can share some high cost structures such as: databases, servers and user-interfaces; this idea was encapsulated in the term digitised economies of scale.

Data-driven approaches to decision making are today applied far and wide. With origins in the field of judgment and decision making (JDM), data-driven decision making (DDDM) has become an emergent topic within I-O psychology, especially within the fields of people analytics and human resource analytics. In light of the current AI revolution, it is evident that the next steps in JDM research include data- driven approaches. The purpose of this Master’s thesis was to compile the research on data-driven decision making conducted across disciplines into a comprehensive overview. Main research questions: based on systematic reviews and scoping reviews about implementations of DDDM affecting individuals, groups, or organizations, what areas of research can be identified? How and to what extent are they linked? To address these questions, this thesis utilizes a scoping meta-review design and bibliometrics. After rigorous search and screening processes, the final sample consisted of n = 1,008 systematic and scoping reviews. The results indicated that there are research areas within the included reviews that are isolated to a varying extent. Based on a multiple correspondence analysis (MCA), five areas of research were identified: business intelligence; learning analytics/education; mHealth/telemedicine; general decision making/decision support; and clinical decision support/diagnosis/healthcare. As a scoping meta-review encompassing a large number of scientific fields and methodologies, this thesis contributes to the progression of DDDM research at large. The results highlight the scattered nature of current research practices within DDDM and identify an opportunity for scientific advancement through interdisciplinary research.