El presente trabajo de investigación se desarrolla en una empresa agroindustrial ubicada en la región de Lambayeque. Esta genera anualmente una elevada cantidad de residuos de cáscaras de maracuyá provenientes de su línea de producción de jugos y concentrados tropicales, las cuales son eliminadas sin mayor aprovechamiento. Por tal motivo se planteó como objetivo general proponer la instalación de una línea de producción de cartón para aprovechar las cáscaras de maracuyá en la empresa, y de este modo aumentar sus utilidades. Para ello se realizó estudios de viabilidad comercial, técnico-tecnológico y económico-financiero con el propósito de determinar la factibilidad del proyecto, dando como resultado la existencia de una demanda insatisfecha a nivel nacional del cartón conocido como kraftliner para caras cubiertas, la disponibilidad de recursos tecnológicos y materiales para diseñar la nueva línea de producción, y la obtención de indicadores económicos positivos con un VAN de $ 793 803,77 un TIR de 46,10%, un TMAR de 18,77% y un Beneficio/Costo de $ 2,12, lo cual indica que la propuesta es rentable y viable. / This research work is carried out in an agro-industrial company located in the Lambayeque region. This generates a high amount of passion fruit shell waste annually from its production line of tropical juices and concentrates, which are disposed of without further use. For this reason, the general objective was to propose the installation of a cardboard production line to take advantage of the passion fruit shells in the company, and thus increase its profits. For this, commercial, technical-technological and economic-financial feasibility studies were carried out with the purpose of determining the feasibility of the project, resulting in the existence of an unsatisfied demand at the national level for cardboard known as kraftliner for covered faces, the availability of technological and material resources to design the new production line, and obtaining positive economic indicators with a NPV of $ 793 803,77, an IRR of 46,10%, a TMAR of 18,77% and a Benefit / Cost of $ 2,12, which indicates that the proposal is profitable and viable.

Denna studie undersöker hur artificiell intelligens (AI) kan optimera beslutsstödet inom verksamhetsstyrning genom analys av ostrukturerad data. Genom att granska begrepp som verksamhetsstyrning, Business Intelligence (BI), AI och maskininlärning (ML), belyser studien hur dessa teknologier kan förbättra organisationers beslutsprocesser. Verksamhetsstyrning syftar till att samordna och optimera organisationens delar för att nå gemensamma mål. AI (NLP, ML) samt särskilt genom BI spelar en avgörande roll genom att förbättra effektivitet och kvalitet. BI samlar och analyserar affärsinformation, medan ML möjliggör automatisk lärande från data. Studiens problemområde identifierar utmaningen med att hantera stora mängder ostrukturerad data. Trots AI:s potential att förbättra beslutsfattandet har dess fulla potential ännu inte realiserats. Genom att undersöka effektiv användning av AI för ostrukturerad data, bidrar studien till en bättre förståelse av hur AI kan förbättra beslutsstödet.Den kvalitativa ansatsen använde semistrukturerade intervjuer med IT-experter för att samla insikter om AI:s användning i beslutsfattande. Respondenterna beskrev hur AI analyserar data, förutsäger trender, optimerar processer och personaliserar kundupplevelser. AI automatiserar också tidskrävande uppgifter, vilket ökar effektiviteten och frigör tid för strategiskt arbete. Det visar att AI kan förbättra datakvalitet, automatisera processer och ge djupare insikter i kundbeteenden och marknadstrender. AI:s förmåga att hantera ostrukturerad data möjliggör identifiering av trender och mönster som annars skulle vara svåra att upptäcka. Utmaningar med AI-implementering inkluderar systemintegrering och behovet av teknisk expertis. Sammanfattningsvis visar studien att AI har stor potential att optimera beslutsstödet inom verksamhetsstyrning genom analys av ostrukturerad data.

This research study focuses on the application models of Data Mining and Machine Learning covering cellular network traffic, in the objective to arm Mobile Network Operators with full view of performance branches (Services, Device, Subscribers). The purpose is to optimize and minimize the time to detect service and subscriber patterns behaviour. Different data mining techniques and predictive algorithms have been applied on real cellular network datasets to uncover different data usage patterns using specific Key Performance Indicators (KPIs) and Key Quality Indicators (KQI). The following tools will be used to develop the concept: RStudio for Machine Learning and process visualization, Apache Spark, SparkSQL for data and big data processing and clicData for service Visualization. Two use cases have been studied during this research. In the first study, the process of Data and predictive Analytics are fully applied in the field of Telecommunications to efficiently address users’ experience, in the goal of increasing customer loyalty and decreasing churn or customer attrition. Using real cellular network transactions, prediction analytics are used to predict customers who are likely to churn, which can result in revenue loss. Prediction algorithms and models including Classification Tree, Random Forest, Neural Networks and Gradient boosting have been used with an exploratory Data Analysis, determining relationship between predicting variables. The data is segmented in to two, a training set to train the model and a testing set to test the model. The evaluation of the best performing model is based on the prediction accuracy, sensitivity, specificity and the Confusion Matrix on the test set. The second use case analyses Service Quality Management using modern data mining techniques and the advantages of in-memory big data processing with Apache Spark and SparkSQL to save cost on tool investment; thus, a low-cost Service Quality Management model is proposed and analyzed. With increase in Smart phone adoption, access to mobile internet services, applications such as streaming, interactive chats require a certain service level to ensure customer satisfaction. As a result, an SQM framework is developed with Service Quality Index (SQI) and Key Performance Index (KPI). The research concludes with recommendations and future studies around modern technology applications in Telecommunications including Internet of Things (IoT), Cloud and recommender systems. / Cellular networks have evolved and are still evolving, from traditional GSM (Global System for Mobile Communication) Circuit switched which only supported voice services and extremely low data rate, to LTE all Packet networks accommodating high speed data used for various service applications such as video streaming, video conferencing, heavy torrent download; and for say in a near future the roll-out of the Fifth generation (5G) cellular networks, intended to support complex technologies such as IoT (Internet of Things), High Definition video streaming and projected to cater massive amount of data. With high demand on network services and easy access to mobile phones, billions of transactions are performed by subscribers. The transactions appear in the form of SMSs, Handovers, voice calls, web browsing activities, video and audio streaming, heavy downloads and uploads. Nevertheless, the stormy growth in data traffic and the high requirements of new services introduce bigger challenges to Mobile Network Operators (NMOs) in analysing the big data traffic flowing in the network. Therefore, Quality of Service (QoS) and Quality of Experience (QoE) turn in to a challenge. Inefficiency in mining, analysing data and applying predictive intelligence on network traffic can produce high rate of unhappy customers or subscribers, loss on revenue and negative services’ perspective. Researchers and Service Providers are investing in Data mining, Machine Learning and AI (Artificial Intelligence) methods to manage services and experience. This research study focuses on the application models of Data Mining and Machine Learning covering network traffic, in the objective to arm Mobile Network Operators with full view of performance branches (Services, Device, Subscribers). The purpose is to optimize and minimize the time to detect service and subscriber patterns behaviour. Different data mining techniques and predictive algorithms will be applied on cellular network datasets to uncover different data usage patterns using specific Key Performance Indicators (KPIs) and Key Quality Indicators (KQI). The following tools will be used to develop the concept: R-Studio for Machine Learning, Apache Spark, SparkSQL for data processing and clicData for Visualization. / Electrical and Mining Engineering / M. Tech (Electrical Engineering)

Decision‐makers in organisations and businesses make numerous decisions every day, and these decisions are expected to be based on facts and carried out in a rational manner. However, most decisions are not based on precise information or careful analysis due to several reasons. People are, e.g., unable to behave rationally as a result of their experiences, socialisation, and additionally, because humans possess fairly limited capacities for processing information in an objective manner. In order to circumvent this human incapacity to handle decision situations in a rational manner, especially those involving risk and uncertainty, a widespread suggestion, at least in managerial decision making, is to take advantage of support in the form of decision support systems. One possibility involves decision analytical tools, but they are, almost without exception, not efficiently employed in organisations and businesses. It appears that one reason for this is the high demands the tools place on the decision‐maker in a variety of ways, e.g., by presupposing that reliable input data is obtainable by an exogenous process. Even though the reliability of current decision analytic tools is highly dependent on the quality of the input data, they rarely contain methods for eliciting data from the users. The problem focused on in this thesis is the unavailability and inefficiency of methods for eliciting decision information from the users. The aim is to identify problem areas regarding the elicitation of decision data in real decision making processes, and to propose elicitation methods that take people’s natural choice strategies and natural behaviour into account. In this effort, we have identified a conceptual gap between the decision‐makers, the decision models, and the decision analytical tools, consisting of seven gap components. The gap components are of three main categories (of which elicitation is one). In order to study elicitation problems, a number of empirical studies, involving more than 400 subjects in total, have been carried out in Sweden and Brazil. An iterative research approach has been adopted and a combination of quantitative and qualitative methods has been used. Findings made in this thesis include the fact that decision‐makers have serious problems in many decision situations due to not having access to accurate and relevant data in the first place, and secondly, not having the means for retrieving such data in a proper manner, i.e. lacking elicitation methods for this purpose. Employing traditional elicitation methods in this realm yield results that reveal an inertia gap, i.e. an intrinsic inertia in people’s natural behaviour to shift between differently framed prospects, and different groups of decisionmakers displaying different choice patterns. Since existing elicitation methods are unable to deal with the inertia, we propose a class of methods to take advantage of this natural behaviour, and also suggest a representation for the elicited information. An important element in the proposed class of methods is also that we must be able to fine‐tune methods and measuring instruments in order to fit into different types of decision situations, user groups, and choice behaviours.

Den ökade globaliseringen och senaste finanskrisen ställer höga krav på uppföljning och medvetenhet av ett företags prestation. Business Intelligence (BI) är ett område vars syfte är att förbättra en organisations prestation genom analys av historisk data. BI är ett komplext område som inte bara handlar om tekniska lösningar, även om det är en förutsättning. För närvarande investeras det mycket i olika BI-lösningar och företagen behöver veta vad resurserna bör läggas på. I dagsläget finns det ingen modell som bedömer ett företags arbete med Business Intelligence utifrån ett flertal dimensioner. Syftet med den här studien var att utveckla en mognadsmodell för Business Intelligence och sedan jämföra de undersökta företagen Axfood, Scania och Systembolagets mognad. För att uppnå studiens syfte avsåg vi att besvara följande frågeställningar, ”Hur skulle en modell för att bedöma ett företags mognad inom Business Intelligence kunna se ut?” samt ”Vilka förutsättningar påverkar ett företags mognad inom Business Intelligence”. Mognadsmodellen (BIOMA) kom att bestå av fyra hörnstenar som i sin tur delades in i en eller flera underkategorier. Varje delkategori ger poäng som sedan infogas i ett koordinatsystem där axlarna motsvarar hörnstenarna och poängen utgår från origo. Att mäta ett företags mognad inom BI är komplext, då ett antal aspekter såsom organisationsstruktur, användarmedverkan samt klyftan mellan IT-avdelning och verksamhet kan påverka. Den teoretiska modellen är empiriskt testad. Respondenterna på respektive företag har bedömt hur långt de kommit inom varje hörnsten samt ge synpunkter på modellens utformning. Modellen har sedan förädlats utifrån det empiriska materialet. Vi anser att BIOMA har ett stort värde då det saknas en modell som visuellt och relativt enkelt beskriver ett företags mognad inom Business Intelligence. Modellen kan användas i olika syften, såsom benchmarking mellan processer och företag, säljstöd för konsulter samt vid förstudie för att klargöra ett företags nuläge. / The increased globalization and the recent financial crisis have put high demands on the monitoring and awareness of an organization's performance. Business Intelligence (BI) is an area which aims to improve this performance through analysis of historical data. BI is a complex question for organization’s because it involves more than just technical solutions for maximum performance. Organizations are currently investing in different BI solutions and a list of priorities has to be made to ensure balanced resource allocation within a BI-implementation. To this day no single business intelligence model exists that can adequately measure a company’s work from several perspectives. The purpose of this study was to develop a maturity model for BI and use it in a case study of three different well-known Swedish companies; Axfood, Scania and Systembolaget, to measure their BI-maturity. To achieve the purpose of the study, three distinct research questions arose; "What would a model for measuring a company’s Business Intelligence maturity look like? How would this model be constructed? And finally “What conditions could potentially affect an organization’s maturity in Business Intelligence?". The Maturity Model BIOMA (Business Intelligence Organizational Maturity Analysis) is made up of four categories, which in turn are divided into one or more sub-categories. A subcategory consists of several statements. Each statement carries a certain number of points. When the points are combined, the summarized amount is inserted into a coordinate system. Within this, the axies correspond to the pillars and the score is based on the origo-point. Measuring a company's BI-maturity is a complex research question, where a number of aspects such as organizational structure, end-user involvement, and the gap between IT department and business can be of great importance. BIOMA was empirically tested in the case study. The responders in each company judged their company based on the statements in each subcategory. Following this they made suggestions on ways to change the model. By applying these suggestions to the original material, the model was then redeveloped to create a final version. The model can be used for various purposes, such as processes within organizations or in benchmarking. It can also be used by consultants in Sales support as a pilot study for clarifying a company’s present BI-maturity. In this absence of a model that could visually describe a company’s BI maturity multidimensionally, we believe that BIOMA has substantial and existing business potential.

Public sector decision makers are confronted by pressures to make faster and better decisions as a result of the competitive environment they operate in. However, there is a trend in the public sector, including the Department of Transport (DoT) in South Africa, to invest in management information systems (MIS) that are highly fragmented and not aiding effective and timely decision-making. As a result, the country witnessed several service delivery protests since 2008 which also affected the public transport sector, such as the widespread burning of Metrorail trains several times by angry commuters. In most instances, poor service delivery emanates from the fact that public servants do not have information at their fingertips to make decisions. This quantitative study utilised Control Objectives for Information and Related Technologies 5 (COBIT 5) as a theoretical framework to investigate the integration of MIS at the DoT with a view to enhancing business intelligence for effective decision-making. Data were collected through a questionnaire directed at middle managers and senior managers that were selected through stratification of business units at the DoT, as well as analysis of documents such as system specifications and strategic plans. The study established that the DoT has several systems such as Alfresco, BAS, GIS, Logis and Persal to name a few, which serve different purposes. However, in most instances, the systems are not integrated as the current infrastructure did not support integration needs and plans to accommodate changing requirements. This is compounded by the system policy implementation constraints, as well as ageing legacy systems that are obsolete. The only component where MIS was found to be integrated, was in the financial business units (Supply Chain Management, Finance and Budgeting). Core business units use off-the-shelf systems and, in some cases, custom-made applications that do not integrate with any other system and thus hinder decision-making. In conclusion, decisions are made based on thumb-sucking, as management does not have access to comprehensive information that is stored in fragmented unintegrated systems. The study recommends that governance structures should be set up to deal with a more holistic business, information and technology architecture for the DoT that enable integration of various systems for effective decision-making. Failure to transform this pattern would lead to service delivery protests persisting. A further study on a framework to integrate MIS in the public sector is recommended. / Information Science / M. Inf.

Most organisations use several data management and business intelligence solutions which are on-premise and, or cloud-based to manage and analyse their constantly growing business data. Challenges faced by organisations nowadays include, but are not limited to growth limitations, big data, inadequate analytics, computing, and data storage capabilities. Although these organisations are able to generate reports and dashboards for decision-making in most cases, effective use of their business data and an appropriate business intelligence solution could achieve and retain informed decision-making and allow competitive reaction to the dynamic external environment. A data management and analytic model has been proposed on which organisations could rely for decisive guidance when planning to procure and implement a unified business intelligence solution. To achieve a sound model, literature was reviewed by extensively studying business intelligence in general, and exploring and developing various deployment models and architectures consisting of naïve, on-premise, and cloud-based which revealed their benefits and challenges. The outcome of the literature review was the development of a hybrid business intelligence model and the accompanying architecture as the main contribution to the study.In order to assess the state of business intelligence utilisation, and to validate and improve the proposed architecture, two case studies targeting users and experts were conducted using quantitative and qualitative approaches. The case studies found and established that a decision to procure and implement a successful business intelligence solution is based on a number of crucial elements, such as, applications, devices, tools, business intelligence services, data management and infrastructure. The findings further recognised that the proposed hybrid architecture is the solution for managing complex organisations with serious data challenges. / Computing / M. Sc. (Computing)

PhD (Business Management) / Department of Business Management / This thesis demonstrates the researcher’s efforts to put into practice the theoretical foundations of information systems research, in order to come up with a context-aware business intelligence framework (CABIF), for the South African higher education institutions. Using critical realism as the philosophical underpinning and mixed methods research design, a business intelligence (BI) survey was deployed within the South African public higher education institutions to measure the respondents’ satisfaction and importance of business intelligence characteristics. The 258 respondents’ satisfaction and importance of the 34 observed business intelligence variables, were subjected to principal components analysis and design science research to come up with the CABIF. The observable BI variables were drawn from four latent variables namely technology and business alignment; organizational and behavioural strategies; business intelligence domain; and technology strategies. The study yielded good values for all the observed satisfaction and importance business intelligence variables as indicated by the Kaiser- Meyer-Olkin (KMO) Measure of Sampling Adequacy and the Bartlett Test of Sphericity. The data set collected from the survey deployed at the South African public higher education institutions, was reliable and valid based on the Cronbach α values which were all above 0.9. The researcher then used the descriptive and prescriptive knowledge of design science research, and the meta-inferences of the results from the principal components analysis to produce five contexts of CABIF. The BI contexts developed were, the Basic Context; the Business Processes Context which was divided into Macro and Micro business process contexts; the Business Intelligence Context; and the Governance Context. These contexts were extrapolated within the University of Venda’s business processes and this researcher concluded that the CABIF developed, could be inferred within the South African higher education institutions. At the University of Venda, this researcher managed to draw up CABIF based business intelligence tools that spanned from leveraging the existing ICT infrastructure, student cohort analysis, viability of academic entities, strategic enrolment planning and forecasting government block grants. The correlations and regression measures of the technology acceptance variables of the business intelligence tools modelled using CABIF at University of Venda, revealed high acceptance ratio. Overall, this research provides a myriad of conceptual and practical insights into how contextualised aspects of BI directly or indirectly impact on the quality of managerial decision making within various core business contexts of South African higher education institutions.