Prescriptive Control of Business Processes - New Potentials Through Predictive Analytics of Big Data in the Process Manufacturing Industry

This paper proposes a concept for a prescriptive control of business processes by using event-based process predictions. In this regard, it explores new potentials through the application of predictive analytics to big data while focusing on production planning and control in the context of the process manufacturing industry. This type of industry is an adequate application domain for the conceived concept, since it features several characteristics that are opposed to conventional industries such as assembling ones. These specifics include divergent and cyclic material flows, high diversity in end products’ qualities, as well as non-linear production processes that are not fully controllable. Based on a case study of a German steel producing company – a typical example of the process industry – the work at hand outlines which data becomes available when using state-of-the-art sensor technology and thus providing the required basis to realize the proposed concept. However, a consideration of the data size reveals that dedicated methods of big data analytics are required to tap the full potential of this data. Consequently, the paper derives seven requirements that need to be addressed for a successful implementation of the concept. Additionally, the paper proposes a generic architecture of prescriptive enterprise systems. This architecture comprises five building blocks of a system that is capable to detect complex event patterns within a multi-sensor environment, to correlate them with historical data and to calculate predictions that are finally used to recommend the best course of action during process execution in order to minimize or maximize certain key performance indicators

Az Ipar 4.0 fejlődése, használata és kihívásai napjainkban = The Development, Use and Challenges of Industry 4.0 Today

Az Ipar 4.0 célja a gyártási és tervezési folyamatok optimalizálása, annak érdekében, hogy a vállalatok időt, pénzt és feleslegesen elhasznált erőforrásokat tudjanak megtakarítani. A tervezési folyamat során a dokumentálásnál és a prototípus elkészítésénál a költség jelentős része megtakarítható, a veszteségek csökkentése pedig hozzájárul a vállalatok növekedéséhez. Célom, hogy átfogó képet adjak az ipari forradalmakat kiváltó technológiai, gazdasági újításokról, a napjaink fejlett technológiai lehetőségeiről, az Ipar 4.0-t megvalósító rendszerekről. Az Ipar 4.0 nem azt jelenti, hogy ki kell dobni minden 5 évnél régebbi gyártó gépet. Létezik olyan technológia, aminek a segítségével az analóg készülékek is ráköthetők a negyedik ipari forradalom fogaskerekére. Vizsgálatomban kitérek az egyik ilyen technológiára a PHM módszerre, amely a kiber-fizikai rendszerek vállalati üzemi tervezését segíti elő. Kutatásom során ismertetem az Ipar 4.0 lehetőségeit, veszélyeit és a vállalkozások Ipar 4.0-ról alkotott véleményét. Fontos, hogy a kis- és középvállalkozások is reagáljanak az Ipar 4.0 által hozott új technológiai lehetőségekre, hiszen, ha nem teszik, akkor csúnyán lemaradnak. Industry 4.0 aims to optimize manufacturing and design processes to help companies save time, money, and prevent the waste of resources. During the design process, a significant part of the cost can be saved in documentation and prototyping, and loss reduction con- tributes to the growth of companies. My goal is to give a comprehensive picture of the technological, economic innovations that trigger industrial revolutions, the advanced technological possibilities of today, and the systems that implement Industry 4.0. Industry 4.0 does not mean that every production machine older than 5 years has to be discarded. There is a technology that can be used to connect analogue devices to the gear of the fourth industrial revolution. In my study, I discuss one of these technologies with the PHM method, which supports corporate business planning of cyber-physical systems. In my research, I present the opportunities, dangers of Industry 4.0 and the views of businesses on Industry 4.0. It is important that small and medium-sized enterprises also respond to the new technological opportunities brought by Industry 4.0, because if they do not, then they will miss out

Az Ipar 4.0 fejlődése, használata és kihívásai napjainkban

Az Ipar 4.0 célja a gyártási és tervezési folyamatok optimalizálása, annak érdekében, hogy a vállalatok időt, pénzt és feleslegesen elhasznált erőforrásokat tudjanak megtakarítani. A tervezési folyamat során a dokumentálásnál és a prototípus elkészítésénál a költség jelentős része megtakarítható, a veszteségek csökkentése pedig hozzájárul a vállalatok növekedéséhez. Célom, hogy átfogó képet adjak az ipari forradalmakat kiváltó technológiai, gazdasági újításokról, a napjaink fejlett technológiai lehetőségeiről, az Ipar 4.0-t megvalósító rendszerekről. Az Ipar 4.0 nem azt jelenti, hogy ki kell dobni minden 5 évnél régebbi gyártó gépet. Létezik olyan technológia, aminek a segítségével az analóg készülékek is ráköthetők a negyedik ipari forradalom fogaskerekére. Vizsgálatomban kitérek az egyik ilyen technológiára a PHM módszerre, amely a kiber-fizikai rendszerek vállalati üzemi tervezését segíti elő. Kutatásom során ismertetem az Ipar 4.0 lehetőségeit, veszélyeit és a vállalkozások Ipar 4.0-ról alkotott véleményét. Fontos, hogy a kis- és középvállalkozások is reagáljanak az Ipar 4.0 által hozott új technológiai lehetőségekre, hiszen, ha nem teszik, akkor csúnyán lemaradnak. ----- ndustry 4.0 aims to optimize manufacturing and design processes to help companies save time, money, and prevent the waste of resources. During the design process, a significant part of the cost can be saved in documentation and prototyping, and loss reduction con- tributes to the growth of companies. My goal is to give a comprehensive picture of the technological, economic innovations that trigger industrial revolutions, the advanced technological possibilities of today, and the systems that implement Industry 4.0. Industry 4.0 does not mean that every production machine older than 5 years has to be discarded. There is a technology that can be used to connect analogue devices to the gear of the fourth industrial revolution. In my study, I discuss one of these technologies with the PHM method, which supports corporate business planning of cyber-physical systems. In my research, I present the opportunities, dangers of Industry 4.0 and the views of businesses on Industry 4.0. It is important that small and medium-sized enterprises also respond to the new technological opportunities brought by Industry 4.0, because if they do not, then they will miss out

Gestión empresarial en el desarrollo de las MYPES en zonas mineras del Perú

Las Micro y Pequeñas Empresas (MYPE´s) son la fuerza empresarial que sostienen el crecimiento económico de los países en desarrollo; lograr gestionarlo es el reto de los propietarios y profesionales. El objetivo principal que pretende la investigación es determinar la influencia de la gestión empresarial en el desarrollo de las MYPE´s en zonas mineras en el sur de Perú. El enfoque es el cuantitativo, con alcance explicativo, de diseño no experimental, los datos se recogieron en un solo momento, la muestra incluyó a 243 empresas a quienes se aplicó un cuestionario a través de la encuesta; los resultados se analizaron con el modelo de regresión ordinal para probar las hipótesis planteadas. En los resultados descriptivos, el 63,8% consideran que la gestión empresarial es excelente, mientras que el 76,5% califican de excelente al desarrollo de las MYPE´s; a nivel inferencial se ha encontrado que la gestión empresarial explica el 83,9% del desarrollo de las micro y pequeñas empresas. La investigación concluye que la gestión empresarial tiene una alta influencia en el desarrollo de este tipo de empresas en un contexto de países en desarrollo, específicamente en zonas mineras

Propuesta de metodología para el desarrollo de proyectos de analítica prescriptiva a partir de un Metaanálisis

Trabajo de investigaciónEste trabajo propone una metodología para el desarrollo de proyectos de Analítica Prescriptiva a partir de un Metaanálisis, en cual se reviso de manera sistemática el estado del arte, metodologías y usos en distintas áreas del conocimiento de dicha analítica, encontrando patrones en sus procesos que son comunes a metodologías orientadas a Data Mining como KDD, CRISP-DM y SEMMA.GLOSARIO RESUMEN INTRODUCCIÓN 1. PLANTEAMIENTO DEL PROBLEMA 2. JUSTIFICACIÓN 3. OBJETIVOS 4. ALCANCES Y LIMITACIONES 5. MARCO CONCEPTUAL 6. MARCO TEÓRICO 7. ESTADO DEL ARTE 8. METODOLOGÍA 9. DESARROLLO DEL PROYECTO 10. CONCLUSIONES REFERENCIAS ANEXOSPregradoIngeniero de Sistema

Analysing the Influence of Macroeconomic Factors on Credit Risk in the UK Banking Sector

Macroeconomic factors have a critical impact on banking credit risk, which cannot be directly controlled by banks, and therefore, there is a need for an early credit risk warning system based on the macroeconomy. By comparing different predictive models (traditional statistical and machine learning algorithms), this study aims to examine the macroeconomic determinants’ impact on the UK banking credit risk and assess the most accurate credit risk estimate using predictive analytics. This study found that the variance-based multi-split decision tree algorithm is the most precise predictive model with interpretable, reliable, and robust results. Our model performance achieved 95% accuracy and evidenced that unemployment and inflation rate are significant credit risk predictors in the UK banking context. Our findings provided valuable insights such as a positive association between credit risk and inflation, the unemployment rate, and national savings, as well as a negative relationship between credit risk and national debt, total trade deficit, and national income. In addition, we empirically showed the relationship between national savings and non-performing loans, thus proving the “paradox of thrift”. These findings benefit the credit risk management team in monitoring the macroeconomic factors’ thresholds and implementing critical reforms to mitigate credit risk

Smart Tech is all Around us – Bridging Employee Vulnerability with Organizational Active Trust-Building

Public and academic opinion remains divided regarding the benefits and pitfalls of datafication technology in organizations, particularly regarding their impact on employees. Taking a dual-process perspective on trust, we propose that datafication technology can create small, erratic surprises in the workplace that highlight employee vulnerability and increase employees’ reliance on the systematic processing of trust. We argue that these surprises precipitate a phase in the employment relationship in which employees more actively weigh trust-related cues, and the employer should therefore engage in active trust management to protect and strengthen the relationship. Our paper develops a framework of symbolic and substantive strategies to guide organizations’ active trust management efforts to (re-)create situational normality, root goodwill intentions, and enable a more balanced interdependence between the organization and its employees. We discuss the implications of our paper for reconciling competing narratives about the future of work and for developing an understanding of trust processes.</p

Predictive Big Data Analytics for Supply Chain Demand Forecasting: Methods, Applications, and Research Opportunities

Big data analytics (BDA) in supply chain management (SCM) is receiving a growing attention. This is due to the fact that BDA has a wide range of applications in SCM, including customer behavior analysis, trend analysis, and demand prediction. In this survey, we investigate the predictive BDA applications in supply chain demand forecasting to propose a classification of these applications, identify the gaps, and provide insights for future research. We classify these algorithms and their applications in supply chain management into time-series forecasting, clustering, K-nearest-neighbors, neural networks, regression analysis, support vector machines, and support vector regression. This survey also points to the fact that the literature is particularly lacking on the applications of BDA for demand forecasting in the case of closed-loop supply chains (CLSCs) and accordingly highlights avenues for future research

The Effects of Advanced Analytics and Machine Learning on the Transportation of Natural Gas

This qualitative single case study describes the effects of an advanced analytic and machine learning system (AAML) has on the transportation of natural gas pipelines and the causes for failure to fully utilize the advanced analytic and machine learning system. This study\u27s guiding theory was the Unified Theory of Acceptance and Use of Technology (UTAUT) model and Transformation Leadership. The factors for failure to fully utilize AAML systems were studied, and the factors that made the AAML system successful were also examined. Data were collected through participant interviews. This study indicates that the primary factors for failure to fully utilize AAML systems are training and resource allocation. The AAML system successfully increased the participants\u27 productivity and analytical abilities by eliminating the many manual steps involved in producing reports and analyzing business conditions. The AAML system also allowed the organization to gather and analyze real-time data in a volume and manner that would have been impossible before the AAML system was installed. The leadership team brought about the AAML system\u27s success through transformation leadership by encouraging creativity, spurring innovation while providing the proper funding, time, and personnel to support the AAML system

Adoption of AI-based Information Systems from an Organizational and User Perspective

Artificial intelligence (AI) is fundamentally changing our society and economy. Companies are investing a great deal of money and time into building corresponding competences and developing prototypes with the aim of integrating AI into their products and services, as well as enriching and improving their internal business processes. This inevitably brings corporate and private users into contact with a new technology that functions fundamentally differently than traditional software. The possibility of using machine learning to generate precise models based on large amounts of data capable of recognizing patterns within that data holds great economic and social potential—for example, in task augmentation and automation, medical diagnostics, and the development of pharmaceutical drugs. At the same time, companies and users are facing new challenges that accompany the introduction of this technology. Businesses are struggling to manage and generate value from big data, and employees fear increasing automation. To better prepare society for the growing market penetration of AI-based information systems into everyday life, a deeper understanding of this technology in terms of organizational and individual use is needed. Motivated by the many new challenges and questions for theory and practice that arise from AI-based information systems, this dissertation addresses various research questions with regard to the use of such information systems from both user and organizational perspectives. A total of five studies were conducted and published: two from the perspective of organizations and three among users. The results of these studies contribute to the current state of research and provide a basis for future studies. In addition, the gained insights enable recommendations to be derived for companies wishing to integrate AI into their products, services, or business processes. The first research article (Research Paper A) investigated which factors and prerequisites influence the success of the introduction and adoption of AI. Using the technology–organization–environment framework, various factors in the categories of technology, organization, and environment were identified and validated through the analysis of expert interviews with managers experienced in the field of AI. The results show that factors related to data (especially availability and quality) and the management of AI projects (especially project management and use cases) have been added to the framework, but regulatory factors have also emerged, such as the uncertainty caused by the General Data Protection Regulation. The focus of Research Paper B is companies’ motivation to host data science competitions on online platforms and which factors influence their success. Extant research has shown that employees with new skills are needed to carry out AI projects and that many companies have problems recruiting such employees. Therefore, data science competitions could support the implementation of AI projects via crowdsourcing. The results of the study (expert interviews among data scientists) show that these competitions offer many advantages, such as exchanges and discussions with experienced data scientists and the use of state-of-the-art approaches. However, only a small part of the effort related to AI projects can be represented within the framework of such competitions. The studies in the other three research papers (Research Papers C, D, and E) examine AI-based information systems from a user perspective, with two studies examining user behavior and one focusing on the design of an AI-based IT artifact. Research Paper C analyses perceptions of AI-based advisory systems in terms of the advantages associated with their use. The results of the empirical study show that the greatest perceived benefit is the convenience such systems provide, as they are easy to access at any time and can immediately satisfy informational needs. Furthermore, this study examined the effectiveness of 11 different measures to increase trust in AI-based advisory systems. This showed a clear ranking of measures, with effectiveness decreasing from non-binding testing to providing additional information regarding how the system works to adding anthropomorphic features. The goal of Research Paper D was to investigate actual user behavior when interacting with AI-based advisory systems. Based on the theoretical foundations of task–technology fit and judge–advisor systems, an online experiment was conducted. The results show that, above all, perceived expertise and the ability to make efficient decisions through AI-based advisory systems influence whether users assess these systems as suitable for supporting certain tasks. In addition, the study provides initial indications that users might be more willing to follow the advice of AI-based systems than that of human advisors. Finally, Research Paper E designs and implements an IT artifact that uses machine learning techniques to support structured literature reviews. Following the approach of design science research, an artifact was iteratively developed that can automatically download research articles from various databases and analyze and group them according to their content using the word2vec algorithm, the latent Dirichlet allocation model, and agglomerative hierarchical cluster analysis. An evaluation of the artifact on a dataset of 308 publications shows that it can be a helpful tool to support literature reviews but that much manual effort is still required, especially with regard to the identification of common concepts in extant literature