Movement Analytics: Current Status, Application to Manufacturing, and Future Prospects from an AI Perspective

Data-driven decision making is becoming an integral part of manufacturing companies. Data is collected and commonly used to improve efficiency and produce high quality items for the customers. IoT-based and other forms of object tracking are an emerging tool for collecting movement data of objects/entities (e.g. human workers, moving vehicles, trolleys etc.) over space and time. Movement data can provide valuable insights like process bottlenecks, resource utilization, effective working time etc. that can be used for decision making and improving efficiency. Turning movement data into valuable information for industrial management and decision making requires analysis methods. We refer to this process as movement analytics. The purpose of this document is to review the current state of work for movement analytics both in manufacturing and more broadly. We survey relevant work from both a theoretical perspective and an application perspective. From the theoretical perspective, we put an emphasis on useful methods from two research areas: machine learning, and logic-based knowledge representation. We also review their combinations in view of movement analytics, and we discuss promising areas for future development and application. Furthermore, we touch on constraint optimization. From an application perspective, we review applications of these methods to movement analytics in a general sense and across various industries. We also describe currently available commercial off-the-shelf products for tracking in manufacturing, and we overview main concepts of digital twins and their applications

New Approach for Market Intelligence Using Artificial and Computational Intelligence

Small and medium sized retailers are central to the private sector and a vital contributor to economic growth, but often they face enormous challenges in unleashing their full potential. Financial pitfalls, lack of adequate access to markets, and difficulties in exploiting technology have prevented them from achieving optimal productivity. Market Intelligence (MI) is the knowledge extracted from numerous internal and external data sources, aimed at providing a holistic view of the state of the market and influence marketing related decision-making processes in real-time. A related, burgeoning phenomenon and crucial topic in the field of marketing is Artificial Intelligence (AI) that entails fundamental changes to the skillssets marketers require. A vast amount of knowledge is stored in retailers’ point-of-sales databases. The format of this data often makes the knowledge they store hard to access and identify. As a powerful AI technique, Association Rules Mining helps to identify frequently associated patterns stored in large databases to predict customers’ shopping journeys. Consequently, the method has emerged as the key driver of cross-selling and upselling in the retail industry. At the core of this approach is the Market Basket Analysis that captures knowledge from heterogeneous customer shopping patterns and examines the effects of marketing initiatives. Apriori, that enumerates frequent itemsets purchased together (as market baskets), is the central algorithm in the analysis process. Problems occur, as Apriori lacks computational speed and has weaknesses in providing intelligent decision support. With the growth of simultaneous database scans, the computation cost increases and results in dramatically decreasing performance. Moreover, there are shortages in decision support, especially in the methods of finding rarely occurring events and identifying the brand trending popularity before it peaks. As the objective of this research is to find intelligent ways to assist small and medium sized retailers grow with MI strategy, we demonstrate the effects of AI, with algorithms in data preprocessing, market segmentation, and finding market trends. We show with a sales database of a small, local retailer how our Åbo algorithm increases mining performance and intelligence, as well as how it helps to extract valuable marketing insights to assess demand dynamics and product popularity trends. We also show how this results in commercial advantage and tangible return on investment. Additionally, an enhanced normal distribution method assists data pre-processing and helps to explore different types of potential anomalies.Små och medelstora detaljhandlare är centrala aktörer i den privata sektorn och bidrar starkt till den ekonomiska tillväxten, men de möter ofta enorma utmaningar i att uppnå sin fulla potential. Finansiella svårigheter, brist på marknadstillträde och svårigheter att utnyttja teknologi har ofta hindrat dem från att nå optimal produktivitet. Marknadsintelligens (MI) består av kunskap som samlats in från olika interna externa källor av data och som syftar till att erbjuda en helhetssyn av marknadsläget samt möjliggöra beslutsfattande i realtid. Ett relaterat och växande fenomen, samt ett viktigt tema inom marknadsföring är artificiell intelligens (AI) som ställer nya krav på marknadsförarnas färdigheter. Enorma mängder kunskap finns sparade i databaser av transaktioner samlade från detaljhandlarnas försäljningsplatser. Ändå är formatet på dessa data ofta sådant att det inte är lätt att tillgå och utnyttja kunskapen. Som AI-verktyg erbjuder affinitetsanalys en effektiv teknik för att identifiera upprepade mönster som statistiska associationer i data lagrade i stora försäljningsdatabaser. De hittade mönstren kan sedan utnyttjas som regler som förutser kundernas köpbeteende. I detaljhandel har affinitetsanalys blivit en nyckelfaktor bakom kors- och uppförsäljning. Som den centrala metoden i denna process fungerar marknadskorgsanalys som fångar upp kunskap från de heterogena köpbeteendena i data och hjälper till att utreda hur effektiva marknadsföringsplaner är. Apriori, som räknar upp de vanligt förekommande produktkombinationerna som köps tillsammans (marknadskorgen), är den centrala algoritmen i analysprocessen. Trots detta har Apriori brister som algoritm gällande låg beräkningshastighet och svag intelligens. När antalet parallella databassökningar stiger, ökar också beräkningskostnaden, vilket har negativa effekter på prestanda. Dessutom finns det brister i beslutstödet, speciellt gällande metoder att hitta sällan förekommande produktkombinationer, och i att identifiera ökande popularitet av varumärken från trenddata och utnyttja det innan det når sin höjdpunkt. Eftersom målet för denna forskning är att hjälpa små och medelstora detaljhandlare att växa med hjälp av MI-strategier, demonstreras effekter av AI med hjälp av algoritmer i förberedelsen av data, marknadssegmentering och trendanalys. Med hjälp av försäljningsdata från en liten, lokal detaljhandlare visar vi hur Åbo-algoritmen ökar prestanda och intelligens i datautvinningsprocessen och hjälper till att avslöja värdefulla insikter för marknadsföring, framför allt gällande dynamiken i efterfrågan och trender i populariteten av produkterna. Ytterligare visas hur detta resulterar i kommersiella fördelar och konkret avkastning på investering. Dessutom hjälper den utvidgade normalfördelningsmetoden i förberedelsen av data och med att hitta olika slags anomalier

Digital anthropology

The textbook supplements the lecture material with topical issues of the philosophy of neural technologies. The material belongs to the section "Philosophy of natural science and technology" of the lecture course on the philosophy and methodology of science. The natural-science aspects of human conscious-ness and technological trends in the evolution of convergent structures of digital ecosystems are described. The evolution of system computer engineering is analyzed

Forecasting: theory and practice

Forecasting has always been in the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The lack of a free-lunch theorem implies the need for a diverse set of forecasting methods to tackle an array of applications. This unique article provides a non-systematic review of the theory and the practice of forecasting. We offer a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts, including operations, economics, finance, energy, environment, and social good. We do not claim that this review is an exhaustive list of methods and applications. The list was compiled based on the expertise and interests of the authors. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of the forecasting theory and practice

Forecasting: theory and practice

Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases.info:eu-repo/semantics/publishedVersio

AI: Limits and Prospects of Artificial Intelligence

The emergence of artificial intelligence has triggered enthusiasm and promise of boundless opportunities as much as uncertainty about its limits. The contributions to this volume explore the limits of AI, describe the necessary conditions for its functionality, reveal its attendant technical and social problems, and present some existing and potential solutions. At the same time, the contributors highlight the societal and attending economic hopes and fears, utopias and dystopias that are associated with the current and future development of artificial intelligence

Application of Business Analytics Approaches to Address Climate-Change-Related Challenges

Climate change is an existential threat facing humanity, civilization, and the natural world. It poses many multi-layered challenges that call for enhanced data-driven decision support methods to help inform society of ways to address the deep uncertainty and incomplete knowledge on climate change issues. This research primarily aims to apply management, decision, information, and data science theories and techniques to propose, build, and evaluate novel data-driven methodologies to improve understanding of climate-change-related challenges. Given that we pursue this work in the College of Management, each essay applies one or more of the three distinct business analytics approaches (i.e., descriptive, prescriptive, and predictive analysis) to aid in developing decision support capabilities. Given the rapid growth in data availability, we evaluate important data characteristics for each analysis, focusing on the data source, granularity, volume, structure, and quality. The final analysis consideration is the methods used on the data output to help coalesce the various model outputs into understandable visualizations, tables, and takeaways. We pursue three distinct business analytics challenges. First, we start with a natural language processing analysis to gain insights into the evolving climate change adaptation discussion in the scientific literature. We then create a stochastic network optimization model with recourse to provide coastal decision-makers with a cost-benefit analysis tool to simultaneously assess risks and costs to protect their community against rising seas. Finally, we create a decision support tool for helping organizations reduce greenhouse gas emissions through strategic sustainable energy purchasing. Although the three essays vary on their specific business analysis approaches, they all have a common theme of applying business analytics techniques to analyze, evaluate, visualize, and understand different facets of the climate change threat

Forecasting: theory and practice

Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases

Urban Informatics

This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity

Urban Informatics

This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity