The use of Asset-Backed Securitisation as a strategic control tool by Edcon's Onthecards Investments.

Thesis (MBA)-University of KwaZulu-Natal, Durban, 2004.This research report studies the application of Edcon's Asset-Backed Securitisation of OntheCards Investments in the retail industry. Asset-Backed Securitisation is a new concept in South Africa that has been largely practised in the financial sector. The research investigates Edcon's ability to adapt and apply the onerous and rigorous Asset-Backed Securitisation process to achieve their strategic and financial objectives. As Asset-Backed Securitisation is a new concept in South Africa, which was traditionally applied in the financial sector, the application of this tool in the retail industry would require drastic changes to the business practice of Edcon's credit operations and their strategic evaluation process. The first part of the research therefore concentrated on the evolution and theory of strategy. This discussion focussed on the evolution of the strategic planning process and eventually concluded with developments in the resource based theory. Strategic frameworks and evaluation techniques were also presented and reviewed. As part of the literature review, the theory on Asset-Backed Securitisation was also presented and International and South African Perspectives were reviewed with respect to the market for Asset-Backed Securitisation. Drawing from the literature review on strategy, a strategic evaluation model was developed for the evaluation of Edcon's strategic control tool of OntheCards Investments, their Asset-Backed Securitisation. The evaluation of OntheCards Investments was focussed on the structure of OntheCards Investments with respect to the theory of Asset-Backed Securitisation, an analysis of Edcon prior to the OntheCards Investments which took the form of a PEST Analysis, SWOT Analysis, Porter's Five Forces Model and a Life Cycle Analysis. The evaluation was concluded by conducting a v situational analysis of Edcon after OntheCards Investments and an assessment of OntheCards Investments with respect to its acceptability and suitability to Edcon's business. The conclusion drawn from the study was that Edcon had stretch goals and aspirations in OntheCards Investments. Edcon identified the critical success factors for OntheCards Investment and actively achieved the improvement in their credit management operations, which has now yielded an extreme competitive advantage that will not be easily imitated by their competition without major capital investment

Customer Relationship Management : Concept, Strategy, and Tools -3/E

Customer relationship management (CRM) as a strategy and as a technology has gone through an amazing evolutionary journey. After the initial technological approaches, this process has matured considerably – both from a conceptual and from an applications point of view. Of course this evolution continues, especially in the light of the digital transformation. Today, CRM refers to a strategy, a set of tactics, and a technology that has become indispensable in the modern economy. Based on both authors’ rich academic and managerial experience, this book gives a unified treatment of the strategic and tactical aspects of customer relationship management as we know it today. It stresses developing an understanding of economic customer value as the guiding concept for marketing decisions. The goal of this book is to be a comprehensive and up-to-date learning companion for advanced undergraduate students, master students, and executives who want a detailed and conceptually sound insight into the field of CRM

Augmenting IDEs with Runtime Information for Software Maintenance

Object-oriented language features such as inheritance, abstract types, late-binding, or polymorphism lead to distributed and scattered code, rendering a software system hard to understand and maintain. The integrated development environment (IDE), the primary tool used by developers to maintain software systems, usually purely operates on static source code and does not reveal dynamic relationships between distributed source artifacts, which makes it difficult for developers to understand and navigate software systems. Another shortcoming of today's IDEs is the large amount of information with which they typically overwhelm developers. Large software systems encompass several thousand source artifacts such as classes and methods. These static artifacts are presented by IDEs in views such as trees or source editors. To gain an understanding of a system, developers have to open many such views, which leads to a workspace cluttered with different windows or tabs. Navigating through the code or maintaining a working context is thus difficult for developers working on large software systems. In this dissertation we address the question how to augment IDEs with dynamic information to better navigate scattered code while at the same time not overwhelming developers with even more information in the IDE views. We claim that by first reducing the amount of information developers have to deal with, we are subsequently able to embed dynamic information in the familiar source perspectives of IDEs to better comprehend and navigate large software spaces. We propose means to reduce or mitigate the information by highlighting relevant source elements, by explicitly representing working context, and by automatically housekeeping the workspace in the IDE. We then improve navigation of scattered code by explicitly representing dynamic collaboration and software features in the static source perspectives of IDEs. We validate our claim by conducting empirical experiments with developers and by analyzing recorded development sessions

From Controlled Data-Center Environments to Open Distributed Environments: Scalable, Efficient, and Robust Systems with Extended Functionality

The past two decades have witnessed several paradigm shifts in computing environments. Starting from cloud computing which offers on-demand allocation of storage, network, compute, and memory resources, as well as other services, in a pay-as-you-go billingmodel. Ending with the rise of permissionless blockchain technology, a decentralized computing paradigm with lower trust assumptions and limitless number of participants. Unlike in the cloud, where all the computing resources are owned by some trusted cloud provider, permissionless blockchains allow computing resources owned by possibly malicious parties to join and leave their network without obtaining permission from some centralized trusted authority. Still, in the presence of malicious parties, permissionlessblockchain networks can perform general computations and make progress. Cloud computing is powered by geographically distributed data-centers controlled and managed by trusted cloud service providers and promises theoretically infinite computing resources. On the other hand, permissionless blockchains are powered by open networks of geographically distributed computing nodes owned by entities that are not necessarily known or trusted. This paradigm shift requires a reconsideration of distributed data management protocols and distributed system designs that assume low latency across system components, inelastic computing resources, or fully trusted computing resources.In this dissertation, we propose new system designs and optimizations that address scalability and efficiency of distributed data management systems in cloud environments. We also propose several protocols and new programming paradigms to extend the functionality and enhance the robustness of permissionless blockchains. The work presented spans global-scale transaction processing, large-scale stream processing, atomic transaction processing across permissionless blockchains, and extending the functionality and the use-cases of permissionless blockchains. In all these directions, the focus is on rethinking system and protocol designs to account for novel cloud and permissionless blockchain assumptions. For global-scale transaction processing, we propose GPlacer, a placement optimization framework that decides replica placement of fully and partial geo-replicated databases. For large-scale stream processing, we propose Cache-on-Track (CoT) an adaptive and elastic client-side cache that addresses server-side load-imbalances that occur in large-scale distributed storage layers. In permissionless blockchain transaction processing, we propose AC3WN, the first correct cross-chain commitment protocol that guarantees atomicity of cross-chain transactions. Also, we propose TXSC, a transactional smart contract programming framework. TXSC provides smart contract developers with transaction primitives. These primitives allow developers to write smart contracts without the need to reason about the anomalies that can arise due to concurrent smart contract function executions. In addition, we propose a forward-looking architecture that unifies both permissioned and permissionless blockchains and exploits the running infrastructure of permissionless blockchains to build global asset management systems

Memory Subsystem Optimization Techniques for Modern High-Performance General-Purpose Processors

abstract: General-purpose processors propel the advances and innovations that are the subject of humanity’s many endeavors. Catering to this demand, chip-multiprocessors (CMPs) and general-purpose graphics processing units (GPGPUs) have seen many high-performance innovations in their architectures. With these advances, the memory subsystem has become the performance- and energy-limiting aspect of CMPs and GPGPUs alike. This dissertation identifies and mitigates the key performance and energy-efficiency bottlenecks in the memory subsystem of general-purpose processors via novel, practical, microarchitecture and system-architecture solutions. Addressing the important Last Level Cache (LLC) management problem in CMPs, I observe that LLC management decisions made in isolation, as in prior proposals, often lead to sub-optimal system performance. I demonstrate that in order to maximize system performance, it is essential to manage the LLCs while being cognizant of its interaction with the system main memory. I propose ReMAP, which reduces the net memory access cost by evicting cache lines that either have no reuse, or have low memory access cost. ReMAP improves the performance of the CMP system by as much as 13%, and by an average of 6.5%. Rather than the LLC, the L1 data cache has a pronounced impact on GPGPU performance by acting as the bandwidth filter for the rest of the memory subsystem. Prior work has shown that the severely constrained data cache capacity in GPGPUs leads to sub-optimal performance. In this thesis, I propose two novel techniques that address the GPGPU data cache capacity problem. I propose ID-Cache that performs effective cache bypassing and cache line size selection to improve cache capacity utilization. Next, I propose LATTE-CC that considers the GPU’s latency tolerance feature and adaptively compresses the data stored in the data cache, thereby increasing its effective capacity. ID-Cache and LATTE-CC are shown to achieve 71% and 19.2% speedup, respectively, over a wide variety of GPGPU applications. Complementing the aforementioned microarchitecture techniques, I identify the need for system architecture innovations to sustain performance scalability of GPG- PUs in the face of slowing Moore’s Law. I propose a novel GPU architecture called the Multi-Chip-Module GPU (MCM-GPU) that integrates multiple GPU modules to form a single logical GPU. With intelligent memory subsystem optimizations tailored for MCM-GPUs, it can achieve within 7% of the performance of a similar but hypothetical monolithic die GPU. Taking a step further, I present an in-depth study of the energy-efficiency characteristics of future MCM-GPUs. I demonstrate that the inherent non-uniform memory access side-effects form the key energy-efficiency bottleneck in the future. In summary, this thesis offers key insights into the performance and energy-efficiency bottlenecks in CMPs and GPGPUs, which can guide future architects towards developing high-performance and energy-efficient general-purpose processors.Dissertation/ThesisDoctoral Dissertation Computer Science 201

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

MyEcoCost - forming the nucleus of a novel environmental accounting system: vision, prototype and way forward

The innovative software system "myEcoCost" enables to gather and communicate resource and environmental data for products and services in global value chains. The system has been developed in the consortium of the European research project myEcoCost and forms a basis of a new, highly automated environmental accounting system für companies and consumers. The prototype of the system, linked to financial accounting of companies, was developed and tested in close collaboration with large and small companies. This brochure gives a brief introduction to the vision linked to myEcoCost: a network formed by collaborative environmental accounting nodes collecting environmental data at each step in a product's value chains. It shows why better life cycle data are needed and how myEcoCost addresses and solves this problem. Furthermore, it presents options for a future upscaling of highly automated environmenal accounting for prodcuts and services

Information Search and Personalization in Electronic Commerce

This thesis studies consumer search behaviour online and its implications on firm performance. The first chapter introduces the overarching topic, providing an overview of the research methodology and key findings. The second chapter examines behavioural implications of consumer types on information search and choice of smartphones online, using demographic, behavioral, browsing history, and detailed product data under laboratory settings. The key finding suggests that opposing personal traits such as conformism and self direction are both associated with extensive search, where the former is steered by bandwagon effects and the latter, by snob effects (demand for a good by individuals of a higher income level is inversely related to its demand by those of a lower income level). Additionally for conformists, price of the purchased good is not reflective of the searched levels, which may be driven by their propensity to choose the most popular alternative rather than the cheapest. This is indicative of conspicuous motives, especially relevant for luxury goods. The third chapter investigates optimal search paths of online shoppers for experience versus search goods, as they engage in continuous sequential search for product information. An optimal stopping rule is designed, based on reservation utilities where the instantaneous utility at each search is modelled as a continuous stochastic process. Furthermore, an empirical model validates the theoretical finding using browsing and purchase data from a Finnish multi-product retailer. The main finding is that, experience goods are associated with three times lower search intensities as compared to search goods. A proxy for the agents' prior information is calculated based on historic search data via novel methodology from the field of information retrieval, such as Text frequency-Inverse document frequency, which exhibits an estimated twelve percent increase in search for search goods, while having no effect on experience goods. Finally, the role of personalised recommendations is studied in the context of online search and choice, which has completely opposing effects on the two product types. The fourth chapter investigates the incentives of e-commerce platforms to show personalized recommendations and its effects on performance. A theoretical framework is developed that characterizes the optimal decision policy of a firm, given current state of shoppers. The key finding is that the firm must always show recommendations to shoppers in the high state above a certain price or value threshold. In the low state, recommending is optimal if the "salience effect" is above a threshold that maximizes discounted future stream of profits. An empirical model provides support to the theoretical findings, highlighting the reputation effects of platform recommendations, using browsing and purchase data from a Finnish multi-product platform. While recommendations are associated with a 29% increase in firm revenue, relevance of such recommendations potentially boost revenue by a significant 30%. Furthermore, strong evidence is presented that consumer state is endogenous in firm revenue regressions. A three-step IV process extracts the direct effect of consumer state on revenue which shows positive association between reputation effects and firm performance.Tässä opinnäytetyössä tutkitaan kuluttajien verkkohakukäyttäytymistä ja sen vaikutuksia yrityksen toimintaan. Ensimmäisessä luvussa esitellään aihe yleisesti sisältäen yleiskatsauksen tutkimusmenetelmistä ja keskeisistä havainnoista. Toisessa luvussa tarkastellaan kuluttajatyyppien käyttäytymisvaikutuksia tiedonhakuun ja älypuhelimilla tehtävistä valinnoista verkkokäytössä käyttämällä väestö- ja käyttäytymistietoja, selaushistoriaa sekä yksityiskohtaisia tuotetietoja laboratorioympäristössä. Keskeinen havainto viittaa siihen, että vastakkaiset henkilökohtaiset arvot, kuten konformismi ja itseohjaus, liittyvät molemmat laajaan hakuun, jossa ensin mainittua ohjaavat bandwagon-ilmiö ja jälkimmäistä snobivaikutukset (korkeamman tulotason yksilöiden kysyntä tavaralle on kääntäen verrannollinen alemman tulotason yksilöiden kysyntään). Lisäksi konformisteille ostetun tavaran hinta ei heijasta haettuja tasoja, mikä saattaa johtua heidän taipumuksestaan valita suosituin vaihtoehto halvimman sijaan. Tämä on osoitus havaittavista motiiveista, erityisesti ylellisyystuotteiden kohdalla. Kolmannessa luvussa tarkastellaan verkko-ostajien optimaalisia hakupolkuja elämysten ja tavaroiden etsimiseen, kun he tekevät jatkuvaa peräkkäistä tuotetietojen hakua. Suunnitellaan optimaalinen pysäytyssääntö, joka perustuu varausjärjestelmiin, jossa jokaisen haun hetkellinen hyödyllisyys mallinnetaan jatkuvana stokastisena prosessina. Lisäksi empiirinen malli validioi teoreettisen havainnon suomalaisen monituotekauppiaan selailu- ja ostotietojen avulla. Tärkein havainto on, että elämyshakujen intensiteetti on kolme kertaa pienempi verrattuna tavarahakuihin. Valtuutettujen edustajien ennakkotiedot on laskettu hakuhistorian tietojen perusteella uudenlaisella tiedonhaun menetelmällä, kuten esim. Tekstin tiheys - Käänteinen asiakirjatiheys, jolla on arviolta kahdentoista prosentin lisäys tavaroiden haussa, kun taas elämyshakuihin sillä ei ole vaikutusta. Lopuksi tutkitaan henkilökohtaisten suositusten, joilla on täysin vastakkaiset vaikutukset kahteen hakukohteeseen, merkitystä verkkohaun ja valinnan yhteydessä. Neljännessä luvussa tarkastellaan sähköisen kaupankäynnin alustojen kannustimia yksilöityjen suosituksien esittämiseeen ja sen vaikutuksia toimintaan. Kehitetään teoreettinen viitekehys, joka luonnehtii yrityksen optimaalista päätöksentekopolitiikkaa ottaen huomioon ostajien nykytilan. Keskeinen havainto on, että yrityksen on aina osoitettava suosituksia korkeatasoisille ostajille, jotka ylittävät tietyn hinta- tai arvorajan. Alemmalla tasolla suositteleminen on optimaalista, jos "näkyvyysvaikutus" ylittää kynnyksen, joka maksimoi diskontatun tuottovirran. Empiirinen malli tukee teoriahavaintoja, joissa korostetaan alustasuositusten mainevaikutuksia käyttämällä selailu- ja ostotietoja suomalaiselta monituotealustalta. Vaikka suositukset on yhdistetty 29%:n kasvuun yritysten liikevaihdossa, paikkaansa pitävien suositusten merkitys saattaa lisätä liikevaihtoa merkittävät 30%. Lisäksi on esitetty vahvaa näyttöä siitä, että kuluttajien asema on sisäsyntyinen yrityksen liikevaihtoregressioissa. Kolmivaiheisessa IV-menettelyssä otetaan huomioon kuluttajan aseman suora vaikutus liikevaihtoon, mikä osoittaa mainevaikutusten ja yrityksen suoriutumisen välisen positiivisen yhteyden