Desain Aplikasi B2b Sistem Manajemen Pergudangan dalam Penunjang Keputusan Bisnis

Warehousing data processing based on products entering and leaving the expedition in Super Tata Raya Steel companies has several obstacles in the delivery of information. Data management carried out by the warehouse section of the company is still manual, especially in terms of the recording and bookkeeping system of product receipts and shipments. In dealing with these problems, this research uses several stages among observation, analysis, planning and design methods with object-oriented approaches that use UML (Unified Modeling Language). So that this research can develop it into a system design in the form of web-based applications using the PHP and MySQL programming languages. The existence of a new system aims to support business-to-business between the expedition services with the company and to reduce the occurrence of errors during warehousing data processing then data management becomes faster and more accurate so that the resulting report is in accordance with the existing data. Therefore, this research can be used as a support for the leadership\u27s decision in determining business patterns in the warehousing sector, so management of company services become effective and efficien

A digital marketing strategy in a fintech start-up: Advicefront

Advicefront is a start-up founded by Portuguese whose core business is developing software planning solutions for financial advisers. The company started in 2015 and attracted the attention of multiple investors and firms interested in accompanying the evolution of the product. Through time, Advicefront crossed some uncertainty periods which eventually led to a shift in product offering. Instead of offering a single software solution the company is now selling a modular approach where separate modules address specific challenges advisers face in their daily workflow. Advicefront has always maintained true to its unique selling proposition, based on showcasing pristinely designed interfaces, integrating with popular tools among advisers, and offering an unmatched user experience for advisers and investors, adapting to each user profile’s actions and intended outcomes. The financial advice software market is still attached to old-fashioned paper-based processes and bureaucracies which are becoming obsolete. Adding to this, the sector going through not only a shift in players, since many advisers are approaching retirement, but also a renovation of investors, as millennials are now becoming financially independent with an investment-oriented approach to their income. Thereby, considering this market in turmoil, Advicefront has an incredible market opportunity to present a software shaped to serve these new populations of advisers and investors. The present project aims to propose a marketing plan for Advicefront branching in a strategic plan as well as an operational plan with recommended actions for each aspect of the marketing-mix. It is also presented a communication plan to be implemented during 2019.A Advicefront é uma start-up cujo principal negócio é desenvolver software de planeamento para consultores financeiros. A empresa surgiu em 2015 e atraiu a atenção de investidores e empresas interessadas em acompanhar a evolução do produto. Com o tempo, a Advicefront ultrapassou períodos de instabilidade que culminaram numa mudança na oferta. Ao invés de oferecer uma única solução de software a empresa desenvolveu um sistema onde módulos separados respondem a necessidades específicas do fluxo de trabalho de consultoria financeira. A Advicefront manteve-se sempre fiel à sua proposta de valor, baseada na criação de interfaces com um design de excelência, na integração com ferramentas populares, e oferecendo uma experiência de utilização inigualável, adaptando-se ao perfil do usuário. O mercado de software de consultoria financeira está ligado a processos antiquados e burocráticos que estão a tornar-se cada vez mais obsoletos. Somando o facto de ser ainda um setor a atravessar um rejuvenescimento de consultores, uma vez que muitos estão a aproximar-se da reforma, mas também uma renovação de investidores. Os millennials estão a tornar-se financeiramente independentes e com orientados para investirem os seus rendimentos. Considerando este mercado em tumulto, a Advicefront tem uma oportunidade no mercado para apresentar um software desenvolvido para responder à nova população de consultores e investidores. Este projeto visa propor um plano de marketing para a Advicefront que se ramifica num plano estratégico bem como um plano operacional com ações recomendadas para cada aspeto do marketing-mix. É ainda apresentado um plano de comunicação a ser implementado em 2019

Contributions to chatbots and digital analytics in industry

Diese kumulative Dissertation umfasst zehn wissenschaftliche Artikel, die zur Forschung digitaler Analytik, Messung von Technologieakzeptanz und Chatbots beitragen. Ziel der Artikel ist es, die Entwicklung, Implementierung und Verwaltung von Technologien zu vereinfachen und zu unterstützen. Modelle werden entwickelt, welche die wichtigsten Schritte beschreiben und unter anderem relevante damit zusammenhängende Fragen auflisten, die zu beteiligenden Interessengruppen benennen und geeignete Tools vorstellen, welche berücksichtigt werden sollten. Es werden Chatbot Taxonomien entwickelt und vorgestellt, welche die Bandbreite der derzeit bestehenden Gestaltungsmöglichkeiten aufzeigen, während identifizierte Archetypen zu beobachtende Kombinationen aufzeigen. Die Identifizierung der häufigsten Gründe für Misserfolge und die Entwicklung kritischer Erfolgsfaktoren tragen ebenfalls zu dem Ziel bei, den Entwicklungs- und Managementprozess zu erleichtern. Da die Endnutzer über die Akzeptanz und Nutzung und damit über den Erfolg einer Technologie entscheiden, werden Ansätze genutzt, wie die Nutzerakzeptanz von Technologien gemessen werden kann und wie Nutzer frühzeitig in den Entwicklungsprozess eingebunden werden können

A survey on context awareness in big data analytics for business applications

The concept of context awareness has been in existence since the 1990s. Though initially applied exclusively in computer science, over time it has increasingly been adopted by many different application domains such as business, health and military. Contexts change continuously because of objective reasons, such as economic situation, political matter and social issues. The adoption of big data analytics by businesses is facilitating such change at an even faster rate in much complicated ways. The potential benefits of embedding contextual information into an application are already evidenced by the improved outcomes of the existing context-aware methods in those applications. Since big data is growing very rapidly, context awareness in big data analytics has become more important and timely because of its proven efficiency in big data understanding and preparation, contributing to extracting the more and accurate value of big data. Many surveys have been published on context-based methods such as context modelling and reasoning, workflow adaptations, computational intelligence techniques and mobile ubiquitous systems. However, to our knowledge, no survey of context-aware methods on big data analytics for business applications supported by enterprise level software has been published to date. To bridge this research gap, in this paper first, we present a definition of context, its modelling and evaluation techniques, and highlight the importance of contextual information for big data analytics. Second, the works in three key business application areas that are context-aware and/or exploit big data analytics have been thoroughly reviewed. Finally, the paper concludes by highlighting a number of contemporary research challenges, including issues concerning modelling, managing and applying business contexts to big data analytics. © 2020, Springer-Verlag London Ltd., part of Springer Nature

Sustainable Value Co-Creation in Welfare Service Ecosystems : Transforming temporary collaboration projects into permanent resource integration

The aim of this paper is to discuss the unexploited forces of user-orientation and shared responsibility to promote sustainable value co-creation during service innovation projects in welfare service ecosystems. The framework is based on the theoretical field of public service logic (PSL) and our thesis is that service innovation seriously requires a user-oriented approach, and that such an approach enables resource integration based on the service-user’s needs and lifeworld. In our findings, we identify prerequisites and opportunities of collaborative service innovation projects in order to transform these projects into sustainable resource integration once they have ended

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

Revisiting the theory of business-to-business advertising

Creating effective business-to-business (B2B) communications is an increasingly complex challenge for marketing managers. It requires a theoretical understanding of a number of puzzling, interacting components of an advertising stimulus. However, few academicians have pursued the goal of integrating and modeling how the B2B advertising process should be conceptualized. Gilliland and Johnston (1997) provided the first comprehensive model of the process, but B2B advertising has changed dramatically since this paper and demands an update to capture the new dimensions of the phenomenon. Using a systematic literature review to summarize recent trends, this paper incorporates the key changes in B2B advertising over the last 20 years. In particular, the authors explore a revised model of B2B effects, including (1) social media, (2) creativity and emotional appeals, (3) national culture, (4) brand equity and credibility, (5) ad experience social context, and (6) competing messages