19,674 research outputs found
E-Fulfillment and Multi-Channel Distribution – A Review
This review addresses the specific supply chain management issues of Internet fulfillment in a multi-channel environment. It provides a systematic overview of managerial planning tasks and reviews corresponding quantitative models. In this way, we aim to enhance the understanding of multi-channel e-fulfillment and to identify gaps between relevant managerial issues and academic literature, thereby indicating directions for future research. One of the recurrent patterns in today’s e-commerce operations is the combination of ‘bricks-and-clicks’, the integration of e-fulfillment into a portfolio of multiple alternative distribution channels. From a supply chain management perspective, multi-channel distribution provides opportunities for serving different customer segments, creating synergies, and exploiting economies of scale. However, in order to successfully exploit these opportunities companies need to master novel challenges. In particular, the design of a multi-channel distribution system requires a constant trade-off between process integration and separation across multiple channels. In addition, sales and operations decisions are ever more tightly intertwined as delivery and after-sales services are becoming key components of the product offering.Distribution;E-fulfillment;Literature Review;Online Retailing
Fatores que afetam a adoção de análises de Big Data em empresas
With the total quantity of data doubling every two years, the low price of computing and data storage, make Big
Data analytics (BDA) adoption desirable for companies, as a tool to get competitive advantage. Given the availability
of free software, why have some companies failed to adopt these techniques? To answer this question,
we extend the unified theory of technology adoption and use of technology model (UTAUT) adapted for the BDA
context, adding two variables: resistance to use and perceived risk. We used the level of implementation of
these techniques to divide companies into users and non-users of BDA. The structural models were evaluated
by partial least squares (PLS). The results show the importance of good infrastructure exceeds the difficulties
companies face in implementing it. While companies planning to use Big Data expect strong results, current
users are more skeptical about its performance.Con la cantidad total de datos duplicándose cada dos años, el bajo precio de la informática y del almacenamiento
de datos, la adopción del análisis Big Data (BDA) es altamente deseable para las empresas, como un
instrumento para conseguir una ventaja competitiva. Dada la disponibilidad de software libre, ¿por qué algunas
empresas no han adoptado estas técnicas? Para responder a esta pregunta, ampliamos la teoría unificada
de la adopción y uso de tecnología (UTAUT) adaptado para el contexto BDA, agregando dos variables: resistencia
al uso y riesgo percibido. Utilizamos el grado de implantación de estas técnicas para dividir las empresas
entre: usuarias y no usuarias de BDA. Los modelos estructurales fueron evaluados con partial least squres (PLS).
Los resultados muestran que la importancia de una buena infraestructura excede las dificultades que enfrentan
las empresas para implementarla. Mientras que las compañías que planean usar BDA esperan muy buenos
resultados, las usuarias actuales son más escépticos sobre su rendimiento.Com a quantidade total de dados duplicando a cada dois anos, o baixo preço da computação e do armazenamento
de dados tornam a adoção de análises de Big Data (BDA) desejável para as empresas, como aquelas
que obterão uma vantagem competitiva. Dada a disponibilidade de software livre, por que algumas empresas
não adotaram essas técnicas? Para responder a essa pergunta, estendemos a teoria unificada de adoção e uso
de tecnologia (UTAUT) adaptado para o contexto do BDA, adicionando duas variáveis: resistência ao uso e risco
percebido. Usamos a nível da implementação da tecnologia para dividir as empresas em usuários e não usuários
de técnicas de BDA. Os modelos estruturais foram avaliados por partial least squares (PLS). Os resultados
mostram que a importância de uma boa infraestrutura excede as dificuldades que as empresas enfrentam para
implementá-la. Enquanto as empresas que planejam usar Big Data esperam resultados fortes, os usuários
atuais são mais céticos em relação ao seu desempenho
Data Mining For Customer Relationship Management
Data mining has various applications for customer relationship management. In this article, we introduce a framework for identifying appropriate data mining techniques for various CRM activities. This article attempts to integrate the data mining and CRM models and to propose a new model of Data mining for CRM. The new model specifies which types of data mining processes are suitable for which stages/processes of CRM. In order to develop an integrated model it is important to understand the existing Data mining and CRM models. Hence the article discusses some of the existing data mining and CRM models and finally proposes an integrated model of data mining for CRM
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