Customers Grouping Using Data Mining Techniques in the Food Distribution Industry (A Case Study)

Significant data development has required organizations to use a tool to understand the relationships between data and make various appropriate decisions based on the information obtained. Customer segmentation and analysis of their behavior in the manufacturing and distribution industries according to the purposefulness of marketing activities and effective communication and with customers has a particular importance. Customer segmentation using data mining techniques is mainly based on the variables of recency purchase (R), frequency of purchase (F) and monetary value of purchase (M) in RFM model. In this article, using the mentioned variables, twelve customer groups related to the BTB (business to business) of a food production company, are grouped. The grouping in this study is evaluated based on the K-means algorithm and the Davies-Bouldin index. As a result, customer grouping is divided into three groups and, finally the CLV (customer lifetime value) of each cluster is calculated, and appropriate marketing strategies for each cluster have been proposed.

Using Machine Learning Methods in the Financial Market for Technical Analysis Based on Hybrid Models

In this study, it is presented a new hybrid model based on deep neural networks to predict the direction and magnitude of the Forex market movement in the short term. The overall model presented is based on the scalping strategy and is provided for high frequency transactions. The proposed hybrid model is based on a combination of three models based on deep neural networks. The first model is a deep neural network with a multi-input structure consisting of a combination of Long Short Term Memory layers. The second model is a deep neural network with a multi-input structure made of a combination of one-dimensional Convolutional Neural network layers. The third model has a simpler structure and is a multi-input model of the Multi-Layer Perceptron layers. The overall model was also a model based on the majority vote of three top models. This study showed that models based on Long Short-Term Memory layers provided better results than the other models and even hybrid models with more than 70% accurate.

The Effect of Cultural Components on Organizational Performance in Startups

The main purpose of this study was to investigate the effect of cultural components on organizational performance in startups. For this purpose, by literature reviewing and theoretical foundations which were derived from independent and dependent variables, the research model was designed. The independent variable of research included cultural components including the dimensions of diversity and breadth, trust and messaging and dependent variable of research included organizational performance in startups. According to the designed model, a questionnaire was distributed among the statistical sample that included all managers of startups in Tehran City. In this regard, 168 questionnaires were distributed by simple random sampling method and by collecting it, the data were analyzed using the structural equation modeling and Amos software. According to the results, all research hypotheses in line with the impact of organizational culture on the dimensions of diversity and breadth, trust and easy communication in startups on organizational performance in startups were confirmed. Also, according to the analysis of the relationship between independent and dependent indices, the variables of messaging, diversity, breadth and trust dimensions showed the highest relationship with 0.86, 0.83 and 0.82, respectively.

The Impact of Supply-Chain and Quality Management Procedures on the Innovation Performance of Small and Medium Enterprises

Small and medium-sized organizations are considered as the engine of economic development and employment. The share of small and medium-sized organizations for more than 95% of businesses is creating 50% of value added worldwide and depending on the country, production between 60% to 90% of all new jobs. The present paper, with examining the information obtained from 73 small, medium-sized manufacturing enterprises, studied the relationship between information sharing in the supply chain and innovation performance of the organization by considering factors such as quality management and supplier-specific investment. In this study, 4 main hypotheses regarding the relationship between chain information sharing, supply of quality management, supplier-specific investment, and the effect of relationship between quality management and supplier-specific investment on innovation performance of the organization have been examined. The results of the study indicated that information sharing in the supply chain has a positive and direct effect on quality management and supplier-specific investment. The results also showed that the impact of information sharing in the supply chain on the specific investment of the supplier is higher than its impact on quality management. Finally, the impact of quality management on organizational innovation performance is far greater than the impact of supplier-specific investment on organizational innovation performance.