A new methodology to study customer electrocardiogram using RFM analysis and clustering

One of the primary issues on marketing planning is to know the customer's behavioral trends. A customer's purchasing interest may fluctuate for different reasons and it is important to find the declining or increasing trends whenever they happen. It is important to study these fluctuations to improve customer relationships. There are different methods to increase the customer's willingness such as planning good promotions, an increase on advertisement, etc. This paper proposes a new methodology to measure customer's behavioral trends called customer electrocardiogram. The proposed model of this paper uses K-means clustering method with RFM analysis to study customer's fluctuations over different time frames. We also apply the proposed electrocardiogram methodology for a real-world case study of food industry and the results are discussed in details

Intelligent Sales Prediction for Pharmaceutical Distribution Companies: A Data Mining Based Approach

One of the problems of pharmaceutical distribution companies (PDCs) is how to control inventory levels in order to prevent costs of excessive inventory and to prevent losing customers due to drug shortage. Consequently, the purpose of this study is to propose a novel method to forecast sales of PDCs. The presented method is a combination of network analysis tools and time series forecasting methods. Due to lack of enough past sales records of each drug, an explorative network based analysis is conducted to find clique sets and group members and to use comembers’ sales data in their sales prediction. Afterwards, time series sales forecasting models were built with three different approaches including ARIMA methodology, neural network, and an advanced hybrid neural network approach. The offered hybrid method by applying each drug and its comembers past records facilitates capturing both linear and nonlinear patterns of sales accurately. The performance of the proposed method was evaluated by a real dataset provided by one of the leading PDCs in Iran. The results indicated that the proposed method is able to cope with low number of past records while it forecasts medicines sales accurately

Efficient resource management and pricing in a two-echelon supply chain with cooperative advertising: A bi-level programming approach

The growing online marketplace has opened a plethora of opportunities for businesses across various industries. Manufacturers, seeking to bypass intermediaries and directly reach end-users, have been increasingly adopting online sales channels in addition to their traditional retail sales. A key challenge, however, lies in determining optimal pricing strategies and advertising investments for both manufacturers and retailers while considering various constraints. This study contemplates a two-echelon supply chain model involving one manufacturer and two retailers. The manufacturer sells its product both through retailers (offline channel) and directly to consumers via an online channel. The model features both global and local advertising. The influence of global advertising is realized through distinct advertising channels, each with a unique impact on demand. To further motivate retailers, the manufacturer contributes to the cost of local advertising. In response to these challenges, this research formulates a bi-level model and employs the concept of Variational Inequalities to solve it. The model also contends with production capacity and budget constraints, leading to a Generalized Nash-Stackelberg game. The validity of the model and the efficacy of the solution method are assessed through numerical experiments performed. Finally, a set of valuable managerial insights are provided

Carrier Collaboration with the Simultaneous Presence of Transferable and Non-transferable Utilities

This database contains experimental problems designed to study Carrier Collaboration with the Simultaneous Presence of Transferable and Non-transferable Utilities. It contains a relatively large set of experimental instances ranging from small to large sizes. The data for each instance is organized in a separate folder that contains two files. The first file includes the specifications of network nodes, including depots and customers. The second file shows the specifications of the vehicles. Read the metadata file for more details and a better understanding of file structures