Using data mining techniques for improving customer relationship management

Customer relationship management (CRM) refers to the managerial efforts to technologies and processes that helped to understand firms’ customers. For this reason data mining techniques have an important role to extract the hidden knowledge and information which is inherited in the data used by researchers. This investigation focuses on the current automotive maintenance industry in Iran and applies various data mining technologies to partitioning customers. Its purpose is to determine the group of potential customers who are more likely to purchase optional services. Whereas the dataset used in this study is the real data of company, many steps of preprocess were applied and dataset records have been divided into two categories by attributing labels to the records. After preprocess steps, CAID and C5.0 methods of decision tree have been applied to classify customers and help the desired organization to make decision. By the results of two decision tree methods, there are some more important features for the firm to making decision

Using data mining techniques for improving customer relationship management

Customer relationship management (CRM) refers to the managerial efforts to technologies and processes that helped to understand firms’ customers. For this reason data mining techniques have an important role to extract the hidden knowledge and information which is inherited in the data used by researchers. This investigation focuses on the current automotive maintenance industry in Iran and applies various data mining technologies to partitioning customers. Its purpose is to determine the group of potential customers who are more likely to purchase optional services. Whereas the dataset used in this study is the real data of company, many steps of preprocess were applied and dataset records have been divided into two categories by attributing labels to the records. After preprocess steps, CAID and C5.0 methods of decision tree have been applied to classify customers and help the desired organization to make decision. By the results of two decision tree methods, there are some more important features for the firm to making decision

Using data mining techniques for improving customer relationship management

Customer relationship management (CRM) refers to the managerial efforts to technologies and processes that helped to understand firms’ customers. For this reason data mining techniques have an important role to extract the hidden knowledge and information which is inherited in the data used by researchers. This investigation focuses on the current automotive maintenance industry in Iran and applies various data mining technologies to partitioning customers. Its purpose is to determine the group of potential customers who are more likely to purchase optional services. Whereas the dataset used in this study is the real data of company, many steps of preprocess were applied and dataset records have been divided into two categories by attributing labels to the records. After preprocess steps, CAID and C5.0 methods of decision tree have been applied to classify customers and help the desired organization to make decision. By the results of two decision tree methods, there are some more important features for the firm to making decision

Data mining in manufacturing: a review based on the kind of knowledge

In modern manufacturing environments, vast amounts of data are collected in database management systems and data warehouses from all involved areas, including product and process design, assembly, materials planning, quality control, scheduling, maintenance, fault detection etc. Data mining has emerged as an important tool for knowledge acquisition from the manufacturing databases. This paper reviews the literature dealing with knowledge discovery and data mining applications in the broad domain of manufacturing with a special emphasis on the type of functions to be performed on the data. The major data mining functions to be performed include characterization and description, association, classification, prediction, clustering and evolution analysis. The papers reviewed have therefore been categorized in these five categories. It has been shown that there is a rapid growth in the application of data mining in the context of manufacturing processes and enterprises in the last 3 years. This review reveals the progressive applications and existing gaps identified in the context of data mining in manufacturing. A novel text mining approach has also been used on the abstracts and keywords of 150 papers to identify the research gaps and find the linkages between knowledge area, knowledge type and the applied data mining tools and techniques

A data mining-based framework for supply chain risk management

Increased risk exposure levels, technological developments and the growing information overload in supply chain networks drive organizations to embrace data-driven approaches in Supply Chain Risk Management (SCRM). Data Mining (DM) employs multiple analytical techniques for intelligent and timely decision making; however, its potential is not entirely explored for SCRM. The paper aims to develop a DM-based framework for the identification, assessment and mitigation of different type of risks in supply chains. A holistic approach integrates DM and risk management activities in a unique framework for effective risk management. The framework is validated with a case study based on a series of semi-structured interviews, discussions and a focus group study. The study showcases how DM supports in discovering hidden and useful information from unstructured risk data for making intelligent risk management decisions

Data mining of the essential causes of different types of fatal construction accidents

Accident analysis is used to discover the causes of workplace injuries and devise methods for preventing them in the future. There has been little discussion in the previous studies of the specific elements contributing to deadly construction accidents. In contrast to previous studies, this study focuses on the causes of fatal construction accidents based on management factors, unsafe site conditions, and workers' unsafe actions. The association rule mining technique identifies the hidden patterns or knowledge between the root causes of fatal construction accidents, and one hundred meaningful association rules were extracted from the two hundred and fifty-three rules generated. It was discovered that many fatal construction accidents were caused by management factors, unsafe site circumstances, and risky worker behaviors. These analyses can be used to demonstrate plausible cause-and-effect correlations, assisting in building a safer working environment in the construction sector. The study findings can be used more efficiently to design effective inspection procedures and occupational safety initiatives. Finally, the proposed method should be tested in a broader range of construction situations and scenarios to ensure that it is as accurate as possible

A dynamic type-1 fuzzy logic system for the development of a new warehouse assessment scheme

A new dynamic assessment algorithm based on the Type-1 Fuzzy Logic System (T1FLS) is proposed in this research work to develop a dynamic warehouse assessment scheme. First, the criteria and the sub-criteria that affect a warehouse performance are identified and, then, classified into a number of clusters. Second, the warehouse performance score is determined by employing the T1FLS that is developed by using expert knowledge and/or digital data. The data for the new assessed warehouses are then evaluated to ensure that the new data are not redundant and, thus, can lead to meaningful information. Finally, such new data are utilized to dynamically update the T1FLS. The algorithm has been validated on a series of actual warehouses in Jordan, and it has been shown that the presented scheme can successfully assess the warehouses with respect to the identified criteria. In addition to being dynamic, the newly proposed assessment framework can take into consideration uncertainties naturally, this being due to fuzzy logic which has the ability to model them intrinsically via the concept of vagueness