International Journal of Industrial Engineering & Production Research

---

Abstract: The big share of electrical breakdown in electrical devices failure among other factors is caused by multitasking such as electrical insulation, mechanical support, energy dissipation, Energy storage, etc. which brings many attentions to lifetime estimation of said insulation material. Up to now, there was no-general theory had been suggested for lifetime estimation of mentioned insulation material the main reason of that was the lack of knowledge on interfering mechanisms. This paper is devoted to suggest a new state-of-art lifetime estimation method with the interest to reduce test procedure time consumption. At first briefly, suggested method has been surveyed to bold its advantages and drawbacks. The lifetime of insulating material estimated from our method, which has been named as HAMD, was better than estimated from the other tests and found to show good agreement with the experimental results.

  

---

Modern business organizations have appreciated the significance of having competitive advantage through the delivery of continuous improvement towards the customers, and being knowledge-oriented. Indisputably, Knowledge Management (KM) plays a key role in the success of Customer Relationship Management (CRM). In this regard, Customer Knowledge Management (CKM) is a newly developed concept that deals with knowledge from customers rather than knowledge about customers. However, little research has been done on the application of CKM in e-business. In this paper, after an overview of the literature, an application of CKM in Customer Lifetime Value (CLV) measurement is studied in an e-retailer case where Corporate Image and Reputation are taken into consideration.

---

Abstract Customer Lifetime Value (CLV) is known as an important concept in marketing and management of organizations to increase the captured profitability. Total value that a customer produces during his/her lifetime is named customer lifetime value. The generated value can be calculated through different methods. Each method considers different parameters. Due to the industry, firm, business or product, the parameters of CLV may vary. Companies use CLV to segment customers, analyze churn probability, allocate resources or formulate strategies related to each segment. In this article we review most presented models of calculating CLV. The aim of this survey is to gather CLV formulations of past 3 decades, which include Net Present Value (NPV), Markov chain model, probability model, RFM, survival analysis and so on.

---

Data mining is a powerful tool for firms to extract knowledge from their customers’ transaction data. One of the useful applications of data mining is segmentation. Segmentation is an effective tool for managers to make right marketing strategies for right customer segments. In this study we have segmented retailers of a hygienic manufacture. Nowadays all manufactures do understand that for staying in the competitive market, they should set up an effective relationship with their retailers. We have proposed a LRFMP (relationship Length, Recency, Frequency, Monetary, and Potential) model for retailer segmentation. Ten retailer clusters have been obtained by applying K-means algorithm with K-optimum according Davies-Bouldin index on LRFMP variables. We have analyzed obtained clusters by weighted sum of LRFMP values, which the weight of each variable calculated by Analytic Hierarchy Process (AHP) technique. In addition we have analyzed each cluster in order to formulate segment-specific marketing actions for retailers. The results of this research can help marketing managers to gain deep insights about retailers.

---

In the study of the reliability of systems in reliability engineering, it has been defined several measures in the reliability and survival analysis literature. The reliability function, the mean residual lifetime and the hazard rate are helpful tools to analyze the maintenance policies and burn-in. In this paper, we consider a network consisting of n components having the property that the network has two states up and down (connected and disconnected). Suppose that the network is subject to shocks that each may cause the component failures. We further suppose that the number of failures at each shock follows a truncated binomial distribution and the process of shocks is nonhomogeneous Poisson process. This paper investigates the reliability function, the mean residual lifetime and the hazard rate of the network under shock model. An example and illustrative graph is also provided.

---

One of the key issues in the studies on customer relationship management (CRM) and modalities of marketing budget allocation is to calculate the customer’s lifetime value and applying it to macro-management decisions. A major challenge in this sector pertains to making calculations so as to incorporate the possibility of changes in the behavior of customers with the turn of time in the model.
In this article, we first classify the customers of ISACO using clustering techniques and use multilayer neural network to calculate the monetary value of each group of customers during the specific period of time. Then, we use the Markov chain approach to develop a model for calculating the lifetime value of ISACO’s customers by taking into consideration the possibility of changes in their behavior in future time periods.
In this study, a new approach has been used to estimate the parameters of the model proposed for calculating the future lifetime value of ISACO’s customers. This method takes into consideration the possibility of changes in the customer behavior throughout their interaction with the company.
The results obtained here may be used in the allocation of marketing budget and adoption of macro-management decisions to envisage various projects for customers with different lifetime value.

---

The subjects of reliability acceptance sampling plans and failure-censored life tests have usually been investigated from the viewpoint of statistical properties; indeed, few researchers have shed light on the economic aspects of these issues. In this research, a constrained mathematical model is developed to optimally design a reliability sampling plan under failure censoring life testing. Minimizing the expected total cost (ETC) involved in the sampling and life testing is considered as the objective function of the model. Ensuring the producer’s and the consumer’s risks is taken into consideration as the constraint of the model. To minimize the ETC, the model optimally determines three decision variables including the total number of the items put to the life test, the number of the failed items to terminate the test, and a criterion to make decisions about the acceptance or rejection of the lot. Examples are provided and analyses are conducted to gain some insight regarding the model performance. 

---

Organizations have used Customer Lifetime Value (CLV) as an appropriate pattern to classify their customers. Data mining techniques have enabled organizations to analyze their customers’ behaviors more quantitatively. This research has been carried out to cluster customers based on factors of CLV model including length, recency, frequency, and monetary (LRFM) through data mining. Based on LRFM, transaction data of 1865 customers in a software company has been analyzed through Crisp-DM method and the research roadmap. Four CLV factors have been developed based on feature selection algorithm. They also have been prepared for clustering using quintile method. To determine the optimum number of clusters, silhouette and SSE indexes have been evaluated. Additionally, k-means algorithm has been applied to cluster the customers. Then, CLV amounts have been evaluated and the clusters have been ranked. The results show that customers have been clustered in 4 groups namely high value loyal customers, uncertain lost customers, uncertain new customers, and high consumption cost customers. The first cluster customers with the highest number and the highest CLV are the most valuable customers and the fourth, third, and second cluster customers are in the second, third, and fourth positions respectively. The attributes of customers in each cluster have been analyzed and the marketing strategies have been proposed for each group.