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Customer lifetime value (CLV) is one of the key indicators to measure the success or health of an organization. How can an organization assess the organization's customers' lifetime value (LTV) and offer relevant strategies to retain prospective and profitable customers? This study offers an integrated view of different methods for calculating CLVs for both loyalty members and non-membership customers.

This study outlines eleven methods for calculating CLV considering (1) the deterministic aspect of NPV (Net present value) models in both finite and infinite timespans, (2) the geometric pattern and (3) the probabilistic aspect of parameter estimates through simulation modeling along with (4) the migration models for including “the probability that customers will return in the future” as a key input for CLV calculation.

The CLV models are validated in the context of complementary and alternative medicine (CAM)in the healthcare industry. The results show that understanding CLV can help the organization develop strategies to retain valuable customers while maintaining profit margins.

The integrated CLV models provide an overview of the mathematical estimation of LTVs depending on the nature of the customers and the business circumstances and can be applied to other business settings.

Many power producers are looking for ways to develop smarter energy capabilities to tackle challenges in the sophisticated, non-linear dynamic processes due to the complicated operating conditions. One prominent strategy is to deploy advanced intelligence systems and analytics to monitor key performance indicators, capture insights about the behavior of the electricity generation processes, and identify factors affecting combustion efficiency. Thus, the purpose of this paper is to outline a way to incorporate a business intelligence framework into existing coal-fired power plant data to transform the data into insights and deliver analytical solutions to power producers.

The proposed ten-step business intelligence framework combines the architectures of database management, business analytics, business performance management, and data visualization to manage existing enterprise data in a coal-fired power plant.

The results of this study provide plant-wide signals of any unusual operational and coal-quality factors that impact the level of NO_x and consequently explain and predict the leading causes of variation in the emission of NO_x in the combustion process.

Once the framework is integrated into the power generation process, it is important to ensure that the top management and the data analysts at the plants have the same perceptions of the benefits of big data and analytics in the long run and continue to provide support and awareness of the use of business intelligence technology and infrastructure in operational decision making.

The key finding of this study helps the power plant prioritize the important factors associated with the emission of NO_x; closer attention to those factors can be promptly initiated in order to improve the performance of the plant.

The use of big data is not just about implementing new technologies to store and manage bigger databases but rather about extracting value and creating insights from large volumes of data. The challenge is to strategically and operationally reconsider the entire process not only to prepare, integrate, and manage big data but also to make proper decisions as to which data to select for the analysis and how to apply analytical techniques to create value from the data that is in line with the strategic direction of the enterprise. This study seeks to fill this gap by outlining how to implement the proposed business intelligence framework to provide plant-wide signals of any unusual operational and coal-quality factors that impact the level of NO_x and to explain and predict the leading causes of variation in the emission of NO_x in the combustion process.

Since works-in-process (WIPs) are highly vulnerable to defects because of the variety and complexity of manufacturing processes, the purpose of this paper is to describe how to utilize existing analytics techniques to reduce defects, improve production processes, and reduce the cost of operations.

Three alternatives for diagnosing causes of defects and variations in the production process are presented in order to answer the following research question: “What are the most important factors to be included in prognostic analysis to prevent defects?”

The key findings for the proposed alternatives help explain the characteristics of defects that have a great impact on manufacturing yield and the quality of products. Consequently, any corrective action and preventive maintenance addressing the common causes of defects and variations in the process can be regularly evaluated and monitored.

Although the focus of this study is on improving shop-floor operations by reducing defects, further experimentation with business analytics in other areas such as machine utilization and maintenance, process control, and safety evaluation remains to be done.

This study has been validated with several scenarios in a manufacturing company, and the results demonstrate the practical validity of the approach, which is equally applicable to other manufacturing sub-sectors.

This study is different from the others by providing alternatives for diagnosing the root causes of defects. Control charts, costs of defects, and clustering-based defect prediction scores are utilized to reduce defects. Additionally, the key contribution of this study is to demonstrate different methods for understanding WIP behaviors and identifying any irregularities in the production process.

This paper aims to examine the role of organizational innovative capabilities (OIC) on the relationship between knowledge sharing (KS), corporate entrepreneurship (CE) and firm performance (FP). Specifically, this study uses the knowledge-based view to develop a model that examines the mentioned relationship.

Using survey data from 520 participants across 75 service sector companies in Thailand, measurement and structure models are tested through structural equation modeling to quantify the impact between constructs.

This study shows that KS and CE positively affect OIC and FP. A positive relationship is also found between KS and CE. The mediating impact of OIC strengthens the relationship between KS and CE on FP.

Like all research using survey methods, the research is prone to respondent biases and generalizability. However, this paper has put the best effort to minimize such effects by rigorous methodological testing to avoid such biases.

The findings of this study suggest that to improve organizational learning and knowledge-based performance, commitment and understanding of the employees in the entire organization is crucial. KS significantly contributes to developing innovative abilities because of its characteristics of providing firm-specific and socially complex advantages. The way a firm transforms and exploits its knowledge may ascertain its level of innovativeness, such as coming up with certain problem-solving procedures and new product development according to the rapid change in the market demand. However, organizations may only instigate to effectively organize knowledge when their employees are ready to share knowledge. Continuous KS boosts entrepreneurial practices and contributes innovativeness across individuals, groups, units or the entire organization.

The relationship between CE, organization innovative capabilities and FP in the presence of KS is rarely discussed in both theoretical and empirical literature. This study contributes to the literature by arguing that apart from the direct impact of KS on FP, KS can lead the firms toward generating important competitive advantage by forming innovative capabilities that can significantly influence FP.