How Kano’s Performance Mediates Perceived SERVQUAL Impact on Kansei

Through Kansei Engineering (KE) methodology in services, the perceived service quality shows a direct impact on Kansei response. In order to strengthen the KE methodology, Kano model is embedded considering the attractive [A] and one-dimensional [O] performances. However, to what extent the Kano performance brings significant impact on Kansei is questionable and has not been explored yet. It is beneficial to measure the effort spent to improve a certain service attribute, considering the Kano performance and its impact on Kansei. This study on logistics services confirms that the Kano’s attractive category [A] shows the highest impact on Kansei (with loading of 0.502), followed by one-dimensional [O] and must-be [M] ones (with loadings of 0.514 and 0.507), respectively. The service provider should prioritize Kano’s [A] service attributes first for improvement. Keywords - Kano, logistics services, Kansei, SERVQUA

Systematic Analysis of Engineering Change Request Data - Applying Data Mining Tools to Gain New Fact-Based Insights

Large, complex system development projects take several years to execute. Such projects involve hundreds of engineers who develop thousands of parts and millions of lines of code. During the course of a project, many design decisions often need to be changed due to the emergence of new information. These changes are often well documented in databases, but due to the complexity of the data, few companies analyze engineering change requests (ECRs) in a comprehensive and structured fashion. ECRs are important in the product development process to enhance a product. The opportunity at hand is that vast amount of data on industrial changes are captured and stored, yet the present challenge is to systematically retrieve and use them in a purposeful way.This PhD thesis explores the growing need of product developers for data expertise and analysis. Product developers increasingly refer to analytics for improvement opportunities for business processes and products. For this reason, we examined the three components necessary to perform data mining and data analytics: exploring and collecting ECR data, collecting domain knowledge for ECR information needs, and applying mathematical tools for solution design and implementation.Results from extensive interviews generated a list of engineering information needs related to ECRs. When preparing for data mining, it is crucial to understand how the end user or the domain expert will and wants to use the extractable information. Results also show industrial case studies where complex product development processes are modeled using the Markov chain Design Structure Matrix to analyze and compare ECR sequences in four projects. In addition, the study investigates how advanced searches based on natural language processing techniques and clustering within engineering databases can help identify related content in documents. This can help product developers conduct better pre-studies as they can now evaluate a short list of the most relevant historical documents that might contain valuable knowledge.The main contribution is an application of data mining algorithms to a novel industrial domain. The state of the art is more up for the algorithms themselves. These proposed procedures and methods were evaluated using industrial data to show patterns for process improvements and cluster similar information. New information derived with data mining and analytics can help product developers make better decisions for new designs or re-designs of processes and products to ensure robust and superior products

Fractal Dimension as a Predictor of Organizational Change Success

As many as two thirds of organizational change (OC) initiatives fail to achieve their outcome objectives. Researchers have demonstrated that successful change requires alignment among all levels of an organization. However, contemporary OC models do not quantify the degree of hierarchical alignment during the change process. The purpose of this quantitative, correlational study was to examine whether the fractal dimension of hierarchical alignment (predictor variable) was associated with OC success (criterion variable) as described by the self-organizing fractal theory (SOFT). The research question addressed the association between the fractal dimension related to the alignment of OC beliefs and behavioral intentions across an organizational hierarchy and subsequent OC success. The instrument included creolization and change resistance themes to collect primary survey data through the self-selection of 125 North American aerospace workers who had participated in a formal change process. Pearson’s product-moment, Spearman rank, and Kendall’s tau correlation coefficients revealed a strong positive association between fractal dimension and OC success. Subsequent regression analysis reinforced the positive correlation and explained at least 56% of the observed variation in OC success. The results contributed to scholarly OC research by providing proof-of-concept demonstration that SOFT is applicable to OC research. This study also contributed to social change by creating measures that may lead to improved change management, resulting in less resource waste, lower employee stress, and improved change outcomes

Proceedings of the 2nd Conference on Production Systems and Logistics (CPSL 2021)

Proceedings of the CPSL 202