Qingyu Zhang

Manufacturing flexibility: defining and analyzing relationships among competence, capability, and customer satisfaction

Abstract Fast and dramatic changes in customer expectations, competition, and technology are creating an increasingly uncertain environment. To respond, manufacturers are seeking to enhance flexibility across the value chain. Manufacturing flexibility, a critical dimension of value chain flexibility, is the ability to produce a variety of products in the quantities that customers demand while maintaining high performance. It is strategically important for enhancing competitive position and winning customer orders. This research organizes literature on manufacturing flexibility and classifies it according to competence and capability theory. It describes a framework to explore the relationships among flexible competence (machine, labor, material handling, and routing flexibilities), flexible capability (volume flexibility and mix flexibility), and customer satisfaction. It develops valid and reliable instruments to measure the sub-dimensions of manufacturing flexibility, and it applies structural equation modeling to a large-scale sample ( n =273). The results indicate strong, positive, and direct relationships between flexible manufacturing competence and volume flexibility and between flexible manufacturing competence and mix flexibility. Volume flexibility and mix flexibility have strong, positive, and direct relationships with customer satisfaction.

Supply chain collaboration: conceptualisation and instrument development

Facing intensified global competition, firms strive for greater supply chain collaboration by leveraging the resources and knowledge of key suppliers and valued customers to reduce uncertainty, lower transaction costs, build core competence, capitalise on opportunities for learning and knowledge creation, and improve competitive position. This study examines the nature and characteristics of supply chain collaboration. Through an extensive literature review, it conceptualises supply chain collaboration as seven interconnecting elements: information sharing, goal congruence, decision synchronisation, incentive alignment, resource sharing, collaborative communication, and joint knowledge creation. It develops valid and reliable instruments to measure supply chain collaboration through rigorous empirical and statistical analysis including structured interviews, Q-sort, and a large-scale study. Predictive validity is evaluated by demonstrating a strong and positive relationship between a firms level of supply chain collaboration and its performance. The definitions and measures that result provide a rich and structured understanding of supply chain design and operation. They also facilitate empirical research and provide analytical consistency that enables greater sharing and comparison of research results.

Logistics flexibility and its impact on customer satisfaction

Purpose – To succeed in an uncertain environment, firms must respond to changing customer needs, and logistics flexibility is an important part of the response. This paper defines logistics flexibility, creates a framework to understand it, and shows how it relates to customer satisfaction.Design/methodology/approach – The study uses a large sample survey (n=273) from the Society of Manufacturing Engineers to develop valid and reliable instruments to measure logistics flexibility and to test the relationships among the variables using structural equation modeling.Findings – The results indicate strong, positive, and direct relationships between flexible logistics competence and capability, and between flexible logistics capability and customer satisfaction.Research limitations/implications – The dependent and independent variables were measured through a single respondent, which may introduce common‐method bias. The respondents were manufacturing executives, which provides only a single perspective.Practi...

Achieving flexible manufacturing competence

Purpose – To respond to increasing environmental uncertainty, manufacturers are enhancing flexible manufacturing competence (FMC) as a source of competitive advantage. This study aims to extend research on FMC to its antecedent variables by investigating the impacts of advanced manufacturing technology and operations improvement practices (OIP) on FMC.Design/methodology/approach – Using survey data from 273 manufacturing firms, this research tests additive, mediating, and moderating models that relate these variables.Findings – Among three alternative models, the moderating model is best supported. The results indicate that advanced manufacturing technology has a stronger positive impact on FMC when OIP are effectively implemented than when they are not.Research limitations/implications – The research focuses attention on the antecedent variables to production competence, rather than on the concept and positive performance effect of production competence.Practical implications – For managers, the issue is...

WEB MINING: A SURVEY OF CURRENT RESEARCH, TECHNIQUES, AND SOFTWARE

The purpose of this paper is to provide a more current evaluation and update of web mining research and techniques available. Current advances in each of the three different types of web mining are reviewed in the categories of web content mining, web usage mining, and web structure mining. For each tabulated research work, we examine such key issues as web mining process, methods/techniques, applications, data sources, and software used. Unlike previous investigators, we divide web mining processes into the following five subtasks: (1) resource finding and retrieving, (2) information selection and preprocessing, (3) patterns analysis and recognition, (4) validation and interpretation, and (5) visualization. This paper also reports the comparisons and summaries of selected software for web mining. The web mining software selected for discussion and comparison in this paper are SPSS Clementine, Megaputer PolyAnalyst, ClickTracks by web analytics, and QL2 by QL2 Software Inc. Applications of these selected web mining software to available data sets are discussed together with abundant presentations of screen shots, as well as conclusions and future directions of the research.

Spanning flexibility: supply chain information dissemination drives strategy development and customer satisfaction

Purpose – To respond to an increasingly uncertain environment, firms are seeking to enhance flexibility across the value chain. Spanning flexibility, a critical dimension of value chain flexibility, is the ability of a firm to provide horizontal information connections across the value chain to meet a variety of customer needs. This research organizes literature on spanning flexibility and classifies it according to competence and capability theory.Design/methodology/approach – The study collected data from 273 manufacturing executives related to spanning flexibility. The instruments used to collect these data have been validated via literature review and structured interviews with executives. Structural equation modeling was applied to these data to test relationships among the variables in the study.Findings – This study develops valid and reliable instruments to measure the sub‐dimensions of spanning flexibility. The results indicate strong, positive, and direct relationships between flexible spanning ...

Innovation‐driven learning in new product development: a conceptual model

This paper develops a conceptual framework that incorporates three innovation strategies and three learning activities through the mechanism of four conversion processes in new product development. Also included is the impact of the three learning activities on performance. The framework can provide a tool for systematically examining the effectiveness of product innovation efforts.

Business process reengineering for flexibility and innovation in manufacturing

In response to an increasingly global and competitive environment, the flexibility to adapt to changing market needs and develop innovative cross‐functional processes is quintessential to success. Emphasizes that, in order to succeed in BPR, the firm must make the organizational structure change from a hierarchical to a flat form, management goals must change from being functional sub‐optimized to global‐optimization, process‐oriented measurement, and employees’ work must change from being fragmented to team‐oriented. It is important that the firm combine core business process reengineering and holistic outsourcing and rethink its business from an integrated, systematic “whole” and value chain viewpoint to beat competition with flexibility and innovation.

The impact of flexible process capability on the product-process matrix: an empirical examination

Abstract Hayes and Wheelwright (Har. Bus. Rev. 57 (1) (1979) 127). provided the product–process matrix to emphasize the importance of the cooperation of manufacturing (processes) and marketing (products) to achieve the unified goals based on Skinners tradeoff theory. But the environments have dramatically changed, and recently, some authors (Ferdows and De Meyer, J. Oper. Manage. 9 (1990) 168; Noble, Dec. Sci. J. 26(5) (1995) 693) have empirically verified that firms can achieve multiple competitive performances simultaneously. Their research especially illustrates how the high flexible process capability that a firm possesses greatly enlarges the feasible zones and reduces the constrained zone in the product–process matrix. Therefore, the product–process matrix needs some modifications to guide researchers and practitioners. This paper discusses and empirically tests the impact of flexible process capability on the product–process matrix and provides further evidence of the compatibility of multiple competitive performances rather than tradeoffs.

Big data analytics with swarm intelligence

– The quality and quantity of data are vital for the effectiveness of problem solving. Nowadays, big data analytics, which require managing an immense amount of data rapidly, has attracted more and more attention. It is a new research area in the field of information processing techniques. It faces the big challenges and difficulties of a large amount of data, high dimensionality, and dynamical change of data. However, such issues might be addressed with the help from other research fields, e.g., swarm intelligence (SI), which is a collection of nature-inspired searching techniques. The paper aims to discuss these issues. , – In this paper, the potential application of SI in big data analytics is analyzed. The correspondence and association between big data analytics and SI techniques are discussed. As an example of the application of the SI algorithms in the big data processing, a commodity routing system in a port in China is introduced. Another example is the economic load dispatch problem in the planning of a modern power system. , – The characteristics of big data include volume, variety, velocity, veracity, and value. In the SI algorithms, these features can be, respectively, represented as large scale, high dimensions, dynamical, noise/surrogates, and fitness/objective problems, which have been effectively solved. , – In current research, the example problem of the port is formulated but not solved yet given the ongoing nature of the project. The example could be understood as advanced IT or data processing technology, however, its underlying mechanism could be the SI algorithms. This paper is the first step in the research to utilize the SI algorithm to a big data analytics problem. The future research will compare the performance of the method and fit it in a dynamic real system. , – Based on the combination of SI and data mining techniques, the authors can have a better understanding of the big data analytics problems, and design more effective algorithms to solve real-world big data analytical problems.