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EXPRESS (a language used to define standard data models) is used to develop data models for products, categories, and chain stores allowing for ISO‐integrated product data management. By applying EXPRESS to the task of managing product and market information, retail data are integrated in a central database and are accessible in real time by members of the distribution channel Point of Sales (POS) systems at the store level provide the streams of data from retail stores. These data are collected and kept in a central database used for dynamic sales analysis and merchandise planning. The decision models for automated shelf layout, continuous sales analysis, and real‐time logistic management are incorporated into the marketing information system (MIS), which can be utilized by store managers through an easy‐accessed Web‐based interface. The WWW is the communicators’ medium used to link the retail headquarters with the distributed retail chain. The central object‐oriented database is based on the proposed EXPRESS data model and provides a means to manage large amounts of rapidly‐changing information. Stores that implement retail information systems of this nature can easily expand to suitable and profitable economies of scale without loss of information and control.

The purpose of this paper is to provide a clustering approach to segment supply chain partners in the automobile industry and prioritize services offered by third party logistics service (3PL) providers.

In total, 98 automobile and auto‐parts manufacturers are surveyed to identify service needs, preferences, and outsourcing commitments. By applying a two‐stage clustering approach combined with Ward's minimum‐variance method and the K‐means algorithm, the logistics companies prioritize their services to better satisfy groups of customers with specific preferences.

Four distinctive groups of manufacturers are identified using the two‐stage clustering approach. The clusters separate logistic preferences and outsourcing patterns of after market parts suppliers, original equipment service parts suppliers, original equipment manufacturer parts suppliers, and tier one car makers. The paper finds that distribution and delivery services hold the highest percentage of services outsourced among the manufacturers.

This paper models logistic services as customizable services and develops a data system methodology to define the profiles of automobile manufacturers and their preferred logistic services. Through the analysis of service preferences and clustering, the paper identifies the key logistic services that can be customized for members of the automobile supply chain. A case is provided which demonstrates how a logistics company can provide customized service designs for specific target markets and customers.

Links and analyzes qualitative and quantitative sources of information about the Taiwan retail market using an adaptation of Leites’ method. Qualitative interviews are abstracted and integrated with quantitative secondary source data and survey data. Outlines, categorizes, and summarizes information useful for making management decisions about consumers, retail formats, products and services, retail development projects, market strategies, and commercial laws and regulations. Demonstrates an efficient means to survey general trends in newly developed retail and consumer markets and shows promise for implementation as a soft OR method to derive management strategies.

In an era of rapidly expanding digital content, the number of e‐documents and the amount of knowledge frequently overwhelm the R&D teams and often impede intellectual property management. The purpose of this paper is to develop an automatic patent summarization method for accurate knowledge abstraction and effective R&D knowledge management.

This paper develops an integrated approach for automatic patent summary generation combining the concepts of key phrase recognition and significant information density. Significant information density is defined based on the domain‐specific key concepts/phrases, relevant phrases, title phrases, indicator phrases and topic sentences of a given patent document.

The document compression ratio and the knowledge retention ratio are used to measure both quantitative and qualitative outcomes of the new summarization methodology. Both measurements indicate the significant benefits and superior results of the method.

In order to implement the methodology with practical success, the accurate and efficient pre‐processing of identifying key concepts and relevant phrases of patent documents is required. The approach relies on a powerful text‐mining engine as the pre‐process module for key phrase extraction.

The methodology helps R&D companies consistently and automatically process, extract and summarize the core knowledge of related patent documents. This enabling technology is critical to R&D companies when they are competing to create new technologies and products for short life cycle marketplaces.

This research addresses a new perspective in R&D knowledge management, particularly in solving the knowledge‐overloading issue. The methodology helps R&D collaborative teams consistently to summarize the core knowledge of patent documents with efficiency. Efficient R&D knowledge management helps the firm to take advantage of IP positioning while avoiding patent conflict and infringement.

This paper aims to study the development of radio frequency identification (RFID) technology through an analysis of patents filed with and issued by the US Patent and Trademark Office. A close analysis of these clusters reveals the patent development strategies of two competing factions of RFID technology developers. This paper provides an analysis of the patents along with insights into the contents of the patents held by these two groups.

The analysis is based on Intermec Technologies and the RFID Patent Pool, the two major players in this domain. The comparison of Intermec Technologies and RFID Patent Pool is conducted using meta‐data analysis and patent content clustering. The methodology and approach includes data pre‐processing, key phrase extraction using term frequency‐inverse document frequency, ontology construction, key phrase correlation measurement, patent technology clustering and patent document clustering. Clusters are derived using the K‐means approach and a prototype Legal Knowledge Management Platform.

The findings support a strong link between intellectual property and competitive advantage – specifically Intermec Technologies, which have not joined the RFID Patent Pool. The patent search results show that Intermec Technologies hold basic RFID patents in the early stages of technology development, which has placed the company in a dominant position.

The features of each cluster clearly depict the niches and specialties of companies and provide a historical framework of RFID technology development.

The RFID patent analysis shows that if a company holds crucial patents in the early stages of a developing technology which relate to the fundamental key aspects of the technology, then the company will be more likely to maintain a leading and dominant position in that industry segment (i.e. RFID in this study).

This research uses patent content cluster analysis to explain the rationale behind an alliance strategy decision.

With the growing trend toward the use of international supply chain and e‐commerce, logistic service providers for product warehousing, transportation and delivery are placing great emphasis on information technology (IT) to be competitive globally. Realizing that the current service tracking system merely supports order status tracking within a service provider, applies mobile agent technology for online order tracking across the global logistic alliances. Utilizes a three‐tier architecture for mobile agent technology and develops a prototype system for global logistic service tracking. Demonstrates the concept and technology proposed. The online service tracking services enable customers to monitor the real‐time status of their service requests and therefore becomes key tool for modern enterprises to compete successfully in a global marketplace.

The Greater China region (China, Taiwan and Hong Kong) has more than 1.2 billion people, about one fifth of the world’s total population. This incredibly large market continues to modernize rapidly, and over the last five years, the region has maintained a very high economic growth rate in comparison to the rest of the world. The combination of market size and economic growth makes Greater China the most promising place in the world for Internet products and services. China, Taiwan and Hong Kong recognize the opportunities and via public and private initiatives are investing in the development of information technology (IT) and the Internet infrastructure. This paper outlines the key electronic commerce (EC) trends and events in the region. Further, the research analyzes the current impediments to Internet commerce in China, Taiwan and Hong Kong and provides strategy and directions for the region’s EC development.

Explains that Taiwan’s retailing is predominantly a mix of traditional markets and department stores, an abundance of convenience stores, a group of supermarket and hypermarket chains, many direct marketing companies and a dynamic fast‐food industry. Outlines the history of Taiwan’s development as a democratic society in order to provide a framework for describing the modernization process. From the 1600s through the period of Japanese colonization ending in 1944, Taiwan was an agrarian society. Beginning less than 50 years ago, rapid industrialization brought greater wealth to the 21 million people who inhabit the island and the recent transition to a more democratic society has resulted in consumer awareness and greater interest in life quality. Suggests that the new retail formats have done a lot to satisfy consumer demands for professional service, product value and variety, and a safe, clean environment. However, single‐site retail formats that require long‐term and large‐scale investments, such as malls, shopping centres and retail streets, are very slow to develop. Better civic co‐ordination, greater openness to international retail planners and financial institutions, as well as increased confidence about the future, are key to the next stage of retail development. Provides details of current retail statistics (in US dollars). Describes Taiwan’s retail history and discusses future retail opportunities.

To determine the status of internet and e‐commerce adoption by the Taiwan semiconductor industry, the research is designed to help government and enterprise in formulating strategic plans and making resource allocation decisions.

Using the three‐level model of internet commerce adoption (MICA), a survey of 287 companies and web sites was designed. Semiconductor firms were placed into five categories: integrated circuit (IC) design, manufacturing, packaging, IC testing, and peripheral device manufacturing.

The MICA model shows the internet adoption ratio for semiconductor firms as 82.6 percent, significantly higher than the electronics and electrical machinery industry sector (56.5 percent). The IC manufacturing and packaging segment are in the processing stage, the final stage of development for the MICA model. One‐third of the IC testing industry segment falls into the provision stage, and 36.1 percent web sites are in the processing stage. The IC design and peripherals industrial segments are located in the provision stage.

The IC manufacturing segment is conducting more financial transactions than the other segments – a result that matches earlier research showing that larger companies are most likely to implement e‐business applications. Many enterprises in the industry are lagging with the adoption of the internet indicating a need for education and training.

This benchmark study provides a framework for evaluating the internet adoption status of semiconductor and other high technology firms. The MICA model is demonstrated to be suitable for evaluating the different stages of internet adoption.

The purpose of this paper is to review the relevant literature on low carbon supply chain management (LCSCM) and classify it on contextual base. It also aims at identifying key decision-making issues in LCSCM. This paper also highlights some of the future challenges and scope of research in this domain.

A content analysis is carried out by systematically collecting the literature from major academic sources over a period of 18 years (2000-2017), identifying structural dimensions and classifying it on contextual base.

There is an increasing trend of research on LCSCM, but this research is still in a nascent stage. All supply chain functions such as supplier selection, inventory planning, network design and logistic decisions have been redefined by integrating emissions-related issues.

Limitation of this study is inherent in its unit of analysis. Only peer-reviewed journal articles published in English language have been considered in this study.

Findings of prior studies on low carbon inventory control, transportation planning, facility allocation, location selection and supply chain coordination have been highlighted in this study. This will help supply chain practitioners in decision making.

Though there are an increasing number of studies about carbon emission-related issues in supply chain management, the present literature lacks to provide a review of the overarching publications. This paper addresses this gap by providing a comprehensive review of literature on emissions-related issues in supply chain management.