Search results

The emergence of advanced information technologies strengthens the capability to the entrepreneur to manage and manipulate data. However, the quality of information, the capability of providing the right information to the right person, and the utilization of information are still in doubt. Therefore, increasing numbers of firms have realized and started to develop as well as improve their existing information systems to fit the ever‐changing business needs of the organization to support decision‐making for the volatile business environment. Indeed, previous research studies have found that logistics management is the great frontier of cost reduction. Therefore, in this paper, an infrastructure of a decision support system is proposed to capture and maintain the business and resources allocation information with the adoption of the neural network for its artificial intelligent characteristic that mimic the operation of human brain to generate solutions systematically. The proposed system is adopted by a shipping company to assist allocation of containers.

Evaluates a simulation approach to measuring supply chain performance which incorporates order release theory. Within manufacturing a number of order release mechanisms have been developed. The importance of order release is first examined and its applicability to monitoring the performance of the supply chain is proposed. A simulation model of a typical, single channel logistics network was developed. Using the simulation model, each of the order release mechanisms was assessed and close agreement was obtained with the work of previous researchers. A new order release approach is proposed which is found to be superior to those analysed previously and should lead to improved supply chain performance.

The principal aim of this study was to provide more realistic output data based on imprecise measurements of product quality. The real‐world problems in fuzzy testing and selecting better processes performance are considered.

The Taguchi index, which provides numerical measures on process performance, has been widely used in the industry. In practice, the Taghchi index is estimated by sample data, thus it is of interest to obtain the confidence limits of the estimate C_pm for assessing processes. In addition, it is much more realistic, because in general the output quality characteristics of continuous quantities are more or less imprecise. Using the approach taken by Buckley, with some extensions, a general method is used to combine the vector of fuzzy numbers to produce the membership function of a fuzzy estimator of C_pm for further fuzzy testing and selection of better process performances.

As the rapid advancement of manufacturing technology occurs, current firms are increasing their levels of out sourcing and are relying more heavily on their supply chain as a source of their competitive advantage. Supplier selection decisions have become an important component of production and logistics management. Those decisions have a significant impact on manufacturers' ability to compete as purchases from outside suppliers may account for a large proportion of a product's costs.

The authors assume that measurements are taken from normally distributed populations in this research. Using fuzzy inference to assess manufacturing process capability processed using imprecise data under mild and severe departures from normality would be an interesting issue for further research.

From a managerial standpoint, considering stochastic uncertainty and fuzziness of data during testing and selecting the better supplier often provides a strong incentive to suppliers to adhere to the conscious gathering of data and variance reductions, as well as to quality requirements and standards.

An obvious advantage of process capability analysis over traditional classical approaches, which use binomial distribution for estimating low fractions of NC, is that reviewing a smaller sample reduces time, effort, and expenses.

As a response to increasing global market competition, companies in various industries tend to identify and manage customer relationship to increase profit performance. Companies commit more resources to identify their VIP customers and retain them by all means. The purpose of this paper is to develop a customer relationship management (CRM) business process management (BPM) model to identify airline customers with different degree of relationship and profit potential, and select the highly profitable customers for developing retention strategy and processes, and convert the less profitable into profitable corporate accounts.

This study innovatively apply the well-known techniques including CRM and relationship marketing models, fuzzy analytic hierarchy process (FAHP), and technique for order preference by similarity to ideal solution (TOPSIS) in the BPM research. This novel approach analyzes longer term customer profit and value potential, and prioritizes corporate accounts as the basis for setting appropriate customer service levels and improving the CRM process. This hybrid model is able to capitalize on the benefits of these methods and offset their deficiencies. Most importantly, it can be customized to various industries without complex modification.

This study uses data of an airline company to validate feasibility of the proposed CRM BPM model. The results indicate that this model is able to classify the customers based on various criteria and sub-criteria, thus allowing companies to introduce appropriate service levels to deal with different categories of customers, and improve CRM process so as to maximize customer profit and value potential.

This CRM BPM model and analysis provide managers extensive customer knowledge, more analytical and fact-based decision-making support, and a stronger focus on return on investment in sales and marketing. Knowing the profit and value potential generated by individual corporate customer makes it easier to establish the link between the CRM and the profit outcome. This model also benefits the organization and its stakeholders by allocating more resources to the targeted customer relationships that are profitable or valuable, and makes marketing more accountable in its marketing programs.

This study makes the first move to innovatively apply the well-known techniques including CRM and relationship marketing models, FAHP, and TOPSIS in the BPM research.

The purpose of this paper is to present a systematic review and analysis of existing research articles on new product development (NPD) published in the 12-year period starting from 1998 to 2009.

To explore the articles related to NPD, four key words namely new product, product design, product development and product innovation were used in combination of title or abstract or keyword of the articles through several knowledge databases. The abstracts of journal papers were read and a decision as to whether article belongs to any NPD research issue or not was made. In total, 1,127 research articles were categorized systematically and then analyzed on various principal NPD information groups.

Analysis of selected articles led to a certain level of dispersion in the publication of NPD research in different journals. It is found that more attention needs to be on knowledge and creativity management, communication and information transfer in any NPD process.

By observing extended literature from authors reviewing articles from various journals, growth in research, and variety of topics covered in NPD, a broad systematic multi journal review of NPD literature is clearly overdue. The authors have developed a comprehensive listing of publications on NPD where they have classified the surveyed papers according to various principal NPD information groups like: published year, NPD research stream, type of organization studied (industrial/consumer/service), level of innovation (high/moderate/low), NPD focus on frameworks, performance perspective (success, failure or both), NPD research design (conceptual/empirical and qualitative/quantitative) and NPD relevant best practice element. Based on the classification scheme, the issues were analyzed from the system's perspective and their implications to NPD research.

The use of AI for operations management, with its ability to evolve solutions, handle uncertainty and perform optimisation continues to be a major field of research. The growing body of publications over the last two decades means that it can be difficult to keep track of what has been done previously, what has worked, and what really needs to be addressed. Hence, the purpose of this paper is to present a survey of the use of AI in operations management aimed at presenting the key research themes, trends and directions of research.

The paper builds upon our previous survey of this field which was carried out for the ten‐year period 1995‐2004. Like the previous survey, it uses Elsevier's Science Direct database as a source. The framework and methodology adopted for the survey is kept as similar as possible to enable continuity and comparison of trends. Thus, the application categories adopted are: design; scheduling; process planning and control; and quality, maintenance and fault diagnosis. Research on utilising neural networks, case‐based reasoning (CBR), fuzzy logic (FL), knowledge‐Based systems (KBS), data mining, and hybrid AI in the four application areas are identified.

The survey categorises over 1,400 papers, identifying the uses of AI in the four categories of operations management and concludes with an analysis of the trends, gaps and directions for future research. The findings include: the trends for design and scheduling show a dramatic increase in the use of genetic algorithms since 2003 that reflect recognition of their success in these areas; there is a significant decline in research on use of KBS, reflecting their transition into practice; there is an increasing trend in the use of FL in quality, maintenance and fault diagnosis; and there are surprising gaps in the use of CBR and hybrid methods in operations management that offer opportunities for future research.

This is the largest and most comprehensive study to classify research on the use of AI in operations management to date. The survey and trends identified provide a useful reference point and directions for future research.

The competitive advantage in manufacturing has shifted from the mass production paradigm to the one that is based on fast‐responsiveness and flexibility. A worldwide wave of industrial restructuring and the re‐emergence of “small firms” have paved ways for the evolution of dispersed manufacturing networks in various degrees of complexity. With the rapid advances in internet technology, the emerging Factory‐on‐Demand mode of electronic production will create a greater opportunity for both producers and customers in the co‐creation of products and markets. Such a change will have far‐reaching implications in production practice beyond that of mass customization.

An electrical company is responsible for the maintenance of a transmission network of high voltage electricity. The maintenance schedule must be planned so as to minimize outage costs, taking into consideration various factors such as system security/reliability, system availability, and manpower utilization. With the rapid growth of organization, planning engineers are required to fulfill additional roles in order to increase productivity. To this end, a fast response and accurate mechanism is required to assist the planning engineers in dealing with the daily operation. This paper describes how a proposed maintenance schedule can be obtained automatically by the adoption of genetic algorithm. The main aim is to determine the maintenance schedule of circuit outage with minimizing the maintenance cost and maximizing the circuit availability under certain unavoidable system constraints. Further, an additional search mechanism called “final tuning search” is developed to enhance the system performance.

Getting the right information at the right time is one of the success factors of product development. However, there exist a great variety of data formats which make the bi‐directional data interchange among various development groups inefficient and costly. Due to the lack of adequate product and engineering data, design practitioners may find it difficult to transform the customer’s requirements to a value‐added product. This paper attempts to propose a hybrid database approach (HDA) to support effective data exchange among a case based reasoning knowledge repository and a corporate relational database model, presenting an integrated view of a company’s knowledge sources so as to enable better decision‐making by providing immediate access to key business information. To introduce machine learning capability to the whole system, case based reasoning (CBR) is embraced to capture past experience and knowledge for case matching in terms of product development issues. The significance of this research is the provision of a cross‐platform data exchange system, which is able to facilitate the creation of a conducive product development environment.

Attempts to propose an infrastructure of a supply chain information system, focusing on the component modules necessary for the building up of such a system with a description of the creation of these modules. The proposed supply chain information system embraces the concept of distributed object technology to enable efficient data exchange among various data objects which may reside in distributed platforms over geographically isolated regions, thereby leveraging the responsiveness of the supply chain network. The significance of contribution of this research is the provision of a cross‐platform data exchange system which is able to facilitate the formulation of a customer‐driven supply chain network, thereby enhancing the competitive edge of companies on a local and global basis.