Search results

In the current competitive environment, each company faces a number of challenges: quick response to customers’ demands, high quality of products or services, customers’ satisfaction, reliable delivery dates, high efficiency, and others. As a result, during the last five years many firms have proceeded to the adoption of enterprise resource planning (ERP) solutions. ERP is a packaged software system, which enables the integration of operations, business processes and functions, through common data‐processing and communications protocols. However, the majority, if not all, of these systems do not support the production scheduling process that is of crucial importance in today’s manufacturing and service industries. In this paper, the authors propose a knowledge‐based system for production‐scheduling that could be incorporated as a custom module in an ERP system. This system uses the prevailing conditions in the industrial environment in order to select dynamically and propose the most appropriate scheduling algorithm from a library of many candidate algorithms.

From the very earliest moments in the modern history of the computer, scientists have dreamed of creating advanced systems which would simulate human meditation and reasoning. Of all the modern technological quests, this research to create artificially intelligent computer systems has been one of the most ambitious and fascinating. Although attempts had been made more than 30 years ago to develop and apply such systems to the medical sciences, the field languished for decades. In this framework, this paper aims to share thoughts and estimations on the important role of expert systems in medicine and address their future as well as the trends that are foreseen in this area.

The purpose of this paper is to examine the association between the extent of adoption of TQM, using Kaynak’s (2003) core TQM practices (quality data and reporting, supplier quality management, product/service design, process management) model and competitive advantage. In addition, the study examines the antecedent role of organisational-related factors (intensity of market competition, information technology (IT), expert systems and the supplier evaluation programme) in respect to the extent of adoption of TQM.

A survey was conducted of 673 business units of garment organisations in a developing country, Bangladesh.

The findings indicate that the extent of adoption of TQM practices was positively associated with competitive advantage. In addition, two organisational-related factors (intensity of market competition and IT) were positively associated with the extent of adoption of TQM.

The findings provide an insight into the ability of TQM to facilitate competitive advantage in developing countries. Practitioners are also provided with an insight into which organisational factors facilitate the adoption of TQM practices.

The findings provide an important insight into the use and value of TQM practices in developing countries.

This paper aims to consider an “-ological” (ontological, epistemological and methodological) triad in the context of construction management (CM) research, and to explore the triad in terms of ontological/epistemological viewpoints, paradigmatic approaches to CM research and, ultimately, CM methodological decisions.

Derivation of critical narrative and graphical models using literature synthesis combined with experiential, methodological views of the authors.

Conceptions of ontology, epistemology and methodology (the “ological-triad”) demonstrate high variability – resultantly, their use in CM research is equally inconsistent, sometimes questionable and, in the extreme, sometimes overlooked. Accordingly, this study concludes that greater recognition of the “ological-triad” is called for in CM research, especially at the design stage. A framework for doing this is proffered.

Combined study of the “ologies” within CM research uniquely consolidates previous disparate knowledge into a single, cogent, subject-specific discourse that, inter-alia, both informs and illuminates CM research challenges. It also encourages critical debate on the issues highlighted.

Based on the perspective of knowledge management, this study aims to discuss how to build cross-city emergency management collaboration mechanism in major emergencies and explore the important role of knowledge management in emergency management collaboration.

Based on the theoretical analysis of knowledge management and the typical case study of cross-city emergency management collaborative rescue, this study provides an in-depth analysis of how these cities achieve high emergency management performance through multidimensional and multilevel knowledge collaboration, thus revealing the mechanism of knowledge transfer, integration and sharing in achieving high emergency management performance.

Through analyzing typical cases, this study finds that building a smooth mechanism for multichannel emergency rescue information can promote the diversification of knowledge transfer methods, building a platform-based integration mechanism for emergency rescue information can enhance knowledge integration capabilities and building a linkage mechanism for emergency rescue materials between cities can promote knowledge-sharing level, thereby improving emergency management performance level.

This study has great significance for how to build cross-city emergency management collaboration mechanism in the digital era. In the future, the authors need to further discuss the following two aspects in depth: research on the impact of cross-city emergency management collaboration mechanism on improving the knowledge management capabilities of government emergency management departments; and research on the impact mechanism of knowledge management capabilities on city resilience.

Through case analysis of cross-city emergency management collaborative rescue for major emergencies in China in recent years, this study proposes three specific strategies for cross-city emergency management (smooth, integration and linkage mechanisms) and reveals that these three strategies are essentially aimed at improving the government’s knowledge management level.

The development of a rule‐based expert system (ES), driven by a discrete event simulation model, that performs dynamic shop scheduling is described. Based on a flowtime prediction heuristic that has been developed and base‐line runs to establish the efficacy of scheduling strategies such as shortest processing time (SPT), critical ratio, total work, etc., a rescheduling‐based dispatching strategy is investigated in a dynamic job shop environment. The results are discussed and analyzed.

The purpose of this paper is to describe the process of productivity management and potential expert systems applications at each stage of productivity analysis. Based on literature reviews it discusses the strengths and limitations of these technologies. Describes several tasks in the measurement, interpretation and evaluation phases and examines the appropriateness of an expert systems application. Finds that expert systems applications could be useful in interpretation and evaluation. Focuses on productivity analysis at the organizational‐level only. Opines that business managers with limited or no knowledge of productivity models may want to have expert systems applications developed to diagnose problems and take corrective actions in a timely manner. The paper could be useful to business practitioners as well as researchers. Contributions include a detailed description of productivity analysis and how and where expert systems applications could make a difference. Productivity management is critical for long‐term business survival.

The need effectively to integrate decision making tasks together with knowledge representation and inference procedures has caused recent research efforts towards the integration of decision support systems with knowledge‐based techniques. Explores the potential benefits of such integration in the area of business forecasting. Describes the forecasting process and identifies its main functional elements. Some of these elements provide the requirements for an intelligent forecasting support system. Describes the architecture and the implementation of such a system, the theta intelligent forecasting information system (TIFIS) that that first‐named author had developed during his dissertation. In TIFIS, besides the traditional components of a decision‐support onformation system, four constituents are included that try to model the expertise required. The information system adopts an object‐oriented approach to forecasting and exploits the forecasting engine of the theta model integrated with automated rule based adjustments and judgmental adjustments. Tests the forecasting accuracy of the information system on the M3‐competition monthly data.

The purpose of this paper is to propose an integrated approach to process integration, automation, and optimization through enhanced business process models.

The approach is based on a framework of process integration for functional applications, automation for business workflows, and additional functionalities for process optimization. The proposed approach is illustrated using enhanced process models over business integration, automation, and optimization with data elements, structures, and organizational elements. The standard sales order process cycle, quotation approval process, and production order cycle are chosen for illustrating process integration, automation, and optimization, respectively.

The proposed approach combines applications and workflows using integrated process/data models and forms a foundation for business process optimization. It is shown that the integrated approach can improve existing business processes in enterprise resource planning (ERP), beyond business process re‐engineering (BPR) principles, once enhanced business process models are implemented. This approach eliminates need for a hierarchical representation of business processes and highlights the flexibility and visibility of business process implementation in ERP system environment.

Although process integration, automation, and optimization are illustrated using selected business process examples, it requires generalization of these enhancements over entire business blueprint of ERP system. Thus, one key limitation of this research is that it is not generalized for the entire business blueprint of ERP. This also requires changes to data structures beyond current relational data in many ERP systems.

This research provides an integrated approach to business process modeling beyond traditional functional and workflow applications by eliminating hierarchical nature of process and data elements.

Decision‐making in executive information systems (EIS) frequently involves scanning complex sets of multidimensional data, which can be difficult with traditional data portrayal techniques. Therefore, schematic faces may be useful in EIS. Schematic faces can represent up to 20 variables by mapping those variables to the salient facial features so that positive outcomes result in a happier looking face, thus simplifying the data scanning process. The composites of the variables, i.e. the faces, aid management by providing a sophisticated graphical interface between the data and the decision maker. An experiment was conducted to compare decision time and accuracy when basing decisions on tables, bar charts, and schematic faces. The gender and cognitive style of subjects were also considered. MANOVA results indicate that both decision speed and accuracy are better when viewing schematic faces. Cognitive style was also a significant factor in the model.