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To propose an infrastructure of a knowledge‐based system to capture and maintain the procurement information and purchasers' knowledge, regarding how to choose partners in the supply chain network, with the adopting of the neural networks that mimic the operation of human brain to generate solutions systematically.

The proposed system encompasses hybrid artificial intelligence (AI) technologies, Online analytical processing (OLAP) applications and neural networks.

Be able to capture the procurement data and vendors' information that are generated in the workflows to ensure tthat he knowledge and structured information are captured without additional time and effort. Recognizes the void of research in the infrastructure of the hybrid AI technologies for knowledge discovery.

Neural network does not have the sensibility characteristic of the purchasing staff, it is not able to identify the environment changes, which need to re‐adjust the output to fit the environment.

The proposed system obtains useful information related to the trend of sales demand in terms of customer preference and expected requirement using the OLAP module and then based on this information, the neural network provides recommendation related to the supported suppliers that are capable of fulfilling the requirements.

This paper proposes a knowledge‐based system that offers expandability and flexibility to allow users to add more related factors for analysis to enhance the quality of decision making.

When making sourcing decisions, both cost optimization and customer demand fulfillment are equally important for firm competitiveness. The purpose of this paper is to develop a stochastic search technique, hybrid genetic algorithm (HGA), for cost-optimized decision making in wholesaler inventory management in a supply chain network of wholesalers, retailers and suppliers.

This study develops a HGA by using a mixture of greedy-based and randomly generated solutions in the initial population and a local search method (hill climbing) applied to individuals selected for performing crossover before crossover is implemented and to the best individual in the population at the end of HGA as well as gene slice and integration.

The application of the proposed HGA is illustrated by considering multiple scenarios and comparing with the other commonly adopted methods of standard genetic algorithm, simulated annealing and tabu search. The simulation results demonstrate the capability of the proposed approach in producing more effective solutions.

The pragmatic importance of this method is for the inventory management of wholesaler operations and this can be scalable to address real contexts with multiple wholesalers and multiple suppliers with variable lead times.

The proposed stochastic-based search techniques have the capability in producing good-quality optimal or suboptimal solutions for large-scale problems within a reasonable time using ordinary computing resources available in firms.

Every company or manufacturing system is vulnerable to breakdowns. This research aims to analyze the role of Multi-Agent Technology (MAT) in minimizing breakdown probabilities in Manufacturing Industries.

This study formulated a framework of six factors and twenty-eight variables (explored in the literature). A hybrid approach of Multi-Criteria Decision-Making Technique (MCDM) was employed in the framework to prioritize, rank and establish interrelationships between factors and variables grouped under them.

The research findings reveal that the “Manufacturing Process” is the most essential factor, while “Integration Manufacturing with Maintenance” is highly impactful on the other factors to eliminate the flaws that may cause system breakdown. The findings of this study also provide a ranking order for variables to increase the performance of factors that will assist manufacturers in reducing maintenance efforts and enhancing process efficiency.

The ranking order developed in this study may assist manufacturers in reducing maintenance efforts and enhancing process efficiency. From the manufacturer’s perspective, this research presented MAT as a key aspect in dealing with the complexity of manufacturing operations in manufacturing organizations. This research may assist industrial management with insights into how they can lower the probability of breakdown, which will decrease expenditures, boost productivity and enhance overall efficiency.

This study is an original contribution to advancing MAT’s theory and empirical applications in manufacturing organizations to decrease breakdown probability.

As the manufacturing market is becoming more global and borderless with respect to national borders, a number of global manufacturing networks have been established, taking the advantage of fast‐growing networking and information technologies. Networked enterprises consist of a number of firms that are geographically dispersed and do not belong to the same head company, a system with central management capability, possessing the ability of central task decomposition/allocation, co‐ordination and monitoring. To ensure appropriate manufacturing operations are assigned to the appropriate firms, to the best interest of the “virtual” consortium, there is a need for the development of a collaborative platform which is capable of facilitating the efficient performance of resources and task allocations. In this paper, the development of a Web‐based enterprise collaborative platform is presented. The applications of the platform on business process management for networked enterprises are also discussed.

The US Government adopted the base realignment and closure (BRAC) to resolve the military, economic and political issue of excess base capacity. There have been five rounds of BRAC since 1988, and more are expected to come in the years ahead. The complexity of the closure and realignment decisions and the plethora of factors that are often involved necessitate the need for a sound theoretical framework to structure and model the decision‐making process. This paper aims to address the issues.

The paper presents a multiple criteria benchmarking system that integrates the employment, environmental, financial, strategic, and tactical impacts of the closure and realignment decisions into a weighted‐sum measure called the “survivability index.” The proposed index is used to determine whether the returns generated by each military base on the Department of Defense (DoD) hit list meet a sufficient target benchmark.

There is a significant amount of evidence that intuitive decision making is far from optimal and it deteriorates exponentially with problem complexity. The benchmarking system presented in this study helps decision makers (DMs) crystallize their thoughts and reduce the environmental complexities inherent in the BRAC decisions. The presented model is intended to create an even playing field for benchmarking and pursuing consensus not to imply a deterministic approach to BRAC decisions.

An iterative process is used to consistently analyze the objective and subjective judgments of multiple DMs within a structured framework based on the analytic network process and fuzzy logic. This iterative and interactive preference modeling procedure is the basic distinguishing feature of the presented model as opposed to statistical and optimization decision‐making approaches.

Attempts to introduce a quality measurement scheme (QMS) that is able to assess the immediate feedback of customers globally in real time, followed by a data mining process, which is an interactive process that involves assembling the data into a format conducive to producing a multi‐dimensional analysis using an online analytical processing (OLAP) approach. In addition, an XML schema, which provides a universal syntax to facilitate the exchange of data, is used in the design of the QMS to support the data mining process. To validate the feasibility of QMS in real industrial situations, a case example is covered, showing promising test results based on the proposed scheme.

The purpose of this study is to survey knowledge management (KM) practices and to examine the applications and technologies adopted when developing the knowledge management system (KMS) in build‐to‐order supply chains (BOSC).

This paper uses a literature review of research articles from 1996 to 2007 with keyword indexes to survey the KM practice, KMS technology and its application in BOSC. Such keyword indexes include: BOSC, SCM, KM, KMS, expert system, knowledge‐based system and information system on the Elsevier online database, ScienceDirect, EBSCO, Proquest, Emerald, DOAJ, and Wiley Inter Science. A total of 1,500 articles were found but only 149 articles related to the keywords of KMS application and KM practices within SCM and BOSC.

The important findings indicate that the KMS application is solely focused on single knowledge problem for enabling individual SC members to attain operational excellence. There is a need for further research into the development of KMS with features of knowledge coordination that cross organizational borders in attaining the BOSC integration.

Perhaps, the limitation of this study was the narrowness of the scope of the paper based on the keywords used for searching.

Validation of the multi‐disciplines of KM practices and KMS applications provides enterprises with useful guidelines for implementing KM‐ and KMS‐related projects within their current BOSC practices.

This paper provides useful knowledge by highlighting the characteristics of KMS technology within BOSC and empirical insights into the relationship between KM and BOSC practices.

Process mining tools can help discover and improve the business processes of urban community services from historical service event records. However, for the community service domains with small datasets, the effects of process mining are generally limited due to process incompleteness and data noise. In this paper, a cross-domain knowledge transfer method is proposed to help service process discovery with small datasets by making use of rich knowledge in similar domains with large datasets.

First, ontology modeling is used to reduce the effects of cross-domain semantic ambiguity on knowledge transfer. Second, association rules (of the activities in the service processes) are extracted with Bayesian network. Third, applicable association rules are retrieved using an applicability assignment function. Further, the retrieved association rules in domains with large datasets are mapped to those with a small dataset using a linear programming method, with a heuristic miner being adopted to generate the process model.

The proposed method is verified based on the empirical data of 10 service domains from Beidaihe, China. Results show that process discovery performance of all 10 domains were improved with the overall robustness score, precision, recall and F1 score increased by 13%, 13%, 17% and 15%, respectively. For the domains with only small datasets, the cross-domain knowledge transfer method outperforms popular state-of-the art methods.

The limitations of sample sizes are greatly reduced. This scheme can be followed to establish business process management systems of community services with reasonable performance and limited sample sizes.

This paper proposes a framework that describes the boundary spanning supply chain management (SCM) initiatives taken by leading companies. Supported by existing literature and interviews with managers from large companies reflecting a cross section of businesses, the framework suggests four motivating domains or factors that could support SCM initiatives. They are supply chain understanding, design, improvement, and coordination. Based on the sand cone model, the framework also suggests four levels of SCM integration over which these motivating factors are relevant to the firm and/or supply chain. They range from no integration outside the functional silos of a single firm to a fully integrated multi‐tier supply chain. Unlike existing frameworks that are based upon the flow of material and information through the supply chain, our framework is derived by combining the concept of integration with the motivating domains that characterize SCM initiatives. It captures the combined and overlapping impact of supply chain initiatives from a more strategic perspective and is a useful additional resource for practitioners who seek to chart potential improvements to their supply chain from a competitive standpoint.

In today’s volatile business environment, whether a company is able to compete in the local and global arena depends heavily on how effective the company can build up the partnership synergy with their business counterparts. Indeed, it is important that companies are able to monitor effectively on the manufacturing activities outside its boundary, such as business partners with dissimilar business nature and probably geographically isolated from each other. With the emergence of a dispersed manufacturing network, the important issue is the communication with the partners, collection of data, and making use of data. This paper calls for an effort to suggest practical and agile approaches and models to avoid a poorly managed supply chain in order to improve productivity and cost‐effectiveness to support the business. The agility of the supply chain models would also be concerned to ensure that there is enough adaptability in the system to respond and tackle problems that may occur in the turbulent market.