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An organization’s supply chain is the upstream fraction of the value chain activities, aiming to ensure that the right materials, services, and technologies are purchased from the right sources, at the right time, and in the right quality. It is essential that a company is able to utilize its supply chain with the support of a good monitoring scheme. This paper considers a framework of supply chain management embracing the principles of fuzzy logic for analyzing and monitoring performance of suppliers based on the criteria of product quality and delivery time. The proposed system recommends the quantity that should be placed in the next purchase order by indicating the possible issues to be considered prior to final confirmation with the relevant suppliers.

The purpose of this paper is to propose a three-tier assessment model (TAM), aiming to identify and evaluate the competitiveness level of companies. The existing problem is that companies find it difficult to choose a proper model which can be deployed to benchmark with competitors in terms of their competiveness level in the marketplace. Most of the available models are not appropriate or easy to use. The proposed assessment model is able to provide an insight for better planning and preparation so as to gain a better chance of success comparing with their competitors. Most importantly, the proposal model adopts a pragmatic approach and can be implemented without going through tedious mathematical calculations and analysis.

TAM embraces three different approaches deployed in various stages of the application process. The first stage is to identify the relevant criteria using hierarchical holographic modeling and the second stage is to assess the associated weightings of these criteria used to rate the potential competitiveness of related companies. The technique used in stage two is known as fuzzy analytic hierarchy process (FAHP) which is a combination of two well-established methods including fuzzy logic and analytical hierarchical programming. In stage three, a technique known as technique for order preference by similarity to the ideal solution (TOPSIS) is adopted to benchmark the level of competitiveness covering several companies in the same industry.

In this paper, a case study is conducted in order to validate the feasibility and practicality of the proposed model. Results indicate that TAM can be easily applied in various industrial settings by practitioners in the field for supporting operations management practices.

Significant amount of work is needed to ensure that the proposed model can be practically deployed in real industrial settings.

This proposed model is able to capitalize on the benefits of the HMM, FAHP and TOPSIS methods and offset their deficiencies. Most importantly, it can be applied to various industries without complex modification.

This paper suggests a hybrid model to assess competitiveness level embracing three different techniques with the unique feature which is able to provide an insight for better planning and preparation in order to excel competitors. Companies may be able to follow the procedures and steps suggested in the paper to implement the model which is proven to be pragmatic and can be applied in real situations.

In today’s competitive industrial environment, it is essential that companies are able to focus on their core activities and collaborate with business partners to achieve the common objective of meeting the best satisfaction of customer demands. However, selecting partners based on accumulated experience may not be effective due to subjective judgement and lack of systematic analysis. This paper attempts to propose a partners benchmarking assessment system (PBAS) which incorporates computational intelligence technologies into partners’ benchmarking process to support decision making. Evidence suggests that the undesirables occur in companies such as extensive delays in the planned schedule, serious quality problems and cost overruns are, to a certain extent, related to the unfulfilled promises of business partners. In this paper, the PBAS is designed to propose an alternative approach to benchmark the business partners based on case‐based reasoning and neural network. To validate the proposed system, a prototype has been developed and tested in an emulated industrial environment. The case example is outlined with analysis of the feasibility of this proposed system based on test results.

This paper aims to develop a genetic algorithm (GA)‐based process knowledge integration system (GA‐PKIS) for generalizing a set of nearly optimal fuzzy rules in quality enhancement based on the extracted fuzzy association rules in a supply chain network.

The proposed methodology provides all levels of employees with the ability to formulate nearly optimal sets of fuzzy rules to identify possible solutions for eliminating the number of defect items.

The application of the proposed methodology in the slider manufacturer has been studied. After performing the spatial analysis, the results obtained indicate that it is capable of ensuring the finished products with promising quality.

In order to demonstrate the feasibility of the proposed approach, only some processes within the supply chain are chosen. Future studies can advance this research by applying the proposed approach in different industries and processes.

Because of the complexity of the logistics operations along the supply chain, the traditional quality improvement approaches cannot address all the quality problems automatically and effectively. This newly developed GA‐based approach can help to optimize the process parameters along the supply chain network.

The purpose of this paper is to propose an outcome-based process optimization model which can be deployed in companies to enhance their business operations, strengthening their competitiveness in the current industrial environment. To validate the approach, a case example has been included to assess the practicality and validity of this approach to be applied in actual environment.

This model embraces two approaches including: fuzzy logic for mimicking the human thinking and decision making mechanism; and data mining association rules approach for optimizing the analyzed knowledge for future decision-making as well as providing a mechanism to apply the obtained knowledge to support the improvement of different types of processes.

The new methodology of the proposed algorithm has been evaluated in a case study and the algorithm shows its potential to determine the primary factors that have a great effect upon the final result of the entire operation comprising a number of processes. In this case example, relevant process parameters have been identified as the important factors causing significant impact on the result of final outcome.

The proposed methodology requires the dependence on human knowledge and personal experience to determine the various fuzzy regions of the processes. This can be fairly subjective and even biased. As such, it is advisable that the development of artificial intelligence techniques to support automatic machine learning to derive the fuzzy sets should be promoted to provide more reliable results.

Recent study on the relevant topics indicates that an intelligent process optimization approach, which is able to interact seamlessly with the knowledge-based system and extract useful information for process improvement, is still seen as an area that requires more study and investigation. In this research, the process optimization system with an effective process mining algorithm embedded for supporting knowledge discovery is proposed for use to achieve better quality control.

This paper aims to investigate the characteristics of demand and supply in relation to the real-world supply chain strategies of local urban fresh food supply chains (FFSC). It generates insights into how a range of strategies is adopted by urban retailer businesses in attempting to cater for the particular requirements of food-literate urban consumers and small-scale local growers.

Using a multiple case study method, 12 urban local fresh food retailers in Sydney were studied and interview data were analyzed using thematic analysis.

Local fresh produce has characteristics of both functional and innovative products. Retailers with strong upstream and downstream collaborations adopt hybrid strategies for increased time efficiency and product variety. The dominance of strategies for time efficiency in downstream activities is aimed at maximising the product’s freshness and taste, while product range improvement strategies mean innovative retailers are working with growers to introduce new product types and offering new recipes to consumers that encourage a wider use of products. Urban retailers of local fresh produce leverage on their relationships with upstream and downstream supply chain entities in implementing hybrid strategies.

Policymakers will make use of the new knowledge generated about the real enablers of contemporary urban food systems in designing developmental policies; findings will inform urban FFSC retailers about how harmonious relationships can be leveraged for sustainability.

The study generates new knowledge on the implementation of a leagile approach by studying the adoption of innovative hybrid strategies by urban local FFSCs in relations to demand and supply characteristics and the utilization of strong vertical relationships in a short supply chain.

This paper proposes an infrastructural framework, involving various emerging technologies, for the development of an agent‐based quality enhancement system with the distinct feature of the ability to cope with the potential quality problems from various customers' requirements.

The proposed system is equipped with the “distributed problem solving” feature which automatically identifies and divides the potential quality problems into sub‐problems and directs straight to the associated agents that are capable of interacting and negotiating with each other to support customer needs. The new feature of this agile quality enhancement system is characterized with the incorporation of the quality mining engine through the capabilities of the online analytical processing, case‐based reasoning, fuzzy logic and artificial neural networks, achieving the optimization of the performance of organizations.

In this paper, the detailed architecture and the components included in the proposed system are described with a case study to validate its feasibility of application in actual environment.

The proposed system is to enhance customer satisfaction by automating problem solving procedures in all business processes.

This paper attempts to propose a virtual case‐based benchmarking system (VCBS) which incorporates computational intelligence technologies into partners' benchmarking process to support decision‐making.

The proposed system consists of three main modules: data repository module, OLAP module and case‐based reasoning (CBR) module. The VCBS is a web‐based application that enables users to access the system and submit information to the system in anywhere at anytime. The database repository, on the other hand, maintains and acquires the data that are generated in the transactions processes and other workflow processes. It also ensures the entire valuable data which are accessible for the management to make decisions. The OLAP and the CBR modules are considered as the brain of the VCBS. The CBR module is aimed for short‐listing candidate, while the OLAP module is utilized for benchmarking the short‐listed candidate.

The VCBS is particularly useful in situations where multiple supply chain partners are involved to achieve the common objective to produce the products to the best satisfaction of customer demands with the lowest possible cost.

Since data warehouse does not update in real time it only performs update periodically during non‐office hours to avoid network traffic. The solution provided to the company may not be the most updated information.

The proposed system improves the current practice of partner selection by adopting the computational intelligence technologies into the traditional partner selection process with the assimilation of data repository, CBR and OLAP to form the integrated system for evaluation of potential partners prior to the final decision.

Globalization and the borderless marketplace have created an opportunity for companies to come together to work on projects that could otherwise not be completed through the efforts of one or two companies acting independently. The formation of virtual enterprise networks combines the advantages of the various core competencies of members of the network to deliver customer satisfaction. Virtual enterprise networks consist of several firms that can be geographically dispersed, and that might be technically disparate in terms of system platforms. Coordination among members of the network and the the transfer of quality products and services among members of the network are the key factors in achieving success. The proposed framework presented in this paper utilizes a multi‐agent model to monitor quality within a virtual enterprise network to ensure that the processes among members run smoothly and efficiently.

Notes that a flexible workflow, which is characterized by its fast responsiveness in dealing with customer demands through the integration of various value chain activities, is fundamental to achieve efficient information interchange among companies. Flexible workflow integration, extends the scope of flexible workflow to cover a unified approach for integrating the relevant stages of operations, incorporates workflow design, workflow modeling and system assimilation, all of which are the integral components of such an approach. This paper proposes a system which possesses the features of reconfigurability and customizability, allowing the integration of workflow in a flexible way under an industrial environment. Describes the proposed methodology of this approach, which aims at achieving a seamless assimilation of workflow processes.