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Supplier selection for defence procurement is a crucial function of a Ministry of Defence. The Ministry spends huge amounts of money each year to procure a vast array of equipment, goods and services. The ongoing financial crisis demands less subjective and more cost-saving methods for selecting a supplier. The approach advocated in this article integrates Analytic Hierarchy Process (AHP) with Goal Programming (GP) in order to combine conflicting criteria to select the best suppliers and allocate optimum order quantities among them. This paper presents a model close to real-world situations. Findings demonstrate that cost savings is a feasible result along with a viable combination of conflicting criteria in the suppliers' selection area.

In this paper, a server‐based enterprise collaborative management system using enterprise application integration technology is developed for trial implementation at Honeywell Consumer Products (Hong Kong) Limited, in the area of supplier relationship management. The system facilitates supplier selection using an integrative case‐based supplier selection and help desk approach to select the most appropriate suppliers, based on their past performance records from a case‐based warehouse. Discusses a case study to integrate Honeywell's supplier rating system and product coding system by case‐based reasoning technique to select preferred suppliers during the new product development process. Finds that the outsource cycle time from the searching of potential suppliers to the allocation of orders is greatly reduced while performance of suppliers can be monitored simultaneously.

With the increasing concerns about food management, attention is placed on the monitoring of different potential risk factors for food handling. Therefore, the purpose of this paper is to propose a system that helps facilitate and improve the quality of decision making, reduces the level of substandard goods, and facilitates data capturing and manipulation, to help a warehouses improve quality assurance in the inventory‐receiving process with the support of technology.

This system consists of three modules, which integrate the radio frequency identification (RFID) technology, case‐based reasoning (CBR), and fuzzy reasoning (FR) technique to help monitor food quality assurance activities. In the first module, the data collection module, raw warehouse and work station information are collected. In the second module, the data sorting module, the collected data are stored in a database. In this module, data are decoded, and the coding stored in the RFID tags are transformed into meaningful information. The last module is the decision‐making module, through which the operation guidelines and optimal storage conditions are determined.

To validate the feasibility of the proposed system, a case study was conducted in food manufacturing companies. A pilot run of the system revealed that the performance of the receiving operation assignment and food quality assurance activities improved significantly.

In summary, the major contribution of this paper is to develop an effective infrastructure for managing food‐receiving process and facilitating decision making in quality assurance. Integrating CBR and FR techniques to improve the quality of decision making on food inventories is an emerging idea. The system development roadmap demonstrates the way to future research opportunities for managing food inventories in the receiving operations and implementing artificial intelligent techniques in the logistics industry.

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.

In increasingly competitive markets, customer satisfaction is a vital corporate objective. Key elements to increasing customer satisfaction include producing consistently high‐quality products and providing high‐quality customer service. Also, supplier relationship management (SRM) contributes to the supplier selection and increases the competitive advantage of manufacturers. SRM can enhance customer satisfaction and increase market share. Thus the development of a customer‐SRM system in the areas of outsourcing is essential for a company to remain competitive. Discusses an intelligent customer‐SRM system (ISRMS), using case‐based reasoning to help solve problems such as supplier selection and the help desk problem‐solving approach. By using ISRMS, companies can select the most suitable suppliers from the supplier list, as well as establishing a good customer‐supplier relationship between parties.

This paper conducts a Systematic Literature Review (SLR) of Machine Learning (ML) in Supply Chain Management through bibliometric and network analysis, the authors are able to grasp key features of the contemporary literature. The study makes use of state-of-the-art analytical framework based on a unified approach to reveal insights from the present body of knowledge and the potentials for future research developments.

Unlike standard literature reviews, in SLR, a structured approach is followed. The approach enables utilizing contemporary tools and software packages such as R-package “bibliometrix” and Gephi for exploratory and visual analytics. A number of clustering methods are employed to form clusters. Later, multivariate analysis methodologies are adopted to determine the dominant clusters for the influential co-cited references.

Using contemporary tools from Bibliometric Analysis (BA), the authors identify in an exploratory analysis, the influential authors, sources, regions, affiliations and papers. In addition, the use of network analysis tools reveals research clusters, topological analysis, key research topics, interrelation and authors’ collaboration along with their patterns. Finally, the optimum number of clusters computed for cluster analysis is decided using a systematic procedure based on multivariate analysis such as k-means and factor analysis.

Modern-day supply chains increasingly depend on developing superior insights from large amounts of data available from diverse sources in unstructured and semi-structured formats. In order to maintain a competitive edge, the supply chains need to perform speedy analysis of big data using efficient tools that provide real-time decision-making insights. Such an analysis necessitates automated processing using intelligent ML algorithms. Through a BA followed by a detailed data visualization in a network analysis enabled grasping key features of the contemporary literature. The analysis is based on 155 documents from the period 2008 to 2018 selected using a systematic selection procedure.

The purpose of this paper is to provide a decision support system (DSS) to enhance the performance of cross‐border supply chain, the goal of which is to improve order planning and fulfill customer orders within the warehouse.

An intelligent DSS, namely order picking planning system (OPPS) with the adoption of case‐based reasoning, is proposed to support managers in making appropriate order fulfilling decisions when an order involves cross‐border activities. Similar cases in the past are retrieved and adapted in reference to the new order. A case study is then conducted to illustrate the feasibility and effectiveness of the system.

Recommendations are given to replace the objective decision‐making process in cross‐border supply chain with the help of the DSS. The warehouse order planning time has been reduced and useful information from past order records can be applied to solve new problems.

With the increasing demand for material sourcing across different places, cross‐border supply chain has raised the concern for manufacturers to seek lower material and rental costs. The focus on warehouse operations can increase efficiency in order delivery by considering cross‐border requirements.

The emergence of the fast fashion trend has exerted a great pressure on fashion designers who are urged to consider customers’ preferences in their designs and develop new products in an efficient manner. The purpose of this paper is to develop a fuzzy association rule mining (FARM) approach for improving the efficiency and effectiveness of new product development (NPD) in fast fashion.

The FARM identifies the hidden relationships between product styles and customer preferences. The knowledge discovered help the fashion industry design new products which are not only fashionable, but are also saleable in the market.

To evaluate the proposed approach, a case study is conducted in a Hong Kong-based fashion company in which a real-set of data are tested to generate fuzzy association rules. The results reveal that the FARM approach can provide knowledge support to the fashion industry during NPD, shorten the NPD cycle time, and increase customer satisfaction.

Compared with traditional association rule mining, the proposed FARM approach takes the fuzziness of data into consideration and the knowledge represented in the fuzzy rules is in a more human-understandable structure. It captures the voice of the customer into fashion product development and provides a specific solution to deal with the challenges brought by fast fashion. In addition, it helps increase the innovation and technological capability of the fashion industry.

An intelligent supplier relationship management system (ISRMS) integrating a company’s customer relationship management (CRM) system, supplier rating system (SRS) and product coding system (PCS) by the case based reasoning (CBR) technique to select preferred suppliers during the new product development (NPD) process is discussed. By using ISRMS in Honeywell Consumer Product (Hong Kong) Limited, it is found that the outsource cycle time from the searching of potential suppliers to the allocation of order, as well as the delay in delivery of goods of suppliers after order allocation, are greatly reduced. In addition, performance of suppliers can be monitored effectively.