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Many supply contracts are employed in practice to improve the performance of supply chains. But there is a lack of research that can offer guidance to practitioners in choosing the best supply contract among a group of popular contracts. This paper aims to fill this gap by developing an intelligent rule‐based supply contract design system for choosing the best contract and its parameters from a supplier's point of view.

The approach used in this paper is based on the comparison of several supply contracts that are encountered in supply chain practice. The paper aims at identifying the conditions under which one supply contract outperforms another from the supplier's perspective. To facilitate the implementation of the decision‐making rules that are developed in this research, an intelligent decision support system is developed.

Six popular contracts are analysed; returns policy (RP), quantity discount (QD), target rebate (TR), backup agreement (BA), quantity flexibility (QF), and quantity commitment (QC). The main findings are: QD contracts generate larger expected profits for the supplier than TR contracts do when the demand is exogenous, an RP contract is better than a QD contract when the wholesale profit margin is sufficiently large and that the optimal QC contract always provides a higher expected service level than BA and QF contracts.

The paper presents an approach for developing an intelligent supply contract design system that can offer guidance to practitioners in choosing the best supply contract for a particular supplier.

Expert systems are being effectively applied to a variety of engineering problems. A growing number of languages and development tools are available for their building. Expert systems building tools (shells) are not so flexible as the high‐level languages, but they are easier to use. The problem is that there are too many development tools on the market today, no standards for their evaluation are available, so it is quite difficult to choose the ‘best’ tool for the developer's/user's needs. This paper is an attempt to review the situation on the confused market. Eighty‐six development tools are described in a table form for easy comparisons. Tools implemented on the AI machines only are not included in this survey.

The purpose of this paper and its companion (Part I: single and two‐component supply chains) is to investigate methods to tackle complexities, constraints (including time‐varying constraints) and other challenges. In this part, attention is devoted to multi‐silo supply chain and the relationships between the components. The first part of the paper aims to consider two types of experimental supply chains: with one‐to‐many and many‐to‐one relationships. The second half of the paper aims to present two approaches on optimising the material flow in the real‐world supply chain network.

Cooperative coevolutionary and classical sequential approaches are taken to address the experimental multi‐silo supply chains. Due to the nature and the complexity of the supply chain presented in the second half of the paper, evolutionary algorithm was not sufficient to tackle the problem. A fuzzy‐evolutionary algorithm is proposed to address the problem.

The proposed systems produce solutions better than solutions proposed by human experts and in much shorter time.

The paper discusses various algorithms to provide the decision support for the real‐world problems. The system proposed for the real‐world supply chain is in the process of integration to the production environment.

Risk identification is the first and crucial step in supply chain risk management process. Due to the nature and complexity of supply chain networks of manufacturing organizations, risk identification nowadays has become more challenging. The purpose of this paper to present the development of a tool, called Supply Chain Risk Identification System (SCRIS), for assisting decision makers in identifying existing risks, and the interrelationship of risks in supply chain (SC) network, by considering different process strategies, namely make to stock (MTS), make to order (MTO) and engineering to order (ETO).

SCRIS is developed using a knowledge‐based system (KBS) approach. The knowledge is represented in ruled based form and written using CLIPS expert system language program. To ensure its feasibility, SCRIS is validated using real case studies in several manufacturing industries.

Feedback gathered from organizations involved in validations processes imply the benefit of using SCRIS as a decision support tool in identifying SC risks. SCRIS also has additional positive role in supply chain risk management (SCRM) by promoting communication and collaboration between SC partners.

SCRIS provides an extensive tool using KBS approach which covers hundreds of SC risk sub‐factors, risk factors, and risk events, as well as mapping the interactions and considering different process strategies which have not been developed to date. A novel SC risks taxonomy is also proposed which encompasses broader issues in the SC network.

This paper aims to improve operational efficiency and minimize accident frequency in cold storage facilities through adopting an effective occupational safety and health program. The hidden knowledge can be extracted from the warehousing operations to create the comfortable and safe workplace environment.

A fuzzy association rule-based knowledge management system is developed by integrating fuzzy association rule mining (FARM) and rule-based expert system (RES). FARM is used to extract hidden knowledge from real operations to establish the relationship between safety measurement, personal constitution and key performance index measurement. The extracted knowledge is then stored and adopted in the RES to establish an effective occupational and safety program. Afterwards, a case study is conducted to validate the performance of the proposed system.

The results indicate that the aforementioned relationship can be built in the form of IF-THEN rules. An appropriate safety and health program can be developed and applied to all workers, so that they can follow instructions to prevent cold induced injuries and also improve the productivity.

Because of the increasing public consciousness of occupational safety and health, it is important for the workers in cold storage facilities where the ambient temperature is at/below 10°C. The proposed system can address the social problem and promote the importance of occupational safety and health in the society.

This study contributes to the knowledge management system for improving the occupational safety and operational efficiency in the cold storage facilities.

This paper investigates the suitability of fuzzy-logic-based support tools for initial screening of manufacturing reshoring decisions.

Two fuzzy-logic-based support tools are developed together with experts from a Swedish manufacturing firm. The first uses a complete rule base and the second a reduced rule base. Sixteen inference settings are used in both of the support tools.

The findings show that fuzzy-logic-based support tools are suitable for initial screening of manufacturing reshoring decisions. The developed support tools are capable of suggesting whether a reshoring decision should be further evaluated or not, based on six primary competitiveness criteria. In contrast to existing literature this research shows that it does not matter whether a complete or reduced rule base is used when it comes to accuracy. The developed support tools perform similarly with no statistically significant differences. However, since the interpretability is much higher when a reduced rule base is used and it require fewer resources to develop, the second tool is more preferable for initial screening purposes.

The developed support tools are implemented at a primary-criteria level and to make them more applicable, they should also include the sub-criteria level. The support tools should also be expanded to not only consider competitiveness criteria, but also other criteria related to availability of resources and strategic orientation of the firm. This requires further research with regard to multi-stage architecture and automatic generation of fuzzy rules in the manufacturing reshoring domain.

The support tools help managers to invest their scarce time on the most promising reshoring projects and to make timely and resilient decisions by taking a holistic perspective on competitiveness. Practitioners are advised to choose the type of support tool based on the available data.

There is a general lack of decision support tools in the manufacturing reshoring domain. This paper addresses the gap by developing fuzzy-logic-based support tools for initial screening of manufacturing reshoring decisions.

The purpose of this paper is to develop a decision support system (DSS) to assist supply chain (SC) risk managers to select a suitable risk management (RM) strategy and expedite the implementation of corresponding RM enablers. The relationship between RM strategies and RM enablers is explored by identifying the underlying factors between them, which is further used to build the DSS.

The DSS is built by integrating heterogeneous techniques. A systematic review approach is employed to explore both proactive and reactive RM enablers, and they are further mapped to various RM strategies by using correspondence analysis (CA). An in-depth interview is conducted to develop the rules for constructing the decision system. A rule-based fuzzy inference system (FIS) is utilized to counteract the uncertainty involved in the decision variables. The efficacy of the proposed DSS is demonstrated by considering two conjectural scenarios in the case of Indian petroleum SC (IPSC).

The results reveal three primary underlying factors between the risk mitigation strategies viz. SC managers' preparedness to face risk, organization's resource capability to deal with risk and the sophistication of the implementation of the RM enablers; with explained variances of 37%, 29% and 22%, respectively. Risk avoidance strategy comprises of RM enablers such as supplier evaluation, technology adaption, information security, etc. Whereas, the risk-sharing strategy includes revenue sharing, insurance, collaboration, public-private-partnership, etc. as essential RM enablers. The DSS recommends risk-mitigation and risk-sharing as effective RM strategies for the IPSC under the considered scenarios.

This paper develops a decision support framework for recommending an effective risk mitigation strategy and outranking the corresponding enablers. The study explicitly focuses on the risk mitigation step of the supply chain risk management (SCRM) process. Pre- and post-risk mitigation steps of the SCRM process, such as risk assessment and risk monitoring are beyond the scope of this research.

The operational procedure of the proposed DSS is explained by considering a real-life case of petroleum SC in the Indian scenario. The unique contributions of this study are presented as theoretical implications and managerial propositions in the context of a developing country.

This paper investigates the overall system risk for a foodgrains supply chain capturing the interrelationship among the risk factors and the effect of risk mitigation strategies.

This paper first calculates the weight of risk factors using an integrated approach of failure mode, effects analysis and fuzzy VIKOR technique. Next, the weights are utilized as input for the weighted fuzzy Petri-net (WFPN) approach to calculate the system risk.

Two different WFPN models are developed based on the relationships among the risk factors, and both models demonstrate a higher risk value for the overall system.

The proposed methodology will help practitioners or managers understand the complexity involved in the system by capturing the interrelationship behaviour. This study also considers the concurrent effect of risk mitigation strategies for calculating the overall system risk, which helps to improve the system’s performance.

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 a departure from the efficiency theory assumptions implicit in most supply chain risk management (SCRM) literature, this study aims to explore the role that external neo-institutional pressures play in shaping the risk management strategies deployed to mitigate transaction cost risks in construction supply chains (CSC).

A theory-elaborating case study is used to investigate how regulatory, normative and mimetic neo-institutional pressures underpin SCRM strategies in state-led and private-led CSC in China.

The study finds that institutionalized Confucianist networks serve as proxies for regulatory accountability and thereby create a form of dysmorphia in the regulatory, normative and mimetic drivers of SCRM strategies in state-led and private-led CSC in China.

The findings reveal that relational costs such as bargaining, transfer and monitoring costs underpin SCRM in state-led CSC. Behavioral costs associated with search, screening and enforcement are the core drivers of SCRM in private-led CSC. These differences in transaction cost drivers of SCRM arise from the risk-buffering effect of personalized Guanxi networks, creating variants of institutional pressures on actors' risk analysis, identification and treatment strategies in China. Considering China's global hegemony in construction and related industries, this study provides valuable insights for practitioners and researchers on the need for a constrained efficiency view of SCRM in global CSC.