Method and Approach Mapping of Fair and Balanced Risk and Value-added Distribution in Supply Chains: A Review and Future Agenda

This paper proposes a fair and balanced risk and value-added distribution as a novel approach for collaborative supply chain. The objective of this article is to analyze the existing methods and approaches for risk management, value-adding, risk and revenue sharing to develop a new framework for balancing risk and value-adding in collaborative supply chains. The authors reviewed and synthesized 162 scientific articles which were published between 2001 and 2017 and. The reviewed articles were categorized into supply chain management and performance, risk management, value-added, fair risk and value-added distribution and supply chain negotiation. The potentials identified for future research were the importance of decision-making and sustainability for effectiveness of supply chain risk management. Most previous authors have applied an approach of revenue and risk-- sharing with both decentralized and centralized supply chains to achieve the fair risk and value-added distribution. The dominant methods we found in literature were game theory and complex mathematical formulation. Most literature focused on operation research techniques. We identified a lack of discussion of the intelligent system approach and a potential for future exploration. This paper guide future research and application agenda of fair risk and value-added distribution in supply chain collaboration. We developed a new framework for a fair and balanced risk and value-added distribution model. For a future agenda, we point towards the development of a systematic intelligent system applying soft-computing techniques and knowledge transfer for maintaining sustainable supply chains.Keywords Supply chain collaboration, Fair risk and value-added distribution, Revenue sharing, Risk management, Risk sharin

Fuzzy system approaches to negotiation pricing decision support

With the emergence of customisation services, business-to-business price negotiation plays an increasingly important role in economic and management science. Negotiation pricing aims to provide different customers with products/services that perfectly meet their requirements, with the “right" price. In general, pricing managers are responsible for identifying the “right" negotiation price with the goal of maintaining good customer relationship, while maximising profits for companies. However, efficiently and effectively determining the “right" negotiation price boundary is not a simple task; it is often complicated, time-consuming and costly to reach a consensus as the task needs to take a wide variety of pricing factors into consideration, ranging from operation costs, customers’ needs to negotiation behaviours. This paper proposes a systematic fuzzy system (FS) approach, for the first time, to provide negotiation price boundary by learning from available historical records, with a goal to release the burden of pricing managers. In addition, when the number of involved influencing factors increases, conventional FS approach easily suffers from the curse of dimensionality. To combat this problem, a novel method, simplified FS with single input and single output modules (SFS-SISOM), is also introduced in this paper to handle high-dimensional negotiation pricing problems. The utility and applicability of this research is illustrated by three experimental datasets that vary from both data dimensionality and the number of training records. The experimental results obtained from two approaches have been compared and analysed based on different aspects, including interpretability, accuracy, generality and applicability