Contract negotiation and risk preferences in dual-channel supply chain coordination

This study employs profit-sharing contracts to coordinate dual-channel supply chains and examines the selection of profit-sharing parameters and the allocation of extra system profit gained from coordination. We characterise the Pareto-optimal contracts for the two- and three-stage dual-channel supply chains, by developing and maximising system utility function related to risk preferences and negotiating power. Under the optimal profit-sharing parameter in a two-stage supply chain, both members are reluctant to cooperate; however, in a three-stage supply chain, under the optimal two profit-sharing parameters selected by optimising the system utility function, the retailer is always reluctant to cooperate, but the distributor or the supplier may have incentives to deviate from cooperation. In this case, the distributor and the supplier will negotiate again as in a two-stage supply chain so that all three members can benefit from coordination with profit-sharing contracts. Besides acting independently, the distributor, in the process of contract negotiation, may choose to form an alliance with the upstream supplier or the downstream retailer, which means the relationship among the three members involving profit allocation after coordination is quite different from that for a two-stage supply chain and is not necessarily interest-contrary. In the contract negotiation, in any kind of scenario, risk aversion and negotiation power have a significant impact on the selection of optimal profit-sharing parameters and the allocation of extra system profit. One member’s risk aversion or its negotiation power may be advantageous to the other. Mathematical examples are illustrated to clarify the contract negotiation process.     

Pricing and ordering decisions of two competing supply chains with different composite policies: a Stackelberg game-theoretic approach

In today’s global highly competitive markets, competition happens among supply chains instead of companies, as the members of supply chains. So, the partners of the chains seek to apply efficient coordinating strategies like discount, return, refund, buyback, or the other coordinating policies to abate the operation costs of the chains and subsequently increase market shares. Hence, because of the importance and application of these strategies in the current non-exclusive markets, in this study, we introduce different composite coordinating strategies to enhance the coordination of the supply chains. Here, we consider two competing supply chains where both chains launch the same product under different brands to the market by applying different composite coordinating strategies. Each supply chain comprises one manufacturer and a group of non-competing retailers where the manufacturer receives raw materials from an outside supplier and transforms them into a finished product; then, the products are sold to the retailers to satisfy the demands of market. In the first chain, a composite (QFF) policy, which is the combination of quantity and freight discount, as well as free shipping quantity policies, are considered between upstream and downstream members while in the second one, different composite polices are considered between upstream and downstream members such that the supplier offers a composite policy, as the first chain, to the manufacturer and the manufacturer proposes a composite (QPR) policy, which is the combination of quantity discount and partial-refund customer return policies, to the retailers. The main objective of the paper is to determine the optimal selling prices and the order quantities of the manufacturer and the retailers in each chain in presence of different composite coordinating strategies. A Stackelberg game-theoretic approach is employed between the members of each chain where the manufacturer is a follower and the retailers are leaders. The concavity of profit functions is proved. Finally, the applicability of the models is justified by presented numerical examples. Moreover, the effects of these strategies on the decisions of the chains’ partners are examined.     

Review of supplier diversification and pricing strategies under random supply and demand

Uncertainties of supply and demand are two major sources of risk in any supply chain. As a result, the companies are implementing different strategies to mitigate the effects of these risks. Supplier diversification and responsive pricing are two of the main strategies that are used to mitigate the supply and demand risks. In supplier diversification, a firm uses multiple channels of sourcing while in responsive pricing, a firm manipulates demand through pricing to mitigate supply and demand risks. In this paper, we review lot-sizing problems when supply and demand are random. We focus on studies that have considered supplier diversification or responsive pricing as a mitigation strategy. We classify the studies based on their main assumptions and summarise their major findings. Finally, we present some directions for future research. Part of what we have found is that most studies that use multiple decision makers have focused on cases where information is complete and non-cooperative. There is a need to consider more realistic situations when there is information asymmetry between the decision makers. In addition, we have found that there is a lack of studies that look at the impact of joint ordering and pricing in the existence of multiple suppliers.     

Failure Prevention Through the Cataloging of Successful Risk Mitigation Strategies

The objective of this paper is to introduce the method to add mitigation strategy data to the generated risk event effect neutralization (GREEN) method knowledgebase to improve its ability to effectively mitigate risks. Risk mitigation is the creation and selection of mitigation strategies to reduce, measure, or control risks in a system. Currently, a vast majority of risk mitigation strategies are created based on the engineering expertise of the engineers on a project. The GREEN method provides a means for engineers to supplement their experience by generating risk mitigation strategies based on past successful risk mitigation strategies using the failure modes of the potential risks that the product faces. In order to better aid the engineer in selecting the best possible risk mitigation strategy for a particular risk, more information on mitigation strategies needs to be cataloged in the GREEN knowledgebase. This paper outlines and demonstrates the method for adding new data on mitigation strategies to the knowledgebase, and presents a case study of how this information is added and used to mitigate product risks.     

Ripple effect quantification by supplier risk exposure assessment

Supply chain (SC) disruptions are considered events that temporarily change the structural design and operational policies of SCs with significant resilience implications. The SC dynamics and complexity drive such disruptions beyond local event node boundaries to affect large parts of the SC. The propagation of a disruption through a SC and its associated impact is called the ripple effect. Previous approaches to ripple effect modelling have mainly focused on estimating the likelihood of a disruption; our study looks at the disruption consequences. We develop a new model to assess the ripple effect of a supplier disruption, based on possible maximum loss. Our risk exposure model quantifies the ripple effect, comprehensively combining features such as financial, customer, and operational performance impacts, consideration of multi-echelon inventory, disruption duration, and supplier importance. The ripple effect quantification is validated with simulations using actual company data. The findings suggest that the model can be of value in revealing latent high-risk supplier relations, and in prioritising risk mitigation efforts when probability estimations are difficult. The performance indicators proposed can be used by managers to analyse disruption propagation impact and to identify the set of most critical suppliers to be included in the disruption risk analysis.     

A hybrid risks-informed approach for the selection of supplier portfolio

In conventional supplier selection approaches, cost consideration is usually emphasised and it renders a vulnerable supply chain with various risks. This article aims to develop a quantitative approach for modelling both supply chain operational risks and disruption risks to support decision-making with regard to order allocation and risk mitigation. We introduce two types of risk evaluation models: value-at-risk (VaR) and conditional value-at-risk (CVaR). Specifically, VaR is used to measure operational risks caused by improper selection and operations of a supplier portfolio to the stochastic demand, which may frequently occur but result in relatively small losses to supply chains; CVaR is used to evaluate disruption risks that are less frequent and tend to cause significant damage. After incorporating risk factors into a probability-based multi-criteria optimisation model, different methods and parameters are compared and tested to determine the factors that may influence the supplier selection process. Computational examples by simulation are presented to illustrate the approach and how decision-makers make trade-offs between costs and hybrid risks.     

Strategic purchasing participation,supplier selection,supplier evaluation and purchasing performance

In this article, we examine how purchasing’s strategic participation influences supply management activities via the choice of appropriate operational and strategic criteria. Specifically, the study focuses on supplier selection and monitoring ongoing supplier performance evaluation based on operational and strategic criteria. A combination of choosing appropriate supplier selection criteria and monitoring supplier performance ensures that the benefits of purchasing’s participation in strategic planning translate into better purchasing performance of cost, quality, delivery, flexibility and innovation. We test the hypotheses using survey data collected from manufacturing companies in the US by means of a path model. Our results provide support for the tenet that purchasing’s participation in strategic planning influences purchasing performance directly as well as through the mediating effects of supplier selection criteria and supplier performance evaluation. We discuss the theoretical and managerial implications of the findings and propose directions for further research.     

Pricing structures for solutions: an exploratory study within the oil and gas industry†

A growing number of companies have begun to realise the potential for differentiating their product offerings by integrating services to provide customised solutions. Although there is now an extensive and growing literature on this trend, researchers have only recently begun to consider the pricing structures for such solutions. To address this shortcoming, the present study adopts an exploratory case-based approach to investigate a buyer (drilling contractor) and two suppliers of offshore capital equipment, each of whom provides condition-based maintenance solutions for offshore drilling units in the upstream oil and gas industry. The findings of the study identify a number of underlying mechanisms for solution offerings (i.e. innovativeness, benchmarking alternatives, measurability, replicability and operational risk) that are important considerations in the process of determining appropriate pricing structures based on the buyer’s business model, procurement practices and maintenance strategy vis-à-vis the supplier’s capabilities and the buyer–supplier relationship. The present study contributes to the literature by providing empirical evidence on and insight into the complexity of determining the pricing structure for solution offerings from the perspective of the supplier as well as the buyer.     

Employing dynamic fuzzy membership functions to assess environmental performance in the supplier selection process

The proposed system illustrates that logic fuzzy can be used to aid management in assessing a supplier's environmental performance in the supplier selection process. A user-centred hierarchical system employing scalable fuzzy membership functions implement human priorities in the supplier selection process, with particular focus on a supplier's environmental performance. Traditionally, when evaluating supplier performance, companies have considered criteria such as price, quality, flexibility, etc. These criteria are of varying importance to individual companies pertaining to their own specific objectives. However, with environmental pressures increasing, many companies have begun to give more attention to environmental issues and, in particular, to their suppliers’ environmental performance. The framework presented here was developed to introduce efficiently environmental criteria into the existing supplier selection process and to reflect on its relevant importance to individual companies. The system presented attempts to simulate the human preference given to particular supplier selection criteria with particular focus on environmental issues when considering supplier selection. The system considers environmental data from multiple aspects of a suppliers business, and based on the relevant impact this will have on a Buying Organization, a decision is reached on the suitability of the supplier. This enables a particular supplier's strengths and weaknesses to be considered as well as considering their significance and relevance to the Buying Organization.     

The impact of autonomous vehicle technologies on product recall risk

Complex Advanced Driving Assistance Systems (ADAS) and Autonomous Vehicle (AV) technology are increasing the number of vehicle recalls. At the same time, financial risks resulting from extensive product recall events can severely affect vehicle manufacturers and their suppliers, exposing the automotive supply chain to business continuity, legal and reputational risk. However, these risk implications are under-appreciated by large segments of the supply chain. This study shows that product recall events are increasing in general but recall events associated with ADAS/AV technology form an increasingly large percentage of these recall events. Based on this analysis, we describe ADAS/AV-specific aspects of risk mitigation and present a multidimensional approach, combining production-centric risk mitigation avenues in the automotive supply chain with the transfer of residual financial risks via insurance. We find that this comprehensive risk mitigation approach benefits in higher transparency of total production costs and increased resilience of the automotive supply chain. Against the background of an increasing product recall risk resulting from the increasing automation and interconnectedness of modern vehicles, we therefore suggest a closer, more strategic cooperation between insurance companies, car manufacturers and automotive suppliers for the benefit of all parties.