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Procurement planning has always been a huge and challenging activity for business firms, especially in manufacturing. With government legislations about global concern over carbon emissions, the manufacturing firms are enforced to regulate and reduce the emissions caused throughout the supply chain. It is observed that procurement and logistics activities in manufacturing firms contribute heavily toward carbon emissions. Moreover, highly dynamic and uncertain business environment with uncertainty in parameters such as demand, supplier and carrier capacity adds to the complexity in procurement planning. The paper aims to discuss these issues.

This paper is a novel attempt to model environmentally sustainable stochastic procurement (ESSP) problem as a mixed-integer non-linear program. The ESSP optimizes the procurement plan of the firm including lot-sizing, supplier and carrier selection by addressing uncertainty and environmental sustainability. The model applies chance-constrained-based approach to address the uncertain parameters.

The proposed ESSP model is solved optimally for 30 data sets to validate the proposed ESSP and is further demonstrated using three illustrations solved optimally in LINGO 10.

The ESSP model simultaneously minimizes total procurement cost and carbon emissions over the entire planning horizon considering uncertain demand, supplier and carrier capacity.

The purpose of this paper is twofold, first providing researchers with an overview about the uncertainties occurred in procurement including applicable approaches for analyzing different uncertain scenarios, and second proposing directions to inspire future research by identifying research gaps.

Papers related to supply chain risk management and procurement risk management (PRM) from 1995–2017 in several major databases are extracted by keywords and then further filtered based on the relevance to the topic, number of citations and publication year. A total of over 156 papers are selected. Definitions and current approaches related to procurement risks management are reviewed.

Five main risks in procurement process are identified. Apart from summarizing current strategies, suggestions are provided to facilitate strategy selection to handle procurement risks. Seven major future challenges and implications related PRM and different uncertainties are also indicated in this paper.

Procurement decisions making under uncertainty has attracted considerable attention from researchers and practitioners. Despite the increasing awareness for risk management for supply chain, no detail and holistic review paper studied on procurement uncertainty. Managing procurement risk not only need to mitigate the risk of price and lead time, but also need to have sophisticated analysis techniques in supply and demand uncertainty.

The contribution of this review paper is to discuss the implications of the research findings and provides insight about future research. A novel research framework is introduced as reference guide for researchers to apply innovative approach of operations research to resolve the procurements uncertainty problems.

This paper aims to build a novel model and approach that assist an aircraft MRO procurement and overhaul management problems from the perspective of aircraft maintenance service provider, in order to ensure its smoothness maintenance activities implementation. The mathematical model utilizes the data related to warehouse inventory management, incoming customer service planning as well as risk forecast and control management at the decision-making stage, which facilitates to alleviate the negative impact of the uncertain maintenance demands on the MRO spare parts inventory management operations.

A stochastic model is proposed to formulate the problem to minimize the impact of uncertain maintenance demands, which provides flexible procurement and overhaul strategies. A Benders decomposition algorithm is proposed to solve large-scale problem instances given the structure of the mathematical model.

Compared with the default branch-and-bound algorithm, the computational results suggest that the proposed Benders decomposition algorithm increases convergence speed.

The results among the same group of problem instances suggest the robustness of Benders decomposition in tackling instances with different number of stochastic scenarios involved.

Extending the proposed model and algorithm to a decision support system is possible, which utilizes the databases from enterprise's service planning and management information systems.

A novel decision-making model for the integrated rotable and expendable MRO spare parts planning problem under uncertain environment is developed, which is formulated as a two-stage stochastic programming model.

The purpose of this paper is to discuss and to help address the need for quantitative models to support and improve procurement in the context of humanitarian relief efforts.

This research presents a two‐stage stochastic decision model with recourse for procurement in humanitarian relief supply chains, and compares its effectiveness on an illustrative example with respect to a standard solution approach.

Results show the ability of the new model to capture and model both the procurement process and the uncertainty inherent in a disaster relief situation, in support of more efficient and effective procurement plans.

The research focus is on sudden onset disasters and it does not differentiate between local and international suppliers. A number of extensions of the base model could be implemented, however, so as to address the specific needs of a given organization and their procurement process.

Despite the prevalence of procurement expenditures in humanitarian efforts, procurement in humanitarian contexts is a topic that previously has only been discussed in a qualitative manner in the literature. This work provides practitioners with a new approach to quantitatively assess and improve their procurement decision processes.

This study adds to the existing literature by demonstrating the applicability and effectiveness of an analytic modeling technique based on uncertainty, such as stochastic programming with recourse, in the context of humanitarian relief procurement activities.

The purpose of this paper is to propose a portfolio procurement framework to response to uncertain customer demand and purchasing price volatility in a simultaneous manner. Then it aims to obtain optimal procurement and production decisions under the portfolio framework to maximize profit.

The portfolio procurement problem is modeled as a dynamic Stackelberg game and Nash equilibrium solutions are obtained. The portfolio procurement framework is analyzed in the settings, with both risk-neutral objective and downside risk constraints measure of contract prices.

By obtaining the Nash equilibrium solutions for both the buyer’s ordering decisions and the supplier’s optimum production decisions, Stackelberg game model for portfolio procurement is proved to be feasible. Additionally, downside risk constrains are proposed to help supply chain participants’ to evaluate the profitability and risk probabilities of the designed procurement contracts under the uncertain customer demand and spot market.

This paper assumes the supplier is risk averse and the buyer is risk neutral, and it would be interesting to examine the performances of portfolio procurement strategy with different risk attitudes participants.

This research could help the buyer respond to not only demand uncertainty but also the volatile spot price in the procurement process. Related optimal portfolio procurement strategy can be carried out to improve the enterprise’ procurement plan by adjusting the order of long-term contract, option contract and the spot market. The proposed framework could also help suppliers design and evaluate contracts for buyers with different risk preference, and on the other hand help the buyers decide if she should accept the contracts from the supplier.

This research should also increase awareness in both academia and industry on the opportunities of using the dynamic portfolio procurement approach to enhance flexibility and to mitigate the inventory as well as price risks in the procurement process. Effective downside risk constrains on contract prices could also help to protect the bottom line of companies with different risk preference.

The portfolio procurement framework proposed in this research can mitigate inventory and price risks simultaneously. Also, instead of solving the portfolio procurement planning problem in computational simulation experiments as in previous research, this paper proposed a dynamic game model for this portfolio-based procurement problem and obtained its Nash equilibrium solutions for both the buyer’s ordering decisions and the supplier’s optimum production decisions. Finally, an innovative and simple downside risk constraints has been designed to help the buyer evaluate supplier’s contract prices according to their individual risk preference.

Proactive planning strategies for “slow-onset” disruptions that affect humanitarian supply chains (SC) developed to address chronic pressing societal problems, can have a significant impact on boosting the operational and financial performance of these chains. The purpose of this paper is to develop a methodology that quantifies the impact of a risk mitigation strategy widely employed in commercial SCs, namely emergency sourcing (ES), on the performance of humanitarian SCs taking into account backorders’ clearance time, unsatisfied demand, and cost.

Discrete event simulation is employed in order to evaluate alternative ES strategies based on a total cost criterion, which incorporates inventory-related costs, as well as premium contract costs paid for emergency replenishment. Backorders’ clearance time and time-to-recovery are also employed as a design parameters.

The results document the significant impact of disruptions on expected total cost, and the beneficial role of ES in hedging against disruptions. To that end, the proposed methodology determines the optimal emergency contracted capacity for a given premium, or alternatively the maximum premium cost value that ensures the feasibility of the implemented ES strategy in the long-run, along with the associated cost and time savings, and reduction of the unsatisfied demand.

The fundamental objective is to provide a decision-making support methodology for deciding on whether to implement an ES strategy or not in humanitarian SCs, and the level of the optimal contracted reserved capacity. The results could be of great value to aid providers, policy-makers, and regulators.

The purpose of this paper is to investigate a supply chain consisting of a producer and multiple suppliers of a type of component needed for the production of a certain product. The effects of carbon emission taxes, quality of components and human inspection errors as well as the collaboration among the supply chain members are considered.

A mathematical model is formulated for a non-collaborative supply chain, and the optimal policy is shown to be the solution of a constraint optimization problem. The mathematical model is modified to the case of a collaborative supply chain and to account for inspection errors. Algorithms are provided, and a numerical example is given to illustrate the determination of the optimal policy.

This study offers a new conceptual and analytical model that analyzes the production problem from a supply chain perspective. Human resource management practices and environmental aspects were incorporated into the model to reduce risk, optimally select the suppliers and properly maximize profit by accounting for human inspection error as well carbon emission taxes. Algorithms describing the determination of the optimal policy are provided.

This study provides practical results that can be useful to researchers and managers aiming at designing sustainable supply chains that incorporate economic, environmental and human factors.

This study can be useful to researchers and managers aiming for designing sustainable supply chains that incorporate economic and human factors.

Startups across all sectors are affected by the COVID-19 pandemic and are facing a formidable challenge in terms of marketing and operations. Most of the startups have experienced a downturn in demand and supply due to COVID-19 led disturbances in sourcing networks. This paper aims to identify, analyze and categorize the significant risks influencing sourcing operations in startups during the COVID-19 era in India, using the total interpretive structural modeling (TISM) approach.

Eight enablers were identified through literature review and expert opinions from various startups in India. This study adopted the TISM approach to analyze the inter-relationships between the enablers. Multiplication Applied to Classification (MICMAC) analysis was used to rank the sourcing risk enablers and classify them as autonomous, independent, linkage and dependent enablers.

The results indicate “insufficient fund” as the most critical enabler. Network issues and employee flexibility risk were among the other critical enablers that have a high driving power. Supplier risk, quality risk and demand risk were found to have highly dependent on other enablers for implementation.

This study mainly focuses on the sourcing risks in startup operations in India. This study can be extended to many other countries.

This study will help startup industry managers and practitioners understand the interactions of enablers and identify critical enablers to mitigate risks in startup sourcing operations in the COVID-19 era.

The present study identifies the sourcing risk enablers in the COVID-19 era. It is the first attempt to analyze the interrelationship among sourcing risks in startups using the TISM approach.

Nowadays, understanding green consumers has become very critical given its implications for marketers to understand and communicate green purchase patterns on the one hand, and to design and strategize both product offerings and customer services on the other hand. The purpose of this paper is to examine the interaction effect of product attributes on the degree of environment concern, the intention of green purchase and a series of green purchase proposed patterns. This paper is built on the theory of planned behavior, and expands it by replacing subjective norms and perceived behavioral control with respectively environmental knowledge and perceived consumer effectiveness, and also by extending purchase behavior to three types of purchase patterns, namely, unconditional purchase, conditional purchase and accidental purchase.

The interaction effect is analyzed through ANOVA, whereas path analysis is used to understand path strengths of proposed model, which is assessed through standardized regression weights and significance through p-value.

Overall, this study reveals the importance of product attributes in the decision-making process of green purchasers.

This study deals with environmental behavior in general, and further research with a focus on specific behaviors is needed in this field investigating the rise of green consumption.

Product attributes play a role in the decision making of consumers willing to buy green products, and both communication and promotion of green products should integrate product attributes accordingly.

Worldwide, consumers are buying more and more green products, and this study leads to a better understanding of the decision-making process of consumers’ green products.