Search results
1 – 10 of over 30000Zhen Hong, C.K.M. Lee and Linda Zhang
The purpose of this paper is twofold, first providing researchers with an overview about the uncertainties occurred in procurement including applicable approaches for analyzing…
Abstract
Purpose
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.
Design/methodology/approach
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.
Findings
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.
Research limitations/implications
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.
Originality/value
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.
Details
Keywords
Abhijeet Ghadge, Sujoy Bag, Mohit Goswami and Manoj Kumar Tiwari
An uncertain product demand in online retailing leads to loss of opportunity cost and customer dissatisfaction due to instances of product unavailability. On the other hand, when…
Abstract
Purpose
An uncertain product demand in online retailing leads to loss of opportunity cost and customer dissatisfaction due to instances of product unavailability. On the other hand, when e-retailers store excessive inventory of durable goods to fulfill uncertain demand, it results in significant inventory holding and obsolescence cost. In view of such overstocking/understocking situations, this study attempts to mitigate online demand risk by exploring novel e-retailing approaches considering the trade-offs between opportunity cost/customer dissatisfaction and inventory holding/obsolescence cost.
Design/methodology/approach
Four e-retailing approaches are introduced to mitigate uncertain demand and minimize the economic losses to e-retailer. Using three months of purchased history data of online consumers for durable goods, four proposed approaches are tested by developing product attribute based algorithm to calculate the economic loss to the e-retailer.
Findings
Mixed e-retailing method of selling unavailable products from collaborative e-retail partner and alternative product's suggestion from own e-retailing method is found to be best for mitigating uncertain demand as well as limiting customer dissatisfaction.
Research limitations/implications
Limited numbers of risk factor have been considered in this study. In the future, others risk factors like fraudulent order of high demand products, long delivery time window risk, damage and return risk of popular products can be incorporated and handled to reduce the economic loss.
Practical implications
The analysis can minimize the economic losses to an e-retailer and also can maximize the profit of collaborative e-retailing partner.
Originality/value
The study proposes a retailer to retailer collaboration approach without sharing the forecasted products' demand information.
Details
Keywords
Seyed Jafar Sadjadi, Zahra Ziaei and Mir Saman Pishvaee
This study aims to design a proper supply chain network for the vaccine industry in Iran, which considers several features such as uncertainties in demands and cost, perishability…
Abstract
Purpose
This study aims to design a proper supply chain network for the vaccine industry in Iran, which considers several features such as uncertainties in demands and cost, perishability of vaccines, wastages in storage, limited capacity and different priorities for demands.
Design/methodology/approach
This study presents a mixed-integer linear programming (MILP) model and using a robust counterpart approach for coping with uncertainties of model.
Findings
The presented robust model in comparison with the deterministic model has a better performance and is more reliable for network design of vaccine supply chain.
Originality/value
This study considers uncertainty in the network design of vaccine supply chain for the first time in the vaccine context It presents an MILP model where strategic decisions for each echelon and tactical decisions among different echelons of supply chain are determined. Further, it models the difference between high- and low-priority demands for vaccine.
Details
Keywords
Yue Yu, Ruozhen Qiu and Minghe Sun
This work examines the joint pricing and ordering (JPO) decisions for a loss-averse retailer with quantity-oriented reference point (RP) effect under demand uncertainty.
Abstract
Purpose
This work examines the joint pricing and ordering (JPO) decisions for a loss-averse retailer with quantity-oriented reference point (RP) effect under demand uncertainty.
Design/methodology/approach
The demand is assumed to be uncertain with the mean and variance as the only known information. The prospect theory is used to model the retailer's expected utility. An expected utility maximization model in the distribution-free approach (DFA) is then developed. Using duality theory, the expected utility under the worst-case distribution is transformed into tractable piece-wise functions. To examine the effectiveness of the DFA in coping with the demand uncertainty, a stochastic programming model is developed and its solutions are used as benchmarks.
Findings
The proposed model and solution approach can effectively hedge against the demand uncertainty. The JPO decisions are significantly influenced by the LA coefficient and the reference level. The LA has a stronger influence than the reference level does on the expected utility. An excessive LA is detrimental while an appropriate reference level is beneficial to the retailer.
Practical implications
The results of this work are applicable to loss-averse retailers with the quantity-oriented RP when making JPO decisions with difficulty in predicting the demands.
Originality/value
The demand is assumed to be uncertain in this work, but a certain demand distribution is usually assumed in the existing literature. The DFA is used to study JPO decisions for the loss-averse retailer with quantity-oriented RP effect under the uncertain demand.
Details
Keywords
Yichen Qin, Hoi-Lam Ma, Felix T.S. Chan and Waqar Ahmed Khan
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…
Abstract
Purpose
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.
Design/methodology/approach
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.
Findings
Compared with the default branch-and-bound algorithm, the computational results suggest that the proposed Benders decomposition algorithm increases convergence speed.
Research limitations/implications
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.
Practical implications
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.
Originality/value
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.
Details
Keywords
Jizhuang Hui, Shuai Wang, Zhu Bin, Guangwei Xiong and Jingxiang Lv
The purpose of this paper deals with a capacitated multi-item dynamic lot-sizing problem with the simultaneous sequence-dependent setup scheduling of the parallel resource under…
Abstract
Purpose
The purpose of this paper deals with a capacitated multi-item dynamic lot-sizing problem with the simultaneous sequence-dependent setup scheduling of the parallel resource under complex uncertainty.
Design/methodology/approach
An improved chance-constrained method is developed, in which confidence level of uncertain parameters is used to process uncertainty, and based on this, the reliability of the constraints is measured. Then, this study proposes a robust reconstruction method to transform the chance-constrained model into a deterministic model that is easy to solve, in which the robust transformation methods are used to deal with constraints with uncertainty on the right/left. Then, experimental studies using a real-world production data set provided by a gearbox synchronizer factory of an automobile supplier is carried out.
Findings
This study has demonstrated the merits of the proposed approach where the inventory of products tends to increase with the increase of confidence level. Due to a larger confidence level may result in a more strict constraint, which means that the decision-maker becomes more conservative, and thus tends to satisfy more external demands at the cost of an increase of production and stocks.
Research limitations/implications
Joint decisions of production lot-sizing and scheduling widely applied in industries can effectively avert the infeasibility of lot-size decisions, caused by capacity of lot-sing alone decision and complex uncertainty such as product demand and production cost. is also challenging.
Originality/value
This study provides more choices for the decision-makers and can also help production planners find bottleneck resources in the production system, thus developing a more feasible and reasonable production plan in a complex uncertain environment.
Details
Keywords
One of the important characteristics of the hotel business is uncertainty of lodging demand, which can jeopardize hotel operation and ultimately even threaten a hotel’s survival…
Abstract
Purpose
One of the important characteristics of the hotel business is uncertainty of lodging demand, which can jeopardize hotel operation and ultimately even threaten a hotel’s survival during an economic recession. The purpose of this paper is to propose an approach to determine optimal hotel investment issues under uncertain lodging demand.
Design/methodology/approach
Uncertainty of lodging demand is classified into two types: the impact of unexpected economic recession and the temporary imbalance between supply of hotel rooms and lodging demand. A jump-diffusion real option approach is proposed to analyze how these two types affect optimal investment timing and the potential value of new hotel projects. The case of hotel investment in Macao is used to illustrate the jump-diffusion real option approach.
Findings
The results of numerical analysis show that the uncertainty induced by temporary imbalance between supply of hotel rooms and lodging demand increases the threshold of investment and hotel value, while the uncertainty induced by unexpected economic recession has ambiguous effects on the value and optimal investment timing of new hotel projects.
Practical implications
The jump-diffusion real option approach increases managerial flexibility for managers when making investment decisions on new hotel projects, allowing greater value to be generated than is possible with the conventional discounted cash flow method.
Originality/value
The approach separates the impact of unexpected economic recession on lodging demand from that of “normal” fluctuations in lodging demand, and it considers the impact of both types of uncertainty on hotel investment.
Details
Keywords
Linan Zhou, Gengui Zhou, Fangzhong Qi and Hangying Li
This paper aims to develop a coordination mechanism that can be applied to achieve the channel coordination and information sharing simultaneously in the fresh agri-food supply…
Abstract
Purpose
This paper aims to develop a coordination mechanism that can be applied to achieve the channel coordination and information sharing simultaneously in the fresh agri-food supply chain with uncertain demand. It seeks to elucidate how the producer can use an option contract to transfer the risk caused by uncertain demand, impel the retailer to share demand information and improve the performance of supply chain.
Design/methodology/approach
An option contract model based on the basic model of fresh agri-food supply chain is introduced to compare the production, profit, risk and information sharing condition of the supply chain in different cases. In addition, a case study focusing on the sale of autumn peaches produced by a local producer is investigated, which provides evidence of the applicability of the authors’ approach.
Findings
The optimal option contract can help the supply chain achieve channel coordination and reach Pareto improvement. In the meantime, such a contract will encourage the retailer to share market demand information with producer spontaneously and help maintain the strategic cooperation between two parties.
Research limitations/implications
This paper considers a single-producer, single-retailer system and both of them are risk neutral.
Practical implications
Presented results can be used as suggestions for improving the contract design of fresh agri-food supply chain in China and can also provide references for other countries with similar experiences as China in fresh agri-food production.
Originality/value
This research introduces the option contract into fresh agri-food supply chain and takes information sharing and the risk caused by uncertain demand into consideration.
Details
Keywords
Haicheng Jia, Jing Li, Ling Liang, Weicai Peng, Jiqing Xie and Jiaping Xie
The development of low-carbon production is impeded by the investment costs of green technology research and development (R&D) and carbon emission reduction while facing the…
Abstract
Purpose
The development of low-carbon production is impeded by the investment costs of green technology research and development (R&D) and carbon emission reduction while facing the uncertain risk of emission reduction investment. With the government's carbon emission constraints, green manufacturers implement the advance selling strategy to increase both profit and reduction level. However, few studies consider the consumer's green preference and emission constraints in advance selling market and spot market independently. The authors' paper investigates the optimal strategies of advance selling pricing and reduction effort for green manufacturers to maximize profits.
Design/methodology/approach
The authors' paper designs a stochastic model and investigates the manufacturer's optimal strategies of advance selling price and emission reduction efforts by categorizing different purchasing periods of low-carbon consumers. With the challenges of uncertain demand and government's emission constraints, the authors' develop the non-linear optimization model to investigate the manufacturer's profit-oriented decisions.
Findings
The results show the government's carbon constraints cannot influence the manufacturer's profit, but the consumer's low-carbon preference in the advance selling period can. Interestingly, the manufacturer will make fewer reduction efforts even when the consumers have stronger environmental awareness. In addition, the increasing consumer price sensitivity will exacerbate the profit loss from mandatory emissions reduction. Overall, for achieving a win–win situation between emission reduction and profit growth, green manufacturers should not only consider the sales strategies, market demand, and government constraints in a low-carbon market, but also pay attention to the uncertainty of green technology innovation.
Originality/value
With the consideration of the government's carbon emission constraints, uncertain demand, and low-carbon consumer's preferences, the authors' study innovatively incorporates the joint impacts of advance selling strategy and emission reduction effort strategy and then differentiates between two cases that pertain to the diverse carbon emission regulations.
Details
Keywords
Yelin Fu, Lianlian Song, Kin Keung Lai and Liang Liang
The purpose of this paper is to propose robust optimization models addressing the container slot allocation problem with minimum quantity commitment (MQC) under uncertain demand…
Abstract
Purpose
The purpose of this paper is to propose robust optimization models addressing the container slot allocation problem with minimum quantity commitment (MQC) under uncertain demand, which is faced by international companies export to USA.
Design/methodology/approach
A novel robust optimization approach handling linear programming (LP) with right-hand-side uncertainty is developed by incorporating new parameters: uncertainty level, infeasibility tolerance and reliability level. Two types of uncertainty, namely, bounded uncertainty and symmetric uncertainty are considered, respectively.
Findings
The present work finds that the expected revenue increases as the uncertainty level and the MQC decrease, as well as the infeasibility tolerance and the reliability level increase, no matter which type of uncertainty is considered.
Research limitations/implications
Typically, the capacity constraints in a container shipping model should include two major restrictions: (1) number of slots and (2) total weight of loaded and empty containers. However, this study only addresses the first restriction for simplicity. It is recommended that future research explore the optimal solutions with additional restriction (2).
Originality/value
This paper fills a theoretical and practical gap for the problem of slot allocation with MQC in container liner revenue management. Deterministic and tractable mixed integer LP is formulated to derive robust solutions which immunes to demand uncertainty. Illustrative examples are presented to test the proposed models. The present work provides practical and solid advice and examples which demonstrates the application of the proposed robust optimization approach for logistics managers.
Details