Yuan Shi

A portfolio approach to managing procurement risk using multi-stage stochastic programming

Procurement is a critical supply chain management function that is susceptible to risk, due mainly to uncertain customer demand and purchase price volatility. A procurement approach in the form of a portfolio that incorporates the common procurement means is proposed. Such means include long-term contracts, spot procurements and option-based supply contracts. The objective is to explore possible synergies among the various procurement means, and so be able to produce optimal or near optimal results in profit while mitigating risk. The implementation of the portfolio approach is based on a multi-stage stochastic programming model in which replenishment decisions are made at various stages along a time horizon, with replenishment quantities being determined by simultaneously considering the stochastic demand and the price volatility of the spot market. The model attempts to minimise the risk exposure of procurement decisions measured as conditional value-at-risk. Numerical experiments to test the effectiveness of the proposed model are performed using demand data from a large air conditioner manufacturer in China and price volatility data from the Shanghai steel market. The results indicate that the proposed model can fairly reliably outperform other approaches, especially when either the demand and/or prices exhibit significant variability.

A multi-stage financial hedging approach for the procurement of manufacturing materials

This paper addresses the problem of mitigating procurement risk that arises from volatile commodity prices by proposing a hedging strategy within a multi-stage time frame. The proposed multi-stage hedging strategy requires a commodity futures position to be correctly initialised and rebalanced with adequate volumes of short/long positions, so as to reduce the volatility in the total procurement cost that would otherwise be generated by varying commodity spot prices. The novelty in the approach is the introduction of the rebalancing of commodity futures position at defined intermediate stages. To obtain an efficient or near optimal multi-stage hedging strategy, a discrete-time stochastic control model (DSCM) is developed. Numerical experiments and Monte Carlo simulation are used to show that the proposed multi-stage hedging strategy compares favourably with the minimal-variance hedge and the one-stage hedge. A close-form optimal solution is also presented for the case when procurement volume and price are independent.

Fuzzy chance-constrained programming model for a multi-echelon reverse logistics network for household appliances

Efficient planning and design of an appropriate reverse logistics network is crucial to the economical collection and disposal of scrapped household appliances and electrical products. Such systems are commonly modelled as mixed-integer programs, whose solutions will determine the location of individual facilities that optimize material flow. One of the major drawbacks of current models is that they do not adequately address the important issue of uncertainty in demand and supply. Another deficiency in current models is that they are restricted to a two-echelon system. This study addresses these deficiencies by embodying such uncertainties in the model using the technique of fuzzy-chance constrained programming, and by extending the model to a three-echelon system. A heuristic in the form of a hybrid genetic algorithm is then employed to generate low-cost solutions. The overall objective is to find economical solutions to the general problem of determining the volume of appliances to be moved between the three echelons of customer base to collection sites, collection sites to disposal centres and disposal centre to landfill centre/remanufacturing centre; and to the problems of positioning the disposal centres and the landfill centre/remanufacturing centres within the problem domain. A case example in China is presented and the quality and robustness of the solutions are explored through sensitivity analysis.

Choosing reverse channels under collection responsibility sharing in a closed-loop supply chain with re-manufacturing

This paper considers a closed-loop supply chain with re-manufacturing consisting of retailers, manufacturers and third-party logistics service providers; all participating in the product recycling responsibilities. The effectiveness of methods that can be used to share responsibilities amongst these parties is quantified using different reverse channels. First, re-manufacturing models with three different reverse channels for retailer collection, manufacturer collection and third-party collection are developed using collection responsibility sharing. Next, by comparing these models with the case of no collection responsibility sharing, the effectiveness of responsibility sharing is analysed and quantified. The results for the three models support the following conclusions: (i) from the point of view of the retailer, third-party collection is always the worst choice; (ii) the choice between retailer collection and manufacturer collection depends on the cost parameter representing the resources required in performing the reverse collection tasks; (iii) from the point of view the manufacturer, when the value of the cost parameter is small, collection by manufacturer is the best choice; retailer collection will be best for high values of the cost parameter.

Collective Recycling Responsibility in Closed-Loop Fashion Supply Chains with a Third Party: Financial Sharing or Physical Sharing?

We develop three closed-loop supply chain models where manufacturers can utilize financial or physical support to push a third party to collect the used fashion product for remanufacturing. We first examine two strategies for the collective recycling responsibility (CRR), namely, the financial sharing (FS) and the physical sharing (PS), using the model with no CRR as a benchmark. After that, we conduct a detailed comparison among the three models in terms of the retail price, demand, return rate, and the profits received by the supply chain members. With this study, we find the following. (i) The FS or PS support offered by the manufacturer to the third party will result in a lower retail price and a higher demand. (ii) The optimal return rate with PS scheme is always higher than that without the CRR, and the one with FS scheme is at least as high as that without the CRR. (iii) All the members in the closed-loop supply chain can always benefit from the CRR. In addition, (iv) which scheme of the FS, PS, or no CRR is the best for the supply chain members will depend heavily on the transfer price of the used product.

Hybrid flowshop scheduling with family setup time and inconsistent family formation

This research considers a hybrid flowshop scheduling problem where jobs are organised in families according to their machine settings and tools. The family setup time arises when a machine shifts from processing one job family to another. The problem is compounded by the challenges that the formation of job families is different in different stages and only a limited number of jobs can be processed within one setup. This type of problem is common in the production process of standard metal components. This paper aims to propose two approaches to solve this problem. One is a metaheuristic in the form of a genetic algorithm and the other is a heuristic. The proposed approaches are compared and contrasted against the two relevant metaheuristic and heuristic adapted from solving a generalised sequence-dependent setup flowshop problem. Comparative results indicate that the proposed genetic algorithm has better performance on minimising makespan and the heuristic is more effective on reducing family setup time.

A dynamic Stackelberg game model for portfolio procurement

Purpose – 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. Design/methodology/approach – 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. Findings – 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 uncert...

Divergent production scheduling with multi-process routes and common inventory

This research considers a scheduling problem in a divergent production system (DPS) where a single input item is converted into multiple output items. Therefore, the number of finished products is much larger than the number of input items. This paper addresses two important challenges in a real-life DPS problem faced by an aluminium manufacturing company. One challenge is that one product can be produced following different process routes that may have slightly different capabilities and capacities. The other is that the total inventory capacity is very limited in the company in the sense that a fixed number of inventory spaces are commonly shared by raw materials, WIP (work-in-process) items and finished products. This paper proposes a two-step approach to solving this problem. In the first step, an integer programming (IP) model is developed to plan the type and quantity of operations. In the second step, a particle swarm optimisation (PSO) is proposed to schedule the operations determined in the first step. The computational results based on actual production data have shown that the proposed two-step solution is appropriate and advantageous for the DPS scheduling problem in the company.

Inventory hedging and coordination under inventory-level-dependent demand

This paper considers an inventory coordination problem in a group company where the group headquarter manages a centralized distribution center (HQ-CDC) providing inventory spaces and services for its subsidiary companies whose demands are inventory-level-dependent. To deal with uncertainty in inventory management, subsidiaries usually reserve more inventory spaces than their actual demands. This extra inventory space strategy is called “inventory hedging” in this research. As a result, subsidiaries may reserve excessive spaces which are never required for their business operations, leading to inconsistency with the lean warehousing and logistics strategy that the HQ-CDC would like to implement. This paper theoretically examine if the inventory hedging strategy is advantageous to subsidiaries, and if so how best such a strategy should be implemented. A coordination scheme with dynamic pricing is introduced here to coordinate the implementation of the inventory hedging strategy. Two types of prices are introduced. One is the basic price used for block-reserving inventory spaces. The other price is the “hedging price” which is the extra amount to the basic price in addition charged for the space more than the actual demand. Two models are developed. In one model, a Nash game is played to reach an optimal strategy for both parties. In the other model, a Stackelberg game is played in which the HQ-CDC serves as the leader. It is demonstrated that the coordination scheme through dynamic price can successfully reduce inventory hedging amount required by the subsidiaries and can increase the HQ-CDCs profit, as compared to the decentralized decision model without considering the hedging price.

A Portfolio Approach to Procurement Risk Management

Procurement and replenishment are always susceptible to uncertain customer demand and also to purchase price volatility. Single factor approaches such as long-term contracts, spot procurements, or supply contracts with options, can mitigate some specific aspect of the overall risk, but such approaches are often of limited value when several types of risk prevail. This study contributes to the problem of procurement by presenting a portfolio approach that simultaneously deals with the two major types of procurement risk, price and inventory. The specific model presented jointly considers both the procurement planning and risk hedging problems. The model is in the form of a multi-stage stochastic program in which replenishment decisions are made at various stages along a time horizon, with replenishment quantities being jointly determined by the stochastic demand and the price dynamics of the spot market. The model attempts to minimise the risk exposure of procurement decisions measured as conditional value-at-risk. Numerical experiments to test the effectiveness of the proposed model. The results indicate that the proposed model can fairly reliably outperform other approaches, especially when either the demand and/or prices exhibit significant variability.