Optimal core acquisition and pricing strategies for hybrid manufacturing and remanufacturing systems

In this study, we combine two aspects of remanufacturing, namely product acquisition management and marketing (pricing) of the remanufactured products. We consider an original equipment manufacturer (OEM) who decides on the acquisition prices offered for returns from different quality types and on selling prices of new and remanufactured products, in a single period setting. We develop a procedure for determining the optimal prices and corresponding profit of the OEM, and conduct a sensitivity analysis to understand the effect of different model parameters on the optimal strategies and profit. An important managerial insight is that the optimal solution is not to have the same profit per remanufactured item for all return types, but to if the minimum cost for acquisition and remanufacturing of some core type is lower.     

Optimal pricing and core acquisition strategy for a hybrid manufacturing/remanufacturing system

Remanufacturing is one of the product recovery options where the quality of used products (cores) is upgraded to ‘as-good-as-new’ conditions. In this article, we consider a monopolist firm selling new and remanufactured products to quality-conscious primary customers and price-sensitive secondary customers, respectively, with one-way substitution, i.e. some primary customers may substitute new products by remanufactured products while secondary customers can never afford to buy new products. We develop economic models under two scenarios – when the supply of cores is unconstrained and when manufacturers have to procure cores at an acquisition price. The major observations of the article are as follows. A firm is better off when there is no constraint on the supply of cores. Even when cores have to be acquired at an acquisition price, the profitability is higher than that when the firm does not engage in remanufacturing activities. When a larger number of primary customers replace new products with remanufactured products, there is partial cannibalization of new product sales; however, the combined market share and profitability of the firm increase. When core supply is constrained and customers are less sensitive to core prices, the limited supply of cores may render remanufacturing an infeasible option for the firm. Therefore, firms should not only generate awareness among primary customers to buy remanufactured products, but also step up efforts to ensure a steady supply of cores. We conclude the article with managerial implications and directions for future research.     

Optimal configuration of remanufacturing supply network with return quality decision

This research studies the configuration problem of a remanufacturing production network together with the decision for return quality thresholds, in which, the manufacturer has multiple remanufacturing facilities to satisfy different market demands. Quality of returns is stochastic, while demand for remanufactured products is either stochastic or deterministic. The problem we considered is to determine facilities to operate, minimum quality to accept into each operating facility, return quantity and demand allocation simultaneously so that the system’s profit is maximised. The problem is formulated as a mixed integer non-linear programming model. Through the use of a numerical example, the impact of quantity of returns, total spending, quality uncertainty, demand uncertainty and transportation cost on the remanufacturing system is analysed.     

Optimal policy in a hybrid manufacturing/remanufacturing system with financial hedging

This paper proposes a hybrid manufacturing/remanufacturing model with the financial hedging in the case where the randomness in demand is correlated with the financial markets. The provided models are mainly for those risk-averse remanufacturers who faced with random demand and yield. The aim of this paper is to maximise remanufacturer utility by purchasing financial instruments and producing new and remanufactured products. A hybrid manufacturing/remanufacturing system production planning model is first built under mean-variance framework, and then the financial hedging is integrated into the hybrid production system. There are three main findings. First, the variance of profit with financial hedging is always less than the variance of the model without financial hedging. Second, the remanufacturer with high (low) risk aversion is more likely to produce new (remanufactured) products. Third, the model without (with) financial hedging tends to produce new (remanufactured) products unless remanufacturing cost is low (high) enough. All those findings proved that financial hedging can reduce the operational uncertainty effectively and increase the proportion of remanufacturing, which will make remanufacturing firms more economical and environmentally friendly. Therefore, remanufacturing firms can consider using financial hedging to reduce operational uncertainty.     

On the effect of quality overestimation in remanufacturing

We study a simple reverse supply chain consisting of a remanufacturing facility and a number of independent locations where used products are returned by the end-users. At the collection locations, the returned products are graded and classified based on a list of nominal quality metrics provided by the remanufacturer. It is assumed that this classification is subject to errors; specifically, the returns condition is overestimated because of a stochastic proportion of returned units which are classified in classes corresponding to better quality than the actual. The scope of the paper is to study how these classification errors affect the optimal procurement decisions of the remanufacturer as well as the associated profit for the cases of both constant and stochastic demand in a single-period context. Moreover, in the former case we study the impact of these classification errors on profit variability. The quantification of the impact of quality overestimation provides intuition on the value of reliable classification and on the extent of the necessary investments and initiatives to improve classification accuracy.     

Optimal acquisition decision in a remanufacturing system with partial random yield information

When making decisions to acquire used products or components (cores), a remanufacturer faces limited information on the quality or proportional yield of cores during the recovery process. In this paper, we propose and analyse a robust optimisation model for studying the remanufacturing decision problem with partial random yield information, that is, when the quality information of cores is partly unknown in a remanufacturing system. Regarding the impacts of unknown yield information, we only require the support and mean of the proportional yield rather than the true distributions. The closed-form solutions of acquisition quantities are derived based on the minimax regret approach. In addition, to validate the effectiveness of the analytical results, particularly the acquisition of yield information, numerical experiments are designed and implemented using (1) the support and mean of the proportional yield based on the manufacturer’s knowledge and (2) a sampling inspection to evaluate the performance of the robust optimisation approach, the benchmark, and the naïve approach. We observe that the minimax regret approach slightly underperforms compared to the benchmark but performs much better than the naïve approach. As an acceptable choice, this approach is less complicated and extremely easy to implement to meet the needs of practical situations based on its robust closed-form solutions.     

Cost information sharing under competition in remanufacturing

Third-party remanufacturers (TPRs) enter the market and compete with original equipment manufacturers (OEMs). Because of multiple uncertainty of the remanufacturing process, the variable remanufacturing cost is random and its true value cannot be observed by OEMs. In this paper, we consider the competition between one TPR and one OEM, and then investigate incentives of cost information sharing. The remanufactured product quantity is constrained by the new product quantity. If the new production cost is low enough such that the optimal remanufactured product quantity is less than the optimal new product quantity, the TPR should always share cost information with the OEM so that their quantity decisions in equilibrium are more responsive to market conditions to avoid overproduction or underproduction. However, if the quantity constraint is binding, the TPR should not always share cost information; in this case, cost information sharing can allow the OEM to adjust the new product quantity to limit remanufacturing and hence be detrimental to the TPR. When the TPR does not voluntarily share cost information, we further examine the impacts of private information on the society and the environment, and then suggest the government to promote information sharing and subsidise the TPR under certain conditions.     

A robust optimisation model for hybrid remanufacturing and manufacturing systems under uncertain return quality and market demand

In remanufacturing research, most researchers predominantly emphasised on the recovery of whole product (core) rather than at the component level due to its complexity. In contrast, this paper addresses the challenges to focus on remanufacturing through component recovery, so as to solve production planning problems of hybrid remanufacturing and manufacturing systems. To deal with the uncertainties of quality and quantity of product returns, the processing time of remanufacturing, remanufacturing costs, as well as market demands, a robust optimisation model was developed in this research and a case study was used to evaluate its effectiveness and efficiency. To strengthen this research, a sensitivity analysis of the uncertain parameters and the original equipment manufacturer’s (OEM’s) pricing strategy was also conducted. The research finding shows that the market demand volatility leads to a significant increase in the under fulfilment and a reduction in OEM’s profit. On the other hand, recovery cost reduction, as endogenous cost saving, encourages the OEM to produce more remanufactured products with the increase in market demand. Furthermore, the OEM may risk profit loss if they raise the price of new products, and inversely, they could gain more if the price of remanufactured products is raised.     

Optimal collection and remanufacturing decisions in reverse supply chains with collector’s imperfect sorting

We consider a reverse supply chain comprising two independent companies: the collector and the remanufacturer. The collector is responsible for sorting the collected used products and then transporting the ‘remanufacturable’ items to the remanufacturer. As sorting is not perfectly accurate, the misclassification errors affect the optimal decisions in the reverse supply chain. We focus on the optimal collection quantity for the collector and the optimal order quantity for the remanufacturer using game theory under both deterministic and uncertain demand. We characterise the optimal solution and show that under certain conditions, the remanufacturer should allow the collector to transport more ‘remanufacturables’ than the order quantity. We then examine the case where both the collector and the remanufacturer belong to a single company with centralised decision-making and compare the optimal collection quantities under both structures of the reverse supply chain.     

基于普通PC机和智能仪表的远程数据采集系统

介绍了以普通PC机和智能仪表为基础研制开发的一种低成本计量数据采集系统，详细阐述了数据采集的过程、系统的构成、功能和特点。     

Impact of quality regulation policy on performance of a remanufacturing supply chain with non-waste returns

Quality issues in a remanufacturing supply chain with non-waste returns have been reported many times. To address these issues, the government proposes different quality regulation policies. This paper develops a multi-agent simulation model to study how the quality regulation policy affects the performance of a remanufacturing supply chain with non-waste returns. Here, the main performance includes profit and hazard ratio of products. Our results indicate that self-regulation of the market cannot realize a desirable outcome (low hazard ratio and high manufacturer’s profit). Adjusting quality regulation probability is more efficient than adjusting the penalty for inferior products. From the view of increasing manufacturer’s average profit, or improving the government’s efficiency, the best policy is the solely adjusting supervision probability policy. From the view of reducing hazard ratio, the best policy is adjusting supervision probability- penalty policy. The appropriate penalty for inferior products can realize the desirable outcome. With an increasing supervision probability, the hazard ratio decreases, and the manufacturer’s average profit increases because the remanufacturing cost and penalty for inferior products decrease.     

Optimal initial production control period based on a quality growth model

The initial production phase of new products or the initial installation phase of new manufacturing facilities is often unstable because of inexperienced workers and many defective products. An initial production process control, in which the defects in design, production technologies and products are fully fixed and removed, is switched to a normal process control whenever it is ready for actual mass production. This paper discusses a method of deciding the optimal initial production control period, based on a quality growth model. It is determined by the number of products with the minimum expected total quality control cost. Finally a penalty cost due to unattainable loss to the quality goal is introduced in the quality control cost: the realized stabilization level of the initial production process control is lower than the original quality objective. Numerical illustrations of the optimal policy are also presented. Copyright © 1999 John Wiley & Sons, Ltd.     

Optimal manufacturing policy in a reverse logistic system with dependent stochastic returns and limited capacities

The stochastic models of systems with reverse logistics usually assume that the quantity of products returned is independent of sales. This hypothesis is obviously not true and can lead to suboptimal production policies. In this paper a new sales-dependent returns model is described. In this model, the returns depend on the useful life of the products sold and on the probability of an end-of-life product being returned. A Markov decision problem is formulated in order to obtain the optimal manufacturing policy. A numerical example is provided to illustrate the use of the defined model. An approximated Markov decision model is defined where the optimal policy is easily obtained. The optimal policies of the original and the approximated models are compared.     

A note on stochastically acceptable quality

A random variable of quality, characterized by the quality distribution function, is considered both for continuous and discrete cases. The acceptable quality distribution function is introduced and different types of stochastic comparison are discussed: comparison in the mean, ordering in distribution, variability ordering and hazard‐rate ordering. Acceptable, unacceptable and intermediate regions for levels of quality are determined by the discretization procedure. It is assumed that the quality of a product is revealed through usage. A simple reliability setting is considered when the quality of a product is directly dependent on its reliability characteristics in usage. Possible generalizations are discussed. Copyright © 2001 John Wiley & Sons, Ltd.     

Optimal data acquisition in fMRI using prolate spheroidal wave functions

Functional magnetic resonance imaging (fMRI) is a technique that can be used to noninvasively study mental activity in a persons brain. fMRI has the potential to answer many interesting questions regarding the way the brain functions. Unfortunately, the drawback to fMRI studies, as they are traditionally performed, is that the temporal resolution is too low to effectively answer questions regarding what happens in an active region of the brain immediately following stimulation. Shepp and Zhang ( 2000 ) introduced a new method that could potentially lead to a significant increase in temporal resolution. Their method suggests a way to improve the time resolution in fMRI studies by sampling only a fraction of the points needed to recreate a full image. Instead of full image reconstruction, an optimal prolate spheroidal wave function filter is used to obtain a measurement over the total activity in a predefined region in the brain, B , at successive time points. The sampling region and filter are chosen in order to minimize the energy loss over the region of interest (ROI). The region they suggested to sample was chosen heuristically and corresponds to the scaled polar set of B . It was shown to be near optimal through extensive computer searches. In this article the optimal sampling region is found for the case when the ROI is circular or spherical and the sampling size is small. Based on this result, a new heuristic is introduced for other ROIs, which improves upon the results obtained using the polar set of B . © 2003 Wiley Periodicals, Inc. Int J Imaging Syst Technol 13, 126–132, 2003; Published online in Wiley Inter‐Science (www.interscience.wiley.com). DOI 10.1002/ima.10051     

A cubic quality loss function and its applications

The traditional quality evaluation method believed that when the product quality characteristic was within the specification limit, no loss was produced. Taguchi proposed that even if the characteristic was within the range of users' demand, the fluctuation of quality characteristic would still cause loss to users and society. Therefore, he proposed a quadratic quality loss function to describe this loss. The function was established based on the Taylor expansion. It neglected terms with powers higher than 2, which would cause a certain deviation between the calculated and true value. Moreover, the tolerance and loss in the quadratic loss function must satisfied specific relationship, which limited its use. In this paper, the Taylor expansion items are raised to third order. The quality loss coefficients are discussed and analyzed so that the cubic quality loss function is established. In addition, the method of calculating hidden quality cost is given by using the cubic loss function. The hidden quality cost is affected by the quality loss coefficients and should be a range. The cubic quality loss function studies the inapplicability of quadratic loss function, so it widens the scope of application of quality loss function.     

Optimal new product design using quality function deployment with empirical value functions

The house of quality (HOQ) has been widely proposed as a method for capturing the voice of the customer (VOC) when developing requirements for new products. The methodology has lacked a formal mechanism for trading off customer preferences with technical feasibility and economic reality. We propose a non‐linear mathematical program for determining the optimal engineering specifications during new product development as a function of elicited customer value functions, engineering development and production costs and development time constraints. The model is shown to be computationally feasible for realistic‐size problems and to outperform heuristic approaches. Copyright © 1999 John Wiley & Sons, Ltd.     

A methodology for simultaneous optimisation of design parameters for the preventive maintenance and quality policy incorporating Taguchi loss function

The performance of a production system depends on the breakdown-free operation of equipment and processes. Maintenance and quality control play an important role in achieving this goal. In addition to deteriorating with time, equipment may experience a quality shift (i.e. process moves to out-of-control state), which is characterised by a higher rejection rate and a higher tendency to fail. This paper develops an integrated model for joint optimisation of preventive maintenance interval and control parameters incorporating the Taguchi loss function. We consider two types of maintenance policies: minimal corrective maintenance that maintains the state of the equipment without affecting the age and imperfect preventive maintenance that upgrades the equipment in between ‘as good as new’ and ‘as bad as old’ condition. The proposed model enables the determination of the optimal value of each of the four decision variables, i.e. sample size (n), sample frequency (h), control limit coefficient (k), and preventive maintenance interval (t _PM) that minimises the expected total cost of the integration per unit time. A numerical example is presented to demonstrate the effect of the cost parameters on the joint economic design of preventive maintenance and process quality control policy. The sensitivity of the various parameters is also examined.     

Optimal design of a distribution-free quality control scheme for cost-efficient monitoring of unknown location

Traditionally, a cost-efficient control chart for monitoring product quality characteristic is designed using prior knowledge regarding the process distribution. In practice, however, the functional form of the underlying process distribution is rarely known a priori. Therefore, the nonparametric (distribution-free) charts have gained more attention in the recent years. These nonparametric schemes are statistically designed either with a fixed in-control average run length or a fixed false alarm rate. Robust and cost-efficient designs of nonparametric control charts especially when the true process location parameter is unknown are not adequately addressed in literature. For this purpose, we develop an economically designed nonparametric control chart for monitoring unknown location parameter. This work is based on the Wilcoxon rank sum (hereafter WRS) statistic. Some exact and approximate procedures for evaluation of the optimal design parameters are extensively discussed. Simulation results show that overall performance of the exact procedure based on bootstrapping is highly encouraging and robust for various continuous distributions. An approximate and simplified procedure may be used in some situations. We offer some illustration and concluding remarks.     

Heuristic algorithms for minimising total recovery cost of end-of-life products under quality constraints

Recently, the optimisation of end-of-life (EOL) product recovery processes has been highlighted. At the inspection phase after disassembly, each part can have various recovery options such as reuse, reconditioning, remanufacturing, and disposal. Depending on the selected options of parts, the values of recovered products that are made by reassembling parts will be different. Hence, it is important to decide appropriate recovery options of parts at the treatment of EOL products, in order to maximise the values of recovered products. To this end, this study deals with a decision making problem to select the best recovery options of parts for minimising the total recovery cost of products under quality constraints. This problem is formulated with a mixed integer nonlinear programming model and heuristic search algorithms are proposed to resolve it. A case study for a turbocharger product is introduced with computational experiments of the proposed algorithms.