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This paper proposes a novel advanced buyback (ABB) contract to coordinate a supply chain that consists of one supplier and one retailer. The contract stipulates when a buyback contract is executed and the amount of buyback. We model the interaction of supply chain participants as a Stackelberg game and incorporate the participants’ reservation profits into the game to examine the effectiveness of the ABB contract with complete information. We demonstrate that the ABB contract can conveniently coordinate the supply chain, flexibly allocate profit, and facilitate avoidance of the aggressive overordering behavior of the retailer. This model is extended to the situation of information asymmetry, where the retailer holds private information about the cost of sales in discrete or continuous states. The optimal strategies of the supply chain and the participants are derived. Information asymmetry can damage the supplier but be beneficial to the retailer. The condition under which the supply chain achieves coordination is clarified. A numerical simulation is performed to verify and extend the results of the theoretical analysis. We show that the ABB contract always outperforms the ordinary wholesale price and buyback contracts with complete information. Supply chain coordination is more likely to be achieved if the demand variability is lower when weighing whether to share private information. Our findings can explain why retailers always have an incentive to inflate their cost of sales.

Purpose – The purpose of this paper is to investigate the impact on the cost of materials used to conduct preventive maintenance (PM). The main motivation for this work is to demonstrate how the group technology concept can be used to improve PM operations. In assessing improvement, the impact on the cost of materials used to conduct PM is investigated. Design/methodology/approach – Based on the similarities between machines required maintenance and failure types, machines are grouped together into virtual cells using similarity coefficients. These cells, then, are used to come up with more efficient planning and scheduling procedures to conduct PM operations including inventory of parts and the execution of maintenance operations. Findings – The results demonstrated that the proposed PM approach could provide a significant cost savings over a traditional PM program, especially in industries for which there is considerable material cost for the performance of PM. Originality/value – The results presented in this paper are reliable, objective, and may be expanded by using the same concept of developing virtual cells to group other types manufacturing operations.

In forecasting count processes, practitioners often ignore the discreteness of counts and compute forecasts based on Gaussian approximations instead. For both central and non-central point forecasts, and for various types of count processes, the performance of such approximate point forecasts is analyzed. The considered data-generating processes include different autoregressive schemes with varying model orders, count models with overdispersion or zero inflation, counts with a bounded range, and counts exhibiting trend or seasonality. We conclude that Gaussian forecast approximations should be avoided

Risk measures are commonly used to prepare for a prospective occurrence of an adverse event. If we are concerned with discrete risk phenomena such as counts of natural disasters, counts of infections by a serious disease, or counts of certain economic events, then the required risk forecasts are to be computed for an underlying count process. In practice, however, the discrete nature of count data is sometimes ignored and risk forecasts are calculated based on Gaussian time series models. But even if methods from count time series analysis are used in an adequate manner, the performance of risk forecasting is affected by estimation uncertainty as well as certain discreteness phenomena. To get a thorough overview of the aforementioned issues in risk forecasting of count processes, a comprehensive simulation study was done considering a broad variety of risk measures and count time series models. It becomes clear that Gaussian approximate risk forecasts substantially distort risk assessment and, thus, should be avoided. In order to account for the apparent estimation uncertainty in risk forecasting, we use bootstrap approaches for count time series. The relevance and the application of the proposed approaches are illustrated by real data examples about counts of storm surges and counts of financial transaction

Owing to the limited service capacity of express delivery providers, most online retailers have to reject many orders during hot selling seasons. In this paper, we consider an express delivery service supply chain consisting of an express delivery provider and an online retailer whereby the selling season includes both regular periods and online sales periods. Utilizing a modified newsvendor model, we derive the express delivery provider's optimal capacity decision and find that the overloading problem cannot be avoided because delivery service cannot be inventoried. To solve such a problem, we introduce an option contract to coordinate the supply chain. By allowing the online retailer to book the capacity, the express delivery provider can rent capacity from a third party in advance. Results show this approach can mitigate the problem significantly. We also extend our model to a supply chain consisting of a delivery provider and two retailers.

In statistical process control, a state of statistical control is identified with a process generating independent and identically distributed random variables. It is often difficult in practice to attain a state of statistical control in this strict sense; autocorrelations and other systematic time-series effects are often substantial. In the face of these effects, standard control-chart procedures can be seriously misleading. We propose and illustrate statistical modeling and fitting of time-series effects and the application of standard control-chart procedures to the residuals from these fits. The fitted values can be plotted separately to show estimates of the systematic effects.

The paper draws on simple data analysis and time series ideas to assess the limitations of standard control chart procedures in various practical applications in which the underlying assumptions of control charts may be materially violated and, in consequence, the control limits may be misplaced. To give an idea of the kinds and frequency of violations that occur in practice, we summarize an empirical study of a sample of 235 quality control applications. This collection includes two data sets reported by leading statisticians that are explicitly discussed here to illustrate difficulties in the proper placement of control limits. The results of the study of all 235 data sets suggest that violations of assumptions are the rule rather than the exception in practice.