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Patient no‐shows and late cancellations lead to clinic inefficiency, high clinic costs and low patient satisfaction. The two main strategies clinics employed to alleviate the adverse effects of no‐shows are overbooking and patient appointment reminders. Developing effective overbooking schedules depends on accurately predicting each patient's no‐show probability, while developing effective reminder systems requires a patient‐level estimate of communication sensitivity. Current methods of estimating no‐show probabilities do not produce such patient‐level predictions. To remedy this, we develop a Bayesian nested logit model which utilizes appointment confirmation data and estimates individual‐level coefficients for patient‐specific predictors. A log‐likelihood comparison of model fit on 12 months of appointment data shows that the Bayesian model outperforms the standard logit model by about 30% improvement in model fit. Additionally, our Bayesian model allows categorization of patients based on their appointment confirmation behavior. Finally, using patient‐specific no‐show probabilities as an input to a simulated appointment scheduler we find that the Bayesian model improves clinic profit over the standard logit model. The benefit comes mainly from waiting cost reduction when no‐show probability is low and from physician overtime and idle time cost reduction when no‐show probability is high. Our study has two managerial implications. First, the Bayesian method allows customizing appointment reminder effort based on patient's confirmation behavior. Second, the Bayesian method also allows improved overbooking scheduling especially in clinics that experience large patient throughput. [ABSTRACT FROM AUTHOR]

We consider coordination issues in supply chains where supplier's production process is subject to random yield losses. For a simple supply chain with a single supplier and retailer facing deterministic demand, a pay back contract which has the retailer paying a discount price for the supplier's excess units can provide the right incentive for the supplier to increase his production size and achieve coordination. Building upon this result, we consider coordination issues for two other supply chains: one with competing retailers, the other with stochastic demand. When retailers compete for both demand and supply, they tend to over-order. We show that a combination of a pay back and revenue sharing mechanism can coordinate the supply chain, with the pay back mechanism correcting the supplier's under-producing problem and the revenue sharing mechanism correcting the retailers' over-ordering problem. When demand is stochastic, we consider a modified pay-back-revenue-sharing contract under which the retailer agrees to not only purchase the supplier's excess output (beyond the retailer's order), but also share with the supplier a portion of the revenue made from the sales of the excess output. We show that this contract, by giving the supplier additional incentives in the form of revenue share, can achieve coordination. [ABSTRACT FROM AUTHOR]

Supply disruptions are all too common in supply chains. To mitigate delivery risk, buyers may either source from multiple suppliers or offer incentives to their preferred supplier to improve its process reliability. These incentives can be either direct (investment subsidy) or indirect (inflated order quantity). In this study, we present a series of models to highlight buyers' and suppliers' optimal parameter choices. Our base-case model has deterministic buyer demand and two possibilities for the supplier yield outcomes: all-or-nothing supply or partial disruption. For the all-or-nothing model, we show that the buyer prefers to only use the subsidy option, which obviates the need to inflate order quantity. However, in the partial disruption model, both incentives-subsidy and order inflation-may be used at the same time. Although single sourcing provides greater indirect incentive to the selected supplier because that avoids order splitting, we show that the buyer may prefer the diversification strategy under certain circumstances. We also quantify the amount by which the wholesale price needs to be discounted (if at all) to ensure that dual sourcing strategy dominates sole sourcing. Finally, we extend the model to the case of stochastic demand. Structural properties of ordering/subsidy decisions are derived for the all-or-nothing model, and in contrast to the deterministic demand case, we establish that the buyer may increase use of subsidy and order quantity at the same time. [ABSTRACT FROM AUTHOR]

We examine the role of expediting in dealing with lead-time uncertainties associated with global supply chains of 'functional products' (high volume, low demand uncertainty goods). In our developed stylized model, a retailer sources from a supplier with uncertain lead-time to meet his stable and known demand, and the supply lead-time is composed of two random duration stages. At the completion time of the first stage, the retailer has the option to expedite a portion of the replenishment order via an alternative faster supply mode. We characterize the optimal expediting policy in terms of if and how much of the order to expedite and explore comparative statics on the optimal policy to better understand the effects of changes in the cost parameters and lead-time properties. We also study how the expediting option affects the retailer's decisions on the replenishment order (time and size of order placement). We observe that with the expediting option the retailer places larger orders closer to the start of the selling season, thus having this option serve as a substitute for the safety lead-time and allowing him to take increased advantages of economies of scale. Finally we extend the basic model by looking at correlated lead-time stages and more than two random lead-time stages. [ABSTRACT FROM AUTHOR]