Maqbool Dada

A Censored-Data Multiperiod Inventory Problem with Newsvendor Demand Distributions

We study the stochastic multiperiod inventory problem in which demand in excess of available inventory is lost and unobserved so that demand data are censored. A Bayesian scheme is employed to dynamically update the demand distribution for the problem with storable or perishable inventory and with exogenous or endogenous price. We show that the Weibull is the only newsvendor distribution for which the optimal solution can be expressed in scalable form. Moreover, for Weibull demand the cost function is not convex in general. Nevertheless, in all but the storable case, sufficient structure can be discerned so that the optimal solution can be easily computed. Specifically, for the perishable inventory case, the optimal policy can be found by solving simple recursions, whereas the perishable case with pricing requires solutions to more complex one-step look-ahead recursions. Interestingly, for the special case of exponential demand the cost function is convex, so that for the storable inventory case, the optimal policy can be found using simple one-step look-ahead recursions whereas for the perishable case the optimal policy can be expressed by exact closed-form formulas.

New structural properties of (s,S) policies for inventory models with lost sales

We revisit the classical inventory model for which (s,S) policies are optimal. We consider the finite and infinite horizon versions of the lost sales problem. New structural properties are developed for the optimal policy and cost function. These properties are then used to develop computational schemes for the lost sales problem with Erlang demands.

Patient punctuality and clinic performance: observations from an academic-based private practice pain centre: a prospective quality improvement study

Objectives The aim of this study was to examine the effects of an intervention to alter patient unpunctuality. The major hypothesis was that the intervention will change the distribution of patient unpunctuality by decreasing patient tardiness and increasing patient earliness. Design Prospective Quality Improvement. Setting Specialty Pain Clinic in suburban Baltimore, Maryland, USA. Participants The patient population ranged in age from 18 to 93u2005years. All patients presenting to the clinic during the study period were included in the study. The average monthly volume was 86.2 (SD=13) patients. A total of 1500 patient visits were included in this study. Interventions We tracked appointment times and patient arrival times at an ambulatory pain clinic. An intervention was made in which patients were informed that tardy patients would not be seen and would be rescheduled. This policy was enforced over a 12-month period. Primary and secondary outcome measures The distribution of patient unpunctuality was developed preintervention and at 12u2005months after implementation. Distribution parameters were used as inputs to a discrete event simulation to determine effects of the change in patient unpunctuality on clinic delay. Results Data regarding patient unpunctuality were gathered by direct observation before and after implementation of the intervention. The mean unpunctuality changed from −20.5u2005min (110 observations, SD=1.7) preintervention to −23.2 (169, 1.2) at 1u2005month after the intervention, −23.8u2005min (69, 1.8) at 6u2005months and −25.0u2005min (71, 1.2) after 1u2005year. The unpunctuality 12u2005months after initiation of the intervention was significantly different from that prior to the intervention (p<0.05). Conclusions Physicians and staff are able to alter patient arrival patterns to reduce patient unpunctuality. Reducing tardiness improves some measures of clinic performance, but may not always improve waiting times. Accommodating early arriving patients does serve to improve clinic performance.

Applying JIT Principles to Resident Education to Reduce Patient Delays: A Pilot Study in an Academic Medical Center Pain Clinic

OBJECTIVESnThis study investigated the effect on patient waiting times, patient/doctor contact times, flow times, and session completion times of having medical trainees and attending physicians review cases before the clinic session. The major hypothesis was that review of cases prior to clinic hours would reduce waiting times, flow times, and use of overtime, without reducing patient/doctor contact time.nnnDESIGNnProspective quality improvement.nnnSETTINGnSpecialty pain clinic within Johns Hopkins Outpatient Center, Baltimore, MD, United States.nnnPARTICIPANTSnTwo attending physicians participated in the intervention. Processing times for 504 patient visits are involved over a total of 4 months.nnnINTERVENTIONnTrainees were assigned to cases the day before the patient visit. Trainees reviewed each case and discussed it with attending physicians before each clinic session.nnnPRIMARY AND SECONDARY OUTCOME MEASURESnPrimary measures were activity times before and after the intervention. These were compared and also used as inputs to a discrete event simulation to eliminate differences in the arrival process as a confounding factor.nnnRESULTSnThe average time that attending physicians spent teaching trainees while the patient waited was reduced, but patient/doctor contact time was not significantly affected. These changes reduced patient waiting times, flow times, and clinic session times.nnnCONCLUSIONSnMoving some educational activities ahead of clinic time improves patient flows through the clinic and decreases congestion without reducing the times that trainees or patients interact with physicians.

Using process analysis to assess the impact of medical education on the delivery of pain services: A natural experiment

Background: The medical, social, and economic effects of the teaching mission on delivery of care at an academic medical center (AMC) are not fully understood. When a free-standing private practice ambulatory clinic with no teaching mission was merged into an AMC, a natural experiment was created. The authors compared process measures across the two settings to observe the differences in system performance introduced by the added steps and resources of the AMCs teaching mission. Methods: After creating process maps based on activity times realized in both settings, the authors developed discrete-event simulations of the two environments. The two settings were comparable in the levels of key resources, but the AMC process flow included three residents/fellows. Simulation enabled the authors to consider an identical schedule across the two settings. Results: Under identical schedules, the average accumulated processing time per patient was higher in the AMC. However, the use of residents allowed simultaneous processing of multiple patients. Consequently, the AMC had higher throughput (3.5 vs. 2.7 patients per hour), higher room utilization (82.2% vs. 75.5%), reduced utilization of the attending physician (79.0% vs. 93.4%), and a shorter average waiting time (30.0 vs. 83.9 min). In addition, the average completion time for the final patient scheduled was 97.9 min less, and the average number of patients treated before incurring overtime was 37.9% greater. Conclusions: Although the teaching mission of the AMC adds processing steps and costs, the use of trainees within the process serves to increase throughput while decreasing waiting times and the use of overtime.

A Periodic-Review Base-Stock Inventory System with Sales Rejection

We consider a system in which an order is placed every T periods to bring the inventory position up to the base stock S. We accept demand until the inventory position reaches a sales rejection threshold M. Our objective is to find the optimal values of S and M that minimize the long-run average cost per period. We establish the stationary distribution of our system and develop structural properties of the optimal solution that facilitate computation. In particular, we show that in an optimal solution, the optimal value of M is nonnegative under some reasonable conditions. Hence, in our model a mixture of backorders and lost sales may occur. Additionally, we compare our system against traditional systems in which demand during stockouts is either fully backordered or lost.

A make-to-stock manufacturing system with component commonality: A queuing approach

In this article a manufacturing system that features component commonality and a production allocation mechanism that postpones inventory commitment is modeled. Under this mechanism, which is called the First-Use First-Serve (FUFS), approach, a component is committed to an order only when all other required components become available. Furthermore, production of a component can only occur if its inventory is below a threshold. The performance of the proposed model is compared with the benchmark case of the First-Come First-Served (FCFS) approach under the sequence in which orders are received. The presented results show that FUFS outperforms FCFS on most system performance criteria. However, FCFS may outperform FUFS under some performance measures of dispersion, when the workload is heavy.

Designing supply contracts: buy-now, reserve, and wait-and-see

ABSTRACT We consider three types of purchase contracts a manufacturer could offer in order to maximize its profit when supplying a retailer that uses responsive pricing to sell in an uncertain market: buy-now before the selling season starts, reserve stock for possible future purchase, and wait-and-see the market before making purchases. The existing literature has shown that adding a recourse purchase—i.e., the wait-and-see alternative—is always beneficial for the retailer who faces an uncertain demand. We find that this is not necessarily the case for the manufacturer who supplies the retailer, as its optimal contract mix depends on the market uncertainty as well as its production characteristics. The manufacturer should offer only the buy-now alternative if its recourse production is much more costly than advance production. As the recourse production cost decreases, the manufacturer should add a second contract to the portfolio: initially the reserve contract and then the wait-and-see contract. However, when the recourse production is cheaper than advance production, the manufacturer should drop the buy-now contract from the mix. As such, it is only in a small region, which shrinks with decreasing uncertainty in demand, that the manufacturer finds it optimal to offer all three purchasing alternatives.

A centralized ordering and allocation system with backorders and strategic lost sales

This article considers a multi-retailer distribution system that is managed by a central decision maker who, at the start of each period, determines how much to order to replenish the system stock. The decision maker also determines how to allocate incoming pipeline inventory to maintain inventory balance among the retailers. It has been noted in the literature that balancing inventories can equalize service levels among retailers. To improve the efficacy of the allocation, this article allows some demand to be rejected to keep inventories in balance. Consequently, depending on the realized pattern of demand during the delivery lead time, the inventory is dynamically allocated to each of the retailers. For the model with two retailers, an exact representation of the infinite-horizon long-run average cost function is developed. This exact expression is used to develop conditions for the unique solution for the two-retailer case. The presented analysis holds for a wide class of continuously and discretely distributed demand.

A finite-horizon inventory system with partial backorders and inventory holdback

Abstract We consider a periodic-review base stock inventory system with partial backorders. At the beginning of each period t of a T period problem, an order of size q t is placed for delivery one period later. As the stochastic demand is realized, as much as possible of it is filled immediately from the inventory on-hand. If the realized demand exceeds the inventory on-hand, up to q t – k t units of excess demand are backordered to be filled from the pipeline inventory or future orders. The on-order quantity k t denotes the reservation quantity held back for use in subsequent periods. The case k t = q t yields the full lost sales model while the case k t = − ∞ yields the full backorder model.