Monitoring Inventory Accuracy With Statistical Process Control

Inventory accuracy is critical in most industrial environments such as distribution, warehousing, and retail. Many companies use a technique called cycle counting and have realized outstanding results in monitoring and improving inventory accuracy. The time and resources to complete cycle counting are sometimes limited or not available. In this work, we promote statistical process control (SPC) to monitor inventory accuracy. Specifically, we model the complex underlying environments with mixture distributions to demonstrate sampling from a mixed but stationary process. For our particular application, we concern ourselves with data that result from inventory adjustments at the stock keeping unit (SKU) level when a given SKU is found to be inaccurate. We provide estimates of both the Type I and Type II errors when a classic C chart is used. In these estimations, we use both analytical as well as simulation results, and the findings demonstrate the environments that might be conducive for SPC approach

A Capacity Allocation Planning Model for Integrated Care and Access Management

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146954/1/poms12941-sup-0001-AppendixS1.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146954/2/poms12941_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146954/3/poms12941.pd

Use of computer simulation to identify effects on hospital census with reduction of transfers for non-procedural patients in community hospitals.

Peer reviewed: TrueOBJECTIVE: In-person healthcare delivery is rapidly changing with a shifting employment landscape and technological advances. Opportunities to care for patients in more efficient ways include leveraging technology and focusing on caring for patients in the right place at the right time. We aim to use computer modelling to understand the impact of interventions, such as virtual consultation, on hospital census for referring and referral centres if non-procedural patients are cared for locally rather than transferred. PATIENTS AND METHODS: We created computer modelling based on 25 138 hospital transfers between June 2019 and June 2022 with patients originating at one of 17 community-based hospitals and a regional or academic referral centre receiving them. We identified patients that likely could have been cared for at a community facility, with attention to hospital internal medicine and cardiology patients. The model was run for 33 500 days. RESULTS: Approximately 121 beds/day were occupied by transferred patients at the academic centre, and on average, approximately 17 beds/day were used for hospital internal medicine and nine beds/day for non-procedural cardiology patients. Typical census for all internal medicine beds is approximately 175 and for cardiology is approximately 70. CONCLUSION: Deferring transfers for patients in favour of local hospitalisation would increase the availability of beds for complex care at the referral centre. Potential downstream effects also include increased patient satisfaction due to proximity to home and viability of the local hospital system/economy, and decreased resource utilisation for transfer systems

Operating room pooling and parallel surgery processing under uncertainty

Operating room (OR) scheduling is an important operational problem for most hospitals. In this study, we present a novel two-stage stochastic mixed-integer programming model to minimize total expected operating cost given that scheduling decisions are made before the resolution of uncertainty in surgery durations. We use this model to quantify the benefit of pooling ORs as a shared resource and to illustrate the impact of parallel surgery processing on surgery schedules. Decisions in our model include the number of ORs to open each day, the allocation of surgeries to ORs, the sequence of surgeries within each OR, and the start time for each surgeon. Realistic-sized instances of our model are difficult or impossible to solve with standard stochastic programming techniques. Therefore, we exploit several structural properties of the model to achieve computational advantages. Furthermore, we describe a novel set of widely applicable valid inequalities that make it possible to solve practical instances. Based on our results for different resource usage schemes, we conclude that the impact of parallel surgery processing and the benefit of OR pooling are significant. The latter may lead to total cost reductions between 21% and 59% on average

Optimal Allocation of Surgery Blocks to Operating Rooms Under Uncertainty

The allocation of surgeries to operating rooms (ORs) is a challenging combinatorial optimization problem. There is also significant uncertainty in the duration of surgical procedures, which further complicates assignment decisions. In this article, we present stochastic optimization models for the assignment of surgeries to ORs on a given day of surgery. The objective includes a fixed cost of opening ORs and a variable cost of overtime relative to a fixed length-of-day. We describe two types of models. The first is a two-stage stochastic linear program with binary decisions in the first-stage and simple recourse in the second stage. The second is its robust counter-part, in which the objective is to minimize the maximum cost associated with an uncertainty set for surgery durations. We describe the mathematical models, bounds on the optimal solution, and solution methodologies, including an easy-to-implement heuristic. Numerical experiments based on real data from a large health care provider are used to contrast the results for the two models, and illustrate the potential for impact in practice. Based on our numerical experimentation we find that a fast and easy-to-implement heuristic works fairly well on average across many instances. We also find that the robust method performs approximately as well as the heuristic, is much faster than solving than the stochastic recours

Bi-Criteria Scheduling of an Outpatient Procedure Center

A rising proportion of surgeries are performed on an outpatient basis. The demand for outpatient surgery typically occurs at the end of a stream of referrals which leads to considerable uncertainty in the type and number of surgeries to be scheduled on a particular day. Surgical services require coordination of many activities including surgical preparation, surgery, and patient recovery. Constructing surgery schedules that result in smooth patient flow is a complicated task due to the dependencies between these activities. The task is further complicated by the fact that the duration of each of these activities can be highly variable. Combining the high volume of activities in an outpatient surgical suite each day with uncertainty in the durations of activities presents challenging scheduling problems for outpatient clinic administrators. Outpatient procedure clinics typically have multiple operating rooms, with a variety of supporting resources, such as nurses, nurse anesthetists, surgeons, intake rooms, recovery rooms, and various kinds of diagnostic equipment and surgical instrument kits. Surgical procedures occur in three major stages. Intake begins when the patient arrives on the day of surgery to initiate the check-in process and ends with the patient being taken to an operatin