Improving supply chain performance: Real-time demand information and flexible deliveries

In some supply chains, materials are ordered periodically according to local information. This paper investigates how to improve the performance of such a supply chain. Specifically, we consider a serial inventory system in which each stage implements a local reorder interval policy; i.e., each stage orders up to a local basestock level according to a fixed-interval schedule. A fixed cost is incurred for placing an order. Two improvement strategies are considered: (1) expanding the information flow by acquiring real-time demand information and (2) accelerating the material flow via flexible deliveries. The first strategy leads to a reorder interval policy with full information; the second strategy leads to a reorder point policy with local information. Both policies have been studied in the literature. Thus, to assess the benefit of these strategies, we analyze the local reorder interval policy. We develop a bottom-up recursion to evaluate the system cost and provide a method to obtain the optimal policy. A numerical study shows the following: Increasing the flexibility of deliveries lowers costs more than does expanding information flow; the fixed order costs and the system lead times are key drivers that determine the effectiveness of these improvement strategies. In addition, we find that using optimal batch sizes in the reorder point policy and demand rate to infer reorder intervals may lead to significant cost inefficiency. © 2010 INFORMS.

Trends in anemia management in US hemodialysis patients 2004-2010.

BACKGROUND: There have been major changes in the management of anemia in US hemodialysis patients in recent years. We sought to determine the influence of clinical trial results, safety regulations, and changes in reimbursement policy on practice. METHODS: We examined indicators of anemia management among incident and prevalent hemodialysis patients from a medium-sized dialysis provider over three time periods: (1) 2004 to 2006 (2) 2007 to 2009, and (3) 2010. Trends across the three time periods were compared using generalized estimating equations. RESULTS: Prior to 2007, the median proportion of patients with monthly hemoglobin >12 g/dL for patients on dialysis 0 to 3, 4 to 6 and 7 to 18 months, respectively, was 42%, 55% and 46% declined to 41%, 54%, and 40% after 2007, and declined more sharply in 2010 to 34%, 41%, and 30%. Median weekly Epoeitin alpha doses over the same periods were 18,000, 12,400, and 9,100 units before 2007; remained relatively unchanged from 2007 to 2009; and decreased sharply in the patients 3-6 and 6-18 months on dialysis to 10,200 and 7,800 units, respectively in 2010. Iron doses, serum ferritin, and transferrin saturation levels increased over time with more pronounced increases in 2010. CONCLUSION: Modest changes in anemia management occurred between 2007 and 2009, followed by more dramatic changes in 2010. Studies are needed to examine the effects of declining erythropoietin use and hemoglobin levels and increasing intravenous iron use on quality of life, transplantation rates, infection rates and survival.

Real-Time and Data-Driven Operation Optimization and Knowledge Discovery for an Enterprise Information System

An enterprise information system (EIS) is an integrated data-applications platform characterized by diverse, heterogeneous, and distributed data sources. For many enterprises, a number of business processes still depend heavily on static rule-based methods and extensive human expertise. Enterprises are faced with the need for optimizing operation scheduling, improving resource utilization, discovering useful knowledge, and making data-driven decisions.

This thesis research is focused on real-time optimization and knowledge discovery that addresses workflow optimization, resource allocation, as well as data-driven predictions of process-execution times, order fulfillment, and enterprise service-level performance. In contrast to prior work on data analytics techniques for enterprise performance optimization, the emphasis here is on realizing scalable and real-time enterprise intelligence based on a combination of heterogeneous system simulation, combinatorial optimization, machine-learning algorithms, and statistical methods.

On-demand digital-print service is a representative enterprise requiring a powerful EIS.We use real-life data from Reischling Press, Inc. (RPI), a digit-print-service provider (PSP), to evaluate our optimization algorithms.

In order to handle the increase in volume and diversity of demands, we first present a high-performance, scalable, and real-time production scheduling algorithm for production automation based on an incremental genetic algorithm (IGA). The objective of this algorithm is to optimize the order dispatching sequence and balance resource utilization. Compared to prior work, this solution is scalable for a high volume of orders and it provides fast scheduling solutions for orders that require complex fulfillment procedures. Experimental results highlight its potential benefit in reducing production inefficiencies and enhancing the productivity of an enterprise.

We next discuss analysis and prediction of different attributes involved in hierarchical components of an enterprise. We start from a study of the fundamental processes related to real-time prediction. Our process-execution time and process status prediction models integrate statistical methods with machine-learning algorithms. In addition to improved prediction accuracy compared to stand-alone machine-learning algorithms, it also performs a probabilistic estimation of the predicted status. An order generally consists of multiple series and parallel processes. We next introduce an order-fulfillment prediction model that combines advantages of multiple classification models by incorporating flexible decision-integration mechanisms. Experimental results show that adopting due dates recommended by the model can significantly reduce enterprise late-delivery ratio. Finally, we investigate service-level attributes that reflect the overall performance of an enterprise. We analyze and decompose time-series data into different components according to their hierarchical periodic nature, perform correlation analysis,

and develop univariate prediction models for each component as well as multivariate models for correlated components. Predictions for the original time series are aggregated from the predictions of its components. In addition to a significant increase in mid-term prediction accuracy, this distributed modeling strategy also improves short-term time-series prediction accuracy.

In summary, this thesis research has led to a set of characterization, optimization, and prediction tools for an EIS to derive insightful knowledge from data and use them as guidance for production management. It is expected to provide solutions for enterprises to increase reconfigurability, accomplish more automated procedures, and obtain data-driven recommendations or effective decisions.

The effect of a urinary incontinence self-management program for older women in South Korea: A pilot study

© 2015 Chinese Nursing Association.Background Although self-management approaches have shown strong evidence of positive outcomes for urinary incontinence prevention and management, few programs have been developed for Korean rural communities. Objectives This pilot study aimed to develop, implement, and evaluate a urinary incontinence self-management program for community-dwelling women aged 55 and older with urinary incontinence in rural South Korea. Methods This study used a one-group pre- post-test design to measure the effects of the intervention using standardized urinary incontinence symptom, knowledge, and attitude measures. Seventeen community-dwelling older women completed weekly 90-min group sessions for 5 weeks. Descriptive statistics and paired t-tests and were used to analyze data. Results The mean of the overall interference on daily life from urine leakage (pre-test: M = 5.76 ± 2.68, post-test: M = 2.29 ± 1.93, t = -4.609, p < 0.001) and the sum of International Consultation on Incontinence Questionnaire scores (pre-test: M = 11.59 ± 3.00, post-test: M = 5.29 ± 3.02, t = -5.881, p < 0.001) indicated significant improvement after the intervention. Improvement was also noted on the mean knowledge (pre-test: M = 19.07 ± 3.34, post-test: M = 23.15 ± 2.60, t = 7.550, p < 0.001) and attitude scores (pre-test: M = 2.64 ± 0.19, post-test: M = 3.08 ± 0.41, t = 5.150, p < 0.001). Weekly assignments were completed 82.4% of the time. Participants showed a high satisfaction level (M = 26.82 ± 1.74, range 22-28) with the group program. Conclusions Implementation of a urinary incontinence self-management program was accompanied by improved outcomes for Korean older women living in rural communities who have scarce resources for urinary incontinence management and treatment. Urinary incontinence self-management education approaches have potential for widespread implementation in nursing practice.

Nurse-led behavioral management of diabetes and hypertension in community practices: a randomized trial.

BACKGROUND: Several trials have demonstrated the efficacy of nurse telephone case management for diabetes (DM) and hypertension (HTN) in academic or vertically integrated systems. Little is known about the real-world potency of these interventions. OBJECTIVE: To assess the effectiveness of nurse behavioral management of DM and HTN in community practices among patients with both diseases. DESIGN: The study was designed as a patient-level randomized controlled trial. PARTICIPANTS: Participants included adult patients with both type 2 DM and HTN who were receiving care at one of nine community fee-for-service practices. Subjects were required to have inadequately controlled DM (hemoglobin A1c [A1c] ≥ 7.5%) but could have well-controlled HTN. INTERVENTIONS: All patients received a call from a nurse experienced in DM and HTN management once every two months over a period of two years, for a total of 12 calls. Intervention patients received tailored DM- and HTN- focused behavioral content; control patients received non-tailored, non-interactive information regarding health issues unrelated to DM and HTN (e.g., skin cancer prevention). MAIN OUTCOMES AND MEASURES: Systolic blood pressure (SBP) and A1c were co-primary outcomes, measured at 6, 12, and 24 months; 24 months was the primary time point. RESULTS: Three hundred seventy-seven subjects were enrolled; 193 were randomized to intervention, 184 to control. Subjects were 55% female and 50% white; the mean baseline A1c was 9.1% (SD = 1%) and mean SBP was 142 mmHg (SD = 20). Eighty-two percent of scheduled interviews were conducted; 69% of intervention patients and 70% of control patients reached the 24-month time point. Expressing model estimated differences as (intervention--control), at 24 months, intervention patients had similar A1c [diff = 0.1 %, 95 % CI (-0.3, 0.5), p = 0.51] and SBP [diff = -0.9 mmHg, 95% CI (-5.4, 3.5), p = 0.68] values compared to control patients. Likewise, DBP (diff = 0.4 mmHg, p = 0.76), weight (diff = 0.3 kg, p = 0.80), and physical activity levels (diff = 153 MET-min/week, p = 0.41) were similar between control and intervention patients. Results were also similar at the 6- and 12-month time points. CONCLUSIONS: In nine community fee-for-service practices, telephonic nurse case management did not lead to improvement in A1c or SBP. Gains seen in telephonic behavioral self-management interventions in optimal settings may not translate to the wider range of primary care settings.