Essays in Physicians Preference Items and Inventory Management within the Healthcare Supply Chain

This work is composed of a number of topics in the healthcare area, which are approached separately with appropriate methodologies. The two topics deal with physician preference items via two different approaches. The first one investigates stock keeping unit (SKU) proliferation in healthcare organizations due to physician preference items (PPI). It captures perspectives of physicians and supply chain professionals about this problem through two surveys. The second topic builds a decision-making framework for the PPI selection process that can be used by healthcare organizations to make more objective decisions. A Multi-criteria decision making technique is implemented to illustrate the framework

The effect of customer satisfaction on parcel delivery operations using autonomous vehicles: An agent-based simulation study

The quality of Third-Party Logistics (3PL) services represented by delivery time decides the outcome of customer satisfaction. The result of this satisfaction judges the type of Word of Mouth (WoM) that, if positive, plays a vital role in attracting non-customers who are willing in 3PL services to join as customers. In this paper, we investigate the effect of an essential factor represented by Word of Mouth on the number of customers in 3PL companies. Therefore, an agent-based model for parcel delivery is developed to investigate the impact of social factors such as WoM and other operational factors, including vehicle number and speed, on customer number and satisfaction, average service time, and vehicle utilization. As a methodology, state charts of Vehicle, Customer, Hub agents are developed to mimic the messaging protocols between these agents under the WoM concept. A case study based in 3PL in Jordan is used as a test bench of the developed model. A sensitivity analysis study is conducted to test the developed model's performance, including different levels of influential model parameters such as targeting non-customers parameters by Loyal/Unhappy customers. Key results reveal that the best scenario is achieved when the WoM value equals 10, the vehicle number equals 30, and the vehicle speed equals 60 km/h. These model parameters result in higher customer numbers of 873, vehicle utilization equals 63%, and customer satisfaction equals 99%. Video of our proposed model showing it in action can be found at: https://www.youtube.com/watch?v=3rR4l130-QU

An integrated multi-criteria decision-making framework for a medical device selection in the healthcare industry

Medical devices used in healthcare organizations are costly, and the process of selecting these devices requires considering multiple criteria such as effectiveness and ease of use. Careful selection of these devices is daunting since it entails the evaluation of various measures. This research investigates the selection process of the same type of medical devices, especially when alternatives are available, and the organization needs to make a good selection. A Multi-Criteria Decision-Making (MCDM) framework based on the integration of the Analytical Hierarchy Process (AHP) and ELimination Et Choice Translating Reality (ELECTRE) method is developed. The framework model includes 10 criteria, which are selected based on real-life inputs from professional physicians. Seven Ultrasound machines (referred to as alternatives) are evaluated using the developed framework. A case study is conducted on the best selection practice of an Ultrasound machine in a gynecology clinic based in the Kingdom of Jordan. Results revealed that the best and worst alternatives of ultrasound machines are identified and compared with all other options

A Collaborative Planning Forecasting and Replenishment (CPFR) Maturity Model

This paper presents the development of a framework that organizations can use to assess their CPFR maturity. The proposed modeling framework identifies important functional and structural aspects of CPFR processes and formulates a method for evaluation on a variety of characteristics of CPFR. This paper uses a variant of multi-objective decision analysis to structure the framework into a hierarchical model for CPFR maturity assessment. Each area of the model was identified based on standardized, industry-accepted process definitions. Then, easy to answer questions were formulated to develop a multi-attribute assessment and scoring of capabilities. This model provides a structured representation of the CPFR process for maturity assessment and provides a path of progress for improving the state of CPFR within the  underperforming areas. The developed model can be used by engineering managers for assessing an on-going CPFR program across several areas and communicating the identified high impact improvement areas with various segments of the organization

Machine Learning Approaches to Predict Patient’s Length of Stay in Emergency Department

As the COVID-19 pandemic has afflicted the globe, health systems worldwide have also been significantly affected. This pandemic has impacted many sectors, including health in the Kingdom of Jordan. Crises that put heavy pressure on the health systems’ shoulders include the emergency departments (ED), the most demanded hospital resources during normal conditions, and critical during crises. However, managing the health systems efficiently and achieving the best planning and allocation of their EDs’ resources becomes crucial to improve their capabilities to accommodate the crisis’s impact. Knowing critical factors affecting the patient length of stay prediction is critical to reducing the risks of prolonged waiting and clustering inside EDs. That is, by focusing on these factors and analyzing the effect of each. This research aims to determine the critical factors that predict the outcome: the length of stay, i.e., the predictor variables. Therefore, patients’ length of stay in EDs across waiting time duration is categorized as (low, medium, and high) using supervised machine learning (ML) approaches. Unsupervised algorithms have been applied to classify the patient’s length of stay in local EDs in the Kingdom of Jordan. The Arab Medical Centre Hospital is selected as a case study to justify the performance of the proposed ML model. Data that spans a time interval of 22 months, covering the period before and after COVID-19, is used to train the proposed feedforward network. The proposed model is compared with other ML approaches to justify its superiority. Also, comparative and correlation analyses are conducted on the considered attributes (inputs) to help classify the LOS and the patient’s length of stay in the ED. The best algorithms to be used are the trees such as the decision stump, REB tree, and Random Forest and the multilayer perceptron (with batch sizes of 50 and 0.001 learning rate) for this specific problem. Results showed better performance in terms of accuracy and easiness of implementation

Examining the effect of nano-additions of rare earth elements on the hardness of body armor ceramic

64-72Body armor is a very critical entity in protecting soldier's live. Soldiers carry heavy stuff on duties, and the ceramic insert in those body armors is one of them. The purpose of this paper is to investigate the effect of Nano-rare-earth elements as additives to the ceramic base material on the armor's performance. Aluminum oxide (Al2O3) has been selected as the base material of the ceramic in this study. This study has chosen two additives: Zirconium dioxide (ZrO2) and Nano-ceramic lab composite (NCLC). In this work, we have presented results of mechanical characterization for alumina-nanocomposites armor plates. Three different concentrations of NCLC and ZnO2 alumina-based compositions have been prepared and pressed at 40 and 50 MPa and sintered at 1350°C for 120 min. X-ray diffraction and scanning electron microscopy (SEM) techniques have been employed to characterize structural, morphological, and phase identification of the films. Mohs test hardness measurements of samples after sintering have been performed. Results have shown that the compositions with NCLC showed a higher hardness than a composition with ZrO2. This result has indicated that the addition of NCLC to Alumina enhances the microstructure and increases the ceramics' hardness

Application of condition-based maintenance for electrical generators based on statistical control charts

Condition-based maintenance involves activities that are conducted based on the equipment's performance. Continuous monitoring of equipment will ensure that it will be maintained according to a relevant activity plan. This paper proposes a maintenance framework to analyze the application of statistical control charts for condition-based maintenance of electrical generators. The proposed framework consists of four components that collaboratively determine a performance threshold for a given piece of electrical equipment. Based on the slow progression and dynamics of mechanical failures, Long Short-term Memory (LSTM) and Useful Remaining Life (URL) models were used to assist in the maintenance decision-making process. The analysis is based on detecting the dynamics of the process parameters, including vibration, noise, and temperature, based on relevant control charts. With the help of experimental methodology, failures in the performance modes and defined modes are measured. Then empirical analysis reveals how control charts respond to failure detection. The results show that X-bar consistently demonstrates failure detection capability, while R charts sometimes fail when data deviates from normality.Moreover, heat monitoring surpassed vibration and noise in failure detection, where temperature control charts successfully identified failure. The overall results support the significant role of statistical charts in decision-making regarding condition-based maintenance for electrical equipment like generators. • Application of statistical control charts for condition-based maintenance of electrical generators. • Detecting dynamics of the process parameters, including vibration, noise, and temperature, based on relevant control charts. • Long Short-term Memory (LSTM) and Useful Remaining Life (URL) models were used to assist in the maintenance decision-making proces

Decision-Making Framework for Evaluating Physicians’ Preference Items Using Multi-Objective Decision Analysis Principles

Physician preference items or PPIs are medical items recommended by physicians for use in medical procedures and other treatments. The recommendation of PPIs by individual physicians can cause the variety of item types that need to be managed within a health care supply chain to increase over time. To better manage the PPI selection process, healthcare organizations often select items through value analysis and discussion teams, which are highly subjective. To better control PPIs, this work uses multiple-objective decision analysis (MODA) to develop a structured quantitative framework for the PPI selection process. The established decision-making framework is based on the theory of multi-objective value analysis. It offers a structured and educated guide to decision-makers for improving value analysis outcomes, advocating sustainable healthcare management strategies. The model was tested and validated through two case studies on two different items in two hospitals in Jordan