Rising healthcare costs force healthcare organizations worldwide to reconsider their business strategies. Hospital managers are tasked with lowering operating costs while upholding the high quality of care, two goals perceived as contradictory by many healthcare professionals. Attempting to contribute towards reaching these goals, the study explores the digitalization of the healthcare supply chain through the adoption of enterprise resource planning (ERP) systems and its impact on hospital supply chain costs. Established technology adoption theories are synthesized to develop a research model that investigates the adoption of hospital ERP systems and its impact on supply chain cost performance. The framework is validated via structural equation modeling (SEM) with data collected from 107 Greek public hospitals. The results indicate that technological and organizational readiness, hospital size, governmental policies, and perceived benefits have a significant influence on the extent of ERP systems adoption. They also demonstrate a statistically significant association between the use of ERP systems and hospital supply chain costs. The present study highlights the influencing factors of ERP systems adoption in hospitals and provides empirical evidence on the resulting cost benefits., This implies greater urgency for hospitals to digitalize their supply chain by implementing and fully exploiting the ERP systems’ functionality in order to reach their goal of delivering quality care at a lower cost.

Purpose: This case study describes the development and empirical validation of an easy-to-implement practical framework for improving hospital pharmacy inventory management. Summary: Research suggests various inventory optimization models, which can lead to cost reductions while maintaining adequate service levels; however, they are facing limited adoption in healthcare settings. The main barriers appear to be the high effort and complexity of implementation, the dependence on data that are not available or might not be in the right form, and the one-size-fits-all approach often followed without addressing healthcare sector–specific particularities. A research framework was developed by adapting relevant inventory models to the healthcare context using the concept of data segmentation on the basis of a three-dimensional classification of hospital pharmacy inventory items based on their relative importance, clinical criticality, and consumption pattern. Suitable replenishment policies were assigned to high-impact classes, and an integrated performance-measurement component assesses the framework’s effectiveness. The suggested approach was implemented and empirically tested at the pharmacy of a large public hospital using longitudinal data. The results demonstrate substantial improvements with respect to all of the selected key performance indicators and translate into inventory cost savings due to reduced stockholding costs and better synchronization of inventories to demand. Conclusion: Use of standard software functionalities combined with targeted data segmentation efforts significantly improves hospital pharmacy inventory cost performance.

Healthcare supply chains are complex systems facing challenges in delivering high-quality care while maintaining cost-effectiveness. Research on inventory management and control, the heart of the healthcare supply system, has demonstrated that these targets can be reached by managing inventories efficiently while aiming at desired service levels. Although this can be enabled by adopting various supply chain management practices, the healthcare sector appears to lag compared to other industries. Seeking to investigate these aspects, this study draws on operations management literature, identifies and categorizes technology-driven supply chain management practices that are applied in hospital operations, develops a holistic conceptual framework delineating the key factors influencing their adoption in healthcare facilities, and examines their aggregated impact on financial performance. The research model is tested using structural equation modeling with survey data collected from Greek public hospitals. The study results indicate that technological readiness, organizational readiness, perceived benefits, and hospital size significantly influence the adoption of these practices in hospital supply chains. Moreover, they show a statistically significant association between the supply chain management practices applied and improved hospital cost performance, suggesting greater urgency for hospitals to exploit them fully. Theoretical and practical implications of the findings are discussed.

Combined with advances in sensing technologies and big data analytics, critical information can be extracted from continuous production processes for predicting the health state of equipment and safeguarding upcoming failures. This research presents a methodology for applying predictive maintenance (PdM) solutions and showcases a PdM application for health state prediction and condition monitoring, increasing the safety and productivity of centrifugal pumps for a sustainable and resilient PdM ecosystem. Measurements depicting the healthy and maintenance-prone stages of two centrifugal pumps were collected on the university campus. The dataset consists of 5,118 records and includes both running and standstill values. Additionally, Spearman statistical analysis was conducted to measure the correlation of collected measurements with the predicted output of machine conditions and select the most appropriate features for model optimization. Several machine learning (ML) algorithms, namely random forest (RF), Naïve Bayes, support vector machines (SVM), and extreme gradient boosting (XGBoost) were analyzed and evaluated during the data mining process. The results indicated the effectiveness and efficiency of XGBoost for the health state prediction of centrifugal pumps. The contribution of this research is to propose an effective framework collectong multistage health data for PdM applications and showcase its effectiveness in a real-world use case.

The fourth industrial revolution (Industry 4.0) brought significant changes in manufacturing, driven by technologies like artificial intelligence (AI), Internet of Things (IoT), 5G, robotics, and big data analytics. For industries to remain competitive, the primary goals must be the improvement of the efficiency and safety of machinery, the reduction of production costs, and the enhancement of product quality. Predictive maintenance (PdM) utilizes historical data and AI models to diagnose equipment’s health and predict the remaining useful life (RUL), providing critical insights for machinery effectiveness and product manufacturing. This prediction is a critical strategy to maximize the useful life of equipment, especially in large-scale and important infostructures. This study focuses on developing an unsupervised machine state-classification solution utilizing real-world industrial measurements collected from a pneumatic pressing machine. Unsupervised machine learning (ML) models were tested to diagnose and output the working state of the pressing machine at each given point (offline, idle, pressing, defective). Our research contributes to extracting valuable insights regarding real-world industrial settings for PdM and production efficiency using unsupervised ML, promoting operation safety, cost reduction, and productivity enhancement in modern industries.

Benefits and Barriers of E-Sourcing and E-Purchasing in the Healthcare Sector: A Case Study: 10.4018/978-1-4666-8841-4.ch005: This chapter reports on research investigating the benefits and barriers of e-Enabling Technologies such as e-Sourcing and e-Purchasing in the healthcare