Selection process of auto-ID technology in warehouse management: A Delphi study

This thesis was submitted for the degree of Doctor of philosophy and awarded by Brunel UniversityIn a supply chain, a warehouse is a crucial component for linking all chain parties. Automatic identification and data capture (auto-ID) technology, e.g. RFID and barcodes are among the essential technologies in the 21st century knowledge-based economy. Selecting an auto-ID technology is a long term investment and it contributes to improving operational efficiency, achieving cost savings and creating opportunities for higher revenues. The interest in auto-ID research for warehouse management is rather stagnant and relatively small in comparison to other research domains such as transport, logistics and supply chain. However, although there are some previous studies that explored factors for the auto-ID selection decision in a warehouse environment, those factors (e.g., operational factors) have been examined separately and researchers have paid no attention to all key factors that may potentially affect this decision. In fact, yet there is no comprehensive framework in the literature that comprehensively investigates the critical factors influencing the auto-ID selection decision and how the factors should be combined to produce a successful auto-ID selection process in warehouse management. Therefore, the main aim of this research is to investigate empirically the auto-ID technology-selection process and to determine the key factors that influence decision makers when selecting auto-ID technology in the warehouse environment. This research is preceded by a comprehensive and systematic review of the relevant literature to identify the set of factors that may affect the technology selection decision. The Technology-Organisation-Environment (TOE) framework has been used as lens to categorise the identified factors (Tornatzky & Fleischer, 1990). Data were collected by conducting first a modified (mixed-method) two-round Delphi study with a worldwide panel of experts (107) including academics, industry practitioners and consultants in auto-ID technologies. The results of the Delphi study were then verified via follow-up interviews, both face-to-face and telephone, carried out with 19 experts across the world. This research in nature is positivist, exploratory/descriptive, deductive/inductive and quantitative/qualitative. The quantitative data were analysed using the statistical package for social sciences, SPSS V.18, while the qualitative data of the Delphi study and the interviews were analysed manually using quantitative content analysis approach and thematic content analysis approach respectively. The findings of this research are reported on the motivations/reasons of warehouses in seeking to use auto-ID technologies, the challenges in making an auto-ID decision, the recommendations to address the challenges, the key steps that should be followed in making auto-ID selection decision, the key factors and their relative importance that influence auto-ID selection decision in a warehouse. The results of the Delphi study show that the six major factors affecting the auto-ID selection decision in warehouse management are: organisational, operational, structural, resources, external environmental and technological factors (in decreasing order of importance). In addition, 54 key sub-factors have been identified from the list of each of the major factors and ranked in decreasing order of the importance mean scores. However, the importance of these factors depends on the objectives and strategic motivations of warehouse; size of warehouse; type of business; nature of business environment; sectors; market types; products and countries. Based on the Delphi study and the interviews findings, a comprehensive multi-stage framework for auto-ID technology selection process has been developed. This research indicates that the selection process is complex and needs support and closer collaboration from all participants involved in the process such as the IT team, top management, warehouse manager, functional managers, experts, stockholders and vendors. Moreover, warehouse managers should have this process for collaboration before adopting the technology in order to reduce the high risks involved and achieve successful implementation. This research makes several contributions for both academic and practitioners with auto-ID selection in a warehouse environment. Academically, it provides a holistic multi-stage framework that explains the critical issues within the decision making process of auto-ID technology in warehouse management. Moreover, it contributes to the body of auto-ID and warehouse management literature by synthesising the literature on key dimensions of auto-ID (RFID/barcode) selection decision in the warehouse field. This research also provides a theoretical basis upon which future research on auto-ID selection and implementation can be built. Practically, the findings provide valuable insights for warehouse managers and executives associated with auto-ID selection and advance their understanding of the issues involved in the technology selection process that need to be considered.Damascus University, Syria and The British Council, Mancheste

Modernized Management of Biomedical Waste Assisted with Artificial Intelligence

Biomedical waste can lead to severe environmental pollution and pose public health risks if not properly handled or disposed of. The efficient management of biomedical waste poses a significant challenge to healthcare facilities, environmental agencies, and regulatory bodies. Traditional management methods often fall short of efficient handling of biomedical waste due to its enormous quantity, diverse, and complex nature. In recent years, different approaches employing Artificial Intelligence (AI) techniques have been introduced and have shown promising potential in biomedical waste management. Wireless detection and IoT methods have enabled the monitoring of waste bins, predictions for the amount of waste, and optimization of the performance of waste processing facilities. This review paper aims to explore the application of AI through machine learning and deep learning models in optimizing the collection, segregation, transportation, disposal, and monitoring processes, which leads to improved resource allocation with risk mitigation of biomedical waste along with prediction, and decision-making using AI algorithms

The drug logistics process: an innovation experience

Purpose - The purpose of this paper is to present the latest innovations in the drug distribution processes of hospital companies, which are currently dealing with high inventory and storage costs and fragmented organizational responsibilities. Design/methodology/approach - The literature review and the in-depth analysis of a case study support the understanding of the unit dose drug distribution system and the subsequent definition of the practical implications for hospital companies. Findings - Starting from the insights offered by the case study, the analysis shows that the unit dose system allows hospitals to improve the patient care quality and reduce costs. Research limitations/implications - The limitations of the research are those related to the theoretical and exploratory nature of the study, but from a practical point of view, the work provides important indications to the management of healthcare companies, which have to innovate their drug distribution systems. Originality/value - This paper analyzes a new and highly topical issue and provides several insights for the competitive development of a fundamental sector

Effects of Enterprise Digital Assistants in medication dispensing operations: Case hospital pharmacy

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Semantic discovery and reuse of business process patterns

Patterns currently play an important role in modern information systems (IS) development and their use has mainly been restricted to the design and implementation phases of the development lifecycle. Given the increasing significance of business modelling in IS development, patterns have the potential of providing a viable solution for promoting reusability of recurrent generalized models in the very early stages of development. As a statement of research-in-progress this paper focuses on business process patterns and proposes an initial methodological framework for the discovery and reuse of business process patterns within the IS development lifecycle. The framework borrows ideas from the domain engineering literature and proposes the use of semantics to drive both the discovery of patterns as well as their reuse

Industry 4.0 for SMEs

This open access book explores the concept of Industry 4.0, which presents a considerable challenge for the production and service sectors. While digitization initiatives are usually integrated into the central corporate strategy of larger companies, smaller firms often have problems putting Industry 4.0 paradigms into practice. Small and medium-sized enterprises (SMEs) possess neither the human nor financial resources to systematically investigate the potential and risks of introducing Industry 4.0. Addressing this obstacle, the international team of authors focuses on the development of smart manufacturing concepts, logistics solutions and managerial models specifically for SMEs. Aiming to provide methodological frameworks and pilot solutions for SMEs during their digital transformation, this innovative and timely book will be of great use to scholars researching technology management, digitization and small business, as well as practitioners within manufacturing companies

Fashion Industry

Fashion is a lot more than providing an answer to primary needs. It is a way of communication, of distinction, of proclaiming a unique taste and expressing the belonging to a group. Sometimes to an exclusive group. Currently, the fashion industry is moving towards hyperspace, to a multidimensional world that is springing from the integration of smart textiles and wearable technologies. It is far beyond aesthetics. New properties of smart textiles let designers experiment with astonishing forms and expressions. There are also surprising contrasts and challenges: a new life for natural fibers, sustainable fabrics and dyeing techniques, rediscovered by eco-fashion, and "artificial apparel," made of wearable electronic components. How is this revolution affecting the strategies of the fashion industry

A Structured Approach to Analyse Logistics Risks in the Blood Transfusion Process

Blood transfusion is a critical healthcare process due to the nature of the products handled and the complexity driven by the strong interdependence among the sub-processes involved. Most of the errors causing adverse events originate during the blood logistics activities. Several literature contributions apply risk management to the transfusion process but often in a fragmented and reactive way. Moreover, few of them focus on logistics risks and assess the effectiveness of risk responses through operational key performance indicators (KPIs). The present paper applies a comprehensive and structured approach to proactively identify and analyse logistics risks as well as define responses to improve blood bag traceability, focusing on hospital wards. The implementation of such actions is monitored by specific KPIs whose measurement enables an improved communication flow among actors allowing to uncover residual risks. Future research will extend the application to further blood transfusion settings and supply chain echelons. The outcomes of this work might assist practitioners in improving policy making about blood supply chains. As a matter of fact, they allow a better understanding of the associated material and informational flows and the related risks, which supports setting effective strategies to either prevent adverse events or mitigate their effects