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

Studying RFID adoption by SMES in the Taiwanese IT industry

With the advent of Radio Frequency Identification (RFID), organisations have the opportunity to rethink how their organisation will operate and integrate in the supply chain. Especially for Small to Medium Sized Enterprises (SMEs), that they have limited resources adopting such an innovative technology (i.e. RFID) can be daunting. Literature indicates that SMEs that deal with implementation have so far only a few guidelines regarding specific opportunities and risks. This research is therefore trying to fill the gap by employing Exploratory Factor Analysis (EFA) techniques and utilising a questionnaire survey with the aim of exploring the factors that affect SMEs’ RFID adoption in the Taiwan Information Technology (IT) manufacturing industry. In doing so, the adoption factors which are classified into 3 different adopters categories named ready adopter (cost and management), initiator adopter (competitiveness and process efficiency) and unprepared adopter (IT management difficulties, IT implementation difficulties and cost of implementation) using EFA technique. A SMEs RFID adoption model is then proposed. It is anticipated that the findings of this research will not only enhance the research in RFID adoption in SMEs, but can also act as a reference for practitioners in the industry and researchers in the academic field

Conceptualizing Emerging Technology in Local Contexts: An Ethnographic Study of RFID in an Emirate’s Farming Industry

Situated in an emirate’s farming industry, this ethnographic study develops a framework to help bridge the existing knowledge gap about what and how local contexts interact with emerging technology, RFID in particular. Findings suggest that numerous local factors pertaining to the researched emirate’s unique environmental, project, cultural, and societal/political contexts shape and/or are reshaped by RFID implementation. For example, geographic landscape demands systems modification and device adaptation; religious custom increases project difficulty, and the systems, in turn, requires changes in certain religious practice; the notion of social sustainability establishes objectives for RFID project, while the latter helps reshape social welfare systems. As these local factors have rarely been empirically examined, my framework can help contribute to future RFID implementation in different local contexts. More specifically, insights gained urge stakeholders involved to carefully manage unique factors of the emirate or similar contexts for intended RFID projects. The findings also suggest that stakeholders should be aware of RFID’s reshaping effects on the local context particularly because those effects might be unexpected

The Influence of Education and Experience upon Contextual and Task Performance in Warehouse Operations

Supply chain workers make observable, preventable errors while completing their assigned tasks in the shipping process. Previous research has indicated that individuals with a greater grasp of their work and better system knowledge are less likely to commit interpretation errors. We believe worker-performance may, likewise, be affected by an individuals knowledge of why and where they fit into a larger system defined as mission knowledge. To assess our research objectives, we conduct a controlled experiment with 100 workers in the Air Force supply career field to discern how mission clarity, that is, education, experience and subject characteristics affect pick and pack errors in simulated warehouse order fulfillment tasks. Results indicate that participants who received the experience treatment committed fewer errors, resulting in increased task performance

A human-centric perspective exploring the readiness towards smart warehousing: the case of a large retail distribution warehouse

YesThe explosive rise in technologies has revolutionised the way in which business operate, consumers buy, and the pace at which these activities take place. These advancements continue to have profound impact on business processes across the entire organisation. As such, Logistics and Supply Chain Management (LSCM) are also leveraging benefits from digitisation, allowing organisations to increase efficiency and productivity, whilst also providing greater transparency and accuracy in the movement of goods. While the warehouse is a key component within LSCM, warehousing research remains an understudied area within overall supply chain research, accounting for only a fraction of the overall research within this field. However, of the extant warehouse research, attention has largely been placed on warehouse design, performance and technology use, yet overlooking the determinants of Artificial Intelligence (AI) adoption within warehouses. Accordingly, through proposing an extension of the Technology–Organisation–Environment (TOE) framework, this research explores the barriers and opportunities of AI within the warehouse of a major retailer. The findings for this qualitative study reveal AI challenges resulting from a shortage of both skill and mind-set of operational management, while also uncovering the opportunities presented through existing IT infrastructure and pre-existing AI exposure of management

Augmenting the distribution of goods from warehouses in dynamic demand environments using intelligent agents

Warehouses are being impacted by increasing e-commerce and omni-channel commerce. Future innovation may predominantly involve automation but many warehouses remain manually operated. The golden rule of material handling is smooth product flow, but there are day-to-day operational issues that occur in the warehouse that can impact this and order fulfilment. Standard operational process is paramount to warehouse operational control but inflexible processes don’t allow for a dynamic response to real-time operational constraints. The growth of IoT sensor and data analytics technology provide new opportunities for designing warehouse management systems that detect and reorganise around real-time constraints to mitigate the impact of day-to-day warehouse operational issues. This paper presents an intelligent agent framework for basic warehouse management systems that is distributed, is structured around operational constraints and includes the human operator at operational and decision support levels. An agent based simulation was built to demonstrate the viability of the framework

The process of RFID assimilation by supply chain participants in China: A technology diffusion perspective on RFID technology

RFID technology is recently an emerging technology that is being used in many echelons of supply chain participants. Mostrecent IS research on this technology focus on factors which will impact its organizational adoption. However, adoption isjust one part of assimilation process which cannot make sure that RFID can be full-scale deployed in an organization.Assimilation theories also suggested that most information technologies exhibit an “an assimilation gap” which meanswidespread usage tends to lag behind their adoption. Therefore, a stage-based model is necessary for us to understand theassimilation process of RFID technology.In this paper, we will draw on innovation diffusion theory, institutional theory and stage-based model to investigate whichinnovation factors play significant roles during three assimilation stages: initiation-adoption-routinization. Factors under eachcategory of TOE (technological, organizational, and environmental) framework will be potential antecedents of the stagebasedassimilation process and their impacts on each stage will be investigated

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

Investigating the determinants of Big Data Analytics (BDA) adoption in Asian emerging economies

Big Data Analytics (BDA), being an emerging technology, is used in many echelons of business and management. Extant research lack focus on the factors that impact the organizational adoption of this technology. Organizations need to assimilate it in a full-scale and deep level to fully realize its benefits and therefore worthy of study._x000D_ Present paper, drawing upon Technology-Organization-Environment (TOE) framework, proposes and investigates the determinants that influence BDA adoption in context of the firms from two big emerging economies of Asia –China and India. Data collected from 106 organizations is tested and the results and implications contribute to understanding of the determinants affecting BDA adoption._x000D

Utilization and Impact of Internet of Things (IoT) in Food Supply Chains from the Context of Food Loss/Waste Reduction, Shelf-Life Extension and Environmental Impact

openThe Internet of Things (IoT) sensor-based technologies are transforming the realm of food production and consumption by offering the potential to enable real-time tracking and data sharing, thus improving communication in the food supply chain. Specifically, real-time information on the location and state of food products as they travel from farms to processing plants, distribution hubs, and eventually consumers can be provided via IoT-enabled sensors and devices. This enables prompt reaction to deviations from ideal circumstances, delaying spoiling and minimizing food loss and waste (FLW). This approach also allows for dynamic inventory management, mitigating issues of overstocking and understocking often linked to food loss. However, the extent to which the implementation of such technologies can contribute to the mitigation of FLW remains uncertain. Thus, this study explores several IoT applications for food supply chains, including real-time monitoring of temperature, humidity, and other important variables. The research also looks at how IoT may help food goods last longer on the shelf. Moreover, IoT technologies have significant environmental impacts, and it is crucial to carefully consider its total environmental effect. IoT promotes energy-efficient transportation, lessens overstocking and understocking, and decreases the carbon footprint related to food production and distribution by optimizing supply chain processes. Therefore, this study also examines the effects of IoT adoption on the environment, including the manufacturing and decommissioning of IoT infrastructure and devices. It evaluates rigorously whether the possible negative consequences of technological production and waste exceed the beneficial environmental benefits, such as energy-efficient transportation and decreased carbon footprints. Shortly, It is aimed to deeply analyse the use and effects of IoT in the food supply chains, with an emphasis on how it may decrease food loss and waste, increase shelf life, and environmental impacts of its use through an extensive literature search in this study.The Internet of Things (IoT) sensor-based technologies are transforming the realm of food production and consumption by offering the potential to enable real-time tracking and data sharing, thus improving communication in the food supply chain. Specifically, real-time information on the location and state of food products as they travel from farms to processing plants, distribution hubs, and eventually consumers can be provided via IoT-enabled sensors and devices. This enables prompt reaction to deviations from ideal circumstances, delaying spoiling and minimizing food loss and waste (FLW). This approach also allows for dynamic inventory management, mitigating issues of overstocking and understocking often linked to food loss. However, the extent to which the implementation of such technologies can contribute to the mitigation of FLW remains uncertain. Thus, this study explores several IoT applications for food supply chains, including real-time monitoring of temperature, humidity, and other important variables. The research also looks at how IoT may help food goods last longer on the shelf. Moreover, IoT technologies have significant environmental impacts, and it is crucial to carefully consider its total environmental effect. IoT promotes energy-efficient transportation, lessens overstocking and understocking, and decreases the carbon footprint related to food production and distribution by optimizing supply chain processes. Therefore, this study also examines the effects of IoT adoption on the environment, including the manufacturing and decommissioning of IoT infrastructure and devices. It evaluates rigorously whether the possible negative consequences of technological production and waste exceed the beneficial environmental benefits, such as energy-efficient transportation and decreased carbon footprints. Shortly, It is aimed to deeply analyse the use and effects of IoT in the food supply chains, with an emphasis on how it may decrease food loss and waste, increase shelf life, and environmental impacts of its use through an extensive literature search in this study