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

Abstract

In 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.