To Greener Pastures: An Action Research Study on the Environmental Sustainability of Humanitarian Supply Chains

Purpose: While humanitarian supply chains (HSCs) inherently contribute to social sustainability by alleviating the suffering of afflicted communities, their unintended adverse environmental impact has been overlooked hitherto. This paper draws upon contingency theory to synthesize green practices for HSCs, identify the contingency factors that impact on greening HSCs and explore how focal humanitarian organizations (HOs) can cope with such contingency factors. Design/methodology/approach: Deploying an action research methodology, two-and-a-half cycles of collaboration between researchers and a United Nations agency were completed. The first half-cycle developed a deductive greening framework, synthesizing extant green practices from the literature. In the second and third cycles, green practices were adopted/customized/developed reflecting organizational and contextual contingency factors. Action steps were implemented in the HSC for prophylactics, involving an operational mix of disaster relief and development programs. Findings: First, the study presents a greening framework that synthesizes extant green practices in a suitable form for HOs. Second, it identifies the contingency factors associated with greening HSCs regarding funding environment, stakeholders, field of activity and organizational management. Third, it outlines the mechanisms for coping with the contingency factors identified, inter alia, improving the visibility of headquarters over field operations, promoting collaboration and resource sharing with other HOs as well as among different implementing partners in each country, and working with suppliers for greener packaging. The study advances a set of actionable propositions for greening HSCs. Practical implications: Using an action research methodology, the study makes strong practical contributions. Humanitarian practitioners can adopt the greening framework and the lessons learnt from the implementation cycles presented in this study. Originality/value: This is one of the first empirical studies to integrate environmental sustainability and HSCs using an action research methodology

Empirical Evidence of RFID Impacts on Supply Chain Performance

Purpose - The purpose of this paper is to investigate the actual benefits of radio frequency identification (RFID) on supply chain performance through the empirical evidence. Design/methodology/approach - The research reviews and classifies the existing quantitative empirical evidence of RFID on supply chain performance. The evidence is classified by process (operational or managerial) and for each process by effect (automational, informational, and transformational). Findings - The empirical evidence shows that the major effects from the implementation of RFID are automational effects on operational processes followed by informational effects on managerial processes. The RFID implementation has not reached transformational level on either operational or managerial processes. RFID has an automational effect on operational processes through inventory control and efficiency improvements. An informational effect for managerial processes is observed for improved decision quality, production control and the effectiveness of retail sales and promotions coordination. In addition, a three-stage model is proposed to explain the effects of RFID on the supply chain. Research limitations/implications - Limitations of this research include the use of secondary sources and the lack of consistency in performance measure definitions. Future research could focus on detailed case studies that investigate cross-functional applications across the organization and the supply chain. Practical implications - For managers, the empirical evidence presented can help them identify implementation areas where RFID can have the greatest impact. The data can be used to build the business case for RFID and therefore better estimate ROI and the payback period. Originality/value - This research fills a void in the literature by providing practitioners and researchers with a better understanding of the quantitative benefits of RFID in the supply chain

Additive Manufacturing of Slow-Moving Automotive Spare Parts: A Supply Chain Cost Assessment

This study develops a cost model for the additive manufacturing (AM)-produced spare parts supply chain in the automotive industry. Moreover, we evaluate the economic feasibility of AM for slow-moving automotive spare parts by comparing the costs of the traditional manufacturing (TM) spare parts supply chain (SPSC) with centralized, outsourced AM SPSC. Data from a multiple case study of an OEM in the automotive industry regarding SPSC is utilized. The supply chain costs of 14 individual spare parts were analyzed, and the total SPSC cost for the AM and TM, were compared. Three of the fourteen parts showed potential for cost-savings, if they were produced with AM instead of TM. In this context, AM polymer parts showed greater potential than metal to replace TM as the more economical option of manufacturing from a total supply chain cost perspective. This study shows that the AM competitiveness to TM, from a financial perspective, increases for spare parts with low demand, high minimum order quantity, and high TM production price. The SPSC cost model included: cost of production, transport, warehousing, and service costs. This study contributes to the emerging field of part identification for AM and the existing literature regarding cost modeling in SPSCs

Managing supply chain uncertainty arising from geopolitical disruptions: evidence from the pharmaceutical industry and Brexit

Purpose This paper examines how firms of different sizes formulate and implement strategies to achieve fit with an external environment disrupted by a geopolitical event. The context of the study is the pharmaceutical industry and how it managed the supply chain uncertainty created by the United Kingdom's decision to leave the European Union, or Brexit. Design/methodology/approach Data were collected longitudinally from the pro-Brexit vote on 23 June 2016, until the UK’s departure from the EU on 31 January 2020. Twenty-seven interviews were conducted in the pharmaceutical sector, including nineteen interviews with senior managers at eight case companies and eight interviews with experts working for trade associations and standards institutes. The interview findings were triangulated with Brexit policy and strategy documentation. Findings When formulating strategy, multi-national enterprises (MNEs) used worst case assumptions, while large firms, and small and medium sized enterprises (SMEs) gathered knowledge as part of a “wait-and-see” strategy, allowing them to reduce perceptions of heightened supply chain uncertainty. Firms then implemented reactive and/or proactive strategies to mitigate supply chain risks. Originality/value The study elaborates on strategic contingency theory by identifying two important conditions for achieving strategic fit: first, companies deploy intangible resources, such as management time, to gather information and reduce perceptions of heightened supply chain uncertainty. Second, companies deploy tangible resources (supply chain redundancies, new supply chain assets) to lessen the negative outcomes of supply chain risks. Managers are provided with an empirical framework for mitigating supply chain uncertainty and risk originating from geopolitical disruptions

Data Warehousing Implementation and Outsourcing Challenges: An Action Research Project with Solectron

Since the 1980s, Solectron evolved as a contract provider of manufacturing, distribution, product design, and full-service supply chain integration to many Fortune 500 companies in the telecommunications, electronics, and computer industries. In order to achieve this set of capabilities, Solectron established a tightly coupled set of collaborative relationships with supply chain participants- including both suppliers and customers. This action research study explores Solectron\u27s data warehousing implementation and outsourcing experiences to support the organization\u27s strategic direction. We uncover the absence of new set of critical pre-implementation performance metrics for managers and researchers to consider under conditions of outsourcing; multiple, simultaneous projects; and lack of IT internal expertise. Thus, our research offers hypotheses that can be further tested by future research

OGSA first impressions: a case study re-engineering a scientific applicationwith the open grid services architecture

We present a case study of our experience re-engineeringa scientific application using the Open Grid Services Architecture(OGSA), a new specification for developing Gridapplications using web service technologies such as WSDLand SOAP. During the last decade, UCL?s Chemistry departmenthas developed a computational approach for predictingthe crystal structures of small molecules. However,each search involves running large iterations of computationallyexpensive calculations and currently takes a fewmonths to perform. Making use of early implementationsof the OGSA specification we have wrapped the Fortranbinaries into OGSI-compliant service interfaces to exposethe existing scientific application as a set of loosely coupledweb services. We show how the OGSA implementationfacilitates the distribution of such applications across alarge network, radically improving performance of the systemthrough parallel CPU capacity, coordinated resourcemanagement and automation of the computational process.We discuss the difficulties that we encountered turning Fortranexecutables into OGSA services and delivering a robust,scalable system. One unusual aspect of our approachis the way we transfer input and output data for the Fortrancodes. Instead of employing a file transfer service wetransform the XML encoded data in the SOAP message tonative file format, where possible using XSLT stylesheets.We also discuss a computational workflow service that enablesusers to distribute and manage parts of the computationalprocess across different clusters and administrativedomains. We examine how our experience re-engineeringthe polymorph prediction application led to this approachand to what extent our efforts have succeeded

BCAS: A Web-enabled and GIS-based Decision Support System for the Diagnosis and Treatment of Breast Cancer

For decades, geographical variations in cancer rates have been observed but the precise determinants of such geographic differences in breast cancer development are unclear. Various statistical models have been proposed. Applications of these models, however, require that the data be assembled from a variety of sources, converted into the statistical models’ parameters and delivered effectively to researchers and policy makers. A web-enabled and GIS-based system can be developed to provide the needed functionality. This article overviews the conceptual web-enabled and GIS-based system (BCAS), illustrates the system’s use in diagnosing and treating breast cancer and examines the potential benefits and implications for breast cancer research and practice

Comparative Study Of Implementing The On-Premises and Cloud Business Intelligence On Business Problems In a Multi-National Software Development Company

Internship Report presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceNowadays every enterprise wants to be competitive. In the last decade, the data volumes are increased dramatically. As each year data in the market increases, the ability to extract, analyze and manage the data become the backbone condition for the organization to be competitive. In this condition, organizations need to adapt their technologies to the new business reality in order to be competitive and provide new solutions that meet new requests. Business Intelligence by the main definition is the ability to extract analyze and manage the data through which an organization gain a competitive advantage. Before using this approach, it’s important to decide on which computing system it will base on, considering the volume of data, business context of the organization and technologies requirements of the market. In the last 10 years, the popularity of cloud computing increased and divided the computing Systems into On-Premises and cloud. The cloud benefits are based on providing scalability, availability and fewer costs. On another hand, traditional On-Premises provides independence of software configuration, control over data and high security. The final decision as to which computing paradigm to follow in the organization it’s not an easy task as well as depends on the business context of the organization, and the characteristics of the performance of the current On-Premises systems in business processes. In this case, Business Intelligence functions and requires in-depth analysis in order to understand if cloud computing technologies could better perform in those processes than traditional systems. The objective of this internship is to conduct a comparative study between 2 computing systems in Business Intelligence routine functions. The study will compare the On-Premises Business Intelligence Based on Oracle Architecture with Cloud Business Intelligence based on Google Cloud Services. A comparative study will be conducted through participation in activities and projects in the Business Intelligence department, of a company that develops software digital solutions to serve the telecommunications market for 12 months, as an internship student in the 2nd year of a master’s degree in Information Management, with a specialization in Knowledge Management and Business Intelligence at Nova Information Management School (NOVA IMS)

Strategic enterprise management systems : tools for the 21st century

https://egrove.olemiss.edu/aicpa_guides/1228/thumbnail.jp