ABSTRACT
Nowadays, manufacturing companies face more difficulties than ever. Unrest in global supply chains triggered by fluctuating customer demand, raw material shortages and crises (Covid pandemic, global warming, wars) complicate the utilization of production resources necessary for economic success. Also, the rapidly changing environment causes existing production plans to be adapted, which results in order changes, causing additional costs for manufacturers. One solution to cope with these problems is cooperation and sharing resources: requesting capacity from partners when having shortages and offering them temporarily in case of excess capacity. In this paper, a platform-based resource sharing mechanism is investigated from the economic perspective. In the mechanism, requests and offers are matched by a central platform applying a complex matching logic. The platform provides valid alternatives based on the incoming ordersthat the requesting company can choose from. Companies are rating each other's performance after each interaction based on delivery accuracy;choosing between resource offers is made based on the cumulated rating about the offeror and the price of the offer. Within this paper, the aim is to investigate the resource sharing mechanism from the economic point of view based on an approach to the responsiveness of a supply chain structure to turbulence, to support decision-makers trying to cope with unexpected changes. For this purpose, here the mechanism is briefly introduced, and basic concepts about turbulences in supply chains are also presented. Cost types related to resource sharing manufacturing companies are distinguished, and the model is validated with agent-based simulation. A simulation experiment is performed to investigate the use-case of outsourced jobs having different price levels. Based on the experiment, it can be concluded that there is a price level limit in such a resource sharing federation, under which it is worth it to collaborate with partners by outsourcing certain jobs to them. © 2022 Authors. All rights reserved.
ABSTRACT
The Covid-19 pandemic highlighted the need of managing complex relations within the healthcare ecosystem. The role of new technologies in achieving this goal is a topic of current interest. Among them, blockchain technology and Artificial Intelligence are gaining wide application in the healthcare context. The aim of this work is to investigate how these technologies foster value co-creation paths in healthcare ecosystems. The results show the direct and indirect impacts of blockchain adoption on value co-creation in terms of data and resource sharing, patient participation, and collaboration between professionals. Theoretical and practical implications are provided. © 2022 IEEE.
ABSTRACT
While industry tends towards mass personalisation and instant deliveries, the last mile of urban logistics is being challenged to decrease the number of vehicles in circulation and the distances they travel in city centres. The COVID-19 pandemic helped expose the inefficiency of cities in fulfilling citizens' real-time needs. Moreover, the first aim of this paper is to understand the barriers which policymakers face in providing a personalised response to citizens' needs and the second to ascertain how they can proactively serve their communities. In line with these concerns, empirical evidence was collected through a questionnaire to Portuguese policymakers, and the results were discussed in a focus group with experts. The results suggest a lack of tools to enable real-time visualisation and study of scenarios for implementing and organising means of delivery and storage. Moreover, although policymakers feel confident in their capacity to manage the last mile, they would struggle to operate autonomously. Therefore, this paper conceptualises an initial algorithm based on the dynamic collaboration of stakeholders and sharing of resources to guarantee fulfilment of citizens' necessities. Furthermore, future discussions shall emerge about the relationships and technical standards between stakeholders to provide the necessary logistical means for city management and operations.
ABSTRACT
This paper aims to present the proposal of a platform founded on a Virtual Breeding Environment (VBE) as an alternative for resource sharing, survival, and growth of organizations that were impacted by the economic recession caused by COVID-19. Considering this, an informational platform model is presented, based on the theoretical framework of VBE and the potential to meet the needs of actors who are part of this environment. The result is a platform called Collabore. This platform helps with resource sharing between companies and enables the development of new network technologies. It also facilitates the co-creation of value between actors, allows lobby creation to compete with large companies in the global market, ensures new jobs and income generation, and facilitates the collaboration between companies dispersed globally, connected by Information and Communication Technologies (ICTs). © 2022, IFIP International Federation for Information Processing.
ABSTRACT
Medical information systems (MIS) play a vital role in managing and scheduling medical resources to underpin healthcare services, which has become more critically important during major public health emergencies. During the Covid-19 pandemic, MIS is facing significant challenges to cope with the surge in demands of medical resources, resulting in more deaths and wider spreading of the disease. Our research examines how to allocate and utilize the medical resources across hospitals in a more accurate, and effective way to mitigate medical resource shortages and sustain the resource provisions. This paper mainly investigated the hospital's supply-and-demand problems for medical resources under major public health emergencies by analyzing the allocation of medical staff resources. Furthermore, a formal method based on the Colored Petri Nets (CPN) has been proposed to model and characterize the medical business process and resource scheduling tasks. The experiments demonstrate that our approach can correctly and efficiently complete the dynamical scheduling process for surging requests. © 2021 IEEE.