Facets of Work: Enriching the Description, Analysis, Design, and Evaluation of Systems in Organizations

This conceptual contribution introduces the idea of “facets of work” and explains how it can be applied to challenges in today’s IS discipline. The notion of facets of work emerged from earlier attempts to bring more knowledge and richer, more evocative ideas to systems analysis and design (SA&D). Focusing on facets of work when initially discussing requirements could provide guidance without jumping prematurely to details, precision, and formal notation needed for producing testable software. This paper defines facet of work, identifies underlying assumptions and criteria, and uses three examples to illustrate how 18 facets of work can illuminate different aspects of situations that are amenable to discussion as systems. Potential applications of facets of work include supporting SA&D, supporting empirical research, visualizing multiple aspects of digitalization, and identifying some of the knowledge in a body of knowledge for IS. Six lengthy tables in the Appendix identify concepts associated with each facet, evaluation criteria, design trade-offs, sub-facets, and other details that are potentially useful as the basis of tools, methods, and future research

Improvising Safety and Energy Efficiency of IoT based Networks Data Routing

The Internet of Things is also referred to as IoT, outlines the physical object networking which is comprised of sensors, software and other associated technologies and technical tools in order to connect and exchange data over the internet with other devices and systems. The IoT devices range from household to industrial tools. Over the years, one of the most emerging technologies of the 21st century is IoT as it plays a huge role in sophisticated industries to smart application such as cars, household appliances and many more. With the implementation of IoT, people can take an advantage of seamless communication between other people, processes as well as things. Without human intervention, data having key information can be gathered by different means such as computing, cloud, big data, and associated mobile technologies. This paper focuses on making an IOT based network’s data routine safer and more energy efficient

Project Management in the Era of Artificial Intelligence

This study discusses the advantages of AI integration in project management, specifically in areas such as resource allocation, decision-making, risk management, and planning. By interpreting vast amounts of data from various sources, AI provides project managers with valuable insights to make better decisions. Although some tasks can be automated, human intervention is necessary for accuracy and efficacy. Therefore, AI should complement human skills, not replace them. Project managers require analytics skills and stay updated on AI technology to integrate it effectively. Ultimately, this study highlights that AI integration can enhance productivity and efficient project delivery

Navigating the Cloud: An In-Depth Exploration of HISA Load Balancing for Dynamic Task Appropriation

In a cloud computing (CC) environs,job exhibit variations in durations, start times, and execution times when assigned to virtual machines (VMs). Therefore, achieving load balancing (LB) across these VMs becomes crucial to optimize system proficiency and presentation. The present research introduces a novel LB method leveraging two optimization algorithms to address VM load balancing challenges. The initiated Dynamic Improved HISA Load Balancing proposal integrates an augment harmony-inspired algorithm with a simulated annealing algorithm for dynamic task allocation.In the harmony-inspired algorithm, an improved strategy for calculating Harmony Memory Consideration Rate (HMCR) is employed through a linear decreasing approach, updating HMCR and Pitch Adjustment Rate (PAR) values dynamically. A threshold probability is then evaluated to determine the finest suitability of the current Harmony, choosing eachof the make better harmony-inspired algorithm or simulated annealing for task allocation across available cloud resources.Simulations are conducted using the CloudSim simulator, considering scenarios with 3 or 5 VMs and 10 to 50 cloudlets. Each scenario is tested five times under operational conditions, and only the best performance outcomes are reported. Experimental results specify such a initiated Dynamic Enhanced HISA-LB proposal outperforms the prevail LBMPSO approach, demonstrating either minimized makespan or enhanced resource utilization with increased performance

How Facets of Work Illuminate Sociotechnical Challenges of Industry 5.0

This conceptual contribution explains how the idea of “facets of work” can refocus traditional sociotechnical concerns to increase their relevance in increasingly automated and digitalized workplaces far removed from situations studied by early sociotechnical researchers. A background section summarizes how the sociotechnical approach seems pervasive but possibly outdated in some ways. It explains how the idea of “facets of work” emerged from attempting to bring richer, more evocative ide-as to systems analysis and design. Focusing on facets of work during initial discussions of requirements could provide guidance without jumping prematurely to precision and notation needed for producing technical artifacts. Tables with one row for each of 18 facets or one row for the first 9 (reflect-ing length restrictions) illustrates that the 18 facets 1) point to areas where the coexistence of people and robots in workplaces poses challenging sociotechnical issues, 2) apply to both sociotechnical and totally automated systems, 3) are associated with specific sets of concepts, 4) bring evaluation criteria and design trade-offs, 5) have useful sub-facets, and 6) imply open-ended questions for starting discussions. The conclusion summarizes this paper’s contribution to understanding challenges of Industry 5.0 and discusses next steps in developing and applying its ideas

Block Chain Technology Assisted Privacy Preserving Resource Allocation Scheme for Internet of Things Based Cloud Computing

Resource scheduling in cloud environments is a complex task, as it involves allocating suitable resources based on Quality of Service (QoS) requirements. Existing resource allocation policies face challenges due to resource dispersion, heterogeneity, and uncertainty. In this research, the authors propose a novel approach called Quasi-Oppositional Artificial Jellyfish Optimization Algorithm (QO-AJFOA) for resource scheduling in cloud computing (CC) environments. The QO-AJFOA model aims to optimize the allocation of computing power and bandwidth resources in servers, with the goal of maximizing long-term utility. The technique combines quasi-oppositional based learning (QOBL) with traditional AJFOA. Additionally, a blockchain-assisted Smart Contract protocol is used to distribute resource allocation, ensuring agreement on wireless channel utilization. Experimental validation of the QO-AJFOA technique demonstrates its promising performance compared to recent models, as tested with varying numbers of tasks and iterations. The proposed approach addresses the challenges of resource scheduling in cloud environments and contributes to the existing literature on resource allocation policies

An Integrative Decision Support Model for Smart Agriculture Based on Internet of Things and Machine Learning

The Internet of Things (IoT) has achieved an upset in a considerable lot of the circles of our current lives, like automobile, medical services offices, home automation, retail, ed-ucation, manufacturing, and many more. The Agriculture and Farming ventures signifi-cantly affect the acquaintance of the IoT with the world. Machine learning (ML) is a part of artificial intelligence (AI) that permits software applications to turn out to be more precise at foreseeing results without being expressly customized to do as such. It uses historical data as input to predict new result values. In the event, a specific industry has sufficient recorded information to help the machine "learn", AI or ML can create out-standing outcomes. Farming is likewise one such important industry profiting and ad-vancing from machine learning at large. ML can possibly add to the total lifecycle of farming, at all phases. This incorporates computer vision, automated irrigation, and harvesting, predicting the soil, weather, temperature, moisture values, and robots for picking off the crude harvest. In this paper, I'll work on a smart agricultural information monitoring framework that gathers the necessary information from the IoT sensors set in the field, measures it, and drives it, from where it streams to store in the cloud space. The information is then shipped off the prediction module where the necessary analysis is done using ML algorithms and afterward sent to the UI for its corresponding applica-tion

Improving the performance of a SME in the cutlery sector using lean thinking and digital transformation

The main purpose of this paper is to show that if three specific contextual factors are present in a company, it is possible to achieve great performance improvements with a lean and industry 4.0 implementation. In terms of research methods, a case study was carried out of a project to implement digitalization and Lean practices in a cutlery company, which in fact encompassed a project of master’s degree in engineering and industrial management. Thus, the research question is: “It is possible to achieve major improvements in a lean and industry 4.0 implementation if three specific contextual factors are present in the company, namely (i) commitment of top management, (ii) knowledge on digitalization and lean, and (iii) very low Value-Added Ratio?”. Regarding the company project, action-research was adopted, and the project team began by mapping and diagnosing the production processes of the two product families (knives and spoons/forks). High levels of work in process, long throughput times, poor flow planning and control, and high stocks of finished products, quickly stood out in both families. Improvement proposals were developed and implemented, namely: (i) creation of a production scheduling and control system, (ii) improvement of the warehouse stock management system, and (iii) adoption of new routines, management tools, visual management, and kaizen meetings. The results achieved were excellent (e.g., throughput time reduced by 27.6% and productivity increased by 36.5%) and aligned with Sustainable Development Goals SDG 9 and 12. The findings of this study corroborate that exceptional results in the company performance can be achieved through a lean and industry 4.0 intervention, if the three referred contextual factors occur.This work has been supported by FCT—Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020

Box-Behnken Design for Optimization on Biodiesel Production from Palm Oil and Methyl Acetate using Ultrasound Assisted Interesterification Method

Energy demand is currently increasing in line with technological and economic developments, but not accompanied by an increase in energy reserves. So we need another alternative energy that can be renewed, namely biodiesel. Biodiesel has been produced commercially through the transesterification from vegetable oil with methanol using catalyst that produces esters and glycerol. The formation of glycerol which is by-product can reduce its economic value, so it needs to be done the separation process. Therefore, a new route is proposed in this study, namely the interesterification reaction (non-alcoholic route) using methyl acetate as an alkyl group supplier and potassium methoxide catalyst. The superiority of the product produced by the interesterification reaction is biodiesel with triacetin byproducts which have an economical value and can be added to biodiesel formulations because of their solubility so that no side product separation process is needed. To increase the yield of biodiesel and the interesterification rate, the ultrasound method was used in this study. To optimize the factors that affect the interesterification reaction (molar ratio of methyl acetate to oil, catalyst concentration, temperature, and interesterification time), the Box-Behnken design (BBD) is used. Optimal operating conditions to produce the yields of biodiesel of 98.64 % are at molar ratio of methyl acetate to palm oil of 18.74, catalyst concentration of 1.24 %, temperature of 57.84 °C, and interesterification time of 12.69 minutes