Human Operator Research Articles

Note on the Fittingness of Negative Naming in Sacred Theology: The Corpus Dionysiacum and Father Bernard Lonergan, SJ1

AbstractWithin a broadly Thomistic frame, this paper shows how simple apophaticism in the theology of Pseudo‐Dionysius the Areopagite is the more fitting mode of knowing the triune God, beyond the use of all divine names. Specifically, we will proceed using the work of Father Bernard Lonergan, SJ on theological fittingness. After setting forth Father Lonergan's understanding of fittingness, the paper will proceed through Dionysius's cataphatic names, apophatic names, and apophatic silence. The cataphatic and apophatic names, while true, useful, and fittingly said of God, will be shown to be imperfectly applied to God. As such, their fittingness pales in comparison to the simple silence of the intellect orientated towards God, despite such pure apophaticism's dissimilarity to normal human intellectual operations.

Brain functional connectivity under teleoperation latency: a fNIRS study

IntroductionLong-distance robot teleoperation faces high latencies that pose cognitive challenges to human operators. Latency between command, execution, and feedback in teleoperation can impair performance and affect operators’ mental state. The neural underpinnings of these effects are not well understood.MethodsThis study aims to understand the cognitive impact of latency in teleoperation and the related mitigation methods, using functional Near-Infrared Spectroscopy (fNIRS) to analyze functional connectivity. A human subject experiment (n = 41) of a simulated remote robot manipulation task was performed. Three conditions were tested: no latency, with visual and haptic latency, with visual latency and no haptic latency. fNIRS and performance data were recorded and analyzed.ResultsThe presence of latency in teleoperation significantly increased functional connectivity within and between prefrontal and motor cortexes. Maintaining visual latency while providing real-time haptic feedback reduced the average functional connectivity in all cortical networks and showed a significantly different connectivity ratio within prefrontal and motor cortical networks. The performance results showed the worst performance in the all-delayed condition and best performance in no latency condition, which echoes the neural activity patterns.ConclusionThe study provides neurological evidence that latency in teleoperation increases cognitive load, anxiety, and challenges in motion planning and control. Real-time haptic feedback, however, positively influences neural pathways related to cognition, decision-making, and sensorimotor processes. This research can inform the design of ergonomic teleoperation systems that mitigate the effects of latency.

Task Allocation and Sequence Planning for Human–Robot Collaborative Disassembly of End-of-Life Products Using the Bees Algorithm

Remanufacturing, which benefits the environment and saves resources, is attracting increasing attention. Disassembly is arguably the most critical step in the remanufacturing of end-of-life (EoL) products. Human–robot collaborative disassembly as a flexible semi-automated approach can increase productivity and relieve people of tedious, laborious, and sometimes hazardous jobs. Task allocation in human–robot collaborative disassembly involves methodically assigning disassembly tasks to human operators or robots. However, the schemes for task allocation in recent studies have not been sufficiently refined and the issue of component placement after disassembly has not been fully addressed in recent studies. This paper presents a method of task allocation and sequence planning for human–robot collaborative disassembly of EoL products. The adopted criteria for human–robot disassembly task allocation are introduced. The disassembly of each component includes dismantling and placing. The performance of a disassembly plan is evaluated according to the time, cost, and utility value. A discrete Bees Algorithm using genetic operators is employed to optimise the generated human–robot collaborative disassembly solutions. The proposed task allocation and sequence planning method is validated in two case studies involving an electric motor and a power battery from an EoL vehicle. The results demonstrate the feasibility of the proposed method for planning and optimising human–robot collaborative disassembly solutions.

Robotic Warehousing Operations: A Learn-Then-Optimize Approach to Large-Scale Neighborhood Search

The rapid deployment of robotics technologies requires dedicated optimization algorithms to manage large fleets of autonomous agents. This paper supports robotic parts-to-picker operations in warehousing by optimizing order–workstation assignments, item–pod assignments, and the schedule of order fulfillment at workstations. The model maximizes throughput, managing human workload at the workstations and congestion in the facility. We solve it via large-scale neighborhood search with a novel learn-then-optimize approach to subproblem generation. The algorithm relies on an off-line machine learning procedure to predict objective improvements based on subproblem features and an online optimization model to generate a new subproblem at each iteration. In collaboration with Amazon Robotics, we show that our model and algorithm generate much stronger solutions for practical problems than state-of-the-art approaches. In particular, our solution enhances the utilization of robotic fleets by coordinating robotic tasks for human operators to pick multiple items at once and by coordinating robotic routes to avoid congestion in the facility. Funding: This research was partially funded by Amazon.com Services LLC under award number 2D-12552417. Supplemental Material: The online appendix is available at https://doi.org/10.1287/ijoo.2024.0033 .

The critical impact of remote pilot modelling in evaluation of detect-and-avoid systems explained for ACAS Xu

Detect-and-avoid (DAA) systems for remotely piloted aircraft systems (RPAS) can provide remain well clear (RWC) guidance as well as shorter term resolution advisories (RAs) for collision avoidance, which are both provided in the vertical and horizontal planes. Simulation-based studies for large sets of encounter scenarios are used in the development and evaluation of DAA systems, which encompass safety and operational acceptability of the DAA supported operations. Given the key role of the remote pilot (RP) in responding to RWC guidance and RAs, a RP model is an essential element in such simulations. This paper describes the development of a RP model for evaluation of encounter scenarios involving the ACAS Xu DAA system. The model describes RP situation awareness (perception, comprehension, projection) as basis for decision-making, modes for responding to RAs and/or RWC guidance, response delays, response strengths, and the flight control actions. The RP model includes deterministic and stochastic settings. It is integrated in a simulation environment for encounters of manned and/or unmanned aircraft, the involved DAA and airborne collision avoidance systems, the surveillance and communication systems, and the human operators. Simulation results are provided for a set of encounters between pairs of RPAS both having ACAS Xu for various configurations of the RP model, and for cases with and without sensor errors. The results show that there can be large differences between the results of deterministic and Monte Carlo simulations, indicating that limited sensor errors can have a large impact on the nonlinear system dynamics. Furthermore it is shown that deadlock conditions can exist where the RPAS show oscillatory behaviour and do not manage to effectively pass each other, dependent on the encounter geometry and RP model settings. It is advised to perform a broad sensitively study for RP performance and to study extending the scope of DAA systems to include guidance for efficiently returning to mission without triggering new conflicts.

Autonomous Intelligent Monitoring of Photovoltaic Systems: An In‐Depth Multidisciplinary Review

ABSTRACTThis study presents a comprehensive multidisciplinary review of autonomous monitoring and analysis of large‐scale photovoltaic (PV) power plants using enabling technologies, namely artificial intelligence (AI), machine learning (ML), deep learning (DL), internet of things (IoT), unmanned aerial vehicle (UAV), and big data analytics (BDA), aiming to automate the entire condition monitoring procedures of PV systems. Autonomous monitoring and analysis is a novel concept for integrating various techniques, devices, systems, and platforms to further enhance the accuracy of PV monitoring, thereby improving the performance, reliability, and service life of PV systems. This review article covers current trends, recent research paths and developments, and future perspectives of autonomous monitoring and analysis for PV power plants. Additionally, this study identifies the main barriers and research routes for the autonomous and smart condition monitoring of PV systems, to address the current and future challenges of enabling the PV terawatt (TW) transition. The holistic review of the literature shows that the field of autonomous monitoring and analysis of PV plants is rapidly growing and is capable to significantly improve the efficiency and reliability of PV systems. It can also have significant benefits for PV plant operators and maintenance staff, such as reducing the downtime and the need for human operators in maintenance tasks, as well as increasing the generated energy.

Population dynamics of sympatric Phortica spp. and first record of stable presence of Phortica oldenbergi in a Thelazia callipaeda-endemic area of Italy

BackgroundFive species of the Phortica genus (Diptera: Drosophilidae) are known in Europe and the Middle East. Among these, Phortica variegata and Phortica okadai are better known for their role as vectors of the zoonotic eyeworm Thelazia callipaeda. Other species, such as Phortica semivirgo and Phortica oldenbergi, have been studied less. Given the paucity of data about these Phortica spp. vectors, we explored the population dynamics and ecology of Phortica spp. in an area highly endemic for T. callipeada (Manziana, Rome, Central Italy).MethodsPhortica spp. flies were collected over a 3-year period (2018–2020) during their active season (April–October) with a sweep net while hovering around fermenting fruits or a human operator acting as baits. Collected flies were morphologically identified and tested for a T. callipeada infection and for the presence of Wolbachia, by polymerase chain reaction (PCR). Population dynamics of species collected was associated to environmental drivers through generalized additive models.ResultsOf the 5564 flies collected, 90.8% were P. variegata, 9.1% were P. oldenbergi, 0.05% were P. semivirgo, and one specimen was P. okadai. Only P. variegata scored molecularly infected with T. callipeada throughout the 3-year sampling period (1.8%). Phortica oldenbergi, observed consistently during the entire sampling period, exhibited a marked preference for fruit traps, contrasting with the lachryphagous activity of P. variegata. Analysis of environmental drivers of P. oldenbergi and P. variegata population dynamics indicated temperature, wind speed, and pressure as significant factors. In addition, Wolbachia pipientis endosymbiont was detected in P. oldenbergi and P. okadai.ConclusionsFor the first time, this study analysed several ecological aspects of Phortica species coexisting in a T. callipeada endemic area, highlighting different behaviors in the same environment and their vectorial role. Notably, this is also the first report of the presence of P. oldenbergi in Italy and P. okadai in Europe, underscoring the importance of extensive sampling for detecting potential vectors and alien species with direct implications for vector-borne disease epidemiology.Graphical

“I will raise my hand and say ‘I over-trust Autopilot’. I use it too liberally” – Drivers’ reflections on their use of partial driving automation, trust, and perceived safety

Introduction: Partially automated cars are on the road. Trust in automation and perceived safety are critical factors determining use of automation. Background: Drivers misuse partially automated driving systems. Misuse is associated with mis-calibrated trust in the automation. Research gap: Little is known about the factors impacting the perceived safety when using partial driving automation. Research objective: The main objective of the present study is to provide a comprehensive driver perspective on the psychological aspects of automation use pertaining to trust in automation, perceived safety, and its relationship with use of automation. Method: Semi-structured interviews (n = 103) were conducted with users of partially automated driving systems. Supplemented with content analysis, natural language processing (NLP) techniques were applied to perform automatic text processing. Guided seed-term analysis was conducted to identify the number of occurrences of the subcategories in the dataset. Main results: We identified human operator-related, automation-related, and environmental factors of trust and perceived safety. The identified factors were more strongly associated with perceived safety than with trust. Participants with physical and visual impairments reported to feel safer using the automation compared to driving manually. Neurotic behavior during manual driving contributed to lower trust and perceived safety using the automation. A correct mental model of the capabilities and limitations of the automation did not guarantee proper automation use. A novel conceptual, process-oriented model, titled PTS-a (predicting trust in and perceived safety of automation use), synthesizes the results of the data analysis. Informed by the cognition-leads-to-emotions approach, the model posits that trust as cognition precedes perceived safety as affective construct. Trust and perceived safety determine how human operators (mis-, dis-)use the automation. Future research: We recommend future research to perform experimental studies to identify cognitive-related thoughts and beliefs pertaining to trust in automation and perceived safety to contribute to the operationalization of these constructs, and unravel the nature of their relationship.

Unmanned aerial vehicle for magnetic detection of metallic landmines in military applications

Landmines significantly hinder the rapid movement of military forces and pose major obstacles during critical operations. Unmanned aerial vehicles (UAVs), commonly known as drones, offer several significant advantages for landmine detection. Most studies have tested drone-based systems at controlled sites and demonstrated their effectiveness in limited scenarios, with no examples of using these systems to detect landmines and relay the results to mine clearance machines (MCMs) for actual removal in real military operations. This study evaluated the use of UAVs equipped with magnetometers to detect metallic landmines in military applications. By conducting a series of controlled experiments, the research identified optimal flight conditions—2 m/s flight speed, 1 m survey interval, and 0.5 m sensor altitude—that balance accuracy and operational efficiency. The findings demonstrate that UAV-based magnetometer systems can significantly enhance mine clearance operations by providing near real-time data to MCMs. This approach offers a safer, faster, and more cost-effective alternative to traditional landmine detection methods by addressing the limitations of ground-based operations, such as high risk to human operators and inefficiency.

Normal Accident Theory and Learning from Major Accidents at the National Aeronautics and Space Administration (NASA).

A review of NASA’s accident history through the lens of Normal Accident Theory (NAT) offers insights into the prospects for safety during the Artemis Program. NAT is applied to NASA’s four major human spaceflight accidents: the Apollo launch pad fire, Apollo 13, Challenger and Columbia. NAT predicts that in the complex and tightly coupled systems used for human spaceflight, an accident type known as the “normal accident”, is inevitable. These accidents are attributable to hidden system interactions that overwhelm the cognitive abilities of human operators and, thereby, escape detection. A review of the four official accident reports and supporting secondary analyses suggests NASA has had one such accident: Apollo 13. It was also the only major accident without injury and a successful recovery effort. NAT would categorize the other three as component failure accidents. Unlike normal accidents, component failure accidents are not inevitable. They stem from an organization’s failure to appropriately monitor, assess and mitigate the risk associated with a faulty component. In such cases, NAT would ask if production pressures, often rooted in scheduling and resource constraints, were a factor.Production pressures are ultimately imposed on organizations through the exercise of power. These pressures impede an organization’s ability to adequately assess risk and increase the likelihood of component failure accidents. The substantial organizational literature on NASA’s human spaceflight accidents has yielded potential remedies to help mitigate the risks associated with production pressure. This paper discusses these remedies and their potential safety benefits. It also proposes a modification to a safety governance mechanism recommended by the Columbia Accident Investigation Board (CAIB). The resulting changes could help NASA, and other organizations administering high risk technologies, further improve safety.

NASA’s Top Human System Research and Technology Needs for Mars

NASA is working with industry and international partners to return humans to the Moon and to eventually enable humans to explore Mars. Within NASA, several organizations work together to identify, prioritize, fund, execute, and operationalize the research and technology development (R&TD) that will be necessary to enable crew health and performance (CHP) during these future missions. These organizations include flight programs, the Health and Medical Technical Authority (HMTA), the Human Research Program, the Space Technology Mission Directorate, System Capability Leadership Teams, and other organizations, many of which existed for several years prior to the creation of the Moon-to-Mars (M2M) Program Office in 2023. A variety of constructs, vocabularies, and processes exist for managing risks and supporting strategic planning across these organizations. For example, M2M objectives, program risks, human system risks, human research gaps, capability gaps, and envisioned futures are all constructs currently used within NASA to identify and prioritize R&TD needs. These strategic planning constructs are evolving to allow M2M objectives and R&TD investments to be aligned and traced at a detailed level. A recognized need exists among stakeholder organizations to identify and communicate the highest CHP R&TD priorities in a unified and digestible way that addresses the perspectives of NASA’s CHP community. To achieve this, the HMTA arranged a series of discussions with representatives of NASA’s CHP community, during which the 8 highest priority CHP capabilities that will enable human missions to Mars, referred to as the “top human system capability needs for Mars”, were identified. The list includes Earth-independent human operations; Mars-duration food system; Mars-duration effects on human physiology; risk mitigations for vehicle atmospheres; computational injury and anthropometric models; exploration exercise countermeasures; individual variability in responses to spaceflight; and sensorimotor countermeasures. Existing tools and processes for strategic planning and risk management were evaluated, as well as the technical practicalities, cost, and schedule feasibility associated with potential R&TD investments in different capability need areas. This capability needs report is not owned by any one NASA organization and does not replace existing strategic or program planning processes; rather it aims to complement and inform them with a unified set of community generated priorities. These top capability needs will be re-evaluated periodically based on R&TD progress and the evolving M2M architecture.

Devising a method for integrated dataset formation and selecting a model for recognizing the technical condition of unmanned aerial vehicle

The object of this study is the process of forming a training dataset for diagnosing the technical condition of unmanned aerial vehicles (UAVs) using machine-learning algorithms. UAV flights are extremely important for various aspects of troop deployment. Combat UAV flights are performed under the influence of negative factors that cause flight special cases (FSC), which hinder the execution of combat missions, lead to mission failures, and result in the aircraft damage or loss. The available capabilities of autopilots are not enough for control under complex conditions, and in certain situations, the human operator cannot timely recognize a flight special case, including evaluation of the destructive impact of enemy’s electronic warfare systems on communication channels and operation of UAV. Therefore, the urgent issue is the intellectualization of onboard control systems, particularly towards recognizing the current technical state of UAV using artificial intelligence methods. To design such systems, labeled datasets are required. The procedure for forming datasets that consider the specificity of UAV construction and their combat use under adversarial conditions is not defined, necessitating the development of an appropriate method. Based on the well-known CRISP-DM methodology, a method for dataset formation has been proposed for subsequent use in artificial intelligence systems that use various machine-learning methods. This method differs from existing ones by considering the specificity of combat mission execution under adversarial conditions, which allowed for an 8.0 % increase in the accuracy of recognizing special cases in UAV flights by the onboard system. It also enabled timely detection of electronic warfare impacts on UAV and evaluation of the effectiveness of radio signal receivers jamming

직장 내 갑질과 이직의도와의 영향 관계에서 회복탄력성의 조절효과 연구 - 호텔 F&B 종사원을 중심으로

Objective : This study tested the moderating effect of resilience in the relationship between workplace incivility and turnover intention by targeting hotel F&B employees. Methods : This study conducted a survey using an online questionnaire from June 1 to June 30, 2024. Then, this study conducted an empirical analysis using the SPSS statistical program on a total of 252 subjects. Results : The analysis results are as follows:First, workplace incivility was found to have a significant effect on turnover intention. Second, resilience (self-regulation ability, interpersonal ability, and positivity) had a moderating effect in the relationship between workplace incivility and turnover intention. Implications : Based on these results, this study attempted to provide basic data for efficient human resource management operation in hotel companies.

Smart automation in luxury leather shoe polishing: a human centric robotic approach

ABSTRACT The polishing of luxury leather shoes is a delicate, labor-intensive process traditionally performed by skilled craftsmen. Footwear companies aim to automate parts of this process to enhance quality, productivity and operator well-being, but the unique nature of luxury shoe production presents challenges. This paper introduces a solution involving a collaborative robotic cell to assist in shoe polishing. A collaborative robotic manipulator, equipped with a specialized tool and governed by force control, executes the polishing tasks. Key factors such as trajectory design, applied force, polishing speed and polish amount were analyzed. Polishing trajectories are designed using CAM software and transferred to the robot’s control system. Human operators design the process, supervise the robot and perform final finishing, ensuring their expertise is integral to achieving quality. Extensive testing on various shoe models showed significant improvements in quality and reliability, leading to successful implementation on an industrial production line.

A Robotic Teleoperation System with Integrated Augmented Reality and Digital Twin Technologies for Disassembling End-of-Life Batteries

Disassembly is a key step in remanufacturing, especially for end-of-life (EoL) products such as electric vehicle (EV) batteries, which are challenging to dismantle due to uncertainties in their condition and potential risks of fire, fumes, explosions, and electrical shock. To address these challenges, this paper presents a robotic teleoperation system that leverages augmented reality (AR) and digital twin (DT) technologies to enable a human operator to work away from the danger zone. By integrating AR and DTs, the system not only provides a real-time visual representation of the robot’s status but also enables remote control via gesture recognition. A bidirectional communication framework established within the system synchronises the virtual robot with its physical counterpart in an AR environment, which enhances the operator’s understanding of both the robot and task statuses. In the event of anomalies, the operator can interact with the virtual robot through intuitive gestures based on information displayed on the AR interface, thereby improving decision-making efficiency and operational safety. The application of this system is demonstrated through a case study involving the disassembly of a busbar from an EoL EV battery. Furthermore, the performance of the system in terms of task completion time and operator workload was evaluated and compared with that of AR-based control methods without informational cues and ‘smartpad’ controls. The findings indicate that the proposed system reduces operation time and enhances user experience, delivering its broad application potential in complex industrial settings.

Predicting hardwood porosity domains: Toward cascading computer-vision wood identification models

Prior work on computer-vision wood identification (CVWID) for North American hardwoods yielded two independent deep learning models – a 22-class model for diffuse-porous woods and a 17-class model for ring-porous woods – but did not address semi-ring-porous woods nor provide a CVWID solution for an unknown specimen without a human first determining which model to deploy. As untrained human operators would lack the anatomical proficiency to differentiate among porosity domains, it is necessary to develop a consolidated model that can identify diffuse-, ring-, and semi-ring-porous woods. Previous research suggests that prediction accuracy might decrease as class number grows. A potential strategy to reduce the number of classes a CVWID system must consider at a time is to hierarchically deploy a cascade of models. In pursuit of a unified model that can cover North American hardwoods of all porosity types, this study compared the accuracies of a consolidated 39-class (ring- + diffuse-porous) model and a consolidated 42-class (ring- + diffuse- + semi-ring-porous) model with a two-tiered, cascading model scheme whereby images are first differentiated into three porosity domain classes and then again into only those taxonomic classes with that porosity. The results showed that the cascading model scheme can mitigate the accuracy reductions incurred by the 42-class model and nearly eliminate the occurrence of cross-domain misidentifications.

2D Facial Analysis Performed by a Human Operator Versus an Artificial Intelligence Software

Background: Artificial intelligence (AI) has been a contribution in recent years to the development of new tools for dental, surgical, and esthetic treatment. In the case of image diagnosis, AI allows automated analysis of some facial parameters. The aim of this study was to evaluate the precision and reproducibility of these IA analyses compared with a human operator. Methods: Thirty subjects were selected, and frontal and profile photographs were taken of each. The photographs were previously calibrated and then analyzed by a human operator and an AI for vertical, horizontal, symmetry parameters, and facial profile angles. Results: Statistically significant differences were found in at least 1 vertical, horizontal, symmetry, and facial profile measurement. Conclusions: The 2D facial analysis performed on photographs using AI is an interesting and constantly evolving tool. However, there are still many differences with the analysis performed by the human operator, so it should be used carefully. Further studies using metric measurements should be performed to contrast the values obtained by human operators and AI and to analyze the accuracy of point selection in facial soft tissues.

Artificial Intelligence Approach to Business Process Re-Engineering the Information Flow of Warehouse Shipping Orders: An Italian Case Study

Artificial intelligence is revolutionizing and significantly shaping the work environment. It not only presents an innovative and unique challenge, but it also creates numerous opportunities in various sectors. This study aims to examine a case of artificial intelligence use in the logistics sector, specifically the implementation of text recognition algorithms that use advanced artificial intelligence technology to perform tasks and activities typically carried out by human operators. The implementation under analysis was carried out at an Italian company in Tolentino, in the province of Macerata. The implementation of this tool focuses on the ability to autonomously read shipping notes, with the aim of simplifying the flow of information in the goods receipt phase and reducing the workload of warehouse staff. As illustrated in the following chapters, when artificial intelligence follows several trainings, technical improvements and structural changes in shipping notes, the flow is greatly simplified, significantly facilitating this logistics phase.

Distributed intelligence in industrial and automotive cyber-physical systems: a review.

Cyber-physical systems (CPSs) are evolving from individual systems to collectives of systems that collaborate to achieve highly complex goals, realizing a cyber-physical system of systems (CPSoSs) approach. They are heterogeneous systems comprising various autonomous CPSs, each with unique performance capabilities, priorities, and pursued goals. In practice, there are significant challenges in the applicability and usability of CPSoSs that need to be addressed. The decentralization of CPSoSs assigns tasks to individual CPSs within the system of systems. All CPSs should harmonically pursue system-based achievements and collaborate to make system-of-system-based decisions and implement the CPSoS functionality. The automotive domain is transitioning to the system of systems approach, aiming to provide a series of emergent functionalities like traffic management, collaborative car fleet management, or large-scale automotive adaptation to the physical environment, thus providing significant environmental benefits and achieving significant societal impact. Similarly, large infrastructure domains are evolving into global, highly integrated cyber-physical systems of systems, covering all parts of the value chain. This survey provides a comprehensive review of current best practices in connected cyber-physical systems and investigates a dual-layer architecture entailing perception and behavioral components. The presented perception layer entails object detection, cooperative scene analysis, cooperative localization and path planning, and human-centric perception. The behavioral layer focuses on human-in-the-loop (HITL)-centric decision making and control, where the output of the perception layer assists the human operator in making decisions while monitoring the operator's state. Finally, an extended overview of digital twin (DT) paradigms is provided so as to simulate, realize, and optimize large-scale CPSoS ecosystems.

The Role of Technology and Automation in Streamlining Business Processes and Productivity for SMEs

Purpose: This research investigates the vital role of automation in enhancing business process efficiency and overall productivity within SMEs. It also discusses how technology can help overcome bottlenecks arising from human operation, resource limitation, and operational inefficiency, which have commonly been experienced by SMEs. Materials and Methods: The study combines an extensive review of existing literature with case studies from various industries to evaluate the effects of automation tools on SME operations. These tools range from cloud-based solutions to AI-powered systems, providing insights into their impact across different business processes. Findings: Research has shown that automation improves productivity in SMEs by up to 30%, reduces manual errors by 25%, and enhances data accuracy by improving employee focus on strategic tasks. In addition, the standardization of processes through automation ensures less variability in output. Automation also impacts customer experience positively by ensuring speed in service delivery and quality improvement in products. High upfront costs, however, combined with a shortage in skilled personnel and integrating new technologies with existing systems, remain barriers to wider adoption by SMEs. Implications to Theory, Practice and Policy: Accordingly, the study recommends that the following measures be taken to counteract the problems posed: phase in automation, cooperate with technology providers who will devise cost-effective solutions, and invest in comprehensive training programs to fill the skills gap. Government incentives, such as tax breaks and subsidies, could also push SMEs toward broader automation. Thereafter, successful integration of automation will enhance the competitiveness of SMEs and create a sustainable growth path in the digital economy.