Enhanced time-constraint aware tasks scheduling mechanism based on predictive optimization for efficient load balancing in smart manufacturing

Smart manufacturing encompasses a category of manufacturing that employs computer-integrated capabilities and technologies to enhance supply chain optimization, production efficiency, and recyclability. Yet, limited studies have been conducted to optimize these prospects from the perspective of low-resource countries where such innovative studies have not been adequately explored. This paper aims to examine the implications of smart manufacturing practices (Smart Procurement, Smart Supply Chain, Smart Production Planning and Control, Automation and Industrial Robot, and Supply Chain Configuration) on pro-environmental behaviour and develop a baseline moderated mediation model to explain the relationship between smart manufacturing and pro-environmental behaviour as well as the indirect effects of environmental awareness and green dynamic capability. This study employs a quantitative research approach, utilizing inductive reasoning and an explanatory design. Data from 382 manufacturing enterprises in Ghana were collected through a cross-sectional survey. We tested our hypotheses using SMART-PLS software version 3.8.9 and SEM technique. The study found a strong and positive relationship between smart manufacturing practices and pro-environmental behaviour. Additionally, the relationship between smart manufacturing and pro-environmental behaviour is influenced by green dynamic capacity and environmental orientation. The study’s implications involve the creation of a fundamental model that can assist policy makers, practitioners, and academics in understanding the connection between smart manufacturing and sustainable production in developing nations. Again, the social implication of the study includes the realisation of decent job and economic growth, responsible consumption, and production as well as actions taken to combat climate change.

Energy efficiency is one of the key drivers for improving the efficiency of resource utilization in smart manufacturing. Within the manufacturing environments, the importance of energy efficiency has grown. However, in most cases the energy consumption of the manufacturing systems are considered using average energy consumption models or simple scheduling models for the needs of discrete event simulation. This paper researches on energy-efficiency scheduling based on the scenario of multi-tasks and multi-resources in smart manufacturing. The scheduling problem is analyzed and modeled as a mixed integer linear programming (MILP) problem with constraints, such as the feasibility of time domain or space, qualification and initial state. A solution of the mixed integer linear programming which optimized by a fast algorithm based on score ranking is designed under time of use (TOU) electricity pricing scheme in smart grid, in which the electricity price varies throughout a day. The feasibility of the method is verified by simulation case. Under the premise of ensuring the reasonable allocation of manufacturing tasks, the goal of smart manufacturing process and long-term energy optimization would be achieved.

In the competitive landscape of modern manufacturing, achieving optimal profit margins necessitates a balanced integration of cost management, strategic pricing, and production efficiency. Manufacturing firms operate in a dynamic environment where fluctuating raw material prices, labour costs, and technological advancements directly impact profitability. To remain sustainable and competitive, companies must adopt systematic approaches that enhance cost efficiency, optimize pricing models, and improve overall production processes. This research paper explores various methodologies and frameworks that manufacturers can employ to maximize profitability while maintaining operational effectiveness. Cost management techniques such as activity-based costing, target costing, and lean manufacturing principles are examined to highlight their role in reducing waste and improving efficiency. By integrating these methodologies, manufacturers can better allocate resources, streamline production, and achieve economies of scale. Moreover, leveraging automation, IoT-enabled predictive maintenance, and data analytics can further enhance cost control and operational agility. Pricing strategy plays a crucial role in profit margin optimization. While traditional cost-plus pricing ensures a predictable margin, dynamic pricing models and value-based pricing provide manufacturers with flexibility to adjust prices based on demand, competition, and customer perception. This paper delves into data-driven pricing mechanisms that enable businesses to maximize revenue while maintaining market competitiveness. Furthermore, penetration and skimming pricing strategies are discussed to illustrate their applicability across different market scenarios. Production efficiency is another key factor influencing profitability. The adoption of lean manufacturing, Six Sigma methodologies, and total quality management (TQM) has proven to enhance operational performance while minimizing defects and production downtime. This study provides an in-depth analysis of how firms can optimize supply chain logistics, enhance workforce productivity, and reduce bottlenecks in manufacturing processes. By implementing smart manufacturing techniques, companies can improve throughput, enhance product quality, and achieve just-in-time production goals. This study also examines real-world case studies and empirical data to illustrate successful applications of these strategies. For instance, the integration of AI-driven analytics in a leading automotive manufacturing company resulted in a 20% reduction in operational costs and a 15% improvement in productivity. Similarly, a consumer electronics firm implemented dynamic pricing algorithms to adapt to market fluctuations, leading to a 10% increase in revenue without additional marketing expenditure. Furthermore, emerging trends such as AI-driven analytics, smart manufacturing, and supply chain digitalization are examined to highlight their potential impact on future profit optimization. The role of Industry 4.0, blockchain technology in supply chain transparency, and automation in reducing human intervention are also discussed. These innovations are transforming the traditional manufacturing landscape and setting new benchmarks for efficiency and profitability.

Recently, along with several technological advancements in cyber-physical systems, the revolution of Industry 4.0 has brought in an emerging concept named digital twin (DT), which shows its potential to break the barrier between the physical and cyber space in smart manufacturing. However, it is still difficult to analyze and estimate the real-time structural and environmental parameters in terms of their dynamic changes in digital twinning, especially when facing detection tasks of multiple small objects from a large-scale scene with complex contexts in modern manufacturing environments. In this article, we focus on a small object detection model for DT, aiming to realize the dynamic synchronization between a physical manufacturing system and its virtual representation. Three significant elements, including equipment, product, and operator, are considered as the basic environmental parameters to represent and estimate the dynamic characteristics and real-time changes in building a generic DT system of smart manufacturing workshop. A hybrid deep neural network model, based on the integration of MobileNetv2, YOLOv4, and Openpose, is constructed to identify the real-time status from physical manufacturing environment to virtual space. A learning algorithm is then developed to realize the efficient multitype small object detection based on the feature integration and fusion from both shallow and deep layers, in order to facilitate the modeling, monitoring, and optimizing of the whole manufacturing process in the DT system. Experiments and evaluations conducted in three different use cases demonstrate the effectiveness and usefulness of our proposed method, which can achieve a higher detection accuracy for DT in smart manufacturing.

This empirical study, “Augmented Reality and Artificial Intelligence in Smart Manufacturing,” reveals how these two technologies are revolutionizing the manufacturing industry. The results, which are based on real data, highlight the significant effects of integrating AI and AR. Notably, after installation, productivity indicators saw an average improvement of 8.5% across production lines, highlighting the effectiveness of AR and AI in improving production operations. Furthermore, the average number of completed product faults dropped by 3.5, demonstrating the effectiveness of AI and AR in quality control. The average 47.5% decrease in repair requests highlights the predictive maintenance's potential for cost savings made possible by AR and AI. The relevance of AR and AI as critical factors influencing productivity, quality, and affordability in smart manufacturing is further supported by this empirical data.

Modern manufacturing systems are complex and heterogeneous distributed information systems that consist of manufacturing hardware and networking software. Unlike traditional manufacturing environments, modern ones generate large amounts of data in real-time that take the form of sensor measurements from the shopfloor, images from the product inspection, technical text from maintenance records, and many more. The issue of collecting, processing, and analyzing this data is at the core of the Industry 4.0 paradigm's success. What is more important, challenging, and rewarding is that such valuable real-time streaming data can be used for diagnostics, prognostics, and health monitoring of systems and products. This dissertation explores the area of health monitoring of modern manufacturing environments to support the state-of-the-art Industry 4.0 paradigms. We deploy Machine Learning (ML), Deep Learning (DL), and Natural Language Processing (NLP) based approaches to assess the performance of systems and products by identifying anomalies and defects, estimating the Remaining Useful Life (RUL), and determining what sort of corrective maintenance actions may be necessary. In the context of the foregoing, advanced manufacturing environments are very intricate and have a vast array of complex problems that can be researched. This dissertation focuses on four important questions: (1) what are the requirements to achieve interoperability in smart manufacturing? (2) how can the next hardware failure, be it of a machine tool or a cutting tool, be predicted before it occurs? (3) how can we identify the products that fail inspection, and how can we improve the overall quality inspection process by considering the factors affecting it? and (4) how can we leverage the resource-rich maintenance logs from manufacturing systems to find the most frequently occurring problems, and more importantly, how can we predict corrective actions for future maintenance problems? This dissertation addresses the above problems by making the following research contributions. In the first work, we discuss interoperability in manufacturing by reviewing semantic, syntactic, factory, and cloud-based interoperability. We then identify key research challenges that impact the implementation of interoperable manufacturing, and review frameworks for modern manufacturing such as Reference Architecture Model for Industry 4.0 (RAMI 4.0), Industrial Internet Reference Architecture (IIRA), and several others. The second part of the research proposes a framework for Smart Prognostics and Health Management (SPHM) to support manufacturing system health. We apply it to a milling machine operation and assess the condition of the milling machine's tool tip by identifying whether it is safe or degraded and estimate its RUL using ML models. To address the third question, we introduce a methodology for Smart Quality Inspection (SQI) of manufacturing products by using a custom Convolutional Neural Network (CNN) architecture. A shopfloor computer application is developed that helps minimize the effects of the factors affecting the visual inspection process. The fourth component of this dissertation introduces a Technical Language Processing (TLP) framework to deal with the unstructured and complex nature of technical text data from maintenance work orders. We demonstrate its capabilities by applying it to maintenance logs of aircraft to predict corrective actions, and link maintenance problems to standardized maintenance codes as prescribed by the industry. The rapid growth of Artificial Intelligence (AI) suggests that its potential to transform health monitoring capabilities is going to improve in the coming years. Future research in health monitoring of manufacturing systems and products can be aimed towards the following areas: development of standardized communication protocols for interoperability, further automation of the inspection process by integrating localized fault detection, and incorporation of TLP with predictive maintenance approaches and quality control to form a prescriptive approach, to suggest a few.--Author's abstract

The abilities of monitoring a manufacturing process in real-time and of generating insights from the acquired data are the fundamental pillars of Smart Manufacturing. This contribution proposes an architecture for data collection from field-level controllers, and for its subsequent visualization according to specific types of users. To achieve the former, KepserverEX has been used which acts as a bridge between the incoming data and further applications that operate on this data. MATLAB and its in-built app designer act as a client running on the same physical system, and provides users with visualization and insights from the gathered field data. OPC-UA, with its client–server model, has been used for communication between these two applications. The resulting MATLAB application is then deployed on Intranet, so as to enable access to authorized users. This general framework is applicable for different manufacturing environments, and one such example consisting of a Smart Lathe Machine has been described in this paper.KeywordsMATLABOPC-UAData visualizationReal-time monitoringKepserverEXIndustry 4.0Smart manufacturing

Artificial intelligence (AI), imitation learning, big data, cloud and distributed computing, robotics cells, and information communication technology, are some of the key tools and elements of the future digital and smart manufacturing facility. There are a number of challenges that digital and smart manufacturing is facing, especially with the complication of AI (i.e., machine, deep and cognitive learning) algorithms, great amount of data to process, and essential complex coding required, which makes immediate changes needed in manufacturing facilities not straightforward. This is notable in small manufacturing cells which is an integrated part of future smart factories such manufacturing facilities are usually needed some annual and regular updates to meet the update in the design specifications of next generation of products. Imitation learning is offering a great opportunity to overcome these challenges and simplify such complications, where human skills, ability to perform specific tasks, knowledge, and talent could be transferred. This is conveying the knowledge, and skills transfer using imitation learning. However, smart manufacturing and industrial revolution needs robotics cells that has skills beyond this, especially when it comes to process optimisation. Therefore, deep imitation learning could come in to help in the development of self-learning robotic systems and cells. Of course with the powerful tools such as distributed computing, blockchain, cloud computing, edge computing, and 5G the collaboration between such self-learning robotic cells will be possible. This will certainly not eliminate human existence but will enhance the manufacturing environment. This paper is focused on presenting the outcomes of CAD simulation and modelling phase of the ongoing research programme that focused on developing a self-learning robotic system using imitation learning. CAD tools have been used and some initial results is presented. Further work is still undertaken, and this will focus on learning from more than one expert, optimisation, impact of dynamic manufacturing environment.

Due to the rapidly increasing of the mobile devices connected to the internet, a lot of researches are being conducted to maximize the benefit of such integration. The main objective of this paper is to enhance the performance of the scheduling mechanism of the mobile computing environment by distributing some of the responsibilities of the access point among the available attached mobile devices. To this aim we investigate a scheduling mechanism framework that comprises an algorithm provides the mobile device with the authority to evaluate itself as a resource. The proposed mechanism is based on the proposing of “self ranking algorithm (SRA)” which provides a lifetime opportunity to reach a proper solution. This mechanism depends on event-based programming approach to start its execution in a pervasive computing environment. Using such mechanism will simplify the scheduling process by grouping the mobile devices according to their self -ranking value and assign tasks to these groups. Moreover, it will maximize the benefit of the mobile devices incorporated with the already existing grid systems by using their computational power as a subordinate value to the overall power of the system. Furthermore, we evaluate the performance of the investigated algorithm extensively, to show how it overcomes the connection stability problem of the mobile devices. Experimental results emphasized that, the proposed SRA has a great impact in reducing the total error and link utilization compared with the traditional mechanism.

Self-decision mechanisms of smart production systems based on improved uncertainty theory and user-CFA

The rapid evolution of manufacturing technologies under the Industry 4.0 paradigm has driven the need for efficient data management and real-time decision-making. The client-server architecture is Central to this transformation, enabling seamless communication, data sharing, and process automation across smart manufacturing environments. This literature review explores the pivotal role of client-server architecture and key communication protocols in enabling Smart Manufacturing and Industry 4.0 initiatives. The study examines the fundamental components of client-server architecture, including clients, servers, and communication protocols, and their interactions in industrial settings. It focuses on three primary protocols, MQTT, HTTP, and OPC UA, analyzing their mechanisms, benefits, and applications in smart manufacturing environments. The research highlights how MQTT’s lightweight, publish-subscribe model facilitates efficient real-time data exchange in IoT-heavy industrial networks. HTTP’s widespread adoption and versatility in handling diverse data types are discussed, emphasizing its role in web-based dashboards and cloud integration. The review also delves into OPC UA’s platform-independent nature and advanced features, which promote interoperability and future-proofing of manufacturing systems. This review synthesizes current knowledge of these technologies and underscores their collective importance in addressing critical requirements of smart manufacturing, including scalability, security, real-time capabilities, and cross-system compatibility. The findings suggest that integrating these communication protocols and architectures is crucial for realizing the full potential of Industry 4.0, paving the way for more agile, efficient, and data-driven production ecosystems.

Today, data scientists in the manufacturing domain are confronted with various communication standards, protocols and technologies to save and transfer various kinds of data. These circumstances makes it hard to understand, find, access and extract data needed for use case depended applications. One solution could be a data pipelining approach enforced by a semantic model which describes smart manufacturing assets itself and the access to their data along their life-cycle. Many research contributions in smart manufacturing already came out with with reference architectures like the RAMI 4.0 or standards for meta data description or asset classification. Our research builds upon these outcomes and introduces a semantic model based DIN Spec 91345 (RAMI 4.0) compliant data pipelining approach with the smart manufacturing domain as exemplary use case. This paper has a focus on the developed semantic model used to enable an easy data exploration, finding, access and extraction of data, compatible with various used communication standards, protocols and technologies used to save and transfer data.

The Industrial robots are widely used in smart manufacturing environments to improve productivity and automation, but their increasing energy consumption has become a critical issue. Traditional time-based control methods update control signals at fixed sampling intervals, which often leads to actuator redundancy and energy inefficiency, especially in steady-state or low-dynamic operating modes. To address this issue, this paper proposes an energy-efficient event-based control strategy for energy-efficient operation of industrial robots in smart manufacturing systems. The proposed approach integrates an event-based mechanism with nonlinear robot control so that control adjustments are made only when tracking accuracy or energy-related metrics exceed specified thresholds. This design significantly reduces unnecessary control signals while ensuring satisfactory trajectory tracking accuracy. A Lyapunov function-based analysis is performed to demonstrate the stability of the closed-loop control system using the proposed event-based method. Furthermore, the triggering mechanism guarantees a strictly positive lower bound for the intervals between events, thereby eliminating the Zeno effect and ensuring practical feasibility. The energy efficiency of the proposed strategy is further investigated by analyzing the behavior of the actuators and the overall energy consumption. Experimental results show that the proposed approach achieves significant energy savings compared to traditional time-based control methods without compromising control performance. This highlights its potential for energy-efficient operation of industrial robots in smart manufacturing applications.

The objective of this study is to analyze the export performance of the Thai agriculture products and manufacturing products to Japan, as well as examine some factors that impact on the expansion those major export items to the Japan. Six products among those major export products to Japan are identified as two categories, the agricultural products and manufacturing products which each categories have three products each ,Frozen shrimps, natural rubber and sugar are classified as agricultural products, while automatic data processing machines and parts thereof, belong to the manufacturing products. The statistical analysis, the Constant Market Share (CMS) Model and Revealed Comparative advantage (RCA) Index Model are the methods used to study the export performance of all these products. From the statistical analysis for the years 1989-1998, it can be seen that the structure of the Thai exports to Japan had trended upward but the value decreased during economic crisis period 1997-1998. The manufacturing products were most export to Japan The products had trended upward such as sugar and electronic integrated circuits and The products had trended downward such as frozen shrimps, natural rubber, automatic data processing machines and parts thereof, radio broadcast receives, television receives and parts thereof. The study under the CMS model was divided into two periods: 1995-1996 and 1997-1998 The overall picture shows that the expansion of Thai exports were reliant on the growth of the market and the demand for the products. Furthermore, only natural rubber, sugar and radio broadcast receives, television receives and parts thereof had comparative advantage. For the 1997- 1998 period, the expansion of the Thai exports was still reliant on the market growth. What’s more there was a fall in the demands for some products, and only automatic data processing machines and parts thereof and radio broadcast receives, television receives and parts thereof were in demand. There was also an increase in the Thai export competitiveness, especially in the products such as natural rubber, sugar, electronic integrated circuits and radio broadcast receives, television receives and parts thereof. By using the RCA model for the year 1995-1998, comparing the Thai exports to Chains Singapore, Malaysia Philippines and Indonesia, the results show that Thailand has the most comparative advantage in sugar, while frozen shrimps, natural rubber and radio broadcast receives, television receives and parts thereof have also comparative advantage. Thailand has on comparative advantage in automatic data processing machines and parts thereof, and electronic integrated circuits.

Carcinogen risk exposure within manufacturing environments is a major occupational health risk which could have long-term implications for workers. The basic methods of monitoring carcinogen exposure include periodic inspections and periodic sampling, which cannot ensure real-time detection and prevention risk. This article presents a novel carcinogen risk assessment framework to combine IoT-enabled AI sensors with predictive technology to potentially address this issue. The framework proposed here would continuously monitor environmentally hazardous conditions in manufacturing settings where carcinogens exist. It would reliably detect whether harmful chemicals, particulate matter, or gases which could pose carcinogenic risks are present. The hazard prediction-program would use best practice machine learning (ML) models to predict exposure levels before they reach potentially hazardous levels, enabling workers to take preventative action in order to avoid the hazard altogether. Subscriber data-stream from IoT sensors that can detect hazardous gases or particulate matter will be accessed via edge AI modules for primary analysis, while cloud-based predictive modeling (i.e., long short-term memory (LSTM) networks and randomized forests) will advise them about the nature of future levels of exposure and exposures. This framework has empirical evidence demonstrating reasonable accuracy in detecting hazardous conditions and only takes a matter of seconds to deliver an alert. Upon notification of hazardous exposure, immediate real-time action is deployed, including cutting production, ventilation control, etc. Findings from simulations demonstrate the system’s ability to detect risk at over 96% accuracy and predict exposure events 2 hours in advance. This gives managers not only increased worker safety, but environmental health compliance as well. In the future, utilizing predictive modeling with IoT technology will represent a real leap forward in smart manufacturing, creating a scalable solution for managing carcinogen exposure risks and protecting worker health in industrial settings.