Figure 7 - available from: Journal of Intelligent Manufacturing
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The Smart Factory is an important topic worldwide as a means for achieving Industry 4.0 in the manufacturing domain. Contemporary research on the Smart Factory has been concerned with application of the so-called Internet of Things (IoT) to the shop floor. However, IoT in this context is often restricted to solving local problems such as managing p...
Citations
... One of the main challenges in implementing data-based decision support through PQ is the pre-processing and integration of diverse data sources (Groggert (Wang 2017). Common data management methods, such as Data Warehouse (Bauer and Günzel 2013) and Smart Factory Information (Yoon et al. 2019), mostly consider the technical implementation rather than a clear structure for the data which is needed for PQ applications. This results in the necessity of a data model with a comprehensive data structure. ...
In short-term production management of the Internet of Production (IoP) the vision of a Production Control Center is pursued, in which interlinked decision-support applications contribute to increasing decision-making quality and speed. The applications developed focus in particular on use cases near the shop floor with an emphasis on the key topics of production planning and control, production system configuration, and quality control loops.
Within the Predictive Quality application, predictive models are used to derive insights from production data and subsequently improve the process- and product-related quality as well as enable automated Root Cause Analysis . The Parameter Prediction application uses invertible neural networks to predict process parameters that can be used to produce components with desired quality properties. The application Production Scheduling investigates the feasibility of applying reinforcement learning to common scheduling tasks in production and compares the performance of trained reinforcement learning agents to traditional methods. In the two applications Deviation Detection and Process Analyzer, the potentials of process mining in the context of production management are investigated. While the Deviation Detection application is designed toidentify and mitigate performance and compliance deviations in production systems, the Process Analyzer concept enables the semi-automated detection of weaknesses in business and production processes utilizing event logs.
With regard to the overall vision of the IoP, the developed applications contribute significantly to the intended interdisciplinary of production and information technology. For example, application-specific digital shadows are drafted based on the ongoing research work, and the applications are prototypically embedded in the IoP.
... The emergence of the digital transformation enables us to detect information on the shop floor within the SC network [14]. It is feasible to increase the overall factory's efficiency by evaluating the obtained data through simulations [23]. Industries, like manufacturers, are attempting to visualize the status of the SC network at a glance using dashboards [21]; however, a feasible dashboard system to monitor the status of the whole supply chain network remains undiscovered. ...
... Smart manufacturing research has become popular recently. The introduction of the Internet of Things helps use information regarding production status, analysis, and different levels of stakeholders, such as machines, factories, and Enterprise Resource Planning; however, Industry 4.0 can bring networking, visualization, and automation to monitor resources, manage industrial lines, and assist with auto-set ups [23]. Inventory management is a perennial issue in the manufacturing industry, and a method to prevent the bull-whip effect has been researched [5]. ...
... Kim et al. (2015) treated the digital thread for AM as 'a set of the interconnected manufacturing process, end to end: from scan or design to analysis and simulation, through build planning and fabrication, to end use of the part, all connected in a series of feedback and feed-forward loops. Due to the smart factory revolution (Yoon et al. 2016), the Industry 4.0 concept (Lee, Bagheri, and Kao 2015), including cyber-physical systems, the Internet of things (IoT) and cloud computing, enables the surge of numerical data exchange in various manufacturing projects (Lu and Xun 2017). A data-intensive digital chain can lead to redundancy and loss of information, increasing the duration and cost of a manufacturing project (Bonnard and Hascoet 2017). ...
... Due to the various sources, there are a variety of formats and data types (Wang 2017). Common data management methods, such as Data Warehouse (Bauer and Günzel 2013) and Smart Factory Information (Yoon et al. 2019), ...
In short-term production management of the Internet of Production (IoP) the vision of a Production Control Center is pursued, in which interlinked decision-support applications contribute to increasing decision-making quality and speed. The applications developed focus in particular on use cases near the shop floor with an emphasis on the key topics of production planning and control, production system configuration, and quality control loops.
Within the Predictive Quality application, predictive models are used to derive insights from production data and subsequently improve the process- and product-related quality as well as enable automated Root Cause Analysis . The Parameter Prediction application uses invertible neural networks to predict process parameters that can be used to produce components with desired quality properties. The application Production Scheduling investigates the feasibility of applying reinforcement learning to common scheduling tasks in production and compares the performance of trained reinforcement learning agents to traditional methods. In the two applications Deviation Detection and Process Analyzer, the potentials of process mining in the context of production management are investigated. While the Deviation Detection application is designed to identify and mitigate performance and compliance deviations in production systems, the Process Analyzer concept enables the semi-automated detection of weaknesses in business and production processes utilizing event logs.
With regard to the overall vision of the IoP, the developed applications contribute significantly to the intended interdisciplinary of production and information technology. For example, application-specific digital shadows are drafted based on the ongoing research work, and the applications are prototypically embedded in the IoP.
... Among them, the label system is the importance label related to the identification of customers and products. For example, the importance labels defined for customers include: basic information, identity information, customer status information, potential customer stage information, purchase intention information, consumption information, online interaction information (Cheng et al., 2016;Yoon et al., 2019). In this way, the basic and important information of customers can be better obtained, and it can pave the way for analyzing the monitoring of customers' needs. ...
In the Internet era, the industrial Internet platform has become a bridge between customers and enterprises. In addition, with the continuous improvement of customers’ living standards, customers not only meet the basic needs for the functionality of products, but also yearn to participate in the production process of products and interactive experience activities with enterprises while pursuing high quality. In view of the difficulties in identifying and capturing the dynamically changing needs of customer- product interaction whole life cycle under the new environment of industrial Internet, and the problem of large amount and redundancy of demand data, this paper develops a set of models and methods covering the identification, definition, acquisition and parsing of customer needs, which provides theoretical support and decision-making basis for enterprises to accurately capture and predict customer needs, excavates potential market customers of enterprises, and realizes value-added of the value chain. Moreover, the methodology of personalized requirements for CILC proposed in this paper is helpful to the transformation of enterprise manufacturing model from mass manufacturing to mass customization and even to mass personalization. It can reduce enterprise production costs, and improve customer satisfaction and service experience.
... STs work cooperatively with conventional information technology systems: enterprise resource planning (ERP), computer-aided process planning and product data management/product lifecycle management, as well as with traditional automation systems: programmable logic controllers and supervisory control and data acquisition systems (Lu and Ju, 2017). A smart manufacturing factory entails smart products (intelligent service), smart data (big data and machine learning) and smart operation (human-technology integration), all of which are hosted on highly secured cloud infrastructures (Yoon et al., 2019). The textile sector embraces smart technologies as well. ...
Purpose
Manufacturing small and medium-sized enterprises (SMEs) are heading towards smart manufacturing despite growing challenges caused by globalisation and rapid technological advancement. These SMEs, particularly textile SMEs of Bangladesh, also face challenges in implementing sustainability and organisational ambidexterity (OA) due to resource constraints and limitations of conventional leadership styles. Adopting paradoxical leadership (PL) and entrepreneurial bricolage (EB) is important to overcome the challenges. However, these dynamics are less explored in academia, especially in the Bangladeshi textile SMEs context. Hence, the purpose of this study is to investigate the influence of the adoption of smart technologies (ASTs), PL and OA, EB on sustainable performance (SP) of textile SMEs in Bangladesh.
Design/methodology/approach
A cross-sectional and primary quantitative survey was conducted. Data from 361 textile SMEs were collected using a structured self-administrated questionnaire and analysed by partial least square structural equation modelling (PLS-SEM).
Findings
The statistical outcome confirms that ASTs and PL significantly influence SP and OA. OA plays a significant mediating role for PL and is insignificant for ASTs, and EB significantly moderates among ASTs, PL and SP.
Research limitations/implications
As this study is cross-sectional and focussed on a single city (Dhaka, Bangladesh), conducting longitudinal studies and considering other parts of the country can provide exciting findings.
Practical implications
This research provides valuable insights for policymakers, management and textile SMEs in developing and developed countries. By adopting unique and innovative OA, PL and EB approaches, manufacturing SMEs, especially textile companies, can be more sustainable.
Originality/value
This study has a novel, pioneering contribution, as it empirically validates the role of multiple constructs such as AST, PL, OA and EB towards SP in the context of textile SMEs in a developing country like Bangladesh.
... At the same time, through intelligent services, SIPs guide enterprises in the park to use high technology, and promote the development of the industry [60,76]. Yoon pointed out that it is necessary to actively use information technology to cover the entire manufacturing system, and to use the IoT, big data, cloud computing, and other technologies to empower the manufacturing system, which has become a research hotspot [77]. For the other three dimensions, intelligent facilities improve the management efficiency of the park, the green and low-carbon dimension refers to society's expectation of low-carbon energy conservation in the SIPs under the situation of increasingly serious environmental problems, and the operational benefits dimension is a measurement of the operation effect of SIPs [78]. ...
The intelligent development of smart industrial parks (SIPs) can not only promote the development of smart cities, but also promote the development of intelligent large-scale buildings. China is strengthening the construction of SIPs; however, the development of SIPs is limited. Due to different understandings of SIPs, the intelligence level of each SIP varies greatly. It is necessary to develop a SIP intelligence level assessment model to check these limitations. Most of the existing evaluations focus on the qualitative evaluation of the overall intelligence level of SIPs, ignoring the influence of each individual dimension. Therefore, this study used quantitative methods to measure the intelligence level of SIPs from the overall and dimensional levels. The evaluation method included five processes: (1) Classifying the intelligence level of SIPs through expert interviews; (2) Using the literature analysis method to identify various dimensions of the intelligence level; (3) Using literature analysis and expert interviews to determine the evaluation indicators (4) Weighting indicators based on correlation and induced ordered weighted average (IOWA) operator; (5) Using grey clustering analysis to calculate the overall intelligence performance of SIPs and each dimension. Finally, the developed model was verified by Z SIP. The analysis results show that the developed model can measure both overall and dimensional performance of SIPs, and demonstrated that enterprise information services, public information services, SIP security, and energy consumption monitoring platform construction make the greatest contributions to the improvement of the intelligence level. Our research results will help to improve the intelligence level of SIPs, and lay the foundation for the determination of the operating costs of SIPs and the formulation of national standards related to SIPs in the future.
... These paradigms have shifted the focus of the nowadays industry from the machine-based operations to the intelligent ones within the context of smart factory (Yoon et al., 2019) and smart manufacturing (Lu et al., 2020). To better support this transition, digitalized manufacturing systems (Mourtzis et al., 2019) were developed. ...
The implementation of smart manufacturing devices in the manufacturing industry has provided an effective and intelligent way to monitor their facilities and thus control their integrated manufacturing processes. Despite the ongoing technological advancements, the same industry is often involved with complex problems that are associated with increasing costs related to the persistent deterioration of the manufacturing/remanufacturing systems. In literature, these systems are often called “circular manufacturing systems”. Furthermore, this deterioration affects the quality of the manufactured products. To address these problems, a novel integrated design and operation management-based framework is introduced in order to obtain joint policies for the authorization of production, recycling, maintenance and remanufacturing activities in the context of deteriorating circular manufacturing systems. This framework utilizes a reinforcement learning technique and ad-hoc production control policies, such as Base Stock and CONWIP, in an effort to improve the flexibility and the resilience of the examined systems to the ever-changing customer demand. Finally, a series of simulation experiments evaluate the behavior and the efficiency of the proposed mechanism within the context of single-stage and 2-stage deteriorating manufacturing/remanufacturing systems. Results suggest that the presented approach can effectively generate sufficient inventory of ready-to-be-sold products due to the enhanced awareness of the ongoing customer demand compared to the traditional reinforcement learning joint control.
... R&D provides a better model-based platform for defining a relationship between architecture and requirements. WSDL (Web Description Services Language) is used for web-based requirements [24]. ...
Software Architecture describes system components and their connections. Requirement elicitation catering the perspective of software architecture is quite challenging and relatively less explored research area for the rapid software development. It has gain growing interest due to reusability of existing modules with less cost and quick developmental time. Software architecture in the context of requirement engineering is an abstraction of software system performing a particular task with the help of group of executable architectural components. In this paper, systematic literature review is adapted as a methodology to explore software architectural elements that provides better performance and simplicity in requirement engineering. We analyzed, reviewed and listed the strategies, tools & techniques along with state-of-the-art mechanisms, pros and cons and application areas. Architectural components that are already implemented in the requirement elicitation process for effective software architectural design are briefly analyzed. Purpose of the paper is to explore and discuss the elements that make software architecture more integral and flexible for traceability of requirements. Another purpose is to identify relation between the software requirements and architecture along with exploring the components to bridge gap between requirements and architecture by critically evaluating industrially and academically proposed methods, tools and frameworks. We also highlighted the open research challenges of Software architecture in requirement elicitation for better software development. In the later section, a resource bank is created acting as a valuable model that encompasses targeted relevant groups, sub-groups with latest software architecture tools & techniques, methods and framework sources to facilitate effective requirement engineering.
... The importance of stepwise starting from a small scale were argued [36,37]. The new technology application in a limited area were demonstrated by building a smart factory [38] Existing studies introduced different models for the smart factory model. Reference [39] proposed a human-centered model, [40] suggested IoT-based, [41] proposed IoT and cloud computing, and [35] proposed a cloud-based control system as smart control systems. ...
... As suggested in previous studies, starting on a small scale [36,37] or limited area [38] seems to be an effective way to minimize risk, but there may be situations in which it is stopped or scaled down due to various obstacles in the process. Procedures for smart conversion of specific tasks are limited in their application of transforming the entire plant. ...
The conceptualization and framework of smart factories have been intensively studied in previous studies, and the extension to various business areas has been suggested as a future research direction. This paper proposes a method for extending the smart factory concept in the ship building phase to the ship servicing phase through actual examples. In order to expand the study, we identified the differences between manufacturing and maintenance. We proposed a smart transformation procedure, framework, and architecture of a smart maintenance factory. The transformation was a large-scale operation for the entire factory beyond simply applying a single process or specific technology. The transformations were presented through a vessel maintenance depot case and the effects of improvements were discussed.
