Review of semantic modelling technologies in support of virtual factory design

Comprehensive data models and standardised terminologies are required across tools and their developers to rapidly design and prototype digitised virtual factories. Also, various types of software are required to be integrated for cost-effective modelling exercises. To meet this requirement, semantic modelling technologies involving ontologies have featured prominently in many manufacturing applications. This paper considers, extensively, the current semantic requirements for digitised virtual factory design and prototyping and compares them with the capabilities of existing semantic technologies. As a result, a review of methodologies, tools and languages for creating ontologies has been reported. The review concludes with the next generation requirements for methodologies, tools and languages for creating ontologies suitable for the virtual factory design process, and further explains ongoing work associated with the creation of ontologies and reasoning mechanisms through the integrated use of ObjectLogic, OntoStudio and OntoBroker.     

Recommender systems using cluster-indexing collaborative filtering and social data analytics

As a result of the extensive variety of products available in e-commerce settings during the last decade, recommender systems have been highlighted as a means of mitigating the problem of information overload. Collaborative filtering (CF) is the most widely used algorithm to build such systems, and improving the predictive accuracy of CF-based recommender systems has been a major research challenge. This research aims to improve the prediction accuracy of CF by incorporating social network analysis (SNA) and clustering techniques. Our proposed model identifies the most influential people in an online social network by SNA and then conducts clustering analysis using these people as initial centroids (cluster centres). Finally, the model makes recommendations using cluster-indexing CF based on the clustering outcomes. In this step, our model adjusts the effect of neighbours in the same cluster as the target user to improve prediction accuracy by reflecting hidden information about his or her social community. The experimental results indicate that the proposed model outperforms other comparison models, including conventional CF, with statistical significance.     

Manufacturing process analysis with support of workflow modelling and simulation

Process analysis is recognized as a major stage in business process reengineering that has developed over the last two decades. Manufacturing process analysis (MPA) is defined as performance analysis of the production process. A manufacturing process analysis framework is outlined with emphasis on linking a company's strategy to operational process. Two issues, namely process modelling and simulation based analysis, are investigated. A compound workflow model (CWM) is proposed to provide graphic presentation of the production process that can be easily understood. Also it can be used directly by simulation to study the impacts of scheduling policy and analyse the process performance. A two-stage simulation analysis method is provided to quantitatively and efficiently define cause-and-effect relations to identify drivers for improvement. The manufacturing environment, PSC (production planning, scheduling and control) factors and the process structure are three main concerns considered in the simulation. An example is discussed in the final part of the paper.     

Modelling quality dynamics,business value and firm performance in a big data analytics environment

Big data analytics have become an increasingly important component for firms across advanced economies. This paper examines the quality dynamics in big data environment that are linked with enhancing business value and firm performance (FPER). The study identifies that system quality (i.e. system reliability, accessibility, adaptability, integration, response time and privacy) and information quality (i.e. completeness, accuracy, format and currency) are key to enhance business value and FPER in a big data environment. The study also proposes that the relationship between quality and FPER is mediated by business value of big data. Drawing on the resource-based theory and the information systems success literature, this study extends knowledge in this domain by linking system quality, information quality, business value and FPER.     

Power-level sampling of metal cutting machines for data representation in discrete event simulation

An extension to the application area for discrete event simulation (DES) has been ongoing since the last decade and focused only on economic aspects to include ecologic sustainability. With this new focus, additional input parameters, such as electrical power consumption of machines, are needed. This paper aim at investigating how NC machine power consumption should be represented in simulation models of factories. The study includes data-sets from three different factories. One factory producing truck engine blocks, one producing brake disc parts for cars and one producing forklift components. The total number of data points analysed are more than 2,45,000, where of over 1,11,000 on busy state for 11 NC machines. The low variability between busy cycles indicates that statistical representations are not adding significant variability. Furthermore, results show that non-value-added activities cause a substantial amount of the total energy consumption, which can be reduced by optimising the production flow using dynamic simulations such as DES.     

Big data analytics business value and firm performance: linking with environmental context

Previous studies, grounded on the resource based view, have already explored the relationship between the business value that Big Data Analytics (BDA) can bring to firm performance. However, the role played by the environmental characteristics in which companies operate has not been investigated in the literature. We inform the theory, in that direction, via the integration of the contingency theory to the resource based view theory of the firm. This original and integrative model examines the moderating influence of environmental features on the relationship between BDA business value and firm performance. The combination of survey data and secondary financial data on a representative sample of medium and large companies makes possible the statistical validation of our research model. The results offer evidence that BDA business value leads to higher firm performance, namely financial performance, market performance and customer satisfaction. More original is the demonstration that this relationship is stronger in munificent environments, while the dynamism of the environment does not have any moderating effect on the performance of BDA solutions. It means that managers working for firms in markets with a growing demand are in the best position to profit from BDA.     

Investments in big data analytics and firm performance: an empirical investigation of direct and mediating effects

Big data has recently been recognised as one of the most important areas of future technology. It has attracted the attention of many industries, since it has the potential to provide companies with high business value. This paper examines the forms of business value that companies can create from big data analytics investments, the direct impacts it has on the financial performance of a firm, and the mediating effects of market performance and customer satisfaction. Drawing on the resource-based view theory, this study demonstrates that the business value achieved from investments in big data analytics leads to advantages in terms of the financial performance of a firm. The results offer evidence of the existence of a customer satisfaction mediation effect and of the absence of a market performance mediation effect. Theoretical and practical implications are discussed at the end of the paper.     

Beyond patent analytics: Insights from a scientific and technological data mashup based on a case example

Although access to open and commercial digital sources is easily available thanks to the proliferation of the internet, R&D departments still face the challenge of how to analyze information from several sources. This paper addresses this issue specifically when technological and scientific information needs to be analyzed in an integrated manner. 12,577 families of patents, 2601 scientific papers and 706 news articles are combined, normalized and analyzed using their own metadata and text. A software tool is used to extract insights from semi-structured and unstructured data by means of text mining. Additionally, interactive force-directed graph visualization is employed to show the multiple relations of concepts during different time periods with regard to the entire technology ecosystem. Through a case study of 3D printing technology, this paper shows how to apply mashup and obtain the benefits, and it defines the challenges of using interactive visualization representation.     

Supply chain data analytics for predicting supplier disruptions: a case study in complex asset manufacturing

Although predictive machine learning for supply chain data analytics has recently been reported as a significant area of investigation due to the rising popularity of the AI paradigm in industry, there is a distinct lack of case studies that showcase its application from a practical point of view. In this paper, we discuss the application of data analytics in predicting first tier supply chain disruptions using historical data available to an Original Equipment Manufacturer (OEM). Our methodology includes three phases: First, an exploratory phase is conducted to select and engineer potential features that can act as useful predictors of disruptions. This is followed by the development of a performance metric in alignment with the specific goals of the case study to rate successful methods. Third, an experimental design is created to systematically analyse the success rate of different algorithms, algorithmic parameters, on the selected feature space. Our results indicate that adding engineered features in the data, namely agility, outperforms other experiments leading to the final algorithm that can predict late orders with 80% accuracy. An additional contribution is the novel application of machine learning in predicting supply disruptions. Through the discussion and the development of the case study we hope to shed light on the development and application of data analytics techniques in the analysis of supply chain data. We conclude by highlighting the importance of domain knowledge for successfully engineering features.     

Iot based laundry services: an application of big data analytics,intelligent logistics management,and machine learning techniques

The authors propose an innovative Internet of Things (IoT) based E-commerce business model Cloud Laundry for mass scale laundry services. The model utilises big data analytics, intelligent logistics management, and machine learning techniques. Using GPS and real-time update of big data, it calculates the best transportation path and update and re-route the logistic terminals quickly and simultaneously. Cloud laundry intelligently and dynamically provides the best laundry solutions based on the current state spaces of the laundry terminals through the user's specifications and thus offers local hotel customers with convenient, efficient, and transparent laundry services. Taking advantage of the rapid development of the big data industry, user interest modelling, and information security and privacy considerations, cloud laundry uses smartphone terminal control and big data models to maintain customers’ security needs. Different from the traditional laundry industry, cloud laundry companies have higher capital turnover, more liquidity, and stronger profitability. Therefore, this new generation of smart laundry business model could be of interest to not only academic researchers, but E-commerce entrepreneurs as well.     

面向微加工的虚拟光刻系统建模与实现

提出了一种面向微加工的虚拟光刻系统Litho3D.该系统采用傅立叶光学成像模型、光刻胶曝光及显影模型,实现了投影式光学光刻的三维模拟.它拥有标准的GDSII、CIF版图格式接口和支持各种光学参数(包括数值孔径、波长、离焦量,光刻胶厚度、表面折射率等)的模拟设置.模拟结果的显示采用了体绘制与网格相结合的方法,增强了结果的可视性.此外,光刻模拟结果可以直接导入到虚拟工艺系统ZProcess中作为刻蚀工艺的掩膜输入,实现了光刻工艺与其他微机电系统(MEMS)工艺模拟的无缝集成.一系列模拟结果验证了该系统的可行性.     

Motion generation and virtual simulation in a digital environment

The growing complexity in product design and manufacturing processes has made virtual prototyping an important new approach that enhances products and process development. Considering the ergonomic issues evident in product lifecycles, digital human modelling (DHM) is adopted for virtual simulation and proactive evaluation. A motion generation from semantics (MGS) system is proposed in this research. The MGS system features include virtual prototyping, natural language instruction, a method time measurement (MTM) translator and motion generator. The system was implemented using product lifecycle management (PLM) software and validated in an automotive manufacturing company. The practice of intuitively generating manual operations and conducting virtual simulations enables the system planners to quickly respond to manufacturing process changes and recursively improve the tooling and process design flexibility and efficiency.     

Supplier selection using analytic network process and data envelopment analysis

The present study intends to present a supplier selection method, which also considers green indicators due to environmental protection issues, by using an analysis network process (ANP) as well as data envelopment analysis (DEA). ANP which is able to consider the interdependency between criteria releases the constraint of DEA that the users cannot set up criteria weight preferences. The model evaluation results for high-tech industry show that the proposed method can provide not only reasonable outcomes with a smaller number of decision making units, but can also provide more consistent results.     

Production system redesign using realistic visualisation

The process of redesigning production systems is usually complex, for which virtual design tools are available. These tools are used to analyse and evaluate planned changes prior to implementation, making it possible to identify and prevent costly design mistakes. Despite this, design mistakes arise during and after the implementation. A source for design mistakes is incorrect or insufficient spatial data of the production systems used in the virtual design tools. The aim of this paper is to show how to reduce the time required for planning and implementing the redesign by supporting the process with realistic visualisation, created from accurate spatial data of the real production systems. Three industrial studies were carried out to evaluate how address realistic visualisation in order to support the redesign process. The result shows terrestrial 3D laser scanning to be suitable for capturing spatial data for realistic visualisation of production systems. The realistic visualisation can be used to virtually analyse design alternatives of the production systems, by, for example, combining the 3D laser scan data with 3D CAD models. The realistic visualisation enabling effective and accurate planning, which gives the opportunity to reduce the time required for planning and implementing redesigned production systems.     

The analytics of product-design requirements using dynamic internet data: application to Chinese smartphone market

To accommodate the diverse users demands for consumer products, enterprises need to design and develop different lines of products according to different groups of users. Dynamic internet data, including product reviews, user attributes, and product configurations, are utilised to model users' stochastic product choice behaviours and mine the product design requirements of features, performance levels, and quantity. First, the web crawler is applied to collect internet data, and then the data are structured and the demand information is retrieved. Second, a product choice model is employed to capture the heterogeneity and correlation of user demands on product features. In particular, users' implicit requirements in terms of product function and performance are elicited from the text mining of product reviews. Third, incorporating various user requirements mined from dynamic internet data, graph theory analysis is introduced into design generation, product improvement, and market analysis. A case study on Chinese smartphones is presented, where the results show that the proposed method is practical and suitable for product-design analysis using the large volume of dynamic internet data.     

Supply chain modelling and managing,using timed coloured Petri nets: a case study

This paper aims at developing a new methodology for designing and managing a supply chain (SC) and, at the same time, for evaluating the performance of every stakeholder involved in a production chain. The methodology proposed has been applied to a footwear supply chain and is based on coloured Petri nets (CPNs). The supply chain analysed in this paper is a complex production system consisting of a network of manufacturers and service suppliers related to logistics systems that provide transportation and storage. The model developed uses coloured, timed Petri nets to represent a supply chain and it is such that resources are the Petri Net (PN) places, the tokens are jobs, orders and/or products, while the colours represent job attributes. These colours are used to encode different data types and values that are attached to tokens. A “coloured token” represents a specific production order or a certain amount of a particular material supplied. Thus, it can be processed in different ways and it can be easily localised within the CPN model. The use of coloured Petri nets allows companies to create a compact representation of states, actions and events of the modelled system. The particular structure of this network allows the designers the easy realisation of a simulator using an “object-oriented”, dedicated programming, which is a useful tool for developing what-if analyses.     

A graphical representation for a statistical test of average equivalence and variance comparison with process data

This paper provides a graphical approach for presenting results of a statistical test of average equivalence with process data. In addition, a graphical representation of process change in variance is presented. An example from the biopharmaceutical industry is provided to illustrate the information contained in the chart. Copyright © 2010 John Wiley & Sons, Ltd.     

冗余驱动地震模拟台虚拟样机动力学分析

以实体造型软件SolidWorks(R)及多刚体动力学软件ADAMS(R)为工具,对一种冗余驱动地震模拟台进行虚拟样机搭建及动力学分析.首先在SolidWorks(R)中搭建好冗余驱动地震模拟台的装配体模型,然后导入ADAMS(R)中完成虚拟样机构建.利用ADAMS(R)软件对地震模拟台末端添加运动,通过运动学与动力学逆解计算出滑块的位置、速度、加速度、驱动力矩及功率,采用后处理模块对驱动功率的绝对值积分求解出驱动电机的作功.根据给定的轨迹,仿真结果表明:在8个电机中,第3个电机的最大驱动力矩、驱动功率与作功均大于其余电机.该过程免去了繁琐的理论建模及编程工作,可快速实现冗余驱动地震模拟台的动力学分析,为物理样机的建造提供依据,体现了虚拟样机技术的优越性.     

Developing a virtual machining model to generate MTConnect machine-monitoring data from STEP-NC

The ability to predict performance of manufacturing equipment during early stages of process planning is vital for improving efficiency of manufacturing processes. In the metal cutting industry, measurement of machining performance is usually carried out by collecting machine-monitoring data that record the machine tool’s actions (e.g. coordinates of axis location and power consumption). Understanding the impacts of process planning decisions is central to the enhancement of the machining performance. However, current methodologies lack the necessary models and tools to predict impacts of process planning decisions on the machining performance. This paper presents the development of a virtual machining model (called STEP2M model) that generates machine-monitoring data from process planning data. The STEP2M model builds upon a physical model-based analysis for the sources of energy on a machine tool, and adopts STEP-NC and MTConnect standardised interfaces to represent process planning and machine-monitoring data. We have developed a prototype system for 2-axis turning operation and validated the system by conducting an experiment using a Computer Numerical Control lathe. The virtual machining model presented in this paper enables process planners to analyse machining performance through virtual measurement and to perform interoperable data communication through standardised interfaces.     

复杂网络理论在离散车间生产过程的应用研究综述

目前复杂网络的理论基础已得到深入研究,但其在生产领域的应用状况和趋势分析尚不明确。为此,提出了一个面向离散制造过程的复杂网络理论应用研究框架;通过分析国内外相关文献资料,围绕与制造过程密切相关的产品族/产品设计、零部件制造、产品装配和效能评价4个方面进行综述,对当前取得的成果进行了总结和分析。并通过与Petri网等复杂系统建模方法比较,发现采用复杂网络理论重新定义当前的生产过程,可为生产研究提供新的视角和思路。最后对未来的研究趋势作了进一步展望。