新形势下制造业物流体系布局优化研究

制造业物流体系是制造业与物流业协同发展衍生出的新体系模式。当前,我国已经迈入经济转型的攻关期。在这种新形势下,制造业物流发展面临着新的机遇。以沈阳市为例,探讨了物流体系布局优化的方法,并给出了布局优化模型。在阐述沈阳市制造业物流现状及存在问题的基础上,提出了沈阳市制造业物流体系布局优化方法及模型,旨为制造业物流体系的布局优化提供支持和应用参考。     

中国建设“世界制造业强国”背景下的制造业技能人才研究

处于后工业化经济时代的当代世界,技能人才对推动国家经济的发展具有举足轻重的作用,因此,确保“质优量足”的技能人才成为制造业强国广泛关注的一个焦点,这加速造成了全球技能人才的激烈争夺,进而引发世界范围内大面积的“技能荒”现象.由此,文章在中国建设“世界制造业强国”的背景下,基于对制造业中心国家——英国、美国和日本制造业技能人才短缺现状和成因探讨的基础上,深入分析三国政府和企业采取的措施以及效果,以期为中国有效应对技能人才短缺提供参考.     

研发部门体制改革与企业创新行为——以A股制造业上市公司为例

结合沪深A股制造业上市公司年报数据,构建Tobit回归模型对企业研发行为进行影响因素分析和稳健性检验。结论发现,研发强度与企业规模、大股东持股比例、经营年限负相关,但与技术人员人数占比正相关;此外,销售毛利率、存货周转率、现金比率以及市销率、市净率等指标也明显左右着企业创新积极性的发挥。为此旨在透过研发部门体制改革推动企业创新发展,建议由传统龙头企业拆分设立研发型子公司,对新兴初创企业引入战略投资者;鼓励企业将生产、销售业务逐步外包,探索建立多元化内源性融资渠道;试点成立创新型创投、私募等专项基金,制定针对性财税政策扶持等。     

零部件进口对中国制造业技术进步的影响

在中国制造业转型时期,零部件进口对促进我国技术进步至关重要,将出口品国内技术含量作为衡量技术进步的标准,对零部件进口与中国制造业技术进步的关系进行量化分析。研究结果表明:扩大零部件进口规模对技术进步有正向影响,但发挥作用存在一定时滞,且在高技术行业中促进作用尤为显著;扩大零部件进口种类能显著促进技术进步,主要体现在低技术行业;外商直接投资、人均资本和研发投入均对技术进步有正向影响,但对不同技术层次行业的影响程度存在差异。     

The implementation of digital technologies for operations management: a case study for manufacturing apps

Abstract

Manufacturing applications address business to business (B2B) with highly customised applications developed for specific requirements, offering highly specialised solution-oriented and service-based software components, systems, and digital tools that aim at a fast and accurate decision-making support system. The purpose of this paper is to describe the implementation of digital technologies for operations management using manufacturing or engineering apps (eApps), for product design and manufacturing processes. In particular, starting from the specific needs of two companies from mature European industries as automotive and food, this work depicts how this kind of solutions can support companies and improve their operations. In particular, related benefits and challenges faced for the full implementation of the developed tools are highlighted. Moreover a business model to exploit the manufacturing apps is also proposed. The business model proposed for the exploitation of the eApps supports the commercialisation of all the revenue streams offered by this rapidly growing sector taking into account the specific needs of the concerned stakeholders through a diversified value proposition.     

Cloud manufacturing system for sheet metal processing

Abstract

Cloud computing is changing the way industries and enterprises run their businesses. Cloud manufacturing is emerging as an approach to transform the traditional manufacturing business model, while helping the manufacturer to align production efficiency with its business strategy, and creating intelligent factory networks that enable collaboration across the whole enterprise. Many production planning and control (PPC) problems are essentially optimisation problems, where the objective is to develop a plan that meets the demand at minimum cost or maximum profit. Because the underlying optimisation problem will vary in the different business and operation phases, it is important to think about optimisation in a dynamic mechanism and in a number of interlinked sub-problems at the same time. Cloud manufacturing has the potential to offer decision support as a service and medium of communication in PPC. To solve these problems and produce collaboration across the supply chain, this paper provides an overview of the state of the art in cloud manufacturing and presents a model of cloud-based production planning and production system for sheet metal processing.     

A reference activity model for smart factory design and improvement

Smart manufacturing systems (SMSs) are envisioned to contain highly automated and IT-driven production systems. To address the complexity that arises in such systems, a standard and holistic model for describing its activities and their interrelationships is needed. This paper introduces a factory design and improvement (FDI) activity model and illustrates a case study of FDI in an electromechanical component factory. In essence, FDI is a reference activity model that encompasses a range of manufacturing system activities for designing and improving a factory during its initial development and also its operational phases. The FDI model shows not only the dependency between activities and manufacturing control levels but also the pieces of information and software functions each activity relies on. We envision that the availability of these pieces of information in digital form to integrate across the software functions will increase the agility of factory design and improvement projects. Therefore, our future work lies in contributing to standards for exchanging such information.     

高端装备制造业知识网络有效性影响因素分析

分析影响高端装备制造业知识网络有效性的五个因素,构建知识网络有效性模型并进行仿真分析,得出结论:企业为提高知识吸收能力和知识释放意愿所做投入在一定范围内可以提升知识网络有效性,投入超过一定值后,这种提升作用不再明显;企业较高的知识水平、企业间良好的合作关系与适当的知识势差可以提高知识网络有效性,并根据仿真结果提出相关建议。     

基于支持向量机的管理信息系统故障识别模型研究

管理信息系统故障影响着企业信息工程的开展。及时识别故障,能够为企业争取更多时间处理故障。因此,管理信息系统故障识别有着重要的研究意义和实践意义。基于支持向量机,构建管理信息系统故障识别模型,通过把样本应用于该模型,结果表明该模型具有较好的准确性,能有效识别管理信息系统故障。     

Machine Learning for Drug Overdose Surveillance

ABSTRACT

We describe two recently proposed machine learning approaches for discovering emerging trends in fatal accidental drug overdoses. The Gaussian Process Subset Scan (Herlands, McFowland, Wilson, & Neill, 2017 Neill, D. B. (2017). Multidimensional tensor scan for drug overdose surveillance. Journal of Public Health Informatics, 9(1), e20. doi:10.5210/ojphi.v9i1.7598[Crossref] , [Google Scholar]) enables early detection of emerging patterns in spatio-temporal data, accounting for both the complex, correlated nature of the data and the fact that detecting subtle patterns requires integration of information across multiple spatial areas and multiple time steps. We apply this approach to 17 years of county-aggregated data for monthly opioid overdose deaths in the New York City metropolitan area, showing clear advantages in the utility of discovered patterns as compared to typical anomaly detection approaches. To detect and characterize emerging overdose patterns that differentially affect a subpopulation of the data, including geographic, demographic, and behavioral patterns (e.g., which combinations of drugs are involved), we apply the Multidimensional Tensor Scan (Neill, 2017 Neill, D. B. (2017). Multidimensional tensor scan for drug overdose surveillance. Journal of Public Health Informatics, 9(1), e20. doi:10.5210/ojphi.v9i1.7598[Crossref] , [Google Scholar]) to 8 years of case-level overdose data from Allegheny County, Pennsylvania. We discover previously unidentified overdose patterns which reveal unusual demographic clusters, show impacts of drug legislation, and demonstrate potential for early detection and targeted intervention. These approaches to early detection of overdose patterns can inform prevention and response efforts, as well as understanding the effects of policy changes.