State of the art of information systems failure managements

Information systems (IS) play a vital role in our daily life. They collect, store, organize and distribute data in structured and organized ways to improve people's daily activities in the most efficient and effective manner. To achieve that, functions and structures are mixed together in a dynamic way to construct an information system with sophisticated capabilities beyond sending emails and publishing data online. Consequently, enterprises and government departments allocate huge financial and human resources for the development of interconnected information systems. However, failures in information systems projects have been growing in the last few years despite their massive allocated resources. That means any IS projects that do not meet their objectives and goal have gone beyond budget or not completed within the agreed time. While there are several factors triggering these failures, this research aims to investigate and address the key factors that are responsible for failure within information systems in various regions, The work conducted includes identifying different role of information systems components, comparing two important information systems success/failure models namely DeLone and Mclean model and ITPOSMO model, and summarising critical success/factors for information systems from various regions. The research findings can be used for developing an framework for effectively managing Information System

Configuration management in aerospace industry

In this paper, first the basis of the Configuration Management is defined. Next, how Configuration Management is practiced in aerospace industry is explored. Further, the current challenges of CM in Aerospace industries are discussed with a brief review of the strategic actions taken by aerospace industry to implement Configuration Management. Last, current trends in Configuration Management are explored

Cost modelling to support optimised selection of the end of life options for automotive components.

In automotive sector, the End-of-Life components, especially the uni-material components e.g. steel, plastics etc., traditionally normally go to material recycling. However, this conventional disposing approach has nowadays moved towards the secondary utilization approach which closes the loop in the material flow process, i.e. reuse via remanufacturing, reconditioning, and repairing etc. However, the economic benefit of different End-of-Life options for automotive components remain unclear, there is a need to quantitatively evaluate the economic benefit of different End-of-Life options. This project aims to develop a cost estimation model to assess the cost- effectiveness between recovery alternatives for End-of-Life automotive components. Firstly, the remanufacturing process for automotive components has been modelled consisting different stages and activities involved. Thereafter, the cost elements in each stage and the cost drivers for each cost element have been identified; cost breakdown structure has been established. Next, cost estimation relationships between cost elements and cost drivers have been established. A cost estimation model has been developed, validated and implemented in MS Excel@ platform. Finally, two case studies about comparison of different End-of-Life options for crankshaft and composite oil pan has been performed, it has been shown that the developed cost model can inform which End-of-Life option is more cost effective

An Internet of Things based framework to enhance just-in-time manufacturing

Just-in-time manufacturing is a main manufacturing strategy used to enhance manufacturers’ competitiveness through inventory and lead time reduction. Implementing just-in-time manufacturing has a number of challenges, for example, effective, frequent and real-time information sharing and communication between different functional departments, responsive action for adjusting the production plan against the continually changing manufacturing situation. Internet of Things technology has the potential to be used for capturing desired data and information from production environment in real time, and the collected data and information can be used for adjusting production schedules corresponding to the changing production environment. This article presents an Internet of Things based framework to support responsive production planning and scheduling in just-in-time manufacturing. The challenges of implementing just-in-time manufacturing are identified first and then an Internet of Things based solution is proposed to address these challenges. A framework to realise the proposed Internet of Things solution is developed and its implementation plan is suggested based on a case study on automotive harness parts manufacturing. This research contributes knowledge to the field of just-in-time manufacturing by incorporating the Internet-of-Things technology to improve the connectivity of production chains and responsive production scheduling capability

Design and implementation of ergonomic risk assessment feedback system for improved work posture assessment

Ergonomic risk factors which include force, repetition and awkward postures, can result in Work-Related Musculoskeletal Disorders (WMSDs) among workers. Hence, systems that provide real-time feedback to the worker concerning his current ergonomic behaviours are desirable. This paper presents the design and implementation of a human-machine interface posture assessment feedback system whose conceptual model is developed through a model-driven development perspective using the Unified Modeling Language (UML) and interface flow diagrams. The resulting system provides a shop floor with a simple, cost-effective and automatic tool for real-time display of worker's postures. Testing the system on volunteer participants reveals that it is easy to use, achieves real-time posture assessment and provides easy-to-understand feedback to workers. This system may be useful for reducing the rate of occurrence of awkward postures, one of the contributing factors to risk of WMSDs among workers

Does China’s stock market react to COVID-19 differently at industry level? Evidence from China

Since the outbreak of the COVID-19 pandemic in 2020, global economic growth has been negatively affected. The reaction of financial markets was particularly dramatic, especially in countries severely affected by the outbreak. Based on Shanghai Stock Exchange (SSE) data from August 13, 2019 to December 31, 2020, this study investigates the short-term and the long-term market reactions of industry indices. The event study method and the Fama-French five-factor model are used to analyse the effect of the COVID-19 pandemic. Findings reveal that cumulative abnormal returns (CARs) in most industries followed a similar short-term trajectory. However, the excess returns of the SSE Information Technology, SSE Telecommunication Services and SSE Materials show different performance in the long term. This study facilitates the analysis of the impact of large public emergencies, such as global pandemics, on investors’ expectations and decision-making. It also helps investors to make rational decisions and the government to formulate targeted policies

Multi-Objective Considered Process Parameter Optimization of Welding Robots Based on Small Sample Size Dataset

The welding process is characterized by its high energy density, making it imperative to optimize the energy consumption of welding robots without compromising the quality and efficiency of the welding process for their sustainable development. The above evaluation objectives in a particular welding situation are mostly influenced by the welding process parameters. Although numerical analysis and simulation methods have demonstrated their viability in optimizing process parameters, there are still limitations in terms of modeling accuracy and efficiency. This paper presented a framework for optimizing process parameters of welding robots in industry settings, where data augmentation was applied to expand sample size, auto machine learning theory was incorporated to quantify reflections from process parameters to evaluation objectives, and the enhanced non-dominated sorting algorithm was employed to identify an optimal solution by balancing these objectives. Additionally, an experiment using Q235 as welding plates was designed and conducted on a welding platform, and the findings indicated that the prediction accuracy on different objectives obtained by the enlarged dataset through ensembled models all exceeded 95%. It is proven that the proposed methods enabled the efficient and optimal determination of parameter instructions for welding scenarios and exhibited superior performance compared with other optimization methods in terms of model correctness, modeling efficiency, and method applicability

Vegetation patches increase wind-blown litter accumulation in a semi-arid steppe of northern China

Litter decomposition is an important source of soil organic matter and nutrients; however, few studies have explored how vegetation patches affect wind-driven litter mobility and accumulation. In this study, we aimed to test the following hypotheses: (1) vegetation patches can reduce litter removal and facilitate litter accumulation, (2) litter mobility results in the heterogeneous redistribution of carbon and nutrients over the land surface, and (3) litter removal rates differ among different litter types (e.g., leaf and stem). Four vegetation patch types and six litter types were used to investigate the impacts of vegetation patches on litter mobility and accumulation. The results show that compared with almost bare ground patches, patches with vegetation cover had significantly higher litter accumulation, with the shrub patch type having the highest accumulation amount. The rate of litter removal due to wind was highest for the almost bare surface type (P4) and lowest for the shrub patch (P1) and Stipa grandis community (P2) types. There were significant differences in the removal rate among the different litter types. These findings indicate that wind-based litter redistribution among bare, S. grandis -dominated, and shrub-dominated patches is at least partially responsible for increasing the spatial heterogeneity of resources on a landscape scale

Bi-objective optimization for low-carbon product family design

[EN] Consumers, industry, and government entities are becoming increasingly concerned about the issue of global warming. With this in mind, manufacturers have begun to develop products with consideration of low-carbon. In recent years, many companies are utilizing product families to satisfy various customer needs with lower costs. However, little research has been conducted on the development of a product family that considers environmental factors. In this paper, a low-carbon product family design that integrates environmental concerns is proposed. To this end, a new method of platform planning is investigated with considerations of cost and greenhouse gas (GHG) emission of a product family simultaneously. In this research, a lowcarbon product family design problem is described at first, and then a GHG emission model of product family is established. Furthermore, to support lowcarbon product family design, an optimization method is applied to make a significant trade-off between cost and GHG emission to implement a feasible platform planning. Finally, the effectiveness of the proposed method is illustrated through a case study. (C) 2016 Elsevier Ltd. All rights reserved.This research was carried out as a part of the CASES project which is supported by a Marie Curie International Research Staff Exchange Scheme Fellowship within the 7th European Community Framework Programme under the Grant agreement no. 294931. This research was also supported by National Natural Science Foundation of China (Nos. 51175262, 51575264); and Jiangsu Province Science Foundation for Excellent Youths under Grant BK2012032.Wang, Q.; Dunbing, T.; Yin, L.; Salido, MA.; Giret Boggino, AS.; Xu, Y. (2016). Bi-objective optimization for low-carbon product family design. Robotics and Computer-Integrated Manufacturing. 41:53-65. https://doi.org/10.1016/j.rcim.2016.02.001S53654

Dynamic Scheduling Method for Job-shop Manufacturing Systems by Deep Reinforcement Learning with Proximal Policy Optimization

With the rapid development of Industry 4.0, modern manufacturing systems have been experiencing profound digital transformation. Development of new technologies can help to improve the efficiency of production and the quality of products. With the increasingly complex production systems, operational decision-making has encountered challenges in the sustainable manufacturing process to satisfy customers and markets' ever-changing demands. Nowadays, the rule-based heuristics approaches are widely used for scheduling management in production systems, which however significantly depends on the expert domain knowledge. In this way, the efficiency of decision-making cannot be guaranteed nor meet the dynamic scheduling requirements in the job-shop manufacturing environment. In this study, we propose using deep reinforcement learning (DRL) methods to tackle the dynamic scheduling problem in the job-shop manufacturing system. The proximal policy optimization (PPO) algorithm has been used in the DRL framework to accelerate the learning process and improve performance. The proposed method has been testified within a real-world dynamic production environment, and it performs better compared with the state-of-the-art method