Simulating Business Processes of Manufacturing SMEs on the Cloud

Simulating the efficiency of business processes could reveal crucial bottlenecks for manufacturing companies and could lead to significant optimizations resulting in decreased time to market, more efficient resource utilization, and larger profit. While such business optimization software is widely utilized by larger companies, SMEs typically do not have the required expertise and resources to efficiently exploit these advantages. The aim of this work is to explore how simulation software vendors and consultancies can extend their portfolio to SMEs by providing business process optimization based on a cloud computing platform. By executing simulation runs on the cloud, software vendors and associated business consultancies can get access to large computing power and data storage capacity on demand, run large simulation scenarios on behalf of their clients, analyze simulation results, and advise their clients regarding process optimization. The solution is mutually beneficial for both vendor/consultant and the end-user SME. End-user companies will only pay for the service without requiring large upfront costs for software licenses and expensive hardware. Software vendors can extend their business towards the SME market with potentially huge benefits.

Development of a knowledge sharing framework for improving the testing processes in global product development

As identified by Griffin (1997) and Kahn (2012), manufacturing organisations typically improve their market position by accelerating their product development (PD) cycles. One method for achieving this is to reduce the time taken to design, test and validate new products, so that they can reach the end customer before competition. This paper adds to existing research on PD testing procedures by reporting on an exploratory investigation carried out in a UK-based manufacturing plant. We explore the organisational and managerial factors that contribute to the time spent on testing of new products during development. The investigation consisted of three sections, viz. observations and process modelling, utilisation metrics and a questionnaire-based investigation, from which a proposed framework to improve and reduce the PD time cycle is presented. This research focuses specifically on the improvement of the utilisation of product testing facilities and the links to its main internal stakeholders - PD engineers.

Extending Science Gateway Frameworks to Support Big Data Applications in the Cloud

Cloud computing offers massive scalability and elasticity required by many scien-tific and commercial applications. Combining the computational and data handling capabilities of clouds with parallel processing also has the potential to tackle Big Data problems efficiently. Science gateway frameworks and workflow systems enable application developers to implement complex applications and make these available for end-users via simple graphical user interfaces. The integration of such frameworks with Big Data processing tools on the cloud opens new oppor-tunities for application developers. This paper investigates how workflow sys-tems and science gateways can be extended with Big Data processing capabilities. A generic approach based on infrastructure aware workflows is suggested and a proof of concept is implemented based on the WS-PGRADE/gUSE science gateway framework and its integration with the Hadoop parallel data processing solution based on the MapReduce paradigm in the cloud. The provided analysis demonstrates that the methods described to integrate Big Data processing with workflows and science gateways work well in different cloud infrastructures and application scenarios, and can be used to create massively parallel applications for scientific analysis of Big Data.

A Formal Approach to Support Interoperability in Scientific Meta-workflows

Scientific workflows orchestrate the execution of complex experiments frequently using distributed computing platforms. Meta-workflows represent an emerging type of such workflows which aim to reuse existing workflows from potentially different workflow systems to achieve more complex and experimentation minimizing workflow design and testing efforts. Workflow interoperability plays a profound role in achieving this objective. This paper is focused at fostering interoperability across meta-workflows that combine workflows of different workflow systems from diverse scientific domains. This is achieved by formalizing definitions of meta-workflow and its different types to standardize their data structures used to describe workflows to be published and shared via public repositories. The paper also includes thorough formalization of two workflow interoperability approaches based on this formal description: the coarse-grained and fine-grained workflow interoperability approach. The paper presents a case study from Astrophysics which successfully demonstrates the use of the concepts of meta-workflows and workflow interoperability within a scientific simulation platform.

A process-based Knowledge Management framework supported by ARIS Enterprise Modelling architecture

Knowledge and its management have been respectively accepted as a critical resource and a core business competency. Despite that literature proves the existence of a gap between the theoretical considerations of Knowledge Management (KM) and their efficient application. Such lacking, we argue, derives from the missing link between a framework of Knowledge Management and the particular methods and guidelines of its implementation. In an attempt to bridge this gap, an original, process- based holistic Knowledge Management framework is proposed, aiming to address the problem of knowledge management application and performance by utilising a set of well accepted Enterprise Modelling (EM) methods and tools.

Methods, Models & Systems for Dealing with Some Business, Management & Engineering Problems

This keynote presentation will report some of our research work and experience on the development and applications of relevant methods, models, systems and simulation techniques in support of different types and various levels of decision making for business, management and engineering. In particular, the following topics will be covered. Modelling, multi-agent-based simulation and analysis of the allocation management of carbon dioxide emission permits in China (Nanfeng Liu & Shuliang Li Agent-based simulation of the dynamic evolution of enterprise carbon assets (Yin Zeng & Shuliang Li) A framework & system for extracting and representing project knowledge contexts using topic models and dynamic knowledge maps: a big data perspective (Jin Xu, Zheng Li, Shuliang Li & Yanyan Zhang) Open innovation: intelligent model, social media & complex adaptive system simulation (Shuliang Li & Jim Zheng Li) A framework, model and software prototype for modelling and simulation for deshopping behaviour and how companies respond (Shawkat Rahman & Shuliang Li) Integrating multiple agents, simulation, knowledge bases and fuzzy logic for international marketing decision making (Shuliang Li & Jim Zheng Li) A Web-based hybrid intelligent system for combined conventional, digital, mobile, social media and mobile marketing strategy formulation (Shuliang Li & Jim Zheng Li) A hybrid intelligent model for Web & social media dynamics, and evolutionary and adaptive branding (Shuliang Li) A hybrid paradigm for modelling, simulation and analysis of brand virality in social media (Shuliang Li & Jim Zheng Li) Network configuration management: attack paradigms and architectures for computer network survivability (Tero Karvinen & Shuliang Li)