Self-explanation in adaptive systems based on runtime goal-based models

The behaviour of self adaptive systems can be emergent, which means that the system’s behaviour may be seen as unexpected by its customers and its developers. Therefore, a self-adaptive system needs to garner confidence in its customers and it also needs to resolve any surprise on the part of the developer during testing and maintenance. We believe that these two functions can only be achieved if a self-adaptive system is also capable of self-explanation. We argue a self-adaptive system’s behaviour needs to be explained in terms of satisfaction of its requirements. Since self-adaptive system requirements may themselves be emergent, we propose the use of goal-based requirements models at runtime to offer self-explanation of how a system is meeting its requirements. We demonstrate the analysis of run-time requirements models to yield a self-explanation codified in a domain specific language, and discuss possible future work.

Marketing performance assessment systems and the business context

Purpose ‐ This study provides empirical evidence for the contextuality of marketing performance assessment (MPA) systems. It aims to introduce a taxonomical classification of MPA profiles based on the relative emphasis placed on different dimensions of marketing performance in different companies and business contexts. Design/methodology/approach ‐ The data used in this study (n=1,157) were collected using a web-based questionnaire, targeted to top managers in Finnish companies. Two multivariate data analysis techniques were used to address the research questions. First, dimensions of marketing performance underlying the current MPA systems were identified through factor analysis. Second, a taxonomy of different profiles of marketing performance measurement was created by clustering respondents based on the relative emphasis placed on the dimensions and characterizing them vis-á-vis contextual factors. Findings ‐ The study identifies nine broad dimensions of marketing performance that underlie the MPA systems in use and five MPA profiles typical of companies of varying sizes in varying industries, market life cycle stages, and competitive positions associated with varying levels of market orientation and business performance. The findings support the previously conceptual notion of contextuality in MPA and provide empirical evidence for the factors that affect MPA systems in practice. Originality/value ‐ The paper presents the first field study of current MPA systems focusing on combinations of metrics in use. The findings of the study provide empirical support for the contextuality of MPA and form a classification of existing contextual systems suitable for benchmarking purposes. Limited evidence for performance differences between MPA profiles is also provided.

Design and development of logistics workflow systems for demand management with RFID

This paper discusses demand and supply chain management and examines how artificial intelligence techniques and RFID technology can enhance the responsiveness of the logistics workflow. This proposed system is expected to have a significant impact on the performance of logistics networks by virtue of its capabilities to adapt unexpected supply and demand changes in the volatile marketplace with the unique feature of responsiveness with the advanced technology, Radio Frequency Identification (RFID). Recent studies have found that RFID and artificial intelligence techniques drive the development of total solution in logistics industry. Apart from tracking the movement of the goods, RFID is able to play an important role to reflect the inventory level of various distribution areas. In today’s globalized industrial environment, the physical logistics operations and the associated flow of information are the essential elements for companies to realize an efficient logistics workflow scenario. Basically, a flexible logistics workflow, which is characterized by its fast responsiveness in dealing with customer requirements through the integration of various value chain activities, is fundamental to leverage business performance of enterprises. The significance of this research is the demonstration of the synergy of using a combination of advanced technologies to form an integrated system that helps achieve lean and agile logistics workflow.

Using a systems approach in logistics design and planning

The international economic and business environment continues to develop at a rapid rate. Increasing interactions between economies, particularly between Europe and Asia, has raised many important issues regarding transport infrastructure, logistics and broader supply chain management. The potential exists to further stimulate trade provided that these issues are addressed in a logical and systematic manner. However, if this potential is to be realised in practice there is a need to re-evaluate current supply chain configurations. A mismatch currently exists between the technological capability and the supply chain or logistical reality. This mismatch has sharpened the focus on the need for robust approaches to supply chain re-engineering. Traditional approaches to business re-engineering have been based on manufacturing systems engineering and business process management. A recognition that all companies exist as part of bigger supply chains has fundamentally changed the focus of re-engineering. Inefficiencies anywhere in a supply chain result in the chain as a whole being unable to reach its true competitive potential. This reality, combined with the potentially radical impact on business and supply chain architectures of the technologies associated with electronic business, requires organisations to adopt innovative approaches to supply chain analysis and re-design. This paper introduces a systems approach to supply chain re-engineering which is aimed at addressing the challenges which the evolving business environment brings with it. The approach, which is based on work with a variety of both conventional and electronic supply chains, comprises underpinning principles, a methodology and guidelines on good working practice, as well as a suite of tools and techniques. The adoption of approaches such as that outlined in this paper helps to ensure that robust supply chains are designed and implemented in practice. This facilitates an integrated approach, with involvement of all key stakeholders throughout the design process.

Using patient knowledge for better health systems

Purpose – This paper describes a “work in progress” research project being carried out with a public health care provider in the UK, a large NHS hospital Trust. Enhanced engagement with patients is one of the Trust’s core principles, but it is recognised that much more needs to be done to achieve this, and that ICT systems may be able to provide some support. The project is intended to find ways to better capture and evaluate the “voice of the patient” in order to lead to improvements in health care quality, safety and effectiveness. Design/methodology/approach – We propose to investigate the use of a patient-orientated knowledge management system (KMS) in managing knowledge about and from patients. The study is a mixed methods (quantitative and qualitative) investigation based on traditional action research, intended to answer the following three research questions: (1) How can a KMS be used as a mechanism to capture and evaluate patient experiences to provoke patient service change (2) How can the KMS assist in providing a mechanism for systematising patient engagement? (3) How can patient feedback be used to stimulate improvements in care, quality and safety? Originality/value –This methodology aims to involve patients at all phases of the study from its initial design onwards, thus leading to an understanding of the issues associated with using a KMS to manage knowledge about and for patients that is driven by the patients themselves. Practical implications – The outcomes of the project for the collaborating hospital will be firstly, a system for capturing and evaluating knowledge about and from patients, and then as a consequence, improved outcomes for both the patients and the service provider. More generally, it will produce a set of guidelines for managing patient knowledge in an NHS hospital that have been tested in one case example.

The handbook of engineering self-aware and self-expressive systems

When faced with the task of designing and implementing a new self-aware and self-expressive computing system, researchers and practitioners need a set of guidelines on how to use the concepts and foundations developed in the Engineering Proprioception in Computing Systems (EPiCS) project. This report provides such guidelines on how to design self-aware and self-expressive computing systems in a principled way. We have documented different categories of self-awareness and self-expression level using architectural patterns. We have also documented common architectural primitives, their possible candidate techniques and attributes for architecting self-aware and self-expressive systems. Drawing on the knowledge obtained from the previous investigations, we proposed a pattern driven methodology for engineering self-aware and self-expressive systems to assist in utilising the patterns and primitives during design. The methodology contains detailed guidance to make decisions with respect to the possible design alternatives, providing a systematic way to build self-aware and self-expressive systems. Then, we qualitatively and quantitatively evaluated the methodology using two case studies. The results reveal that our pattern driven methodology covers the main aspects of engineering self-aware and self-expressive systems, and that the resulted systems perform significantly better than the non-self-aware systems.

CEPBen:a benchmark for complex event processing systems

Complex Event processing (CEP) has emerged over the last ten years. CEP systems are outstanding in processing large amount of data and responding in a timely fashion. While CEP applications are fast growing, performance management in this area has not gain much attention. It is critical to meet the promised level of service for both system designers and users. In this paper, we present a benchmark for complex event processing systems: CEPBen. The CEPBen benchmark is designed to evaluate CEP functional behaviours, i.e., filtering, transformation and event pattern detection and provides a novel methodology of evaluating the performance of CEP systems. A performance study by running the CEPBen on Esper CEP engine is described and discussed. The results obtained from performance tests demonstrate the influences of CEP functional behaviours on the system performance. © 2014 Springer International Publishing Switzerland.

A mixture density network approach to modelling and exploiting uncertainty in nonlinear control problems

In nonlinear and stochastic control problems, learning an efficient feed-forward controller is not amenable to conventional neurocontrol methods. For these approaches, estimating and then incorporating uncertainty in the controller and feed-forward models can produce more robust control results. Here, we introduce a novel inversion-based neurocontroller for solving control problems involving uncertain nonlinear systems which could also compensate for multi-valued systems. The approach uses recent developments in neural networks, especially in the context of modelling statistical distributions, which are applied to forward and inverse plant models. Provided that certain conditions are met, an estimate of the intrinsic uncertainty for the outputs of neural networks can be obtained using the statistical properties of networks. More generally, multicomponent distributions can be modelled by the mixture density network. Based on importance sampling from these distributions a novel robust inverse control approach is obtained. This importance sampling provides a structured and principled approach to constrain the complexity of the search space for the ideal control law. The developed methodology circumvents the dynamic programming problem by using the predicted neural network uncertainty to localise the possible control solutions to consider. A nonlinear multi-variable system with different delays between the input-output pairs is used to demonstrate the successful application of the developed control algorithm. The proposed method is suitable for redundant control systems and allows us to model strongly non-Gaussian distributions of control signal as well as processes with hysteresis. © 2004 Elsevier Ltd. All rights reserved.

A hybrid measurement systems analysis and uncertainty of measurement approach for industrial measurement in the light controlled factory

The uncertainty of measurements must be quantified and considered in order to prove conformance with specifications and make other meaningful comparisons based on measurements. While there is a consistent methodology for the evaluation and expression of uncertainty within the metrology community industry frequently uses the alternative Measurement Systems Analysis methodology. This paper sets out to clarify the differences between uncertainty evaluation and MSA and presents a novel hybrid methodology for industrial measurement which enables a correct evaluation of measurement uncertainty while utilising the practical tools of MSA. In particular the use of Gage R&R ANOVA and Attribute Gage studies within a wider uncertainty evaluation framework is described. This enables in-line measurement data to be used to establish repeatability and reproducibility, without time consuming repeatability studies being carried out, while maintaining a complete consideration of all sources of uncertainty and therefore enabling conformance to be proven with a stated level of confidence. Such a rigorous approach to product verification will become increasingly important in the era of the Light Controlled Factory with metrology acting as the driving force to achieve the right first time and highly automated manufacture of high value large scale products such as aircraft, spacecraft and renewable power generation structures.

Early design verification of complex assembly variability using a hybrid - model based and physical testing - methodology

Design verification in the digital domain, using model-based principles, is a key research objective to address the industrial requirement for reduced physical testing and prototyping. For complex assemblies, the verification of design and the associated production methods is currently fragmented, prolonged and sub-optimal, as it uses digital and physical verification stages that are deployed in a sequential manner using multiple systems. This paper describes a novel, hybrid design verification methodology that integrates model-based variability analysis with measurement data of assemblies, in order to reduce simulation uncertainty and allow early design verification from the perspective of satisfying key assembly criteria.

Practical design considerations of cascaded hybrid battery storage systems

The high cost of batteries has led to investigations in using second-life ex-transportation batteries for grid support applications. Vehicle manufacturers currently all have different specifications for battery chemistry, arrangement of cells, capacity and voltage. With anticipated new developments in battery chemistry which could also affect these parameters, there are, as yet, no standards defining parameters in second life applications. To overcome issues relating to sizing and to prevent future obsolescence for the rest of the energy storage system, a cascaded topology with an operating envelope design approach has been used to connect together modules. This topology offers advantages in terms of system reliability. The design methodology is validated through a set of experimental results resulting in the creation of surface maps looking at the operation of the converter (efficiency and inductor ripple current). The use of a pre-defined module operating envelope also offers advantages for developing new operational strategies for systems with both hybrid battery energy systems and also hybrid systems including other energy sources such as solar power.

WASAN : the development of a facilitated methodology for structuring a waste minimisation problem

This paper contributes a new methodology called Waste And Source-matter ANalyses (WASAN) which supports a group in building agreeable actions for safely minimising avoidable waste. WASAN integrates influences from the Operational Research (OR) methodologies/philosophies of Problem Structuring Methods, Systems Thinking, simulation modelling and sensitivity analysis as well as industry approaches of Waste Management Hierarchy, Hazard Operability (HAZOP) Studies and As Low As Reasonably Practicable (ALARP). The paper shows how these influences are compiled into facilitative structures that support managers in developing recommendations on how to reduce avoidable waste production. WASAN is being designed as Health and Safety Executive Guidance on what constitutes good decision making practice for the companies that manage nuclear sites. In this paper we report and reflect on its use in two soft OR/problem structuring workshops conducted on radioactive waste in the nuclear industry. Crown Copyright © 2010.

Measurement resource planning:a methodology that uses quality characteristics mapping

Integration of the measurement activity into the production process is an essential rule in digital enterprise technology, especially for large volume product manufacturing, such as aerospace, shipbuilding, power generation and automotive industries. Measurement resource planning is a structured method of selecting and deploying necessary measurement resources to implement quality aims of product development. In this research, a new mapping approach for measurement resource planning is proposed. Firstly, quality aims are identified in the form of a number of specifications and engineering requirements of one quality characteristics (QCs) at a specific stage of product life cycle, and also measurement systems are classified according to the attribute of QCs. Secondly, a matrix mapping approach for measurement resource planning is outlined together with an optimization algorithm for combination between quality aims and measurement systems. Finally, the proposed methodology has been studied in shipbuilding to solve the problem of measurement resource planning, by which the measurement resources are deployed to satisfy all the quality aims. © Springer-Verlag Berlin Heidelberg 2010.