Nonlinear penalties in dynamic optical networks employing autonomous transponders

We report for the first time on the limitations in the operational power range of network traffic in the presence of heterogeneous 28-Gbaud polarization-multiplexed quadrature amplitude modulation (PM-mQAM) channels in a nine-channel dynamic optical mesh network. In particular, we demonstrate that transponders which autonomously select a modulation order and launch power to optimize their own performance will have a severe impact on copropagating network traffic. Our results also suggest that altruistic transponder operation may offer even lower penalties than fixed launch power operation.

Supporting decision-making for self-adaptive systems:from goal models to dynamic decision networks

Context/Motivation - Different modeling techniques have been used to model requirements and decision-making of self-adaptive systems (SASs). Specifically, goal models have been prolific in supporting decision-making depending on partial and total fulfilment of functional (goals) and non-functional requirements (softgoals). Different goalrealization strategies can have different effects on softgoals which are specified with weighted contribution-links. The final decision about what strategy to use is based, among other reasons, on a utility function that takes into account the weighted sum of the different effects on softgoals. Questions/Problems - One of the main challenges about decisionmaking in self-adaptive systems is to deal with uncertainty during runtime. New techniques are needed to systematically revise the current model when empirical evidence becomes available from the deployment. Principal ideas/results - In this paper we enrich the decision-making supported by goal models by using Dynamic Decision Networks (DDNs). Goal realization strategies and their impact on softgoals have a correspondence with decision alternatives and conditional probabilities and expected utilities in the DDNs respectively. Our novel approach allows the specification of preferences over the softgoals and supports reasoning about partial satisfaction of softgoals using probabilities. We report results of the application of the approach on two different cases. Our early results suggest the decision-making process of SASs can be improved by using DDNs. © 2013 Springer-Verlag.

Dynamic decision networks for decision-making in self-adaptive systems:a case study

Bayesian decision theory is increasingly applied to support decision-making processes under environmental variability and uncertainty. Researchers from application areas like psychology and biomedicine have applied these techniques successfully. However, in the area of software engineering and speci?cally in the area of self-adaptive systems (SASs), little progress has been made in the application of Bayesian decision theory. We believe that techniques based on Bayesian Networks (BNs) are useful for systems that dynamically adapt themselves at runtime to a changing environment, which is usually uncertain. In this paper, we discuss the case for the use of BNs, speci?cally Dynamic Decision Networks (DDNs), to support the decision-making of self-adaptive systems. We present how such a probabilistic model can be used to support the decision making in SASs and justify its applicability. We have applied our DDN-based approach to the case of an adaptive remote data mirroring system. We discuss results, implications and potential bene?ts of the DDN to enhance the development and operation of self-adaptive systems, by providing mechanisms to cope with uncertainty and automatically make the best decision.

Technology transfer through international manufacturing networks:valuing technology from an owner’s perspective

Technology is a key part of organisational knowledge and gives its owners their distinctive capabilities and competitive advantages. However, to best use these assets technology often needs to be transferred and shared with others through a form of technology collaboration. This raises the important question of how technology should be valued when it is being transferred. Technology valuation has become a critical issue in most transfer transactions. Transfer arrangements and terms of payment have a significant effect on the generation and sharing of joint benefits in commercial, technical and strategic aspects. In this paper the concept of “owner's value” is explored by highlighting its structure and components and assessing the importance of factors affecting value. The influence on technology valuation of the transfer arrangement, the associated terms of payment and the interaction between the shared benefits, cost and risks are discussed.

Industrial networks of the future:review of research and practice

Academic researchers have followed closely the interest of companies in establishing industrial networks by studying aspects such as social interaction and contractual relationships. But what patterns underlie the emergence of industrial networks and what support should research provide for practitioners? Firstly, it appears that manufacturing is becoming a commodity rather than a unique capability, which accounts especially for low-technology approaches in downstream parts of the network, for example in assembly operations. Secondly, the increased tendency towards specialization has forced other, upstream, parts of industrial networks to introduce advanced manufacturing technologies to supply niche markets. Thirdly, the capital market for investments in capacity, and the trade in manufacturing as a commodity, dominates resource allocation to a larger extent than previously was the case. Fourthly, there is a continuous move towards more loosely connected entities that comprise manufacturing networks. More traditional concepts, such as the “keiretsu” and “chaibol” networks of some Asian economies, do not sufficiently support the demands now being placed on networks. Research should address these four fundamental challenges to prepare for the industrial networks of 2020 and beyond.

Aspects of the design and operation of production control systems in manufacturing industry

Computerised production control developments have concentrated on Manufacturing Resources Planning (MRP II) systems. The literature suggests however, that despite the massive investment in hardware, software and management education, successful implementation of such systems in manufacturing industries has proved difficult. This thesis reviews the development of production planning and control systems, in particular, investigates the causes of failures in implementing MRP/MRP II systems in industrial environments and argues that the centralised and top-down planning structure, as well as the routine operational methodology of such systems, is inherently prone to failure. The thesis reviews the control benefits of cellular manufacturing systems but concludes that in more dynamic manufacturing environments, techniques such as Kanban are inappropriate. The basic shortcomings of MRP II systems are highlighted and a new enhanced operational methodology based on distributed planning and control principles is introduced. Distributed Manufacturing Resources Planning (DMRP), was developed as a capacity sensitive production planning and control solution for cellular manufacturing environments. The system utilises cell based, independently operated MRP II systems, integrated into a plant-wide control system through a Local Area Network. The potential benefits of adopting the system in industrial environments is discussed and the results of computer simulation experiments to compare the performance of the DMRP system against the conventional MRP II systems presented. DMRP methodology is shown to offer significant potential advantages which include ease of implementation, cost effectiveness, capacity sensitivity, shorter manufacturing lead times, lower working in progress levels and improved customer service.

A review of research and practice for the industrial networks of the future

Academic researchers have followed closely the interest of companies in establishing industrial networks by studying aspects such as social interaction and contractual relationships. But what patterns underlie the emergence of industrial networks and what support should research provide for practitioners? First, it appears that manufacturing is becoming a commodity rather than a unique capability, which accounts especially for low-technology approaches in downstream parts of the network, for example, in assembly operations. Second, the increased tendency towards specialisation has forced other, upstream, parts of industrial networks to introduce advanced manufacturing technologies for niche markets. Third, the capital market for investments in capacity, and the trade in manufacturing as a commodity, dominates resource allocation to a larger extent than was previously the case. Fourth, there is becoming a continuous move towards more loosely connected entities that comprise manufacturing networks. Finally, in these networks, concepts for supply chain management should address collaboration and information technology that supports decentralised decision-making, in particular to address sustainable and green supply chains. More traditional concepts, such as the keiretsu and chaibol networks of some Asian economies, do not sufficiently support the demands now being placed on networks. Research should address these five fundamental challenges to prepare for the industrial networks of 2020 and beyond. © 2010 Springer-Verlag London.

Industrial networks of the future:a critical commentary on research and practice

Academia has followed the interest by companies in establishing industrial networks by studying aspects such as social interaction and contractual relationships. But what patterns underlie the emergence of industrial networks and what support should research provide for practitioners? Firstly, it seems that manufacturing is becoming a commodity rather than a unique capability, which accounts especially for low-technology approaches in downstream parts of the network, for example in assembly operations. Secondly, the increased tendency to specialize forces other parts of industrial networks to introduce advanced manufacturing technologies for niche markets. Thirdly, the capital market for investments in capacity and the trade in manufacturing as a commodity dominates resource allocation to a larger extent. Fourthly, there will be a continuous move toward more loosely connected entities forming manufacturing networks. More traditional concepts, like keiretsu and chaibol networks, do not sufficiently support this transition. Research should address these fundamental challenges to prepare for the industrial networks of 2020 and beyond.

A solution for information management in logistics operations of modern manufacturing chains

One dominant feature of the modern manufacturing chains is the movement of goods. Manufacturing companies would remain an unprofitable investment if the supplies/logistics of raw materials, semi-finished products or final goods are not handled in an effective way. Both levels of a modern manufacturing chain-actual production and logistics-are characterized by continuous data creation at a much faster rate than they can be meaningfully analyzed and acted upon manually. Often, instant and reliable decisions need to be taken based on huge, previously inconceivable amounts of heterogeneous, contradictory or incomplete data. The paper will highlight aspects of information flows related to business process data visibility and observability in modern manufacturing networks. An information management platform developed in the framework of the EU FP7 project ADVANCE will be presented.

Infer user interests via link structure regularization

Learning user interests from online social networks helps to better understand user behaviors and provides useful guidance to design user-centric applications. Apart from analyzing users' online content, it is also important to consider users' social connections in the social Web. Graph regularization methods have been widely used in various text mining tasks, which can leverage the graph structure information extracted from data. Previously, graph regularization methods operate under the cluster assumption that nearby nodes are more similar and nodes on the same structure (typically referred to as a cluster or a manifold) are likely to be similar. We argue that learning user interests from complex, sparse, and dynamic social networks should be based on the link structure assumption under which node similarities are evaluated based on the local link structures instead of explicit links between two nodes. We propose a regularization framework based on the relation bipartite graph, which can be constructed from any type of relations. Using Twitter as our case study, we evaluate our proposed framework from social networks built from retweet relations. Both quantitative and qualitative experiments show that our proposed method outperforms a few competitive baselines in learning user interests over a set of predefined topics. It also gives superior results compared to the baselines on retweet prediction and topical authority identification. © 2014 ACM.

A world full of surprises:Bayesian theory of surprise to quantify degrees of uncertainty

In the specific area of software engineering (SE) for self-adaptive systems (SASs) there is a growing research awareness about the synergy between SE and artificial intelligence (AI). However, just few significant results have been published so far. In this paper, we propose a novel and formal Bayesian definition of surprise as the basis for quantitative analysis to measure degrees of uncertainty and deviations of self-adaptive systems from normal behavior. A surprise measures how observed data affects the models or assumptions of the world during runtime. The key idea is that a "surprising" event can be defined as one that causes a large divergence between the belief distributions prior to and posterior to the event occurring. In such a case the system may decide either to adapt accordingly or to flag that an abnormal situation is happening. In this paper, we discuss possible applications of Bayesian theory of surprise for the case of self-adaptive systems using Bayesian dynamic decision networks. Copyright © 2014 ACM.

The future of manufacturing

Anyone who looks at the title of this special issue will agree that the intent behind the preparation of this volume was ambitious: to predict and discuss “The Future of Manufacturing”. Will manufacturing be important in the future? Even though some sceptics might say not, and put on the table some old familiar arguments, we would strongly disagree. To bring subsidies for the argument we issued the call-for-papers for this special issue of Journal of Manufacturing Technology Management, fully aware of the size of the challenge in our hands. But we strongly believed that the enterprise would be worthwhile. The point of departure is the ongoing debate concerning the meaning and content of manufacturing. The easily visualised internal activity of using tangible resources to make physical products in factories is no longer a viable way to characterise manufacturing. It is now a more loosely defined concept concerning the organisation and management of open, interdependent, systems for delivering goods and services, tangible and intangible, to diverse types of markets. Interestingly, Wickham Skinner is the most cited author in this special issue of JMTM. He provides the departure point of several articles because his vision and insights have guided and inspired researchers in production and operations management from the late 1960s until today. However, the picture that we draw after looking at the contributions in this special issue is intrinsically distinct, much more dynamic, and complex. Seven articles address the following research themes: 1.new patterns of organisation, where the boundaries of firms become blurred and the role of the firm in the production system as well as that of manufacturing within the firm become contingent; 2.new approaches to strategic decision-making in markets characterised by turbulence and weak signals at the customer interface; 3.new challenges in strategic and operational decisions due to changes in the profile of the workforce; 4.new global players, especially China, modifying the manufacturing landscape; and 5.new techniques, methods and tools that are being made feasible through progress in new technological domains. Of course, many other important dimensions could be studied, but these themes are representative of current changes and future challenges. Three articles look at the first theme: organisational evolution of production and operations in firms and networks. Karlsson's and Skold's article represent one further step in their efforts to characterise “the extraprise”. In the article, they advance the construction of a new framework, based on “the network perspective” by defining the formal elements which compose it and exploring the meaning of different types of relationships. The way in which “actors, resources and activities” are conceptualised extends the existing boundaries of analytical thinking in operations management and open new avenues for research, teaching and practice. The higher level of abstraction, an intrinsic feature of the framework, is associated to the increasing degree of complexity that characterises decisions related to strategy and implementation in the manufacturing and operations area, a feature that is expected to become more and more pervasive as time proceeds. Riis, Johansen, Englyst and Sorensen have also based their article on their previous work, which in this case is on “the interactive firm”. They advance new propositions on strategic roles of manufacturing and discuss why the configuration of strategic manufacturing roles, at the level of the network, will become a key issue and how the indirect strategic roles of manufacturing will become increasingly important. Additionally, by considering that value chains will become value webs, they predict that shifts in strategic manufacturing roles will look like a sequence of moves similar to a game of chess. Then, lastly under the first theme, Fleury and Fleury develop a conceptual framework for the study of production systems in general derived from field research in the telecommunications industry, here considered a prototype of the coming information society and knowledge economy. They propose a new typology of firms which, on certain dimensions, complements the propositions found in the other two articles. Their telecoms-based framework (TbF) comprises six types of companies characterised by distinct profiles of organisational competences, which interact according to specific patterns of relationships, thus creating distinct configurations of production networks. The second theme is addressed by Kyläheiko and SandstroÍm in their article “Strategic options based framework for management of dynamic capabilities in manufacturing firms”. They propose a new approach to strategic decision-making in markets characterised by turbulence and weak signals at the customer interface. Their framework for a manufacturing firm in the digital age leads to active asset selection (strategic investments in both tangible and intangible assets) and efficient orchestrating of the global value net in “thin” intangible asset markets. The framework consists of five steps based on Porter's five-forces model, the resources-based view, complemented by means of the concepts of strategic options and related flexibility issues. Thun, GroÍssler and Miczka's contribution to the third theme brings the human dimension to the debate regarding the future of manufacturing. Their article focuses on the challenges brought to management by the ageing of workers in Germany but, in the arguments that are raised, the future challenges associated to workers and work organisation in every production system become visible and relevant. An interesting point in the approach adopted by the authors is that not only the factual problems and solutions are taken into account but the perception of the managers is brought into the picture. China cannot be absent in the discussion of the future of manufacturing. Therefore, within the fourth theme, Vaidya, Bennett and Liu provide the evidence of the gradual improvement of Chinese companies in the medium and high-tech sectors, by using the revealed comparative advantage (RCA) analysis. The Chinese evolution is shown to be based on capabilities developed through combining international technology transfer and indigenous learning. The main implication for the Western companies is the need to take account of the accelerated rhythm of capability development in China. For other developing countries China's case provides lessons of great importance. Finally, under the fifth theme, Kuehnle's article: “Post mass production paradigm (PMPP) trajectories” provides a futuristic scenario of what is already around us and might become prevalent in the future. It takes a very intensive look at a whole set of dimensions that are affecting manufacturing now, and will influence manufacturing in the future, ranging from the application of ICT to the need for social transparency. In summary, this special issue of JMTM presents a brief, but undisputable, demonstration of the possible richness of manufacturing in the future. Indeed, we could even say that manufacturing has no future if we only stick to the past perspectives. Embracing the new is not easy. The new configurations of production systems, the distributed and complementary roles to be performed by distinct types of companies in diversified networked structures, leveraged by the new emergent technologies and associated the new challenges for managing people, are all themes that are carriers of the future. The Guest Editors of this special issue on the future of manufacturing are strongly convinced that their undertaking has been worthwhile.

Strategic innovation networks

This thesis draws on two key areas of the innovation literature, the strategic management of technology (SMOT) and innovation networks. The aim is to integrate these two areas of the management of innovation literature to develop a framework which I describe as the Strategic Innovation Network (SIN). The key proposition that the revised framework (SIN) aims to address is based on the work of Chandler (1962). Chandler's (1962) conclusion that 'structure follows strategy' is examined in relation to the interaction between corporate/technology strategy and network structure. The SIN is intended to address weaknesses in both the SMOT and network literature. The research data is based on five detailed longitudinal case studies. The organisations are defined as mid-corporate firms operating in traditional manufacturing sectors. Each organisation was chosen on the basis that it was aiming to develop its innovative capacity through product or process innovation projects. The research was carried out over an 18 month period with interviews being held regularly to develop the longitudinal aspect of the study analysis. The data for each individual case study is examined using the SIN framework. The longitudinal approach addresses the objective to provide a dynamic model of the innovation processes by mapping the changes in network structure during the course of individual projects. The network structural changes are examined in relation to each organisation's strategy and five key dynamic network stages are identified in relation to the innovation process. These network stages show the influence strategy has on the structures adopted by the five case studies.

A process framework for selecting supply system architecture in manufacturing supply chains and networks

The process framework comprises three phases, as follows: scope the supply chain/network; identify the options for supply system architecture and select supply system architecture. It facilitates a structured approach that analyses the supply chain/network contextual characteristics, in order to ensure alignment with the appropriate supply system architecture. The process framework was derived from comprehensive literature review and archival case study analysis. The review led to the classification of supply system architectures according to their orientation, whether integrated; partially integrated; co-ordinated or independent. The classification was combined with the characteristics that influence the selection of supply system architecture to encapsulate the conceptual framework. It builds upon existing frameworks and methodologies by focusing on structured procedure; supporting project management; facilitating participation and clarifying point of entry. The process framework was initially tested in three case study applications from the food, automobile and hand tool industries. A variety of industrial settings was chosen to illustrate transferability. The case study applications indicate that the process framework is a valid approach to the problem; however, further testing is required. In particular, the use of group support system technologies to support the process and the steps involving the participation of software vendors need further testing. However, the process framework can be followed due to the clarity of its presentation. It considers the issue of timing by including alternative decision-making techniques, dependent on the constraints. It is useful for ensuring a sound business case is developed, with supporting documentation and analysis that identifies the strategic and functional requirements of supply system architecture.