An approach for selecting cost estimation techniques for innovative high value manufacturing products

This paper presents an approach for determining the most appropriate technique for cost estimation of innovative high value manufacturing products depending on the amount of prior data available. Case study data from the United States Scheduled Annual Summary Reports for the Joint Strike Fighter (1997-2010) is used to exemplify how, depending on the attributes of a priori data certain techniques for cost estimation are more suitable than others. The data attribute focused on is the computational complexity involved in identifying whether or not there are patterns suited for propagation. Computational complexity is calculated based upon established mathematical principles for pattern recognition which argue that at least 42 data sets are required for the application of standard regression analysis techniques. The paper proposes that below this threshold a generic dependency model and starting conditions should be used and iteratively adapted to the context. In the special case of having less than four datasets available it is suggested that no contemporary cost estimating techniques other than analogy or expert opinion are currently applicable and alternate techniques must be explored if more quantitative results are desired. By applying the mathematical principles of complexity groups the paper argues that when less than four consecutive datasets are available the principles of topological data analysis should be applied. The preconditions being that the cost variance of at least three cost variance types for one to three time discrete continuous intervals is available so that it can be quantified based upon its geometrical attributes, visualised as an n-dimensional point cloud and then evaluated based upon the symmetrical properties of the evolving shape. Further work is suggested to validate the provided decision-trees in cost estimation practice

Differences in forecasting approaches between product firms and product-service systems (PSS)

This paper examines the forecasting implications for Product-Service Systems (PSS) applications in manufacturing firms. The approach taken is to identify the scope of operations for PSS applications by identifying all the activities associated with the total cost of ownership (TCO). The paper then develops a revenue model for manufacturing firms providing PSS applications. The revenue model identifies three generic revenue streams that provide the basis for discussion on the differences in forecasting approaches between product firms and Product-Service Systems (PSS) in manufacturing firms. The forecasting approaches are different due to the nature of customer involvement in the service aspect of PSS applications. This necessitates an understanding of the customer service experience and the factors affecting this such as the service profit chain which links profitability, customer loyalty and service value to employee satisfaction, capability and productivity. The forecasting approaches identified raises forecasting challenges for each of the three generic revenue sources. These challenges vary from the difficulty in obtaining the service user’s viewpoint through to difficulties in determining market acceptance of PSS applications

Strategic, tactical decisions and information in Rapid Manufacturing supply chain

The efficiency and agility of its supply chain is vital to the commercial success of any product. Sharing strategic and tactical information effectively within the supply chain is often a key factor in achieving this goal. This paper proposes a framework to identify strategic, tactical decisions and information. The framework is used to conduct a sector based analysis of the Rapid Manufacturing (RM) industry. The decisions and information identified include amongst others various supply chain strategies and technical information

Managing the trade-off implications of global supply

The cost versus response trade-off is a growing logistics issue due to many markets being increasingly characterized by demand uncertainty and shorter product life cycles. This is exacerbated further with supply increasingly moving to low cost global sources. However, the poor response implications of global supply are often not addressed or even acknowledged when undertaking such decisions. Consequently, various practical approaches to minimising, postponing or otherwise managing the impact of the demand uncertainty are often only adopted retrospectively. Even though such generic solutions are documented through case examples we lack effective tools and concepts to support the proactive identification and resolution of such trade-offs. This paper reports on case-based theory building research, involving three cases from the UK and USA used in developing a conceptual model with associated tools, in support of such a process

Compensating mode conversion due to bend discontinuities through intentional trace asymmetry

In this letter, a comparative analysis is carried out between the mechanism of mode conversion in differential microstrip lines due to bend discontinuities on one side and trace asymmetry on the other side. With the help of equivalent modal circuits, a theoretical basis is provided for the idea to compensate the undesired common mode (CM), due to the presence of the bend, by intentionally designing asymmetric traces. As an application example, the proposed CM-reduction strategy is used in conjunction with another recently-presented wideband CM suppression filter for differential microstrip lines. It is shown that the proposed solution enhances the overall CM-reduction performance of the filter by some decibels, while preserving its transmission properties

Construction vs. Development: Polarizing Models of Human Gestation

This essay argues that the polarization of our public debate over embryo-destructive research may be due, to a large extent, not to different valuations of individual human life but to different conceptions of the process of gestation, with one group treating the process as a making or construction and the other treating it as a development. These two incompatible models of reproduction are shown to explain the various positions commonly encountered in this debate over the treatment of embryos, and to a significant degree those encountered in the debate over abortion as well. Finally, the historical, theoretical, and intuitive strengths of each model are examined

Machine learning and its applications in reliability analysis systems

In this thesis, we are interested in exploring some aspects of Machine Learning (ML) and its application in the Reliability Analysis systems (RAs). We begin by investigating some ML paradigms and their- techniques, go on to discuss the possible applications of ML in improving RAs performance, and lastly give guidelines of the architecture of learning RAs. Our survey of ML covers both levels of Neural Network learning and Symbolic learning. In symbolic process learning, five types of learning and their applications are discussed: rote learning, learning from instruction, learning from analogy, learning from examples, and learning from observation and discovery. The Reliability Analysis systems (RAs) presented in this thesis are mainly designed for maintaining plant safety supported by two functions: risk analysis function, i.e., failure mode effect analysis (FMEA) ; and diagnosis function, i.e., real-time fault location (RTFL). Three approaches have been discussed in creating the RAs. According to the result of our survey, we suggest currently the best design of RAs is to embed model-based RAs, i.e., MORA (as software) in a neural network based computer system (as hardware). However, there are still some improvement which can be made through the applications of Machine Learning. By implanting the 'learning element', the MORA will become learning MORA (La MORA) system, a learning Reliability Analysis system with the power of automatic knowledge acquisition and inconsistency checking, and more. To conclude our thesis, we propose an architecture of La MORA

Estimating the cost of a new technology intensive automotive product: A case study approach.

Estimating cost of new technology intensive products is very ad hoc within the automotive industry. There is a need to develop a systematic approach to the cost estimating, which will make the estimates more realistic. This research proposes a methodology that uses parametric, analogy and detailed estimating techniques to enable a cost to be built for an automotive powertrain product with a high content of new technology. The research defines a process for segregating new or emerging technologies from current technologies to enable the various costing techniques to be utilised. The cost drivers from an internal combustion engine's characteristics to facilitate a cost estimate for high- volume production are also presented. A process to enable a costing expert to either build an estimate for the new technology under analysis or use a comparator and then develop a variant for the new system is also discussed. Due to the open nature of the statement ‘new technology’, research is also conducted to provide a meaningful definition applicable to the automotive industry and this pro

Manufacturing a mathematical group: a study in heuristics

I examine the way a relevant conceptual novelty in mathematics, that is, the notion of group, has been constructed in order to show the kinds of heuristic reasoning that enabled its manufacturing. To this end, I examine salient aspects of the works of Lagrange, Cauchy, Galois and Cayley (Sect. 2). In more detail, I examine the seminal idea resulting from Lagrange’s heuristics and how Cauchy, Galois and Cayley develop it. This analysis shows us how new mathematical entities are generated, and also how what counts as a solution to a problem is shaped and changed. Finally, I argue that this case study shows us that we have to study inferential micro-structures (Sect. 3), that is, the ways similarities and regularities are sought, in order to understand how theoretical novelty is constructed and heuristic reasoning is put forwar

The strategic integration of agile and lean supply

Lean supply is closely associated with enabling flow and the elimination of wasteful variation within the supply chain. However, lean operations depend on level scheduling and the growing need to accommodate variety and demand uncertainty has resulted in the emergence of the concept of agility. This paper explores the role of inventory and capacity in accommodating such variation and identifies how TRIZ separation principles and TOC tools may be combined in the integrated development of responsive and efficient supply chains. A detailed apparel industry case study is used to illustrate the application of these concepts and tools