When costs from being a constraint become a driver for concept generation

Managing innovation requires solving issues related to the internal development and engineering processes of a company (supply side), in addition to facing the market and competition (demand side). In this context, the product development process is crucial, as different tradeoffs and issues that require managerial attention tend to arise. The main challenges result in managers requiring practical support tools that can help them in planning and controlling the process, and of designers requiring them for supporting their design decisions. Hence, the thesis aims to focus on product costs to understand its influence on design decisions as well as on the overall management of the product development process. The core part of the thesis is based on the models and methods developed for enhancing cost analysis at the beginning of the product development process. This investigation aims to determine the importance of cost estimation in improving the overall performance of a newly designed product. The focus on post-sales and, more generally, on the customer, has become so relevant that manufacturers have to take into account not only the most obvious aspects about the product and related services, but even consider the associated implications for customers during product use. However, implementing a product life cycle perspective is still a challenging process for companies. From a methodological perspective, the reasons include uncertainty regarding the available approaches and ambiguity about their application. In terms of implementation, the main challenge is the long-term cost management, when one considers uncertainty in process duration, data collection, and other supply chain issues. In fact, helping designers and managers efficiently understand the strategic and operational consequences of a cost analysis implementation is still a problem, although advanced methodologies for more in-depth and timely analyses are available. And this is even more if one considers that product lifecycle represents a critical area of investment, particularly in light of the new challenges and opportunities provided by big data analysis in the Industry 4.0 contexts. This dissertation addresses these aspects and provides a methodological approach to assess a rigorous implementation of life-cycle cost while discussing the evidence derived from its operational and strategic impacts. The novelty lies in the way the data and information are collected, dynamically moving the focus of the investigation with regard to the data aggregation level and the product structure. The way the techniques have been combined represents a further aspect of novelty. In fact, the introduced approach contributes to a new trend in the Product Cost Estimation (PCE) literature, which suggests the integration of different techniques for product life-cycle cost analysis. The findings obtained at the end of the process can be employed to assess the impact of platform design strategy and variety proliferations on the total life-cycle costs. By evaluating the possible mix of options, and hence offering the optimal product configuration, a more conscious way for planning the product portfolio has been provided. In this sense, a detailed operational analysis (as the cost estimation) is used to inform and drive the strategic planning of the portfolio. Finally, the thesis discusses the future opportunities and challenges for product cost analysis, assessing how digitalisation of manufacturing operations may affect the data gathering and analysis process. In this new environment, the opportunity for a more informed, cost-driven decision-making will multiply, leading to varied opportunities in this research field

Mapping customer needs to engineering characteristics: an aerospace perspective for conceptual design

Designing complex engineering systems, such as an aircraft or an aero-engine, is immensely challenging. Formal Systems Engineering (SE) practices are widely used in the aerospace industry throughout the overall design process to minimise the overall design effort, corrective re-work, and ultimately overall development and manufacturing costs. Incorporating the needs and requirements from customers and other stakeholders into the conceptual and early design process is vital for the success and viability of any development programme. This paper presents a formal methodology, the Value-Driven Design (VDD) methodology that has been developed for collaborative and iterative use in the Extended Enterprise (EE) within the aerospace industry, and that has been applied using the Concept Design Analysis (CODA) method to map captured Customer Needs (CNs) into Engineering Characteristics (ECs) and to model an overall ‘design merit’ metric to be used in design assessments, sensitivity analyses, and engineering design optimisation studies. Two different case studies with increasing complexity are presented to elucidate the application areas of the CODA method in the context of the VDD methodology for the EE within the aerospace secto

Estimation of life-cycle costs of buildings: regression vs artificial neural network

Purpose – The purpose of this paper is to compare the performance of regression and artificial-neural-networks (ANNs) methods to estimate the running cost of building projects towards improved accuracy. Design/methodology/approach – A data set of 20 building projects is used to test the performance of these two (ANNs/regression) models in estimating running cost. The concept of cost-significant-items is identified as important in assisting estimation. In addition, a stepwise technique is used to eliminate insignificant factors in regression modelling. A connection weight method is applied to determine the importance of cost factors in the performance of ANNs. Findings – The results illustrate that the value of the coefficient of determination=99.75 per cent for ANNs model(s), with a value of 98.1 per cent utilising multiple regression (MR) model(s); second, the mean percentage error (MPE) for ANNs at a testing stage is 0.179, which is less than that of the MPE gained through MR modelling of 1.28; and third, the average accuracy is 99 per cent for ANNs model(s) and 97 per cent for MR model(s). On the basis of these results, it is concluded that an ANNs model is superior to a MR model when predicting running cost of building projects. Research limitations/implications – A means for continuous improvement for the performance of the models accuracy has been established; this may be further enhanced by future extended sample. Originality/value – This work extends the knowledge base of life-cycle estimation where ANNs method has been found to reduce preparation time consumed and increasing accuracy improvement of the cost estimation

Life cycle cost modelling as an aircraft design decision support tool

This report summarizes the work that has been carried out as part of the FLAVIIR project, a 5 year research program looking at technologies for future unmanned air vehicles. A novel classification of aircraft product definition is utilised and a framework to estimate the life cycle cost of aircraft using the product definition is presented. The architecture to estimate the life cycle cost and the associated models are described.The acquisition costs are estimated using a hierarchical structure and a discrete simulation model is used to estimate the maintenance and operation costs. The acquisition cost model uses an object oriented approach with libraries of materials and processes integrated into the cost model. Risk analysis is performed to identify the important design parameters and uncertainty in the model. The acquisition cost model developed has the capability to estimate the costs of aircraft structures manufactured using metal-based materials as well as non-metal-based materials.The discrete event simulation model estimates the operation and maintenance costs of a fleet of aircraft using the mission characteristics, aircraft performance and the logistics data as input. The aircraft performance parameters are calculated by using aerodynamic analysis along with performance analysis models and the simulation model utilises a novel methodology to link aircraft performance with survivability analysis for estimating the maintenance costs.A framework is presented in which the cost models developed can be integrated into the conceptual design process to facilitate the comparison between different configurations. The usage of the life cycle cost framework as a decision support tool is outlined and three case studies are presented which include composites vs metals trade-off analysis, optimisation studies and web deployment for real time cost estimation. The novel contributions of this research are outlined and interesting avenues for future research that can be pursued are identified

Insights on Research Techniques towards Cost Estimation in Software Design

Software cost estimation is of the most challenging task in project management in order to ensuring smoother development operation and target achievement. There has been evolution of various standards tools and techniques for cost estimation practiced in the industry at present times. However, it was never investigated about the overall picturization of effectiveness of such techniques till date. This paper initiates its contribution by presenting taxonomies of conventional cost-estimation techniques and then investigates the research trends towards frequently addressed problems in it. The paper also reviews the existing techniques in well-structured manner in order to highlight the problems addressed, techniques used, advantages associated and limitation explored from literatures. Finally, we also brief the explored open research issues as an added contribution to this manuscript

Effective Utilization of Historical Data to Increase Organizational Performance: Focus on Sales/ Tendering and Projects

Master's thesis in Offshore technologyIn Oil and Gas industry there was not enough focus on this topic as cost was not a big factor in good olden days. But the sensational drop in oil prices below US$40 per barrel at the end of 2015 made the price more than 60 percent down compared to the one in previous years. It’s clear that the sector is going through one of the most transformative periods in its history. This situation has created more challenges to all O&G company leaders by forcing them to change their business strategies. The operating companies in the Oil and Gas industry have been focusing to reduce costs and increase organizational performance. Accordingly suppliers companies need to acknowledge their focus on the efficiency and optimization of resources to be able to sustain and grow in a competitive market. It demands better control of estimates and cost on future sales/tendering process. As quoted by one of the Operations Managers “An informed organization saves cost and wins faster”. The only way to get reliable information for any organization is by analyzing ‘what happened in the past’ and what we learned from it. In other words this is achieved through utilization of historical data from previous projects and by developing benchmarking metrics. Further, usage of the historical data can improve estimation and scheduling, support strategic planning, and improve the organizational processes. The historical project data or information can help in making strategic business decisions in any Organization. It can play a significant role in providing very distinct advantage over the competitors. Historical data can help the management to decide what projects are right for the future of the company and which projects can be avoided. Further, it can help to learn from past mistakes and win future bids by not repeating them. Most of the top management understands the importance of having and using historical project information or data. The problem is that very few companies have the methodologies, procedures, and systems in place to effectively use this information to improve their project processes and to support the estimation, scheduling, and control of future projects (opportunities). The present work focuses on historical data, estimation process and lessons learned for enhancing organizational performance. Further, the work includes a case study and number of expert interviews conducted at ABB. The work discusses how to collect, normalize, and analyze historical project data to develop practical information. Three models have been developed for project estimation process with a feedback loop, Lessons learned process model and Historical data utilization process. The recommendations have been made to use the historical data for establishing references for the sales/tendering department for future estimates, which can reduce the dependency on manual or a single person’s judgment and improve the estimation process. Some suggestions have also been made for establishing lessons learned process which can improve organizational performance. The results from analysis show that by applying the recommended processes, organizations can achieve efficiency through easy access and storage of historical database, easy access to lessons learned, measurable KPIs. Also use of key variables like project complexity and severity of requirements for estimation process and historical data process can form a better relation for data analysis and utilization.AB

System Qualities Ontology, Tradespace and Affordability (SQOTA) Project – Phase 4

This task was proposed and established as a result of a pair of 2012 workshops sponsored by the DoD Engineered Resilient Systems technology priority area and by the SERC. The workshops focused on how best to strengthen DoD’s capabilities in dealing with its systems’ non-functional requirements, often also called system qualities, properties, levels of service, and –ilities. The term –ilities was often used during the workshops, and became the title of the resulting SERC research task: “ilities Tradespace and Affordability Project (iTAP).” As the project progressed, the term “ilities” often became a source of confusion, as in “Do your results include considerations of safety, security, resilience, etc., which don’t have “ility” in their names?” Also, as our ontology, methods, processes, and tools became of interest across the DoD and across international and standards communities, we found that the term “System Qualities” was most often used. As a result, we are changing the name of the project to “System Qualities Ontology, Tradespace, and Affordability (SQOTA).” Some of this year’s university reports still refer to the project as “iTAP.”This material is based upon work supported, in whole or in part, by the U.S. Department of Defense through the Office of the Assistant of Defense for Research and Engineering (ASD(R&E)) under Contract HQ0034-13-D-0004.This material is based upon work supported, in whole or in part, by the U.S. Department of Defense through the Office of the Assistant of Defense for Research and Engineering (ASD(R&E)) under Contract HQ0034-13-D-0004

-ilities Tradespace and Affordability Project – Phase 3

One of the key elements of the SERC’s research strategy is transforming the practice of systems engineering and associated management practices – “SE and Management Transformation (SEMT).” The Grand Challenge goal for SEMT is to transform the DoD community’s current systems engineering and management methods, processes, and tools (MPTs) and practices away from sequential, single stovepipe system, hardware-first, document-driven, point- solution, acquisition-oriented approaches; and toward concurrent, portfolio and enterprise- oriented, hardware-software-human engineered, model-driven, set-based, full life cycle approaches.This material is based upon work supported, in whole or in part, by the U.S. Department of Defense through the Office of the Assistant Secretary of Defense for Research and Engineering (ASD(R&E)) under Contract H98230-08- D-0171 (Task Order 0031, RT 046).This material is based upon work supported, in whole or in part, by the U.S. Department of Defense through the Office of the Assistant Secretary of Defense for Research and Engineering (ASD(R&E)) under Contract H98230-08- D-0171 (Task Order 0031, RT 046)

Learning From Mistakes: Machine Learning Enhanced Human Expert Effort Estimates

In this paper, we introduce a novel approach to predictive modeling for software engineering, named Learning From Mistakes (LFM). The core idea underlying our proposal is to automatically learn from past estimation errors made by human experts, in order to predict the characteristics of their future misestimates, therefore resulting in improved future estimates. We show the feasibility of LFM by investigating whether it is possible to predict the type, severity and magnitude of errors made by human experts when estimating the development effort of software projects, and whether it is possible to use these predictions to enhance future estimations. To this end we conduct a thorough empirical study investigating 402 maintenance and new development industrial software projects. The results of our study reveal that the type, severity and magnitude of errors are all, indeed, predictable. Moreover, we find that by exploiting these predictions, we can obtain significantly better estimates than those provided by random guessing, human experts and traditional machine learners in 31 out of the 36 cases considered (86%), with large and very large effect sizes in the majority of these cases (81%). This empirical evidence opens the door to the development of techniques that use the power of machine learning, coupled with the observation that human errors are predictable, to support engineers in estimation tasks rather than replacing them with machine-provided estimates