Engineering Cost Model Research Articles

The Convergence of COSYSMO Parametric Cost Estimation with Model‐Based Systems Engineering

AbstractThe promise of Model‐Based Systems Engineering (MBSE) and its advertised benefits hinge on the ability of our profession to integrate engineering disciplines and project management across the system life cycle. In particular, connecting system architecture and design decisions to the economics of the system's development is a critically important topic, but has often been overlooked by the system engineering community. Achieving such integration requires two key elements: (1) the standardization of multidisciplinary terms and functions, and (2) the establishment of rules governing relationships between cross‐functional models and modeling environments. This paper contributes to these areas by (1) establishing common terminology describing systems engineering and cost estimating, and (2) proposing specific cost drivers and counting rules within an MBSE environment that can be applied to estimating systems engineering effort using the COSYSMO cost model. COSYSMO is a systems engineering cost model, originally developed in 2005, and was grounded on document‐based systems engineering methods. This paper facilitates the convergence of COSYSMO and MBSE by updating the COSYSMO counting rules to specifically address size driver selection and assessment in a SysML model. It illustrates how advanced modeling tool features streamline and automate cost estimation activities, while queries and crosscutting views offered by modern MBSE tools enhance the completeness, quality, and consistency of parametric cost estimation process. Additionally, it provides guidelines for identifying system model content and level of detail for cost estimating, and proposes an approach to link attributes and properties in a system model to the variables in the COSYSMO cost estimating relationship.

Retracted: Improved Design of Engineering Cost Model Based on Improved Genetic Algorithm

Retracted: Improved Design of Engineering Cost Model Based on Improved Genetic Algorithm

Assessment of capital expenditure for fixed-bottom offshore wind farms using probabilistic engineering cost model

The capital expenditure (CAPEX) for the fixed-bottom offshore wind farm is assessed using a probabilistic engineering cost model and the cost reduction scenarios in Japan are analyzed. Firstly, the engineering cost model is described to assess the capital expenditure. A new export cable length model is also proposed considering the landing point distance and the vessel size model is proposed as the function of turbine rated power. The proposed engineering cost model succeeds in explaining the mechanism of the increase and decrease of CAPEX experienced in the UK. The uncertainties of model parameters are identified from the reported data and modeled by the normal distribution function. The workability is predicted using the discrete event simulation. The predicted CAPEX is then compared with the existing 30 fixed-bottom offshore wind farms in the United Kingdom. The predicted mean and standard deviation values of CAPEX show good agreement with the reported ones, while the conventional parametric model underestimates the mean value and cannot predict the standard deviation. Finally, the cost reduction scenarios and their uncertainties of offshore wind farms in Japan are analyzed using the proposed probabilistic engineering cost model. The levelized cost of wind energy reduced from 20.0 JPY/kWh to 17.0 JPY/kWh, 13.6 JPY/kWh and 10.1 JPY/kWh by the reduction of installation days using the specific installation vessel, the turbine enlargement and the improvement of operation and maintenance efficiency. The predicted supply prices for each cost reduction scenario agree well with those reported at the first auction conducted in 2021 in Japan.

Improved Design of Engineering Cost Model Based on Improved Genetic Algorithm

In order to solve the problems of traditional engineering cost model derivation process based on genetic algorithm complex, low convergence efficiency, and low accuracy of cost results, an improved design method of engineering cost model based on improved genetic algorithm is proposed. Through this algorithm, the cost model is optimized, better simulation data is selected, and the cost function model is constructed with the cost of the simulation data. The data parameter value is the accurate cost data calculated by the formula. Practice has proved that the dynamic cost precontrol system based on the improved genetic algorithm can effectively realize the dynamic precontrol of the project cost in the postearthquake building repair construction stage. As the number of cost samples increases, the error rate will gradually decrease, and the precontrol performance is good. Moreover, the flexibility of precontrol is always greater than 95%, and the use limit is only 0.04, which is highly practical and can be used as a reference for cost control of postearthquake building repair construction departments. The designed model can accurately and quickly estimate the project cost.

Development of engineering cost models for integrated design optimization of onshore and offshore wind farms

The goal of any multidisciplinary design optimization problem for wind power plants is to reduce the overall levelized cost of the energy. When it comes to designing a wind farm, one has to find the best combination of multiple parameters, such as turbine types, turbine dimensions and farm layout, to ensure the minimum cost. Clearly, since any design parameter may affect several cost items and performance indices of a farm, the most cost-effective solution should handle the mutual coupling among all design variables. For example, increasing the turbine spacing surely has a positive impact on energy production due to the minimization of wake losses, but, at the same time, may have a detrimental impact on the cost of cabling. In order to assist multidisciplinary design activities for wind farms and wind turbines, the present work is aimed at developing a tool for preliminary estimation of the levelized cost of energy of land- and sea-based wind farms. Such a tool is based on a modular architecture, which will ease the integration of the tool in a multi-level design framework. Each module of the tool implements one or more engineering models to estimate all cost items along with the annual energy production of the farm, starting from a few pieces of information related to turbine types and dimensions, farm geometry and wind conditions.

Genetic-algorithm-based layout optimization of an offshore wind farm under real seabed terrain encountering an engineering cost model

Optimizing the layout of an offshore wind farm can increase its total energy output. However, previous studies did not consider the seabed terrain, which can affect the lengths of the turbine supporting structures and thus affect the cost of electricity (COE) of the wind farm. At present, it is not clear whether layouts that have been optimized without considering the terrain can be applied under real terrains. Consequently, this study used genetic algorithm to innovatively optimize the offshore wind farm layouts under different seabed terrains, including a real one at Nanao Island. Moreover, an engineering cost model was developed and employed to predict the initial capital costs of the wind farms. A Gaussian-based wake model was used to predict the wind fields at these wind farms. For complex terrains, when the layouts that were optimized without considering the terrain were applied, the COEs of the wind farms increased by 27.3%. For flattened terrains, they only increased by 3.8%. Thus, layout optimizations that consider the terrain are necessary for real applications. Besides, the COEs of wind farms had almost no sensitivity to the terrain if they had the same mean water depth, with a maximum difference of 4.79% between various terrains.

Evaluation of Requirements Management Processes Utilizing System Modeling Language (SysML) Executable Models

AbstractLike many project processes, requirements management can be time consuming and costly if not well planned. The fundamentals of the process steps can be obtained through literature search, however the variation of application can lead to program cost challenges if not tailored for the project's risk posture and budget scope. This paper investigates various published requirements management processes, presents a fundamental process model, demonstrates a method to capture the process steps using a Systems Modeling Language (SysML), and shows the results of a simulation of the process steps enabling a comparison of outcomes for a project. This method investigates an option of using duration constraints to calculate labor hours based on process steps, and uses inputs from the project's requirement data to provide a method of calculating systems engineering labor hours utilizing the Constructive Systems Engineering Cost Model (COSYSMO) in a SysML model. This work is intended to show how SysML can be used to support development and refinement of project processes in general, with specific focus on how this method can enable implementation of requirements management in a way that provides insight into potential cost and schedule impacts to the project.

Comparison of dynamic response and levelized cost of energy on three platform concepts of floating offshore wind turbine systems

Spar, semisubmersible and barge platforms are modelled based on the full-scale demonstration projects in Japan for a generic 5 MW turbine. Three platforms are evaluated with the structural and stability parameters. The characteristic of floater motions and mooring forces are clarified by performing the dynamic analysis. The dynamic responses are validated with the water tank tests. The levelized cost of energy is assessed by using the engineering cost model.

Cost Estimation in Strategic Procurement Practices:Methodology and Applications in Indian Defense Sector

Estimation of cost plays very important and significant role in strategic procurement decisions. Cost is defined as an amount be paid or given up to get something in business, whereas estimate described as to form an approximate judgement or opinion regarding the worth, amount, size, weight etc. The ability to generate reliable cost estimates of a project, product or service is very critical for a Company to meet and execute its business requirements. Without this ability, Company is at risk of experiencing missed deadlines and performance shortfalls. In case of overestimate scenarios, companies could lead to loss of future orders against competition. This paper provides comprehensive review of various costing methods and its underlined assumptions specifically for defense industry. The unique fundamental characteristic of this sector is that sales are always restricted to Governments and their armed forces. The selection criteria by government remain to be through competitive tender bidding and based on L1 (Lowest bidder) selection. It is important to choosing a right and appropriate cost model based on its size, complexity, timeline & development stage both during participating and life cycle management to meet business objectives. During this descriptive research, various interviews and interactions done with key decision makers of International Defense Industry. It is observed that, Standard regular products follow costing methods like Expert judgment, price to win, Top-Down and Bottom-up approach, whereas complex & large ones are estimated using statistical based Parametric, COSYMO (constructive System Engineering Cost Model), Analogy, Engineering and actual costing techniques. In new era of highly sophisticated weapon systems and radars technology developments, software's are another big-ticket item in defense industry, while doing life cycle cost estimation. An attempt has also been made to highlight challenges that are faced by organizations specially those entering to the defense sector for the first time in India. Non- availability of existing structure, local industrial base, product lines, engineering capabilities & technology makes cost estimation more challenging. This paper also come up with suggestions that can be used as high level reference guide while calibrating international cost data points for using in India.

Reliability Improvement of Major Defense Acquisition Program Cost Estimates—Mapping DoDAF to COSYSMO

ABSTRACTMajor defense acquisition programs (MDAPs) are notoriously prone to substantial cost overruns, and engineering and design issues are often partially to blame. Accordingly, as the Department of Defense (DoD) develops increasingly complex systems, additional emphasis is being placed on systems engineering to ensure that programs remain on schedule and within budget. Notably, in 2013 the DoD mandated the submission of DoD Architecture Framework (DoDAF) models during a program's Materiel Solution Analysis Phase—early in the system life cycle—a period historically characterized by a lack of system specification and parametric estimation. Capitalizing on this change, this paper establishes an explicit connection between the DoDAF and the Constructive Systems Engineering Cost Model (COSYSMO) with the goal of improving the measurement reliability of MDAP cost estimates. In particular, we employ techniques from text mining and social network analysis to produce a mapping between DoDAF's 52 models and COSYSMO's 18 drivers, yielding an untapped source of information for determining functional system size and, ultimately, estimating systems engineering effort.

Comparative Evaluation of Three Sugarcane Supply Strategies in Colombia: Logistics, Energy and GHG Emissions

Sugarcane-based ethanol in Colombia has a prospective opportunity to explore the production of ethanol from lignocellulosic biomass taking into account that a large amount of generated residues (between 50 and 100 t/ha) are left on the field after green harvesting. The use of sugarcane green harvesting residues for ethanol production could be considered as a feasible solution but it is facing high supply costs. A bottom-up engineering cost model was used to estimate the green harvesting residues supply cost for three collection strategies. The model took into account the investment and operation costs of the residues collection, processing, delivery, and machineries including depreciations, repair and maintenance, and labor cost. Overall energy consumptions and life-cycle GHG emissions of the three strategies were analyzed. The integral harvesting showed the best performance in costs (6.01 USD/dry-t), energy consumptions (125.32 MJ/dry-t), and GHG emissions (7.74 kg CO2-eq/dry-t), followed by the baled and chopped residues.

Characterizing the Impact of Requirements Volatility on Systems Engineering Effort

ABSTRACTThis paper describes the results of a study aimed at increasing the understanding of the causes of requirements volatility, its impact on systems engineering effort, and its changing dynamics over the system life cycle. The objective of the research is to improve the ability of systems engineers to anticipate and manage the effects of changing requirements. The ultimate goal of this study is to develop a model for quantifying the impact of requirements volatility on systems engineering effort that can be incorporated into the Constructive Systems Engineering Cost Model. Based on a review of the literature and expert judgment collected through surveys in five workshops, we identify five observations that summarize the key considerations of requirements volatility.First, a set of project organizational, technical, and contextual factors were ranked by subject matter experts in terms of their influence on requirements volatility. Their responses point to poor initial understanding of the system and customer needs as the leading cause of requirements volatility. Second, our results suggest that, while volatility tends to decrease over time, the number of requirements changes may increase during transitions between life cycle phases. Third, requirements volatility increases the functional size of the project and causes rework of engineering products, driving an increase in systems engineering effort. Fourth, the effect of requirements volatility on systems engineering effort increases the later the change occurs in the system life cycle. Fifth, the effort impact of a requirements change varies depending on the type of change (added, deleted, and modified).

Ecohydrological and socioeconomic integration for the operational management of environmental flows

Investment in and operation of flow control infrastructure such as dams, weirs, and regulators can help increase both the health of regulated river ecosystems and the social values derived from them. This requires high-quality and high-resolution spatiotemporal ecohydrological and socioeconomic information. We developed such an information base for integrated environmental flow management in the River Murray in South Australia (SA). A hydrological model was used to identify spatiotemporal inundation dynamics. River ecosystems were classified and mapped as ecohydrological units. Ecological response models were developed to link three aspects of environmental flows (flood duration, timing, and inter-flood period) to the health responses of 16 ecological components at various life stages. Potential infrastructure investments (flow control regulators and irrigation pump relocation) were located by interpreting LiDAR elevation data, digital orthophotography, and wetland mapping information; and infrastructure costs were quantified using engineering cost models. Social values were quantified at a coarse scale as total economic value based on a national survey of willingness-to-pay for four key ecological assets; and at a local scale using mapped ecosystem service values. This information was integrated using a constrained, nonlinear, mixed-integer, compromise programming optimization model and solved using a stochastic Tabu search algorithm. We tested the model uncertainty and sensitivity using 390 Monte Carlo model runs at varying weights of ecological health vs. social values. Integrating ecohydrological and socioeconomic information identified environmental flow management regimes that efficiently achieved both ecological and social objectives. Using an ecologically weighted efficient and socially weighted efficient scenario, we illustrated model outputs including a suite of cost-effective infrastructure investments and an operational plan for new and existing flow control structures including dam releases, weir height manipulation, and regulator operation on a monthly time step. Both the investments and management regimes differed substantially between the two scenarios, suggesting that the choice of weightings on ecological and social objectives is important. This demonstrates the benefit of integrating high-quality and high-resolution spatiotemporal ecohydrological and socioeconomic information for guiding the investment in and operational management of environmental flows.

4.6.3 Affordable Systems: Balancing the Capability, Schedule, Flexibility, and Technical Debt Tradespace

AbstractWith the increasing demands for affordable system capabilities that can be provided quickly to the user community, developers must explore a variety of options for identifying “satisficing” solutions. The system capability affordability tradespace must balance expedited systems engineering to reduce schedule and cost, encourage flexibility in architecture decisions to support future evolution of the system, and minimize technical debt that either results in later rework or adversely impacts future options. This paper shows how the University of Southern California (USC) Center for Systems and Software Engineering (CSSE) software and systems engineering cost models can be used in the analysis of this tradespace to show the range of options and the resulting consequences.

6.3.1 COSYSMO Calibration Steps and Results

AbstractThis paper provides insight into calibrating a systems engineering cost model (COSYSMO) by providing a process that organizations can follow to improve their estimation accuracy. A specific example is provided from Raytheon Missile Systems to illustrate how data are collected and how they influence the cost estimating relationship. The results from this specific case are generalized to other organizations who wish to improve their cost estimation capabilities in systems engineering.

9.2.2 Quantifying Systems Engineering Reuse – a Generalized Reuse Framework in COSYSMO

AbstractThis paper further elaborates a generalized reuse framework for systems engineering reuse previously published and defines two interrelated reuse processes – development for reuse (DFR) and development with reuse (DWR). It proposes a quantitative approach for assessing such a reuse effort by defining a set of reuse categories for both the DFR and the DWR processes and by extending the model definition and cost estimating relationship of COSYSMO, a systems engineering cost model.

Harmonising software engineering and systems engineering cost estimation

Complex systems are developed with the help of numerous engineering disciplines. In software-intensive systems, software and systems engineers play a major role in design, development, test and deployment. Since these roles are often very tightly coupled, it is difficult to determine which life cycle functions can be performed jointly versus separately. This makes it particularly difficult for resource estimation and project planning. To resolve this issue, we examine the gaps and overlaps between software engineering and systems engineering cost models with intent to harmonise the estimates for project estimation. In particular, we evaluate the central assumptions of the constructive cost model II and constructive systems engineering cost model and propose an approach to identify gaps and overlaps between them. Furthermore, we provide guidelines on how to reconcile and resolve the identified gaps and overlaps. The ultimate purpose of this work is to develop effective techniques for accurately estimating the combined software and systems engineering effort for software-intensive systems.

The impact of different fibre access network technologies on cost, competition and welfare

Using a novel approach to the evaluation of new network technologies that combines an engineering cost model with a differentiated multi-player oligopoly model with wholesale access regulation this article evaluates the choice among different Fibre-to-the-Home (FTTH) architectures. The cost modelling relies upon an engineering bottom-up approach that feeds into a competition model. For addressing competition the pyramid model was chosen, which is an extension of the Hotelling model to multiple firms/services. The paper solves for price setting Nash equilibria between an incumbent, wholesale-access-based entrants and cable as an additional fully integrated network competitor. Welfare tradeoffs are highlighted with respect to cost differences and QoS differences between the various FTTH architectures and between the modes of regulation. According to the analysis architectures that can be unbundled (and that allow for greater speeds) outperform, from a social welfare perspective, architectures that (realistically) allow only for bitstream access.

Fractal-COSYSMO Systems Engineering Cost Estimation for Complex Projects

Cost estimation for engineering projects has advanced beyond the bottom-up counting of costs and the top-down use of analogies. Parametric cost estimation is now employed to estimate volatile software costs, as well as the cost of Systems Engineering (SE) effort in engineering projects. The Constructive Systems Engineering Cost Model (COSYSMO) estimates the number of Person-Months (PM) necessary to complete systems engineering projects by using project size and cost parameters. On re-examining the nature of systems engineering and the structures of complex projects that must be built in an orderly fashion on a variety of scales, it is apparent that a parametric formula that employs fractal dimensionality principles should be well suited for their cost estimation. This thesis therefore develops the connections between fractal dimensionality and cost estimation with COSYSMO. The result is Fractal-COSYSMO, a novel cost estimating formulation that can be used to determine the cost of developing systems that show complexity on a broad scale.

Reuse in Systems Engineering

Reuse in systems engineering is a frequent but poorly understood phenomenon. Nevertheless, it has a significant impact on system development and on estimating the appropriate amount of systems engineering effort with models like the Constructive Systems Engineering Cost Model (COSYSMO). Practical experience showed that the initial version of COSYSMO, based on a “build from the scratch” philosophy, needed to be refined in order to incorporate reuse considerations that fit today's industry environment. The notion of reuse recognizes the effect of legacy system definition in engineering a system and introduces multiple reuse categories for classifying the four COSYSMO size drivers-requirements, interfaces, algorithms, and operational scenarios. It fundamentally modifies the driver counting rules and updates its definition of system size. It provides an enabling framework for estimating a system under incremental and spiral development. In this paper, we present: 1) the definition of the COSYSMO reuse extension and the approach employed to define this extension; 2) the updated COSYSMO size driver definitions to be consistent with the reuse model; 3) the method applied to defining the reuse weights used in the modified parametric relationship; 4) a practical implementation example that instantiates the reuse model by an industry organization and the empirical data that provided practical validation of the extended COSYSMO model; and 5) recommendations for organizational implementation and deployment of this extension.