Short interval control for the cost estimate baseline of novel high value manufacturing products – a complexity based approach

Novel high value manufacturing products by default lack the minimum a priori data needed for forecasting cost variance over of time using regression based techniques. Forecasts which attempt to achieve this therefore suffer from significant variance which in turn places significant strain on budgetary assumptions and financial planning. The authors argue that for novel high value manufacturing products short interval control through continuous revision is necessary until the context of the baseline estimate stabilises sufficiently for extending the time intervals for revision. Case study data from the United States Department of Defence Scheduled Annual Summary Reports (1986-2013) is used to exemplify the approach. In this respect it must be remembered that the context of a baseline cost estimate is subject to a large number of assumptions regarding future plausible scenarios, the probability of such scenarios, and various requirements related to such. These assumptions change over time and the degree of their change is indicated by the extent that cost variance follows a forecast propagation curve that has been defined in advance. The presented approach determines the stability of this context by calculating the effort required to identify a propagation pattern for cost variance using the principles of Kolmogorov complexity. Only when that effort remains stable over a sufficient period of time can the revision periods for the cost estimate baseline be changed from continuous to discrete time intervals. The practical implication of the presented approach for novel high value manufacturing products is that attention is shifted from the bottom up or parametric estimation activity to the continuous management of the context for that cost estimate itself. This in turn enables a faster and more sustainable stabilisation of the estimating context which then creates the conditions for reducing cost estimate uncertainty in an actionable and timely manner

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

A geometrical framework for forecasting cost uncertainty in innovative high value manufacturing.

Increasing competition and regulation are raising the pressure on manufacturing organisations to innovate their products. Innovation is fraught by significant uncertainty of whole product life cycle costs and this can lead to hesitance in investing which may result in a loss of competitive advantage. Innovative products exist when the minimum information for creating accurate cost models through contemporary forecasting methods does not exist. The scientific research challenge is that there are no forecasting methods available where cost data from only one time period suffices for their application. The aim of this research study was to develop a framework for forecasting cost uncertainty using cost data from only one time period. The developed framework consists of components that prepare minimum information for conversion into a future uncertainty range, forecast a future uncertainty range, and propagate the uncertainty range over time. The uncertainty range is represented as a vector space representing the state space of actual cost variance for 3 to n reasons, the dimensionality of that space is reduced through vector addition and a series of basic operators is applied to the aggregated vector in order to create a future state space of probable cost variance. The framework was validated through three case studies drawn from the United States Department of Defense. The novelty of the framework is found in the use of geometry to increase the amount of insights drawn from the cost data from only one time period and the propagation of cost uncertainty based on the geometric shape of uncertainty ranges. In order to demonstrate its benefits to industry, the framework was implemented at an aerospace manufacturing company for identifying potentially inaccurate cost estimates in early stages of the whole product life cycle

GENOTYPIC VARIATION IN THE ACCUMULATION OF RARE EARTH ELEMENTS (REE) IN PHALARIS ARUNDINACEA L

Rare earth elements (REEs) represent a number of economically valuable elements whose increasing demand is closely associated with rapidly growing high-tech sectors such as high-tech electronics and "green energy technologies". In soils REEs are actually not rare but occur widespread with concentrations comparable to some essential plant nutrients (e.g. Zn). Thus, a promising chance to improve supply of these resources could be phytomining

A framework for geometric quantification and forecasting of cost uncertainty for aerospace innovations

Quantification and forecasting of cost uncertainty for aerospace innovations is challenged by conditions of small data which arises out of having few measurement points, little prior experience, unknown history, low data quality, and conditions of deep uncertainty. Literature research suggests that no frameworks exist which specifically address cost estimation under such conditions. In order to provide contemporary cost estimating techniques with an innovative perspective for addressing such challenges a framework based on the principles of spatial geometry is described. The framework consists of a method for visualising cost uncertainty and a dependency model for quantifying and forecasting cost uncertainty. Cost uncertainty is declared to represent manifested and unintended future cost variance with a probability of 100% and an unknown quantity and innovative starting conditions considered to exist when no verified and accurate cost model is available. The shape of data is used as an organising principle and the attribute of geometrical symmetry of cost variance point clouds used for the quantification of cost uncertainty. The results of the investigation suggest that the uncertainty of a cost estimate at any future point in time may be determined by the geometric symmetry of the cost variance data in its point cloud form at the time of estimation. Recommendations for future research include using the framework to determine the “most likely values” of estimates in Monte Carlo simulations and generalising the dependency model introduced. Future work is also recommended to reduce the framework limitations noted

SIDEROPHORES FOR SELECTIVE SOLID PHASE EXTRACTION OF STRATEGIC ELEMENTS

All over the world, industrial mining is leaving contaminated areas and dumps that, although being full of valuable metals, have high concentrations of toxic heavy metals that pollute the environment. The development of sustainable alternative biomining and bioremediation processes offers the potential to fully exploit these unexploited mining sites

Micro-Blogging Adoption in the Enterprise: An Empirical Analysis

Given the increasing interest in using social software forcompany-internal communication and collaboration, this paperexamines drivers and inhibitors of micro-blogging adoption at theworkplace. While nearly one in two companies is currentlyplanning to introduce social software, there is no empiricallyvalidated research on employees’ adoption. In this paper, we buildon previous focus group results and test our research model in anempirical study using Structural Equation Modeling. Based on ourfindings, we derive recommendations on how to foster adoption.We suggest that micro-blogging should be presented to employeesas an efficient means of communication, personal brand building,and knowledge management. In order to particularly promotecontent contribution, privacy concerns should be eased by settingclear rules on who has access to postings and for how long theywill be archived

Need for nursing care support in cancer patients: Registry-linkage study in Germany

Aim: In Germany, very little is known about the need for assistance and nursing care support among cancer patients after hospitalization. The aim of this study was to describe nursing care support for cancer patients and to analyse whether these patients need more care assistance than other persons in need for care. Methods: This was a registry linkage study conducted in 2011. Cases were identified from the population-based cancer registry for the Muenster District in north-western Germany and in factually anonymised form linked by a semi-automatic probabilistic procedure (the standard procedure of the cancer registry) with medical examination records of patients applying for assistance and nursing care support from the regional statutory health insurance. The application records of 4,029 patients with colon, breast and prostate cancer were compared to a reference group of 13,104 non-cancer patients. Results: In only 41.7% of colon, 45.8% of breast and 37.4% of prostate cancer patients was the malignancy the main underlying diagnostic cause for the application of assistance and nursing care. These patients were on average younger (mean age 71.1 vs. 76.8 years) than the non-cancer reference group, required higher levels of support (79.5 vs. 58.1% “considerable” or higher level care need) and their applications were less likely to be rejected (odds ratios [ORs] 0.26, 0.28, and 0.31, respectively). By contrast, the proportion of successful applications and the level of support granted did not differ between multimorbid cancer patients with other main diagnoses as compared to non-cancer applicants. Conclusion: Patients with colon, breast or prostate cancer do not need per se more nursing care than non-cancer patients. Only if cancer is the main underlying diagnosis for nursing care support, higher levels of support are needed

On the change of cost risk and uncertainty throughout the life cycle of manufacturing products

In practice cost estimators typically assume that cost risk and uncertainty continuously decrease across the whole product life cycle. Industry case studies and semi-structured interviews indicate that while cost risk and uncertainty decreases between technology readiness levels / stage gates, it increases when technology readiness levels / stage gates change. This increase can lead to cost risk and uncertainty levels above those at previous technology readiness levels / stage gates. This difference between assumptions in practice and evidence from case studies and semi-structured interviews may lead to the over- and / or under-assignment of capital reserves over time, thus resulting in binding project capital unnecessarily and / or the need to increase projects budgets in an unplanned manner. Further research is suggested regarding the scale of changes in cost risk and uncertainty when technology readiness level changes / stage gates are arrived at in order to improve robustness of forecasting effort

Dynamics of cost uncertainty for innovative high value manufacturing products - a geometric phenomenon

In practice the forecasting of cost uncertainty for high value manufacturing products is typically a statistical exercise focused on predicting a static cost range at a future point in time. This only leads to robust forecasts if sufficient historical data is available, robust knowledge of cost estimating relationships exists and these relationships do not change in the time between creating the forecast and verifying its accuracy. The more innovative the product is the less likely it however is that these prerequisites are met. Using cost data from the U.K. Ministry of Defence Royal Air Force A400M transport aircraft from 2002 to 2014 as an example, the dynamics of cost estimating relationships over time are examined using a novel non-statistical forecasting approach. The approach considers cost uncertainty as a geometric phenomenon, does not rely on prior information and permits easy identification of patterns in changes of cost estimating relationships over time