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 review of multi-criteria decision making methods for enhanced maintenance delivery

Conventionally there is a strong relation between manufacturing and services in complex engineering industries. For companies which aim to last in the competitive manufacturing market choosing appropriate decision making methods to improve their maintenance delivery has a vital role. The aim of this paper is to review Multi Criteria Decision Making (MCDM) models, evaluate each method and do a critical comparison to assess them from a maintenance management point of view. The first section of this paper reviews MCDM methods in different literature, and then the second part develops a set of criteria to classify different techniques. At the end methods are compared based on developed criteria. This paper assesses different MCDM models, and provides a framework to select approaches for maintenance management

Discrete Event Simulation Modelling for Dynamic Decision Making in Biopharmaceutical Manufacturing

With the increase in demand for biopharmaceutical products, industries have realised the need to scale up their manufacturing from laboratory-based processes to financially viable production processes. In this context, biopharmaceutical manufacturers are increasingly using simulation-based approaches to gain transparency of their current production system and to assist with designing improved systems. This paper discusses the application of Discrete Event Simulation (DES) and its ability to model the various scenarios for dynamic decision making in biopharmaceutical manufacturing sector. This paper further illustrates a methodology used to develop a simulation model for a biopharmaceutical company, which is considering several capital investments to improve its manufacturing processes. A simulation model for a subset of manufacturing activities was developed that facilitated ‘what-if’ scenario planning for a proposed process alternative. The simulation model of the proposed manufacturing process has shown significant improvement over the current process in terms of throughout time reduction, better resource utilisation, operating cost reduction, reduced bottlenecks etc. This visibility of the existing and proposed production system assisted the company in identifying the potential capital and efficiency gains from the investments therefore demonstrating that DES can be an effective tool for making more informed decisions. Furthermore, the paper also discusses the utilisation of DES models to develop a number of bespoke productivity improvement tools for the company

Olfactory-based augmented reality support for industrial maintenance

Augmented reality (AR) applications have opened innovative ways for performance improvement in the IoT industry. It can enhance user perception of the real-world by providing valuable information about an industrial environment and provide visual virtual information onto a head-mounted device (HMD). Such information is important for maintainers to quickly detect abnormalities, reduces nugatory routines and facilitate preventive maintenance.Since odors are made up of volatile compounds at low concentration, they can be used for olfactory-based identification.The prototype comprises of three modules: an electronic nose, a database and an AR application integrated with Microsoft HoloLens. After diagnosing an odor, the results are then sent wirelessly through a local network to the HMD worn by the user. To validate the technology, four odors have been used, including engine oil, sun lotion, medical alcohol and perfume, to record behaviors and demonstrate the repeatability of the process. The presented technology incorporates sampling methods, cleaning processes and statistical analysis that can be further scrutinized to allow correct smell identification

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

A novel approach for No Fault Found decision making

Within aerospace and defence sectors, organisations are adding value to their core corporate offerings through services. These services tend to emphasise the potential to maintain future revenue streams and improved profitability and hence require the establishment of cost effective strategies that can manage uncertainties within value led services e.g. maintenance activities. In large organisations, decision-making is often supported by information processing and decision aiding systems; it is not always apparent whose decision affects the outcome the most. Often, accountability moves away from the designated organisation personnel in unforeseen ways, and depending on the decisions of individual decision makers, the structure of the organisation, or unregulated operating procedures may change. This can have far more effect on the overall system reliability – leading to inadequate troubleshooting, repeated down-time, reduced availability and increased burden on Through-life Engineering Services. This paper focuses on outlining current industrial attitudes regarding the No Fault Found (NFF) phenomena and identifies the drivers that influence the NFF decision-making process. It articulates the contents of tacit knowledge and addresses a knowledge gap by developing NFF management policies. The paper further classifies the NFF phenomenon into five key processes that must be controlled by using the developed policies. In addition to the theoretical developments, a Petri net model is also outlined and discussed based on the captured information regarding NFF decision-making in organisations. Since NFF decision-making is influenced by several factors, Petri nets are sought as a powerful tool to realise a meta-model capability to understand the complexity of situations. Its potential managerial implications can help describe decision problems under conditions of uncertainty. Finally, the conclusions indicate that engineering processes, which allow decision-making at various maintenance echelons, can often obfuscate problems that then require a systems approach to illustrate the impact of the issue

Identifying information asymmetry challenges in the defence sector

Nowadays austerity policy and reduced funding promote the Defence Sector (DS) interested in improving interactions across the supply network to achieve more outcomes with less expense. The quality of the information link plays a key role in the supply chain. The information is often lost causing costs increase. Information Asymmetry (IA) exists when two or more parties in a contract/project have different types or amounts of information, and choose not to share or fail to understand information that is shared. This paper aims to outline some of the challenges faced within the DS as a result of the existence of IA. This is the first step towards improving the management of IA and has been supported by a literature review and through semi-structured interviews with subject matter experts. Therefore, the conclusions in this paper can be used for further developments in this area of study

Mitigating the risk of software obsolescence in the oil and gas sector

This paper focuses on how on-going energy demand, shift in water depth and heavy crude oil type are creating huge obsolescence issues especially software in the offshore oil and gas industry. The aim is to identify and quantify software obsolescence using a novel framework developed to evaluate major software types, their associated obsolescence impact and risk exposure, cost implications, and resolution qualification. Validation of the framework confirmed the role of the framework for life cycle support and guidance related to software obsolescence. The tool can be used for engineering and procurement contracts thereby reducing capital and operating expenditure

Defining next-generation additive manufacturing applications for the Ministry of Defence (MoD)

“Additive Manufacturing” (AM) is an emerging, highly promising and disruptive technology which is catching the attention of the Defence sector due to the versatility it is offering. Through the combination of design freedom, technology compactness and high deposition rates, technology stakeholders can potentially exploit rapid, delocalized and flexible production. Having the capability to produce highly tailored, fully dense, potentially optimized products, on demand and next to the point of use makes this emerging and immature technology a game changer in the “Defence Support Service” (DS2) sector. Furthermore, if the technology is exploited for the Royal Navy, featured with extended and disrupted supply chains, the benefits are very promising. While most of the AM research and efforts are focusing on the manufacturing/process and design opportunities/topology optimization, this paper aims to provide a creative but educated and validated forecast on what AM can do for the Royal Navy in the future. This paper aims to define the most promising next generation Additive Manufacturing applications for the Royal Navy in the 2025 – 2035 decade. A multidisciplinary methodology has been developed to structure this exploratory applied research study. Moreover, different experts of the UK Defence Value Chain have been involved for primary research and for verification/validation purposes. While major concerns have been raised on process/product qualification and current AM capabilities, the results show that there is a strong confidence on the disruptive potential of AM to be applied in front-end of DS2 systems to support “Complex Engineering Systems” in the future. While this paper provides only next-generation AM applications for RN, substantial conceptual development work has to be carried out to define an AM based system which is able to, firstly satisfy the “spares demands” of a platform and secondly is able to perform in critical environments such as at sea