A basis function approach to Bayesian inference in diffusion processes

In this paper, we present a framework for Bayesian inference in continuous-time diffusion processes. The new method is directly related to the recently proposed variational Gaussian Process approximation (VGPA) approach to Bayesian smoothing of partially observed diffusions. By adopting a basis function expansion (BF-VGPA), both the time-dependent control parameters of the approximate GP process and its moment equations are projected onto a lower-dimensional subspace. This allows us both to reduce the computational complexity and to eliminate the time discretisation used in the previous algorithm. The new algorithm is tested on an Ornstein-Uhlenbeck process. Our preliminary results show that BF-VGPA algorithm provides a reasonably accurate state estimation using a small number of basis functions.

Modelling and benchmarking business processes: The supply-line example

Benchmarking techniques have evolved over the years since Xerox’s pioneering visits to Japan in the late 1970s. The focus of benchmarking has also shifted during this period. By tracing in detail the evolution of benchmarking in one specific area of business activity, supply and distribution management, as seen by the participants in that evolution, creates a picture of a movement from single function, cost-focused, competitive benchmarking, through cross-functional, cross-sectoral, value-oriented benchmarking to process benchmarking. As process efficiency and effectiveness become the primary foci of benchmarking activities, the measurement parameters used to benchmark performance converge with the factors used in business process modelling. The possibility is therefore emerging of modelling business processes and then feeding the models with actual data from benchmarking exercises. This would overcome the most common criticism of benchmarking, namely that it intrinsically lacks the ability to move beyond current best practice. In fact the combined power of modelling and benchmarking may prove to be the basic building block of informed business process re-engineering.

Technological and organisational change in small- to medium-sized manufacturing companies:a learning organisation perspective

This paper examines the extent to which a learning organisation perspective is attainable in small- to medium-sized manufacturing companies. An audit tool is developed from the literature on organisational learning and recognised processes that lead towards becoming a learning organisation. The paper focuses on the application of the audit tool in three UK automotive component suppliers which are all experiencing pressures for change imposed by the major vehicle manufacturers. The main changes are concerned with tiering of the supply chain and substantial delegation of responsibilities to component suppliers including an increasing emphasis on innovation and continuous improvement. The companies presented in the paper are taken from a research project into the impact of changes in supply chain relationships on the operation of small- and medium-sized manufacturing firms in the West Midlands region of the UK. The ways in which the companies are responding to change are presented together with the results of a self-assessment using the developed audit tool. These results suggest that companies of this type tend to focus on change in those areas that involve least challenge to the established power and authority of management.

An application of decision process modelling to manufacturing system design

As levels of investment in advanced manufacturing systems increase, effective project management becomes ever more critical. This paper demonstrates how the model proposed by Mintzberg, Raisinghani and Theoret in 1976, which structures complicated strategic decision processes, can be applied to the design of new production systems for both descriptive and analytical research purposes. This paper sets a detailed case study concerning the design and development of an advanced manufacturing system within the Mintzberg decision model and so breaks down the decision sequence into constituent parts. It thus shows how a structured model can provide a framework for the researcher who wishes to study decision episodes in the design of manufacturing facilities in greater depth.

Understanding the factors affecting e-business adoption and impact on logistics processes

Purpose: The purpose of this paper is to conceptualize e-business adoption and to generate understanding of the range of factors affecting the adoption process. The paper also aims at exploring the perceived impact of e-business adoption on logistics-related processes. Design/methodology/approach: Case study research, by conducting in-depth interviews in eight companies. Findings: E-business adoption is not exclusively a matter of resources. Increased e-business adoption and impact are caused by increased operational compatibility, as well as increased levels of collaboration. In terms of e-business impact this mainly refers to cycle time reductions and quality improvements, rather than direct cost reductions as reported by other authors. Research limitations/implications: The intrinsic weakness of the research method and the way concepts are operationalized limits the ability to generalize findings. Practical implications: Managers should emphasize developing their relationships with theirsuppliers/customers, in an effort to do common e-business investments, and should aim to increase their partners' commitment to the use of these applications. Originality/value: This paper provides empirical evidence from a sector where limited research efforts have taken place. Explanations can be helpful to other researchers involved in the understanding of the adoption of e-business and its impact. © Emerald Group Publishing Limited.

A systems methodology for analysing and simulating manufacturing systems

The Systems Engineering Group (SEG) at De Montfort University are developing the Boardman Soft Systems Methodology (BSSM) which allows complex human systems to be modelled, this work builds upon Checkland's Soft Systems Methodology (1981). The BSSM has been applied to the modelling of the systems engineering process as used in design and manufacturing companies. The BSSM is used to solicit information from a company and this data is then transformed into systemic diagrams (systemigrams). These systemigrams are posited to be accurate and concise representations of the system which has been modelled. This paper describes the collaboration between SEG and a manufacturing company (MC) in Leicester, England. The purpose of this collaboration was twofold. First, it was to create an objective view of the MC's processes, in the form of systemigrams. It was important to get this modelled by a source outside of the company, as it is difficult for people within a system being modelled to be unbiased. Secondly, it allowed a series of systemigrams to be produced which can then be subjected to simulation, for the purpose of aiding risk management decisions and to reduce the project cycle time

A detailed design study of leaching and ancillary processes in the hydrometallurgical extraction of iron from iron bearing feedstocks

A systematic survey of the possible methods of chemical extraction of iron by chloride formation has been presented and supported by a comparable study of :feedstocks, products and markets. The generation and evaluation of alternative processes was carried out by the technique of morphological analysis vihich was exploited by way of a computer program. The final choice was related to technical feasibility and economic viability, particularly capital cost requirements and developments were made in an estimating procedure for hydrometallurgjcal processes which have general applications. The systematic exploration included the compilation of relevant data, and this indicated a need.to investigate precipitative hydrolysis or aqueous ferric chloride. Arising from this study, two novel hydrometallurgical processes for manufacturing iron powder are proposed and experimental work was undertaken in the following .areas to demonstrate feasibility and obtain basic data for design purposes: (1) Precipitative hydrolysis of aqueous ferric chloride. (2) Gaseous chloridation of metallic iron, and oxidation of resultant ferrous chloride. (3) Reduction of gaseous ferric chloride with hydrogen. (4) Aqueous acid leaching of low grade iron ore. (5) Aqueous acid leaching of metallic iron. The experimentation was supported by theoretical analyses dealing with: (1) Thermodynamics of hydrolysis. (2) Kinetics of ore leaching. (3) Kinetics of metallic iron leaching. (4) Crystallisation of ferrous chloride. (5) Oxidation of anhydrous ferrous chloride. (6) Reduction of ferric chloride. Conceptual designs are suggested fbr both the processes mentioned. These draw attention to areas where further work is necessary, which are listed. Economic analyses have been performed which isolate significant cost areas, und indicate total production costs. Comparisons are mode with previous and analogous proposals for the production of iron powder.

Estimating parameters in stochastic systems:a variational Bayesian approach

This work is concerned with approximate inference in dynamical systems, from a variational Bayesian perspective. When modelling real world dynamical systems, stochastic differential equations appear as a natural choice, mainly because of their ability to model the noise of the system by adding a variation of some stochastic process to the deterministic dynamics. Hence, inference in such processes has drawn much attention. Here a new extended framework is derived that is based on a local polynomial approximation of a recently proposed variational Bayesian algorithm. The paper begins by showing that the new extension of this variational algorithm can be used for state estimation (smoothing) and converges to the original algorithm. However, the main focus is on estimating the (hyper-) parameters of these systems (i.e. drift parameters and diffusion coefficients). The new approach is validated on a range of different systems which vary in dimensionality and non-linearity. These are the Ornstein–Uhlenbeck process, the exact likelihood of which can be computed analytically, the univariate and highly non-linear, stochastic double well and the multivariate chaotic stochastic Lorenz ’63 (3D model). As a special case the algorithm is also applied to the 40 dimensional stochastic Lorenz ’96 system. In our investigation we compare this new approach with a variety of other well known methods, such as the hybrid Monte Carlo, dual unscented Kalman filter, full weak-constraint 4D-Var algorithm and analyse empirically their asymptotic behaviour as a function of observation density or length of time window increases. In particular we show that we are able to estimate parameters in both the drift (deterministic) and the diffusion (stochastic) part of the model evolution equations using our new methods.

A methodology for evaluating and selecting processes for natural gas conditioning

The aim of this work was to develop a generic methodology for evaluating and selecting, at the conceptual design phase of a project, the best process technology for Natural Gas conditioning. A generic approach would be simple and require less time and would give a better understanding of why one process is to be preferred over another. This will lead to a better understanding of the problem. Such a methodology would be useful in evaluating existing, novel and hybrid technologies. However, to date no information is available in the published literature on such a generic approach to gas processing. It is believed that the generic methodology presented here is the first available for choosing the best or cheapest method of separation for natural gas dew-point control. Process cost data are derived from evaluations carried out by the vendors. These evaluations are then modelled using a steady-state simulation package. From the results of the modelling the cost data received are correlated and defined with respect to the design or sizing parameters. This allows comparisons between different process systems to be made in terms of the overall process. The generic methodology is based on the concept of a Comparative Separation Cost. This takes into account the efficiency of each process, the value of its products, and the associated costs. To illustrate the general applicability of the methodology, three different cases suggested by BP Exploration are evaluated. This work has shown that it is possible to identify the most competitive process operations at the conceptual design phase and illustrate why one process has an advantage over another. Furthermore, the same methodology has been used to identify and evaluate hybrid processes. It has been determined here that in some cases they offer substantial advantages over the separate process techniques.

Projected sequential Gaussian processes: flexible interpolation for large data sets

Recently within the machine learning and spatial statistics communities many papers have explored the potential of reduced rank representations of the covariance matrix, often referred to as projected or fixed rank approaches. In such methods the covariance function of the posterior process is represented by a reduced rank approximation which is chosen such that there is minimal information loss. In this paper a sequential framework for inference in such projected processes is presented, where the observations are considered one at a time. We introduce a C++ library for carrying out such projected, sequential estimation which adds several novel features. In particular we have incorporated the ability to use a generic observation operator, or sensor model, to permit data fusion. We can also cope with a range of observation error characteristics, including non-Gaussian observation errors. Inference for the variogram parameters is based on maximum likelihood estimation. We illustrate the projected sequential method in application to synthetic and real data sets. We discuss the software implementation and suggest possible future extensions.

Variational inference for diffusion processes

Diffusion processes are a family of continuous-time continuous-state stochastic processes that are in general only partially observed. The joint estimation of the forcing parameters and the system noise (volatility) in these dynamical systems is a crucial, but non-trivial task, especially when the system is nonlinear and multimodal. We propose a variational treatment of diffusion processes, which allows us to compute type II maximum likelihood estimates of the parameters by simple gradient techniques and which is computationally less demanding than most MCMC approaches. We also show how a cheap estimate of the posterior over the parameters can be constructed based on the variational free energy.

Setting parameters in MRP for the effective management of bought-out inventory in a JIT assembly environment

In recent years, UK industry has seen an explosive growth in the number of `Computer Aided Production Management' (CAPM) system installations. Of the many CAPM systems, materials requirement planning/manufacturing resource planning (MRP/MRPII) is the most widely implemented. Despite the huge investments in MRP systems, over 80 percent are said to have failed within 3 to 5 years of installation. Many people now assume that Just-In-Time (JIT) is the best manufacturing technique. However, those who have implemented JIT have found that it also has many problems. The author argues that the success of a manufacturing company will not be due to a system which complies with a single technique; but due to the integration of many techniques and the ability to make them complement each other in a specific manufacturing environment. This dissertation examines the potential for integrating MRP with JIT and Two-Bin systems to reduce operational costs involved in managing bought-out inventory. Within this framework it shows that controlling MRP is essential to facilitate the integrating process. The behaviour of MRP systems is dependent on the complex interactions between the numerous control parameters used. Methodologies/models are developed to set these parameters. The models are based on the Pareto principle. The idea is to use business targets to set a coherent set of parameters, which not only enables those business targets to be realised, but also facilitates JIT implementation. It illustrates this approach in the context of an actual manufacturing plant - IBM Havant. (IBM Havant is a high volume electronics assembly plant with the majority of the materials bought-out). The parameter setting models are applicable to control bought-out items in a wide range of industries and are not dependent on specific MRP software. The models have produced successful results in several companies and are now being developed as commercial products.

Rolling bearing vibrations: the effects of varying compliance, manufacturing tolerances and wear

In this study some common types of Rolling Bearing vibrations are analysed in depth both theoretically and experimentally. The study is restricted to vibrations in the radial direction of bearings having pure radial load and a positive radial clearance. The general vibrational behaviour of such bearings has been investigated with respect to the effects of varying compliance, manufacturing tolerances and the interaction between the bearing and the machine structure into which it is fitted. The equations of motion for a rotor supported by a bearing in which the stiffness varies with cage position has been set up and examples of solutions,obtained by digital simulation. is given. A method to calculate amplitudes and frequencies of vibration components due to out of roundness of the inner ring and varying roller diameters has been developed. The results from these investigations have been combined with a theory for bearing/machine frame interaction using mechanical impedance technique, thereby facilitating prediction of the vibrational behaviour of the whole set up. Finally. the effects of bearing fatigue and wear have been studied with particular emphasis on the use of vibration analysis for condition monitoring purposes. A number of monitoring methods have been tried and their effectiveness discussed. The experimental investigation was carried out using two purpose built rigs. For the purpose of analysis of the experimental measurements a digital mini computer was adapted for signal processing and a suite of programs was written. The program package performs several of the commonly used signal analysis processes and :include all necessary input and output functions.

Evaluating the impact of design decisions on the financial performance of manufacturing companies

Product design decisions can have a significant impact on the financial and operation performance of manufacturing companies. Therefore good analysis of the financial impact of design decisions is required if the profitability of the business is to be maximised. The product design process can be viewed as a chain of decisions which links decisions about the concept to decisions about the detail. The idea of decision chains can be extended to include the design and operation of the 'downstream' business processes which manufacture and support the product. These chains of decisions are not independent but are interrelated in a complex manner. To deal with the interdependencies requires a modelling approach which represents all the chains of decisions, to a level of detail not normally considered in the analysis of product design. The operational, control and financial elements of a manufacturing business constitute a dynamic system. These elements interact with each other and with external elements (i.e. customers and suppliers). Analysing the chain of decisions for such an environment requires the application of simulation techniques, not just to any one area of interest, but to the whole business i.e. an enterprise simulation. To investigate the capability and viability of enterprise simulation an experimental 'Whole Business Simulation' system has been developed. This system combines specialist simulation elements and standard operational applications software packages, to create a model that incorporates all the key elements of a manufacturing business, including its customers and suppliers. By means of a series of experiments, the performance of this system was compared with a range of existing analysis tools (i.e. DFX, capacity calculation, shop floor simulator, and business planner driven by a shop floor simulator).

The design of a support department simulator for use in the design of manufacturing systems

Manufacturing firms are driven by competitive pressures to continually improve the effectiveness and efficiency of their organisations. For this reason, manufacturing engineers often implement changes to existing processes, or design new production facilities, with the expectation of making further gains in manufacturing system performance. This thesis relates to how the likely outcome of this type of decision should be predicted prior to its implementation. The thesis argues that since manufacturing systems must also interact with many other parts of an organisation, the expected performance improvements can often be significantly hampered by constraints that arise elsewhere in the business. As a result, decision-makers should attempt to predict just how well a proposed design will perform when these other factors, or 'support departments', are taken into consideration. However, the thesis also demonstrates that, in practice, where quantitative analysis is used to evaluate design decisions, the analysis model invariably ignores the potential impact of support functions on a system's overall performance. A more comprehensive modelling approach is therefore required. A study of how various business functions interact establishes that to properly represent the kind of delays that give rise to support department constraints, a model should actually portray the dynamic and stochastic behaviour of entities in both the manufacturing and non-manufacturing aspects of a business. This implies that computer simulation be used to model design decisions but current simulation software does not provide a sufficient range of functionality to enable the behaviour of all of these entities to be represented in this way. The main objective of the research has therefore been the development of a new simulator that will overcome limitations of existing software and so enable decision-makers to conduct a more holistic evaluation of design decisions. It is argued that the application of object-oriented techniques offers a potentially better way of fulfilling both the functional and ease-of-use issues relating to development of the new simulator. An object-oriented analysis and design of the system, called WBS/Office, are therefore presented that extends to modelling a firm's administrative and other support activities in the context of the manufacturing system design process. A particularly novel feature of the design is the ability for decision-makers to model how a firm's specific information and document processing requirements might hamper shop-floor performance. The simulator is primarily intended for modelling make-to-order batch manufacturing systems and the thesis presents example models created using a working version of WBS/Office that demonstrate the feasibility of using the system to analyse manufacturing system designs in this way.