Inventory parameter management and focused continuous improvement for repetitive batch manufacturers

What this thesis proposes is a methodology to assist repetitive batch manufacturers in the adoption of certain aspects of the Lean Production principles. The methodology concentrates on the reduction of inventory through the setting of appropriate batch sizes, taking account of the effect of sequence dependent set-ups and the identification and elimination of bottlenecks. It uses a simple Pareto and modified EBQ based analysis technique to allocate items to period order day classes based on a combination of each item's annual usage value and set-up cost. The period order day classes the items are allocated to are determined by the constraints limits in the three measured dimensions, capacity, administration and finance. The methodology overcomes the limitations associated with MRP in the area of sequence dependent set-ups, and provides a simple way of setting planning parameters taking this effect into account by concentrating on the reduction of inventory through the systematic identification and elimination of bottlenecks through set-up reduction processes, so allowing batch sizes to reduce. It aims to help traditional repetitive batch manufacturers in a route to continual improvement by: Highlighting those areas where change would bring the greatest benefits. Modelling the effect of proposed changes. Quantifying the benefits that could be gained through implementing the proposed changes. Simplifying the effort required to perform the modelling process. It concentrates on increasing flexibility through managed inventory reduction through rationally decreasing batch sizes, taking account of sequence dependent set-ups and the identification and elimination of bottlenecks. This was achieved through the development of a software modelling tool, and validated through a case study approach.

Process improvement within a HR division at a UK police force

A structured approach to process improvement is described in the context of the human resources division of a UK police force. The approach combines a number of established techniques of process improvement such as the balanced scorecard and process mapping with a scoring system developed to prioritise processes for improvement. The methodology described presents one way of ensuring the correct processes are identified and redesigned at an operational level in such a way as to support the organisation's strategic aims. In addition, a performance measurement system is utilised to attempt to ensure that the changes implemented do actually achieve the desired effect over time. The case demonstrates the need to choose and in some cases develop in-house tools and techniques dependent on the context of the process improvement effort.

Process integration and improvement using systemic diagrams and a human-centred approach

If product cycle time reduction is the mission, and the multifunctional team is the means of achieving the mission, what then is the modus operandi by which this means is to accomplish its mission? This paper asserts that a preferred modus operandi for the multifunctional team is to adopt a process-oriented view of the manufacturing enterprise, and for this it needs the medium of a process map [16] The substance of this paper is a methodology which enables the creation of such maps Specific examples of process models drawn from the product develop ment life cycle are presented and described in order to support the methodology's integrity and value The specific deliverables we have so far obtained are a methodology for process capture and analysis, a collection of process models spanning the product development cycle, and, an engineering handbook which hosts these models and presents a computer-based means of navigating through these processes in order to allow users a better understanding of the nature of the business, their role in it, and why the job that they do benefits the work of the company We assert that this kind of thinking is the essence of concurrent engineering implementation, and further that the systemigram process models uniquely stim ulate and organise such thinking.

Improvement of bio-oil stability in wood pyrolysis process

Pyrolysis is one of several thermochemical technologies that convert solid biomass into more useful and valuable bio-fuels. Pyrolysis is thermal degradation in the complete or partial absence of oxygen. Under carefully controlled conditions, solid biomass can be converted to a liquid known as bie-oil in 75% yield on dry feed. Bio-oil can be used as a fuel but has the drawback of having a high level of oxygen due to the presence of a complex mixture of molecular fragments of cellulose, hemicellulose and lignin polymers. Also, bio-oil has a number of problems in use including high initial viscosity, instability resulting in increased viscosity or phase separation and high solids content. Much effort has been spent on upgrading bio-oil into a more usable liquid fuel, either by modifying the liquid or by major chemical and catalytic conversion to hydrocarbons. The overall primary objective was to improve oil stability by exploring different ways. The first was to detennine the effect of feed moisture content on bio-oil stability. The second method was to try to improve bio-oil stability by partially oxygenated pyrolysis. The third one was to improve stability by co-pyrolysis with methanol. The project was carried out on an existing laboratory pyrolysis reactor system, which works well with this project without redesign or modification too much. During the finishing stages of this project, it was found that the temperature of the condenser in the product collection system had a marked impact on pyrolysis liquid stability. This was discussed in this work and further recommendation given. The quantity of water coming from the feedstock and the pyrolysis reaction is important to liquid stability. In the present work the feedstock moisture content was varied and pyrolysis experiments were carried out over a range of temperatures. The quality of the bio-oil produced was measured as water content, initial viscosity and stability. The result showed that moderate (7.3-12.8 % moisture) feedstock moisture led to more stable bio-oil. One of drawbacks of bio-oil was its instability due to containing unstable oxygenated chemicals. Catalytic hydrotreatment of the oil and zeolite cracking of pyrolysis vapour were discllssed by many researchers, the processes were intended to eliminate oxygen in the bio-oil. In this work an alternative way oxygenated pyrolysis was introduced in order to reduce oil instability, which was intended to oxidise unstable oxygenated chemicals in the bio-oil. The results showed that liquid stability was improved by oxygen addition during the pyrolysis of beech wood at an optimum air factor of about 0.09-0.15. Methanol as a postproduction additive to bio-oil has been studied by many researchers and the most effective result came from adding methanol to oil just after production. Co-pyrolysis of spruce wood with methanol was undertaken in the present work and it was found that methanol improved liquid stability as a co-pyrolysis solvent but was no more effective than when used as a postproduction additive.

Developing and evaluating a methodology for business process improvement

Purpose – There appears to be an ever-insatiable demand from markets for organisations to improve their products and services. To meet this, there is a need to provide business process improvement (BPI) methodologies that are holistic, structured and procedural. Therefore, this paper describes research that has formed and tested a generic and practical methodology termed model-based and integrated process improvement (MIPI) to support the implementation of BPI; and to validate its effectiveness in organisations. This methodology has been created as an aid for practitioners within organisations. Design/methodology/approach – The research objectives were achieved by: reviewing and analysing current methodologies, and selecting a few frameworks against key performance indicators. Using a refined Delphi approach and semi-structured interview with the “experts” in the field. Intervention, case study and process research approach to evaluating a methodology. Findings – The BPI methodology was successfully formed and applied by the researcher and directly by the companies involved against the criteria of feasibility, usability and usefulness. Research limitations/implications – The paper has demonstrated a new knowledge on how to systematically assess a BPI methodology in practice. Practical implications – Model-based and integrated process improvement methodology (MIPI) methodology offers the practitioner (experienced and novice) a set of step-by-step aids necessary to make informed, consistent and efficient changes to business processes. Originality/value – The novelty of this research work is the creation of a holistic workbook-based methodology with relevant tools and techniques. It extends the capabilities of existing methodologies.

Wind power forecasts using Gaussian processes and numerical weather prediction

Since wind at the earth's surface has an intrinsically complex and stochastic nature, accurate wind power forecasts are necessary for the safe and economic use of wind energy. In this paper, we investigated a combination of numeric and probabilistic models: a Gaussian process (GP) combined with a numerical weather prediction (NWP) model was applied to wind-power forecasting up to one day ahead. First, the wind-speed data from NWP was corrected by a GP, then, as there is always a defined limit on power generated in a wind turbine due to the turbine controlling strategy, wind power forecasts were realized by modeling the relationship between the corrected wind speed and power output using a censored GP. To validate the proposed approach, three real-world datasets were used for model training and testing. The empirical results were compared with several classical wind forecast models, and based on the mean absolute error (MAE), the proposed model provides around 9% to 14% improvement in forecasting accuracy compared to an artificial neural network (ANN) model, and nearly 17% improvement on a third dataset which is from a newly-built wind farm for which there is a limited amount of training data. © 2013 IEEE.

Short-term wind power forecasting using Gaussian processes

Since wind has an intrinsically complex and stochastic nature, accurate wind power forecasts are necessary for the safety and economics of wind energy utilization. In this paper, we investigate a combination of numeric and probabilistic models: one-day-ahead wind power forecasts were made with Gaussian Processes (GPs) applied to the outputs of a Numerical Weather Prediction (NWP) model. Firstly the wind speed data from NWP was corrected by a GP. Then, as there is always a defined limit on power generated in a wind turbine due the turbine controlling strategy, a Censored GP was used to model the relationship between the corrected wind speed and power output. To validate the proposed approach, two real world datasets were used for model construction and testing. The simulation results were compared with the persistence method and Artificial Neural Networks (ANNs); the proposed model achieves about 11% improvement in forecasting accuracy (Mean Absolute Error) compared to the ANN model on one dataset, and nearly 5% improvement on another.