Cost effectiveness of production control systems

This thesis deals with the problems associated with the planning and control of production, with particular reference to a small aluminium die casting company. The main problem areas were identified as: (a) A need to be able to forecast the customers demands upon the company's facilities. (b) A need to produce a manufacturing programme in which the output of the foundry (or die casting section) was balanced with the available capacity in the machine shop. (c) The need to ensure that the resultant system enabled the company's operating budget to have a reasonable chance of being achieved. At the commencement of the research work the major customers were members of the automobile industry and had their own system of forecasting, from which they issued manufacturing schedules to their component suppliers, The errors in the forecast were analysed and the distributions noted. Using these distributions the customer's forecast was capable of being modified to enable his final demand to be met with a known degree of confidence. Before a manufacturing programme could be developed the actual manufacturing system had to be reviewed and it was found that as with many small companies there was a remarkable lack of formal control and written data. Relevant data with regards to the component and the manufacturing process had therefore to be collected and analysed. The foundry process was fixed but the secondary machining operations were analysed by a technique similar to Component Flow Analysis and as a result the machines were arranged in a series of flow lines. A system of manual production control was proposed and for comparison, a local computer bureau was approached and a system proposed incorporating the production of additional management information. These systems are compared and the relative merits discussed and a proposal made for implementation.

Reliability management of machine tool technology: a case study of current practice

Considerable attention has been given in the literature to identifying and describing the effective elements which positively affect the improvement of product reliability. These have been perceived by many as the 'state of the art' in the manufacturing industry. The applicability, diffusion and effectiveness of such methods and philosophies, as a means of systematically improving the reliability of a product, come in the main from case studies and single and infra-industry empirical studies. These studies have both been carried out within the wider context of quality assurance and management, and taking reliability as a discipline in its own right. However, it is somewhat of a surprise that there are no recently published findings or research studies on the adoption of these methods by the machine tool industry. This may lead one to construct several hypothesised paradigms: (a) that machine tool manufacturers compared to other industries, are slow to respond to propositions given in the literature by theorists or (b) this may indicate that a large proportion of the manufacturers make little use of the reliability improvement techniques as described in the literature, with the overall perception that they will not lead to any significant improvements? On the other hand, it is evident that hypothetical verification of the operational and engineering methods of reliability achievement and improvement adopted in the machine tool industry is less widely researched. Therefore, research into this area is needed in order to explore the 'state of the art' practice in the machine tool industry. This is in terms of the status, structure and activities of the operation of the reliability function. This paper outlines a research programme being conducted with the co-operation of a leading machine tool manufacturer, whose UK manufacturing plant produces in the main Vertical Machining Centres (VMCs) and is continuously undergoing incremental transitions in product reliability improvement.

Technology valuation within the context of transfer collaborations with Chinese machine tool companies

The point of departure for this study was a recognition of the differences in suppliers' and acquirers' judgements of the value of technology when transferred between the two, and the significant impacts of technology valuation on the establishment of technology partnerships and effectiveness of technology collaborations. The perceptions, transfer strategies and objectives, perceived benefits and assessed technology contributions as well as associated costs and risks of both suppliers and acquirers were seen to be the core to these differences. This study hypothesised that the capability embodied in technology to yield future returns makes technology valuation distinct from the process of valuing manufacturing products. The study hence has gone beyond the dimensions of cost calculation and price determination that have been discussed in the existing literature, by taking a broader view of how to achieve and share future added value from transferred technology. The core of technology valuation was argued as the evaluation of the 'quality' of the capability (technology) in generating future value and the effectiveness of the transfer arrangement for best use of such a capability. A dynamic approach comprising future value generation and realisation within the context of specific forms of collaboration was therefore adopted. The research investigations focused on the UK and China machine tool industries, where there are many technology transfer activities and the value issue has already been recognised in practice. Data were gathered from three groups: machine tool manufacturing technology suppliers in the UK and acquirers in China, and machine tool users in China. Data collecting methods included questionnaire surveys and case studies within all the three groups. The study has focused on identifying and examining the major factors affecting value as well as their interactive effects on technology valuation from both the supplier's and acquirer's point of view. The survey results showed the perceptions and the assessments of the owner's value and transfer value from the supplier's and acquirer's point of view respectively. Benefits, costs and risks related to the technology transfer were the major factors affecting the value of technology. The impacts of transfer payment on the value of technology by the sharing of financial benefits, costs and risks between partners were assessed. The close relationship between technology valuation and transfer arrangements was established by which technical requirements and strategic implications were considered. The case studies reflected the research propositions and revealed that benefits, costs and risks in the financial, technical and strategic dimensions interacted in the process of technology valuation within the context of technology collaboration. Further to the assessment of factors affecting value, a technology valuation framework was developed which suggests that technology attributes for the enhancement of contributory factors and their contributions to the realisation of transfer objectives need to be measured and compared with the associated costs and risks. The study concluded that technology valuation is a dynamic process including the generation and sharing of future value and the interactions between financial, technical and strategic achievements.

Machine learning for large-scale wearable sensor data in Parkinson disease:concepts, promises, pitfalls, and futures

For the treatment and monitoring of Parkinson's disease (PD) to be scientific, a key requirement is that measurement of disease stages and severity is quantitative, reliable, and repeatable. The last 50 years in PD research have been dominated by qualitative, subjective ratings obtained by human interpretation of the presentation of disease signs and symptoms at clinical visits. More recently, “wearable,” sensor-based, quantitative, objective, and easy-to-use systems for quantifying PD signs for large numbers of participants over extended durations have been developed. This technology has the potential to significantly improve both clinical diagnosis and management in PD and the conduct of clinical studies. However, the large-scale, high-dimensional character of the data captured by these wearable sensors requires sophisticated signal processing and machine-learning algorithms to transform it into scientifically and clinically meaningful information. Such algorithms that “learn” from data have shown remarkable success in making accurate predictions for complex problems in which human skill has been required to date, but they are challenging to evaluate and apply without a basic understanding of the underlying logic on which they are based. This article contains a nontechnical tutorial review of relevant machine-learning algorithms, also describing their limitations and how these can be overcome. It discusses implications of this technology and a practical road map for realizing the full potential of this technology in PD research and practice. © 2016 International Parkinson and Movement Disorder Society.