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There appears to be no empirical-based method in the literature for estimating if an engineering change proposal (ECP) will occur or the dollar amount incurred. This paper aims to present an empirically based approach to address this shortfall.

Using the cost assessment data enterprise database, 533 contracts were randomly selected via a stratified sampling plan to build two regression models: one to predict the likelihood of a contract experiencing an ECP and the other to determine the expected median per cent increase in baseline contract cost if an ECP was likely. Both models adopted a stepwise approach. A validation set was placed aside prior to any model building.

Not every contract incurs an ECP; approximately 80 per cent of the contracts in the database did not have an ECP. The likelihood of an ECP and the additional amount incurred appears to be statistically independent of acquisition phase, branch of service, commodity, contract type or any other factor except for the basic contract amount and the number of contract line item numbers; both of these later variables equally affected the contract percentage increase because of an ECP. The combined model overall bested current anecdotal approaches to ECP withhold.

This paper both serves as a published reference point for ECP withholds in the archival forum and presents an empirically based method for determining per cent ECP withhold to use.

The main objective of this study is to investigate what are the critical success factors that exist for continuous improvement (CI) methodology deployment in the Irish medical technology (MedTech) industry. The research will, in particular, seek to establish if the highly regulated nature of the global MedTech industry is an additional critical failure factor (CFF) for the deployment of CI methodology. The study involves the analysis of the benefits, challenges, CFFs and tools most utilised for the application to the deployment of CI methodologies in the Irish medical device (MD) industry.

A quantitative survey was utilised in this study. The main participants were made up of senior quality professionals working in operational excellence, quality consultants, quality directors, quality engineers, quality managers and quality supervisors working in both manufacturing and service sectors from Irish MD companies. A total of 94 participants from the Irish MedTech industry responded to the survey.

The main finding of this study is that 42% of participants perceived that a highly regulated environment was a CFF to CI, whilst 79% of respondents utilised Lean Six Sigma in their organisations, and productivity and financial factors were found to be the highest reasons for CI deployment amongst the Irish MedTech industry. The top CFFs highlighted for CI in regulated industries were fear of extra validation activity, compliance versus quality culture and a regulatory culture of being “safe”. Another relevant finding presented in this paper is that just over 48% of participants felt that CI tools are very strongly integrated into the industries quality management systems (QMSs) such as the corrective and preventative action system, non-conformance and audit systems.

All data collected in the survey came from professionals working for Irish indigenous and multinational MedTech companies. It is important to highlight that n = 94 is a low sample size, which is enough for a preliminary survey but reinforcing the limitation in terms of generalisation of the results. A further study on a wider European and global scale as well as a comparison with the highly regulated pharma industry would be informative.

The authors understand that this is the very first research focussed on the CFFs for CI in the MedTech/MD manufacturing industry with a specific focus on the highly regulated nature of the industry as a potential CFF. The results of this study represent an important first step towards a full understanding of the applicability and use of CI in the medical-device-manufacturing industries on a global scale.