A QFD-based method to support SMEs in benchmarking co-design tools

Efficient collaborative product design is crucial for extended enterprises willing to develop complex products pursuing a short time to market. However, successful collaborative product design depends on the ability to effectively manage and share engineering knowledge and data throughout the entire product development process. Co-design software platforms aim to facilitate cooperation in distributed teams. In the context of Small and Medium Enterprises (SMEs) the advanced co-design software implementation to support the supply chain is not a trivial task. SMEs have peculiar characteristics such as flexibility, ICT skills and financial resources, which are difficult to be integrated within a structured design network. This paper presents a method to define and evaluate a co-design platform dedicated to SMEs in the mechanical product field. System architecture is defined by applying suitable metrics based on collaborative process characteristics in order to assess functionality performance of the available tools. Benchmarking is based on different levels of collaboration recognized in the typical product development process in SMEs. Correlation between process metrics, software functionalities and specific collaboration requirements is managed by adopting Quality Function Deployment (QFD) techniques. A practical case study allows the robustness of the proposed method to be verified and the main advantages and future developments to be discussed

A new method for measuring process flexibility of product design

International audienceProcess flexibility enables product designers to accommodate changing requirements in a timely and cost‐effective way. However, in order to effectively guide process flexibility in terms of investment in product design, one must be able to objectively measure process flexibility. Hence, this paper introduces a new quantitative method to measure the process flexibility of product design. Instead of directly measuring the available process flexibility, this method introduces a surrogate value (i.e., the reduced project cost resulting from process flexibility) to indirectly measure process flexibility, with consideration of requirement variations. Then, the relationships between process flexibility and impact factors of requirement variations are investigated based on the proposed method. A set of simulation experiments indicates that the method proposed and related propositions are also suitable for complex product design

Prevalence of multimorbidity and its impact on survival in people with motor neuron disease

Background and purpose: This study was undertaken to determine the prevalence of multimorbidity in people with motor neuron disease (MND) and to identify whether specific patterns of multimorbidity impact survival beyond age alone.Methods: We performed a retrospective analysis of the Scottish national MND register from 1 January 2015 to 29 October 2019. People with amyotrophic lateral sclerosis, primary lateral sclerosis, progressive muscular atrophy, or progressive bulbar palsy were included. We fitted latent class regression models incorporating comorbidities (class indicators), age, sex, and bulbar onset (covariates), and survival (distal outcome) with multimorbidity as a hypothesised latent variable. We also investigated the association between the Charlson Comorbidity Index and survival in Cox regression and compared its discrimination and calibration to age alone.Results: A total of 937 people with MND were identified (median age = 67 years, 60.2% male); 64.8% (n = 515) had two or more comorbidities. We identified a subpopulation with high prevalence of cardiovascular disease, but when accounting for the relationship between age and individual comorbidities, there was no difference in survival. Both Charlson Comorbidity Index (hazard ratio [HR] per unit increase = 1.11, 95% confidence interval [CI] = 1.07-1.15, p &lt; 0.0001) and age (HR per year increase = 1.04, 95% CI = 1.03-1.05, p &lt; 0.0001) were significantly associated with survival, but discrimination was higher for age compared to Charlson Comorbidity Index (C-index = 0.63 vs. 0.59).Conclusions: Multimorbidity is common in MND, necessitating holistic interdisciplinary management, but age is the dominant predictor of prognosis in people with MND. Excluding people with MND and multimorbidity from trial participation may do little to homogenise the cohort in terms of survival potential and could harm generalisability.</p