Improving coordination in an engineer-to-order supply chain using a soft systems approach

An empirical study in the ship-building sector has been undertaken to understand the problems associated with the coordination of engineer-to-order (ETO) supply chains and to reveal insights into opportunities for improvements based on the application of soft systems methodology. A number of alternatives to improve coordination of supply chain have been proposed based on the comparison between a soft systems model and actual practice. These alternatives were summarised into seven general principles that help define the role of individual companies in coordinating ETO supply chains, highlighting the company’s structures and interdependencies that lead to project tardiness. Due to the specific nature of a project which changes according to the context, it is difficult to generalise the soft systems model. Nevertheless, future research can further explore some of the principles proposed to deal with coordination problems experienced in other types of ETO project operations, such as construction and oil and gas

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe