The Estimation of ERP Lifecyle Costs: A Quantitative Analysis of Cost Types and Cost Drivers for German Small and Mid-Sized Companies

Contextualisation: Enterprise Resource Planning (ERP) systems have become one of the largest IT investments in recent years. Yet the implementation of this IT technology often involves some problems. Analyses and studies have identified very high cost overruns and project fiascos. There is an obvious need for better cost estimation, allowing imple­menting organisations a more precise or realistic specification of costs. Unfortunately, neither has a suitable model been developed nor are traditional software estimation mod­els suitable to be transferred to ERP cost prediction. Research about this issue is relatively fragmented and the analysis of ERP costs is still in its fledging stages. Purpose: This thesis aims to analyse the cost fields and cost drivers during the whole lifespan of an ERP lifecycle in German SMEs in the industrial sector with 30 to 1,500 employees. Different approaches for predicting ERP costs will be deduced on the basis of these findings. Conceptual Framework: Within this thesis, the three factors "cost types", "cost drivers" and the "ERP lifecycle" are combined into one conceptual framework. The conducted systematic literature review identified the five different costs types "internal personnel costs", "external personnel costs", "hardware costs", "licence costs" and "ERP software costs" and found 35 cost drivers to be relevant in this thesis. The lifecycle is divided into three stages: evaluation, implementation and maintenance. The combination of the differ­ent cost types and different lifecycle phases results in 12 different cost fields. The cost driver candidates are analysed for their impact on each cost field. Method: In order to access this research issue, a quantitative survey design that involved asking responsible managers in the target group about their ERP expenditures was con­ducted. This was accomplished by way of self-completion questionnaires provided by an online survey tool. The sampling strategy was a self-selecting one that yielded 72 eligible respondents. Based on this sample, the data was analysed for correlations, and multiple regressions were conducted using SPSS. Findings: Firstly, this thesis identifies a cost structure of cost fields for the costs arising during each ERP lifecycle phase and for its whole lifespan. Secondly, it maps which of the identified cost drivers have an influence on each of the 12 cost fields. Thirdly, it cre­ates a regression model of how to predict ERP costs for its whole lifespan. The developed model yields a mean magnitude of relevant error (MMRE) of 34%. Comparing this value to other approaches shows that it contributes to an improved prediction model. So far it is the best fit in ERP effort estimation

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