Advanced Business Simulations-Incorporating business and process execution data

Key Performance Indicators (KPIs) and their predictions are widely used by the enterprises for informed decision making. Nevertheless , a very important factor, which is generally overlooked, is that the top level strategic KPIs are actually driven by the operational level business processes. These two domains are, however, mostly segregated and analysed in silos with different Business Intelligence solutions. In this paper, we are proposing an approach for advanced Business Simulations, which converges the two domains by utilising process execution & business data, and concepts from Business Dynamics (BD) and Business Ontologies, to promote better system understanding and detailed KPI predictions. Our approach incorporates the automated creation of Causal Loop Diagrams, thus empowering the analyst to critically examine the complex dependencies hidden in the massive amounts of available enterprise data. We have further evaluated our proposed approach in the context of a retail use-case that involved verification of the automatically generated causal models by a domain expert.

Task sharing within a managed clinical network to improve child health in Malawi

BACKGROUND: Eighty per cent of Malawi's 8 million children live in rural areas, and there is an extensive tiered health system infrastructure from village health clinics to district hospitals which refers patients to one of the four central hospitals. The clinics and district hospitals are staffed by nurses, non-physician clinicians and recently qualified doctors. There are 16 paediatric specialists working in two of the four central hospitals which serve the urban population as well as accepting referrals from district hospitals. In order to provide expert paediatric care as close to home as possible, we describe our plan to task share within a managed clinical network and our hypothesis that this will improve paediatric care and child health.

PRESENTATION OF THE HYPOTHESIS: Managed clinical networks have been found to improve equity of care in rural districts and to ensure that the correct care is provided as close to home as possible. A network for paediatric care in Malawi with mentoring of non-physician clinicians based in a district hospital by paediatricians based at the central hospitals will establish and sustain clinical referral pathways in both directions. Ultimately, the plan envisages four managed paediatric clinical networks, each radiating from one of Malawi's four central hospitals and covering the entire country. This model of task sharing within four hub-and-spoke networks may facilitate wider dissemination of scarce expertise and improve child healthcare in Malawi close to the child's home.

TESTING THE HYPOTHESIS: Funding has been secured to train sufficient personnel to staff all central and district hospitals in Malawi with teams of paediatric specialists in the central hospitals and specialist non-physician clinicians in each government district hospital. The hypothesis will be tested using a natural experiment model. Data routinely collected by the Ministry of Health will be corroborated at the district. This will include case fatality rates for common childhood illness, perinatal mortality and process indicators. Data from different districts will be compared at baseline and annually until 2020 as the specialists of both cadres take up posts.

IMPLICATIONS OF THE HYPOTHESIS: If a managed clinical network improves child healthcare in Malawi, it may be a potential model for the other countries in sub-Saharan Africa with similar cadres in their healthcare system and face similar challenges in terms of scarcity of specialists.

Identifying the Impact of Incomplete Datasets on Process Cycle Time Prediction in an Aerospace Assembly Line

Supply Chain Simulation (SCS) is applied to acquire information to support outsourcing decisions but obtaining enough detail in key parameters can often be a barrier to making well informed decisions.
One aspect of SCS that has been relatively unexplored is the impact of inaccurate data around delays within the SC. The impact of the magnitude and variability of process cycle time on typical performance indicators in a SC context is studied.
System cycle time, WIP levels and throughput are more sensitive to the magnitude of deterministic deviations in process cycle time than variable deviations. Manufacturing costs are not very sensitive to these deviations.
Future opportunities include investigating the impact of process failure or product defects, including logistics and transportation between SC members and using alternative costing methodologies.

Glycosylation is an Androgen-Regulated Process Essential for Prostate Cancer Cell Viability

Steroid androgen hormones play a key role in the progression and treatment of prostate cancer, with androgen deprivation therapy being the first-line treatment used to control cancer growth. Here we apply a novel search strategy to identify androgen-regulated cellular pathways that may be clinically important in prostate cancer. Using RNASeq data, we searched for genes that showed reciprocal changes in expression in response to acute androgen stimulation in culture, and androgen deprivation in patients with prostate cancer. Amongst 700 genes displaying reciprocal expression patterns we observed a significant enrichment in the cellular process glycosylation. Of 31 reciprocally-regulated glycosylation enzymes, a set of 8 (GALNT7, ST6GalNAc1, GCNT1, UAP1, PGM3, CSGALNACT1, ST6GAL1 and EDEM3) were significantly up-regulated in clinical prostate carcinoma. Androgen exposure stimulated synthesis of glycan structures downstream of this core set of regulated enzymes including sialyl-Tn (sTn), sialyl Lewis(X) (SLe(X)), O-GlcNAc and chondroitin sulphate, suggesting androgen regulation of the core set of enzymes controls key steps in glycan synthesis. Expression of each of these enzymes also contributed to prostate cancer cell viability. This study identifies glycosylation as a global target for androgen control, and suggests loss of specific glycosylation enzymes might contribute to tumour regression following androgen depletion therapy.

Development of a multivariable online monitoring system for the thermoforming process

The thermoforming industry has been relatively slow to embrace modern measurement technologies. As a result researchers have struggled to develop accurate thermoforming simulations as some of the key aspects of the process remain poorly understood. For the first time, this work reports the development of a prototype multivariable instrumentation system for use in thermoforming. The system contains sensors for plug force, plug displacement, air pressure and temperature, plug temperature, and sheet temperature. Initially, it was developed to fit the tooling on a laboratory thermoforming machine, but later its performance was validated by installing it on a similar industrial tool. Throughout its development, providing access for the various sensors and their cabling was the most challenging task. In testing, all of the sensors performed well and the data collected has given a powerful insight into the operation of the process. In particular, it has shown that both the air and plug temperatures stabilize at more than 80C during the continuous thermoforming of amorphous polyethylene terephthalate (aPET) sheet at 110C. The work also highlighted significant differences in the timing and magnitude of the cavity pressures reached in the two thermoforming machines. The prototype system has considerable potential for further development. 

Process Selection Using Variation and Cost Relations

Variations are inherent in all manufacturing processes and can significantly affect the quality of a final assembly, particularly in multistage assembly systems. Existing research in variation management has primarily focused on incorporating GD&T factors into variation propagation models in order to predict product quality and allocate tolerances. However, process induced variation, which has a key influence on process planning, has not been fully studied. Furthermore, the link between variation and cost has not been well established, in particular the effect that assembly process selection has on the final quality and cost of a product. To overcome these barriers, this paper proposes a novel method utilizing process capabilities to establish the relationship between variation and cost. The methodology is discussed using a real industrial case study. The benefits include determining the optimum configuration of an assembly system and facilitating rapid introduction of novel assembly techniques to achieve a competitive edge.

PIPc study: development of indicators of potentially inappropriate prescribing in children (PIPc) in primary care using a modified Delphi technique

Objective There is limited evidence regarding the quality of prescribing for children in primary care. Several prescribing criteria (indicators) have been developed to assess the appropriateness of prescribing in older and middle-aged adults but few are relevant to children. The objective of this study was to develop a set of prescribing indicators that can be applied to prescribing or dispensing data sets to determine the prevalence of potentially inappropriate prescribing in children (PIPc) in primary care settings.


Design Two-round modified Delphi consensus method.


Setting Irish and UK general practice.


Participants A project steering group consisting of academic and clinical general practitioners (GPs) and pharmacists was formed to develop a list of indicators from literature review and clinical expertise. 15 experts consisting of GPs, pharmacists and paediatricians from the Republic of Ireland and the UK formed the Delphi panel.


Results 47 indicators were reviewed by the project steering group and 16 were presented to the Delphi panel. In the first round of this exercise, consensus was achieved on nine of these indicators. Of the remaining seven indicators, two were removed following review of expert panel comments and discussion of the project steering group. The second round of the Delphi process focused on the remaining five indicators, which were amended based on first round feedback. Three indicators were accepted following the second round of the Delphi process and the remaining two indicators were removed. The final list consisted of 12 indicators categorised by respiratory system (n=6), gastrointestinal system (n=2), neurological system (n=2) and dermatological system (n=2).


Conclusions The PIPc indicators are a set of prescribing criteria developed for use in children in primary care in the absence of clinical information. The utility of these criteria will be tested in further studies using prescribing databases.