Root cause analysis with enriched process logs

In the field of process mining, the use of event logs for the purpose of root cause analysis is increasingly studied. In such an analysis, the availability of attributes/features that may explain the root cause of some phenomena is crucial. Currently, the process of obtaining these attributes from raw event logs is performed more or less on a case-by-case basis: there is still a lack of generalized systematic approach that captures this process. This paper proposes a systematic approach to enrich and transform event logs in order to obtain the required attributes for root cause analysis using classical data mining techniques, the classification techniques. This approach is formalized and its applicability has been validated using both self-generated and publicly-available logs.

Process compliance analysis based on behavioural profiles

Process compliance measurement is getting increasing attention in companies due to stricter legal requirements and market pressure for operational excellence. In order to judge on compliance of the business processing, the degree of behavioural deviation of a case, i.e., an observed execution sequence, is quantified with respect to a process model (referred to as fitness, or recall). Recently, different compliance measures have been proposed. Still, nearly all of them are grounded on state-based techniques and the trace equivalence criterion, in particular. As a consequence, these approaches have to deal with the state explosion problem. In this paper, we argue that a behavioural abstraction may be leveraged to measure the compliance of a process log – a collection of cases. To this end, we utilise causal behavioural profiles that capture the behavioural characteristics of process models and cases, and can be computed efficiently. We propose different compliance measures based on these profiles, discuss the impact of noise in process logs on our measures, and show how diagnostic information on non-compliance is derived. As a validation, we report on findings of applying our approach in a case study with an international service provider.

Patient safety and the RCA: A document analysis

This research examined the function of Queensland Health's Root Cause Analysis (RCA) to improve patient safety through an investigation of patient harm events where permanent harm and preventable death, Severity Assessment Code 1, were the outcome of healthcare. Unedited and highly legislated RCAs from across Queensland Health public hospitals from 2009, 2010 and 2011 comprised the data. A document analysis revealed the RCAs opposed organisational policy and dominant theoretical directives. If we accept the prevailing assumption that patient harm is a systemic issue, then the RCA is failing to address harm events in healthcare.

Quality-aware business process management

Business Process Management (BPM) has emerged as a popular management approach in both Information Technology (IT) and management practice. While there has been much research on business process modelling and the BPM life cycle, there has been little attention given to managing the quality of a business process during its life cycle. This study addresses this gap by providing a framework for organisations to manage the quality of business processes during different phases of the BPM life cycle. This study employs a multi-method research design which is based on the design science approach and the action research methodology. During the design science phase, the artifacts to model a quality-aware business process were developed. These artifacts were then evaluated through three cycles of action research which were conducted within three large Australian-based organisations. This study contributes to the body of BPM knowledge in a number of ways. Firstly, it presents a quality-aware BPM life cycle that provides a framework on how quality can be incorporated into a business process and subsequently managed during the BPM life cycle. Secondly, it provides a framework to capture and model quality requirements of a business process as a set of measurable elements that can be incorporated into the business process model. Finally, it proposes a novel root cause analysis technique for determining the causes of quality issues within business processes.

RCA method for fault diagnosis in digital substations of power systems

The authors present a Cause-Effect fault diagnosis model, which utilises the Root Cause Analysis approach and takes into account the technical features of a digital substation. The Dempster/Shafer evidence theory is used to integrate different types of fault information in the diagnosis model so as to implement a hierarchical, systematic and comprehensive diagnosis based on the logic relationship between the parent and child nodes such as transformer/circuit-breaker/transmission-line, and between the root and child causes. A real fault scenario is investigated in the case study to demonstrate the developed approach in diagnosing malfunction of protective relays and/or circuit breakers, miss or false alarms, and other commonly encountered faults at a modern digital substation.

Improving the performance of nutrition screening through a series of quality improvement initiatives

Background Nutrition screening identifies patients at risk of malnutrition to facilitate early nutritional intervention. Studies have reported incompletion and error rates of 30-90% for a range of commonly used screening tools. This study aims to investigate the incompletion and error rates of 3-Minute Nutrition Screening (3-MinNS) and the effect of quality improvement initiatives in improving the overall performance of the screening tool and the referral process for at risk patients. Methods Annual audits were carried out from 2008-2013 on 4467 patients. Value Stream Mapping, Plan-Do-Check-Act cycle and Root Cause Analysis were used in this study to identify gaps and determine the best intervention. The intervention included 1) implementing a nutrition screening protocol, 2) nutrition screening training, 3) nurse empowerment for online dietetics referral of at-risk cases, 4) closed-loop feedback system and 5) removing a component of 3-MinNS that caused the most error without compromising its sensitivity and specificity. Results Nutrition screening error rates were 33% and 31%, with 5% and 8% blank or missing forms, in 2008 and 2009 respectively. For patients at risk of malnutrition, referral to dietetics took up to 7.5 days, with 10% not referred at all. After intervention, the latter decreased to 7% (2010), 4% (2011) and 3% (2012 and 2013), and the mean turnaround time from screening to referral was reduced significantly from 4.3 ± 1.8 days to 0.3 ± 0.4 days (p < 0.001). Error rates were reduced to 25% (2010), 15% (2011), 7% (2012) and 5% (2013) and percentage of blank or missing forms reduced to and remained at 1%. Conclusion Quality improvement initiatives are effective in reducing the incompletion and error rates of nutrition screening, and led to sustainable improvements in the referral process of patients at nutritional risk.

Copyright enforcement in the networked society : guiding principles for protecting copyright

This paper argues that any future copyright policy should be proportional and flexible and be developed from a clear and evidence-based approach. An approach is required that carefully balances the incentives and rewards provided to economic rights holders against fundamental rights of privacy, self-expression, due process and the user rights embodied in copyright law to protect access, learning, critique, and reuse. This paper also suggests that while adequate enforcement measures are certainly part of a solution to a well functioning lawful, enforcement alone can never solve the root cause of unlawful file-sharing, since it utterly fails to address supply-side market barriers. Focus on enforcement measures alone continues to leave out a legitimate but un-served market demand, susceptible to unlawful alternatives. A competitive and consumer friendly digital content market and an appropriate legal framework to enable easy lawful access to digital content are essential preconditions for the creation of a culture of lawful, rather than unlawful, consumption.

Bayesian hierarchical models in statistical quality control methods to improve healthcare in hospitals

Quality oriented management systems and methods have become the dominant business and governance paradigm. From this perspective, satisfying customers’ expectations by supplying reliable, good quality products and services is the key factor for an organization and even government. During recent decades, Statistical Quality Control (SQC) methods have been developed as the technical core of quality management and continuous improvement philosophy and now are being applied widely to improve the quality of products and services in industrial and business sectors. Recently SQC tools, in particular quality control charts, have been used in healthcare surveillance. In some cases, these tools have been modified and developed to better suit the health sector characteristics and needs. It seems that some of the work in the healthcare area has evolved independently of the development of industrial statistical process control methods. Therefore analysing and comparing paradigms and the characteristics of quality control charts and techniques across the different sectors presents some opportunities for transferring knowledge and future development in each sectors. Meanwhile considering capabilities of Bayesian approach particularly Bayesian hierarchical models and computational techniques in which all uncertainty are expressed as a structure of probability, facilitates decision making and cost-effectiveness analyses. Therefore, this research investigates the use of quality improvement cycle in a health vii setting using clinical data from a hospital. The need of clinical data for monitoring purposes is investigated in two aspects. A framework and appropriate tools from the industrial context are proposed and applied to evaluate and improve data quality in available datasets and data flow; then a data capturing algorithm using Bayesian decision making methods is developed to determine economical sample size for statistical analyses within the quality improvement cycle. Following ensuring clinical data quality, some characteristics of control charts in the health context including the necessity of monitoring attribute data and correlated quality characteristics are considered. To this end, multivariate control charts from an industrial context are adapted to monitor radiation delivered to patients undergoing diagnostic coronary angiogram and various risk-adjusted control charts are constructed and investigated in monitoring binary outcomes of clinical interventions as well as postintervention survival time. Meanwhile, adoption of a Bayesian approach is proposed as a new framework in estimation of change point following control chart’s signal. This estimate aims to facilitate root causes efforts in quality improvement cycle since it cuts the search for the potential causes of detected changes to a tighter time-frame prior to the signal. This approach enables us to obtain highly informative estimates for change point parameters since probability distribution based results are obtained. Using Bayesian hierarchical models and Markov chain Monte Carlo computational methods, Bayesian estimators of the time and the magnitude of various change scenarios including step change, linear trend and multiple change in a Poisson process are developed and investigated. The benefits of change point investigation is revisited and promoted in monitoring hospital outcomes where the developed Bayesian estimator reports the true time of the shifts, compared to priori known causes, detected by control charts in monitoring rate of excess usage of blood products and major adverse events during and after cardiac surgery in a local hospital. The development of the Bayesian change point estimators are then followed in a healthcare surveillances for processes in which pre-intervention characteristics of patients are viii affecting the outcomes. In this setting, at first, the Bayesian estimator is extended to capture the patient mix, covariates, through risk models underlying risk-adjusted control charts. Variations of the estimator are developed to estimate the true time of step changes and linear trends in odds ratio of intensive care unit outcomes in a local hospital. Secondly, the Bayesian estimator is extended to identify the time of a shift in mean survival time after a clinical intervention which is being monitored by riskadjusted survival time control charts. In this context, the survival time after a clinical intervention is also affected by patient mix and the survival function is constructed using survival prediction model. The simulation study undertaken in each research component and obtained results highly recommend the developed Bayesian estimators as a strong alternative in change point estimation within quality improvement cycle in healthcare surveillances as well as industrial and business contexts. The superiority of the proposed Bayesian framework and estimators are enhanced when probability quantification, flexibility and generalizability of the developed model are also considered. The empirical results and simulations indicate that the Bayesian estimators are a strong alternative in change point estimation within quality improvement cycle in healthcare surveillances. The superiority of the proposed Bayesian framework and estimators are enhanced when probability quantification, flexibility and generalizability of the developed model are also considered. The advantages of the Bayesian approach seen in general context of quality control may also be extended in the industrial and business domains where quality monitoring was initially developed.

A simulated annealing algorithm for energy efficient virtual machine placement

Improving energy efficiency has become increasingly important in data centers in recent years to reduce the rapidly growing tremendous amounts of electricity consumption. The power dissipation of the physical servers is the root cause of power usage of other systems, such as cooling systems. Many efforts have been made to make data centers more energy efficient. One of them is to minimize the total power consumption of these servers in a data center through virtual machine consolidation, which is implemented by virtual machine placement. The placement problem is often modeled as a bin packing problem. Due to the NP-hard nature of the problem, heuristic solutions such as First Fit and Best Fit algorithms have been often used and have generally good results. However, their performance leaves room for further improvement. In this paper we propose a Simulated Annealing based algorithm, which aims at further improvement from any feasible placement. This is the first published attempt of using SA to solve the VM placement problem to optimize the power consumption. Experimental results show that this SA algorithm can generate better results, saving up to 25 percentage more energy than First Fit Decreasing in an acceptable time frame.

Acquisition modelling for heavy engineering assets in mining industry

Each year, organizations in Australian mining industry (asset intensive industry) spend substantial amount of capital (A$86 billion in 2009-10) (Statistics, 2011) in acquiring engineering assets. Engineering assets are put to use in operations to generate value. Different functions (departments) of an organization have different expectations and requirements from each of the engineering asset e.g. return on investment, reliability, efficiency, maintainability, low cost of running the asset, low or nil environmental impact and easy of disposal, potential salvage value etc. Assets are acquired from suppliers or built by service providers and or internally. The process of acquiring assets is supported by procurement function. One of the most costly mistakes that organizations can make is acquiring the inappropriate or non-conforming assets that do not fit the purpose. The root cause of acquiring non confirming assets belongs to incorrect acquisition decision and the process of making decisions. It is very important that an asset acquisition decision is based on inputs and multi-criteria of each function within the organization which has direct or indirect impact on the acquisition, utilization, maintenance and disposal of the asset. Literature review shows that currently there is no comprehensive process framework and tool available to evaluate the inclusiveness and breadth of asset acquisition decisions that are taken in the Mining Organizations. This thesis discusses various such criteria and inputs that need to be considered and evaluated from various functions within the organization while making the asset acquisition decision. Criteria from functions such as finance, production, maintenance, logistics, procurement, asset management, environment health and safety, material management, training and development etc. need to be considered to make an effective and coherent asset acquisition decision. The thesis also discusses a tool that is developed to be used in the multi-criteria and cross functional acquisition decision making. The development of multi-criteria and cross functional inputs based decision framework and tool which utilizes that framework to formulate cross functional and integrated asset acquisition decisions are the contribution of this research.

Ratchetting of railhead in the vicinity of the gap of the insulated rail joints

Insulated Rail Joints (IRJs) are designed to electrically isolate two rails in rail tracks to control the signalling system for safer train operations. Unfortunately the gapped section of the IRJs is structurally weak and often fails prematurely especially in heavy haul tracks, which adversely affects service reliability and efficiency. The IRJs suffer from a number of failure modes; the railhead ratchetting at the gap is, however, regarded as the root cause and attended to in this thesis. Ratchetting increases with the increase in wheel loads; in the absence of a life prediction model, effective management of the IRJs for increased wagon wheel loads has become very challenging. Therefore, the main aim of this thesis is to determine method to predict IRJs' service life. The distinct discontinuity of the railhead at the gap makes the Hertzian theory and the rolling contact shakedown map, commonly used in the continuously welded rails, not applicable to examine the metal ratchetting of the IRJs. Finite Element (FE) technique is, therefore, used to explore the railhead metal ratchetting characteristics in this thesis, the boundary conditions of which has been determined from a full scale study of the IRJ specimens under rolling contact of the loaded wheels. A special purpose test set up containing full-scale wagon wheel was used to apply rolling wheel loads on the railhead edges of the test specimens. The state of the rail end face strains was determined using a non-contact digital imaging technique and used for calibrating the FE model. The basic material parameters for this FE model were obtained through independent uniaxial, monotonic tensile tests on specimens cut from the head hardened virgin rails. The monotonic tensile test data have been used to establish a cyclic load simulation model of the railhead steel specimen; the simulated cyclic load test has provided the necessary data for the three decomposed kinematic hardening plastic strain accumulation model of Chaboche. A performance based service life prediction algorithm for the IRJs was established using the plastic strain accumulation obtained from the Chaboche model. The predicted service lives of IRJs using this algorithm have agreed well with the published data. The finite element model has been used to carry out a sensitivity study on the effects of wheel diameter to the railhead metal plasticity. This study revealed that the depth of the plastic zone at the railhead edges is independent of the wheel diameter; however, large wheel diameter is shown to increase the IRJs' service life.

Proactive behavior-based safety management for construction safety improvement

Construction is one of the most hazardous industries due to its dynamic, temporary, and decentralized nature. The Hong Kong Commissioner for Labor identifies worker behavior as the root cause of construction accidents. Behavior-based safety (BBS) is one effective approach in managing employee safety issues. However, there is little research on the application of BBS in the construction industry. This research proposes an extension of the BBS approach, proactive behavior-based safety (PBBS), to improve construction safety. PBBS integrates the theory of BBS with the technology of Proactive Construction Management System (PCMS). The innovations of PBBS are: (1) automatically monitoring location-based behaviors; (2)quantitatively measuring safety performance; (3) investigating potential causes of unsafe behaviors; and (4) improving the efficiency of safety management. A pilot study of a Hong Kong construction site practicing PBBS was conducted. The experiment results showed that PBBS performed well on construction accident prevention and the Safety Index (SI) of the two project teams, with increased improvements by 36.07% and 44.70% respectively. It is concluded that PBBS is effective and adaptable to construction industry.

Socioeconomic inequalities in diet and bodyweight: Evidence, causes, and intervention options

Diets low in fruits, vegetables, and whole grains, and high in saturated fat, salt, and sugar are the major contributors to the burden of chronic diseases globally. Previous research, and studies in this issue of Public Health Nutrition (PHN), show that unhealthy diets are more commonly observed among socioeconomically disadvantaged groups, and are key contributors to their higher rates of chronic disease. Most research examining socioeconomic inequalities in diet and bodyweight has been descriptive, and has focused on identifying the nature, extent, and direction of the inequalities. These types of studies are clearly necessary and important. We need however to move beyond description of the problem and focus much more on the question of why inequalities in diet and bodyweight exist. Furthering our understanding of this question will provide the necessary evidence-base to develop effective interventions to reduce the inequalities. The challenge of tackling dietary inequalities however doesn’t finish here: a maximally effective approach will also require equity-based policies that address the unequal population-distribution of social and economic resources, which is the fundamental root-cause of dietary and bodyweight inequalities.

Not so not-for-profit

To address the sub-theme of the journal: Artistic Practices in a Time of Crisis, the author discusses the context of economic cuts and recent international crises on his PhD interactive and visual design research project undertaken in Australia. Identifying an apparent root-cause of current global crises, the author in reply, has structured a research plan and created a suite of new media, interactive, technology artworks, and installation art. Notions of Zen Buddhism, and stillness through meditation, are applied in the research and context of the artworks to support awareness of wellbeing, in response to the root-cause condition. The discussion will focus on the overarching question: how can one obtain value through the arts during current times of economic reduction conditioning?