Léa Amandine Deleris

Incorporating risk into business process models

Although business process modeling is considered as a core activity in enterprise risk management, existing process modeling languages do not include a complete notation for documenting how processes can fail. This paper develops a conceptual framework for extending standard business process metamodels to include comprehensive information that is useful for managing and quantifying operational risk in business processes. We provide formal extensions of the Business Process Modeling Notation standard, as well as a step-by-step process for creating a risk-extended process model.

Quantitative Risk Assessment in Supply Chains: A Case Study Based on Engineering Risk Analysis Concepts

In recent years, numerous events have shown the extent to which companies, and subsequently their supply chains, are vulnerable to uncertain events. We have witnessed many supply chain malfunctions (with substantial consequences) due to supply and demand disruptions: affected companies reported, on average, a 14% increase in inventories, an 11% increase in cost, and a 7% decrease in sales in the year following the disruption (Hendricks and Singhal 2005). Component shortages, labor strikes, natural and manmade disasters, human errors, changes in customer taste, technological failures, malicious activities, and financially distressed and, in extreme cases, bankrupt partners, among many others, can cause disruptions in supply chains:

The Human Behaviour-Change Project: harnessing the power of artificial intelligence and machine learning for evidence synthesis and interpretation

BackgroundBehaviour change is key to addressing both the challenges facing human health and wellbeing and to promoting the uptake of research findings in health policy and practice. We need to make better use of the vast amount of accumulating evidence from behaviour change intervention (BCI) evaluations and promote the uptake of that evidence into a wide range of contexts. The scale and complexity of the task of synthesising and interpreting this evidence, and increasing evidence timeliness and accessibility, will require increased computer support.The Human Behaviour-Change Project (HBCP) will use Artificial Intelligence and Machine Learning to (i) develop and evaluate a ‘Knowledge System’ that automatically extracts, synthesises and interprets findings from BCI evaluation reports to generate new insights about behaviour change and improve prediction of intervention effectiveness and (ii) allow users, such as practitioners, policy makers and researchers, to easily and efficiently query the system to get answers to variants of the question ‘What works, compared with what, how well, with what exposure, with what behaviours (for how long), for whom, in what settings and why?’.MethodsThe HBCP will: a) develop an ontology of BCI evaluations and their reports linking effect sizes for given target behaviours with intervention content and delivery and mechanisms of action, as moderated by exposure, populations and settings; b) develop and train an automated feature extraction system to annotate BCI evaluation reports using this ontology; c) develop and train machine learning and reasoning algorithms to use the annotated BCI evaluation reports to predict effect sizes for particular combinations of behaviours, interventions, populations and settings; d) build user and machine interfaces for interrogating and updating the knowledge base; and e) evaluate all the above in terms of performance and utility.DiscussionThe HBCP aims to revolutionise our ability to synthesise, interpret and deliver evidence on behaviour change interventions that is up-to-date and tailored to user need and context. This will enhance the usefulness, and support the implementation of, that evidence.

From Reliability Block Diagrams to Fault Tree Circuits

Reliability block diagrams (RBDs) depict the functional relationships between components comprising a system, whereas Bayesian networks (BNs) represent probabilistic relationships between uncertain variables. Previous research has described how one can transform an RBD into a BN. In parallel, developments in the artificial intelligence literature have shown how a BN can be transformed into another graphical representation, an arithmetic circuit, which can subsequently be used for efficient inference. In this paper, we introduce a new graphical representation that we call a fault tree circuit, which is a special kind of arithmetic circuit constructed specifically for an RBD. A fault tree circuit can be constructed directly from an RBD and is more efficient than an arithmetic circuit that is compiled from the BN corresponding to that RBD. We develop several methods for fault tree circuits, highlighting how they can aid the analyst in efficient diagnosis, sensitivity analysis, and decision support for many typical reliability problems. The circuit framework can complement tools that are popular in the reliability analysis community. We use a simple pump system example to illustrate the concepts.

A simulation model for improving the maintenance of high cost systems, with application to an offshore oil installation

We describe a Generalized Semi-Markov Model paired with Monte Carlo simulation that represents the evolution of the systems that constitute an offshore installation. We use the model to assess the performance of a maintenance plan in terms of a production penalty and unplanned shutdowns. In addition to estimating, comparing and improving maintenance plans, our approach enables the determination of the consequences of planning capital expenditures and installation modifications and the value of process improvements. Moreover, the entire framework is designed to be sufficiently flexible to accommodate various ‘what if’ scenarios in addition to other modifications. The simulation model was tested on data from an offshore oil installation, and was well received by the installations operations personnel. Copyright

Service operation classification for risk management

We propose an empirical service-operation risk-classification model to provide managerial insights to service providers in terms of risk management. The model is developed through an investigation of the dependencies between the characteristics of service operations in consumer services and the broad classes of provider risk to which they are exposed. A survey of professional managers has been conducted in which respondents were asked to assess 30 service operations across six service dimensions and five factors representing provider risk. The data have been analyzed using statistical methods, in particular Bayesian network analysis and hierarchical clustering. The results indicate relationships between service operations, service dimensions, and risk factors. Due to the limited sample size, our findings should be regarded as preliminary. The proposed model should help determine the most relevant types of service risks based on the specific characteristics of the service provided and therefore help to develop risk mitigation strategies.

Adaptive Project Risk Management

IT projects tend to be associated with over-budget and late deliveries. In this paper, we describe a method for improving project management based on (a) a thorough analysis of risks affecting activities in a project plan, i.e., the root factors leading to cost and time overruns, and (b) an optimization of the resources allocated to each activity in the project plan in order to maximize the probability of completing on time and within-budget. One key feature of our method is its capability to adapt and learn the risk factors affecting activities during the course of the project, which enables project managers to dynamically reallocate resources to ensure a better outcome given the updated risk profile.

Three key enablers to successful enterprise risk management

Enterprise risk management (ERM) refers to a set of processes that enables the effective management of the risks, opportunities, and expected and unexpected events that may affect the enterprise. The successful implementation of ERM is a challenging task in part because it requires collaboration among multiple business units of different sizes, scope, and capability, each facing what it perceives as unique risks. Other difficulties with ERM implementations include lack of adoption of an enterprise-wide governance model, lack of a common risk language (e.g., taxonomy), and uneven levels of maturity within an organization regarding the management of risks. This paper establishes three conceptual frameworks that provide a basis for an enterprise embarking on ERM: 1) a risk management cycle; 2) a risk-related taxonomy; and 3) an ERM maturity model. The risk management cycle provides a discipline to consistently and coherently manage virtually all risks in the enterprise. The risk taxonomy provides a foundation for clear and concise communication about risk across the enterprise to enable better risk management. The ERM maturity model, and its associated capability assessment, allows an organization to determine gaps in its current risk management processes and define ways to improve those ERM capabilities. Together, these three frameworks are key enablers for a successful ERM implementation and ongoing operation.

Simulation of adaptive project management analytics

Typically, IT projects are delivered over-budget and behind schedule. In this paper, we explore the effects of common project management practices that contribute to these problems and suggest a better alternative that can utilize resources more effectively. Our alternative approach uses (a) a thorough analysis of risks affecting activities in a project plan (i.e., the root factors leading to cost and time overruns), and (b) an optimization of the resources allocated to each activity in the project plan to maximize the probability of on time and within budget project completion. One key feature of our method is its capability to adapt and learn the risk factors affecting activities during the course of the project, enabling project managers to reallocate resources dynamically to ensure a better outcome given the updated risk profile. We use simulations to test the performance of our optimization algorithm and to gain insights into the benefits of adaptive re-planning.

Bayesian Network Structure Learning with Messy Inputs: The Case of Multiple Incomplete Datasets and Expert Opinions

In this paper, we present an approach to build the structure of a Bayesian network from multiple disparate inputs. Specifically, our method accepts as input multiple partially overlapping datasets with missing data along with expert opinions about the structure of the model and produces an associated directed acyclic graph representing the graphical layer of a Bayesian network. We provide experimental results where we compare our algorithm with an application of Structural Expectation Maximization. We also provide a real world example motivating the need for combining disparate sources of information even when noisy and not fully aligned with one another.