Reassessing the scope of OR practice:the influences of problem structuring methods and the analytics movement

This paper argues that if OR is to prosper it needs to more closely reflect the needs of organizations and its practitioners. Past research has highlighted a gap between theoretical research developments, applications and the methods most frequently used in organizations. The scope of OR applications has also been contested with arguments as to the expanding boundaries of OR. But despite this, anecdotal evidence suggests that OR has become marginalized in many contexts. In order to understand these changes, IFORS (International Federation of OR Societies) in 2009 conducted a survey of global OR practice. The aim was to provide current evidence on the usage of OR tools, areas of application, and the barriers to OR’s uptake, as well as the educational background of OR practitioners. Results presented here show practitioners falling into three segments, which can be loosely characterized as those practicing ‘traditional’ OR, those adopting a range of softer techniques including Problem Structuring Methods (PSMs), and a Business Analytics cluster. When combined with other recent survey evidence, the use of PSMs and Business Analytics is apparently extending the scope of OR practice. In particular, the paper considers whether the Business Analytics movement, with an overlapping skill set to traditional OR but with a fast growing organizational base, offers a route to diminishing the gap between academic research and practice. The paper concludes with an exploration of whether this represents not just an opportunity for OR but also a serious challenge to its established practices

A two-stage framework for designing visual analytics systems to augment organizational analytical processes

A perennially interesting research topic in the field of visual analytics is how to effectively develop systems that support organizational knowledge worker’s decision-making and reasoning processes. The primary objective of a visual analytic system is to facilitate analytical reasoning and discovery of insights through interactive visual interfaces. It also enables the transfer of capability and expertise from where it resides to where it is needed–across individuals, and organizations as necessary. The problem is, however, most domain analytical practices generally vary from organizations to organizations. This leads to the diversified design of visual analytics systems in incorporating domain analytical processes, making it difficult to generalize the success from one domain to another. Exacerbating this problem is the dearth of general models of analytical workflows available to enable such timely and effective designs. To alleviate these problems, this dissertation presents a two-stage framework for informing the design of a visual analytics system. This two-stage design framework builds upon and extends current practices pertaining to analytical workflow and focuses, in particular, on investigating its effect on the design of visual analytics systems for organizational environments. It aims to empower organizations with more systematic and purposeful information analyses through modeling the domain users’ reasoning processes. The first stage in this framework is an Observation and Designing stage, in which a visual analytic system is designed and implemented to abstract and encapsulate general organizational analytical processes, through extensive collaboration with domain users. The second stage is the User-centric Refinement stage, which aims at interactively enriching and refining the already encapsulated domain analysis process based on understanding user’s intentions through analyzing their task behavior. To implement this framework in the process of designing a visual analytics system, this dissertation proposes four general design recommendations that, when followed, empower such systems to bring the users closer to the center of their analytical processes. This dissertation makes three primary contributions: first, it presents a general characterization of the analytical workflow in organizational environments. This characterization fills in the blank of the current lack of such an analytical model and further represents a set of domain analytical tasks that are commonly applicable to various organizations. Secondly, this dissertation describes a two-stage framework for facilitating the domain users’ workflows through integrating their analytical models into interactive visual analytics systems. Finally, this dissertation presents recommendations and suggestions on enriching and refining domain analysis through capturing and analyzing knowledge workers’ analysis processes. To exemplify the generalizability of these design recommendations, this dissertation presents three visual analytics systems that are developed following the proposed recommendations, including Taste for Xerox Corporation, OpsVis for Microsoft, and IRSV for the U.S. Department of Transportation. All of these systems are deployed to domain knowledge workers and are adopted for their analytical practices. Extensive empirical evaluations are further conducted to demonstrate efficacy of these systems in facilitating domain analytical processes

Proceedings of the Conference on Production Systems and Logistics: CPSL 2022

[no abstract available

Advanced Analytics Success Factors - A Case Study

Companies are increasingly taking into use advanced analytics solutions. Advanced analytics solutions are computer programs that analyze data, make predictions on the future, and give optimization-based recommendations on courses of action for achieving pre-determined business goals. Analytics solutions employ sophisticated statistical and mathematical models, and are often offered by third parties. Companies use analytics solutions to improve the efficiency of their operations. This thesis studies whether the distinction between analytics and advanced analytics made in literature is well-founded. The second aim of this study is to find out, what contributes to an analytics initiative’s success. The study begins with a literature review synthesizing the findings of previous analytics research. The resulting synthesis identifies four distinct stages in an analytics project. They are acquiring data, transforming it into insights, communicating the insights, making business decisions, and finally implementing the decisions. Factors that contribute to each stage’s success are identified. The hypotheses that were developed in the theoretical part of the thesis are subsequently tested empirically using the single case study method and semi-structured interviews. The case study confirms the findings of earlier research. Analytics can be viewed as a process with clearly identifiable stages. Specific measures can be taken to improve the success of each stage. The results obtained suggest that an analytics initiative should always be preceded by a thorough goal definition stage. This is a finding that earlier research has not emphasized sufficiently. The study offers business executives a clear roadmap for managing analytics initiatives. It formulates clear action points and allocates parties the responsibility for executing them. The study also highlights some ordinary pitfalls preventing companies from fully benefitting from the results of analytics initiatives. Finally, the study points out new interesting research opportunities in the intersection of information systems science and cognitive science. A key difficulty in using analytics effectively is that the reasoning behind the insights created by the solutions are often complex. Cognitive science could provide us tools for making the insights easier to digest. Lastly, the study highlights that process decoupling will eventually be applied to analytics initiatives. Future studies should research how the stages of an analytics initiative can be separated from each other, and outsourced to parties performing them the most effectively

Problem structuring for decision consensus among students / Dianne Lee Mei Cheong, Louis Sanzogni and Luke Houghton

Decision-problem structuring is conceptualized to be a process comprising activities characterized by the students. The activities are cognitive efforts of a group coming to an understanding and determining the representation of the decision-problem and of what knowledge is relevant to the decision-problem. Cognitive effort refers to the fraction of limited attention with respect to resources that are momentarily allocated to a process. The consensual representation of the decision-problem provides the basis for modeling those activities in some form and order. Knowing how a decision-problem is structured by students based on Management Information System domain will enable the modeling to be based on a simple descriptive behaviour in problem structuring. One such method would be a mathematical model to quantify the problem which ultimately becomes well-structured

Incorporating supra-local social structure into social impact assessment using causal network analysis

This paper discusses the incorporation of supra-local social structure (SLSS) analysis into social impact assessment (SIA) practice in order to afford a deeper and more complex understanding of the social production of the impacts of planned interventions. We define SLSS as the total set of political, economic, socio-cultural and ideological driving forces and external structural phenomena shaping the social vulnerability of affected communities. We advocate causal network analysis for effectively incorporating SLSS into SIA and we take the conflict over the HydroAysén project in Chilean Patagonia as an empirical case study. While previous applications have interpreted planned interventions as the root cause of impacts, this paper analyses the dialectical interaction of four elements: the SLSS, the local community, the planned intervention and its impacts. This application revealed two fundamental issues. First, on a theoretical-conceptual level, it showed the capacity of SLSS to mould the causal pathways of a project's impacts on the affected community. Second, on an applied level, it enabled identification of the elements that should be addressed to facilitate social management of the project.This work was supported by the National Commission for Scientific and Technological Research of Chile (CONICYT) [Project Fondecyt, grant number 1150576 and Project REDES, grant number 150052]. Also, this study has been conducted within the grant received from the Programa Nacional de Formación de Profesorado Universitario (FPU) conceded by the Spanish Ministry of Science, Innovation and Universities to the third author

Is operational research in UK universities fit-for-purpose for the growing field of analytics?

Over the last decade considerable interest has been generated into the use of analytical methods in organisations. Along with this, many have reported a significant gap between organisational demand for analytical-trained staff, and the number of potential recruits qualified for such roles. This interest is of high relevance to the operational research discipline, both in terms of raising the profile of the field, as well as in the teaching and training of graduates to fill these roles. However, what is less clear, is the extent to which operational research teaching in universities, or indeed teaching on the various courses labelled as analytics , are offering a curriculum that can prepare graduates for these roles. It is within this space that this research is positioned, specifically seeking to analyse the suitability of current provisions, limited to master s education in UK universities, and to make recommendations on how curricula may be developed. To do so, a mixed methods research design, in the pragmatic tradition, is presented. This includes a variety of research instruments. Firstly, a computational literature review is presented on analytics, assessing (amongst other things) the amount of research into analytics from a range of disciplines. Secondly, a historical analysis is performed of the literature regarding elements that can be seen as the pre-cursor of analytics, such as management information systems, decision support systems and business intelligence. Thirdly, an analysis of job adverts is included, utilising an online topic model and correlations analyses. Fourthly, online materials from UK universities concerning relevant degrees are analysed using a bagged support vector classifier and a bespoke module analysis algorithm. Finally, interviews with both potential employers of graduates, and also academics involved in analytics courses, are presented. The results of these separate analyses are synthesised and contrasted. The outcome of this is an assessment of the current state of the market, some reflections on the role operational research make have, and a framework for the development of analytics curricula. The principal contribution of this work is practical; providing tangible recommendations on curricula design and development, as well as to the operational research community in general in respect to how it may react to the growth of analytics. Additional contributions are made in respect to methodology, with a novel, mixed-method approach employed, and to theory, with insights as to the nature of how trends develop in both the jobs market and in academia. It is hoped that the insights here, may be of value to course designers seeking to react to similar trends in a wide range of disciplines and fields

Enhancing discrete-event simulation with big data analytics: a review

This article presents a literature review of the use of the OR technique of discrete-event simulation (DES) in conjunction with the big data analytics (BDA) approaches of data mining, machine learning, data farming, visual analytics, and process mining. The two areas are quite distinct. DES represents a mature OR tool using a graphical interface to produce an industry strength process modelling capability. The review reflects this and covers commercial off-the-shelf DES software used in an organisational setting. On the contrary the analytics techniques considered are in the domain of the data scientist and usually involve coding of algorithms to provide outputs derived from big data. Despite this divergence the review identifies a small but emerging literature of use-cases and from this a framework is derived for a DES development methodology that incorporates the use of these analytics techniques. The review finds scope for two new categories of simulation and analytics use: an enhanced capability for DES from the use of BDA at the main stages of the DES methodology as well as the use of DES in a data farming role to drive BDA techniques