Evaluating cost taxonomies for information systems management

The consideration of costs, benefits and risks underpin many Information System (IS) evaluation decisions. Yet, vendors and project-champions alike tend to identify and focus much of their effort on the benefits achievable from the adoption of new technology, as it is often not in the interest of key stakeholders to spend too much time considering the wider cost and risk implications of enterprise-wide technology adoptions. In identifying a void in the literature, the authors of the paper present a critical analysis of IS-cost taxonomies. In doing so, the authors establish that such cost taxonomies tend to be esoteric and difficult to operationalize, as they lack specifics in detail. Therefore, in developing a deeper understanding of IS-related costs, the authors position the need to identify, control and reduce IS-related costs within the information systems evaluation domain, through culminating and then synthesizing the literature into a frame of reference that supports the evaluation of information systems through a deeper understanding of IS-cost taxonomies. The paper then concludes by emphasizing that the total costs associated with IS-adoption can only be determined after having considered the multi-faceted dimensions of information system investments

Taxonomic classification of planning decisions in health care: a review of the state of the art in OR/MS

We provide a structured overview of the typical decisions to be made in resource capacity planning and control in health care, and a review of relevant OR/MS articles for each planning decision. The contribution of this paper is twofold. First, to position the planning decisions, a taxonomy is presented. This taxonomy provides health care managers and OR/MS researchers with a method to identify, break down and classify planning and control decisions. Second, following the taxonomy, for six health care services, we provide an exhaustive specification of planning and control decisions in resource capacity planning and control. For each planning and control decision, we structurally review the key OR/MS articles and the OR/MS methods and techniques that are applied in the literature to support decision making

Human-Machine Collaborative Optimization via Apprenticeship Scheduling

Coordinating agents to complete a set of tasks with intercoupled temporal and resource constraints is computationally challenging, yet human domain experts can solve these difficult scheduling problems using paradigms learned through years of apprenticeship. A process for manually codifying this domain knowledge within a computational framework is necessary to scale beyond the ``single-expert, single-trainee" apprenticeship model. However, human domain experts often have difficulty describing their decision-making processes, causing the codification of this knowledge to become laborious. We propose a new approach for capturing domain-expert heuristics through a pairwise ranking formulation. Our approach is model-free and does not require enumerating or iterating through a large state space. We empirically demonstrate that this approach accurately learns multifaceted heuristics on a synthetic data set incorporating job-shop scheduling and vehicle routing problems, as well as on two real-world data sets consisting of demonstrations of experts solving a weapon-to-target assignment problem and a hospital resource allocation problem. We also demonstrate that policies learned from human scheduling demonstration via apprenticeship learning can substantially improve the efficiency of a branch-and-bound search for an optimal schedule. We employ this human-machine collaborative optimization technique on a variant of the weapon-to-target assignment problem. We demonstrate that this technique generates solutions substantially superior to those produced by human domain experts at a rate up to 9.5 times faster than an optimization approach and can be applied to optimally solve problems twice as complex as those solved by a human demonstrator.Comment: Portions of this paper were published in the Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI) in 2016 and in the Proceedings of Robotics: Science and Systems (RSS) in 2016. The paper consists of 50 pages with 11 figures and 4 table

Information systems evaluation: Navigating through the problem domain

Information systems (IS) make it possible to improve organizational efficiency and effectiveness, which can provide competitive advantage. There is, however, a great deal of difficulty reported in the normative literature when it comes to the evaluation of investments in IS, with companies often finding themselves unable to assess the full implications of their IS infrastructure. Although many of the savings resulting from IS are considered suitable for inclusion within traditional accountancy frameworks, it is the intangible and non-financial benefits, together with indirect project costs that complicate the justification process. In exploring this phenomenon, the paper reviews the normative literature in the area of IS evaluation, and then proposes a set of conjectures. These were tested within a case study to analyze the investment justification process of a manufacturing IS investment. The idiosyncrasies of the case study and problems experienced during its attempts to evaluate, implement, and realize the holistic implications of the IS investment are presented and critically analyzed. The paper concludes by identifying lessons learnt and thus, proposes a number of empirical findings for consideration by decisionmakers during the investment evaluation process