Cardoen, Brecht; Beliën, Jeroen; Vanhoucke, Mario (2015)
A custom pack combines medical disposable items into a single sterile package that is used for surgical procedures. Although custom packs are gaining importance in hospitals due to their potential benefits in reducing surgery setup times, little is known on methodologies to configure them, especially if the number of medical items, procedure types and surgeons is large. In this paper, we propose a mathematical programming approach to guide hospitals in developing or reconfiguring their custom packs. In particular, we are interested in minimising points of touch, which we define as a measure for physical contact between staff and medical materials. Starting from an integer non-linear programming model, we develop both an exact linear programming (LP) solution approach and an LP-based heuristic. Next, we also describe a simulated annealing approach to benchmark the mathematical programming methods. A computational experiment, based on real data of a medium-sized Belgian hospital, compares the optimised results with the performance of the hospital's current configuration settings and indicates how to improve future usage. Next to this base case, we introduce scenarios in which we examine to what extent the results are sensitive for waste, i.e. adding more items to the custom pack than is technically required for some of the custom pack's procedures, since this can increase its applicability towards other procedures. We point at some interesting insights that can be taken up by the hospital management to guide the configuration and accompanying negotiation processes.
When KBC Bank introduced their mobile banking application in 2011 they delivered much more than a piece of software. Mobile banking was a way to focus on their customers and rebuild trust. Yet, this case is not so much about what they delivered but how they delivered it. Agile development brought along both challenges and opportunities for KBC. The case explores how agile compares to traditional ways of ICT development. Can banks, given the new digital challenges, really build their future ICT portfolios on agile development?
Trkman, Peter; Mertens, Willem; Viaene, Stijn; Gemmel, Paul (2015)
Purpose - The purpose of this paper is to argue that in order to achieve customer centricity through business process management (BPM), companies have to obtain the profound understanding of customers' processes and when necessary change not only the interactions with but also the processes of their customers. A method is presented that allows doing this in a systematic manner. Design/methodology/approach - A case study of a large multinational company was conducted. Several different sources and methods were used, including document analysis, interviews and a qualitative analysis of responses to open-ended questions. Data were gathered at three points in time: before, during and after the implementation of the presented approach. Findings - The method that was successfully employed by the case organisation consisted of combining BPM with service blueprinting, and of extending these efforts by integrating the customers' internal processes into the scope of improvement. Research limitations/implications The paper does not thoroughly evaluate the long-term effects of the proposed approach. Some results of the case study analysis had to be excluded from this paper due to reasons of confidentiality. Practical implications - The paper presents an approach for organisations to not only understand the needs of their customers but also the way in which their product is used in customers' processes. In this way BPM can be implemented in a truly customer-oriented way. Originality/value - This paper extends previous work by presenting one way in which BPM can follow up on its promise of increasing an organisations customer orientation. While servitisation has received a lot of attention in various disciplines, its application within BPM research and practice has been scarce.
Support Vector Machines are methods that stem from Artificial Intelligence and attempt to learn the relation between data inputs and one or multiple output values. However, the application of these methods has barely been explored in a project control context. In this paper, a forecasting analysis is presented that compares the proposed Support Vector Regression model with the best performing Earned Value and Earned Schedule methods. The parameters of the SVM are tuned using a cross-validation and grid search procedure, after which a large computational experiment is conducted. The results show that the Support Vector Machine Regression outperforms the currently available forecasting methods. Additionally, a robustness experiment has been set up to investigate the performance of the proposed method when the discrepancy between training and test set becomes larger.
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