Application of simulation and modelling in managing unplanned healthcare demand

Patients who attend Accident and Emergency (A & E) departments with problems that could be dealt with by their general practitioners (GPs) use time and resources of the department that could be otherwise used for patients with more appropriate needs. Hospital managers throughout the world are facing increasing pressure to introduce measures and initiatives to significantly ease the problem of such inappropriate attendances at A&E departments. This study looks at an initiative in which primary care clinicians are used to help deflect patients with non-urgent needs away from A&E. Simulation and modelling was used to assess the impact that this initiative would have on A&E workflow. The results suggest that the deflection of patients attending A&E with non-urgent needs may reduce the time spent in A&E by all patients attending A&E

Modelling and prediction of intermittent demand distributions

The management of spare part inventories is an issue of strategic concern for mostindustrial firms. However, the demand for spare parts is typically intermittent in naturemeaning that orders arrive sporadically and the order sizes may be highly variable. Anumber of authors have suggested that compound distributions could be used to modelintermittent demand patterns. There is however a lack of theoretical analysis and relevantempirical evidence on this issue. In this work, we assess whether compound Poissondistributions provide a good fit for the demand distributions of spare part items. Aframework that links demand classification and the distributional properties of demand isproposed and the empirical validity of the framework is assessed by means ofexperimentation with real data.This study also examines a number of different approaches for managing inventory itemswith intermittent demand. The literature on inventory management is dominated by the'frequentist-approach'; this is the term that is being used in this thesis to refer to all thesolutions that rely on frequentist inference in order to obtain the demand distribution. Thefrequentist-based approach is characterised by a reliance on a number of assumptions(including, at a minimum, that the demand distribution and the associated parameters areknown). As demonstrated in this study, such assumptions may pose considerable practicalproblems when demand is intermittent. An argument is being made in favour of otherinventory management approaches that rely on fewer, less restrictive, assumptions. Thealternative approaches considered in this study include the bootstrapping-based solutionproposed by Willemain et al. (1994), a new solution based on the work by Efron (1979)and a new approach that is based on the Bayesian paradigm. A comparison of the stockcontrol performance of these alternatives suggests that non-frequentist approaches mayperform as well as the frequentist one

Spare parts management: Linking distributional assumptions to demand classification

Spare parts are known to be associated with intermittent demand patterns and such patterns cause considerable problems with regards to forecasting and stock control due to their compound nature that renders the normality assumption invalid. Compound distributions have been used to model intermittent demand patterns; there is however a lack of theoretical analysis and little relevant empirical evidence in support of these distributions. In this paper, we conduct a detailed empirical investigation on the goodness of fit of various compound Poisson distributions and we develop a distribution-based demand classification scheme the validity of which is also assessed in empirical terms. Our empirical investigation provides evidence in support of certain demand distributions and the work described in this paper should facilitate the task of selecting such distributions in a real world spare parts inventory context. An extensive discussion on parameter estimation related difficulties in this area is also provided

A compound-Poisson Bayesian approach for spare parts inventory forecasting

Spare parts are often associated with intermittent demand patterns that render their forecasting a challenging task. Forecasting of spare parts demand has been researched through both parametric and non-parametric approaches. However, little has been contributed in this area from a Bayesian perspective, and most of such research is built around the Poisson demand distributional assumption. However, the Poisson distribution is known to have certain limitations and, further, empirical evidence on the inventory performance of Bayesian methods is lacking. In this paper, we propose a new Bayesian method based on compound Poisson distributions. The proposed method is compared to the Poisson-based Bayesian method with a Gamma prior distribution as well as to a parametric frequentist method and to a non-parametric one. A numerical investigation (on 7400 theoretically generated series) is complemented by an empirical assessment on demand data from about 3000 stock keeping units in the automotive sector to analyse the performance of the four forecasting methods. We find that both Bayesian methods outperform the other methods with a higher inventory efficiency reported for the Poisson Bayesian method with a Gamma prior. This outperformance increases for higher demand variability. From a practical perspective, the outperformance of the proposed method is associated with some added complexity. We also find that the performance of the non-parametric method improves for longer lead-times and higher demand variability when compared to the parametric one