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Intermittent demand appears sporadically, with some time periods not even displaying any demand at all. Even so, such patterns constitute considerable proportions of the total stock in many industrial settings. Forecasting intermittent demand is a rather difficult task but of critical importance for corresponding cost savings. The current study aims to examine the empirical outcomes of three heuristics towards the modification of established intermittent demand forecasting approaches.

First, optimization of the smoothing parameter used in Croston's approach is empirically explored, in contrast to the use of an a priori fixed value as in earlier studies. Furthermore, the effect of integer rounding of the resulting forecasts is considered. Lastly, the authors evaluate the performance of Theta model as an alternative of SES estimator for extrapolating demand sizes and/or intervals. The proposed heuristics are implemented into the forecasting support system.

The experiment is performed on 3,000 real intermittent demand series from the automotive industry, while evaluation is made both in terms of bias and accuracy. Results indicate increased forecasting performance.

The current research explores some very simple heuristics which have a positive impact on the accuracy of intermittent demand forecasting approaches. While some of these issues have been partially explored in the past, the current research focuses on a complete in‐depth analysis of easy‐to‐employ modifications to well‐established intermittent demand approaches. By this, the authors enable the application of such heuristics in an industrial environment, which may lead to significant inventory and production cost reductions and other benefits.

Proposes a new real estate valuation methodology and presents the architecture for a decision support system for real estate analysis based on Geographic Information Systems (GIS) techniques integrated with fuzzy theory and spatial analysis.

The proposed information system architecture/problem‐solving methodology uses GIS technology integrated with two approaches: fuzzy logic and spatial analysis. The steps required in the proposed methodology are: database design and implementation; criteria and rules; system design; and implementation. The components/modules included in the proposed methodology are: requirement and definition analysis; data production; topology; integrated database; visualization; variables; quantification; valuation; and implementation.

The applicability of the system is evaluated via a case study in estimation of house sale prices. The proposed system/methodology was used in order to valuate property values in one municipality of Attica in Greece. The estimation, market analysis, forecasting and management of property values are of great importance and a prerequisite for real estate development.

The proposed methodology is innovative, easy to implement and has a vast theoretical background. Following the methodology/architecture, a prototype information system is presented in order to move from theory to practice. The value of the paper is the combination of new technology assessments and GIS tools, integrated with fuzzy theory and spatial analysis.