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The need effectively to integrate decision making tasks together with knowledge representation and inference procedures has caused recent research efforts towards the integration of decision support systems with knowledge‐based techniques. Explores the potential benefits of such integration in the area of business forecasting. Describes the forecasting process and identifies its main functional elements. Some of these elements provide the requirements for an intelligent forecasting support system. Describes the architecture and the implementation of such a system, the theta intelligent forecasting information system (TIFIS) that that first‐named author had developed during his dissertation. In TIFIS, besides the traditional components of a decision‐support onformation system, four constituents are included that try to model the expertise required. The information system adopts an object‐oriented approach to forecasting and exploits the forecasting engine of the theta model integrated with automated rule based adjustments and judgmental adjustments. Tests the forecasting accuracy of the information system on the M3‐competition monthly data.

Some analysts have claimed that the volatility of an asset is caused solely by the random arrival of new information about the future returns from the asset. Others have claimed that volatility is mainly caused by trading. In any case it is a common belief that volatility is of great importance in finance and it is the factor that plays the most important role in determining option prices. This paper discusses the development of a decision support system (D‐TIFIS) for options trading based on volatility forecasting. In order to evaluate the system, data were used from the Greek FTSE/ASE 20 stock index as well as at the money call and put prices on the specific index.

A great challenge for today’s companies is not only how to adapt to the changing business environment but also how to gain a competitive advantage from the way in which they choose to do so. As a basis for achieving such advantages, companies have started to seek to improve the performance of various operations. Forecasting is one of them; it is important to firms because it can help ensure that effective use of resources is made. In the market there are a number of off‐the‐shelf system products, which provide forecasts. The new trend, of moving traditional software packages to Web services, has pushed forecasting to a new dimension, named by the authors as “e‐forecasting”. In this paper, a first approach to e‐forecasting is made by throwing light on several aspects and a survey is presented which aims at identifying existing Web forecasting services.

The main scope of the paper is to demonstrate the capabilities of PYTHIA forecasting platform, to compare time series forecasting techniques, which were used to forecast mortgage loans in UK, and to show how PYTHIA can be useful for a bank.

The paper outlines the methods used to forecast the time series data, which are included in PYTHIA. Theta, the time‐series used to forecast average mortgage loan prices, were grouped in: all buyers – average loan prices in UK; first‐time buyers – average loan prices in UK; and home‐movers – average loan prices in UK. The case of all buyers – average loan prices in UK, was presented in detail.

After the comparison of the methods, the best forecasts are produced by WINTERS and this is maybe due to the fact that there is seasonality in the data. The Theta method comes next in the row and generally produces good forecasts with small mean absolute percentage errors. In order to tell with grater certainty which method produces the most accurate forecasts we could compare the rest error statistics provided by PYTHIA too.

The paper presents the PYTHIA forecasting platform and shows how it can be used by the managers of a Bank to forecast mortgage loan values. PYTHIA can provide the forecasts required by practically all business situations demanding accurate predictions. It is designed and developed with the purpose of making the task of managerial forecasting straightforward, user‐friendly and practical. It incorporates a lot of knowledge and experience in the field of forecasting, modeling and monitoring while fully utilizing new capabilities of computers and software.

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.

During the last decade, many companies have made large investments in the development and implementation of enterprise resource planning (ERP) systems. However, only few of these systems developed or installed have actually considered maintenance strategies. Maintenance is a complex process that is triggered by planned periodic repair (scheduled or planned maintenance), equipment breakdown or deterioration indicated by a monitored parameter (unplanned or emergency maintenance). This process requires planning, scheduling, monitoring, quality assurance and deployment of necessary resources (workshop, manpower, machines, equipment, tools, spare parts, materials). Proper design and integration of maintenance management into ERP systems enable enterprises to effectively manage their production planning and scheduling, as well as to analyze their maintenance history so as to carry out cost analysis and produce future projections of failure trends. The present work presents the design of an object‐oriented maintenance management model and its integration into an ERP system. The proposed model was designed towards the development of innovative industrial software regarding the optimum management of maintenance in a wide range of business areas.

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.

This paper aims to identify and determine regional tourism axes in Hormozgan Province, Iran, as a region with significant potential

The research method is quantitative and uses the fuzzy accreditation tool and TOPSIS model; the identification, determination and ranking of regional tourism axes have been performed by analyzing the spatial distribution of tourism attractions in the GIS environment.

The results show that given the capacities of Hormozgan Province, at least 15 axes are recognizable. This paper highlights regional tourism planning as a tool for urban and rural socio-economic development in potential provinces such Hormozgan.

This study provides a number of practical implications for regional tourism development as follows: it identifies some of the most important potential axes in Hormozgan Province, which can be considered as investment areas in the national and regional tourism development strategy. The spatial results of this study could be embedded in all urban and rural developmental plans in the province. Tourism investment should shift its spatial concentration from the spot approach, especially islands and cities, to the axis approach while equipping those axes as comprehensive spatial strategic regional tourism plans. Sectoral tourism in each sector including sports, economy and nature could be planned as if sectoral institutions and organizations are going to develop their own tourism goals.

Demand foresting significantly impacts supply chain (SC) design and recovery planning. The more accurate the demand forecast, the better the recovery plan and the more resilient the SC. Given the paucity of research about machine learning (ML) applications and the pharmaceutical industry’s need for disruptive techniques, this study aims to investigate the applicability and effect of ML algorithms on demand forecasting. More specifically, the study identifies machine learning algorithms applicable to demand forecasting and assess the forecasting accuracy of using ML in the pharmaceutical SC.

This research used a single-case explanatory methodology. The exploratory approach examined the study’s objective and the acquisition of information technology impact. In this research, three experimental designs were carried out to test training data partitioning, apply ML algorithms and test different ranges of exclusion factors. The Konstanz Information Miner platform was used in this research.

Based on the analysis, this study could show that the most accurate training data partition was 80%, with random forest and simple tree outperforming other algorithms regarding demand forecasting accuracy. The improvement in demand forecasting accuracy ranged from 10% to 41%.

This study provides practical and theoretical insights into the importance of applying disruptive techniques such as ML to improve the resilience of the pharmaceutical supply design in such a disruptive time.

The finding of this research contributes to the limited knowledge about ML applications in demand forecasting. This is manifested in the knowledge advancement about the different ML algorithms applicable in demand forecasting and their effectiveness. Besides, the study at hand offers guidance for future research in expanding and analyzing the applicability and effectiveness of ML algorithms in the different sectors of the SC.

Energy usage of households accounts for a significant portion of total energy consumption and carbon emissions. Scottish homes today are highly energy consumers emitting on average 3 tonnes of CO₂ per house annually and the amount exceeds the UK average of 2.75 tonnes of CO₂. Moreover, 26% of the households are actually facing fuel poverty and it is therefore a critical task to efficiently manage and minimise energy trends in housing in order to meet carbon dioxide (CO₂) emission reduction and energy consumption cut targets such as 80% overall cuts in carbon emissions by 2080 for the UK, compared with 1990 levels. The study has been undertaken within the Zero Energy Mass Custom Homes (ZEMCH) research network's demonstration projects e.g. ‘ZEMCH 109’. The existing post-council end-terraced house was intended to be extended in South Ayrshire, Scotland in 2012. As part of the project, the Building Environments Analysis Unit (BEAU) research centre has conducted a post occupancy monitoring of the energy and indoor environmental conditions e.g. indoor air temperature, relative humidity and CO₂ levels in the Scottish affordable home which will also continue even after the construction of the newly built extension and the refurbishment of the existing home. It is therefore important for the successful demonstration of the ZEMCH 109 project and for the purpose of this study that a detailed monitoring and a post occupancy evaluation (POE) of the exiting NRGStyle home are performed sufficiently in order to investigate the relationship between energy consumption and the indoor environmental conditions and cross-checked with the accepted standards.