Search results

With access to the large-scale search data from Rightmove plc, the paper firstly indicated the possibility of using user-generated data from online property portals to predict housing market activities and secondly embraced a GIS approach to explore what people search for housing and what they chose and investigated the issue of mismatch between search patterns and revealed patterns. Based on the analysis, the paper contributes a visual GIS-based approach which may help planners and designers to make more informed decisions related to new housing supply, particularly where to build, what to build and how many to build.

The paper used the 2013 housing search data from Rightmove and the 2013 price data from Land Registry with transactions made after the search period and embraced a GIS approach to explore the potential housing demand patterns and the mismatch between searches and sales. In the analysis, the paper employed the K-means approach to group prices into five levels and used GIS software to draw maps based on these price levels. The paper also employed a simple analysis of linear regression based on the coefficient of determination to investigate the relationship between online property views and values of house sales.

The result indicated the strong relationship between online property views and the values of house sales, implying the possibility of using search data from online property portals to predict housing market activities. It then explore the spatial housing demand patterns based on searches and showed a mismatch between the spatial patterns of housing search and actual moves across submarkets. The findings may not be very surprising but the main objective of the paper is to open up a potentially useful methodological approach which could be extended in future research.

It is important to identify search patterns from people who search with the intention to buy houses and from people who search with no intention to purchase properties. Rightmove data do not adequately represent housing search activity, and therefore more attention should be paid to this issue. The analysis of housing search helps us have a better understanding of households' preferences to better estimate housing demand and develop search-based prediction models. It also helps us identify spatial and structural submarkets and examine the mismatches between current housing stock and housing demand in submarkets.

The GIS approach in this paper may help planners and designers better allocate land resources for new housing supply based on households' spatial and structural preferences by identifying high and low demand areas with high searches relative to low housing stocks. Furthermore, the analysis of housing search patterns helps identify areas with latent demand, and when combined with the analysis of transaction patterns, it is possible to realise the areas with a lack of housing supply relative to excess demand or a lack of latent demand relative to the housing stock.

The paper proves the usefulness of a GIS approach to investigate households' preferences and aspirations through search data from online property portals. The contribution of the paper is the visual GIS-based approach, and based on this approach the paper fills the international knowledge gap in exploring effective approaches to analysing user-generated search data and market outcome data in combination.

This paper is concerned with the development of an intelligent inventory management system which aims at bridging the substantial gap between the theory and the practice of inventory management. The proposed system attempts to achieve this by providing automatic demand and lead time pattern identification and model selection facilities. The process of demand pattern identification together with the statistical tests used is discussed. The models incorporated cover deterministic demand models including: constant, quasi‐constant, trended and seasonal demand as well as stochastic demand models. This paper includes an empirical evaluation of the system on real data from the manufacturing and airline industries which shows that this system can lead to significant savings in inventory cost.

One strategic design parameter in capacity management is the setting of a planned level of capacity utilization at which the manufacturing operation will operate long term. Seeks to examine systematically the implications of varying levels of capacity utilization within an assemble‐to‐order firm through experiments with a simulation model. Four performance measures and a total weekly cost measure are analysed under nine capacity utilization levels, two demand patterns, and 11 ratios of the costs of idle capacity to the costs of late orders. The prescribed capacity utilization level is a function of the firm′s competitive goals, demand pattern, and cost structures.

Service managers are continually challenged with balancing customer demand and service capacity. Recent studies have raised awareness of various demand and capacity management practices available to services, but little numerical work has been done to identify how these decisions work together and how they relate to one another. For instance, reducing prices may attract customers during a slow period, but the extent of impact this should have on cross‐training staff is not clear. A simulation based on theoretical and empirical insights explores the impact of various decisions on profitability and operations. The decisions modelled include the impact of: automation, customer participation, cross training employees, informing customers about the operation, and others. It is shown that demand and capacity decisions do indeed impact on each other – sometimes in ways that are not initially obvious. Results provide useful thought‐starters for service managers striving to improve their operations.

The purpose of this paper is to present a dynamic model to measure the degree of system’s leanness under dynamic demand conditions using a novel integrated metric.

The multi-stage production system model is based on a system dynamics approach. The leanness level is measured using a new developed integrated metric that combines efficiency, WIP performance as well as service level. The analysis includes design of experiment technique at the initial analysis to examine the most significant parameters impacting the leanness score and then followed by examining different dynamic demand scenarios. Two scenarios were examined: one focussed low demand variation with various means (testing the impact of demand volumes) while the second focussed on high demand variation with constant means (testing the impact of demand variability).

Results using the data from a real case study indicated that given the model parameters, demand rate has the highest impact on leanness score dynamics. The next phase of the analysis thus focussed on investigating the effect of demand dynamics on the leanness score. The analysis highlighted the different effects of demand variability and volumes on the leanness score and its different components leading to various demand and production management recommendations in this dynamic environment.

The presented lean management policies and recommendations are verified within the scope of similar systems to the considered company in terms of manufacturing settings and demand environment. Further research will be carried to extend the dynamic model to other dynamic manufacturing and service settings.

The developed metric can be used not only to assess the leanness level of the systems which is very critical to lean practitioners but also can be used to track lean implementation progress. In addition, the presented analysis outlined various demand management as well as lean implementation policies that can improve the system leanness level and overall performance.

The presented research develops a novel integrated metric and adds to the few literature on dynamic analysis of lean systems. Furthermore, the conducted analysis revealed some new aspects in understanding the relation between demand (variability and volume) and the leanness level of the systems. This will aid lean practitioners to set better demand and production management policies in today’s dynamic environment as well as take better decisions concerning lean technology investments.

Labour scheduling heuristic methods have been applied in service operating environments using both actual and synthetic demand patterns. Two important characteristics of these demand patterns are (1) demand smoothness and (2) mean demand. Investigates the effects of demand smoothness and mean demand on the solution quality associated with four prominent heuristic methods. Indicates that both characteristics can affect the performance of the heuristic methods. An especially important finding is that the two methods which use information from linear programming solutions are far more robust to changes in the degree of demand smoothness. Concludes that managers should consider linear programming methods as an alternative or supplement for making their scheduling decisions. Also recommends that labour scheduling researchers use multiple levels of mean demand and demand smoothness when evaluating new heuristic methods.

The purpose of this paper is to study determining factors of international tourism demand in Middle Eastern countries.

Panel data pattern is used for data analysis of 1995 to 2013.

Results indicate variables like trade freedom index and gross domestic product (GDP) have positive and significant impact upon tourism demand of the countries of the region. Purchasing power parity (PPP) and GDP per capita are indicators which affect the tourism demand rate in Middle East negatively.

It is estimated that Middle East region will claim for the bulk of tourist arrivals in following years. Therefore, this study is vital for destination managers to plan for demand in future.

This paper presents a study on the impacts of information sharing and ordering co‐ordination on the performance of a supply chain with one capacitated supplier and multiple retailers under demand uncertainty. In particular, a computer model is proposed to simulate inventory replenishment decisions by the retailers and production decisions by the supplier under different demand patterns and capacity tightness. It is found that information sharing and ordering co‐ordination significantly impact the supply chain performance in terms of both total cost and service level. It is also found that the value of sharing information and ordering co‐ordination is significantly affected by demand patterns and capacity tightness. Guidelines are developed for companies to share information and co‐ordinate orders under different conditions. These guidelines can help companies reduce costs and improve customer service levels in the supply chain.

The main objective of this paper is to analyze patterns of consumption expenditure and the effects of income, prices and socioeconomic and demographic factors on demand among elderly- and young-adult-headed households in Brazil.

The authors estimated a Quadratic Almost Ideal Demand System demand system using the main household consumption good groups – food, housing, clothing, transportation, health care and other expenses – with data from three Brazilian Household Budget surveys.

The study results showed that elderly- and young-adult-headed households have different consumption patterns. The consumption of food, transportation and health care was more price-sensitive for households headed by the elderly, while higher income increases health care expenses in elderly-headed households to a greater extent than it does in younger-headed households.

The limitations are due to the structure of the data used, such as the effects of seasonality and individualized demand analyses, and sample design in the estimates. However, due to the structure of the demand models, which when estimating by seemingly unrelated regressions do not allow to take into account the sample design.

As a consequence of population aging, the Brazilian economy will experience changes in the composition of household consumption, mainly for food, housing, transportation and health-care-related products.

This paper fulfills the lack of studies that analyze the consumption patterns and how demand varies across different types of elderly-headed households in a developing country, such as Brazil.

The study aims to investigate the impact of additive manufacturing (AM) on the performance of a spare parts supply chain with a particular focus on underlying spare part demand patterns.

This work evaluates various AM-enabled supply chain configurations through Monte Carlo simulation. Historical demand simulation and intermittent demand forecasting are used in conjunction with a mixed integer linear program to determine optimal network nodal inventory policies. By varying demand characteristics and AM capacity this work assesses how to best employ AM capability within the network.

This research assesses the preferred AM-enabled supply chain configuration for varying levels of intermittent demand patterns and AM production capacity. The research shows that variation in demand patterns alone directly affects the preferred network configuration. The relationship between the demand volume and relative AM production capacity affects the regions of superior network configuration performance.

This research makes several simplifying assumptions regarding AM technical capabilities. AM production time is assumed to be deterministic and does not consider build failure probability, build chamber capacity, part size, part complexity and post-processing requirements.

This research is the first study to link realistic spare part demand characterization to AM supply chain design using quantitative modeling.