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Accurate forecasting of intermittent demand is very important since parts with intermittent demand characteristics are very common. The purpose of this paper is to bring an easier way of handling the hard work of intermittent demand forecasting by using commonly used Excel spreadsheet and also performing parameter optimization.

Smoothing parameters of the forecasting methods are optimized dynamically by Excel Solver in order to achieve the best performance. Application is done on real data of Turkish Airlines’ spare parts comprising 262 weekly periods from January 2009 to December 2013. The data set are composed of 500 stock-keeping units, so there are 131,000 data points in total.

From the results of implementation, it is shown that using the optimum parameter values yields better performance for each of the methods.

Although it is an intensive study, this research has some limitations. Since only real data are considered, this research is limited to the aviation industry.

This study guides market players by explaining the features of intermittent demand. With the help of the study, decision makers dealing with intermittent demand are capable of applying specialized intermittent demand forecasting methods.

The study brings simplicity to intermittent demand forecasting work by using commonly used spreadsheet software. The study is valuable for giving insights to market players dealing with items having intermittent demand characteristics, and it is one of the first study which is optimizing the smoothing parameters of the forecasting methods by using spreadsheet in the area of intermittent demand forecasting.

Investment in Australia’s property market, whether directly or indirectly through Australian real estate investment trusts (A-REITs), grew remarkably since the 1990s. The degree of segregation between the property market and other financial assets, such as shares and bonds, can influence the diversification benefits within multi-asset portfolios. This raises the question of whether direct and indirect property investments are substitutable. Establishing how information transmits between asset classes and impacts the predictability of returns is of interest to investors. The paper aims to discuss these issues.

The authors study the linkages between direct and indirect Australian property sectors from 1985 to 2013, with shares and bonds. This paper employs an Autoregressive Fractionally Integrated Moving Average (ARFIMA) process to de-smooth a valuation-based direct property index. The authors establish directional lead-lag relationships between markets using bi-variate Granger causality tests. Johansen cointegration tests are carried out to examine how direct and indirect property markets adjust to an equilibrium long-term relationship and short-term deviations from such a relationship with other asset classes.

The authors find the use of appraisal-based property data creates a smoothing bias which masks the extent of how information is transmitted between the indirect property sector, stock and bond markets, and influences returns. The authors demonstrate that an ARFIMA process accounting for a smoothing bias up to lags of four quarters can overcome the overstatement of the smoothing bias from traditional AR models, after individually appraised constituent properties are aggregated into an overall index. The results show that direct property adjusts to information transmitted from market-traded A-REITs and stocks.

The study shows direct property investments and A-REITs are substitutible in a multi-asset portfolio in the long and short term.

The authors apply an ARFIMA(p,d,q) model to de-smooth Australian property returns, as proposed by Bond and Hwang (2007). The authors expect the findings will contribute to the discussion on whether direct property and REITs are substitutes in a multi-asset portfolio.

Applies time‐series forecasting, a traditional operations analysis methodology, to develop a forecasting procedure and ordering policy for a natural‐gas customer of Columbia Gas of Ohio, USA. Evaluates six time‐series methods and four operating policies against four commonly used measures of error and the cost consequences of error to the customer. Demonstrates that time‐series forecasting and decision theory developed by operations and applied in an actual industrial situation can become a powerful marketing technique. Provides further insights into evaluating forecasting models and ordering policies, demonstrating that introducing optimal planned bias is a robust decision‐making/forecasting approach within services. There are three parts to the study. The first is a straightforward testing of forecasting methods, using the forecasts as the natural‐gas ordering policy. Results vary depending upon how well forecasts are fitted to the data. For example, one inaccurate forecast with a poor fit incurs a penalty cost of $179,270, while the best forecast results in a penalty cost of $27,081. The second part evaluates two additional complex ordering rules with the same forecasting methods, further reducing the lowest cost to $17,709. The third part is a technical analysis reflecting a redesign of the study, demonstrating the difficulty of generalizing when characteristics of the underlying demand change. Concludes that the best forecasting model/operating policy is to use the very basic forecasting model of simple moving average (or the equivalent, first‐order exponential smoothing) combined with an optimal planned bias ordering policy, i.e. with the planned introduction of bias.

The purpose of this paper is to investigate the vibration suppression of industrial track robot and propose a practical solution.

Root‐cause analysis through dynamic modeling, and vibration suppression using the acceleration smoother.

The vibration is due to insufficient damping based on the model analysis. The solution achieved significant performance improvement without redesign of robot hardware and controller.

The design of the proposed acceleration smoother is still empirical based, which is unable to achieve optimal design.

This solution is very easy to implement. It is robust, reliable and is able to generate consistent results.

A very practical industrial solution, especially useful for upgrading the existing systems in the field without redesign the hardware and controller.

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.

The purpose of this paper is to address the variation of efficiency of local office markets. It has long been argued that as data in the property market are based on valuations, it has a tendency toward smoothing or stickiness. The accuracy of valuations is shown to be partially dependent on local variable factors such as the extent of information, the variability of local cycles and the heterogeneity of the stock. This paper assesses the efficiency of local office markets in nine cities of the UK by estimating unsmoothed annual time series of rents and returns and comparing with the original valuations.

The paper uses an econometric approach to identify true unobserved returns from observed smoothed data. It uses desmoothing techniques and compares the volatility of smoothed and desmoothed underlying returns. It then looks for autocorrelation over time in the errors where the presence of autocorrelation rejects the assumption of market efficiency.

Examining, regional city office markets, the results suggest that financial centres have the least efficient markets because of their high level of variability. Other provincial cities are characterised by weak‐form efficiency. Market cyclicality is found to be a key factor affecting valuation accuracy.

Research in regional markets is often constrained by shortness and low frequency of time series observations. This limits the analysis and the ways in which it could be developed. Issues also relate to the method of desmoothing adopted.

Key financial centre are found to be less efficient markets than other cities. Thus, price changes fully embody all previous information in regional centres but not in London or Edinburgh. Periods of significant cyclical volatility tend to cause valuation inaccuracy and pricing problems.

There are spillovers from inefficiencies in property market pricing that can affect other sectors of society directly (through increased costs) or indirectly (by contributing to macroeconomic cyclicality).

This is the first paper to explicitly consider the efficiency of regional city office markets and to identify the true unobserved returns series in each city office market. Its findings, perhaps unexpectedly, suggest that most regional city office markets are more efficient at processing pricing information than London.

Construction labour productivity is often influenced by variations in work conditions and management effectiveness. It is substantially important to understand the nature and extent to which individual parameters affect productivity. The purpose of this paper is to focus on providing insight on parameters that affect daily job‐site labour productivity by investigating their relative significance and influence on work output.

The methodology is based on the illustration and use of three different data analysis techniques to rank parameters that affect a certain process. These techniques include Fuzzy Subtractive Clustering, Neural Network Modelling and Stepwise Variable Selection Procedure. The first one belongs to inferential statistics, while the other two are artificial intelligence based techniques. The collection of field information, spanning over a time period of ten months, comprised of daily real time observations of job‐site operations, work progress information collected from project managers and supervisors by using customized forms, and daily weather condition recorded through internet sources. Nine parameters are considered in the study presented in this paper. The data on these parameters is examined and their relative influence and contribution in productivity estimates are assessed. The approach was to consider a limited set of parameters relating to daily job‐site productivity. The methodology presented in this paper provides insight on the relative impact of parameters, affecting labour productivity on short term or daily basis. The results based on each of the three methods are analyzed and transformed into a final ranking of parameters.

The three most important parameters are identified in the same order by the fuzzy logic and neural networks methods. Regression analysis, however, provided somewhat different results.

This research investigates the contribution of a set of parameters towards the variations in daily job‐site labour productivity. For practitioners such as site engineers, this is of practical importance for making daily work plans. On the other hand, the structured approach presented to perform significance ranking of parameters relevant to an engineering process, may also be of interest to other researchers and practitioners.

The paper aims to describe and apply a commercially oriented method of forecast performance measurement (cost of forecast error – CFE) and to compare the results with commonly adopted statistical measures of forecast accuracy in an enterprise resource planning (ERP) environment.

The study adopts a quantitative methodology to evaluate the nine forecasting models (two moving average and seven exponential smoothing) of SAP^®'s ERP system. Event management adjustment and fitted smoothing parameters are also assessed. SAP^® is the largest European software enterprise and the third largest in the world, with headquarters in Walldorf, Germany.

The findings of the study support the adoption of CFE as a more relevant commercial decision‐making measure than commonly applied statistical forecast measures.

The findings of the study provide forecast model selection guidance to SAP^®'s 12+ million worldwide users. However, the CFE metric can be adopted in any commercial forecasting situation.

This study is the first published cost assessment of SAP^®'s forecasting models.

Quantitative forecasting techniques see their greatest application as part of production and inventory systems. Perhaps unfortunately, the problem has been left to systems analysts while the professional societies have contented themselves with exhortations to improve forecasting, neglecting recent developments from forecasting research. However, improvements in accuracy have a direct and often substantial financial impact. This article shows how the production and inventory control forecasting problem differs from other forecasting applications in its use of information and goes on to consider the characteristics of inventory type data. No single forecasting method is suited to all data series and the article then discusses how recent developments in forecasting methodology can improve accuracy. Considers approaches to extending the database beyond just the time‐series history of the data series. Concludes with a discussion of an “ideal” forecasting system and how far removed many popular programs used in production and inventory control are from this ideal.

When forecasting intermittent demand the method derived by Croston (1972) is often cited. Previous research favorably compared Croston's forecasting method for demand with simple exponential smoothing assuming a nonzero demand occurs as a Bernoulli process with a constant probability. In practice, however, the assumption of a constant probability for the occurrence of nonzero demand is often violated. This research investigates Croston's method under violation of the assumption of a constant probability of nonzero demand. In a simulation study, forecasts derived using single exponential smoothing (SES) are compared to forecasts using a modification of Croston's method utilizing double exponential smoothing to forecast the time between nonzero demands assuming a normal distribution for demand size with different standard deviation levels. This methodology may be applicable to forecasting intermittent demand at the beginning or end of a product's life cycle.