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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.

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.

Control charts are designed to be effective in detecting a shift in the distribution of a process. Typically, these charts assume that the data for these processes follow an approximately normal distribution or some known distribution. However, if a data-generating process has a large proportion of zeros, that is, the data is intermittent, then traditional control charts may not adequately monitor these processes. The purpose of this study is to examine proposed control chart methods designed for monitoring a process with intermittent data to determine if they have a sufficiently small percentage of false out-of-control signals. Forecasting techniques for slow-moving/intermittent product demand have been extensively explored as intermittent data is common to operational management applications (Syntetos & Boylan, 2001, 2005, 2011; Willemain, Smart, & Schwarz, 2004). Extensions and modifications of traditional forecasting models have been proposed to model intermittent or slow-moving demand, including the associated trends, correlated demand, seasonality and other characteristics (Altay, Litteral, & Rudisill, 2012). Croston’s (1972) method and its adaptations have been among the principal procedures used in these applications. This paper proposes adapting Croston’s methodology to design control charts, similar to Exponentially Weighted Moving Average (EWMA) control charts, to be effective in monitoring processes with intermittent data. A simulation study is conducted to assess the performance of these proposed control charts by evaluating their Average Run Lengths (ARLs), or equivalently, their percent of false positive signals.

Research in the area of forecasting and stock inventory control for intermittent demand is designed to provide robust models for the underlying demand which appears at random, with some time periods having no demand at all. Croston’s method is a popular technique for these models and it uses two single exponential smoothing (SES) models which involve smoothing constants. A key issue is the choice of the values due to the sensitivity of the forecasts to changes in demand. Suggested selections of the smoothing constants include values between 0.1 and 0.3. Since an ARIMA model has been illustrated to be equivalent to SES, an optimal smoothing constant can be selected from the ARIMA model for SES. This chapter will conduct simulations to investigate whether using an optimal smoothing constant versus the suggested smoothing constant is important. Since SES is designed to be an adapted method, data are simulated which vary between slow and fast demand.

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.

The purpose of this paper is to explore and propose how product-in-use data can be used in, and improve the performance of, the demand planning process for automotive aftermarket services.

A literature review and a single case study investigate the underlying reasons for the demand for spare parts by conducting in-depth interviews, observing actual demand-generating activities, and studying the demand planning process.

This study identifies the relevant product-in-use data and divides them into five main categories. The authors have analysed how product-in-use data are best utilised in planning spare parts with different attributes, e.g. different life cycle phases and demand frequencies. Furthermore, the authors identify eight potentially relevant areas of application of product-in-use data in the demand planning process, and elaborate on their performance effects.

This study details the understanding of what impact context has on the potential performance effects of using product-in-use data in aftermarket demand planning. Propositions generate several strands for future research.

This study shows the potential impact of using product-in-use data, using eight different types of interventions for spare parts, in the aftermarket demand planning.

The literature focusses on single applications of product-in-use data, but would benefit from considering the context of application. This study presents interventions and explores how these enable improved demand planning by analysing usage and effects.

Spare parts have become ubiquitous in modern societies and managing their requirements is an important and challenging task with tremendous cost implications for the organisations that are holding relevant inventories. An important operational issue involved in the management of spare parts is that of categorising the relevant stock keeping units (SKUs) in order to facilitate decision‐making with respect to forecasting and stock control and to enable managers to focus their attention on the most “important” SKUs. This issue has been overlooked in the academic literature although it constitutes a significant opportunity for increasing spare parts availability and/or reducing inventory costs. Moreover, and despite the huge literature developed since the 1970s on issues related to stock control for spare parts, very few studies actually consider empirical solution implementation and with few exceptions, case studies are lacking. Such a case study is described in this paper, the purpose of which is to offer insight into relevant business practices.

The issue of demand categorisation (including forecasting and stock control) for spare parts management is addressed and details reported of a project undertaken by an international business machine manufacturer for the purpose of improving its European spare parts logistics operations. The paper describes the actual intervention within the organisation in question, as well as the empirical benefits and the lessons learned from such a project.

This paper demonstrates the considerable scope that exists for improving relevant real word practices. It shows that simple well‐informed solutions result in substantial organisational savings.

This paper provides insight into the empirical utilisation of demand categorisation theory for forecasting and stock control and provides some very much needed empirical evidence on pertinent issues. In that respect, it should be of interest to both academics and practitioners.

The UK Chinook helicopter is a utility and attack helicopter being operated by the Royal Air Force (RAF). Its versatile nature is of enormous importance to the strategic capability of the RAF's operations. The purpose of this paper is to utilise systems‐based forecasting to conduct an evaluation of inventory and forecasting systems being used to support its maintenance programme.

A case study is conducted. Data are collected from existing monthly Component Repair (CRP) data and performance evaluation of software. For propriety reasons, all data have been sanitised.

Analysis of the current inventory and forecasting system suggests a possible lack of forecasting precision. Current non‐specific formulation of forecasting techniques implied several of the cost driver's demands were being miscalculated. This lack of precision is possibly a result of the smoothing value of 0.01 being too low, especially as the results of statistical modelling suggest that current parameter values of 0.01 might be too low.

The paper reports on work conducted jointly between Boeing and the University of Southampton that sought to create an intermittent demand forecasting model.

A Bayesian approach to demand forecasting to optimize spare parts inventory that requires periodic replenishment is examined relative to a non-Bayesian approach when the demand rate is unknown. That is, optimal inventory levels are decided using these two approaches at consecutive time intervals. Simulations were conducted to compare the total inventory cost using a Bayesian approach and a non-Bayesian approach to a theoretical minimum cost over a variety of demand rate conditions including the challenging slow moving or intermittent type of spare parts. Although Bayesian approaches are often recommended, this study’s results reveal that under conditions of large variability across the demand rates of spare parts, the inventory cost using the Bayes model was not superior to that using the non-Bayesian approach. For spare parts with homogeneous demand rates, the inventory cost using the Bayes model for forecasting was generally lower than that of the non-Bayesian model. Practitioners may still opt to use the non-Bayesian model since a prior distribution for the demand does not need to be identified.

One aspect of forecasting intermittent demand for slow-moving inventory that has not been investigated to any depth in the literature is seasonality. This is due in part to the reliability of computed seasonal indexes when many of the periods have zero demand. This chapter proposes an innovative approach which adapts Croston's (1970) method to data with a multiplicative seasonal component. Adaptations of Croston's (1970) method are popular in the literature. This method is one of the most popular techniques to forecast items with intermittent demand. A simulation is conducted to examine the effectiveness of the proposed technique extending Croston's (1970) method to incorporate seasonality.