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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.

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.

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.

The modern concept of labor hoarding emerged in early 1960s, and soon became a standard part of mainstream economists’ explanation of the working of labor markets. The concept represents the convergence of three important elements: an empirical finding that labor productivity was procyclical; a framing of this finding as a “puzzle” or anomaly for the basic neoclassical theory of the firm, and a proposed resolution of the puzzle based on optimizing behavior of the firm in the presence of costs of hiring, firing, and training workers. This paper recounts the history of each of these elements, and how they were woven together into the labor hoarding concept. Each history involves people associated with various research traditions and motivated by an array of questions, many of which were unrelated to the questions that the modern labor hoarding concept was ultimately created to address.

The accuracy of forecasts has a critical impact on an organization. A new, practical, and meaningful forecast performance measure, percentage forecasting error (PFE), was introduced by the authors in an earlier publication. In this chapter, we examined the accuracy of the PFE under several different scenarios and found the results to indicate that PFE offers forecasters an accurate and practical alternative to assess forecast accuracy.

In the last few decades, there has been growing interest in forecasting with computer intelligence, and both fuzzy time series (FTS) and artificial neural networks (ANNs) have gained particular popularity, among others. Rather than the conventional methods (e.g., econometrics), FTS and ANN are usually thought to be immune to fundamental concepts such as stationarity, theoretical causality, post-sample control, among others. On the other hand, a number of studies significantly indicated that these fundamental controls are required in terms of the theory of forecasting, and even application of such essential procedures substantially improves the forecasting accuracy. The aim of this paper is to fill the existing gap on modeling and forecasting in the FTS and ANN methods and figure out the fundamental concepts in a comprehensive work through merits and common failures in the literature. In addition to these merits, this paper may also be a guideline for eliminating unethical empirical settings in the forecasting studies.

The 2008 U.S. presidential election was of great interest nationally and internationally. Interest in the 2008 election was sufficient to drive a $2.8 million options market by a U.K.-based company INTRADE. The options in this market are priced as European style fixed return options (FRO). In 2008, the Security and Exchanges Commission approved, and both the American Stock Exchange and the Chicago Board Options Exchange began to trade FROs. Little research is available on trading in FROS because these markets are very new. This chapter uses the INTRADE options market data to construct exponential smoothing forecasts, which are then compared under a hypothetical trading strategy. The trading returns indicate that this market is relatively efficient at least in the short term but that because of the all or nothing payout structure of a FRO, there may exist small arbitrage opportunities.

Traditionally, hiring indefinite duration contract employees has been the dominant method used by U.S. organizations to staff their labor needs. Indefinite duration contract employees, hereafter referred to as “regular” employees, have three defining characteristics: (1) they are hired directly as employees of the organization whose work they perform; (2) the duration of the employment relationship is unspecified, with a mutual expectation that it will continue as long as it is mutually satisfactory; and (3) the employment relationship provides ongoing – as opposed to intermittent – work. When their demand for labor increases, organizations staffed exclusively by regular employees can respond by having their employees work overtime or by hiring additional regular employees. Conversely, when their demand for labor decreases, such organizations can either maintain “inventories” of excess regular employees or reduce labor inputs by laying-off or reducing the work hours of regular employees.