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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.

Companies are nowadays called to manage increasingly larger and more complex quantities of data. Making good predictions and estimating a possible risky situation in advance is therefore more important than ever. The statistical tools are a key element to support quality management and production process analyses, allowing managers to anticipate, in various ways, the situations that will arise. After a general introduction to statistics, this chapter discusses the main statistical techniques used to describe, visualize, and analyze data, with particular attention to the methods that allow to verify the quality and consistency of the conclusions (i.e., confidence intervals and hypothesis verification). Finally, some advanced and widespread quality management statistical tools are presented: quality control charts, historical series, and stochastic control.

Academic libraries have endured rapid change in the past two decades that has had repercussions on how they manage their organization and deliver library services. Skyrocketing costs, especially for journals, explosive growth in new technologies, fiscal exigencies caused by a tightening of public financing of most academic institutions, demands for greater accountability, and the onslaught of electronic delivery of networked information, are just some of the major obstacles libraries are encountering (Lubans, 1996; Riggs, 1993; Shaughnessy, 1987). Customers of academic libraries are increasingly less satisfied because of limited resources and the difficulties they encounter in accessing printed material in a traditional library facility (Doughtery, 1992). The emergence of textual materials in electronic form has added a new dimension to this discontent. While such resources have the potential for meeting the information needs more dynamically, the costs for information have been exorbitant, particularly since full electronic texts have not been sufficient in coverage to supplant printed resources (Tenopir, 1993). These phenomena require academic libraries to use a more integrated and flexible approach to problem solving (Gapen, Hampton & Schmitt, 1993).