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ABC inventory classifications are widely used in practice, with demand value and demand volume as the most common ranking criteria. The standard approach in ABC applications is to set the same service level for all stock keeping units (SKUs) in a class. In this paper, we show (for three large real life datasets) that the application of both demand value and demand volume as ABC ranking criteria, with fixed service levels per class, leads to solutions that are far from cost optimal. An alternative criterion proposed by Zhang et al. performs much better, but is still considerably outperformed by a new criterion proposed in this paper. The new criterion is also more general in that it can take criticality of SKUs into account. Managerial insights are obtained into what class should have the highest/lowest service level, a topic that has been disputed in the literature.

Citation for final published version:Syntetos, Argyrios, Zied Babai, M. and Gardner, Everette S. 2015. Forecasting intermittent inventory demands: simple parametric methods vs. ABSTRACTAlthough intermittent demand items dominate service and repair parts inventories in many industries, research in forecasting such items has been limited. A critical research question is whether one should make point forecasts of the mean and variance of intermittent demand with a simple parametric method such as simple exponential smoothing or else employ some form of bootstrapping to simulate an entire distribution of demand during lead time. The aim of this work is to answer that question by evaluating the effects of forecasting on stock control performance in more than 7,000 demand series. Tradeoffs between inventory investment and customer service show that simple parametric methods perform well, and it is questionable whether bootstrapping is worth the added complexity.

If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services.Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation. AbstractPurpose -The technology of time temperature integrators (TTI) is used to ensure the safety and quality of temperature sensitive goods such as food and drugs along their entire lifespan. This work aims to provide a better understanding of potential benefits that can be expected from the use of TTIs in terms of supply chain improvement. Design/methodology/approach -Based on the different types of information provided by TTIs: information on products' freshness and information on products' remaining shelf lives, the paper identifies qualitatively the benefits that would stem from each type of information. Findings -A framework is built to evaluate the benefits, in terms of cost reduction and/or quality service improvement, that would stem from information provided by TTIs. Illustrative models are also developed in order to quantify some of these benefits.Research limitations/implications -The coexistence on products' packaging of a printed use by date and a TTI device can be misleading for consumers. Besides, the benefits that supply chain actors will achieve by using TTIs will vary by product category and are dependent upon the level at which the TTI device is used. Further research and case studies have to be developed in order to bring further answers to these issues. Practical implications -This paper is one of the first studies that helps companies in the food and the health care industry to better understand the benefits of using TTIs from an operations management point of view and to evaluate whether it can be advantageous to deploy this technology or not. Originality/value -This work differs from investigations in literature in that it identifies exhaustively and qualitatively the benefits of TTIs and to give perspectives for quantitative models that can be developed to assess these benefits.

Purpose -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. Design/methodology/approach -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. Findings -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. Originality/value -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.

Demand forecasting performance is subject to the uncertainty underlying the time series an organisation is dealing with. There are many approaches that may be used to reduce uncertainty and thus to improve forecasting performance. One intuitively appealing such approach is to aggregate demand in lowerfrequency 'time buckets'. The approach under concern is termed to as Temporal Aggregation and in this paper we investigate its impact on forecasting performance. We assume that the non-aggregated demand follows either a moving average process of order one or a first-order autoregressive process and a Single Exponential Smoothing (SES) procedure is used to forecast demand. These demand processes are often encountered in practice and SES is one of the standard estimators used in industry. Theoretical Mean Squared Error expressions are derived for the aggregated and non-aggregated demand in order to contrast the relevant forecasting performances. The theoretical analysis is supported by an extensive numerical investigation and experimentation with an empirical dataset. The results indicate that performance improvements achieved through the aggregation approach are a function of the aggregation level, the smoothing constant and the process parameters. Valuable insights are offered to practitioners and the paper closes with an agenda for further research in this area.

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