Aris A. Syntetos

Supply chain forecasting: Theory, practice, their gap and the future

Supply Chain Forecasting (SCF) goes beyond the operational task of extrapolating demand requirements at one echelon. It involves complex issues such as supply chain coordination and sharing of information between multiple stakeholders. Academic research in SCF has tended to neglect some issues that are important in practice. In areas of practical relevance, sound theoretical developments have rarely been translated into operational solutions or integrated in state-of-the-art decision support systems. Furthermore, many experience-driven heuristics are increasingly used in everyday business practices. These heuristics are not supported by substantive scientific evidence; however, they are sometimes very hard to outperform. This can be attributed to the robustness of these simple and practical solutions such as aggregation approaches for example (across time, customers and products). n nThis paper provides a comprehensive review of the literature and aims at bridging the gap between theory and practice in the existing knowledge base in SCF. We highlight the most promising approaches and suggest their integration in forecasting support systems. We discuss the current challenges both from a research and practitioner perspective and provide a research and application agenda for further work in this area. Finally, we make a contribution in the methodology underlying the preparation of review articles by means of involving the forecasting community in the process of deciding both the content and structure of this paper.

Efficient inventory control for imperfect quality items

In this paper, we present a general EOQ model for items that are subject to inspection for imperfect quality. Each lot that is delivered to the sorting facility undertakes a 100 per cent screening and the percentage of defective items per lot reduces according to a learning curve. The generality of the model is viewed as important both from an academic and practitioner perspective. The mathematical formulation considers arbitrary functions of time that allow the decision maker to assess the consequences of a diverse range of strategies by employing a single inventory model. A rigorous methodology is utilised to show that the solution is a unique and global optimal and a general step-by-step solution procedure is presented for continuous intra-cycle periodic review applications. The value of the temperature history and flow time through the supply chain is also used to determine an efficient policy. Furthermore, coordination mechanisms that may affect the supplier and the retailer are explored to improve inventory control at both echelons. The paper provides illustrative examples that demonstrate the application of the theoretical model in different settings and lead to the generation of interesting managerial insights.

On the calculation of safety stocks when demand is forecasted

The inventory control literature generally assumes that the demand distribution and all its parameters are known. In practical applications it is often suggested to estimate the demand variance either directly or based on the one-period ahead forecast errors. The variance of the lead time demand, essential for safety stock calculations, is then obtained by multiplying the estimated per-period demand variance by the length of the lead time. However, this is flawed, since forecast errors for different periods of the lead time are positively correlated, even if the demand process itself does not show (process) auto-correlation. As a result these traditional procedures lead to safety stocks that are too low. This paper presents corrected lead time demand variance expressions and reorder levels for inventory systems with a constant lead time where demand fluctuates around a constant level. Firstly, we derive the exact lead time forecast error of mean demand conditional on the true demand variance. Secondly, we derive for normally distributed demand the correct reorder level under uncertainty of both the demand mean and variance. We show how the results can be implemented in inventory models, and particularly discuss batch ordering policies combined with moving average and exponential smoothing forecasts. We find that traditional approaches can lead to safety stocks that are up to 30 percent too low and service levels that are up to 10 percent below the target.

Supply chain forecasting when information is not shared

The operations management literature is abundant in discussions on the benefits of information sharing in supply chains. However, there are many supply chains where information may not be shared due to constraints such as compatibility of information systems, information quality, trust and confidentiality. Furthermore, a steady stream of papers has explored a phenomenon known as Downstream Demand Inference (DDI) where the upstream member in a supply chain can infer the downstream demand without the need for a formal information sharing mechanism. Recent research has shown that, under more realistic circumstances, DDI is not possible with optimal forecasting methods or Single Exponential Smoothing but is possible when supply chains use a Simple Moving Average (SMA) method. In this paper, we evaluate a simple DDI strategy based on SMA for supply chains where information cannot be shared. This strategy allows the upstream member in the supply chain to infer the consumer demand mathematically rather than it being shared. We compare the DDI strategy with the No Information Sharing (NIS) strategy and an optimal Forecast Information Sharing (FIS) strategy in the supply chain. The comparison is made analytically and by experimentation on real sales data from a major European supermarket located in Germany. We show that using the DDI strategy improves on NIS by reducing the Mean Square Error (MSE) of the forecasts, and cutting inventory costs in the supply chain.

A note on the demand distributions of spare parts

In a recent paper published by the International Journal of Production Research (Syntetos, Babai, and Altay 2012), we undertook a detailed empirical investigation of the validity and utility of various statistical distributions in a spare parts demand context. Spare parts are typically characterised by intermittent demand structures and, as such, the findings presented in that paper safely also extend to other inventory settings (like those comprising slow-moving items, etc.). The choice of a statistical distribution constitutes an important input into a stock control system since parametric decisions about replenishments are influenced directly by the hypothesised demand distribution. When reflecting on the paper’s methodology, an error was identified. The goodness-of-fit of the various distributions considered in that study was assessed by employing the Kolmogorov–Smirmov (K–S) test. When calculating the relevant statistic, the number of categories should be derived based on the empirical sample size. However, in the paper under discussion, the number of categories was derived based on the (maximum) demand size rather than the sample size. The purpose of this note is to point out the drawback associated with this analysis and its implications for the results presented in the paper. Suppose that the N observations in a demand series are ordered from the smallest to largest, i.e.

Formation of seasonal groups and application of seasonal indices

Estimating seasonal variations in demand is a challenging task faced by many organisations. There may be many stock-keeping units (SKUs) to forecast, but often data histories are short, with very few complete seasonal cycles. It has been suggested in the literature that group seasonal indices (GSI) methods should be used to take advantage of information on similar SKUs. This paper addresses two research questions: (1) how should groups be formed in order to use the GSI methods? and (2) when should the GSI methods and the individual seasonal indices (ISI) method be used? Theoretical results are presented, showing that seasonal grouping and forecasting may be unified, based on a Mean Square Error criterion, and K-means clustering. A heuristic K-means method is presented, which is competitive with the Average Linkage method. It offers a viable alternative to a companys own grouping method or may be used with confidence if a company lacks a grouping method. The paper gives empirical findings that confirm earlier theoretical results that greater accuracy may be obtained by employing a rule that assigns the GSI method to some SKUs and the ISI method to the remainder.

Perishable Inventory Systems

Distinguishing between economic order quantity and economic production quantity models is common as well as uncommon in literature depending on whom to consult. Here we see that distinction and the authors carefully investigate specific cost comparisons between postponement and non-postponement systems in both cases. Detailed inventory holding policies are considered as well as specific calculations for a system with two end products (simulation results are provided in an appendix). Two case studies in the book relate to the master thesis of one of the authors as well as to a paper from literature presenting a case study along Taiwanese information technology firms. The first case refers to an interview with a managing director of a manufacturer operating in Hong Kong and Guangdong province in South China. The goal of the paper making the second case may be seen as multi-fold: First, four types of form postponement are empirically examined. The measurement scale is developed from and also tested on 102 information technology firms in Taiwan. Second, the authors explore the factors affecting the adoption of different form postponement strategies. Confirmatory factor analysis is used to validate the dimensionality of the considered form postponement strategies, while path analysis is used to examine the relationship between product/demand characteristics and the adoption of postponement strategies. The empirical conclusions of the study give helpful advice for adopting postponement strategies in related production processes. Contrary to the majority of papers in literature on postponement, this book presents quantitative models. Essentially, they contain aspects of inventory theory, scheduling theory as well as queuing theory. One of the main issues in this book is to combine the areas postponement and perishability of products. On the basis of an earlier paper, the authors develop economicorder-quantity-based models with perishable items to evaluate the impact of a form postponement strategy on the retailer in a supply chain. Models are investigated for systems to minimize some cost function for ordering and keeping several perishable end products. The impact of the deterioration rate on the inventory replenishment policies is studied by a theoretical analysis as well as some numerical experiments. Subject to the particular circumstances the results show that a postponement strategy for perishable items may lead to lower total average cost. While there exists a tremendous amount of literature in the context of postponement and supply chain design, on the contrary, apart from very few papers de facto not enough research exists simultaneously dealing with the associated problems of implementing postponement strategies and designing supply chains. Against the background of a broadening globalization and an increasing division of labor, the importance of establishing postponement strategies in supply chains becomes more important which evidently manifests the need for further research. The book is written for an academic readership who will find it pleasant to see mathematical models in operation. The authors quickly come to the point so that one may also read the book as a small set of extended and commented papers put together as a book. At places we would have liked to see some extended material to educate those who are not specialists in operations research and management science. On the other hand, we greatly appreciate the extended awareness for the obvious need for further research in this area opening up a wealth of opportunities for (us and other) researchers. To summarize, this book adds very favorably to the existing literature on postponement and bridges the gap to perishability.

The Impact of Temporal Aggregation on Demand Forecasting of ARMA(1,1) Process: Theoretical Analysis

Abstract Demand forecasting performance will be challenged by demand dispersion underlying the time series related to the Stock Keeping Units (SKUs). Among the strategies that may be used to reduce the demand dispersion, an intuitively appealing approach is to aggregate demand in lower-frequency time buckets. This paper focuses on the impact of non-overlapping temporal aggregation on the performance of demand forecasting by investigating the mean square error (MSE) before and after aggregation. We assume that the non-aggregated demand follows a first-order autoregressive moving average process [ARMA(1,1)] and a Single Exponential Smoothing (SES) procedure is used to estimate the level of demand. The theoretical analysis shows that the temporal aggregation approach has a great potential to improve the forecasting accuracy. The improvement is a function of process parameters, the aggregation level, and the smoothing constant values. We present some insights into the impact of different control parameters on the performance of each approach. The paper concludes with an agenda for further research in this area.

The boomerang returns? Accounting for the impact of uncertainties on the dynamics of remanufacturing systems

Recent years have witnessed companies abandon traditional open-loop supply chain structures in favour of closed-loop variants, in a bid to mitigate environmental impacts and exploit economic opportunities. Central to the closed-loop paradigm is remanufacturing: the restoration of used products to useful life. While this operational model has huge potential to extend product life-cycles, the collection and recovery processes diminish the effectiveness of existing control mechanisms for open-loop systems. We systematically review the literature in the field of closed-loop supply chain dynamics, which explores the time-varying interactions of material and information flows in the different elements of remanufacturing supply chains. We supplement this with further reviews of what we call the three ‘pillars’ of such systems, i.e. forecasting, collection, and inventory and production control. This provides us with an interdisciplinary lens to investigate how a ‘boomerang’ effect (i.e. sale, consumption, and return processes) impacts on the behaviour of the closed-loop system and to understand how it can be controlled. To facilitate this, we contrast closed-loop supply chain dynamics research to the well-developed research in each pillar; explore how different disciplines have accommodated the supply, process, demand, and control uncertainties; and provide insights for future research on the dynamics of remanufacturing systems.

Revisiting the value of information sharing in two-stage supply chains

Abstract There is a substantive amount of literature showing that demand information sharing can lead to considerable reduction of the bullwhip effect/inventory costs. The core argument/analysis underlying these results is that the downstream supply-chain member (the retailer) quickly adapts its inventory position to an updated end-customer demand forecast. However, in many real-life situations, retailers adapt slowly rather than quickly to changes in customer demand as they cannot be sure that any change is structural. In this paper, we show that the adaption speed and underlying (unknown) demand process crucially affect the value of information sharing. For the situation with a single upstream supply-chain member (manufacturer) and a single retailer, we consider two demand processes: stationary or random walk. These represent two extremes where a change in customer demand is never or always structural, respectively. The retailer and manufacturer both forecast demand using a moving average, where the manufacturer bases its forecast on retailer demand without information sharing, but on end-customer demand with information sharing. In line with existing results, the value of information turns out to be positive under stationary demand. One contribution, though, is showing that some of the existing papers have overestimated this value by making an unfair comparison. Our most striking and insightful finding is that the value of information is negative when demand follows a random walk and the retailer is slow to react. Slow adaptation is the norm in real-life situations and deserves more attention in future research – exploring when information sharing indeed pays off.