Forecasting intermittent demand

Methods for forecasting intermittent demand are compared using a large data-set from the UK Royal Air Force (RAF). Several important results are found. First, we show that the traditional per period forecast error measures are not appropriate for intermittent demand, even though they are consistently used in the literature. Second, by comparing target service levels to achieved service levels when inventory decisions are based on demand forecasts, we show that Croston's method (and a variant) and Bootstrapping clearly outperform Moving Average and Single Exponential Smoothing. Third, we show that the performance of Croston and Bootstrapping can be significantly improved by taking into account that each lead time starts with a demand

On the bias of Croston''s forecasting method

Croston's forecasting method has been shown to be appropriate in dealing with intermittent demand items. The method, however, suffers from a positive bias as shown by Syntetos and Boylan (2001, 2005) who proposed a modification. Unfortunately, the modification ignores the damping effect on the bias of the probability that a demand occurs. This leads to overcompensation and a negative bias, which can in fact be larger than the positive bias of the original method. Levén and Segerstedt (2004) also proposed a modified Croston method, but that suffers from an even more severe bias. Building on the results of Syntetos and Boylan (2001, 2005), we propose a new modification that takes the damping effect into account. A numerical study confirms that it considerably outperforms the existing methods. Moreover, the performance is better over the entire range of relevant parameters, which avoids the need to use different methods depending on the demand categorisation as suggested by Syntetos et al. (2005)

A strategic capacity allocation model for a complex supply chain: formulation and solution approach comparison

In this paper, a capacity allocation problem is discussed based on a more complex supply chain than has been typically considered in previous quantitative modelling studies. This study analyses an integrated supply chain operation from raw material purchasing to final product distribution. The aim is to optimize the allocation of capacities among different facilities and product items. In this paper, a mixed integer programming model with dynamic characteristics is presented first, and then alternative solution procedures are introduced. The solution procedures include the development of a decomposition heuristic and an integrated heuristic algorithm. A computation study compares the solution procedures and uses sensitivity analysis to show that the heuristics work well. Thus, by adequately modelling a more realistic sized supply chain problem, this study represents an important advance in supply chain modelling research

The impact of forecasting on the bullwhip effect

There has been strong empirical evidence that demand variability increases as one moves up the supply chain (from the retailer to the raw materials supplier), a phenomenon called bullwhip effect. This paper examines the bullwhip effect and in particular one of its main causes, demand forecasting. Key observations for the studies that deal with the impact of forecasting on the bullwhip effect are that: (1) all allow negative demands as well as negative orders for analytical tractability and (2) none considers the best exponential smoothing forecast without a prefixed smoothing constant. This paper validates the main findings in the literature when negative demands and negative orders are not allowed, using simulation. The main contribution is the inclusion of 'best' exponential smoothing as a forecasting method. This method is shown to explain some structural differences in bullwhip effect that have been observed in comparisons between naïve exponential smoothing and optimal forecasting. Therefore, it provides an important alternative to naïve smoothing for use in practice, especially as it is included in some of the more modern Demand Planning Systems

Economic ordering quantities for recoverable item inventory systems

We study a deterministic EOQ model of an inventory system with items that can be recovered (repaired/refurbished/remanufactured). We use different holding cost rates for manufactured and recovered items, and include disposal. We derive simple square root EOQ formulas for both the manufacturing batch quantity and the recovery batch quantity

Determining optimal dissembly and recovery strategies

We present a stochastic dynamic programming algorithm for determining the optimal disassembly and recovery strategy, given the disassembly tree, the process-dependent quality distributions of assemblies, and the quality-dependent recovery options and associated profits for assemblies. This algorithm generalizes the one proposed by Krikke et al. (International Journal of Production Research 1998; 36(1):111-39) in two ways. First, there can be multiple disassembly processes. Second, partial disassembly is allowed. Both generalizations are important for practise