A state space model for exponential smoothing with group seasonality

We present an approach to improve forecast accuracy by simultaneously forecasting a group of products that exhibit similar seasonal demand patterns. Better seasonality estimates can be made by using information on all products in a group, and using these improved estimates when forecasting at the individual product level. This approach is called the group seasonal indices (GSI) approach, and is a generalization of the classical Holt-Winters procedure. This article describes an underlying state space model for this method and presents simulation results that show when it yields more accurate forecasts than Holt-Winters.Common seasonality; demand forecasting; exponential smoothing; Holt-Winters; state space model.

Relationship between freight accessibility and logistics employment in US counties

This paper analyzes the relationship between freight accessibility and logistics employment in the US. It develops an accessibility measure relevant for logistics companies based on a gravity model. This allows for an analysis of the accessibility of US counties focusing on four different modes of transportation: road, rail, air, and maritime. Using a Partial Least Squares model, these four different freight accessibility measures are combined into two constructs, continental and intercontinental freight accessibility, and related to logistics employment. Results show that highly accessible counties attract more logistics employment than other counties. The analyses show that it is very important to control for the effect of the county population on both freight accessibility and logistics employment. While county population explains the most variation in the logistics employment per county, there is a significant relationship between freight accessibility and logistics employment, when controlling for this effect

Integral stock norms in divergent systems with lot-sizes

In this paper an overview is given of integral stock norm formulae for several periodic review production-inventory systems. The stock norm formulae for all systems are simple and coherent. These formulae are used to study the 2-stage divergent system with lot-sizing. At first glance, integral control of divergent systems seems to be questionable due to the fact that the inventory levels of the final products are unbalanced most of the time. Such imbalance requires additional inventory in the system to obtain the same service level compared to a similar system without imbalance. However, the results of this paper show that in most cases the impact of imbalance is small. This result implies, that many divergent systems can be controlled efficiently with an integral control rule. Furthermore, systems with depot as well as without depot will be considered and compared. It will appear that the following rule holds in general: the positive effect of decreased imbalance in case a depot is present is small compared with the negative effect of decreased ability to satisfy customers' demand. The only exception to this rule are systems with a large lot-size for the common component combined with large coefficients of variation for the final products' demand. Attention will be restricted to the identical products case.</p

Determination of safety stocks in a lost sales inventory system with periodic review, positive lead-time, lot-sizing and a target fill rate

An approximation for the fill rate, i.e. the percentage of demand being delivered from inventory on hand immediately, is derived for items in a periodic review inventory control system with lost sales. We assume demand is stochastic and discrete, lead-times are positive and replenishments are made in multiples of a given fixed case pack size. Most literature on inventory control systems assumes that unmet demand is backordered.The major reason for this is that the analysis of a general lost sales inventory system is known to be hard. To find an approximation for the fill rate, given a safety stock, we start with existing analytical approximations. By applying linear regression, we slightly modify these existing approximations. The new approximation is tested for a wide set of parameters and performs very well: the average approximation error for the fill rate is only 0.0028 and the standard deviation of the approximation error is 0.0045. Since the approximations are very fast,this result enables inventory controllers dealing with a lost sales inventory system to set safety stocks in accordance with the target service level set by their management in an effectiveway. The results of our study also show that the assumption that the lost sales system can simply be approximated by a backordering system if the target fill rate is at least 95%, may lead to serious approximation errors. These errors are particularly large when the lead-time is large or demand uncertainty is low and when on average there is at least one replenishment order outstanding

The expected outdating versus the fill rate for perishable products in a periodic review inventory system

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Quantifying the potential to improve on food waste, freshness and sales for perishables in supermarkets

\u3cp\u3eThe focus of this paper is on improving the performance of fresh departments in supermarkets by reducing food waste, increasing freshness and/or increasing sales. First, two concepts will be introduced to quantify the improvement potential. Next, these concepts will be applied on empirical data for 3 product categories in 27 stores from 3 large retailers in Europe. The two concepts to quantify the improvement potential are called the Fresh Case Cover and the Efficient Frontier. The Fresh Case Cover is defined as the case pack size divided by the average demand during the store shelf life. A regression analysis shows that this single variable explains 42% of the variation in waste. The Efficient Frontier represents a lower bound on the waste needed in a store for any given On-Shelf Availability (OSA). It is demonstrated how the Efficient Frontier can be used to quantify the benefits from supply chain improvement projects and to evaluate fresh departments within a store. To quantify product freshness, an exact expression is derived and an approximation is developed and tested. To quantify waste an existing approximation is generalized. The results show that the improvement potential is very large. For example, increasing the store shelf life with just one day results in 43.1% less waste and 17% more freshness (or in 3.4% higher OSA) and unpacking in the DC results in 34.8% less waste and 1.6% more freshness (or in 2.0% higher OSA). Improving the store replenishment and execution is especially beneficial for medium and large stores.\u3c/p\u3