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

Seriegroottebepaling als capaciteit een dominante factor is

No abstract

Heuristics for setting reorder levels in periodic review inventory systems with an aggregate service constraint

Inventory managers are responsible for the trade-off between inventory holding costs and customer service. In this paper we consider a periodic review multi-item inventory system with exogenous lot-sizes and backordering. The objective is to minimize the total inventory holding costs subject to the constraint that the aggregate fill rate should be at least equal to a target level. The aggregate fill rate is a weighted average of the fill rates of all items in the assortment. We consider three ways of defining this aggregate fill rate: using generic weights, weights based on the average demand (volume) or weights based on the average (monetary) turnover. We show that the definition of the aggregate service can have huge effects on the performance of the system. So, inventory managers should be very careful on which definition to apply. We also derive four heuristics to determine the reorder levels for all items. One heuristic is very generic and can be applied to many problems including multi- item multi-echelon inventory systems and systems with a constrained aggregate ready rate. Since multiple assumptions made to derive the heuristics are common assumptions made in the literature, we first test the accuracy of these approximations using simulation. Next, we evaluate the heuristics based on data from a large international reseller. The heuristic based on the most accurate approximation performs best, is close to optimal and very efficient. Savings compared to no service level differentiation are large (up to 28.7%) and depend on the definition of the aggregate service

Decision support for selecting the optimal product unpacking location in a retail supply chain

AbstractThe purpose of this research is to investigate the optimal product unpacking location in a bricks-and-mortar grocery retail supply chain. Retail companies increasingly are investing in unpacking operations at their distribution centres (DC). Given the opportunity to unpack at the DC requires a decision as to which products should be selected for unpacking at the DC and which should be shipped to stores in a case pack (CP) or outer pack provided by the supplier. The combined unpacking and unit size decision is evaluated by focusing on the relevant costs at the DC and in-store, i.e., picking in the DC, unpacking either in the DC or in the store, shelf stacking in the store and refilling from the backroom. For replenishing stores, an (R, s, nQ) inventory policy is considered when using the supplier CP and a (R, s, S) policy when the product is unpacked at the DC. Expressions are developed to quantify the relevant volumes and to calculate the corresponding costs on which the unpacking decision is based. A numerical example with empirical data from a European modern retailer demonstrates that unpacking a subset of the stock keeping units (SKUs) at the DC results in a significant cost reduction potential of 8% compared to no unpacking at the DC. The example shows that DC unpacking can generally be highly favorable for a large share of products

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

Ordering Behavior in Retail Stores and Implications for Automated Replenishment

Retail store managers may not follow order advices generated by an automated inventory replenishment system if their incentives differ from the cost-minimization objective of the system or if they perceive the system to be suboptimal. We study the ordering behavior of retail store managers in a supermarket chain to characterize such deviations in ordering behavior, investigate their potential drivers, and thereby devise a method to improve automated replenishment systems. Using orders, shipments, and point-of-sale data for 19,417 item-store combinations over five stores, we show that (i) store managers consistently modify automated order advices by advancing orders from peak to nonpeak days, and (ii) this behavior is explained significantly by product characteristics such as case pack size relative to average demand per item, net shelf space, product variety, demand uncertainty, and seasonality error. Our regression results suggest that store managers improve upon the automated replenishment system by incorporating two ignored factors: in-store handling costs and sales improvement potential through better in-stock. Based on these results, we construct a method to modify automated order advices by learning from the behavior of store managers. Motivated by the management coefficients theory, our method is efficient to implement and outperforms store managers by achieving a more balanced handling workload with similar average days of inventory.retail operations, inventory management, behavioral operations