34 research outputs found

    The effect of correlation between demands on hierarchical forecasting

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    The forecasting needs for inventory control purposes are hierarchical. For SKUs in a product family or a SKU stored across different depot locations, forecasts can be made from the individual series’ history or derived top-down. Many discussions have been found in the literature, but it is not clear under what conditions one approach is better than the other. Correlation between demands has been identified as a very important factor to affect the performance of the two approaches, but there has been much confusion on whether positive or negative correlation. This paper summarises the conflicting discussions in the literature, argues that it is negative correlation that benefits the top-down or grouping approach, and quantifies the effect of correlation through simulation experiments

    Non-stationary demand forecasting by cross-sectional aggregation

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    In this paper the relative effectiveness of top-down (TD) versus bottom-up (BU) approaches is compared for cross-sectionally forecasting aggregate and sub-aggregate demand. We assume that the sub-aggregate demand follows a non-stationary Integrated Moving Average (IMA) process of order one and a Single Exponential Smoothing (SES) procedure is used to extrapolate future requirements. Such demand processes are often encountered in practice and SES is one of the standard estimators used in industry (in addition to being the optimal estimator for an IMA process). Theoretical variances of forecast error are derived for the BU and TD approach in order to contrast the relevant forecasting performances. The theoretical analysis is supported by an extensive numerical investigation at both the aggregate and sub-aggregate level, in addition to empirically validating our findings on a real dataset from a European superstore. The results demonstrate the increased benefit resulting from cross-sectional forecasting in a non-stationary environment than in a stationary one. Valuable insights are offered to demand planners and the paper closes with an agenda for further research in this area. © 2015 Elsevier B.V. All rights reserved

    A state space model for exponential smoothing with group seasonality

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    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.

    Formation of seasonal groups and application of seasonal indices

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    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 company’s 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

    A Multi-Phase Approach for Product Hierarchy Forecasting in Supply Chain Management: Application to MonarchFx Inc

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    Hierarchical time series demands exist in many industries and are often associated with the product, time frame, or geographic aggregations. Traditionally, these hierarchies have been forecasted using top-down, bottom-up, or middle-out approaches. The question we aim to answer is how to utilize child-level forecasts to improve parent-level forecasts in a hierarchical supply chain. Improved forecasts can be used to considerably reduce logistics costs, especially in e-commerce. We propose a novel multi-phase hierarchical (MPH) approach. Our method involves forecasting each series in the hierarchy independently using machine learning models, then combining all forecasts to allow a second phase model estimation at the parent level. Sales data from MonarchFx Inc. (a logistics solutions provider) is used to evaluate our approach and compare it to bottom-up and top-down methods. Our results demonstrate an 82-90% improvement in forecast accuracy using the proposed approach. Using the proposed method, supply chain planners can derive more accurate forecasting models to exploit the benefit of multivariate data.Comment: 25 pages, 2 figures, 8 table

    A Brand New CROLEI: Do We Need a New Forecasting Index?

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    The aim of this paper is to determine whether the existing leading indicators system CROLEI (CROatian Leading Economic Indicators) and its derivative, the CROLEI forecasting index, predict overall Croatian economic activity reliably. The need to evaluate the CROLEI system and the index stems from the modification of the barometric method on which the system and the index are founded on in its application in Croatia. The evaluation of the forecasting power involved the construction of six alternative forecasting indices, which not only challenge the original CROLEI index, but also enable comparisons of forecasting power. The construction of the alternative forecasting indices is also based on the barometric method. The authors then proceed to adjust more complex measurements i.e. forecasting power evaluation matrix, in order to obtain credible forecasting power estimates. Forecasting power is also estimated using two regression models that allow for the forecasting of reference series and yield measurements of forecasting power. The results of both approaches indicate not only that the original CROLEI has by far the greatest forecasting power, but also that it is able to predict the turning points in the economic cycle with the highest probability.CROLEI (CROatian Leading Economic Indicators), forecasting indicator,barometric method, signaling method

    An application of forecasting techniques for a small geographical area

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    It was the purpose of this thesis to formulate and test short-run economic forecasting methodologies that are useful for small geographical areas. The major concern was to derive an accurate monthly revenue forecast for the Chesapeake and Potomac Telephone Company of Washington, D.C. A survey was made of the literature dealing with techniques used to forecast telephone demand. This review suggested methodologies that were appropriate given the special problems of the Washington area. A narrative analysis of current economic trends affecting telephone demand in Washington further refined the development of a proper forecasting methodology. Next, an analysis of the available time series data was presented. This analysis provided an understanding of the underlying characteristics of the data and led to the formulation of specific forecasting models for testing. Five separate empirical models were developed for forecasting telephone demand in Washington, D.C. These models were analyzed for their statistical significance and tested for their ability to produce accurate forecasts. Monthly forecasts were generated with each model, and a final forecast was selected

    Generalised estimators for seasonal forecasting by combining grouping and shrinkage approaches

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    In this paper, generalised estimators are proposed to estimate seasonal indices for certain forms of additive and mixed seasonality. The estimators combine one of two group seasonal indices methods, Dalhart’s group method and Withycombe’s group method, with a shrinkage method in different ways. Optimal shrinkage parameters are derived to maximise the performance of the estimators. Then, the generalised estimators, with the optimal shrinkage parameters, are evaluated based on forecasting accuracy. Moreover, the effects of three factors are examined, namely, the length of data history, variance of random components and the number of series. Finally, a simulation experiment is conducted to support the evaluation
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