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Book part
Publication date: 17 November 2010

Joanne S. Utley and J. Gaylord May

This study examines the use of forecast combination to improve the accuracy of forecasts of cumulative demand. A forecast combination methodology based on least absolute…

Abstract

This study examines the use of forecast combination to improve the accuracy of forecasts of cumulative demand. A forecast combination methodology based on least absolute value (LAV) regression analysis is developed and is applied to partially accumulated demand data from an actual manufacturing operation. The accuracy of the proposed model is compared with the accuracy of common alternative approaches that use partial demand data. Results indicate that the proposed methodology outperforms the alternative approaches.

Details

Advances in Business and Management Forecasting
Type: Book
ISBN: 978-0-85724-201-3

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Book part
Publication date: 12 November 2014

Joanne Utley

This paper presents a mathematical programming model to reduce bias for both aggregate demand forecasts and lower echelon forecasts comprising a hierarchical forecasting

Abstract

This paper presents a mathematical programming model to reduce bias for both aggregate demand forecasts and lower echelon forecasts comprising a hierarchical forecasting system. Demand data from an actual service operation are used to illustrate the model and compare its accuracy with a standard approach for hierarchical forecasting. Results show that the proposed methodology outperforms the standard approach.

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Advances in Business and Management Forecasting
Type: Book
ISBN: 978-1-78441-209-8

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Modern Management in the Global Mining Industry
Type: Book
ISBN: 978-1-78973-788-2

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Book part
Publication date: 17 January 2009

Joanne S. Utley and J. Gaylord May

This chapter uses advance order data from an actual manufacturing shop to develop and test a forecast model for total demand. The proposed model made direct use of…

Abstract

This chapter uses advance order data from an actual manufacturing shop to develop and test a forecast model for total demand. The proposed model made direct use of historical time series data for total demand and time series data for advance orders. Comparison of the proposed model to commonly used approaches showed that the proposed model exhibited greater forecast accuracy.

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Advances in Business and Management Forecasting
Type: Book
ISBN: 978-1-84855-548-8

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Book part
Publication date: 17 January 2009

Mark T. Leung, Rolando Quintana and An-Sing Chen

Demand forecasting has long been an imperative tenet in production planning especially in a make-to-order environment where a typical manufacturer has to balance the…

Abstract

Demand forecasting has long been an imperative tenet in production planning especially in a make-to-order environment where a typical manufacturer has to balance the issues of holding excessive safety stocks and experiencing possible stockout. Many studies provide pragmatic paradigms to generate demand forecasts (mainly based on smoothing forecasting models.) At the same time, artificial neural networks (ANNs) have been emerging as alternatives. In this chapter, we propose a two-stage forecasting approach, which combines the strengths of a neural network with a more conventional exponential smoothing model. In the first stage of this approach, a smoothing model estimates the series of demand forecasts. In the second stage, general regression neural network (GRNN) is applied to learn and then correct the errors of estimates. Our empirical study evaluates the use of different static and dynamic smoothing models and calibrates their synergies with GRNN. Various statistical tests are performed to compare the performances of the two-stage models (with error correction by neural network) and those of the original single-stage models (without error-correction by neural network). Comparisons with the single-stage GRNN are also included. Statistical results show that neural network correction leads to improvements to the forecasts made by all examined smoothing models and can outperform the single-stage GRNN in most cases. Relative performances at different levels of demand lumpiness are also examined.

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Advances in Business and Management Forecasting
Type: Book
ISBN: 978-1-84855-548-8

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Article
Publication date: 2 July 2020

Xiaoxi Zhou, Jianfei Meng, Guosheng Wang and Qin Xiaoxuan

This paper examines the problem of lack of historical data and inadequate consideration of factors influencing demand in the forecasting of demand for fast fashion…

Abstract

Purpose

This paper examines the problem of lack of historical data and inadequate consideration of factors influencing demand in the forecasting of demand for fast fashion clothing and proposes an improved Bass model for the forecasting of such a demand and the demand for new clothing products.

Design/methodology/approach

From the perspective of how to solve the lack of data and improve the precision of the clothing demand forecast, this paper studies the measurement of clothing similarity and the addition of demand impact factors. Using the fuzzy clustering–rough set method, the degree of resemblance of clothing is determined, which provides a basis for the scientific utilisation of historical data of similar clothing to forecast the demand for new clothing. Besides, combining the influence of consumer preferences and seasonality on demand forecasting, an improved Bass model for a fast fashion clothing demand forecast is proposed. Finally, with a forecasting example of demand for clothing, this study also tests the validity of the method.

Findings

The objective measurement method of clothing similarity in this paper solves the problem of the difficult forecasting of demand for fast fashion clothing due to a lack of sales data at the preliminary stage of the clothing launch. The improved Bass model combines, comprehensively, consumer preferences and seasonality and enhances the forecast precision of demand for fast fashion clothing.

Originality/value

The paper puts forward a scientific, quantitative method for the forecasting of new clothing products using historical sales data of similar clothing, thus solving the problem of lack of sales data of the fashion.

Details

International Journal of Clothing Science and Technology, vol. 33 no. 1
Type: Research Article
ISSN: 0955-6222

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Article
Publication date: 8 June 2015

Masayasu Nagashima, Frederick T. Wehrle, Laoucine Kerbache and Marc Lassagne

This paper aims to empirically analyze how adaptive collaboration in supply chain management impacts demand forecast accuracy in short life-cycle products, depending on…

Abstract

Purpose

This paper aims to empirically analyze how adaptive collaboration in supply chain management impacts demand forecast accuracy in short life-cycle products, depending on collaboration intensity, product life-cycle stage, retailer type and product category.

Design/methodology/approach

The authors assembled a data set of forecasts and sales of 169 still-camera models, made by the same manufacturer and sold by three different retailers in France over five years. Collaboration intensity, coded by collaborative planning forecasting and replenishment level, was used to analyze the main effects and specific interaction effects of all variables using ANOVA and ordered feature evaluation analysis (OFEA).

Findings

The findings lend empirical support to the long-standing assumption that supply chain collaboration intensity increases demand forecast accuracy and that product maturation also increases forecast accuracy even in short life-cycle products. Furthermore, the findings show that it is particularly the lack of collaboration that causes negative effects on forecast accuracy, while positive interaction effects are only found for life cycle stage and product category.

Practical implications

Investment in adaptive supply chain collaboration is shown to increase demand forecast accuracy. However, the choice of collaboration intensity should account for life cycle stage, retailer type and product category.

Originality/value

This paper provides empirical support for the adaptive collaboration concept, exploring not only the actual benefits but also the way it is achieved in the context of innovative products with short life cycles. The authors used a real-world data set and pushed its statistical analysis to a new level of detail using OFEA.

Details

Supply Chain Management: An International Journal, vol. 20 no. 4
Type: Research Article
ISSN: 1359-8546

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Article
Publication date: 1 March 1987

Leslie Bernard Trustrum, F. Robert Blore and William James Paskins

Demand forecasting models are past the point of academic curiosity, and although they are still in the early stages of their life cycle, they are well beyond the…

Abstract

Demand forecasting models are past the point of academic curiosity, and although they are still in the early stages of their life cycle, they are well beyond the development stage. The modelling of demand phenomena may be viewed as having two main thrusts: the first is a scientific one that leads to a greater understanding of the phenomena. Here, the goal is to build either normative or descriptive models which advance knowledge. The second is a pragmatic thrust concerned with the capability of management science to aid decision makers. A model is demonstrated and its future potential assessed.

Details

Marketing Intelligence & Planning, vol. 5 no. 3
Type: Research Article
ISSN: 0263-4503

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Article
Publication date: 27 July 2012

Asli Aksoy, Nursel Ozturk and Eric Sucky

Demand forecasting in the clothing industry is very complex due to the existence of a wide range of product references and the lack of historical sales data. To the…

Abstract

Purpose

Demand forecasting in the clothing industry is very complex due to the existence of a wide range of product references and the lack of historical sales data. To the authors' knowledge, there is an inadequate number of literature studies to forecast the demand with the adaptive network based fuzzy inference system for the clothing industry. The purpose of this paper is to construct a decision support system for demand forecasting in the clothing industry.

Design/methodology/approach

The adaptive‐network‐based fuzzy inference system (ANFIS) is used for forecasting demand in the clothing industry.

Findings

The results of the proposed study showed that an ANFIS‐based demand forecasting system can help clothing manufacturers to forecast demand more accurately, effectively and simply.

Originality/value

In this study, the demand is forecast in terms of clothing manufacturers by using ANFIS. ANFIS is a new technique for demand forecasting, it combines the learning capability of the neural networks and the generalization capability of the fuzzy logic. The input and output criteria are determined based on clothing manufacturers' requirements and via literature research, and the forecasting horizon is about one month. The study includes the real life application of the proposed system and the proposed system is tested by using real demand values for clothing manufacturers.

Details

International Journal of Clothing Science and Technology, vol. 24 no. 4
Type: Research Article
ISSN: 0955-6222

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Article
Publication date: 14 May 2018

Erik Hofmann and Emanuel Rutschmann

Demand forecasting is a challenging task that could benefit from additional relevant data and processes. The purpose of this paper is to examine how big data analytics…

Abstract

Purpose

Demand forecasting is a challenging task that could benefit from additional relevant data and processes. The purpose of this paper is to examine how big data analytics (BDA) enhances forecasts’ accuracy.

Design/methodology/approach

A conceptual structure based on the design-science paradigm is applied to create categories for BDA. Existing approaches from the scientific literature are synthesized with industry knowledge through experience and intuition. Accordingly, a reference frame is developed using three steps: description of conceptual elements utilizing justificatory knowledge, specification of principles to explain the interplay between elements, and creation of a matching by conducting investigations within the retail industry.

Findings

The developed framework could serve as a guide for meaningful BDA initiatives in the supply chain. The paper illustrates that integration of different data sources in demand forecasting is feasible but requires data scientists to perform the job, an appropriate technological foundation, and technology investments.

Originality/value

So far, no scientific work has analyzed the relation of forecasting methods to BDA; previous works have described technologies, types of analytics, and forecasting methods separately. This paper, in contrast, combines insights and provides advice on how enterprises can employ BDA in their operational, tactical, or strategic demand plans.

Details

The International Journal of Logistics Management, vol. 29 no. 2
Type: Research Article
ISSN: 0957-4093

Keywords

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