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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

Book part
Publication date: 17 November 2010

Kenneth D. Lawrence, Dinesh R. Pai and Sheila M. Lawrence

This chapter proposes a fuzzy approach to forecasting using a financial data set. The methodology used is multiple objective linear programming (MOLP). Selecting an…

Abstract

This chapter proposes a fuzzy approach to forecasting using a financial data set. The methodology used is multiple objective linear programming (MOLP). Selecting an individual forecast based on a single objective may not make the best use of available information for a variety of reasons. Combined forecasts may provide a better fit with respect to a single objective than any individual forecast. We incorporate soft constraints and preemptive additive weights into a mathematical programming approach to improve our forecasting accuracy. We compare the results of our approach with the preemptive MOLP approach. A financial example is used to illustrate the efficacy of the proposed forecasting methodology.

Details

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

Book part
Publication date: 30 April 2008

J. Gaylord May and Joanne M. Sulek

This chapter will present a goal programming model which simultaneously generates forecasts for the aggregate level and for lower echelons in a multilevel forecasting

Abstract

This chapter will present a goal programming model which simultaneously generates forecasts for the aggregate level and for lower echelons in a multilevel forecasting context. Data from an actual service firm will be used to illustrate and test the proposed model against a standard forecast technique based on the bottom-up/top-down approach.

Details

Advances in Business and Management Forecasting
Type: Book
ISBN: 978-0-85724-787-2

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.

Details

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

Keywords

Book part
Publication date: 18 July 2016

John F. Kros and William J. Rowe

Business schools are tasked with matching curriculum to techniques that industry practitioners rely on for profitability. Forecasting is a significant part of what many…

Abstract

Business schools are tasked with matching curriculum to techniques that industry practitioners rely on for profitability. Forecasting is a significant part of what many firms use to try to predict budgets and to provide guidance as to the direction the business is headed. This chapter focuses on forecasting and how well business schools match the requirements of industry professionals. Considering its importance to achieving successful business outcomes, forecasting is increasingly becoming a more complex endeavor. Firms must be able to forecast accurately to gain an understanding of the direction the business is taking and to prevent potential setbacks before they occur. Our results suggest that, although techniques vary, in large part business schools are introducing students to the forecasting tools that graduates will need to be successful in an industry setting. The balance of our chapter explores the forecasting tools used by business schools and firms, and the challenge of aligning the software learning curve between business school curriculum and industry expectations.

Details

Advances in Business and Management Forecasting
Type: Book
ISBN: 978-1-78635-534-8

Keywords

Book part
Publication date: 13 March 2013

Joanne Utley

Past research has shown that forecast combination typically improves demand forecast accuracy even when only two component forecasts are used; however, systematic bias in…

Abstract

Past research has shown that forecast combination typically improves demand forecast accuracy even when only two component forecasts are used; however, systematic bias in the component forecasts can reduce the effectiveness of combination. This study proposes a methodology for combining demand forecasts that are biased. Data from an actual manufacturing shop are used to develop the methodology and compare its accuracy with the accuracy of the standard approach of correcting for bias prior to combination. Results indicate that the proposed methodology outperforms the standard approach.

Details

Advances in Business and Management Forecasting
Type: Book
ISBN: 978-1-78190-331-5

Keywords

Book part
Publication date: 30 April 2008

Stephen DeLurgio

This is a study of forecasting models that aggregate monthly times series into bimonthly and quarterly models using the 1,428 seasonal monthly series of the M3 competition…

Abstract

This is a study of forecasting models that aggregate monthly times series into bimonthly and quarterly models using the 1,428 seasonal monthly series of the M3 competition of Makridakis and Hibon (2000). These aggregating models are used to answer the question of whether aggregation models of monthly time series significantly improve forecast accuracy. Through aggregation, the forecast mean absolute deviations (MADs) and mean absolute percent errors (MAPEs) were found to be statistically significantly lower at a 0.001 level of significance. In addition, the ratio of the forecast MAD to the best forecast model MAD was reduced from 1.066 to 1.0584. While those appear to be modest improvements, a reduction in the MAD affects a forecasting horizon of 18 months for 1,428 time series, thus the absolute deviations of 25,704 forecasts (i.e., 18*1,428 series) were reduced. Similar improvements were found for the symmetric MAPE.

Details

Advances in Business and Management Forecasting
Type: Book
ISBN: 978-0-85724-787-2

Book part
Publication date: 17 November 2010

Ronald K. Klimberg, George P. Sillup, Kevin J. Boyle and Vinay Tavva

Producing good forecast is a vital aspect of a business. The accuracy of these forecasts could have a critical impact on the organization. We introduce a new, practical…

Abstract

Producing good forecast is a vital aspect of a business. The accuracy of these forecasts could have a critical impact on the organization. We introduce a new, practical, and meaningful forecast performance measure called percentage forecast error (PFE). The results of comparing and evaluating this new measure to traditional forecasting performance measures under several different simulation scenarios are presented in this chapter.

Details

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

Book part
Publication date: 14 November 2011

Ronald K. Klimberg, George P. Sillup and Kevin Boyle

The accuracy of forecasts has a critical impact on an organization. A new, practical, and meaningful forecast performance measure, percentage forecasting error (PFE), was…

Abstract

The accuracy of forecasts has a critical impact on an organization. A new, practical, and meaningful forecast performance measure, percentage forecasting error (PFE), was introduced by the authors in an earlier publication. In this chapter, we examined the accuracy of the PFE under several different scenarios and found the results to indicate that PFE offers forecasters an accurate and practical alternative to assess forecast accuracy.

Details

Advances in Business and Management Forecasting
Type: Book
ISBN: 978-0-85724-959-3

Article
Publication date: 30 November 2022

Luh Putu Eka Yani and Ammar Aamer

Demand foresting significantly impacts supply chain (SC) design and recovery planning. The more accurate the demand forecast, the better the recovery plan and the more…

Abstract

Purpose

Demand foresting significantly impacts supply chain (SC) design and recovery planning. The more accurate the demand forecast, the better the recovery plan and the more resilient the SC. Given the paucity of research about machine learning (ML) applications and the pharmaceutical industry’s need for disruptive techniques, this study aims to investigate the applicability and effect of ML algorithms on demand forecasting. More specifically, the study identifies machine learning algorithms applicable to demand forecasting and assess the forecasting accuracy of using ML in the pharmaceutical SC.

Design/methodology/approach

This research used a single-case explanatory methodology. The exploratory approach examined the study’s objective and the acquisition of information technology impact. In this research, three experimental designs were carried out to test training data partitioning, apply ML algorithms and test different ranges of exclusion factors. The Konstanz Information Miner platform was used in this research.

Findings

Based on the analysis, this study could show that the most accurate training data partition was 80%, with random forest and simple tree outperforming other algorithms regarding demand forecasting accuracy. The improvement in demand forecasting accuracy ranged from 10% to 41%.

Research limitations/implications

This study provides practical and theoretical insights into the importance of applying disruptive techniques such as ML to improve the resilience of the pharmaceutical supply design in such a disruptive time.

Originality/value

The finding of this research contributes to the limited knowledge about ML applications in demand forecasting. This is manifested in the knowledge advancement about the different ML algorithms applicable in demand forecasting and their effectiveness. Besides, the study at hand offers guidance for future research in expanding and analyzing the applicability and effectiveness of ML algorithms in the different sectors of the SC.

Details

International Journal of Pharmaceutical and Healthcare Marketing, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1750-6123

Keywords

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