Search results

1 – 10 of over 13000
Article
Publication date: 1 May 1992

Robert Fildes and Charles Beard

Quantitative forecasting techniques see their greatest applicationas part of production and inventory systems. Perhaps unfortunately, theproblem has been left to systems analysts…

2983

Abstract

Quantitative forecasting techniques see their greatest application as part of production and inventory systems. Perhaps unfortunately, the problem has been left to systems analysts while the professional societies have contented themselves with exhortations to improve forecasting, neglecting recent developments from forecasting research. However, improvements in accuracy have a direct and often substantial financial impact. This article shows how the production and inventory control forecasting problem differs from other forecasting applications in its use of information and goes on to consider the characteristics of inventory type data. No single forecasting method is suited to all data series and the article then discusses how recent developments in forecasting methodology can improve accuracy. Considers approaches to extending the database beyond just the time‐series history of the data series. Concludes with a discussion of an “ideal” forecasting system and how far removed many popular programs used in production and inventory control are from this ideal.

Details

International Journal of Operations & Production Management, vol. 12 no. 5
Type: Research Article
ISSN: 0144-3577

Keywords

Article
Publication date: 16 March 2012

Pradip Kumar Bala

The purpose of this paper is to develop a forecasting model for retailers based on customer segmentation, to improve performance of inventory.

4774

Abstract

Purpose

The purpose of this paper is to develop a forecasting model for retailers based on customer segmentation, to improve performance of inventory.

Design/methodology/approach

The research makes an attempt to capture the knowledge of segmenting the customers based on various attributes as an input to the demand forecasting in a retail store. The paper suggests a data mining model which has been used for forecasting of demand. The proposed model has been applied for forecasting demands of eight SKUs for grocery items in a supermarket. Based on the proposed forecasting model, the inventory performance has been studied with simulation.

Findings

The proposed forecasting model with the inventory replenishment system results in the reduction of inventory level and increase in customer service level. Hence, the proposed model in the paper results in improved performance of inventory.

Practical implications

Retailers can make use of the proposed model for demand forecasting of various items to improve the inventory performance and profitability of operations.

Originality/value

With the advent of data mining systems which have given rise to the use of business intelligence in various domains, the current paper addresses one of the most pressing issues in retail management, as demand forecasting with minimum error is the key to success in inventory and supply chain management. The proposed forecasting model with the inventory replenishment system results in the reduction of inventory level and increase in customer service level. The proposed model outperforms other widely used existing models.

Details

Journal of Modelling in Management, vol. 7 no. 1
Type: Research Article
ISSN: 1746-5664

Keywords

Open Access
Article
Publication date: 21 August 2023

Yue Zhou, Xiaobei Shen and Yugang Yu

This study examines the relationship between demand forecasting error and retail inventory management in an uncertain supplier yield context. Replenishment is segmented into…

1476

Abstract

Purpose

This study examines the relationship between demand forecasting error and retail inventory management in an uncertain supplier yield context. Replenishment is segmented into off-season and peak-season, with the former characterized by longer lead times and higher supply uncertainty. In contrast, the latter incurs higher acquisition costs but ensures certain supply, with the retailer's purchase volume aligning with the acquired volume. Retailers can replenish in both phases, receiving goods before the sales season. This paper focuses on the impact of the retailer's demand forecasting bias on their sales period profits for both phases.

Design/methodology/approach

This study adopts a data-driven research approach by drawing inspiration from real data provided by a cooperating enterprise to address research problems. Mathematical modeling is employed to solve the problems, and the resulting optimal strategies are tested and validated in real-world scenarios. Furthermore, the applicability of the optimal strategies is enhanced by incorporating numerical simulations under other general distributions.

Findings

The study's findings reveal that a greater disparity between predicted and actual demand distributions can significantly reduce the profits that a retailer-supplier system can earn, with the optimal purchase volume also being affected. Moreover, the paper shows that the mean of the forecasting error has a more substantial impact on system revenue than the variance of the forecasting error. Specifically, the larger the absolute difference between the predicted and actual means, the lower the system revenue. As a result, managers should focus on improving the quality of demand forecasting, especially the accuracy of mean forecasting, when making replenishment decisions.

Practical implications

This study established a two-stage inventory optimization model that simultaneously considers random yield and demand forecast quality, and provides explicit expressions for optimal strategies under two specific demand distributions. Furthermore, the authors focused on how forecast error affects the optimal inventory strategy and obtained interesting properties of the optimal solution. In particular, the property that the optimal procurement quantity no longer changes with increasing forecast error under certain conditions is noteworthy, and has not been previously noted by scholars. Therefore, the study fills a gap in the literature.

Originality/value

This study established a two-stage inventory optimization model that simultaneously considers random yield and demand forecast quality, and provides explicit expressions for optimal strategies under two specific demand distributions. Furthermore, the authors focused on how forecast error affects the optimal inventory strategy and obtained interesting properties of the optimal solution. In particular, the property that the optimal procurement quantity no longer changes with increasing forecast error under certain conditions is noteworthy, and has not been previously noted by scholars. Therefore, the study fills a gap in the literature.

Details

Modern Supply Chain Research and Applications, vol. 5 no. 2
Type: Research Article
ISSN: 2631-3871

Keywords

Article
Publication date: 8 May 2007

Tien‐Hsiang Chang, Hsin‐Pin Fu, Wan‐I Lee, Yichen Lin and Hsu‐Chih Hsueh

To propose and test an augmented collaborative planning, forecasting, and replenishment (A‐CPFR) model in a retailer‐supplier context with a view to improving forecasting accuracy…

4867

Abstract

Purpose

To propose and test an augmented collaborative planning, forecasting, and replenishment (A‐CPFR) model in a retailer‐supplier context with a view to improving forecasting accuracy and then reducing the “bullwhip effect” in the supply chain.

Design/methodology/approach

After a literature review, the paper presents a real case in which the present authors provided assistance. The description of the case includes: case company background; an “as‐is” model analysis; a “to‐be” (CPFR) model analysis; and a description of the results and potential benefits. The paper then proposes an A‐CPFR model for the case and performs a simulation of the new model for comparison with the existing CPFR model.

Findings

The results show that the mean absolute deviation of forecasting and the inventory variance are both better in the proposed model than in the existing CPFR model. The proposed model can thus improve the accuracy of sales forecasting, reduce inventory levels, and reduce the “bullwhip effect”.

Practical implications

In addition to information provided by the retailer, a logistics supplier should also obtain competitors' promotional information from the market as another factor for forecasting – thus enabling timely responses to demand fluctuations.

Originality/value

The proposed model is an original and useful development on the existing CPFR model. It could become a reference model for the retail industry in implementing CPFR in the future.

Details

Supply Chain Management: An International Journal, vol. 12 no. 3
Type: Research Article
ISSN: 1359-8546

Keywords

Article
Publication date: 20 February 2009

A.A. Syntetos, M. Keyes and M.Z. Babai

Spare parts have become ubiquitous in modern societies and managing their requirements is an important and challenging task with tremendous cost implications for the organisations…

5213

Abstract

Purpose

Spare parts have become ubiquitous in modern societies and managing their requirements is an important and challenging task with tremendous cost implications for the organisations that are holding relevant inventories. An important operational issue involved in the management of spare parts is that of categorising the relevant stock keeping units (SKUs) in order to facilitate decision‐making with respect to forecasting and stock control and to enable managers to focus their attention on the most “important” SKUs. This issue has been overlooked in the academic literature although it constitutes a significant opportunity for increasing spare parts availability and/or reducing inventory costs. Moreover, and despite the huge literature developed since the 1970s on issues related to stock control for spare parts, very few studies actually consider empirical solution implementation and with few exceptions, case studies are lacking. Such a case study is described in this paper, the purpose of which is to offer insight into relevant business practices.

Design/methodology/approach

The issue of demand categorisation (including forecasting and stock control) for spare parts management is addressed and details reported of a project undertaken by an international business machine manufacturer for the purpose of improving its European spare parts logistics operations. The paper describes the actual intervention within the organisation in question, as well as the empirical benefits and the lessons learned from such a project.

Findings

This paper demonstrates the considerable scope that exists for improving relevant real word practices. It shows that simple well‐informed solutions result in substantial organisational savings.

Originality/value

This paper provides insight into the empirical utilisation of demand categorisation theory for forecasting and stock control and provides some very much needed empirical evidence on pertinent issues. In that respect, it should be of interest to both academics and practitioners.

Details

International Journal of Operations & Production Management, vol. 29 no. 3
Type: Research Article
ISSN: 0144-3577

Keywords

Abstract

Details

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

Article
Publication date: 1 May 1992

T.S. Lee, Steven J. Feller and Everett E. Adam

Applies time‐series forecasting, a traditional operations analysismethodology, to develop a forecasting procedure and ordering policy fora natural‐gas customer of Columbia Gas of…

Abstract

Applies time‐series forecasting, a traditional operations analysis methodology, to develop a forecasting procedure and ordering policy for a natural‐gas customer of Columbia Gas of Ohio, USA. Evaluates six time‐series methods and four operating policies against four commonly used measures of error and the cost consequences of error to the customer. Demonstrates that time‐series forecasting and decision theory developed by operations and applied in an actual industrial situation can become a powerful marketing technique. Provides further insights into evaluating forecasting models and ordering policies, demonstrating that introducing optimal planned bias is a robust decision‐making/forecasting approach within services. There are three parts to the study. The first is a straightforward testing of forecasting methods, using the forecasts as the natural‐gas ordering policy. Results vary depending upon how well forecasts are fitted to the data. For example, one inaccurate forecast with a poor fit incurs a penalty cost of $179,270, while the best forecast results in a penalty cost of $27,081. The second part evaluates two additional complex ordering rules with the same forecasting methods, further reducing the lowest cost to $17,709. The third part is a technical analysis reflecting a redesign of the study, demonstrating the difficulty of generalizing when characteristics of the underlying demand change. Concludes that the best forecasting model/operating policy is to use the very basic forecasting model of simple moving average (or the equivalent, first‐order exponential smoothing) combined with an optimal planned bias ordering policy, i.e. with the planned introduction of bias.

Details

International Journal of Operations & Production Management, vol. 12 no. 5
Type: Research Article
ISSN: 0144-3577

Keywords

Article
Publication date: 24 May 2011

Matthew Downing, Maxwell Chipulu, Udechukwu Ojiako and Dinos Kaparis

The UK Chinook helicopter is a utility and attack helicopter being operated by the Royal Air Force (RAF). Its versatile nature is of enormous importance to the strategic…

2136

Abstract

Purpose

The UK Chinook helicopter is a utility and attack helicopter being operated by the Royal Air Force (RAF). Its versatile nature is of enormous importance to the strategic capability of the RAF's operations. The purpose of this paper is to utilise systems‐based forecasting to conduct an evaluation of inventory and forecasting systems being used to support its maintenance programme.

Design/methodology/approach

A case study is conducted. Data are collected from existing monthly Component Repair (CRP) data and performance evaluation of software. For propriety reasons, all data have been sanitised.

Findings

Analysis of the current inventory and forecasting system suggests a possible lack of forecasting precision. Current non‐specific formulation of forecasting techniques implied several of the cost driver's demands were being miscalculated. This lack of precision is possibly a result of the smoothing value of 0.01 being too low, especially as the results of statistical modelling suggest that current parameter values of 0.01 might be too low.

Originality/value

The paper reports on work conducted jointly between Boeing and the University of Southampton that sought to create an intermittent demand forecasting model.

Details

The International Journal of Logistics Management, vol. 22 no. 1
Type: Research Article
ISSN: 0957-4093

Keywords

Article
Publication date: 1 February 1991

John Gattorna, Abby Day and John Hargreaves

Key components of the logistics mix are described in an effort tocreate an understanding of the total logistics concept. Chapters includean introduction to logistics; the…

6106

Abstract

Key components of the logistics mix are described in an effort to create an understanding of the total logistics concept. Chapters include an introduction to logistics; the strategic role of logistics, customer service levels, channel relationships, facilities location, transport, inventory management, materials handling, interface with production, purchasing and materials management, estimating demand, order processing, systems performance, leadership and team building, business resource management.

Details

Logistics Information Management, vol. 4 no. 2
Type: Research Article
ISSN: 0957-6053

Keywords

Book part
Publication date: 12 November 2014

Matthew Lindsey and Robert Pavur

A Bayesian approach to demand forecasting to optimize spare parts inventory that requires periodic replenishment is examined relative to a non-Bayesian approach when the demand…

Abstract

A Bayesian approach to demand forecasting to optimize spare parts inventory that requires periodic replenishment is examined relative to a non-Bayesian approach when the demand rate is unknown. That is, optimal inventory levels are decided using these two approaches at consecutive time intervals. Simulations were conducted to compare the total inventory cost using a Bayesian approach and a non-Bayesian approach to a theoretical minimum cost over a variety of demand rate conditions including the challenging slow moving or intermittent type of spare parts. Although Bayesian approaches are often recommended, this study’s results reveal that under conditions of large variability across the demand rates of spare parts, the inventory cost using the Bayes model was not superior to that using the non-Bayesian approach. For spare parts with homogeneous demand rates, the inventory cost using the Bayes model for forecasting was generally lower than that of the non-Bayesian model. Practitioners may still opt to use the non-Bayesian model since a prior distribution for the demand does not need to be identified.

Details

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

Keywords

1 – 10 of over 13000