Incorporating demand uncertainty and forecast error in supply chain planning models

Fildes, Robert; Kingsman, Brian G.

doi:10.1057/jors.2010.40

Cited by 62 publications

(41 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…They observe that many studies are dominated by a research paradigm that emphasizes mathematics, and they think that information sharing in the supply chain is valuable. They conclude with the observation that "more empirical modeling that includes forecast error and less reliance on spurious mathematical simplicity is required" (Fildes & Kingsman, 2011).…”

Section: Mrp Weaknesses and Optimizationsmentioning

confidence: 89%

Maintenance Enterprise Resource Planning: Information Value Among Supply Chain Elements

Ascef¹,

Bordetsky²,

Ferrer³

2014

View full text Add to dashboard Cite

Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instruction, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to The maintenance supply chain involves maintenance, repair, and overhaul organizations and the relationships within and across suppliers and customers. These organizations work with the probability of equipment failure, maintenance, and the use requirements of spare parts. All of these elements increase uncertainty in this environment. Furthermore, it is difficult to integrate and process information to maintain effective inventory control. This high level of uncertainty and lack of integration of information cause inventory excesses and shortages of spare parts needed in maintenance, which results in unnecessary costs.This research proposes a new model based on information processing theories to connect the lateral elements of the supply chain, increase vertical information integration, and transform the maintenance supply chain into an efficient system to decrease shortages and excesses of inventory thereby reducing costs. This research will incorporate a simulation to compare the proposed new model with the traditional inventory models. This study claims that, when using the new model in different situations, inventory performance is better than in the traditional models of inventory control. The importance of the results for the maintenance organizations relates to potential improvements in cost and in inventory control while fulfilling mission requirements.14. SUBJECT TERMS information processing, enterprise resource planning (ERP), economic order quality (EOQ), information integration, maintenance supply chain ABSTRACTThe maintenance supply chain involves maintenance, repair, and overhaul organizations and the relationships within and across suppliers and customers. These organizations work with the probability of equipment failure, maintenance, and the use requirements of spare parts. All of these elements increase uncertainty in this environment. Furthermore, it is difficult to integrate and process information to maintain effective inventory control.

show abstract

Section: Mrp Weaknesses and Optimizationsmentioning

confidence: 89%

Maintenance Enterprise Resource Planning: Information Value Among Supply Chain Elements

Ascef¹,

Bordetsky²,

Ferrer³

2014

View full text Add to dashboard Cite

show abstract

“…A summary of time series forecasting techniques and models are presented by Jan G. De Gooijer [9]. A comprehensive review of research in forecasting for supply chains is given by Fildes and Kingsman [5], who conclude that there are [3,20] and other life cycle assessment models of the product to forecast the demand. Although this is one of the best method but requires correct data of similar technology to find the constants involve in calculations.…”

Section: Litrature Reviewmentioning

confidence: 99%

Estimation of Appropriate Forecasts using Neural Network Learning of Previous Sales Data

Bissa¹

2017

IJRASET

View full text Add to dashboard Cite

A forecast is an estimate of the level of demand to be expected for the particular product or several products for some period of time in the future. It can be said that forecast is an educated guess; certainly is should contain as little error as is humanly possible. To make a forecast more meaningful, it should be in turns of units to be planned or scheduled, and it should cover a time period at least as long as the period of time required to make a decision and to put that decision into effect .The nature of product and its demand pattern affect the type of forecast to be made and the time period which must be covered or spanned; like demand may be constant, may have cyclic variations, long term upward trend etc.. Generally we find that forecast can be done by subjective opinion, market research, using certain index or can be computed using averages or some combination methods. Most of the forecaster assumes that use of statistical methods applied to the past data is a realistic way of forecasting future demands. New researchers use the bass model and other life cycle assessment models of the product to forecast the demand but these require a similar product life cycle to compute certain constants which again weaken the forecast. We think that the possibility of getting the almost exact forecast depends on the process (may be statistical or any) that are used to forecast, and these processes are selected by the forecaster; this means that these all forecasts basically dependant on the experiences of the forecaster and the knowledge regarding the trend of the market, and also the ability of the forecaster to predict the remaining life cycle of the product. This is the basic region that we get the values which, many times, are far from actual one. Considering the requirements of forecasts i.e. a huge knowledge about the product whose demand is to be forecasted and certainty of irrelevance or missing data, the author experiments on a new tradition of forecasting and uses an artificial neural network technology to forecast the future demand and represent some of them in this paper. This technology uses mathematical formulations to model nervous system operations and is used to learn pattern and relationships in data. There is verity of software available to model the neural network but among those the author fined more convenient software which is a add-ins of MS EXEL known as Neuro XL Predictor which is very simple to use as it is a part of Microsoft Excel spreadsheets.

show abstract

“…Existe material referencial que aborda este tipo de enfoque (Gaur, Kesavan, Raman y Fisher, 2007;Fildes y Kingsman, 2011;Chase, 2014), pero hay diferencias en la práctica local de aspectos vitales debido a que en el mercado retail peruano existe un uso limitado de técnicas cuantitativas para estimar la demanda y establecer una periodicidad adecuada de abastecimiento.…”

Section: Marco Referencialunclassified

Un enfoque bayesiano de planeación agregada orientado all retail marketing

Taquía-Gutiérrez¹

2015

Interfases

View full text Add to dashboard Cite

show abstract

Incorporating demand uncertainty and forecast error in supply chain planning models

Cited by 62 publications

References 33 publications

Maintenance Enterprise Resource Planning: Information Value Among Supply Chain Elements

Maintenance Enterprise Resource Planning: Information Value Among Supply Chain Elements

Estimation of Appropriate Forecasts using Neural Network Learning of Previous Sales Data

Un enfoque bayesiano de planeación agregada orientado all retail marketing

Contact Info

Product

Resources

About