Conducting a sales forecasting audit

Moon, Mark A.; Mentzer, John T.; Smith, Carlo D.

doi:10.1016/s0169-2070(02)00032-8

Cited by 74 publications

(84 citation statements)

References 29 publications

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“…Fildes and Hastings (1994) in an examination of various divisions of a multi-national organisation found the forecasters adopted very different roles depending on the organisational priority given to forecasting, the data availability and the product market. Moon, Mentzer and Smith (2003) have recently documented the forecasting function in a variety of US companies on four dimensions including functional integration and systems, arguing that for forecasting to be effective, information must be shared across 'owners' of the forecasting process and that information needs to be integrated within the components of the information system. However, none of these studies has focussed on how FSS are used in organisations and on the critical element of the interaction of the managerial judgement and the model.…”

Section: Forecasting Support Systems and Their Use In Organisationsmentioning

confidence: 99%

The design features of forecasting support systems and their effectiveness

Fildes

Goodwin

Lawrence

2006

Decision Support Systems

109

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Forecasts play a key role in the management of the supply chain. In most organisations such forecasts form part of an information system on which other functions such as scheduling, resource planning and marketing depend. Forecast accuracy is, therefore, an important component in the delivery of an effective supply chain. Typically, the forecasts are produced by integrating managerial judgment with quantitative forecasts within a forecasting support system (FSS). However, there is much evidence that this integration is often carried out poorly with deleterious effects on accuracy. This study considers the role that a well-designed FSS might have in improving this situation. It integrates the literatures on forecasting and decision support to explain the causes of the problem and to identify design features of FSSs that might help to ameliorate it. An assessment is made of the extent to which currently available business forecasting packages, which are widely employed in supply chain management, possess these features.

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Section: Forecasting Support Systems and Their Use In Organisationsmentioning

confidence: 99%

The design features of forecasting support systems and their effectiveness

Fildes

Goodwin

Lawrence

2006

Decision Support Systems

109

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“…Accurate forecasts can affect positively to the operational management of companies leading to significant monetary savings, greater competitiveness, enhanced channel relationships and customer satisfaction, lower inventory investment, reduced product obsolescence, improve distribution operations, schedule more efficient production and distribution, and more profitable financial decisions (Moon et al, 2003). For most of these companies, a particular type of a Decision Support System, known as a Forecasting Support System (FSS) is employed to prepare the forecasts (Fildes et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

Nonlinear identification of judgmental forecasts effects at SKU level

2010

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Prediction of demand is a key component within supply chain management. Improved accuracy in forecasts affects directly all levels of the supply chain, reducing stock costs and increasing customer satisfaction. In many application areas, demand prediction relies on statistical software which provides an initial forecast subsequently modified by the expert's judgment. This paper outlines a new methodology based on State Dependent Parameter (SDP) estimation techniques to identify the non-linear behaviour of such managerial adjustments. This non-parametric SDP estimate is used as a guideline to propose a non-linear model that corrects the bias introduced by the managerial adjustments. One-step-ahead forecasts of SKU sales sampled monthly from a manufacturing company are utilized to test the proposed methodology. The results indicate that adjustments introduce a non-linear pattern undermining accuracy. This understanding can be used to enhance the design of the Forecasting Support System in order to help forecasters towards more efficient judgmental adjustments.

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“…Greater accuracy in demand forecasting can result in higher profitability, greater competitiveness, enhanced channel relationships, and improved customer satisfaction [1]. The importance of accurate sales forecasts to efficient inventory management has long been recognized.…”

Section: Existing Workmentioning

confidence: 99%

Purchase-driven Classification for Improved Forecasting in Spare Parts Inventory Replenishment

Bala¹

2010

IJCA

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Performance of inventory management depends on the accuracy of demand forecasting. There are many techniques used for forecasting demand in retail sale. Advances in data mining application systems have given rise to the use of business intelligence in various domains of retailing. The current research captures the knowledge of classification of the customers using the purchase-based data of customers for improved forecasting. The model developed in this work suggests a technique for forecasting of demands which results in improved performance of inventory. The suggested forecasting model with the inventory replenishment system results in the reduction of inventory level and increase in customer service level. Moreover, the model makes use of purchase driven information instead of customers" demographic profile or other personal data for developing the decision tree for forecasting.

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Conducting a sales forecasting audit

Cited by 74 publications

References 29 publications

The design features of forecasting support systems and their effectiveness

The design features of forecasting support systems and their effectiveness

Nonlinear identification of judgmental forecasts effects at SKU level

Purchase-driven Classification for Improved Forecasting in Spare Parts Inventory Replenishment

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