| 1. |
- Schaer, Oliver, et al.
(författare)
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Demand forecasting with user-generated online information
- 2019
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Ingår i: International Journal of Forecasting. - : Elsevier. - 0169-2070 .- 1872-8200. ; 35:1, s. 197-212
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Tidskriftsartikel (refereegranskat)abstract
- Recently, there has been substantial research on the augmentation of aggregate forecasts with individual consumer data from internet platforms, such as search traffic or social network shares. Although the majority of studies have reported increases in accuracy, many exhibit design weaknesses, including a lack of adequate benchmarks or rigorous evaluation. Furthermore, their usefulness over the product life-cycle has not been investigated, even though this may change, as consumers may search initially for pre-purchase information, but later for after-sales support. This study begins by reviewing the relevant literature, then attempts to support the key findings using two forecasting case studies. Our findings are in stark contrast to those in the previous literature, as we find that established univariate forecasting benchmarks, such as exponential smoothing, consistently perform better those that include online information. Our research underlines the need for a thorough forecast evaluation and argues that the usefulness of online platform data for supporting operational decisions may be limited.
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| 2. |
- Trapero, Juan R., et al.
(författare)
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On the identification of sales forecasting models in the presence of promotions
- 2015
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Ingår i: Journal of the Operational Research Society. - : Taylor & Francis. - 0160-5682 .- 1476-9360. ; 66:2, s. 299-307
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Tidskriftsartikel (refereegranskat)abstract
- Shorter product life cycles and aggressive marketing, among other factors, have increased the complexity of sales forecasting. Forecasts are often produced using a Forecasting Support System that integrates univariate statistical forecasting with managerial judgment. Forecasting sales under promotional activity is one of the main reasons to use expert judgment. Alternatively, one can replace expert adjustments by regression models whose exogenous inputs are promotion features (price, display, etc). However, these regression models may have large dimensionality as well as multicollinearity issues. We propose a novel promotional model that overcomes these limitations. It combines Principal Component Analysis to reduce the dimensionality of the problem and automatically identifies the demand dynamics. For items with limited history, the proposed model is capable of providing promotional forecasts by selectively pooling information across established products. The performance of the model is compared against forecasts provided by experts and statistical benchmarks, on weekly data; outperforming both substantially.
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