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CMAF FFT: Alternatives to judgmental forecast adjustments: a retail case study [Video]

CMAF FFT: Alternatives to judgmental forecast adjustments: a retail case study

This is the first webinar in Season 3 of “Friday Forecasting Talks”, hosted by Centre for Marketing Analytics and Forecasting of Lancaster University, UK.
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— Contents of this video ————————–
00:00 – Introduction
04:30 – Human role in supply chain
06:18 – What we know
09:35 – Case study
16:17 – Judgmental adjustments vs model tuning
21:42 – Accuracy evaluation
25:22 – Findings
34:25 – Discussion from John Boylan
44:00 – Q&A

Speaker: Anna Sroginis

The abstract: With the increase in data sources and frequency of decision-making organisations see an increase in the volume of forecasts that needs to be generated. Nonetheless, several studies verify that human intervention in forecasting remains a common practice. There are several ways that experts can augment statistical forecasts with judgment: (i) adjusting forecasts individually for a single item; (ii) batch-adjusting: correcting several time series or categories at the same time; (iii) model tuning, indicating a location of corrections rather than size and feeding it to a statistical model, for example, by introducing indicator variables in a regression model. The literature has explored extensively the first category, but much less the other two. Yet, these are easily scalable for many products at once, making it easier and faster for forecasters to implement changes. There is limited research in the effectiveness and performance of these approaches. Furthermore, due to their ease of use, both batch-adjustments and model tuning might be overused and, as a result, potentially lose their effectiveness. For instance, in model tuning, introducing indicators for spurious events may result in overfitting rather than augmenting the statistical models which increasingly employ more sophisticated algorithms. Using a case study from a UK retailer, which exhibits all three behaviours of adjustments, we provide empirical evidence of the efficacy of these alternatives, as well as explore the conditions where each alternative may be preferable.

Bio: Anna Sroginis is a lecturer in Management Science at Lancaster University, UK. Her research focuses on judgment in forecasting. In particular, the intersection between human experts and support systems with their algorithms is crucial to make better decisions in practice.

Discussant: John Boylan

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