Effective Demand Forecasting and Improvements Strategies for Supply Chain Planning

Daniel Dumke — Mon, 14 Jun 2010 04:05:00 +0000

The article reviewed here takes a look at typical biases in supply chain demand planning and how to avoid it. This work could prove very valuable for many companies who rely on manually adjusted forecasts.
Usually the forecasting process uses two steps:
1) statistical forecast by the forecasting system
2) manual adjustment to include additional effects (eg. additional analysis of demand pattern not included in step 1)

Data

The authors acquired data by four supply chain companies (industries: pharmaceuticals, food, household products and one retailer). As a whole over 68’000 datasets (statistical forecast, final (adjusted) forecast, actual outcome) were analyzed.

Problem

Now the problem seams to be that the human forecaster has several biases (systematic deviations from the real forecast). Eg. so that usually the human forecast is too positive.

Solution

Three mitigation strategies are suggested:

Blattberg-Hoch approach
using the inputs of the human forecaster and the model each with a 50% share of the whole forecast.
Error bootstrap rules
The goal here is to include knowledge about prior errors in the forecasting process
Avoiding small adjustments
as the results show small adjustments do on average not improve the overall forecast

Reference:

Fildes, R., Goodwin, P., Lawrence, M., & Nikolopoulos, K. (2009). Effective forecasting and judgmental adjustments: an empirical evaluation and strategies for improvement in supply-chain planning International Journal of Forecasting, 25 (1), 3-23 DOI: 10.1016/j.ijforecast.2008.11.010

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Tags:

demand uncertainty

forecast

risk

Michael Lawrence

Effective Demand Forecasting and Improvements Strategies for Supply Chain Planning

Data

Problem

Solution