We conducted research to assess how supply chain teams perceive the accuracy of their forecasts and discover the tools and techniques they are using to upgrade the forecasting process.
Our research results are presented in this report.
Here, you’ll find helpful benchmarks and insights offered by peers on the latest demand forecasting techniques, forecast specifics and periodicity, and expectations for the future.
The survey was completed by 301 professionals working in supply chain, finance, sales and commercial / business roles.
Respondents work in many areas, including North America, Europe, Latin America, and Asia Pacific.
Various industries are represented from chemicals to consumer electronics, food and beverage and manufacturing sectors.
Perceptions on forecast accuracy
Find out how supply chain professionals rate the accuracy of their forecast and the benefits they expect to gain from improved forecast accuracy.
Most common techniques and technologies
Find out which methodologies and tools are in use and on the rise.
A New Day For Demand Forecasting?
Like many of the respondents in our survey, we too choose to be optimistic about the future, and we heard their needs loud and clear. This is why we have added demand forecasting capability to the SC Navigator suite of supply chain planning applications.
How supply chain teams realize the accuracy of their forecasts and discover the tools and techniques they are using to upgrade the forecasting process.
Demand forecasting is perceived as an important part of the business planning process.
But, only 2% of respondents reported being “extremely satisfied” with the accuracy of their forecasts.
The typical range of absolute forecast error at an item level is reportedly 20-40% according to 42% of respondents.
Also read: Machine Learning and Exception Management in Logistics Technology
Technology may performance the main role in organizations’ perceptions of forecast accuracy.
Only 2% of respondents said they were “extremely satisfied” with the tools used for demand forecasting.
Neutrality seems to be the norm, with 49% of respondents rating their satisfaction with their current technology between 5 and 7. Roughly 27% are not satisfied.
39% of respondents said that they use spreadsheets for demand forecasting.
About 45% of those using spreadsheets are dissatisfied, showing that this toolset is not robust enough.
30% of respondents stated that they use an expert forecasting package, and 61% of them reported being satisfied.
About a quarter of respondents (23%) use bundled ERP functionality, and the majority feel indifferent about this toolset.
Research shows that organizations are looking at several techniques to improve demand forecasting.
56% of professionals are looking into statistical modeling of historical demand in their organizations, and 41% are looking into machine learning.
Another 41% is currently investigating demand sensing.
Other techniques that are being investigated include the ‘best fit’ functionality and segmentation. 60% of respondents also indicated that they were “extremely curious” about new techniques such as DDMRP for expulsion from the effects of forecast accuracy.
We have focused in this paper on particular performance measurement, i.e. forecast accuracy, and its relevance for improving the quality of firms’ planning processes. Of course, firms use many different performance metrics and forecasting accuracy is clearly just one of them. It is not our interest in this paper to only study forecast accuracy to expand this list. Instead, our study is driven by a long-standing concern with the quality of firms, which has gained momentum recently as researchers became interested in the use of firms’ rolling forecasts and application beyond budget considerations is.
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