Part four of our blog series on improved forecasting using cognitive insights
As described in our earlier blogs, forecast data enrichment processes are complex. It is not always clear who in the demand planning team adjusted and for what reasons. While in many companies the number of forecast items to take into account has grown exponentially, the experienced ‘old school’ planners who knew the ins and outs of each planning item are gradually phasing out. Today’s demand planning teams would therefore benefit greatly from being supported in a smart way to make effective and focused forecast adjustments. Providing these effective and focused data enrichments is one of the key steps of our cognitive insights approach to improve your forecast by nudging the planner to make better enrichment decisions:
- Create awareness
- Assess previously performed enrichments
- Guide planners in providing effective enrichments
- Automate predictable enrichments where possible
This blog focuses on guiding planners to provide effective enrichments. But how to achieve this? The concept of decisional guidance provides the answer to that question.
Decisional data enrichment guidance
Decisional guidance was introduced in literature in the ’90s already: Mark Silver defines it as the way a planning system supports the decision-maker with structuring and executing the decision-making process. Within intentional guidance, literature distinguishes two types: informative guidance and suggestive guidance (Montazemi et al., 1996; Fildes et al., 2006).
Informative guidance: Giving a planner unbiased, relevant information without any suggestions on actions to take. Example: “Based on historical sales, average demand was 598 products”.
Suggestive guidance: Proposing a specific action to the decision maker. Example: “Based on historical sales, the system advises to adjust the forecast for March from 123 to 598 products. Do you accept this change?”
As research on decisional guidance in the context of planning and forecasting is lacking, we decided to perform a study on how decisional guidance can be implemented in planning systems to improve the performance of judgmentally adjusted forecasts. Jochem Geurts, master student Operations, Management and Logistics of Eindhoven University of Technology, performed this research.
The research of Jochem consists of a data analysis on a dataset of one of our customers, and an experiment in which we determine which form of guidance works best in what situation.
To validate the added value of forecast enrichment, we used a dataset of one of our customers. The dataset showed the current process of judgmentally adjusting statistical forecasts. Jochem found that in the current situation, on average planners do not improve the forecast performance. Specifically, adjustments made for products categorized as low to medium volatile decreased the forecast performance. This decline in accuracy is mainly due to the already high quality of the statistical forecast (average forecasting error of 29% for statistical forecasts versus 42% for the enriched forecasts). Adjustments made on volatile products did improve the forecast performance. Next to the distinction between the volatility of the products, Jochem also looked at the direction of the adjustment. The analysis showed that planners are good at choosing the direction of the adjustment, but they have difficulties predicting how much higher or lower the new forecast should be. These two findings are combined in the experiment in which decisional guidance is applied.
The experiment showed that decisional guidance has a positive effect on forecast accuracy. When making a distinction between informative and suggestive guidance, we showed that both forms of guidance have a positive effect on forecast accuracy and there was no big differences between the effect of these two different types. For products with medium to high volatility, decisional guidance on the size (how much) significantly improved the forecast performance. For products with low volatility, decisional guidance on the adjustment (direction) showed nearly the same effects in terms of performance as decisional guidance on the size . It can be useful to use decisional guidance on the adjustment as extra check for planners if they are certain to adjust the forecasts of products with low volatility.
Next, the experiment shows that the participants were more intended to fully accept advice which involved small changes compared to large adjustments. Since large adjustments typically add most value to the forecast performance, we recommend to be careful with proposing many small adjustments as decisional guidance. It might lead to a decreased willingness to accept the large adjustments as advice. These results will be used to further improve the forecast data enrichment process.
Decisional guidance: towards smart-touch forecasting
The promising results of both the analysis and experiment confirm that providing the right decisional guidance to planning teams on making- and accepting changes can significantly improve your forecasting performance. This is a great step towards true ‘smart-touch forecasting’.