Towards smart-touch forecasting

Introduction to our blog series on improved forecasting using cognitive insights

Due to increasing data availability, advanced data science techniques such as machine learning have become a powerful tool to create more accurate statistical forecasts. However, in practice, human planners still include additional information, not known to the statistical forecasting algorithm, to come to a final forecast.

Integrating planner enrichments in a smart way

Despite efforts over the last decade to improve statistical forecasting capabilities and include more input data, this forecast enrichment process is still of eminent importance in most companies. But to really complement the statistical forecast the enrichments should be of high quality. However, research shows that planners do not always add value to the statistical forecast when enriching, and can even make the forecast worse. This inaccurate enriched forecast causes production re-planning that creates purchasing, financing, and scheduling difficulties, next to service level issues and imbalanced inventories. Only by enriching in a structured and focused way planners can truly add value.

At EyeOn we believe that we can integrate the planner enrichments in a smart way. By creating smart-touch planning solutions, we can automate the forecasting process for products that are easy to forecast, we can provide recommendations where it is more difficult, and flag where human intervention is needed. We call this process smart-touch forecasting.

As shown in the figure below, planners obtain a statistical forecast from the planning system, and receive feedback on their enrichment behavior while interacting with the planning tool. This combined effort of planning system and planner results in the best forecasting performance.

EyeOn’s vision on smart-touch planning
EyeOn’s vision on smart-touch planning

 

The challenge of human bias

When evaluating the enrichments of human planners, we need to be aware of their cognitive biases. As humans are not capable to deal with too large amounts of data, they can provide suggestions that are irrational.

Types of human bias
Types of human bias

 

Decreasing human bias with data-driven nudging

Cognitive forecast enrichments
Cognitive forecast enrichments in a nutshell

Therefore a key element of our smart-touch forecasting approach is what we call ‘cognitive forecast enrichments’, in which we nudge the planner to take better enrichment decisions.

In order to take the planner along in providing effective and efficient enrichments, we recommend the following four steps:
  1. Create awareness
  2. Assess previously performed enrichments
  3. Activate planners in providing effective enrichments
  4. Automate predictable enrichments where possible

The blog series ‘improved forecasting using cognitive insights’ will dive into each of these four building blocks in the coming months. Each month, we will present a part of our approach in building an effective forecasting enrichment process. The next blog on creating ‘Awareness’ will be online end of August.

If you have questions, or would like to discuss your enrichment process, please contact Edward Versteijnen!

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