Executive Guide to Forecasting in Manufacturing, Part 2: Mastering the Workflow

Many executives describe forecasting as a “black box.” Data flows in, models produce numbers, and reports are circulated - but too often the process feels disconnected from the business decisions it is meant to support. Without structure, forecasts risk being produced at the wrong level of detail, at the wrong frequency, or for the wrong purpose.

In Demand Forecasting for Executives and Professionals by Stephan Kolassa, Bahman Rostami-Tabar, and Enno Siemsen, the authors emphasize that forecasting should not begin with data or algorithms, but with the decisions that require a forecast. From that starting point, a structured workflow ensures that forecasts are relevant, transparent, and decision-ready. For enterprise manufacturers, adopting such a workflow is critical to move from reactive forecasting toward reliable, value-driving planning.

Key Insights from the Book

The authors outline forecasting as an iterative workflow, not a one-off calculation. Each stage builds on the previous one to link forecasts directly to business decisions:

1. Identify the decision. Forecasts have no value on their own - they matter because they support strategic, tactical, or operational decisions.
2. Define requirements. Establish forecast horizon, frequency, and level of detail. A network design decision might need a 5-year market forecast, while replenishment requires daily SKU-level projections.
3. Gather data. Use historical demand, predictor variables (deterministic like promotions or holidays, stochastic like weather), and domain knowledge from experts.
4. Prepare data. Clean and structure the dataset by fixing errors, handling missing values, and ensuring consistency.
5. Visualize data. Plot the data to detect trends, seasonality, or anomalies that could impact model choice and interpretation.
6. Choose and train models. Select the right methods for the decision at hand, test multiple models, and balance accuracy with interpretability.
7. Produce forecasts. Generate outputs for the required horizon - as point forecasts, prediction intervals, or full probability distributions. Causal models can also incorporate predictors with known or forecasted values.
8. Evaluate quality. Measure not only forecast accuracy but also relevance, speed, and usability for decision-makers.
9. Communicate results. Go beyond single numbers - share uncertainty through ranges, distributions, or scenarios.
10. Incorporate judgment. Adjust forecasts when new information emerges, but document changes to learn which inputs add value.

The book makes one point especially clear: discipline in following the workflow matters more than complexity of methods. Without this structure, forecasts risk being misaligned with the decisions they are supposed to guide.

Quantics Perspective

In complex enterprise manufacturing environments, the starting point for effective forecasting must always be the decision the forecast is intended to support. Without this clarity, forecasts risk missing critical information and will not be trusted or used by stakeholders.

Take a practical example: most regions of a company may outsource outbound transport to third parties, but one business unit operates its own fleet and must decide whether to purchase additional transport resources. For this unit, knowing the expected volumes on specific lanes and distribution modes is essential. If the forecast fails to provide that level of detail, it cannot be used effectively. That is why Quantics experts work closely with clients during the setup phase - reviewing decisions, identifying information requirements, and ensuring the forecasting design is tailored to actual business needs.

Beyond setup, Quantics solutions deliver end-to-end support for data processing, analysis, and visualization, reducing manual effort and making insights accessible to all stakeholders. To ensure robustness and accuracy, the platform continuously reviews more than 60 forecasting methods - including statistical and machine learning approaches - and selects the best fit or weighted combination for each situation. Predictor variables are included if they add measurable value. Importantly, forecasts are not created on a single level of aggregation: the system generates forecasts across the entire organizational structure (from SKU to region to business unit), learns from all levels, and then reconciles them into one final, coherent forecast. Outputs can be delivered as both point and probabilistic forecasts to quantify uncertainty explicitly.

Quantics offers evaluation functions tailored to enterprise manufacturing, enabling forecasts to be assessed from multiple perspectives with metrics that match the needs of diverse stakeholders. This ensures not only technical accuracy but also practical usability across sales, production, logistics, and finance.

Finally, Quantics makes collaboration and override management simple. Users can apply fast, efficient overrides with exception handling, assign reason codes, and maintain a full log of changes. This strengthens process discipline, boosts transparency, and provides valuable insight into the impact of human judgment on forecast accuracy.

Practical Takeaways

Executives can strengthen forecasting effectiveness by:

• Starting with decisions. Define what decision the forecast will inform before choosing models or data.
• Tailoring to the decision level. Match horizon and granularity to strategic, tactical, or operational needs.
• Integrating diverse inputs. Blend historical data with predictors and expert judgment.
• Communicating uncertainty. Present ranges and scenarios, not just single numbers.
• Documenting adjustments. Track judgmental overrides to see what truly adds value.
• Aligning functions in one workflow. Ensure sales, operations, and finance work from a shared process.
• Using dedicated software. Platforms like Quantics enforce workflow discipline, automate manual steps, and scale demand planning across complex organizations.

In Conclusion

Forecasts that lack a clear workflow are just numbers without context. By embedding a structured process, executives ensure that forecasts are directly connected to decisions, transparent across teams, and continuously improved. For manufacturers, this discipline reduces politics, builds trust, and strengthens resilience across the supply chain.

At Quantics, we see that organizations that implement structured forecasting workflows not only improve accuracy but also achieve faster consensus, better collaboration, and more reliable decision-making.

In our next post, we will explore how to manage uncertainty and make smarter decisions under risk - turning forecasts into powerful tools for balancing costs, service levels, and resilience.

Disclaimer

In this post, we share highlights from the book “Demand Forecasting for Executives and Professionals” by Stephan Kolassa, Bahman Rostami-Tabar, and Enno Siemsen, together with our own reflections on how these ideas apply to today’s manufacturing supply chains. This is not a replacement for the book, but rather a guide to spark thought and discussion. For a deeper dive, we encourage you to explore the book itself - available here.