S&OP – More Precise and Efficient Financial Forecasts & Reports

Starting point The S&OP process in a company: the challenge

In the classic S&OP process, sales planning often relies on historical data, manual expert knowledge, and simple trend extrapolations. The following challenges regularly occur:

• Insufficient forecast accuracy with highly volatile demand, seasonal effects, or short-term market changes.
• Data silos and lack of integration between sales, production, procurement, and controlling.
• High manual effort in creating, updating, and reconciling forecasts.
• Reactive instead of proactive planning, as early indicators from the market or supply chain are often not systematically used.
• Many stakeholders (procurement, sales, production, controlling) with different goals and perspectives, resulting in many coordination meetings, phone calls, and the need for patience.

These issues often lead to inefficient decisions, such as overstock, delivery bottlenecks, or unnecessary costs.

What to do How to make the S&OP process more efficient with AI

Targeted use of Artificial Intelligence (AI) and data science can overcome weaknesses in the S&OP process. Possible measures include:

Intelligent sales forecasts

Traditional forecasts are usually based on rigid models, ignore short-term influences, and often deliver unsatisfactory results during rapid market changes. By appropriately using machine learning models and suitable statistical methods, historical sales figures can be predicted very well and objectively. External influencing factors such as price developments, campaigns, and public holidays can be incorporated into the models, as well as weather data, economic or industry indicators. Internal influencing factors such as ERP and CRM data are often useful as well to achieve even better sales forecasts. If such influencing factors indicate a trend reversal early and are identified as predictive, they can also indicate trend reversals for financial figures such as revenue. By generating the forecast on a rolling basis and thus updating it, for example, monthly, weekly, or even daily, current circumstances are taken into account as best as possible, and demand fluctuations can be noticed in a timely manner. This enables a timely reaction and adaptability to changes in the market and demand.

Early warning systems & alerts

Risks such as delivery bottlenecks, budget overruns, or plan deviations are often detected too late – controlling remains reactive instead of proactive. Predictive models can continuously analyze operational key figures (inventory, delivery times, demand fluctuations, supplier OTIF rates, etc.). Monitoring relevant & correlating economic and industry indicators provides hints about how the market and economy are behaving. In case an anomaly is detected, e.g., in the direction of development or in the relationship between different variables, potential risks can be identified early and actions can be proactively initiated.

Scenario analysis & real-time simulations

What-if analyses are often Excel-based, manual, and take a long time – yet flexible planning assumptions are essential for good decisions in S&OP. By changing the values of influencing factors, you can observe live how key performance indicators, such as company revenues or costs, change. It enables the evaluation of the impact of various factors. By mapping multiple plausible scenarios with best-case/worst-case estimations and observing the effects on KPIs, decision-makers have a solid basis for decisions.

Decision support with generative AI

Management reports, commentary, and analyses consume a lot of time, while quick decisions are demanded. By using generative AI, management comments on forecast deviations can, for example, be recorded automatically, KPI trends can be summarized in natural language, or an interactive question-and-answer system can be provided in a dashboard, e.g., for the question: “Why did revenue decrease in Q2?” By linking with BI tools and S&OP dashboards, the tools are always available right where they are needed. From our experience, such tools can ensure that the time required to prepare for an S&OP meeting becomes negligible: information is not stored in PowerPoint slides but generated on-demand using the AI tool as soon as needed, and exactly in the form needed. Those who have the right information at hand faster can make better-informed decisions.

Your benefits Benefits: effective and holistic business planning in S&OP

The integration of AI into forecasting and the S&OP process offers concrete, measurable benefits:

• Higher forecast accuracy & objective second opinion for manual planning figures.
• Better decision-making bases for management and controlling thanks to dynamic “what-if” scenarios.
• Better and more efficient collaboration between departments and stakeholders through data-based transparency, aligned planning bases, and consistent forecasts.
• Bundled signals along the entire supply chain for holistic planning.
• Breaking down information silos and creating a shared view of future demand.
• Reduced tedious coordination & communication needs for more efficient processes.
• Reduction of manual effort, as forecasts are automatically created and regularly updated.

• Systematic analysis of your own company figures with regard to the economic situation.
• Proactive instead of reactive action, as you are prepared for trend reversals.