GenAI in Planning & IBP: From Demo to Operational KPIs

From Forecasting to IBP: A Value Leap

Traditional forecasting relied heavily on historical sales and a handful of economic indicators. GenAI, however, ingests multiple layers of complex data: consumer trends, commodity price fluctuations, marketing campaigns, even weather signals and social media chatter. This turns forecasting from a single number into a probability range, allowing companies to prepare multiple scenarios rather than one rigid plan.

The greater leap is integration into IBP, where supply, demand, finance, and production capacity intersect. Here, GenAI serves as a real-time advisor, suggesting adjustments to production, inventory, or transportation in response to shifting market conditions.

Data – Models – Governance: The Success Triangle

The backbone of GenAI is data. Dirty, inconsistent data yields skewed results and poor adoption. The first step to unlocking GenAI’s value is building a standardized master data dictionary across warehouses, contracts, and financial systems.

Equally important, models must be governed as 'living assets'. Enterprises need oversight mechanisms, before-and-after comparisons, and controlled experiments. Only then can GenAI’s recommendations evolve from theoretical advice into reliable operational decisions.

Start with processes that have clean data and clear decision cycles, such as sales planning or production scheduling. Set specific goals—reduce forecast error by 15% or shorten planning cycles by 30%. Standardize data, establish A/B comparison frameworks to prove ROI, and then transfer operations to a dedicated team. Alongside, train staff to collaborate with machines, ensuring human–machine co-creation.

GenAI Automating Decision Chains

A standout feature of GenAI is its role as an intelligent agent—not only answering questions but also recommending action chains. For instance, if demand spikes in one market, GenAI may recommend boosting production at the nearest plant, rerouting shipments, and alerting procurement simultaneously.

Still, full automation remains aspirational. Today, the optimal approach is hybrid: machines propose, humans validate and decide. This minimizes risks while leveraging GenAI’s computational power.

KPIs and Ethics: Guarding the Boundaries

Measurement is another challenge. Focusing solely on 'cost savings' risks overlooking indirect benefits like delivery reliability, customer satisfaction, or workforce morale. A multi-dimensional KPI set, blending financial and non-financial outcomes, is essential.

Equally critical is ethics. As GenAI embeds deeper into decisions, companies must guarantee explainability, data security, and fairness—avoiding biases that could cause inequities or reputational harm.

1. Running scattered pilots with no measurable KPIs. 2. Ignoring data quality, leading to unreliable results. 3. Expecting machines to fully replace humans instead of enabling collaboration. 4. Lacking monitoring mechanisms, allowing models to drift over time. 5. Neglecting ethics and risk, from data privacy to bias. Solutions: select focused use cases, measure outcomes rigorously, and maintain tight feedback loops.

GenAI is no longer science fiction. In supply chains, it is already shaping planning and operational decisions. But true value emerges only when enterprises choose the right entry points, standardize data, design robust KPIs, and enforce execution discipline. In doing so, GenAI becomes more than just another tool—it becomes a strategic lever that enhances competitiveness, making supply chains more agile, leaner, and more resilient in an age of constant disruption.