How can AI help companies make better planning decisions?

Planning becomes difficult when demand changes, supply is uncertain and decisions are spread across different teams and systems.

Across the supply chain, the gap between intention and execution grows at every stage, from demand forecasting to supply, materials, production, sequencing, execution and replanning. Evidence from LTPlabs’ planning expertise, combined with market benchmarks, highlights the scale of the challenge:

• 78% of planners identify forecast inefficiency as a challenge.
• 75% of cases involve frequent delays, exceptions or ad hoc replanning.
• 70% of supply chain leaders identify supply volatility as an explicit pain point.
• 63% of projects present fragmented decisions across teams, stages or systems.
• 62% depend heavily on manual processes and individual experience.
• In 35% of cases, decisions change when manual sequencing is compared with global optimisation.

These challenges reinforce one another. Demand signals are often unreliable. Supply constraints and lead times make plans unstable. Teams rely on manual intervention to balance multiple constraints, while siloed decisions make it harder to balance service, capacity and cost. Sequencing often depends on individual experience, limiting the number of alternatives that teams can evaluate.

As a result, planning teams become reactive and the original plan becomes increasingly difficult to execute.

The promise and reality of AI

AI is increasingly presented as a solution to planning challenges. Yet, according to a Gartner survey, only 23% of supply chain organisations have a formal AI strategy.

The most relevant applications for planning include:

• Intelligent simulation to test demand and capacity scenarios before decisions are made.
• Agentic AI to support adaptive and automated replanning.
• Decision governance to make decisions more consistent and explainable.

The opportunity is particularly significant when analytical and generative AI work together:  

• Analytical AI can support demand forecasting, anomaly detection, scenario simulation, inventory optimisation, risk prediction and constraint optimisation.
• Generative AI can help interpret exceptions, consult organisational knowledge, summarise deviations and recommend actions.
• Agentic AI can combine these capabilities with process redesign and enterprise integration to orchestrate decisions and trigger actions in connected systems.

Together, these technologies can make planning more predictive, explainable and actionable.

AI maturity changes the nature of the challenge

The main constraints in decision-making also evolve as AI maturity increases.

At the starting point, decisions rely mainly on individual experience, with limited systematic support from data. The next stage involves manually consolidating information from multiple systems and sources. More mature organisations use AI to recommend decisions or support holistic optimisation. At the highest level, well-structured, organised and accessible data enables faster and better-informed decisions.

This progression matters because AI adoption is not only a technology decision. It also depends on the quality of the underlying data, the redesign of planning processes and the integration of AI into the way teams make decisions.

Examples from planning projects

LTPlabs’ planning projects illustrate how these capabilities can be applied in practice.

Faster and more explainable scenario analysis

In many planning environments, testing scenarios requires manual changes and analysis. This effort limits the number of alternatives that teams can explore within each decision window.

An interactive interface using generative AI allows planners to explore scenarios in natural language, compare alternatives and understand their impacts. It also explains the trade-offs behind each option.

This approach enabled up to 10 times more scenarios to be tested per decision window and reduced the effort required for scenario analysis by 20% to 30%.

Identifying opportunities in slow-moving inventory

Slow-moving stock can represent a significant challenge when its commercial potential is difficult to identify through manual processes.

LTPlabs developed an analytical model to identify matches between new orders and existing stock. A conversational generative AI interface then provided automatic recommendations.

The estimated recovered revenue ranged from €130,000 to €500,000. Additional benefits included stock reduction and indirect gains from fewer production setups and less idle time.

Improving demand forecasting

Manual and poorly structured forecasting processes can produce inconsistent forecasts while creating a high workload for commercial teams.

A new forecasting model combining machine learning and statistical methods automated the forecast. A dedicated interface allowed teams to validate and adjust the results using market inputs.

The combination improved accuracy by 5.9 percentage points and reduced forecast bias by 8 percentage points.

From plan to action

AI can help supply chain organisations close the gap between planning and execution. Its value comes from connecting reliable data, analytical models, conversational interfaces and redesigned decision processes.

The most effective applications support better scenario evaluation, more consistent decisions and faster responses to exceptions. They also reduce dependence on manual work and make the reasoning behind recommendations easier to understand.

For organisations planning under pressure, the path forward begins with identifying where decisions become fragmented, manual or unstable. These points often provide the clearest opportunities to combine AI with process redesign and enterprise integration.