Food Decision Engineering transforms science, data, intelligence and human judgement into transparent, practical and continuously improving decisions.

IFoRAC is developing and applying this interdisciplinary approach to help governments, international organizations and food-system actors make better decisions under uncertainty.

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Start with one important decision. Build the capability to make it better.

Food authorities and food-system organizations have access to more evidence than ever: risk assessments, laboratory results, surveillance data, inspections, consumption surveys, scientific publications, international alerts and emerging AI capabilities.

Yet the systems connecting this evidence to decisions are often fragmented.

Evidence may arrive too late. Data systems may not connect. Uncertainty may be described but not used to compare options. Scientific advice may not account for implementation, cost, trade, food security or sustainability.

The result is a persistent decision-system gap: the science may be strong and the data abundant, but the pathway from evidence to action remains under-engineered.

Food systems do not only need better evidence. They need better-engineered decision systems.

Food Decision Engineering (FDE) is the interdisciplinary field dedicated to designing, integrating and continuously improving the systems that enable organizations to make better food-related decisions under uncertainty.

Traditional risk assessment asks: What is the risk?

Food Decision Engineering goes further: Given the evidence, uncertainty, objectives and constraints, how should the decision be made, implemented and improved?

FDE does not replace risk assessment, Codex principles, food-control systems or expert judgement. It designs the architecture that connects them to practical action and measurable results.

• Evidence: Scientific studies, surveillance, inspections, laboratory and consumption data
• Intelligence: Risk understanding, foresight, AI-assisted analysis, uncertainty and scenarios
• Decision architecture: Responsibilities, workflows, governance, models and decision products
• Decisions: Prevention, policy, inspection, import control, innovation and investment
• Learning: Monitoring results, evaluating impact and improving future decisions

FDE turns isolated analyses into a repeatable institutional capability for making, explaining and improving decisions.

1. Define the decision portfolio: Identify the decisions that matter most for public health, resilience, trade, innovation or trust. Output: Priority decision map
2. Frame the decision: Clarify what must be decided, who owns the decision, when it is needed and what is at stake. Output: Decision statement and scope
3. Map the evidence architecture: Determine which data, expertise, standards and external signals should inform the decision. Output: Evidence and intelligence map
4. Engineer the analytical approach: Select fit-for-purpose risk assessment, modeling, AI, economic and uncertainty-analysis methods. Output: Analytical workflow and model plan
5. Compare options and trade-offs: Evaluate expected benefits, residual risks, feasibility, costs and possible unintended consequences. Output: Options and trade-off brief
6. Design the decision product: Translate the analysis into a usable brief, dashboard, ranking tool, scenario model or governance process. Output: Decision-support product
7. Build the learning loop: Monitor implementation and impact so that every decision improves the next one. Output: Monitoring, evaluation and learning plan

• Decision-system diagnosis: Identify where evidence, responsibilities, workflows and decisions are disconnected.
• Decision architecture: Define the decision, owners, timelines, evidence flows and governance arrangements.
• Decision-ready risk assessment: Design risk assessments around the options and decisions they must support.
• Food Decision Intelligence tools: Develop decision briefs, dashboards, ranking models, scenario simulators and AI-enabled workflows.
• Trade-off and impact analysis: Compare safety benefits with cost, feasibility, food security, sustainability, trade and resilience.
• Institutional capability: Build the skills, processes and learning systems needed to internalize FDE.

• Reduce known food-borne disease burden: Identify priority hazard-food pathways and select interventions most likely to reduce illness, deaths and DALYs.
• Prepare for climate-driven and emerging risks: Connect climate, surveillance and supply-chain intelligence to early-warning and prevention decisions.
• Accelerate safe food innovation: Create proportionate and adaptive pathways for evaluating novel foods, ingredients and production technologies.
• Balance competing food-system objectives: Compare decisions across food safety, nutrition, affordability, food security, sustainability, resilience and trade.

Explore Food Decision Missions

Food Decision Engineering should not be limited to highly digitalized institutions. IFoRAC applies a progressive model:

• Essential FDE: Decision mapping, structured expert judgement, simple prioritization, decision briefs and explicit tracking of uncertainty and data gaps.
• Enhanced FDE: Connected operational datasets, risk ranking, scenario analysis, option comparison and decision dashboards.
• Advanced FDE: Integrated data architecture, probabilistic modeling, AI-enabled intelligence, predictive analysis, impact evaluation and adaptive learning.

The objective is not to make every institution advanced immediately. It is to make today’s decisions better while building tomorrow’s capability.

• Decisions, not reports
• Integration, not fragmentation
• Fit-for-purpose methods
• Transparent uncertainty
• Human accountability
• Codex-compatible risk analysis
• Continuous institutional learning

A Food Decision Mission starts with a defined challenge: a known hazard, an emerging risk, a novel food or a difficult food-system trade-off.

IFoRAC brings together the necessary evidence, expertise, analytical methods and stakeholders to design a practical decision pathway—and a system that can improve over time.

Discuss a Food Decision Mission
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The first step can be a focused decision-system diagnosis without requiring a large institutional transformation.