Ingredion Partners with HowGood to Measure Ingredient Sustainability

Recommendation: Roll out a unified sustainability score for ingredients using howgoods data to guide buyers and brands toward measurable outcomes.

Ingredion partnered with howgoods to build a standardized framework that measures environmental, social, and economic facets of ingredients, with stevia and other agricultural materials as early benchmarks. The system translates farm-level signals into a clear outcomes score visible to teams across procurement, sales, and product development. This alignment shows thats why procurement teams value the scores.

The model collects data points on origin, farming practices, input use, processing methods, and supplier status, turning them into a set of functions that support comparison across brands. It has a travaillé in pilots, delivering consistent metrics that help buyers assess risks and plan supplier transitions, with human rights checks woven into the review.

In brazil, pilots captured agricultural practices and materials handling for stevia and other crops, enabling procurement to compare impacts across origin countries. That data supports continued improvements and transparent reporting for brands and their customers, addressing both regulatory requirements and consumer expectations.

For implementation, align procurement, R&D, and marketing around the same data schema; embed scores into supplier contracts and share dashboards with buyers to support informed decisions. Software updates from howgoods will deliver fresher insights and sharper implications for ingredients and materials across brands.

Outline for the article

Adopt a three-tier approach to ingredient sustainability measurement: supplier-level footprinting, product-level footprinting, and brand-level impact. A tier will be defined for supplier data, a tier for product data, and a tier for brand strategy. This structure will meet needs of brands, companys, and consumers by clarifying the footprint behind each decision and by enabling access to consistent data from howgoods.

The article will map how Ingredion will collaborate with suppliers, brands, the organization, and the howgoods partnership to present actionable steps. It will highlight relationships and the practical considerations behind footprint declarations, plus concrete metrics to track progress.

Key considerations include data freshness, privacy, cost, and alignment with regulatory expectations.

This outline will help the organization, meet the needs of brands and consumers, and guide companys teams through practical steps.

Define sustainability indicators using HowGood’s data model

Start with a core indicator set aligned to HowGood’s data model to achieve clear, comparable insights across geographies. Focus on agricultural practices, materials sourcing, and health outcomes to provide a full view for buyers and suppliers, with insights driving decisions. Use this baseline in year one to identify risks and set targets for year two.

Define indicators that come with HowGood’s data model, such as environmental footprint for ingredients, water use, land-use intensity, biodiversity risk, and health and safety metrics; HowGood’s data model comes with standardized fields that map to indicators, complemented by social initiatives that address community health and welfare.

Structure indicators to reveal regional patterns and regulatory implications across geographies, with brazil among focus areas; start with generalized data to establish a baseline and progressively add enhanced data to sharpen the point of view across areas, delivering more than baseline metrics.

Link indicators to platforms used by buyers to act quickly, rapidly translating data into actionable steps, while tracking expense and outcomes; provide insights that help prioritize initiatives and investments for materials suppliers.

Map indicators to regulations and initiatives that shape supply chains, ensuring alignment with regulatory expectations and investor needs; use these mappings to drive action across year-by-year plans, including the brazil market.

These indicators provide a clear framework for providing actionable health, environmental, and economic insights that improve sustainability success for Ingredion, HowGood, and their buyers.

Capture supplier attributes and product-level footprints

Implement a standardized supplier data template and a live dashboard to capture attributes and product-level footprints across the supply chain. This provides an informed view into input origins and footprints, enabling decisions that support regenerative goals and consumer expectations. Data quality matters now.

Capture supplier attributes across origin, certification, farming practices, and capacity. For corn suppliers, add field location, variety, soil health programs, irrigation efficiency, and participation in regenerative schemes. Use a consistent scale (1-5) to simplify aggregation across products and projects.

Attach product-level footprints to each product line: GHG per kilogram, water footprint, land-use intensity, energy consumption, and packaging waste. Link these footprints to supplier attributes so you can assess relationships and prioritize improvement work across the line.

Leverage research from HowGood to calibrate footprint models and verify supplier claims. Through ongoing validation, you can assess the reliability of data and ensure those footprints reflect real-world performance while strengthening collaboration with suppliers who meet consumer expectations.

Whats next is a structured governance and data-collection cadence: quarterly data pulls, supplier validations, and cross-functional review cycles that inform procurement decisions and innovation roadmaps for corn-based products.

Drive regenerative innovation by combining supplier attributes with product footprints to identify high-impact changes – such as switching to regenerative corn sources or reducing packaging weight – then track progress with clear metrics and regular feedback loops to brand teams and retailers through the supply chain. This approach helps brands compare options and align on what to invest next.

Standardize cradle-to-gate and cradle-to-retail methodologies

Adopt a unified cradle-to-gate and cradle-to-retail framework now, anchored by ISO-based LCA principles and a shared data platform. Start with a six-ingredient pilot across three plants to demonstrate value at scale while ensuring ease of access to actionable insights for sales teams and customers.

Key actions include the following steps:

1. Define scope and boundaries: choose a single functional unit (for example, one kilogram of finished product) and standardize system boundaries to include cultivation or extraction, processing, packaging, and distribution to retail. This alignment lets you compare ingredients and plant-based inputs on a like-for-like basis.
2. Establish governance and partnerships: form a cross-functional partnership across sustainability, procurement, R&D, and sales; build relationships with suppliers and growers; set data-sharing rules that protect sensitive information while ensuring access for insights they can use to drive decisions.
3. Standardize data collection and attributes: implement a common data model for biomass feedstock and ingredients, capturing attributes such as origin, cultivation method, processing steps, energy use, and packaging. Use uniform units and timeframes to enable footprinting across facilities.
4. Identify hotspots and footprinting results: use the standardized model to identify the largest emission sources in the chain, such as agricultural inputs, drying and milling, or transportation; quantify impact per unit and per product line to guide improvements while keeping human needs in focus.
5. Leverage the ingredions data platform for insights: integrate HowGood data with internal information to enrich footprinting with sensory and functional attributes; ensure cross-functional visibility so sales can translate insights into customer-facing benefits.
6. Scale reporting and transparency: deliver concise, consistent reports that show regenerative attributes where applicable; share standardized dashboards with business units to support informed decisions on sourcing and product development across markets.
7. Cost and implementation plan: estimate data-collection costs per ingredient and per plant; start with a phased rollout across key ingredients, then scale by adding more items each quarter; optimize processes to keep data collection lean while increasing accuracy.
8. Continuous improvement and human-centric needs: monitor progress toward regenerative objectives, adjust supplier requirements, and maintain open lines of communication with farmers, processors, and customers to align on shared goals and values.

Implement data quality checks and version control

Adopt a centralized data quality gate lié à version control that executes at ingestion and during transformation. Set priority on the most critical fields–product, regionet date–and layer granular checks for ingredient_id, sustainability_score, and data source. Ensure each data request passes validation within minutes, and surface results to owners within weeks to accelerate action. These checks were designed to stay fast and simple.

Define granularity levels that align with HowGood and Ingredion collaboration: per product, per ingredient, per batch, and per region tels que brazil. Implement schema validation, non-null checks, range checks, referential integrity, and cross-source consistency, even for small batches. Use automated tests that run on every commit and after data refreshes, and track failures with a simple dashboard, providing priority issues and emerging opportunities for improvement. Provide clear remediation steps so teams can take corrective actions quickly. As issues emerge, we update checks to stay aligned with evolving data needs.

Établir version control for data and rules: store data quality checks as code in a repo, use Git commits with clear messages, and apply semantic versioning to data schemas. Capture Traçabilité des données so you can answer where each value came from and who changed it, when. For large datasets, consider lightweight data versioning (e.g., data snapshots) to enable quick rollbacks if a rule introduces drift. This supports continuously improving data quality.

In governance, assign owners to requests, including brian and other team members, and set a plan for weekly reviews. If a request fails quality checks, the owner takes action quickly; this plan creates opportunities to improve the product et brand perception. Provide a transparent view to customers et consumers–show them that data is handled with care and that improvements emerge from ongoing feedback. That thing keeps the quality conversation practical.

The approach signals a clear commitment to data integrity; thats why customers gain confidence and Règles : - Fournissez SEULEMENT la traduction, sans explications - Maintenez le ton et le style d'origine - Conservez la mise en forme et les sauts de ligne for teams to collaborate and iterate.

Plan phased rollout with pilots and scalability targets

Launch a three-region pilot within 60 days and set a 12-month scalability target to reach 50 brands and 200 suppliers, with commitment from leadership and onboarding completed for key corn ingredients and regenerative sourcing pilots that deliver measurable outcomes.

Form a cross-functional steering team and allocate a dedicated resource pool. Deploy a dynamic data pipeline to capture sourcing data and changing conditions, and generate a view of progress for customers and brands. Set a goal to increase supplier onboarding by 25% each quarter, reduce Scope 3 emissions in pilots by 5%, and clearly demonstrate outcomes that support continued growth around core markets, and explore further opportunities to apply learnings across regions.

Scale the framework around regions and brands by creating modular pilots that can be replicated in other regions with shorter cycle times. Establish phased gates: complete pilot with at least 85% performance threshold, then extend to 60% of the portfolio in year one and reach full coverage by year two. Align sourcing choices with regenerative practices to achieve measurable benefits in corn quality and brand reputation, and plan to invest in broader regenerative programs in regions with the strongest customer demand.

Continuously monitor outcomes and adjust sourcing strategies; capture learnings in a living playbook to support continued growth and to meet customers’ aims. Build a transparent onboarding and reporting loop so brands can see progress in near real time, with regular updates to the view for leadership and regional teams.