Supply execution
≈$40kper day
Company-reported average savings
A food group reported average savings of about US$40,000 a day after deploying AI-assisted execution in part of its supply network.
Consumer Goods · Food & Beverage
Connect consumer needs to products, channels and supply
ActelosAI helps food and beverage brands and producers scope and build AI applications across consumer insight, product innovation, channel execution and supply. Start with one operating decision, connect the relevant evidence and agree how the team will test and use the output.
01Why choose your product again?
Bring together eating and drinking occasions, taste preferences, price acceptance and repeat-purchase feedback to identify product opportunities worth testing.
02Are shipments turning into sales?Distinguish distributor shipments from sales to consumers. Consider distribution, stockouts, promotional spend and returns to understand sustainable growth.
03Can supply respond when demand changes?Compare options within shelf-life, ingredient, production and delivery constraints, balancing stockouts, excess stock and expiry losses.
The operating challenge
Connect consumer choices with business action
Consumer preferences shape new products. Channel sales inform the next promotion and production plan. Connecting insight, products, channels and supply within the same business scope helps explain where growth comes from—and where inventory and waste accumulate.
Start with one task in a brand or manufacturing business. Use data to support decisions and bring recommendations into everyday work.
AI transformation outcomes
See change in supply coordination and frontline work
Manufacturing waste
20%
Less waste on selected production lines
A food group reported 20% less waste in FY2024 on production lines at some large factories, using AI and digital capabilities.
Sales support
20%–35%
Less time on routine tasks in pilots
A food and beverage group reported 20%–35% time savings on relevant routine tasks for salespeople using an AI assistant in pilot markets.
AI applications
Six starting points across food and beverage
Start with one category, production line or channel. Connect data, human judgment and results that can be tested.
01
Consumer and occasion insight
Understand the needs behind the next purchase
Connect authorized research, reviews and service feedback with purchase and repeat-purchase data. AI helps organize why different groups choose products for breakfast, meals, exercise or leisure, highlighting evidence gaps so teams can choose the next consumer test.
View data, outputs and measures
- Connected data
- Research and sample scope, authorized feedback, product attributes and lawfully linked purchase records
- Work outputs
- Occasions and need themes, evidence references, opportunities and research questions
- Measures to track
- Insight preparation time · evidence traceability · stated intent versus actual trial
02
Product innovation and portfolio
Make product choices with better evidence
Compare concepts, flavors, formats and portfolio choices using consumer preferences, formulation trials, ingredient costs and existing product performance. AI supports screening and the next test; R&D, category and compliance teams decide which options proceed.
View data, outputs and measures
- Connected data
- Concepts, formulation and sensory tests, ingredient and packaging costs, sales, margins and applicable constraints
- Work outputs
- Candidate comparisons, test priorities, portfolio overlap and gaps, conditions to confirm
- Measures to track
- Concept-to-test time · repeated trials · trial and repeat purchase · substitution of existing products
03
Channel sales and promotions
Distinguish shipments from consumer demand
Connect distributor shipments, consumer sales and promotional execution to distinguish inventory loading, underlying growth and additional demand. AI surfaces exceptions, possible causes and spending comparisons to guide support for products, regions and channels.
View data, outputs and measures
- Connected data
- Shipments, consumer sales, channel stock and returns, distribution, stockouts, promotion costs and comparison periods
- Work outputs
- Channel exceptions, promotion reviews, comparable spending options and questions for sales teams
- Measures to track
- Distribution · sales-stock gaps · returns · incremental contribution after promotion costs; attribution needs a comparison
04
Demand and supply planning
Bring demand changes into supply decisions
Combine historical demand, promotions, launches and seasonal signals into demand ranges by product, region and period. AI helps compare purchasing, production and delivery options, showing implications for ingredients, capacity, service and inventory.
View data, outputs and measures
- Connected data
- Demand and stockouts, promotions and launches, ingredient lead times, line capacity, changeovers and delivery needs
- Work outputs
- Forecast ranges and bias, supply-demand gaps, production and replenishment options by scenario
- Measures to track
- Error and bias at consistent granularity and horizon · delivery in full and on time · expediting · stock and write-offs
05
Batch shelf life and inventory risk
Act before expiry and mismatches grow
Combine batch age, remaining shelf life, customer acceptance rules and expected demand to spot expiry, slow-moving stock and allocation risks. AI prioritizes batches and compares feasible destinations for planning, sales and quality teams to review.
View data, outputs and measures
- Connected data
- Batches and locations, production and expiry dates, usable stock status, demand, transit time and customer shelf-life requirements
- Work outputs
- Prioritized batch risks, allocation options, unmet conditions and accountable owners
- Measures to track
- Value exposed to expiry · write-offs and returns · shelf life at delivery · response time
06
Production deviations and quality
Find anomalies sooner and evidence faster
Bring together line signals, inspection, maintenance and batch records. AI helps identify anomalies, suggests possible causes and orders investigation steps for production and quality staff to review. It does not replace food safety decisions, testing or product release.
View data, outputs and measures
- Connected data
- Line and equipment logs, material batches, inspection results, deviations, maintenance and current procedures
- Work outputs
- Alerts, related batches and evidence, possible causes and further checks
- Measures to track
- Detection and investigation time · false and missed alerts · first-pass quality · rework and waste
Data and business results
Track growth, efficiency and execution together
Quality of growth
Track whether trial becomes repeat purchase, channel shipments become consumer sales, and contribution improves after promotion and service costs.
Supply efficiency
Track delivery, inventory, expediting and expiry write-offs together, checking that improvement in one step does not shift costs and risk elsewhere.
Decision and execution quality
Measure the time from finding a problem to completing action, evidence traceability, rework, false alerts and the total burden of human review.
Compare the same business scope against an actual baseline, while tracking quality, service and cost.
Related experience
CHAGEE
Case presented in collaboration with Gaoyan
AI supports product and new-menu selection
An AI product-selection platform supports CHAGEE’s product and menu development, helping teams evaluate new product options and make selection decisions.
Explore the caseCategory focus
Food and beverage share a chain, with different constraints
Segment by storage conditions, packaging, consumption and channels so the approach fits the product.
Food: storage and use matter
Ambient, chilled and frozen products, whether ready to eat or requiring preparation, have different demand, batch and delivery needs. Work at category and product level: formulation, sensory properties, ingredient variability, allergens, shelf life and returns cannot be represented by one average.
Beverages: connect occasion, formulation and pack
Ready-to-drink, powdered, concentrated and freshly prepared drinks require different views of occasions, seasonality, pack formats and channels. Include cold-chain, returnable packaging or in-store preparation constraints where they apply.
Human experts
Bring industry, transformation and supply experience to the problem
Connect category, consumer growth, delivery and supply operations expertise around the needs of a food and beverage business.
Shaun Yang
Expert Partner, Consumer Goods Industry Lead
Connects market insight, consumer signals and operating data to support category strategy, product innovation, commercial execution and practical AI adoption.
Managing Director, Gaoyan
Explore expert experience
Martin Qiu
Expert Partner, AI-Native Transformation & Enterprise Delivery Lead
Brings food and beverage, consumer growth and digital product experience to business priorities and cross-functional delivery.
Expert Partner, Gaoyan
Explore expert experience
Sheldon Lee
Expert Partner, Supply Chain & Operations Lead
Connects supply chain, warehousing and distribution with commercial feasibility and regional execution to clarify the processes and conditions that need to change.
Founder & CEO, Opxiq
Explore expert experienceBefore we begin
Scope AI around a food or beverage decision
The current Consumer Goods focus is food and beverage brands and producers. Consumer research, channel execution and supply decisions need different evidence and owners.
Should we start with insight, channels or supply
Choose the decision that is currently unclear. Consumer insight work starts from authorized research and feedback; channel work distinguishes shipments from consumer sales; supply work includes demand, shelf life and capacity constraints. Start with one category, channel or production scope rather than combining every question into one project.
What could the first output look like
Depending on the agreed scope, the output may be evidence-linked consumer themes, a channel exception review or demand-and-supply options for human review. Define how the team will validate and use it. AI-generated product ideas are not proof of consumer demand, food safety or commercial success.
Start with a business problem worth solving
Choose one category, channel or supply step. Together, identify the decision to improve, the available data and how to measure change, then define an appropriate AI trial.