Easier product decisions
Make product information easier to find, compare and maintain so customers and staff can choose with more confidence.
FROM POSSIBILITY TO PRACTICE
What it does for your business
For your commerce and merchandising team, “Demand forecasting” can become a repeatable workflow rather than a separate task handled from scratch each time. AI and data models can help estimate future outcomes or compare scenarios using the history and assumptions relevant to the task. It fits into product discovery, catalogue operations and customer assistance, using your business information and the standards your team already works to.
The value it could bring
These are the improvements to evaluate against your current process. We agree the scope and test the value with your team.
Plan with a clearer baseline
Bring historical patterns and relevant assumptions into a repeatable planning process instead of rebuilding an estimate each time.
Explore the trade-offs
Compare plausible scenarios before a commitment and see which assumptions have the greatest effect.
EVIDENCE FROM OTHER BUSINESSES
Published ROI evidence
These named businesses implemented related workflows. Their deployment results provide a reference for the opportunity; they are not appana projects or a promised return for your business.
Vallarta Supermarkets
Logile Fresh Inventory Management
1,070%Reported annual deployment ROI
Demand-led fresh production reduced excess stock and spoilage; the study attributes more than $10 million in profits to the deployment by year three.
What the figure covers
A grocery-wide deployment combining production planning, recipe, grind and yield management. This is the suite’s return, not an isolated forecasting result.
Nucleus Research · Published 2025-12-17 · Source checked 2026-10-05
Read the published case study ↗How the returns are assessed
Reported annual ROI uses Nucleus Research’s method: average annual net benefit across three years divided by initial investment. Assessments can include indirect benefits and projected years. Figures cover the named deployment, including software, integration and process changes; they do not isolate AI’s contribution or predict your return.
Nucleus Research’s calculation method ↗How it works in your business
STEP 1
Bring in the right context
Select the relevant information from your commerce platform, product-information system and stock tools. Agree what a good result looks like with your commerce and merchandising team, including the rules, examples and permissions the workflow needs.
STEP 2
Turn the task into a workflow
Project what sells where, before the buy is committed. Prepare the inputs, state the assumptions and compare estimates with a suitable baseline. Show uncertainty and where the available data is insufficient.
STEP 3
Put the result to work
Give planners an estimate or scenario comparison they can challenge, with the underlying assumptions and a way to compare it with actual outcomes. Staff verify stock, product claims and commercial terms before they are presented as confirmed.
AN EXAMPLE IN PRACTICE
A member of your commerce and merchandising team needs help with “Demand forecasting”. They supply product attributes, catalogue records and stock information, together with the relevant instructions and the result they need. The workflow prepares an initial result with its supporting context, flags missing information and returns it for review. The owner can correct it and use the accepted result in the team's commerce platform, product-information system and stock tools.
Start with a focused pilot
Choose one workflow, one team and a representative set of real tasks. We establish the current baseline, build the first version and review the results together before expanding it.
What we would start with
Depending on the scope, useful inputs include:
- Product attributes, catalogue records and stock information
- Store policies and merchandising standards
- Common shopper questions and approved answers
We agree access and integration with your commerce platform, product-information system and stock tools as part of the design.
How we would measure value
- Error against actual outcomes on held-out data
- Planning time and performance against the existing baseline
- Catalogue correction rate, shopper task completion or avoidable returns
Staff verify stock, product claims and commercial terms before they are presented as confirmed.
LET’S BUILD IT TOGETHER
Could this help your business?
Tell us how your team works today and what you would like to improve. We’ll explore the opportunity, shape a practical first step and build it with you.
Talk to us about this use case