What will happen?
Calculates sales volume, revenue shifts, stock shortages, and store risk in advance.
FDAM AI PLATFORMFranchise Intelligence
From prediction to action,
From prediction to action,
the AI platform for franchises
FDAM AI reads your sales, order, inventory, and operations data and turns it into results your team can act on right away: order recommendations, sales-drop and store-closure risk forecasts, meeting notes, review replies, and operations reports.
Q
Which stores should I check first this week?
- Syncing sales from POS + 5 delivery channels1,284 stores
- Detecting deviations from normal patterns23 signals
- Breaking down causes by hour, channel, menu3 factors
- Creating actions and staff tasksready
Priority store · 01Gangnam #3
CautionWeekly sales−14.8%
A drop in lunch delivery orders and an inventory gap in key items were detected at the same time.
View root causeCreate check task
30-second summary
The FDAM AI Platform is the set of AI features built into FDAM, MS Benter's AI operations platform for franchises. It analyzes each store's data to forecast optimal order quantities, sales drops, and store-closure risk, and automates repetitive work like meeting notes, review replies, and operations reports. Every result comes with the reasoning behind it and recommended actions.
POSBaeminCoupang EatsYogiyoDdangyoBaedal TeukgeupSpecial venuesOrders & receivingInventoryFranchise salesOperations checksReviews & CS
01 — Definition
What is a franchise AI platform?
A franchise AI platform unifies sales, order, purchasing, inventory, and operations data from headquarters and franchise stores to forecast demand and risk and recommend the next action. FDAM is an AI operations platform for franchises that builds this AI into both the HQ ERP and Sodam, the store app.
Where general-purpose generative AI focuses on answering questions, FDAM continuously reads changes in your real operating data and alerts you first, right when it matters. Predictions flow directly into real work inside FDAM, such as order approvals, store checks, and operations reports, while your team makes the final call. FDAM is built on MS Benter's 20+ years of building order management, logistics, and HQ operations systems for franchises.
What should we do next?
Drafts orders, summaries, replies, reports, and follow-up tasks.
02 — CapabilitiesAI features that carry you
AI features that carry you
from prediction to action
Each feature does more than show a result. It walks you through what data it read, why it reached that result, and what your team should check, all in one flow.
01 · ORDER RECOMMENDATION
AI order suggestions
Analyzes each store's sales flow and inventory to calculate expected demand and recommended order quantities by item. Your team can review the reasoning and contributing factors, then apply them straight to the order.
- Data analyzed
- Sales history, current stock, incoming deliveries, day-of-week and seasonal patterns
- Output
- Expected demand by item, recommended quantity, stockout and overstock risk
- Next step
- Review the recommendation, then approve the order in Sodam
- Validation
- 90% recommendation accuracy*
*Verified by our in-house lab on real customer data
Tomorrow's order · GangnamAI pick
Recommended18 itemsAuto-calculated
Expected demand+12%Weekend effect
Stockout risk2 itemsNeed order
ItemStock · DemandRec.
Chicken breast12 box
Sauce A8 box
Containers6 box
Saturday sales +18%No delivery scheduled
Send order to Sodam02 · MEETING NOTE
AI Meeting Notes
Summarizes sales consultations and operations meetings, then structures decisions, owners, and deadlines. Meeting records don't end as documents; they turn into real follow-up tasks.
- Input
- Meeting recordings, notes, attendees, and agenda
- Output
- Key summary, decisions, to-dos by owner
- Next step
- Added to the HQ operations calendar and staff task lists
Operations meeting · Oct 1 · 42 minSummarized
Key summary
Discussed the store training schedule ahead of the new menu launch and how to respond to the sales drop at Gangnam #3.
Decisions
- New menu training set for week 2 of October
- Prioritize the Gangnam #3 operations check
To-dos by owner
- KVisit and inspect Gangnam #3
- TDistribute new menu training materials
- LConfirm Sauce A supply schedule
3 follow-up tasks created · Owners notified
03 · AI REPORT
AI Operations Reports
Turns period performance, key changes, at-risk stores, and recommended actions into an easy-to-read report. Generate an executive summary and detailed views for operations staff from the same data.
- Data analyzed
- Unified Sales, operations metrics, AI signals, and resolution results
- Output
- Key summary, causes of change, store rankings, recommended actions
- Next step
- Share weekly and monthly reports and track follow-up tasks
Weekly ops report · Week 4, SepAI-generated
Total sales₩1.28B▲ 8.4%Watch store3 storesNeed reviewActions8 actionsAssigned
This weekLast week
MonTueWedThuFriSatSun
- Gangnam #3−14.8%
- Pangyo−6.2%
- Hongdae−3.9%
04 · SALES SIGNAL
AI Sales Decline Forecast
Detects changes that break from a store's normal sales pattern, not just week-over-week swings. It narrows down causes by store, hour, sales channel, and menu item so you know where to look first.
- Data analyzed
- POS and delivery app sales, order count, average ticket, menu mix
- Output
- Decline signals, contributing factors, most-affected hours and channels
- Next step
- Creates staff check tasks and root-cause reports
Gangnam #3 · Sales anomalyCaution
−14.8%vs. forecast · Last 3 weeks
Expected rangeActual sales
9/89/159/229/29
Drivers of decline
Lunch delivery orders−32%
Avg. order−6%
Dinner dine-in sales+3%
AIFewer lunch delivery orders account for 68% of the decline. We recommend checking delivery channels first.
05 · STORE RISK
AI Store-Closure Risk Forecast
Combines long-term shifts in sales and operations to identify stores that need focused attention. Instead of reducing risk to a single number, it shows the factors and trends driving it.
- Data analyzed
- Long-term sales trends, operating history, contract and management metrics
- Output
- Risk level by store, trend over time, key drivers
- Next step
- Supervisor visit priorities and suggested checklist items
Store-closure risk · PangyoMonthly
72Risk score
Caution
Risk score rose from 48 → 72 over the last 3 months.
- Sales trendDown 12 weeks straightHigh
- Order frequency−24% vs. last monthNormal
- Customer rating4.3 → 3.6Normal
Action · Supervisor visit within 1 weekCreate check task
06 · PURCHASE ANALYSIS
AI Off-Catalog Purchasing Analysis
Compares HQ-supplied items with outside purchases and sales flow to spot unusual buying patterns and potential cost leakage. Quickly narrow down which items and stores need a closer look.
- Data analyzed
- Order, receiving, inventory, sales, and per-supplier purchasing data
- Output
- Off-catalog share, variance by item, unusual purchasing patterns
- Next step
- Cost and inventory checks, review requests to assigned stores
Off-catalog analysis · Sep2 anomalies
Off-catalog share12.4%▲ 3.1%p
Amount to review₩12.4M4 stores
HQ supplyOff-catalog
Meat28%
Sauces16%
Packaging9%
- !Pangyo · SaucesOutside purchases up 41% from last month
- !Hongdae · PackagingSales unchanged, but no HQ orders for 3 weeks
07 · REVIEW RESPONSE
AI review replies
Understands each review's rating and key points, then drafts a reply that fits your brand's response guidelines and tone. Cuts the time stores spend on repetitive replies while keeping response quality consistent.
- Input
- Star ratings, review text, store and brand response guides
- Output
- Review summary, sentiment, tailored reply draft
- Next step
- Post the reply to the channel after staff review
Customer review · Delivery app · Gangnam★★★★☆
It arrived 30 minutes after I ordered and the chicken was still warm and crispy. They packed plenty of sauce too, and my kids loved it. The only downside was that the bag came a little torn.
AIReply draft
Thank you so much for your review! We're glad your order reached you warm and crispy. We'll look into a sturdier bag right away. We'll make sure your next order is just as good.
Brand tone applied · Addresses complaintReview & post
03 — Customer intelligenceReviews and customer feedback
Reviews and customer feedback
become operating data too
Collects customer reactions from multiple delivery channels by store and brand, classifies recurring feedback, and turns it into store improvements and consistent customer service.
Unified delivery reviews
Brings reviews, ratings, timestamps, and reply status from every delivery app into one place. Track rating changes and unanswered reviews by store, and compare customer reactions across channels on the same basis.
- Data checked
- Ratings · review text · timestamp · reply status
- Use
- Track reputation changes and missed replies by store
Customer feedback analysis
Classifies recurring feedback by type, such as taste, packaging, delivery, and service. It finds issues that rose at specific stores or periods and suggests improvements for operations staff to tackle first.
- Data analyzed
- Reviews · CS · keywords · sentiment · frequency
- Use
- Spot recurring issues and prioritize store improvements
04 — How it works
How data turns into action
- 01
Connect data
Connects POS, delivery app, order and inventory, and sales, opening, and operations data according to user permissions.
- 02
Learn patterns
Organizes sales flow, recurring cycles, and drivers of change by store and product into a form the model can learn from.
- 03
Detect & predict
Detects unusual changes and calculates future demand or risk.
- 04
Explain why & what to do
Shows not just the result but the key factors behind it and the data worth checking.
- 05
Act after review
Your team reviews and approves recommendations, turning them into real work like orders, reports, and customer replies.
05 — Data sourcesWhat data
What data
does it analyze?
FDAM scopes analysis around the data your brand actually has and can connect. What matters isn't how much data you have, but whether it's organized on a consistent basis and tied to business context.
POS, special venues, delivery apps
Orders, receiving, shipping, inventory, product, and supplier data
Franchise sales, contracts, opening schedules, operations checks, and communication history
Reviews, CS, campaigns, and other connectable unstructured data
06 — For every role
Different answers for different roles
Executives
Summarize brand-wide performance and at-risk stores, and pinpoint the business issues to address first.
Operations managers
Connect early signs of sales decline, inspection history, and store issues to decide next steps.
Purchasing & SCM
Reduce stockout and overstock risk using demand forecasts and order and inventory flow.
Store owners
Check AI-recommended order quantities and sales and inventory alerts in Sodam, then act in a few taps.
07 — AccuracyHow do you verify
How do you verify
AI accuracy?
Forecast accuracy varies by feature, item, store, training period, and data quality. FDAM validates accuracy by comparing the AI's recommended order quantities against actual sales and inventory outcomes, and keeps checking the same way by store and item after rollout.
08 — Security & governanceAI should work within
AI should work within
your existing permissions
HQ and stores, departments, and individual staff can each see different data. FDAM AI can be configured to use only the data each user needs, based on your existing user permissions and audit framework.
Permission-based access
Limits viewable data by each user's role and store scope.
Human final approval
Critical actions like orders or external replies are reviewed and decided by your staff.
09 — FAQFranchise AI
Franchise AI
FAQ
What is a franchise AI platform?
It's an AI system built for franchises that analyzes sales, order, inventory, franchise sales, and operations data together to forecast demand and risk and recommend next tasks. FDAM is an AI operations platform for franchises that builds this AI into both the HQ ERP and Sodam, the store app, so predictions lead straight into ordering, inspection, and reporting work.
How is FDAM AI different from general AI tools?
General-purpose AI tools answer questions. FDAM AI directly reads the POS and delivery app sales, order, inventory, and HQ operations data connected to FDAM, and alerts you first when something changes. Results are acted on right inside FDAM, as order approvals in Sodam, supervisor inspection tasks, and operations reports.
Do we need to replace our existing POS or ERP?
Not necessarily. FDAM first looks at connecting your existing POS and ERP. After reviewing how your current systems can integrate and what data is available, we design the right approach, such as API integration or file-based collection.
How is AI order recommendation accuracy verified?
We compare FDAM AI's recommended order quantities against actual sales and inventory outcomes. Because accuracy varies by store, item, training period, and data quality, we keep checking the same way after rollout. Our in-house lab verified 90% recommendation accuracy on real customer data.
Does AI order recommendation place orders automatically?
By default, FDAM AI proposes a recommended order first, and the store owner reviews and approves it in Sodam. The actual level of automation is set to match your brand's policies.
Can we start without much data?
Yes. FDAM first assesses the time span and quality of the data you have, and you can start step by step with rule-based recommendations or a limited-scope model.
Are all AI features available right away?
Availability and requirements can differ by FDAM AI feature. During consultation, we clearly distinguish features that are available now, in beta, custom-built, or coming soon.
START WITH YOUR DATASee what AI can do
See what AI can do
with your data.
We'll propose a realistic rollout scope based on the data you have and the problems you need to solve first.
Talk to sales about AI →