Inventory Intelligence (Preview)

Order qty, reorder points, overstock and cash impact for smarter inventory buys.

This connector is available in the following products and regions:

Service Class Regions
Copilot Studio Premium All Power Automate regions except the following:
     -   US Government (GCC)
     -   US Government (GCC High)
     -   China Cloud operated by 21Vianet
     -   US Department of Defense (DoD)
Logic Apps Standard All Logic Apps regions except the following:
     -   Azure Government regions
     -   Azure China regions
     -   US Department of Defense (DoD)
Power Apps Premium All Power Apps regions except the following:
     -   US Government (GCC)
     -   US Government (GCC High)
     -   China Cloud operated by 21Vianet
     -   US Department of Defense (DoD)
Power Automate Premium All Power Automate regions except the following:
     -   US Government (GCC)
     -   US Government (GCC High)
     -   China Cloud operated by 21Vianet
     -   US Department of Defense (DoD)
Contact
Name Saifs AI
URL https://saifs.ai/
Email taher@saifytech.com
Connector Metadata
Publisher Saifs AI
Website https://saifs.ai/
Privacy Policy https://saifs.ai/privacy_policy/inventory-intelligence
Categories AI;Data

Inventory Intelligence & Optimization - Saifs AI

Inventory Intelligence & Optimization - Saifs AI helps purchasing and operations teams decide what to buy, how much, and when. You send a stock list (and sales history when available), and the server returns order quantities, reorder points, overstock flags, cash tied up in inventory, and a buying list ranked by money impact.

It is built for businesses that run inventory from spreadsheets or mid-market systems—not enterprise planning suites—and need clear, repeatable buying decisions they can act on today.

Publisher: Saifs AI
Website: https://saifs.ai
Sign up / Get started: https://saifs.ai
Contact us / Help: https://saifs.ai/contact-us
Privacy: https://saifs.ai/privacy_policy/inventory-intelligence
Terms: https://saifs.ai/eula
Learn more: https://saifs.ai/inventory-intelligence
Authentication: OAuth 2.0

Why choose us

Deterministic results

The reason to use Inventory Intelligence & Optimization - Saifs AI is that buying math is deterministic. The same stock list (and sales history), with the same settings, produces the same order quantities, reorder points, overstock flags, and money-ranked buy list. Results are computed from published inventory models. They are not generated as a new narrative each time you ask.

That matters in Microsoft Copilot Studio because purchasing and finance act on cash and units. They need a recommendation they can rerun, audit, and defend — the same way an operations researcher sizes a reorder point, not the way a language model guesses how much to buy.

How we keep results deterministic

Each layer is established inventory-science research, applied to your stock and demand — not a newly invented model. The same class of math has been used for decades by researchers and practitioners who needed a reproducible number for what to buy, how much, and when.

What it does Established method In use since
Recommends how much to order when order cost and holding cost matter Economic order quantity (Harris) 1913
Sizes one-shot, seasonal, or perishable buys so you cover demand without excess spoil Newsvendor / critical-fractile model (Edgeworth) 1888
Sets when to reorder and how much buffer to hold for a service level Safety-stock and reorder-point models 1950s
Flags overstock and understock ranked by cash tied up or at risk Exception reporting on those optimal bands Standard inventory control
Respects real purchasing constraints (open POs, MOQ, case packs, budget) Constraint-aware application of the same formulas Standard purchasing practice

Every layer above has been published, cited, and run against real stock books for years. This MCP applies those models to your SKUs and turns the output into a buy list ranked by money impact. Ask again with the same file and settings, and you get the same recommendations.

In this product, deterministic means a fitted, reproducible score: the same input returns the same output. Thin demand history is reported as a gap rather than invented precision.

For administrators

This section is for Microsoft 365 and Copilot Studio administrators who decide whether to enable Inventory Intelligence & Optimization - Saifs AI for the tenant, who may connect it, and how it should be used across the organization.

Core value for the organization

Most teams still reorder from habit or a static reorder level that is wrong for half the year. Overstock locks cash and can spoil; understock loses sales. The classical math has existed for more than a century, but it usually lives in ERP modules many mid-market and SMB teams never deploy.

Enabling this MCP gives the organization one approved way to answer what to buy, how much, and when — inside Copilot Studio agents that makers already build. It turns a stock list (and sales history when available) into order quantities, reorder points, overstock flags, and a buying list ranked by money impact.

You are not buying SAP APO. You are giving every enabled maker the same defensible inventory-math stack, so purchase recommendations are consistent across agents, teams, and buy meetings.

How the business benefits

Organization need What this MCP provides
Stop stockouts on the items that matter Reorder points and safety stock at a chosen service level (default 95%)
Free cash stuck in slow movers Overstock flags ranked by cash tied up
Buy enough for a festival or perishable window without guessing Newsvendor-style quantities for short selling periods
Spend a limited purchase budget on the highest-impact orders Money-ranked buy list, optionally grouped by supplier
Avoid double-buying stock already on the way on_order is subtracted when you provide open POs
One method for the whole tenant Deterministic models: the same file and settings produce the same buy list every time
No operations-research team required Makers attach the MCP and a stock CSV / ERP / Tally export
Control who can run it Makers authenticate with OAuth 2.0. Only people you allow to create the connection can call the tools
Lower operational risk than free-form Copilot quantities Recommendations come from EOQ, safety stock, and newsvendor math, not from a language model inventing order qty

Why this is an administrator decision

Makers will otherwise ask Copilot to “tell us how much to order” from a spreadsheet. That produces a different story each time, cannot be audited, and cannot be compared week to week. Enabling this MCP replaces that with a governed connector:

  • You choose who can connect it. Tenant and environment permissions still apply. Connection requires a Saifs AI account and OAuth 2.0 sign-in.
  • You keep a system of record. This MCP recommends and explains. It does not place purchase orders or write back to ERP, Tally, or Dataverse. Warehouse systems remain the source of truth until your organization chooses a separate integration.
  • You can defend the number. Order quantities and reorder points can be rerun on the same file. Missing inputs are reported rather than silently assumed to be perfect.
  • You scale makers, not models. One enablement decision serves purchasing managers, store owners, D2C ops, spare-parts planners, pharmacies, and finance leads who already live in stock spreadsheets.

What administrators should expect after enabling

  1. Approved makers add Inventory Intelligence & Optimization - Saifs AI to an agent and complete OAuth sign-in (see Getting started).
  2. End users provide a stock list with at least sku. On-hand, unit cost, sales history, lead times, and on_order make recommendations sharper.
  3. The agent returns order quantities, reorder points, and a money-ranked buy list. Managers still place POs in their ERP or with suppliers; the MCP does not place those orders for them.
  4. For catalogs larger than about 200 products or 2,500 sales rows, makers use inventory_optimizer_get_engine in a numpy sandbox so large files do not pass through the chat as tokens.

If you enable this MCP, you are standardizing how the organization decides what to buy this week — with a reproducible recommendation that leadership can stand behind.

Who should use this

Use Inventory Intelligence & Optimization - Saifs AI if you own or influence stock and purchase decisions:

Role How it helps
Purchasing / inventory managers (distributors, wholesalers) Nightly-style answers: which SKUs to reorder now, which slow movers are tying up cash, and a supplier-grouped buy list.
Retail and store owners Replace gut reorder levels with quantities and reorder points grounded in demand and lead time.
E-commerce / D2C operations Size seasonal and campaign buys (for example festival windows) instead of guessing order quantities.
Manufacturers / spare-parts planners Balance order cost vs. holding cost and set safety stock for critical parts.
Pharmacy and perishable goods buyers Expiry-aware (newsvendor-style) order quantities so you buy enough to cover demand without excess spoil.
SMB owners and finance leads See cash stuck in overstock and prioritize purchases when budget is limited.
Copilot Studio / Microsoft 365 Copilot makers Add inventory math tools to agents so makers and end users get explained buying recommendations from CSV or ERP exports—without building the formulas themselves.

Best fit: distributors, wholesalers, retailers, D2C/e-commerce, manufacturers, and pharmacies that hold stock and decide order quantity and timing regularly.

Not required: SAP APO, Oracle Advanced Planning, or similar enterprise modules. A CSV (or similar) stock file—and preferably sales history—is enough to get started.

How it is useful

Most teams still reorder from habit or a static reorder level that is wrong for half the year. Overstock locks cash and can spoil; understock loses sales. The classical math (economic order quantity, safety stock, newsvendor for perishables) has existed for decades, but it is usually locked inside expensive ERP modules that many mid-market and SMB teams never deploy.

Inventory Intelligence & Optimization - Saifs AI makes that math available in Copilot and agent experiences:

  • What to order and how much — recommended order quantities (including EOQ-style economics when cost inputs are available).
  • When to reorder — reorder points with safety stock for a chosen service level (default 95%).
  • What is wrong in the warehouse — overstock and understock flagged and ranked by cash impact.
  • What to buy first under a budget — a purchase queue sorted by money, optionally grouped by supplier.
  • Perishables and short selling windows — quantities that balance stockout cost vs. leftover / salvage cost.
  • Respect real purchasing constraints — open purchase orders (on_order), MOQ, case packs, max order qty, and price breaks when you provide them.

Only sku is required to call the tool. Stronger inputs (unit_cost, on_hand, on_order, demand history, lead times) produce sharper recommendations; the tool reports what was missing rather than blocking the call.

Use cases

1. Daily / weekly buying list

Scenario: A distributor purchasing manager pastes or uploads on-hand stock (and optional sales history).
Outcome: A ranked buy list—what to order today, how much, and which SKUs are already covered by open POs—so you do not double-buy stock that is already on the way.

2. Reorder alerts and stockout prevention

Scenario: Lead times and service levels vary by SKU (or you use defaults).
Outcome: Reorder points and safety stock so the agent can say clearly which items are below the reorder point and should be ordered before you run out.

3. Free cash from overstock

Scenario: Slow movers sit for months while fast movers stock out.
Outcome: Overstock flags with cash tied up in excess inventory, ranked so finance and purchasing can cut or pause buys on the worst offenders first.

4. Budget-constrained purchasing

Scenario: You have a fixed purchase budget for the week or month.
Outcome: Recommendations prioritized within purchase_budget so limited cash goes to the highest-impact orders first.

5. Seasonal and campaign buys (e-commerce)

Scenario: Festival or promotion window with a limited selling period.
Outcome: Newsvendor-style order quantities using sale price, cost, salvage, and demand uncertainty—better than “order what we sold last year.”

6. Pharmacy and perishable ordering

Scenario: Items expire or have a short shelf life.
Outcome: Order quantities that account for shelf life / selling window and leftover value, reducing spoil while still protecting availability.

7. Supplier-grouped POs

Scenario: Many SKUs from the same vendor; you want one PO per supplier.
Outcome: Buying list grouped by supplier, with MOQ and case-pack rounding applied when provided.

8. Compare habit vs. recommendation

Scenario: You already order a fixed quantity every cycle.
Outcome: Pass current_order_qty to see how today’s habit compares with the recommended quantity and cost trade-off.

9. Large catalogs (sandbox path)

Scenario: More than about 200 products or 2,500 sales rows.
Outcome: Use inventory_optimizer_get_engine to run the same math locally via a returned script; data stays on the user’s machine for that path.

Supported operations (tools)

Tool When to use
inventory_optimizer About 200 products or fewer and about 2,500 sales rows or fewer. Send stock rows (and optional demand history); get full order / overstock / cash results.
inventory_optimizer_get_engine Larger files. Returns a ready-to-run Python script (numpy only). Save and run it unchanged against the user’s file; report printed decisions and the detail JSON.

Typical inputs

Stock (items) — recommended fields

Field Purpose
sku Product code (required)
name, category Labels for readable results
unit_cost, on_hand Costing and coverage
on_order Units already ordered (avoids rebuying in-transit stock)
backorders Units already promised to customers
annual_demand Optional if you do not send sales history
order_cost, holding_cost EOQ economics (holding cost can default to ~25% of unit cost)
lead_time_days, lead_time_std Reorder point and safety stock
service_level Target in-stock fraction (default 0.95)
sale_price, perishable, shelf_life_days, salvage_value Perishable / seasonal math
supplier, moq, case_pack, max_order_qty, price_breaks Purchasing constraints
current_order_qty Compare recommendation vs. current habit

Demand history (optional): rows with sku, date (or sale_date), and quantity (or sold_qty / qty / units_sold).

Useful options: currency / currency_symbol (display only; no FX conversion), as_of, purchase_budget, group_by_supplier, optional demand-forecaster handoff records in forecasts.

Typical outputs

  • Recommended order quantities and reorder points
  • Safety stock for the chosen service level
  • Overstock / understock flags ranked by cash impact
  • Buying list sorted by money (optionally by supplier)
  • Notes on missing or thin inputs so recommendations stay honest

Prerequisites

  • Access to Microsoft Copilot Studio, Microsoft 365 Copilot, Azure Foundry, or another host that can call this MCP server
  • A Saifs AI account authorized for Inventory Intelligence & Optimization - Saifs AI
  • Ability to complete OAuth 2.0 sign-in when connecting the MCP server
  • Stock data with at least a SKU per item (CSV/export from ERP, Tally, QuickBooks, Shopify, POS, or spreadsheet)
  • Prefer also: on-hand qty, unit cost, and sales history for sharper demand and cash results

Authentication (OAuth 2.0)

This MCP server uses OAuth 2.0. Users and makers authenticate with Saifs AI credentials through the standard OAuth consent flow when they connect the server in Copilot Studio or Microsoft 365 Copilot.

Administrators and makers do not create API keys manually. When you create a new connection, you are redirected to Saifs AI to sign in. After successful login, the connection is created and the agent can call MCP tools under that account.

Getting started

  1. Connect Inventory Intelligence & Optimization - Saifs AI in your Copilot Studio or Microsoft 365 Copilot experience.
  2. Complete the OAuth 2.0 sign-in / consent when prompted.
  3. Provide a stock list (paste, upload, or agent-accessible file). Include on_order when purchase orders are already open.
  4. Ask in plain language, for example:
    • “What should I order this week?”
    • “Which SKUs are overstocked and how much cash is stuck?”
    • “Reorder points for these items at 99% service level.”
    • “Buy list under a ₹5L / $5,000 budget, grouped by supplier.”
  5. Act on the ranked decisions; treat results as decision support—validate critical SKUs against supplier constraints and business policy before placing POs.

Known issues and limitations

  • Active account required. An active Saifs AI account with access to Inventory Intelligence is required. Tools cannot be called until OAuth 2.0 sign-in completes successfully on the connection.
  • Directional decision support, not an ERP replacement. Recommendations guide purchasing; they do not place orders or replace warehouse systems of record.
  • Thin demand history. Very short or sparse sales history weakens safety stock and demand estimates; the tool may fall back to defaults or report gaps instead of inventing precision.
  • Holding cost is often approximate. If you omit holding cost, a default rate (about 20–30% of unit cost per year) is used. Adjust when your true carrying cost differs.
  • on_order matters. If open POs are omitted, the buy list may suggest reordering stock that is already in transit.
  • Currency is a display label only. No foreign-exchange conversion between currencies.
  • Size limits for the direct tool. Prefer inventory_optimizer for roughly ≤200 SKUs and ≤2,500 demand rows; use inventory_optimizer_get_engine for larger catalogs.
  • MOQ / case pack / max qty only apply when you supply them; otherwise quantities are unconstrained by supplier packaging rules.
  • Multi-warehouse and custom policy engines are out of scope for the core tools described here unless your deployment adds them separately.
  • OAuth session required. Tools will not run until OAuth 2.0 connection succeeds; expired tokens may require the user to reconnect or refresh.

Support and feedback

Inventory Intelligence & Optimization - Saifs AI is published by Saifs AI.

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Inventory Intelligence

Order qty, reorder points, overstock and cash impact for smarter inventory buys.

Inventory Intelligence

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InvokeMCP

Order qty, reorder points, overstock and cash impact for smarter inventory buys.