Spreadsheet planning
Demand, lead times, and stock live in tabs one person maintains. When they are on holiday, the team guesses or copies last month's order.
Custom software for e-commerce operations
Turn replenishment from gut feel into order proposals the team can trust
We build inventory replenishment automation for e-commerce and wholesale teams that need order proposals, safety stock rules, and supplier constraints applied consistently, without a planning spreadsheet someone overwrites every Monday.
For operators who know bestsellers should not go out of stock while dead stock ties up cash, but the current plan lives in Excel with last week's lead times.
The pattern
A recommendation you override every week is a second opinion, not automation
The problem is not missing analytics. It is the gap between the suggested order and the purchase order actually sent: MOQ, bundles, warehouse allocation, and exceptions that never make it back into the sheet before the next planning meeting.
What a typical planning week looks like
Bestsellers out, dead stock in
Hero SKUs go out of stock during promotions while cash sits on variants that have not moved in two seasons. The imbalance shows up after the fact, not in the purchase decision.
Gut-feel reorders
Buyers override formulas because they know the supplier or feel a spike coming. Overrides are not logged and the same debate repeats every cycle.
Stale lead times
The sheet still assumes three weeks delivery. The supplier has been taking five for months. Safety stock was never recalculated.
MOQ and pack-size friction
The calculation says forty units. The supplier minimum is one hundred in cases of twenty-five. Someone rounds by eye and the warehouse receives the wrong economic order.
Multi-location blind spots
Stock split across warehouses or stores. The master sheet shows total coverage, but the location fulfilling most orders is the one hitting zero.
If the buyer overrides the sheet every week, the rules are not in the software yet. Tell us how you plan today.
The distinction
The problem is not another forecast chart. The problem is that nobody trusts the number enough to stop overriding it.
If planning software only suggests quantities, the buyer still has to apply MOQ, check bundle component demand, allocate across warehouses, and escalate exceptions. That is where work and stockouts go.
Spreadsheet and meeting workflow
- 1Export sales and stock
- 2Adjust by intuition and supplier memory
- 3Send PO when someone has time
Rules-based replenishment system
- 1Load demand, stock, and open POs from systems of record
- 2Apply safety stock and lead times per SKU or location
- 3Calculate order proposals with MOQ and pack logic
- 4Allocate across warehouses and bundle components
- 5Queue exceptions and approved POs to ERP or buyer
Order proposals can carry numbers and constraints, not just a chart. Let's talk about your replenishment model or book a call.
What we build
A replenishment layer on the inventory data you already have
We do not ask you to replace ERP or WMS. We build software that reads sales velocity, stock, and supplier master data, applies your reorder rules, and produces proposals or purchase orders the team can approve or automate.
Automation here means a proposal you would have sent anyway, with the numbers visible.
Possible integrations depend on the project. Below are examples of systems we can connect to. It is not a promise that every platform is already supported out of the box:
Commerce and ERP
- Shopify
- WooCommerce
- NetSuite
- Microsoft Dynamics
- Custom ERP
Inventory and WMS
- Warehouse systems
- Multi-location stock
- 3PL portals
- Internal stock databases
Planning inputs
- Sales history
- Open purchase orders
- Supplier lead times
- Promotion calendars
Outputs
- ERP purchase orders
- Buyer review queues
- Exception alerts
- Reporting exports
The implementation reflects SKU structure, supplier terms, and how much autonomy buyers want. If a system has a reliable API or export, it can usually be connected. If not, we say so in discovery.
Use cases
Planning work that can be proposed, not reinvented every week
Exact rules depend on catalog, suppliers, and approval model. These are the workflows we automate first because they are recurring and measurable.
Order proposals
Per SKU or variant, the system calculates suggested quantity from velocity, days of cover, and inbound stock, with the formula visible so buyers trust or correct with reason.
Safety stock rules
Minimum cover, seasonality factors, and ABC tiers apply consistently instead of living as conditional formatting in a spreadsheet tab.
Multi-warehouse allocation
Proposals respect which location fulfills which channel and transfer constraints, so the D2C warehouse is not treated as interchangeable with bulk storage.
Bundle and component demand
Kit sales explode into component requirements. The system flags component shortages before the bundle goes out of stock, not after assembly fails.
Supplier MOQ and pack logic
Minimum order, case packs, and price breaks adjust proposals before the buyer sees them, with clear notes when rounding up is required.
Exception escalation
Large swings, new SKUs with thin history, or suppliers with volatile lead times go to review instead of silently generating a PO.
These are the workflows we automate first. Which ones cost you stockouts or trapped cash today?
Architecture
How sales and stock become a purchase order proposal
Replenishment is explicit software: pull data, apply rules, calculate proposals, route exceptions, and log what was suggested versus ordered. Not a black-box forecast without audit trail.
Sources
Sales, stock, open POs, supplier master
Planning engine
Velocity, cover, safety stock, seasonality
Constraints layer
MOQ, packs, bundles, location rules
Target systems
ERP, WMS, buyer queue, alerts
Outcome
Approved PO, held proposal, or exception task
What the implementation typically includes
Data synchronization
Scheduled pulls from commerce, ERP, and WMS so proposals use current stock and inbound, not a forgotten export.
Configurable rules
Cover targets, lead times, and supplier terms live as explicit parameters buyers can inspect and change without editing formulas.
Proposal and approval flow
Buyers approve, modify with reason, or reject proposals. Overrides feed reporting so gut-feel adjustments are visible, not invisible.
Exception monitoring
Stockouts on A SKUs, proposals above threshold, and suppliers without updated lead times surface before the warehouse is empty.
We do not promise a universal forecast model for every catalog shape. Discovery defines which SKUs use statistical demand, which use manual parameters, and which stay fully human.
Economics
Why replenishment automation is worth engineering, when cash is tied up in the wrong SKUs
Stock is cash on the balance sheet. Every unit of dead inventory is money not available to buy what sells. Every stockout on a hero SKU is lost revenue plus support cost and disappointed customer acquisition. Custom automation is an investment against repeated planning labor and working capital trapped in misaligned purchases.
The ROI logic, without invented numbers
Working capital on dead stock
Consistent cover targets and reorder discipline reduce overbuy on variants that tie up cash for months without rotation.
Revenue lost to stockouts
Proposals that respect velocity and lead times on A SKUs reduce avoidable out-of-stock in normal and peak demand.
Buyer time on repeatable math
Hours on exports, pivots, and column recalculation can shift to exception review and supplier negotiation.
Fewer emergency freight purchases
When lead times and safety stock stay current, panic orders and premium freight to cover a spreadsheet error become less frequent.
Build vs buy
When custom replenishment software is the better economic decision
Sometimes a Shopify-native planning app is enough. If one store, one warehouse, and a homogeneous catalog fit a standard product, use it. Custom work is for when bundles, multi-location allocation, ERP POs, and supplier-specific constraints leave the buyer doing the real work in Excel anyway.
Off-the-shelf SaaS
- Predefined demand and reorder models
- Recurring subscription tied to SKU count
- Standard commerce integrations
- Limited bundle and component logic
- The buyer still adapts the process to the tool's assumptions
Custom implementation
- Built around your suppliers, MOQs, and locations
- Reads and writes through the ERP and WMS you already use
- Bundle, kit, and component explosion rules you define
- Approval paths and override logs finance can audit
- You own planning parameters and integration
- Optional maintenance after handover
Prediko or Fabrikator is often the honest answer for a single Shopify store under roughly a thousand homogeneous SKUs. We will say so if it fits. Custom work makes sense when the buyer still rebuilds the plan in a spreadsheet every week because the subscription only gave them another chart.
Engagement
How the work is delivered without betting the whole purchase operation on day one
Implementation can start with one supplier category or warehouse, then expand. You should see trusted proposals on a limited SKU set before extending replenishment logic to the full catalog.
- 01
Discovery
Catalog shape, locations, suppliers, current planning ritual, and what a good proposal means for your buyers.
- 02
Data and rules audit
We trace where sales, stock, lead times, and MOQs live today, including unofficial spreadsheet columns.
- 03
Model design
Cover targets, safety stock, bundle logic, and approval thresholds scoped to the first SKU set.
- 04
Build and integration
We connect sources and outputs with the credentials and PO workflow you designate.
- 05
Parallel run
Proposals run alongside the existing sheet. Buyers compare outcomes and tune rules before anything posts automatically.
- 06
Rollout and tuning
Expand SKU coverage, automate approved paths, optional maintenance when suppliers and seasons change.
We do not quote a universal timeline or fixed package price on this page. Duration follows integration depth, catalog complexity, and how many exception types the first release needs.
Start with one warehouse or supplier group. Expand when proposals match reality. Book a call.
Questions
Direct answers
Let's see where the planning spreadsheet still makes the real decisions
Bring catalog shape, location model, and a sample planning export. We will tell you what is realistically automatable, what stays in buyer review, and when a Shopify-native planner is the more honest recommendation.