Inventory planning software is a different category from inventory counting software, and most comparison pages blur the two. Counting tools tell you what you hold. Inventory planning software forecasts demand, applies supplier lead times and proposes purchase orders. This page compares seven ways to get that capability, and every option ends with a line on who it is not for.
What this page says that ranking pages do not: the deciding factor is rarely the forecast algorithm. It is whether your supplier lead times, SKU mapping and stockout history are clean enough for any algorithm to work, and what it costs you to get them there. Each entry below states that switching effort plainly.
The reader here is an operator on Shopify Plus or a paid subscription platform, past $3M in revenue, with enough SKUs that a person can no longer hold the reorder logic in their head. If you sit below that floor, a spreadsheet and a weekly stock count will serve you better than any tool on this list.
What does inventory planning software actually do that stock-counting software does not?
Planning software produces a number you can act on: how many units of each SKU to order, from which supplier, by which date. Counting software produces a record of what happened. The gap between them is a forecast, a lead time and a rule for how much buffer to hold.
The arithmetic underneath is short. In an illustrative case, a SKU sells 12 units a day, the supplier takes 30 days to deliver, and you want 5 days of buffer. Reorder point is 12 × 30 = 360, plus 12 × 5 = 60 of safety stock, so 420 units. Planning software runs that per SKU, per location, every day.
The lead time and the buffer rule are also where a plan breaks. Change the lead time from 30 to 45 days because a supplier slipped, and the reorder point moves by 180 units in this example. If the tool still holds the old lead time, you place the order late and find out at the stockout.
For the counting side of the problem, see ecommerce inventory software. For the forecasting method itself, demand and inventory planning covers how demand signals turn into reorder decisions. This page assumes you have read neither and only need to choose.
What data must be clean before any tool forecasts well?
Four inputs decide the quality of every recommendation, whichever vendor you pick.
- Sales history by SKU, corrected for stockouts. Days when a SKU was unavailable understate demand.
- Supplier lead times per supplier and per SKU, as they actually ran, not as the supplier quotes them.
- Minimum order quantities and pack sizes. A tool that recommends 173 units from a supplier who ships in cartons of 24 has produced a number nobody can order.
- A single SKU identity across channels. If the same product carries different SKUs on Shopify and on a marketplace, demand is split and every forecast is low.
Fix these before you trial anything. It is the cheapest work in this whole decision, and it is the work vendors are least likely to mention in a sales call.
Which inventory planning tools are worth comparing?
The seven options cover the realistic range for a $3M–$30M brand: purpose-built planning apps, a supply-chain tool for larger catalogues, operations suites with planning inside them, enterprise planning platforms and the spreadsheet you already have. Pricing changes often and is packaged differently by each vendor, so this page gives none. Check each vendor’s own pricing page.
| Option | Built around | Switching effort | Not for |
|---|---|---|---|
| Inventory Planner | Forecasting and purchase orders for Shopify-centred stores | Low to medium; depends on data cleanliness | Teams that need warehouse-level control |
| Netstock | Planning and exception alerts on top of an existing system | Medium; needs an integration to your ERP or platform | Shopify-only brands with a small catalogue |
| Cogsy | Planning for consumer brands, with cash-aware buying | Low to medium | Brands with complex multi-warehouse routing |
| Prediko | Shopify-native planning and stock alerts | Low | Brands selling mainly off Shopify |
| Cin7 | Operations suite with inventory, orders and some planning | High; it becomes the system of record | Teams that only want a forecast layer |
| Brightpearl | Retail operations platform with planning inside it | High; it replaces several systems | Brands that want to keep their current stack |
| Enterprise planning (RELEX, Lokad and similar) | Statistical planning for large, complex catalogues | High; project-scale implementation | Anyone below the catalogue scale that justifies a project |
Take from the table the switching-effort column first. The lowest-effort options add a planning layer over what you already run, and the highest-effort options replace your system of record. Which you need is set by the state of your current stack, not by preference.
Capabilities and packaging in this market change. Each entry describes what the product is built around and what to verify; where a feature is not something we can state with certainty, we say where to check.
1. Inventory Planner
Inventory Planner is a purpose-built forecasting and purchasing app that connects to Shopify and other selling channels. Its core job is turning sales history and lead times into a recommended purchase order per supplier. That narrow focus is the reason to shortlist it: the whole product is the planning layer.
What to check in a trial: how it treats products with short history, whether it adjusts for out-of-stock days, and how it handles bundles. Ask to see one bundle run end to end. Also check how purchase orders leave the tool, since a recommendation you retype into an email is a manual step you have not removed.
Switching effort is usually low to medium. The connection is quick; the slow part is loading accurate lead times and minimum order quantities per supplier. Budget the time for that, not for the install.
Who this is not for: teams who need bin-level warehouse control, pick paths or receiving workflows. It plans purchases; it is not a warehouse system, and you would still need one.
2. Netstock
Netstock is a planning and exception-alerting tool designed to sit on top of an existing business system rather than replace it. It is aimed at businesses with meaningful catalogues, multiple suppliers and an ERP or accounting system already in place. Its strength is telling a buyer which items need attention today.
The trade-off is integration weight. Because it reads from another system, its recommendations are only as good as that feed. Ask which systems it connects to natively, and what the refresh cadence is. Metric to confirm: how often stock and sales data actually update inside the tool.
Switching effort is medium, driven mostly by the integration and the item-master clean-up. Expect an implementation conversation, not a self-serve install.
Who this is not for: a Shopify-only brand with a modest catalogue and no ERP. The setup effort will exceed the value, and a Shopify-native planner will be faster to run.
3. Cogsy
Cogsy positions itself around planning for consumer brands, with attention to cash tied up in stock as well as units. That framing matters at $3M–$30M, where the constraint is often working capital rather than warehouse space. A tool that shows what a purchase order does to your cash position changes how buyers argue about order size.
Check how it handles demand you know is coming, such as a planned promotion or a new-product launch. A pure history-based forecast cannot see either. The useful question is whether a human can enter a known event and have it flow into the recommendation.
Switching effort is low to medium. Data preparation is the same as everywhere: lead times, minimums, SKU identity.
Who this is not for: brands with complex multi-warehouse routing, where stock moves between several locations under rules the planner has to model. Confirm multi-location support against your actual network before you commit.
4. Prediko
Prediko is a Shopify-native planning and stock-alert app. Being built inside the Shopify ecosystem means a short path from install to first recommendation, and it suits a team whose demand comes almost entirely from one Shopify store. Fewer moving parts means fewer places for the data to drift.
The limit is the same as the strength. If a meaningful share of your sales comes from marketplaces or wholesale channels outside Shopify, ask exactly how that demand is brought in. Demand that the tool cannot see is demand it cannot forecast, and you will under-order the SKUs that sell elsewhere.
Switching effort is low. It is the quickest option here to trial on a real catalogue.
Who this is not for: brands that sell mainly off Shopify. For that shape, read multi-channel inventory management software first, because the channel problem comes before the planning problem.
5. Cin7
Cin7 is an inventory and operations platform that covers stock, orders and purchasing, with planning capability inside a wider suite. The case for it is consolidation: one system of record for stock instead of a stack of connected apps. The case against is that it becomes the centre of your operation, and moving the centre is a project.
We claim no specific Cin7 planning feature here. Before you shortlist it for planning, ask the vendor to demonstrate forecasting, reorder recommendations and lead-time handling on your own data, and confirm which parts sit in your plan tier.
Switching effort is high. You are changing where stock is recorded, which touches order flow, fulfilment and finance.
Who this is not for: teams who only want a forecast layer. If your current stock system works and only the buying is broken, replacing the whole system to fix buying is the wrong-sized answer.
6. Brightpearl
Brightpearl is a retail operations platform aimed at merchants outgrowing a stack of separate tools. Planning is one part of a wider system that also handles orders, inventory and accounting connections. That breadth is the reason to consider it when several disconnected systems cause most of your errors.
As with any suite, check that the planning depth matches a dedicated planner on the things you care about: forecasting method, lead-time handling, bundles, purchase-order output. A suite that does ten things adequately may do the one thing you bought it for less well than a specialist.
Switching effort is high, because it replaces several systems at once and needs a real migration plan.
Who this is not for: brands that want to keep their current stack. If your order, fulfilment and finance tools are working, a suite is a large change made to fix a small problem.
7. Enterprise planning platforms (RELEX, Lokad and similar)
Enterprise planning platforms such as RELEX and Lokad use probabilistic and statistical forecasting across very large, complex catalogues. They exist for retailers and distributors whose planning problem is genuinely large: thousands of SKUs, many locations, promotions, perishables or long supply chains.
At $3M–$30M, this category is mostly a poor fit, and you should know that before a sales conversation flatters you into a scoping call. Implementation is project-scale, usually needs data engineering time, and the ongoing cost of feeding it clean data is real.
Switching effort is the highest on this list, measured in project time, not install time.
Who this is not for: anyone below the catalogue scale that justifies a project. If you cannot name the SKU-and-location complexity that a purpose-built planner fails at, you do not need this tier.
What about a spreadsheet plus Shopify exports?
A spreadsheet is a legitimate option and it is the honest baseline every paid tool has to beat. For a small, stable catalogue with two or three suppliers, a weekly export and a reorder-point column will do the job. The cost is one person’s time and one person’s memory.
A spreadsheet plus Shopify exports fails in predictable ways. The export goes stale. The person who built the formulas leaves. Lead times live in someone’s head. A bundle sells and nobody explodes it into components. None of these show up as an error; they show up as a stockout three weeks later.
Who this is not for: a brand with many SKUs, several suppliers with different lead times, or more than one person making buying decisions. The moment two people edit the same file, version drift begins.
How do you choose between them?
Choose by the state of your data and your current stack, not by feature count. If your system of record is fine and only buying is broken, pick a planning layer: Inventory Planner, Cogsy or Prediko for Shopify-centred brands, Netstock if you run an ERP. Pick a suite only when the systems themselves are the fault.
Three questions settle most decisions.
Where does demand come from? One Shopify store points to a Shopify-native planner. Several channels point to a planner with proven channel ingestion, or to fixing the channel problem first.
How good are your lead times? If they are guesses, no tool helps. Spend two weeks recording actual order-to-receipt days per supplier and the forecast improves more than any purchase would.
Who acts on the recommendation? A recommended purchase order that a buyer retypes into a supplier email, a spreadsheet and an accounting system is three manual steps. The value of planning software is capped by how much of that hand-off it removes.
What should you test in a trial?
Run a backtest on your own history. Take 20 to 30 SKUs with known outcomes from last season, including a few that stocked out and a few that overstocked, and see what the tool would have recommended at the time. A demo on the vendor’s sample data proves nothing about your catalogue.
Then test the awkward cases deliberately: one bundle, one product with under a season of history, one SKU that sells on two channels, one supplier with a minimum order quantity. These are where tools diverge, and they are the SKUs that cost you money.
What breaks after you switch?
Three things break most often, and none is a software fault.
Lead times drift and nobody updates them. A supplier moves from 30 to 45 days, the tool still assumes 30, and recommendations run late without any warning. Assign an owner and a review date for supplier lead times.
Stockout days pollute the forecast. If the tool treats out-of-stock days as zero demand, it learns that the product is slow and orders less of it, which causes the next stockout. Confirm the correction is on.
Recommendations get ignored. A buyer who does not trust a number overrides it, and then the tool is a dashboard nobody uses. Trust comes from showing the buyer the inputs, so make sure the tool exposes lead time, demand rate and safety stock per SKU rather than one opaque quantity.
Where does AI belong in inventory planning, and where does it not?
AI earns its place at scale: spotting demand patterns across thousands of SKUs, flagging exceptions a buyer would miss, and grouping new products with similar past ones to borrow their history. Those are jobs where a person cannot read every row.
AI does not belong in approving large purchase orders unsupervised, or anywhere the data underneath is unreliable. A wrong forecast on a big order costs real cash, and a human minute of review is cheap by comparison. If your SKU mapping or lead times are known to be wrong, an AI layer will produce confident numbers built on them.
AI discovery has a comparable limit. AI shopping assistants can surface your products, but for now the model is discover in AI, buy on site: OpenAI’s Instant Checkout in ChatGPT was withdrawn on 4 March 2026. Demand from AI referrals arrives through your own store, so it lands in the same sales history your planner reads.
Is inventory planning a software problem or an operations problem?
Inventory planning is an operations automation problem before it is a purchasing one. The forecast is the easy part; the hard part is the pipeline around it: clean SKU identity, accurate lead times, stock feeds from every channel and 3PL, and purchase orders that leave the tool without being retyped. Pointerflow works on exactly that hand-off layer as part of ops automation.
Sources
- No external figures are quoted. This article is written from standard reorder-point arithmetic (the worked example is illustrative) and general operating practice; check each vendor’s own site for current features and pricing.