Inventory optimization software for a chain of 2 to 50 stores

Ask for software that catches empty shelves and stock that does not sell, and the shortlist fills with replenishment suites. They are serious products, built to forecast demand for every product in every store and turn that forecast into orders.

marql does a different job. It reads the POS and ERP you already run and shows, store by store, where a shelf is empty and where cash is sitting in stock that does not sell. Then it prepares the decision and, later, checks whether it worked. This page puts the two side by side for a chain of 2 to 50 stores, including the cases where a suite is the better buy.

Who replenishment suites are built for

RELEX, SymphonyAI, ToolsGroup and invent.ai are the replenishment suites that come up most often. They are built for retailers with hundreds of stores, a distribution centre and a planning team. They forecast demand for every product in every store and turn the forecast into orders, which either go straight to the ERP or wait for a planner to approve them. Most of the customers they name run hundreds of stores, and their own estimates for implementation range from a few weeks to six months^{5, 6, 7, 8}.

We checked each vendor's site on October 1, 2026. This page leaves their prices out because they change, and each vendor publishes its own.

What the problem looks like at 2 to 50 stores

At this size, most stock-outs start inside the store. The largest worldwide study of the problem, published in 2002, pooled 40 earlier studies and traced 70 to 75 percent of empty shelves to store practice: orders placed too late or too small, and product that was in the building but not on the shelf^{1, 2}. A better forecast helps with the first cause. Neither gets fixed if nobody reads stock store by store every day.

In a chain of 20 stores that looks familiar. One store has an empty shelf on a product that the store down the road holds three months of. Another carries a slow line that ties up a few thousand euros, and nobody notices until the next count.

Both sides cost money. IHL Group estimates that out-of-stocks and overstocks together cost retailers $1.73 trillion a year, about 6.5 percent of global retail sales³. The data to see both is already there, in the POS and the ERP. What is missing is someone, or something, that looks every day, names the store, puts a number on the problem and later checks whether the fix worked.

A suite answers "how much should we order?" At 20 stores the more common question is "where is it going wrong today?"

What marql does with the same data

Every time stock syncs, marql checks each SKU in each store. If a SKU is at zero and sold in the last 30 days, it becomes an incident for that store. Incidents are ranked by the revenue at risk over a week, worked out from that store's own sales in the last four weeks. Part of that revenue walks out the door: in the same worldwide study, 31 percent of shoppers who found an empty shelf bought the item at another store, and 9 percent did not buy it at all¹.

For frozen cash, marql values the stock in each store that has not sold for 28 days. Positions holding more than 60 days of cover get marked as excess, and the figures stay per store instead of rolling up into a chain total.

For each finding, the options are the ones a buyer would weigh anyway: pause replenishment, promote or mark down, or move the stock to a store that sells it. marql drafts the transfer or the supplier order. The quantities come from buffer arithmetic, and the language model only explains them. A person confirms the draft before it leaves the screen as a CSV or XLSX file, and marql writes nothing back to the POS or the ERP.

Once the task is done, the Impact Ledger compares the store with at least four comparable stores, or with its own usual swings if the chain is too small for that. The comparison with peer stores is a difference-in-differences design, the method Card and Krueger used in 1994 to compare fast-food restaurants in New Jersey and Pennsylvania⁴. The baseline is frozen at the moment of the decision. The result is one of four verdicts: helped, no effect, worse or inconclusive.

All of this depends on stock figures per store from your POS or ERP. A connection that carries only sales can show revenue, but it cannot show you a shelf.

At a 20-store grocery chain in Romania, the first stock scan on September 23, 2026 found 792 SKUs holding more than about 90 days of cover. Read the story.

The playbooks on empty shelves and frozen cash work through both problems step by step.

Side by side

When a suite is the better buy

• You want the system to generate orders and send them to the ERP, and you have planners to run it.
• What holds you back is forecast accuracy across a large range and a distribution centre, more than spotting problems in the stores.
• You also need assortment, space or price optimization, and marql has none of those modules.
• You can carry an implementation project that runs for months.

When marql fits

• You run 2 to 50 stores, and the owner or the COO makes the stock calls.
• Sales and stock already sit in your POS or ERP, but nobody reads them store by store every day.
• You want to see each week, per store, where an empty shelf or idle stock is costing money, and whether the fix worked.
• You want to keep your ERP and your ordering exactly as they are.

Pricing is published: €200 per location per month for the first nine locations, and less per location after that. See how marql works for a retail chain.

The comparison hub covers the other options (spreadsheets, POS reports, BI tools and ERP) and says when each one is the better answer.

Sources

1. Gruen, T. W., Corsten, D. S. and Bharadwaj, S. (2002). Retail Out-of-Stocks: A Worldwide Examination of Extent, Causes and Consumer Responses. Grocery Manufacturers of America, Food Marketing Institute and CIES. Executive summary, pp. vi-vii. PDF
2. Corsten, D. and Gruen, T. (2003). Desperately seeking shelf availability: an examination of the extent, the causes, and the efforts to address retail out-of-stocks. International Journal of Retail & Distribution Management, 31(12), 605-617. doi:10.1108/09590550310507731
3. Berthiaume, D. (2025-09-10). IHL Group: Inventory issues cause $1.7T in annual losses. Chain Store Age, reporting IHL Group research. chainstoreage.com
4. Card, D. and Krueger, A. B. (1994). Minimum Wages and Employment: A Case Study of the Fast-Food Industry in New Jersey and Pennsylvania. American Economic Review, 84(4), 772-793. Princeton DataSpace
5. RELEX Solutions. Automatic replenishment system. Accessed October 1, 2026. relexsolutions.com
6. SymphonyAI Retail. Replenishment and allocation. Accessed October 1, 2026. symphonyai.com
7. ToolsGroup. Retail. Accessed October 1, 2026. toolsgroup.com
8. invent.ai. Replenishment. Accessed October 1, 2026. invent.ai