What matters when choosing an ERP in agricultural trade?

In agricultural trade it comes down to whether the system can do three things that do not exist in ordinary trade. First: the price is not yet fixed on delivery, because moisture, foreign matter and further laboratory values decide deductions and surcharges. Second: a substantial part of the business runs on contracts closed months before delivery, and from those arises an open position that has to be right every day. Third: the weighbridge is the start of the document, not the purchase order. Anyone not making these three points the test in the selection gets a system in which the accounting is right and the business runs in spreadsheets beside it.

Which question comes before all others?

How do you keep your open position today, and how current is it? That is: how much is bought, how much is sold, what is still in the silo, and how much of that is already assigned to a contract?

That single question separates vendors faster than any feature list. Anyone in the contract business who does not know daily where they stand carries a price risk that no system resolves afterwards. And anyone who takes the answer today from a well-kept spreadsheet should bring exactly that spreadsheet to the selection meetings: it is the most precise requirements description you have.

Where selection in agricultural trade most often fails

• The weighbridge does not hang off the system. Gross, tare, net, sampling and lot arise in a few minutes at intake. If the weighbridge runs separately, everything is captured twice, and the discrepancies only surface in settlement.
• Quality-based settlement gets rebuilt on the side. Moisture deduction, foreign matter, drying costs, surcharges for protein content: that is calculation logic with an effect on valuation and accounting. As a spreadsheet beside the system it works exactly until the first audit.
• The season is underestimated. At harvest a multiple of the normal document volume arrives within a few weeks, often around the clock. A system that looks good in February has to stay usable under load in August, including at the weighbridge terminal.
• Blending in storage is missing from the model. Knowing batches is not enough. Several deliveries become one silo lot, and traceability has to map the shares. Anyone noticing this only at the audit has a certification problem, not a software problem.

Which criteria genuinely carry?

Contracts as a unit of their own, not an order with a late date. A contract has a quantity, a tolerance, a period, a price or a price formula and partial deliveries. The open position has to be derivable from those at any moment, at the touch of a button and not by an overnight report.

Quality values as part of the document. Laboratory values belong on the lot and affect settlement without anyone recalculating. Test a real case with deduction and surcharge in the demo, from weighing slip to credit note.

Traceability with blending. From intake through the silo lot to delivery, in both directions and retrievable in minutes. That is the requirement on which standard and industry solution really differ.

Do not forget the side business. Farm inputs, seed, fertiliser and crop protection bring hazardous goods and documentation duties with them, and there are the tax specifics of dealing with flat-rate farmers. These topics are rarely the reason for a decision, but often the reason for rework.

The general approach with criteria and checklist: ERP selection: criteria, approach and checklist

Industry solution or standard with an extension?

The dividing line runs at quality-based settlement. Anyone trading mainly farm inputs, that is goods with a fixed price and a fixed quantity, gets a long way with a standard system and a lean add-on. As soon as intake, contracts and laboratory values carry the revenue, the logic reaches so deep into valuation and accounting that a specialised solution becomes more realistic.

The useful question in the selection is therefore not „industry solution yes or no“ but: what share of our revenue hangs on quality values? If it is a fifth, the answer is different from two thirds.

What holds in both cases: the data foundation decides more than the product. Item masters with inconsistent units, supplier masters without clean holding numbers and silo data that only the storekeeper knows all travel along on a switch. That work falls due before the project, or more expensively during it.

Why this is so often underestimated: Why does automation fail on data quality?