It is the first Monday of the month and the purchasing manager is building next month's reorder list. Sales history is in the inventory system. But the big orders coming up are in the CRM, because the sales reps log them there before they become actual orders. Supplier lead times are in a spreadsheet the buyer updates after every phone call. And the seasonal pattern, the one that says pool chemicals triple in September and slow to a trickle in May, lives in somebody's head. The forecast ends up being a spreadsheet that pulls a bit from each place and relies on experience to fill the gaps.
That is how most wholesale and distribution businesses forecast stock. It works until it does not: you run out of a fast mover because nobody updated the lead time after the supplier moved warehouses, or you overbuy a slow line because the sales rep's "big deal" fell through and the forecast still assumed it.
What a useful stock forecast actually needs
A forecast does not have to be complicated. For most wholesalers, it needs four things:
Sales history by item, ideally by month, going back at least twelve to eighteen months. This is the base. It shows what sold, when, and in what quantities. Your inventory or ERP system almost always has this, though getting it out in a usable format varies.
Supplier lead times, meaning the realistic number of days or weeks from placing the order to the stock arriving on the shelf. Not the number the supplier quoted two years ago, but the one that reflects what actually happened in the last few shipments. For imported stock, this includes shipping and customs time. Lead times often live in the inventory system's supplier records, but just as often they are wrong there and the real number is in the buyer's head or a spreadsheet.
Open purchase orders and stock on hand. You need to know what you already have and what is already on the way before you order more. This sounds obvious, but if stock on hand in your inventory system does not match the balance sheet (a common problem we have written about in why your stock value does not match the balance sheet), then the forecast starts from a wrong number.
Seasonality and known future events. The September pool chemical spike. The January slowdown. A price rise from a supplier that will pull orders forward. A big contract starting in March. Some of this is in the sales data (the seasonal pattern repeats), but events like upcoming contracts or price changes live in the CRM, in emails or in somebody's calendar.
Where the data sits, and why it does not join up
In a typical wholesale business, the pieces land like this:
- Inventory system (Cin7, Unleashed, Dear, Fishbowl, MYOB Advanced, NetSuite, or an ERP): sales history, stock on hand, open POs, and sometimes supplier lead times.
- Accounting software (often separate from the inventory system): cost prices, landed costs, and the financial stock value.
- CRM or sales pipeline (HubSpot, Salesforce, Pipedrive, or a spreadsheet): upcoming deals, expected order dates and quantities.
- Spreadsheets: supplier lead times, seasonal adjustments, reorder points, safety stock levels, notes on substitutes.
- Email and phone calls: the supplier telling you they are three weeks behind, or the sales rep mentioning a customer is doubling their order next quarter.
The inventory system knows what sold and what is on hand. The CRM knows what might sell. The spreadsheet knows how long it takes to arrive. None of them talks to the others. So the purchasing manager becomes the human integration layer, opening four tabs and a spreadsheet every Monday morning.
When your stock system's own forecasting is enough
Most mid-range inventory systems (Cin7, Unleashed, MYOB Advanced, NetSuite) have some form of demand forecasting or reorder suggestions built in. These usually work by looking at sales history over a period, calculating an average or trend, and suggesting reorder quantities based on the lead time and safety stock you have set in the system.
For items with steady, predictable demand, this works well enough. If you sell roughly the same number of a product every month, the system's own forecast will catch it.
It starts to fall short in three situations:
- Seasonal items. Some systems handle seasonal patterns, but many use a simple moving average that smooths the spikes out. A product that sells five times as much in summer as in winter needs a forecast that knows that, and some inventory systems do not offer seasonal profiles.
- Long or variable lead times. If the system's lead time field says 14 days but the real lead time for imported stock is 10 weeks including shipping, the reorder suggestion arrives too late. Keeping lead times accurate in the system requires discipline, and nobody has a process for updating them after a shipment.
- Lumpy or project-driven demand. If a handful of large orders drive most of the volume (common in industrial supply and B2B wholesale), the average-based forecast misses them. A single big order distorts the average for months.
If your situation is mostly steady demand with reliable lead times, start with what the system already offers. Set accurate lead times and safety stock levels, review the suggestions weekly, and override them for items you know are seasonal or lumpy. That costs nothing extra and is often better than the spreadsheet it replaces.
When you need something alongside the stock system
When the forecast needs information the inventory system does not have, you need to bring it in from elsewhere. Common examples:
- CRM pipeline data feeding expected orders into the forecast, so purchasing can see a big job coming before it becomes an order.
- Actual lead times calculated from purchase order dates and goods receipt dates, replacing the static number in the supplier record with a rolling average of what really happened.
- Seasonal profiles built from two or three years of sales history, applied to the base forecast to lift or lower quantities by month.
- Landed cost updates from the accounting side, so the forecast considers the real cost (including freight, duty and exchange rates, which we covered in getting freight, duty and FX into your stock cost).
This does not always mean buying a dedicated demand planning tool. For many wholesalers, a well-structured report that combines data from the inventory system, the CRM and a lead-time tracker gives the purchasing manager a better view than any of those systems alone. We have built these as Power BI dashboards, as scheduled reports from a small database, and sometimes as improved spreadsheets that pull from APIs instead of manual exports.
A dedicated demand planning tool (like Netstock, StockTrim or the planning modules inside larger ERPs) makes sense when you carry thousands of SKUs, have complex seasonal and promotional patterns, and the cost of a stockout or overstock is high enough to justify the subscription and setup. For a business with a few hundred active items and a good buyer, a connected report is usually the better first step.
How to start without a big project
If the spreadsheet forecast is working but fragile, here is a practical order of attack:
- Fix lead times first. Go through your top fifty items by sales value and update the lead time in the inventory system to the real number, based on your last three to five receipts. Set a monthly reminder to check them. This one step improves every reorder suggestion the system makes.
- Reconcile stock on hand. If stock on hand in the system does not match the warehouse or the balance sheet, the forecast is building on sand. Our post on stock value not matching the balance sheet walks through why this happens and how to fix it.
- Add safety stock for seasonal and slow items. Set higher safety stock for items that spike in certain months, and lower it for items with steady demand. Most inventory systems let you set this per item.
- Connect the CRM pipeline. If your sales team logs upcoming deals in a CRM, a weekly export or API connection that flags expected large orders for the next month or two gives purchasing early warning. It does not have to be automated on day one; a weekly email from the sales manager works as a proof of concept.
- Build a seasonal overlay. Export monthly sales by item for the last two to three years. For items with clear seasonal patterns, calculate a monthly index (each month's sales as a proportion of the yearly average). Apply that to next year's base forecast. This can live in a spreadsheet or in a report that pulls from the inventory system.
Frequently asked questions
Can Cin7 or Unleashed do stock forecasting?
Both offer reorder point and reorder quantity features based on sales velocity and lead times. Cin7 has demand forecasting in its higher plans. Unleashed relies on reorder levels and minimum stock settings. For steady-demand items with accurate lead times, these work. For seasonal, lumpy or long-lead-time items, you will likely need to supplement them with a report or spreadsheet that accounts for the patterns.
What is safety stock, and how much should I carry?
Safety stock is extra stock held above your expected demand to cover variability in sales or supplier delivery times. How much depends on how variable the demand is, how long the lead time is and how much a stockout costs you. Items with reliable demand and short lead times need less. Items with spiky demand or overseas lead times need more. There is no single formula that fits every item.
Do I need a demand planning tool?
Not necessarily. If you carry a manageable range of products and your buyer knows the business well, a connected report that brings sales history, lead times and pipeline data together is often enough. Dedicated tools like Netstock or StockTrim earn their keep when you manage thousands of SKUs with complex seasonal and promotional patterns.
How do I forecast for a new product with no sales history?
Start with the closest comparable product's history. If there is no close match, use the CRM pipeline or the sales team's estimate of the first three to six months, and review it weekly. Set a short reorder cycle so you can react quickly rather than committing to a large initial buy.
How often should I review the forecast?
Weekly for fast-moving items. Monthly for the full range. Quarterly for a proper review of lead times, seasonal profiles and safety stock levels. The review should be a short, standing meeting between purchasing and sales, not a one-person spreadsheet exercise.
Getting help
If your stock forecast is a spreadsheet that depends on one person's knowledge and you want to connect it to what your systems already know, book a free initial chat and we will talk through what is realistic for your setup. You might also find these useful: