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Consumables Forecasting for Facilities Contracts

Soap, paper and chemicals ordered by instinct produce cupboards full of one thing and none of another. What actually drives usage per site.

Updated 2 min readBy SpiderHunts Technologies

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Quick answer — TL;DR

Usage follows occupancy and site type rather than floor area, which is what most ordering is based on. Forecasting per site against real issue history cuts both emergency purchases at retail prices and stock sitting in cupboards.

The short answer

Consumable usage tracks how many people use a building, not how big it is. Ordering per square metre produces systematic over-supply in quiet buildings and shortages in busy ones.

Issue records against site already tell you the real pattern. Most contractors hold that data and order from memory anyway.

What drives usage

  • Occupancy, which is the dominant factor
  • Site type, since a food site and an office differ enormously
  • Day of week, with hybrid working making midweek peaks sharper
  • Season, for hand hygiene and for entrance matting
  • Dispenser type, which changes consumption per use
  • Whether the public has access

Hybrid working has made the midweek pattern much more pronounced in offices, and ordering built on pre-pandemic assumptions is frequently still in place.

Where the money leaks

ProblemCost
Emergency purchases locallyRetail price, plus the trip
Overstock at siteCash tied up, and it goes missing
Wrong product orderedReturns or write-off
Stockout of a basic itemClient complaint, sometimes contractual
Bulk discount missedOrdering too often, too small

Emergency purchases are the visible cost and usually not the largest. Stock sitting in site cupboards is capital, and in many operations nobody counts it.

Forecast per site, order centrally

  1. Build usage history per site from issue records.
  2. Adjust for known changes in occupancy or scope.
  3. Forecast per site for the ordering period.
  4. Aggregate for purchasing, so you get volume pricing.
  5. Distribute on a schedule rather than on request.

Aggregating for purchase while forecasting per site is the combination that gets both accuracy and price. Most operations do one or the other.

Watch for the signals

A site whose usage suddenly changes is telling you something: occupancy changed, a dispenser is broken and wasting product, or stock is going missing.

Flagging unusual movement per site turns the forecast into an operational control as well as a purchasing tool, and that is frequently where the larger saving is.

FAQ

Frequently asked questions

The questions readers ask us after this guide.

Still have a question?

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Ask about your project

What data do we need?

Issue records against site over a reasonable period, plus something that indicates occupancy. Both usually exist already.

How far ahead should we forecast?

Match it to your ordering cycle plus supplier lead time. Forecasting further out adds error without adding value.

What about sites with no usage history?

Use a comparable site of the same type and occupancy, then switch to its own data once there is enough.

Can this detect theft?

It can flag usage that does not match occupancy, which has several explanations including waste and faulty dispensers. Treat it as a prompt to look.

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