What is the Movements dashboard?
Movements is the audit log of your inventory — every time stock went up or down, and why. Every delivery received, every order fulfilled, every stock count adjustment, every waste entry, every consumption event shows up here. Use it to spot unusual activity, investigate anomalies, and see the full picture of stock flow through your outlet.
Why is it important?
The other inventory dashboards summarise. Movements is where you go to investigate. When Yield Performance shows a big variance, when Waste jumps unexpectedly, or when Ordering suggestions seem wrong — this is where you check what actually happened. It's the ground truth.
The headline numbers
Stat | What it means |
Total Movements | Total number of movement entries in the selected period — every addition and deduction combined. A sudden spike or drop from normal can itself be worth investigating. |
Net Stock Change Value | Net value of all movements — additions (goods receipts, positive count adjustments) minus deductions (order usage, waste, consumption). A large negative number means more value flowed out than in. |
Total Additions | Total value of every positive movement in the period — goods received, production output, and positive count adjustments. |
Total Deductions | Total value of every negative movement — order usage, waste, consumption, negative count adjustments. Shown as a positive number for readability. |
Addition Movements | The count of positive movements. Paired with Total Additions, tells you the average size of each addition. |
Deduction Movements | The count of negative movements. Paired with Total Deductions. |
Largest Single Movement | The single highest-value movement in the period, with the product name and value. A useful quick anomaly check. |
Largest as % of Total | The largest single movement as a share of total movement value. High % means one entry dominates; low % means activity was spread evenly. |
Movement Flow visualisations
Movement Flow (Sankey)
A Sankey diagram showing how stock value flows between product categories and movement types. The left side is where value comes from (categories); the right side is how it was used (order deductions, waste, goods receipts, stock counts, production). The width of each band is the value flowing — wider = more value.
Use it to see at a glance: which categories dominate your movement activity, and where that value ends up. If a category has a wide band flowing into "Waste," that's where to focus.
Movement Flow (Waterfall)
Waterfall chart showing net stock value change by movement type:
Green bars — additions (goods receipts, positive count adjustments, production output)
Red bars — deductions (order usage, waste, consumption, negative count adjustments)
Final bar — net change for the period
The biggest red bar is usually order deductions (normal — you're selling stock). The second-biggest red bar is the one to investigate. If it's waste, check the Waste dashboard. If it's count adjustments, check Yield Performance. If it's consumption, check staff meals or prep waste.
Timing heatmaps
Additions by Time
Heatmap by day of week (y-axis) and hour (x-axis), with darker green cells meaning more additions at that time. Use this to:
See when deliveries actually arrive — it's often later than suppliers promise.
Check whether count adjustments cluster at specific times (end of shift, morning open).
Schedule receiving shifts around when stock actually comes in.
Deductions by Time
Same format, darker red cells mean more deductions (usage). Reveals:
Peak service hours, which should match your revenue peaks.
Prep spikes — if large deductions appear mid-afternoon, that's batch prep.
Unusual patterns like deductions in the middle of the night (worth questioning).
Anomaly Flags
A table of movements the system flagged as statistically unusual. Three detection rules:
Count adjustments where the absolute delta exceeds 3× the product's rolling 28-day average. May indicate counting errors or something going missing.
Wastage entries in the top 5% by value. Unusually expensive single waste events — worth asking why.
Consumption entries that are significantly above normal patterns. Could be an error, a staff event, or a prep overshoot.
Each row shows the product, movement type, value, date, and the rule that flagged it. Treat this as a weekly review list — even if most flags turn out to be legitimate, the ones that aren't are the kind of loss that never shows up otherwise.
Movement tables
Addition Movements
Detailed list of every positive movement — product, quantity, value, source (goods receipt, production, count adjustment), supplier (where applicable), and date. Sortable and filterable. Use this to verify a specific delivery or check a product's receiving history.
Deduction Movements
Same for negative movements — product, quantity, value, type (order usage, waste, consumption, count), and date. Use this to investigate a specific product's usage pattern or trace unusual activity flagged elsewhere.
How it's calculated
💡 Every movement has: product, quantity change (+ or −), type (receipt, order, waste, etc.), unit price, date.
Movement Value = |quantity| × unit price at time of movement
Net Stock Change = Σ additions − Σ deductions
Unit price for each movement is fixed at the time it happened — so older movements reflect the price at that date, not the latest price. This keeps historical accuracy intact when suppliers raise prices.
Worked example
A week with these movements:
Delivery of chicken — +10 kg × ฿220 = +฿2,200 (addition)
Order usage of chicken across sales — −8 kg × ฿220 = −฿1,760 (deduction)
Waste entry for spoiled chicken — −0.5 kg × ฿220 = −฿110 (deduction)
Count adjustment after stock count — −0.2 kg × ฿220 = −฿44 (deduction)
Totals for chicken that week:
Total Additions: ฿2,200
Total Deductions: ฿1,914
Net Stock Change: +฿286
Action Steps
Review Anomaly Flags weekly. This is the most valuable 10 minutes in your week — each flag is either confirmed normal or a loss caught early.
Watch Largest as % of Total. If one movement is 30%+ of all movement value, it's skewing everything. Check it and decide if it's legitimate.
Use Deductions by Time to validate operations. Deductions outside service hours need a reason — prep? Staff meals? Unlogged waste?
When the Waste dashboard shows a spike: come here, filter to that product, and see the specific waste entries that drove it.
When Yield Performance shows a variance: come here, filter to that product, and check if a recent goods receipt or count adjustment explains the gap.
Cross-check Total Additions against goods receipts from your invoices. If totals don't match, a receipt wasn't logged — everything downstream (COGS, Ordering, Profitability) is distorted until it is.
Frequently asked questions
What's the difference between "waste" and "consumption" movements?
Waste is stock that was discarded (spoiled, expired, spilled). Consumption is stock that was used but not sold through an order — typically staff meals, tastings, prep that produced a different SKU, or sampling. Both reduce stock; they're separated so you can see them differently.
Why does an anomaly keep flagging even though it's normal for us?
The rules are statistical, based on the product's own rolling 28-day history. If a genuinely large weekly delivery is normal for you, the first few weeks may flag it until the baseline catches up. After ~4 weeks, it will stop flagging.
Why is the Sankey flow blank for one category?
Either no movements happened in that category during the selected period, or the category has no products with movement activity yet. Expand the date range and check again.
Can I see a single product's full movement history?
Yes — open the Addition or Deduction Movements table and filter by product name. You'll see every movement for that product in the selected period.
What if Total Additions is much higher than Total Deductions?
You're building stock faster than you're using it. If it's one big delivery, that's normal — wait and the usage will catch up. If it's a persistent pattern, your par levels may be too high, and the Waste dashboard will eventually reflect it.
Why are some movements shown with prices different from today's?
Movements are priced at the time they happened, not revalued to today's price. This keeps historical reports accurate. If you're comparing across periods with different prices, look at quantities, not values.