AI for insights and analytics
A kitchen that drops from three deliveries a week to two shows up as a small dip on the monthly report and a sixteen percent loss on the delivery file. AI analytics reads the invoice, delivery and order guide files, and answers the question the district manager actually asks: which kitchens are slipping, and why.
These are the questions foodservice distributors ask. Each one maps to a measure our tools compute from your files. The AI model chooses the measure and explains the result; the arithmetic is done by our code, and every total is checked.
| The question | The measure behind it | What comes back |
|---|---|---|
| Which kitchens have changed their delivery pattern? | Drop frequency against own pattern | Accounts whose drops per week fell against their own history, with the day that went and the revenue lost. |
| Where has order guide compliance slipped? | Order guide compliance | Lines bought against the agreed guide per kitchen and chain, and the lines that moved off it. |
| Which shorts and substitutions preceded a lost kitchen? | Substitutions and shorts | Shorted and substituted lines by kitchen and delivery day, joined to the accounts that then dropped a day. |
| Which drops do not pay for the truck? | Contribution per drop | Revenue and margin per drop less the delivery cost, by route and account. |
| How much of a chain's sites do we hold? | Share at chain and site | Sites served over sites in the chain, and category share within the sites we serve. |
| How are new kitchens ramping? | New account ramp | Weekly revenue of new accounts against the typical curve, with those below it flagged. |
Every figure below was computed by a tool from the rows and checked before it was shown. The lines sum; the percentages match; each line opens to the rows that make it.
How much weekly revenue have we lost to kitchens changing their pattern?
Weekly revenue at risk from pattern changes is $84k across 3 districts, from accounts that dropped a delivery day or halved their order value in the last four weeks. Metro district carries $41k, 49% of it.
| Metro district | $41k | 49% of the total |
| Coastal district | $27k | 32% of the total |
| Valley district | $16k | 19% of the total |
| The 3 lines sum to | $84k | 0 unexplained |
Which Metro kitchens?
3 kitchens make up $41k of the $41k in Metro district, 100% of the district's figure. Two of them had shorted lines on the delivery day that then disappeared.
| The Copper Bistro | $18k | Wednesday drop gone; five shorts on Wednesdays before |
| Harbour Grill group | $14k | three sites down from daily to four days |
| Linden Street Cafe | $9.0k | order value halved; guide compliance 61% |
| These 3 are | $41k | 100% of Metro district |
Who calls, and about what?
The call is about Wednesday and the shorts, not about price. The rep goes with the shorted lines in hand and the substitution offered, and logs what the kitchen says.
| Metro reps | Visit the three kitchens with the shorted-line list | This week |
| Operations | Check Wednesday route capacity and the five shorted SKUs | Before the visits |
| District manager | Run the pattern-change list every Monday | Weekly |
Each measure has one formula, one source and one meaning. They are computed per route and rep and in total, and every one carries an identity that must hold before it is shown.
| Measure | Formula | From | What it tells you |
|---|---|---|---|
| Category share per kitchen | Kitchen spend in category ÷ kitchen total, against the median for its format and cuisine | Ledger with category; account master | Proteins bought, produce and dry goods bought elsewhere |
| Drop frequency against own pattern | Delivery days per week, last 4 weeks, against the kitchen's prior 26 weeks | Delivery file | A delivery day given to a competitor |
| Average drop value | Revenue ÷ drops, per kitchen | Ledger; delivery file | Drops too small to pay for the truck |
| Order guide compliance | Spend on order guide items ÷ total spend; order guide items not bought in 8 weeks | Order guide; ledger | What the kitchen was set up to buy and stopped buying |
| Share at chain and site | Revenue per site against the chain's own median site; sites below half of median | Ledger; account master with chain | The kitchens inside a contract that buy elsewhere |
| Delivery window adherence | Drops inside the agreed window ÷ drops; drops during service hours | Delivery file with timestamps | Deliveries that arrived during lunch service |
| Substitutions and shorts | Lines substituted or shorted ÷ lines ordered, per kitchen | Order and shipment files | The service failures that precede a dropped day |
| New account ramp | Weekly revenue in weeks 1 to 12 against the ramp of accounts that went on to stay | Ledger; account master | New kitchens that are not becoming customers |
| Contribution per drop | Gross margin per drop − delivery cost per drop | Ledger; delivery file; route costs | Kitchens and routes that lose money as served |
| Lost kitchens, by prior value | Prior-year revenue of kitchens past k × own order gap ÷ prior-year revenue | Ledger | Revenue that stopped, with the closure or the competitor to find out |
Each measure is worked through, with the export it comes from and what to drop, in Sales KPIs for foodservice distributors.
From your question and the measures declared for foodservice distributors, the model picks the one that answers it, and the period and comparison the question implies.
Deterministic code reads the rows, computes the measure, and checks the identities below. The same question on the same data gives the same answer, every time.
The AI model writes the sentence around the result, naming the route or account behind it. It states no figure that is not in the result, and every figure links to its rows.
| Category share | Category spend per kitchen sums to the kitchen's ledger total |
| Drop frequency | Every delivery belongs to one kitchen and one day; drops sum to the route total |
| Substitutions | Lines ordered = shipped as ordered + substituted + shorted + cancelled |
| Chain and site | Site revenue sums to the chain total |
The exports foodservice distributors already produce. Column names are mapped once and the mapping is reused. A file is the way in; scheduled delivery and connections to your systems come with the plan, and every source is listed here.
An answer is a list with an owner and a cadence, or it is a chart nobody works.
| Measures | Owner | Cadence |
|---|---|---|
| Drop frequency; lost kitchens; substitutions | District sales manager with operations | Weekly |
| Order guide compliance; new account ramp | Rep; district sales manager | Weekly to monthly |
| Delivery window adherence; contribution per drop | Operations; commercial director | Monthly |
| Category share; chain and site share | Commercial director; national accounts | Quarterly |
From the delivery file. Each account's drops per week and value per drop over the prior months are its own baseline, so a bistro that always took three a week is flagged at two, and a kitchen that always took one a fortnight is not. A fixed rule cannot do that; a per-account baseline can.
Yes, when both files are loaded. The tool joins the shorted and substituted lines to the kitchens that later changed pattern, by delivery day, and lists the ones where a short came first. Whether the short caused the change is the rep's call to make, but the timing is on the page.
The invoice ledger, the delivery file and, if you have one, the order guide by kitchen. Route cost per drop turns revenue per drop into contribution per drop. All of it is a file your systems already export weekly.
Both. Sites are rolled up to the chain for share and concentration, and reported separately for pattern changes and compliance, because the decision to cut a delivery day is made in a kitchen and the contract is signed at head office.
Order guide compliance · Excess drops · Cost to serve · Dormant account · Fill rate · Contribution
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Bring a few thousand rows. The data map opens next, every column mapped once, and the first question is answered in minutes. Free, in your browser, no account.