Sena counts what consumers across a market purchased from real-world data.
Supply chain optimization sets what a network carries, where it carries it and how fast. Three patterns bend the target before the arithmetic starts.
Own shipment history supplies the demand series the network optimizes toward. It records what a company managed to sell in the markets it already serves, filtered through whatever sat in the outlet at the time.
The record runs to the point of dispatch, and the trail goes quiet after it. What the outlet held on the day and what the buyer took home arrive later, as a partner report compiled by the partner.
A single demand figure covers a market where formats, outlet types, and price tiers move in different directions. The parts cancel inside the total, and the network optimizes toward an average that sits away from every segment underneath it.
Sena is the decision AI with access to real-world data. It counts what consumers across a market purchased and records what each outlet held on the day, so the demand side of an optimization rests on the category itself.
Sena counts category purchases across the market, including purchases made from every other player, so the series behind a target describes what buyers did.
Contributors record what each outlet held and which lines had gone, dated to the day of capture.
Sena reports how fast recorded purchases moved in each market, indexed against the others, so route and cover follow demand a team can open.
An optimization holds up on two things: whether the demand series it runs toward measures the category or one company's shipments, and whether availability was recorded apart from wanting.
Supply chain optimization sets the cost, the flow and the service level a network runs at, against a demand target for a named market and period. A network optimized toward a shipment series inherits every stockout, every listing gap and every promotion that shaped those shipments, and carries all of them forward as though buyers wanted that much.
Complete, internal, and shaped by whatever sat in the outlet.
Every player counted, per market, with availability recorded beside it.
Three layers cover most of what runs in practice, and each takes the same demand input.
Where production, holding, and consolidation sit, and which route serves which market. Set once a cycle, and expensive to revisit.
How much to make and hold per period, per market, and per line. Revisited monthly or weekly.
Order sizing, cover levels, and replenishment against the plan. Revisited each cycle.
All three run toward a demand figure, and it is usually the same figure. An error in it propagates through every layer at once.
Four families of methods sit under the three layers, and each one takes a demand figure as a parameter.
States the network as an objective function with constraints, then solves for the lowest cost meeting them. Exact within its own terms, and every term arrives from an input.
Runs the network forward many times against varying conditions and reports the spread of outcomes. Handles variability well and inherits whatever demand spread it was given.
Reorder points, min-max levels, and allocation ratios. Fast, transparent, and widely used, with the demand assumption sitting inside the parameter.
Learns patterns from history and projects them forward. Strong where the future resembles the past, and it learns the constraints alongside the demand.
An optimization runs against an objective, and the common ones pull apart.
| Objective | What it pushes toward | What it trades away |
|---|---|---|
| Lowest landed cost | Fewer locations, larger runs, longer routes. | Speed of response when a market turns. |
| Highest service level | More cover, held closer to the buyer. | Working capital and obsolescence exposure. |
| Lowest working capital | Leaner cover, later commitment. | Availability in the markets moving fastest. |
| Lowest emissions | Shorter routes, consolidated movement, local supply. | Flexibility across markets. |
Four sources supply the demand target in practice, and each carries a known limit.
Fast, complete, and internal. It records supply meeting demand, so it inherits every constraint that shaped it.
Closer to the buyer, compiled by the party being measured, covering the markets that party already reports on.
Real movement, covering the outlets a company already reaches, which leaves the rest of the market dark.
Category purchases counted across every player per market, with what the outlet held recorded beside it.
The demand series comes from four collected inputs, applied per market.
| Input | What it answers |
|---|---|
| Receipts | Category purchases and their dates, so the target counts demand every player met. |
| Store captures | What each outlet held on the day, which separates a demand fall from an availability gap. |
| Geo-verified photos | The outlet as the capture found it, with the market and the date attached. |
| Stated preference | What buyers say they move to when a line has gone, which prices what a gap costs. |
Four things decide whether an optimum survives contact with the market.
Sena reports the demand side and the availability side. Freight, lead times, supplier operations, and stock policy stay with the planning and execution systems, and Sena sets its answer beside them. The split earns its place because an optimization that reads a supply gap as soft demand corrects in the wrong direction and then compounds it.
Real people share what they buy and prefer, under explicit consent. Sena captures it directly at the source, so every figure traces back to where it came from whenever a number comes under question.
Consented purchase supplies the series, and every outlet capture carries the market and the day it was taken.
Sena records what sat in the outlet, so an empty slot and soft wanting read as separate causes.
Sena indexes recorded movement per market, so route and cover follow the markets taking the category fastest.
Any point in the demand series opens to the weeks of purchase and the outlet photographs that produced it.
An optimum carries weight only as far as the demand series underneath it, and most series measure supply meeting demand. Sena builds every figure from real-world signal captured when the question needs it, from consumers purchasing across the category through to the dated capture a service level review is checked against.
Counts what buyers purchased across the category and when, including purchases made from every other player, so the demand target describes the market.
Reads what each outlet held off images captured in real outlets, which is what separates an availability gap from a fall in wanting.
Signal arrives from the consumer network under explicit consent. What buyers move to when a line has gone is stated by those buyers.
Shipment history, service level exports, and cover models join over 250+ integrations, so own movement reads against the category around it.
Every point in the series holds its market and its capture date, so a target reopens on the evidence it was built from.
Historic movement exports and planning models become one base, so every period the team has measured sits together.
Four owners run against the same demand target, and each needs a different cut of it.
| Team | What they own, and what they need |
|---|---|
| Network and planning | The structure. Needs a demand target built on category purchase, per market. |
| Demand planning | The period. Needs formats and outlet types reported apart, with a rate for each. |
| Logistics and fulfillment | The route. Needs confirmation the product reached the outlet it was sent to. |
| Commercial and account teams | The conversation. Needs a dated capture of what a partner's outlets held. |
The same demand series and the same dated capture, read on whatever the category moves through the outlet.
Category purchase by format and tier, with what the outlet held recorded beside it.
Which model reached the market, and which specification buyers found and took home.
Which pack sizes reached the counter, and which ran absent the longest.
Handset availability across the outlets selling the plan, market by market.
One mechanism, applied per market and per category. Each step is documented, which is what keeps a target defensible.
Real contributors share what they buy and pay under explicit consent and record what each outlet held in the markets named.
Sena builds a dated series of category purchases per market, with availability recorded alongside it.
Route, cover, and service level run against that series, with the captures attached.
Most optimizations run toward the one demand series a company holds in full, which is its own shipment history. Sena measures the category that history sits inside.
| Capability area | Typical setup | Sena |
|---|---|---|
| The demand series | Own shipments and orders. | Purchase across the category, every player in that market counted. |
| Demand against availability | Combined in one figure. | Separated, with what the outlet held recorded on the day. |
| Level | Market total, split downward by rules. | Each market, format, and outlet type carries its own series. |
| Reach of the view | The order and the shipment, then the trail goes quiet. | Through to the outlet and through to what the buyer took home. |
| Evidence of a gap | A partner report, compiled by the partner. | A geo-verified photo of the outlet, carrying the market and the day. |
| Why sell-through moved | An estimate, argued between commercial and planning. | The reason buyers give when they switch, reported per market. |
| Auditing the target | An appendix describing the method. | Open the figure, and the outlet photograph sits underneath. |
Three situations where a category-built demand series changes the call.
Rank markets on recorded movement, so holding and route follow the markets taking the category fastest.
Read what the outlet held beside what buyers purchased, so a service level answer and a demand answer stay apart.
Open the market figure to the purchases and the outlet photographs underneath it, in the review where the call gets questioned.
Every supply chain decision Sena answers, in one place
Turns a demand series into what to hold and replenish, market by market.
Explore demand planning → Supply chain visibility What the outlet heldRecords what reached each outlet and which lines had gone, dated to the day.
Explore supply chain visibility → Supply chain analytics Own movement against the marketSets recorded own movement beside what the market absorbed over the same period.
Explore supply chain analytics → Inventory optimization Where cover should sitRanks markets on how fast recorded purchases moved and where a gap sent buyers.
Explore inventory optimization →The walkthrough builds a dated demand series for one category in one market, records what the outlets held across the period, and opens the purchases behind any point in the series while the team watches.