Takes proposed price changes with their margin and volume impact through approval bands and writes the condition records to the ERP and the CRM price book with the right validity.
Tables are read in place through the native Snowflake connector: the app sends only the filter and the columns it needs, Snowflake returns the matching rows. The Backbone landing model (RAW, CONFORMED and HISTORY schemas) is the usual source, so history and aggregates are already prepared.
| Object | Read from | Fields the app uses | Freshness |
|---|---|---|---|
| Price condition | CONFORMED.PRICE_CONDITION | material_id (material) · customer_id (customer) · amount (amount) · currency (unit) | prepared by the Backbone; refreshed on its schedule |
| Customer master | CONFORMED.CUSTOMER | customer_id (id) · name (name) · country (country) · sales_org (sales org) | prepared by the Backbone; refreshed on its schedule |
| Sales order line | CONFORMED.SALES_ORDER_LINE | so_number (number) · so_line (line) · customer_id (customer) · material_id (material) | prepared by the Backbone; refreshed on its schedule |
| Quotation | CONFORMED.QUOTATION | quote_id (number) · customer_id (customer) · net_value (value) · valid_to (valid to) | prepared by the Backbone; refreshed on its schedule |
In plain words: A proposer submits S01 with at least 1 line whose new amount differs from the current condition.
This app starts from a screen, so the first read happens when the instance opens; the criteria on the arrows use the fields above.
A proposer submits S01 with at least 1 line whose new amount differs from the current condition. The instance then reads its rows from Snowflake through the handlers above.
Decisions are recorded in a Lima-owned table in Snowflake through stage, COPY INTO and MERGE, so analytics see them next to the history. The operational write goes to the system of record through its released interface, and the app records the acknowledgement. For this app the operational change (condition records created per material and customer with the new amount and validity) goes to SAP S/4HANA or Oracle Fusion through its released interface, and the pair pages for those systems show the exact call.
| Object | What changes | Interface |
|---|---|---|
| Price condition | condition records created per material and customer with the new amount and validity; the previous record ended the day before | MERGE into LIMA.APP_DECISIONS |
| Quotation | open quotations flagged with the new price for the owner to confirm or re-issue | MERGE into LIMA.APP_DECISIONS |
Each write stores the identifier Snowflake returns beside the decision, the person and the time, so the log answers who changed what, and the reconciliation shows it arrived.
Snowflake roles and row-access policies are mapped to Lima access policies once, so a person sees the same rows in the app that they see in a worksheet.
This app reads price condition from Snowflake because the screens need history at scale: months of rows, aggregates by period, several versions. The Backbone lands the objects in the conformed model and refreshes them on a schedule, and the handler reads them in place with the filter and the columns pushed down. The person's grants in Snowflake decide what they see.
The operational rows the person acts on can come live from the ERP in the same app: one handler per object per system, and a screen that shows both.
The screens read through handlers that leave the rows in Snowflake. The conformed tables it reads were landed there by the Backbone from the ERP; the app reads them there.
The prerequisites above: an integration identity with read rights on the objects listed and write rights on the interfaces named, connectivity, and the scopes agreed with the process owner.
Yes. The screens, criteria and roles stay; the handlers are re-bound to the other system's objects, and the app's other system pages show them.
Bring the process and the systems it touches. In the first session we register the data handlers, draw the levels or the lanes and run the first screen on your live rows.
