Solutions For OMP

OMP, loaded with the master data process industries run on.

OMP plans processes where one run makes several products, quantities change unit by density, batches carry potency and expiry, and the order of runs decides the changeovers. Lima Backbone builds that model from SAP and Oracle — recipes with all their outputs, resources with campaigns, lots with their attributes — checks it before every load, delivers it on the planning cycle, and returns approved plans to the ERP as acknowledged transactions.

Run your first load into OMP Planning integration overview

First load: one dataset, four weeks, fixed price. OMP technology alliance application planned for Q1 2027.

OMP's model for process industries: products with grades, recipes with several outputs, resources with campaigns and changeovers, lots with shelf life. Each box names the SAP and Oracle tables Lima reads to fill it, and the rules Lima keeps that the ERP never held.

All outputs

every process order's co-products and by-products carried into the recipe, ratios checked

1,240

recipes with two to four outputs on the showcase estate; 380 liquids converted by density

2,700

changeover pairs kept as governed planning master data, since ECC holds none

Step 1 · What OMP expects

Recipes with several outputs, resources with campaigns, lots with attributes

OMP's model reflects how process plants work: a recipe turns inputs into several outputs at a rate on a resource; batches carry quality attributes and expiry; the sequence of runs on a resource has changeover costs. Most of that is in the ERP; some of it — changeover matrices, substitution rules — is in nobody's system until Lima holds it.

Object	What it holds, in plain words	Why the plan needs it	SAP source	Oracle source
Products and grades	Materials with attributes, units and density; grades as characteristics	What is planned, in the units it is made and sold in	MARA · MARM · AUSP/CABN classification	Items · OPM item attributes
Recipes / production processes	Inputs and outputs with ratios and yields, run rates, alternatives, the resource used	How products are made, including co-products and by-products	PLKO/PLPO master recipes · MKAL · STPO · AFPO order items	OPM formulas and recipes
Resources	Reactors, lines, tanks and silos with capacity, shifts, campaigns and changeover matrices	Feasibility and sequencing	CRHD · KAKO/KAZY (changeover matrices only in PP/DS)	Resources and calendars
Lots and batches	Quantity, expiry, potency, grade, quality status per batch	Allocation respects shelf life and quality	MCHA · MCHB · AUSP characteristics	MTL_LOT_NUMBERS · lot attributes
Supply and demand	Process orders with one item per output, purchase orders, sales orders, forecast	The balance the plan solves	AFKO/AFPO · EKPO · VBAP · PBIM	OPM batches · PO · OE lines
Locations and lanes	Plants, tank farms, warehouses and the lanes between them	Where stock is and can go	T001W · EORD · stock transport routes	Organizations · sourcing
Calendars	Shifts, maintenance windows, working days per resource	When resources can run	TFACS · CRHD shifts	Calendars
Quality and substitution rules	Grade substitution, minimum remaining shelf life, allowed re-grading	Which stock can serve which demand	Governed planning master data in Lima	Governed planning master data in Lima

OMP receives loads through its integration interfaces (files and APIs, including its SAP integration); Lima produces the loads in OMP's model and keeps the rules the ERP never held.

Step 2 · Where ERP integrations go wrong for OMP

Six places the data breaks, and what it does to the plan

These problems come from process manufacturing itself; a discrete-style integration meets every one of them and usually handles none.

One process makes several products co-products vanish from the planAn SAP process order for a reactor run carries the main product as its first item and the co-products as further order items, with an apportionment of cost and quantity; the recipe defines the output ratios. An integration that reads one output per order drops the co-products, and OMP plans as if they never existed — then the plant makes them anyway and stock appears from nowhere.

Quantities change unit by density mass balance breaksA feedstock is bought in litres, stocked in kilograms and consumed by the recipe in kilograms; a finished product is made in kilograms and sold in drums. The conversions need a density per material, sometimes per batch. A missing density breaks the mass balance and the plan orders the wrong amount.

Batch attributes live in the classification system expired stock allocatedPotency, grade and expiry sit in SAP's classification tables (AUSP, CABN) as characteristic values, tied to the batch record by class. OMP needs them as columns on the lot. Without flattening, every batch looks the same and the plan allocates expired or off-grade stock.

Changeovers and campaigns exist in no ERP table the schedule lives in a spreadsheetThe cost of switching a line from a dark colour to a light one, or from an allergen product to a clean one, decides the schedule, and ECC has no place for a changeover matrix. It lives in a planner's spreadsheet until it lives somewhere governed.

Continuous production runs at a rate rates, minimum runs, tanksA reactor produces at kilograms per hour with a minimum run length; SAP routings state durations per base quantity. OMP wants rates and minimum campaign sizes, and tank capacities constrain how much can be made before something must move.

Yields vary, and the plan needs the actuals stock projection driftsPlanned yield and actual yield differ run by run. Confirmations carry the actual inputs consumed and outputs produced; without them OMP re-plans on planned yields and the stock projection drifts within a week.

Industry flavour: chemicals, plastics and packaging, paper and board, metals, food and beverage, life sciences

Chemicals bring co-products, density and campaigns; plastics and packaging bring colour and material changeovers; paper and board bring grade sequences and reel logic; metals bring alloys and heats; food and beverage bring allergen sequencing and shelf life; life sciences bring potency, expiry and quality status per batch. Each of these has a check in Step 4.

Step 3 · How Lima Backbone handles each one

Each problem has a specific answer, and each answer is something you can see

The figure shows one process order becoming a recipe with three outputs, with units and batch attributes handled on the way. The table maps all six problems.

Problem	What Lima does	The capability behind it	What you see
1 · Several outputs	Reads every order item of a process order and every output of a recipe, carries co-products and by-products with their ratios, and checks that the ratios sum to 100% within tolerance	Co-product-aware transformation operators for PP-PI and OPM	Recipe register with outputs per recipe and ratio checks
2 · Density and units	Converts every quantity with the material's or batch's density from MARM and batch-specific units; a missing density is an exception for the material owner before the load	Unit and density conversion rules; exception routing	Conversions applied per material, with the density used
3 · Batch attributes	Flattens characteristic values from the classification tables into named lot columns — potency, grade, expiry, status — and checks each required one is present and valid	Classification flattening operator; completeness rules	Lot table with attribute coverage per plant
4 · Changeovers	Keeps changeover matrices and campaign rules as governed planning master data in Lima with an owner and an effective date, and loads them with the resources	Governed planning master data; versioning	The matrix, its owner and its version beside the resource
5 · Rates and tanks	Converts routing durations per base quantity into rates, carries minimum run lengths, and models tank and silo capacities as resources	Model-aware transformation for PP-PI routings	Rates and minimum runs per resource, tank capacities per location
6 · Yields	Captures confirmations (actual consumption and production) through change-data capture and loads them as actuals, so OMP re-plans on what happened	Change-data capture on confirmations	Planned vs actual yield per recipe per week

Step 4 · The checks that run before anything loads

Nine checks an OMP load runs every cycle

Every check is a rule with an owner. A failed row is held with the rule and the row; the load continues with the rest and the report says what was held. Examples from the showcase estate:

Check	Kind	Example	When it fails
Output ratios of every recipe alternative sum to 100% within tolerance	Consistency	Recipe R-7: 62 + 30 + 8 = 100	Exception to the recipe owner when outside ± 0.5%
Every recipe has at least one valid alternative on the load date	Completeness	14 recipes with all alternatives expired	Exception to the recipe owner
Every liquid or bulk material has a density; every unit pair has a conversion	Completeness	27 liquids without a density in MARM	Exception to the material owner; rows held
Required batch attributes present and valid: potency numeric, expiry a future or past date, grade in the allowed list	Quality	19% of batches at plant 2000 without a potency value	Exception to the quality owner
Batch stock sums to storage-location and plant stock	Consistency	MCHB total differs from MARD total for one material	Exception with both figures side by side
Every resource has a calendar, a rate and, where campaigns apply, a changeover row for every product pair or a default	Completeness	REACTOR-2 has 2,700 pairs and no default	Exception to the scheduler
Every process order's outputs match its recipe's outputs	Consistency	An order with two items for a three-output recipe	Exception to the production planner
Confirmed quantities within yield tolerance of the plan	Quality	A run at 41% yield against a 62% plan	Flagged for the production planner; loaded as actual
Record counts and quantity control totals per table match the source	Completeness	Every table, every load	Load report stays open until they match

Kinds: completeness (is everything there), consistency (do related numbers agree), quality (is each value valid).

Step 5 · Watching the data flow, correcting it on the way

One screen from the ERP table to the planning object

Integration suites such as Informatica sell data integration, data quality, the catalog and lineage as separate services on one consumption meter, and a team stitches the four together for each load. Lima runs the four as one surface. The screen above is the same for every load into OMP: each step from the SAP or Oracle recipe and batch tables to the OMP recipe and lot records shows its row count; the rule that stopped a row shows the row and the person who owns the fix; the fix is applied where the row stopped and the replay starts from that step. Column-level lineage is recorded from the run itself, so the trace from a number in OMP back to the ERP table is current every morning without a scanning project.

Rules in plain language

Checks a planner can read

A rule such as "confirmed date is on or after order date" is written in words, previewed on live rows, versioned, and owned by a named person.

Exceptions with owners

Nothing disappears

A row that fails a rule becomes an exception with the row, the rule, an owner and a deadline. The load carries on with the valid rows and says what it held.

Lineage from the run

Always current

Every step records what it read and wrote at column level, so the trace from a planning number back to the ERP field is captured as a by-product of running.

Governed planning master data

Parameters with owners

Values the ERP never held — hierarchies, changeover matrices, demand-stream rules — live in Lima with an owner and an effective date.

Acknowledged write-back

The loop closes

Results return to the ERP as transactions the ERP confirms; the confirmation is stored and reconciled against what was sent.

Deadlines on the pipeline

The planning run waits for nothing

Each load carries its window and deadline; a tripped rule pauses the run and the clock, and the run resumes from its checkpoint after the fix.

Step 6 · Load, reconcile, return

On the planning cycle, reconciled, and back to the ERP

OMP is loaded on its cycle; every load ends with a reconciliation report; approved production plans return to the ERP as transactions the ERP confirms.

Nightly net-change

Lots, stock, orders and confirmations as changes since the last load, in dependency order, before the daily scheduling run.

Weekly master data

Recipes, resources, calendars, changeover matrices and substitution rules, with the ratio, density and attribute checks.

Monthly S&OP

Demand and capacity at the aggregate level, with scenario versions tagged.

What goes back to the ERP

Result	SAP transaction	Oracle interface	How it is confirmed
Approved production plan	`BAPI_PLANNEDORDER_CREATE` · process orders via `BAPI_PROCORD_CREATE`	OPM batch interface	Order numbers stored; counts reconciled; failures re-queued with reason
Purchase requisitions and transfers	`BAPI_PR_CREATE` · stock transport orders via `BAPI_PO_CREATE1`	`PO_REQUISITIONS_INTERFACE_ALL` · internal requisitions	Acknowledged and reconciled
Approved master-data corrections	`BAPI_MATERIAL_SAVEDATA` (density, shelf life)	Item interface	Second-person approval in Lima, then the acknowledged write

Standard released interfaces; the exact objects are confirmed per scope during the assessment.

A first load, step by step

Twenty-two days on the showcase estate

How a first load into OMP runs for a chemicals estate with three sites. Figures are from the showcase estate.

Day 1
connect

Three sites, one modelSAP ECC (two European sites) and Oracle EBS with Process Manufacturing (one US site) connected; the dataset scoped to recipes, resources, lots and stock for the scheduling run.

Day 4
recipes

Every output counted1,240 recipes with two to four outputs each; two alternatives whose ratios sum to 97% go to the recipe owner; 14 recipes with only expired alternatives are found before OMP could plan on them.

Day 7
units

Mass balance closes380 liquid materials converted by density; 27 with no density held for the material owner and fixed in Lima; the litres-to-kilograms conversions are recorded with the density used.

Day 9
batches

Attributes become columns48,112 lots with potency, grade and expiry flattened from the classification tables; 19% of batches at one plant lacked a potency value and were held for the quality owner.

Day 11
changeovers

The spreadsheet retiresThe scheduler's changeover matrix — 2,700 product pairs for REACTOR-2 plus a default — is loaded as governed planning master data with an owner and an effective date.

Day 14
first load

ReconciledProducts, recipes, resources, calendars, lots and stock load in order; the reconciliation report matches counts and control totals for every table.

Day 19
write-back

The loop closesApproved planned orders post to ECC through BAPI_PLANNEDORDER_CREATE and to Oracle through the batch interface; confirmations flow back nightly as actual yields.

Day 22
hand-over

LiveThe recipe register, the reconciliation report and the exception register are handed to the planning COE; the nightly cycle runs on its own.

Partner and certification status

Stated as it is

Program	What it gives you	Status
OMP technology alliance	A recognised integration path and a badge for planning COEs	Application planned · Q1 2027
Systems integrator delivery	Delivery by your SI or ours on the same fixed-scope offer	Available

Statuses are stated as they are and dated when they change. Badges appear only after they are granted.

Lifecycle

A OMP integration uses every stage, Return included.

STEP 1

ConnectSAP, Oracle, WMS, files, lakehouses

STEP 2

Extracttable reads, BAPIs, CDS views, CDC

STEP 3

Transformat MRP volume, 150+ operators, Python

STEP 4

Loadon the run window, counts proved

STEP 5

Orchestratedeadlines, pause and resume, replay

STEP 6

Returnwrite-back, acknowledged

One engine underneath every page in this family. It reads from any source and writes to any target, runs batch loads or change-data streams, handles a nightly delta or a multi-terabyte migration, and is licensed by capacity, so the bill stays the same however many rows move. Lima's fabric answers questions on data in place; Lima Backbone moves, transforms and returns data on a deadline. When to federate and when to pipeline · The Backbone hub.

Terms on this page

Plain definitions

RecipeThe process-industry definition of how a product is made: inputs, outputs with ratios, yields, rates and the resource used.

Co-product · by-productAdditional outputs of one process run: a co-product is sold or used as a product; a by-product is disposed of or reused.

DensityMass per volume, used to convert litres to kilograms; needed per material and sometimes per batch.

CharacteristicA quality attribute such as potency or grade stored in SAP's classification system and attached to a batch.

Changeover matrixThe time or cost of switching a resource from one product to another, for every product pair.

CampaignA sequence of runs of similar products on one resource, chosen to keep changeovers small.

Control totalA sum computed on the source and the target and compared, alongside record counts.

Acknowledged writeA transaction the ERP has confirmed, with the confirmation stored and reconciled against what was sent.

Questions we hear

Asked before the first load.

How does Lima load OMP?

Through OMP's integration interfaces, in the shape OMP's model expects, on the cycle you set, with counts proved per load. Your OMP team keeps its model and planning logic.

Where does the changeover matrix live?

In Lima, as governed planning master data with an owner, a version and an effective date, loaded with the resources. The scheduler edits it in Lima; the spreadsheet retires.

How are co-products handled?

Every order item and every recipe output is carried with its ratio; the ratios are checked to sum to 100% within tolerance, and an alternative that fails goes to the recipe owner before the load.

What about batch potency and expiry?

Characteristic values are flattened from the classification tables into lot columns and checked for presence and validity, so allocation respects potency, grade and remaining shelf life.

What comes back?

Approved production plans as planned or process orders, requisitions and transfers, and approved master-data corrections — into SAP through released BAPIs and into Oracle through its interfaces, each acknowledged and reconciled.

Can we start with one site?

Yes. One site or one product line is the usual first dataset.