Supply chain planning data integration is the set of feeds that bring master data, sales, inventory, open orders, lead times and constraints from your ERP, WMS and POS systems into a planning system, and send the resulting plans back. It decides how good the recommendations can be, because a plan built on a wrong lead time or a missing open order is wrong before anyone looks at it. The work has three parts: agree which data is needed and at what grain, pick a transfer method for each feed, and check the data before the first plan runs.
What Data a Planning System Needs
The exact list depends on which decisions you automate, but a replenishment or supply planning scope usually needs these groups:
- Master data: items, locations, product and location hierarchies, units of measure, pack sizes, sourcing relationships (which supplier or DC serves which location) and, for manufacturers, bills of materials and routings.
- Sales or POS history: units sold by item, location and day or week, plus returns. Retailers also need the dates an item was out of stock, so the forecast does not learn from censored sales.
- Inventory positions: on hand, in transit, allocated and blocked stock at every location the plan covers.
- Open orders: purchase orders, transfer orders, production orders and customer orders that are placed but not yet received or shipped.
- Lead times: quoted supplier and transport lead times, and the actual receipt dates to check them against.
- Constraints and policies: minimum order quantities, shelf and DC capacity, truck sizes, production capacity, service level targets and order calendars.
- Demand drivers, where the scope uses them: prices, promotion calendars, store openings, weather.
Agree the grain of each feed early. A store replenishment plan needs item-store-day sales; a tactical supply plan can run on weekly item-DC data. Pulling more detail than the decision needs adds load without improving the plan, and pulling less makes the decision impossible.
Supply Chain Planning Data Integration Patterns
Each feed can use a different method. The usual options are:
- Batch files: CSV, TXT, XML or XLSX extracts dropped on a schedule, often over SFTP. They are simple to set up and easy to inspect, which suits nightly sales and inventory snapshots.
- Database to database: direct transfers over ODBC, for example with SSIS jobs, for high-volume history loads and large daily tables.
- APIs: REST (JSON) or SOAP (XML) web services that push or pull data on demand or on an event, such as a new purchase order or a stock adjustment. They also let other systems trigger planning jobs.
- Native ERP connectors: for SAP, remote function calls (RFC) move master data, transactions and plans without custom middleware.
- Middleware: if you already run an integration platform such as Dell Boomi or MuleSoft, the planning feeds can go through it.
- EDI: standard messages with trading partners, such as purchase orders, order acknowledgements and advance ship notices. EDI matters when suppliers confirm quantities and dates that the plan has to respect.
Plan the return path at the same time. Approved orders, transfers and production plans have to reach the ERP or WMS that executes them, on a schedule that fits the order cutoffs. Feeds can be timed (every night at 02:00) or event-driven (when a file arrives), and the two can be mixed.
Data Quality Checks Before the First Plan
Run checks at ingestion, before any planning model sees the data. The checks that catch the expensive problems are:
- Completeness: every active item-location has a source, a lead time and a pack size; no store or DC is missing a day of sales or stock.
- Consistency: units of measure match between sales, stock and orders; open orders point to items and locations that exist.
- Plausibility: negative stock, sales spikes from data errors, lead times of zero or of several years.
- Actual against master: compare quoted lead times with real receipt dates, and supplier quantities with what arrived. Drift here moves safety stock and order timing.
- Reconciliation: totals in the planning system match the totals in the source system for the same day.
You can start before the data is clean. Teams that plan in spreadsheets often have gaps in history and parameters, and those can be found and filled during onboarding. Our post on supply chain planning in Excel covers where spreadsheet planning breaks and what changes when you move off it.
At Studenac Market, a retailer with 1,000+ stores and inconsistent data, the project began by cleaning and connecting the data into a single source of truth before automating replenishment. Studenac now reports an 81% user acceptance rate and 55% fewer lost sales, as told in the Studenac case study.
How Long Does Data Integration Take?
Integration time depends on how many source systems you have, how clean they are and how many decisions are in scope. At Solvoyo, data integration and validation run inside the onboarding phase, together with discovery and configuration, in the first nine weeks. User acceptance testing follows in weeks 10 to 12, and a production-ready configuration typically takes about 12 weeks. Customers usually see measurable results within three to four months.
The integration itself runs in three steps: map your data to the planning model, agree push or pull and the timing with your IT team, and finalize documented endpoints. The business side often takes longer than the technical work: agreeing which lead time is the right one, who owns pack sizes, and which stock status counts as available.
Keeping Integration Healthy After Go-Live
After go-live, watch for feeds that fail silently: the plan runs on yesterday’s stock and nobody notices. Log every job as success or failure, alert on missing or late files, and keep the ingestion checks running every day.
Scope grows over time. Adding a planning module means adding the feeds it needs, such as production capacity for a production plan or carrier rates for transport planning. Master data also changes as items, stores and suppliers come and go, so keep the item and location setup under the same change control as the ERP.
How Solvoyo Approaches Data Integration
Solvoyo is a supply chain decision automation platform with a single data model for demand, inventory, supply, fulfillment and transportation, so each feed is loaded once and every planning module reads the same numbers. Master data diagnostics flag, isolate and correct broken records before they reach a decision, and planners can record a reason code whenever they change a recommendation. With checked data underneath, the engine plans against the real lead times, pack sizes and capacities, and over 95% of plans execute untouched.
Solvoyo connects through REST and SOAP web services, SFTP files, ODBC and SSIS, SAP RFC, EDI and middleware. The Integration and APIs page lists the options and the IT FAQ, and the customer journey shows each onboarding phase. To see how your own systems would connect, request a demo.
Frequently Asked Questions
Master data (items, locations, hierarchies, units, pack sizes, sourcing and, for manufacturers, bills of materials), sales or POS history, inventory positions, open purchase, transfer, production and customer orders, supplier and transport lead times, and constraints such as minimum order quantities, capacities and service targets. Prices, promotions and weather are added when the scope uses them.
Map the ERP data to the planning model, then choose a method per feed: scheduled batch files, direct database transfers, REST or SOAP APIs, a native connector such as SAP RFC, existing middleware, or EDI for trading partners. Agree push or pull and the timing with IT, check the data at ingestion, and set up the return path so approved plans are written back to the ERP.
It depends on the number of source systems, data quality and scope. With Solvoyo, data integration and validation run within the first nine weeks of onboarding, followed by acceptance testing, and a production-ready configuration typically takes about 12 weeks. Customers typically see measurable results within three to four months.
