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Every manufacturer running an ERP system faces the same fundamental disconnect. SAP, Oracle or Microsoft Dynamics knows what was planned: which products, which quantities, which machines, which materials. The factory floor knows what actually happened: which machines ran, which ones stopped, how long each stoppage lasted, how many conforming parts came out at the end of the shift. Between these two layers sits an information gap that distorts production cost accounting, undermines capacity planning, and makes the OEE discussion at the weekly review meeting a debate about estimates rather than an analysis of facts. MES ERP integration<\/strong> is the discipline of closing that gap \u2014 and the two approaches that exist to close it differ by 12 months, several hundred thousand euros, and the analytical depth of what you get when it is done.<\/p>\n ERP systems operate at planning precision \u2014 they know what a work order requires, not what a machine produced minute by minute during its execution. SAP’s production order confirmation process (MIGO or CO11N) receives aggregated data: total quantity confirmed, total scrap, total labour hours. It does not receive the 47 micro-stoppages that occurred during the shift, the changeover that ran 23 minutes longer than standard, or the fact that the night shift consistently produces 12% more quality deviations on Product Reference A than the day shift. This information exists on the factory floor. It simply has no automated path back to ERP.<\/p>\n The consequence is that production cost accounting in ERP is built on incomplete data. Actual versus standard cost variances are real, but the reasons behind them are invisible. Capacity planning is based on theoretical cycle times rather than measured actual OEE. Maintenance budgets are allocated based on calendar schedules rather than actual equipment degradation signals. The cumulative financial impact of this information gap \u2014 the decisions made on incomplete data, the production losses not identified early enough, the maintenance interventions that came too late \u2014 runs into millions of euros annually for mid-size manufacturing organisations. The ISA-95 \/ IEC 62264 international standard<\/a> was developed specifically to define the data models and interface architecture for closing this gap.<\/p>\n Architecture 1 is the full MES layer: a Manufacturing Execution System sits between ERP and the machines, managing work order execution and feeding confirmed production data back to ERP. SAP DMC, Siemens Opcenter and Rockwell Plex all implement this architecture natively. The data flow is comprehensive \u2014 genealogy, quality results, material consumption and OEE all flow to ERP through the MES. The cost is 200,000 to 1,500,000 euros and 12 to 18 months of implementation. The OEE analytics depth is reporting-level.<\/p>\n Architecture 2 is the ERP-plus-OEE-platform layer: ERP manages production planning and order management as it always has. A specialist OEE platform receives production orders from ERP, measures equipment performance against them in real time, and feeds actual production confirmations and OEE data back to ERP. No full MES layer sits in between. The data flow covers the OEE and production confirmation dimensions \u2014 not genealogy or work order execution management, which remain ERP’s responsibility. The cost is SaaS per production line, recovered from improvement in 4 to 8 weeks. The OEE analytics depth is JEMBA AI \u2014 which no full MES OEE module currently matches. And the implementation time is 48 hours for the first line.<\/p>\n For manufacturers whose primary integration need is production performance data flowing to ERP \u2014 not genealogy or regulatory compliance documentation \u2014 Architecture 2 closes the information gap that matters most, faster and at lower cost than Architecture 1. For manufacturers who need both, Architecture 1 and Architecture 2 coexist: the MES handles compliance management, TEEPTRAK handles OEE analytics, and the two integrate bidirectionally as described in our MES system in manufacturing guide<\/a>.<\/p>\n In a TEEPTRAK-ERP integration, the bidirectional data flow covers the dimensions that drive production performance management. From ERP to TEEPTRAK: production orders with product reference, planned quantity and scheduled machine; work centre master data with ideal cycle times per product; production calendar with planned shift windows. This context allows TEEPTRAK to calculate OEE per order, per shift and per machine against the ERP plan rather than against a generic baseline.<\/p>\n From TEEPTRAK to ERP: actual production confirmations with produced quantity and scrap count for each order completion \u2014 enabling ERP to update stock levels, calculate actual cost variances and trigger customer delivery processing. OEE data at order and shift level for production cost analysis. Downtime events categorised by root cause for maintenance cost allocation. JEMBA AI predictive maintenance alerts that can automatically create maintenance work orders in ERP’s PM module or an integrated CMMS. The integration uses standard ERP web services \u2014 SAP OData and BAPI interfaces, Oracle REST API, Microsoft Dynamics OData \u2014 without custom development on either side for standard scenarios.<\/p>\n A common framing in manufacturing technology discussions positions MES and ERP as competing or overlapping systems where the goal is to find the right boundary between them. This framing is misleading in practice. ERP is not in competition with MES any more than a production planning system is in competition with a machine. They operate at different levels of the manufacturing hierarchy, as defined by ISA-95, and they manage different time horizons: ERP at days-to-months for business process management, MES at hours-to-shifts for production execution, OEE platforms at seconds-to-shifts for equipment performance measurement.<\/p>\n The question is not which system wins; it is which combination of systems closes the information gaps that are costing money. For most discrete manufacturers \u2014 those without regulatory genealogy requirements and without complex work order routing across dozens of workstations \u2014 the ERP plus TEEPTRAK combination closes the economically significant gaps in 48 hours. For manufacturers with regulatory requirements or complex execution management needs, the full MES layer is warranted, with TEEPTRAK complementing it as the OEE analytics engine. The OPC Foundation’s OPC-UA standard<\/a> provides the machine-layer connectivity protocol that both architectures rely on for modern equipment integration.<\/p>\n The financial case for MES ERP integration \u2014 in either architecture \u2014 is built on two improvements that become possible once the data gap is closed. First, production cost accounting becomes accurate: actual versus standard variances are explainable by measured OEE data rather than estimated by production managers. For a factory with 30 million euros of annual production value and a 5% cost variance improvement from better data, that is 1.5 million euros of financial improvement from information quality alone. Second, production improvement accelerates: each OEE point gained on a line generating 200 euros per hour of added value running 4,500 hours per year is worth 9,000 euros annually. TEEPTRAK customers average 29 OEE points improvement in 12 months \u2014 261,000 euros per line per year. The full ROI framework for both architectures is in our OEE software pricing and ROI guide<\/a>.<\/p>\n \nClose the ERP-to-floor data gap in 48 hours \u2014 without a MES project<\/strong> External references: ISA-95 \/ IEC 62264 \u2014 enterprise-control system integration standard<\/a> · OPC Foundation \u2014 OPC-UA industrial connectivity<\/a><\/p>\n See also: MES system in manufacturing<\/a> · MES alternative guide<\/a> · OEE software pricing and ROI<\/a><\/p>\nWhy ERP alone cannot see what is happening on the factory floor<\/h2>\n
The two architectures for MES ERP integration<\/h2>\n
What data actually flows in each direction<\/h2>\n
The MES vs ERP debate that misses the point<\/h2>\n
Measuring the ROI of closing the integration gap<\/h2>\n
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