ERP and EPM: A Guide for Commerce Businesses

Financial operations don’t always scale easily with financial complexity, and CFOs can suffer the consequences. Businesses selling direct-to-consumer (DTC), wholesale, and business-to-business (B2B) at the same time can find financial close turning into a quarterly ordeal. Orders flow across different channels at different margins, under different payment terms, in multiple currencies, and into systems that weren't necessarily designed to reconcile them at the speed multichannel commerce generates them.

Global B2B ecommerce alone was valued at $30.1 trillion in 2025, according to Statista. And many brands building a competitive share of that volume also sell DTC simultaneously. Their enterprise resource planning (ERP) and enterprise performance management (EPM) systems, however, were designed for simpler architectures that often only had one channel, one margin profile, one currency, and predictable transaction volumes.

This guide covers what ERP and EPM platforms actually do, why the traditional distinction between them often breaks down under multi-channel commerce, where a modern API-first commerce platform fits in the financial architecture of enterprise-level commerce, and how to sequence implementation to compress close cycles and gain per-channel profit and loss (P&L) visibility.

What ERP and EPM actually do: Why definitions matter for commerce

Before getting to where enterprises run into problems with these systems, let’s be precise about what each system does. The gap between the definitions and the commerce reality is where some architecture mistakes originate.

ERP: The operational record of what happened

ERPs are systems of record. Some of the biggest platforms like SAP S/4HANA, Oracle ERP Cloud, Microsoft Dynamics 365, and NetSuite all perform the same core function: capturing every operational event, including purchase orders, inventory movements, received payments, and payables, and posting them to a general ledger (GL).

The GL is the financial source of truth for the business. Every downstream deliverable, from the trial balance to audited financials, traces back to the structured data the ERP captures at the transaction level. For manufacturers and distributors with predictable transaction volumes and a single sales channel, ERP does this job well.

The design assumptions baked into most enterprise ERP platforms, however, are two to three decades old, from a time when multichannel ecommerce was nowhere near as ubiquitous as it is today. These systems can still handle hundreds of purchase orders per day, complex fixed-asset depreciation schedules, and multi-entity consolidation without strain; but they weren't architected to ingest 50,000 DTC orders on a Tuesday, reconcile them with wholesale invoices, and process B2B net-30 accounts before the period closes.

EPM: The analytical layer for what should happen next

EPMs, such as Anaplan, Workday Adaptive Planning, OneStream, and Oracle EPM Cloud, sit above the ERP layer. Their job is to support planning, not recording. A CFO uses an EPM to build annual operating plans, run rolling forecasts, model scenarios, consolidate multi-entity financials, and report against actuals.

There is a clear and critical dependency here: An EPM is only as accurate as the data feeding it. If the data isn’t correct, the analysis won’t be. And if the data is correct but the processes supporting it are slow, then the resulting analysis will reflect a reality that, even if it was accurate at one point, is now in the past. 

Every EPM deployment needs a reliable upstream data source, and for multichannel commerce businesses, that source is increasingly not the ERP.

Why the traditional ERP-EPM distinction breaks down in multichannel commerce

For a business with a single sales channel, it may be easier to have a clean ERP-to-EPM data flow, especially if volume is predictable, transaction types are limited, and currency exposure is manageable. The ERP can capture what happened, and the EPM can help plan what happens next.

Consider a business selling on three channels simultaneously. DTC orders close immediately at consumer margin, with settlements arriving on a two-day lag. Wholesale orders may carry net-30 or net-60 terms, with tiered pricing and minimum order quantities (MOQs). B2B orders may carry custom pricing, split fulfillment across locations, and require credit limit approvals before release.

Each channel has a different P&L structure, cost of goods, return rate, and contribution to working capital.

The ERP's GL might not distinguish between these inputs by default. Revenue can post to a revenue account, and cost of goods sold (COGS) might post to a separate COGS account. Getting per-channel margin visibility requires either extensive chart-of-accounts customization or a separate data layer—or a commerce platform that structures transactional data at the channel level before it reaches the GL.

The commerce data problem that ERP vendors don't talk about

Legacy ERPs may be presented as unified financial systems, but the transaction volumes, data structures, and reconciliation requirements of modern multichannel commerce sit well outside the design constraints many of those systems were built for. This isn’t a gap that can be addressed by a simple integration, either. The problem is structural. 

High-velocity transactional data

A mid-market brand running DTC, a wholesale portal, and a B2B storefront might process thousands of transactions per day across channels during a peak period. Each transaction carries SKU-level data, channel attribution, fulfillment location, payment method, applied discounts, tax jurisdiction, and for B2B, customer-specific pricing. This goes well beyond one GL line per order; it can include dozens of structured data points, each a planning-relevant signal.

A TrendCandy survey of 200 manufacturing decision-makers found that manual workflows cost businesses an average of 5% of their annual revenue, with 88% of those businesses losing deals due to inefficient quoting and approval processes. That’s a process problem, but it’s also a data-velocity problem. When the operational system can't capture and structure transactional data fast enough, the planning layer responds too late to act on the signal. Sometimes your competitor’s quote just gets there faster.

Traditional ERP platforms often process this data in batch cycles; sometimes nightly, but in some instances weekly or monthly. For a DTC brand running flash promotions, that latency means the EPM model forecasts against yesterday's inventory and last night's margin. For a B2B team managing credit limits and order approvals, it means decisions can be made based on data that's already out of date.

Multi-currency reconciliation and the financial close challenge

Multi-currency commerce adds complexity that ERP platforms often handle inconsistently. A brand selling in the US, UK, EU, and APAC regions simultaneously posts revenue in four currencies, with exchange rate exposure that changes throughout the day. Each settlement converts at the rate in effect at the time of payment, not the rate used during month-end reconciliation.

The gap between payment gateway data and ERP ledger entries is where finance teams lose close time. Manually mapping settlement files to GL entries, applying the correct exchange rates, and reconciling returns against original payment currencies can extend close cycles for teams relying on ERP-native reconciliation tools. McKinsey found that up to one-third of B2B transactions still rely on manual methods such as fax and direct mail. Where manual processes persist, unstructured order data has to be keyed into the ERP by hand, introducing errors that surface during close and require correction before the books can be signed.

Per-channel revenue recognition

DTC, wholesale, and B2B revenue are recognized at different points in the timeline of the transaction. DTC revenue is recognized at shipment. Wholesale revenue may be recognized on delivery or acceptance, depending on contract terms. B2B orders with milestone billing or subscription components carry their own recognition schedules.

A single GL line marked "revenue" does not reflect that complexity. CFOs reporting channel-level performance to a board, an investor, or a lender need revenue displayed by channel before it hits the consolidated P&L. Getting that segmentation out of a traditional ERP, however, often requires custom reporting configuration, manual journal entries, or a business intelligence (BI) layer doing work the ERP can’t do.

Finance teams can circumvent these limitations with chart-of-accounts tricks, such as adding sub-accounts for each channel. Such workarounds might be a viable option for a small business, but for an enterprise or scaling business with dozens of product categories, multiple fulfillment models, and overlapping promotional structures, the maintenance overhead grows faster than the business it's supposed to serve.

How a modern commerce platform can serve as the ERP data spine

The conventional assumption has been that the ERP is the system of record and the commerce platform is the front end. For multichannel commerce businesses, that hierarchy is reversed. The commerce platform is where every financial event originates: think order creation, payment capture, inventory movement, return processing, settlement, and more. What the ERP receives downstream is an aggregated version of data that was created or captured in the commerce platform.

What "API-first" means for financial data flow

A modern, API-first commerce platform generates structured financial events at the transaction level, available in real time via API. It goes well beyond just processing orders.

Every order object contains channel, customer, line-item pricing, applied discounts, tax, payment method, currency, fulfillment location, and fulfillment status. Every return object carries the original order reference, restocking status, and refund amount. Every payout object maps settlement amounts to individual transactions.

This is the data model that EPM tools need to plan at the sales channel level, and it exists natively in the commerce platform before it ever reaches the ERP. An API-first architecture means that structured financial data can flow directly from the commerce platform to the EPM layer, bypassing the ERP's batch cycle. For rolling cash-flow forecasts and intra-period demand sensing, that direct delivery can change what's operationally possible.

For brands that manage working capital tightly, latency can be the difference between acting on a demand signal and reacting to a trend already past its peak.

What Shopify's data model looks like to an EPM tool

With a legacy system, a brand selling DTC, wholesale via a B2B portal, and in a marketplace might need up to 12 days to complete a financial close: 10 days for the ERP to reconcile all three channels, and 2 more to export GL data into the EPM platform.

With Shopify, they can route transaction data directly to their EPM, allowing the finance team to receive channel-level actuals within 24 hours of period close. The ERP can remain the GL of record for consolidated financials and compliance. For planning purposes, the EPM can read structured commerce data rather than waiting for the ERP's batch cycle. The two systems can then become complementary layers rather than a hierarchy.

Independent research shows that brands transitioning to Shopify see up to a 33% decrease in total cost of ownership (TCO) compared to major competitors. For a brand spending six-figure sums annually on ERP customization to produce channel-level reporting, rerouting that data flow reduces both stack cost and the latency between transaction and insight.

The integration architecture connecting your commerce platform to your EPM

Getting commerce data into an EPM tool requires deliberate choices about middleware, data cadence, and the granularity the EPM platform can handle. Each decision shapes what the financial planning and analysis (FP&A) team can actually do with the data once it arrives, and how quickly they can do it.

How EPM budgets are seeded from commerce transactional data

A traditional budgeting workflow reads from the general ledger. You pull last year's actuals from the ERP, build next year's budget in the EPM, and load the budget back to the ERP for variance tracking. This produces accurate budgets for stable, single-channel businesses. For multichannel commerce, it can flatten the planning signal.

When the EPM is seeded from transactional data rather than just the GL, the budget can reflect channel-level seasonality, SKU-level margin variance, and promotional lift from specific campaigns. The planning model can distinguish between DTC revenue that grew from a paid acquisition push and B2B revenue that grew from the addition of two wholesale accounts. Those are different cost drivers, different sustainability profiles, and different inputs to a capital allocation decision.

Real-time vs. batch sync

Most EPM vendors support both real-time API connections and batch file uploads. What they don't always mention is that many EPM data models are optimized for batch syncs. 

The practical architecture for most multichannel commerce businesses includes a combination of real-time and batch sync. A data warehouse such as Snowflake or BigQuery can aggregate commerce transactional data at whatever granularity the EPM can handle, then sync on a daily or weekly cadence. The ERP can continue to own the GL of record, while the commerce platform owns the operational data. The data warehouse then serves as the translation layer that makes them compatible.

AI-assisted forecasting from commerce data

Many EPM platforms have added AI-assisted forecasting capabilities over the past three years. Workday Adaptive Planning, Anaplan, and OneStream all include machine learning (ML) models that detect seasonality, identify anomalies, and generate probabilistic forecast ranges. The accuracy of those models depends on the quality and granularity of input data.

Commerce transactional data tends to be a better input for forecasting than GL data because it retains valuable signals. A GL entry reading "$2.3M DTC revenue, October" has already aggregated away, for example, the daily demand curve, the promotional spike on day 12, and the geographic concentration of orders. The underlying transaction data, however, still carries all of that—date, time, channel, location, SKU, discount code, acquisition source—and AI forecasting models can use it.

Two-thirds of B2B buyers now use generative AI tools as much as or more than traditional search engines to evaluate vendors, according to Responsive research. The shift AI is driving on the demand side has implications for planning: demand patterns are becoming less linear and less seasonal, and AI-assisted forecasting from granular commerce data is one of the few planning tools that can keep pace.

ERP, EPM, or commerce platform first? A sequencing framework for scaling businesses

The sequencing question is where many CFOs lose the most time. Getting the order wrong—implementing EPM before establishing a clean data layer, or running a legacy ERP migration before stabilizing the commerce platform—creates new integration debt. A clear sequencing framework can protect against that outcome.

The migration-in-flight challenge: Keeping storefronts live

Replatforming a commerce stack while keeping multiple channels live is a real operational risk. DTC storefronts can't go dark during a migration. B2B portals with active accounts need order continuity. Wholesale order processing can't stop for a migration window.

The brands that manage migration well tend to separate data layer migration from storefront migration. The commerce platform goes live first, capturing clean transactional data from day one. ERP integration can follow in phases (GL sync, then inventory sync, then accounts receivable sync) with the legacy system handling historical data and the new platform handling forward transactions. EPM integration of the new data source should come last, once the commerce data model is stable enough to produce reliable actuals.

Decision framework: Complement, replace, or rely

There are three primary sequencing approaches, each applicable to different business profiles. The right choice depends on ERP maturity, compliance complexity, and revenue scale:

Complement: Keep the existing ERP as the GL of record, add the commerce platform as the operational data layer, and route structured transactional data to both the ERP and the EPM via APIs. This approach tends to be best for businesses with mature ERP deployments and complex compliance or multi-entity consolidation requirements that justify retaining the ERP.

Replace: Migrate from legacy ERP to a modern cloud ERP while simultaneously replatforming commerce. Use the migration to redesign the chart of accounts for channel-level reporting. This approach tends to be best for businesses whose ERP is at the end of life, heavily customized beyond maintainability, or no longer supported by the vendor.

Rely: For brands whose transaction volumes and compliance requirements don't justify a full ERP, use the commerce platform as the primary financial data layer and connect directly to EPM. The platform's native financial reporting handles the operational record; the EPM handles planning and consolidation. This approach tends to be best for brands with limited multi-entity complexity.

Often, the most damaging sequencing choice for a growing commerce business is to treat ERP modernization as a prerequisite for EPM implementation. That approach delays planning capability by months or years while the ERP migration runs, meaning the CFO makes acquisition, inventory, and headcount decisions without the per-channel visibility that the EPM was supposed to deliver.

Facing the inaction tax

Every quarter that a multichannel business operates without unified commerce data flowing to the planning layer carries a cost. Close cycles continue to take too long; forecasts continue to miss; and channel-investment decisions continue to rely on incomplete data rather than per-channel margin analysis.

An independent consulting firm found that businesses migrating to Shopify see 15% incremental revenue attributable to direct platform benefits. That uplift compounds quarter over quarter, especially when you account for opportunity cost. And the reverse is true, too: Every quarter you’re not improving, you’re facing the inaction tax—the invisible cost of ongoing friction. 

You can think about the cost of a brand delaying unified financial data by two years in terms of missing two years of better forecasting; and while that’s no small cost, it doesn’t get the whole picture. That brand is missing two years of compounding margin improvement from decisions that cleaner data would have made visible; as well as all the attendant opportunities they could have seen and seized with more revenue to support growth. 

What good looks like: Commerce businesses with unified financial operations

The clearest demonstration of what a well-sequenced commerce data stack produces is the operating results of brands that have built one. 

Russell Hendrix

Russell Hendrix, a foodservice equipment supplier, migrated to Shopify and achieved a 24% increase in revenue and a 43% increase in B2B online order volume. The finance team's most significant operational gain: Sales reps processing draft orders through Shopify ran five times faster than those routing orders directly through the legacy ERP. The commerce platform became the operational entry point for order data, and the ERP received clean, structured input downstream rather than manually keyed raw orders.

“Processing orders in the admin helps our sales team bypass so many steps we used to have to go through before. It's probably five times faster than using our ERP,” says Paul Roy, director of ecommerce and marketing. 

Industry West

Furniture brand Industry West achieved a 90% lift in B2B web order revenue from trade accounts, a 20% lift in average order value (AOV), and a 15% lift in average basket size after migrating to Shopify. They’d faced the inaction tax and chose to stop paying it. 

On their previous platform, development hours were sucked into maintenance work and never seen again. “We were showing all of these hours spent on development without anything appearing to happen. I kept saying, ‘It’s not that nothing is happening, you’re not seeing anything better happening,’” says Ian Leslie, chief marketing officer.

Money and time were being spent, though, and plenty of it. “We were just spending time and money on urgent, back-end things,” Ian explains. “For instance, we poured money into a sunsetting version of [our previous platform], and then just had to spend even more when there was an upgrade. Plus, every quarter there were patches. These things were what we were focusing our time and money on.”

After migrating, Industry West no longer had to pay the inaction tax and could put their time and effort where they really needed to be. 

Kooks

Kooks, an automotive manufacturing brand, achieved a 38% reduction in total cost of ownership and a 22% increase in conversion rate after migrating to Shopify. Thanks to an integrated ERP, they were able to maintain real-time inventory accuracy, reducing their order-management workload. In parallel, the marketing team gained a new set of EPM-like tools through the robust analytics built into the Shopify platform and ecosystem.

When the commerce platform and ERP share a live inventory feed, the CFO can monitor carrying cost exposure and fulfillment risk in the same data stream rather than waiting for a batch reconciliation to surface the gap.

“Transitioning to Shopify has given us the functionality we needed to scale—real-time inventory, integrated analytics, and B2B capabilities that streamline operations,” says George Kook Jr., president.

Building the business case: Key questions to answer before you buy

Before selecting or reconfiguring any part of the ERP-EPM-commerce stack, specific questions need to be answered. 

The key questions to work through before committing to an architecture or a vendor include:

• What is the current close cycle, and which channel's reconciliation is driving it?
• What is the current forecast accuracy at the channel level?
• Does the existing ERP expose transactional data via API, or only via GL extracts?
• What is the chart of accounts' current ability to distinguish channel-level revenue and COGS?
• Which EPM vendors handle commerce dimensionality at transaction granularity?

The wrong answers to any of these risk surfacing as integration failures or data quality problems after go-live. 

From transactional data to strategic planning engine

The ERP-versus-EPM debate tends to be, for commerce businesses, a distraction from the question that actually matters: Is the transactional data layer clean, unified, and fast enough to feed the planning layer in real time?

For most multichannel commerce businesses, the answer is no—not because they lack an ERP or an EPM, but because the data moving between their commerce operations and their planning tools was never designed to travel at commerce velocity. Legacy ERP platforms were built for asset-heavy industries with predictable transaction structures. EPM platforms are only as accurate as their upstream inputs. The GL, by design, aggregates before it reports.

The commerce platform that captures every financial event at the transaction level and exposes that data via API is the architecture shift that changes what's possible for the CFO. 

That shift reframes the CFO's role in the replatforming conversation. Unifying the commerce and planning stack isn't just an IT-migration project managed by engineering and reviewed by finance. Unification can be the highest-leverage finance transformation decision available to a multichannel commerce business—one that compresses close cycles, sharpens forecast accuracy, and makes per-channel insights visible in time for you to act on them.

• Talk to a Shopify commerce expert about unifying your financial data layer — book a consultation
• Download the Shopify Time to Value Guide to see how fast enterprise brands are building the composable commerce stack (https://www.shopify.com/resource/time-to-value-guide)