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When supply chain implementations fail, the root cause is often not system design or process alignment—it’s data readiness.
Too often, organizations assume data readiness = data correctness. In reality, it’s much broader:
- What data do we actually need?
- At what level of detail?
- How do we transform it from ERP into planning systems?
- And most importantly—how do we keep it healthy over time?
📊 The Data Interfaces That Drive Supply Chain Implementations
Every implementation rest on a set of inbound and outbound interfaces. Classifying them upfront provides clarity on what data really matters:
🧩 Where ERP Stops and Planning Starts
ERP systems are excellent at handling transactions (orders, stock, invoices) and basic masters (item, location, vendor, BOM).
But planning requires more nuanced constructs that ERP often doesn’t maintain natively:
Calendars: production, shipping, holiday calendars that govern planning cycles.
Lead Times: procurement, transportation, and production lead times tailored for simulation.
Planning BOM Subsets: collapsing multi-level BOMs into just the levels relevant for MPS or network planning.
Sourcing Rules: allocation logic that goes beyond ERP purchasing defaults.
This is why Data Readiness Templates matter: they don’t just capture “source systems.” They capture the transformation rules and the governance ownership needed to make data fit for planning.
🗂️ SMARTLINKS Way to Handle Data
Master Data (The Structural Backbone)
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- Risk if ignored: Wrong hierarchies, misaligned routings, flawed sourcing rules
- Best practice: Document upfront, validate hierarchies, automate reconciliation
Transaction Data (The Dynamic Signals)
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- Risk if ignored: Over/understated demand, bad supply signals, missed promotions
- Best practice: Align load frequency to business cadence, prioritize freshness
Modes of Handling
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- Delta Loads → incremental updates, efficient but needs strong change-detection.
- Full Loads → complete refresh, reliable but heavy and should be scheduled carefully.
🛠️ Three Stages to Get It Right- The Smartlinks Way
- Preparation (Before Implementation)
- Define inbound/outbound interfaces
- Classify Master vs Transaction, Delta vs Full
- Use templates to capture sources, frequency, ownership, and transformation rules
Some may argue this is too early in the game, but this gives us insight into data and possibilities and prevents future risks and allows you time to think through scenarios
- Execution (During Implementation)
- Stage data logically (collapse BOMs, filter routings, prioritize transactions)
- Validate through trial runs before cutover
- Sustainability (After Go-Live)
- Monitor data health through dashboards
- Set ownership rules for every data object
- Automate delta capture with fallback to full loads
✅ Why This Matters
By treating data readiness as a front-loaded enabler—not a late-stage cleanup—organizations can:
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- Avoid last-minute surprises
- Accelerate cutovers
- Build trust in planning outcomes
In short: Data readiness turns data from a risk factor into a risk mitigator.