Prediko Demand Forecasting and Cin7 Core
Integration Agency & Consultants
Capital efficiency starts to break when stockouts on high-margin items increase despite having inventory in the warehouse. This usually happens because 'In Transit' statuses or inconsistent lead times in Cin7 Core are not correctly mapped to sales velocity. We connect Prediko's demand forecasting to the inventory reality in Cin7 Core, ensuring procurement plans reflect actual stock availability rather than just historical sales. This prevents capital from being locked in overstock and gives the buying team a trustworthy 'open to buy' budget based on reconciled data.
Auditing SKU mapping and inventory logic
Diagnosis begins by examining how Cin7 Core handles stock levels and lead times, as these directly dictate Prediko's procurement accuracy. We audit your source of truth for inventory and SKU mapping to identify where manual workarounds currently mask data gaps. Discovery determines the sync frequency for items and how data should flow for financial reconciliation. We help define the ownership boundaries between finance and procurement to ensure the integration supports your month-end close. Skipping this diagnosis often leads to design flaws where Prediko may suggest stock already in transit, potentially forcing the team back into manual spreadsheets. We ensure your operating model is agreed upon before any technical build commences.
Solution Design
We typically treat Cin7 Core as the source of truth for physical inventory and purchase order history. The design focuses on reconciling warehouse reality with Shopify sales velocity to generate accurate procurement signals in Prediko. A key trade-off involves sync frequency. High-frequency updates for every SKU can increase system load and introduce transient data noise, so we often batch inventory snapshots to ensure Prediko works from a stable, reconciled baseline. We prioritise the flow of purchase recommendations back into Cin7 Core as draft POs to keep the procurement cycle tight. This design ensures finance closes the month based on Cin7 accounts while procurement operates with the velocity data Prediko provides. The result is an operating model where capital is allocated based on actual stockout risk rather than static reorder points.
Synchronising SKU mappings and order triggers
Cin7 Core acts as the authoritative source of truth for inventory levels and purchase order history. Prediko pulls this data, alongside sales velocity, to calculate procurement recommendations. The integration relies on exact SKU mappings. If a variant SKU differs from the ERP SKU, the sync flags the exception. Procurement suggestions generated in Prediko flow back to Cin7 Core for approval, so the ERP remains the master for financial records. We include monitoring to detect when lead time data drifts from historical reality. This helps prevent procurement suggestions from being based on outdated data.
Governing data movement with enterprise middleware
A controlled integration layer governs the data flow between Prediko and Cin7 Core. This layer manages the movement of inventory levels, lead time data, and purchase order suggestions. In high-volume environments, a SKU mismatch or a malformed payload from a renamed warehouse location can stall the procurement cycle. The integration layer handles these failures through business-rule validation at the boundary, a defined retry schedule, and full payload logging. If an inventory sync fails, threshold-based alerting notifies the operations team before it impacts procurement logic. This governance reflects enterprise-grade security standards, including ISO 27001 and SOC 2 compatibility. The layer is actively managed by Cogent consultants and monitoring agents to ensure data integrity between your forecasting and ERP.
Monitoring data integrity and stock exceptions
Standard dashboards usually show that data is moving, but they rarely show if the data is contextually wrong. Hidden issues, such as stock incorrectly counted as available during warehouse transfers or misread 'In Transit' statuses, can compound into over-ordering. Our approach surfaces these operational exceptions. We monitor the delta between ERP stock levels and forecasting snapshots to identify outliers before they become committed purchase orders. Teams receive alerts when lead times change significantly or when a sync failure threatens the accuracy of the next buy, preventing manual reconciliation gaps from slowing down the procurement cycle.
Operating the new demand planning workflow
Handover focuses on the operations and procurement teams, who must own the demand signal and procurement workflow after launch. Training covers the new operating model: how Prediko pulls inventory snapshots from Cin7 Core and where procurement suggestions are approved before being pushed back to the ERP. We define daily checks for stock on hand and weekly reviews of purchase suggestions to ensure the plan remains accurate. Your team learns to read alerts from the integration layer to catch lead time mismatches or SKU sync issues. Documentation is provided as a practical operational reference for the people running the business, not a technical archive for IT.
Maintaining sync stability and lead times
Post-launch, we provide operational monitoring of the sync between Prediko and Cin7 Core to protect forecasting integrity. This includes identifying lead time drift and verifying that stock statuses are correctly mapped so they do not skew procurement suggestions. When sales volumes spike, we monitor the integration to verify sales velocity data remains consistent. This helps prevent inventory drift that often occurs during peak trading periods when API rate limits or mismatched SKUs can quieten critical signals.
Common failures
Inconsistent lead times and 'In Transit' visibility
Operational impact: When Cin7 Core lead times are inaccurate or 'In Transit' stock is hidden from the forecast, Prediko generates flawed purchase suggestions. This creates a cycle of emergency air-freight to stop stockouts or leaves capital trapped in stock that arrived months too early. It forces the procurement team into a state of operational latency where they are always reacting to the warehouse rather than the market.
Prevention: Map Cin7 Core's 'In Transit' stock status precisely so Prediko accounts for it in replenishment logic. The integration must treat Cin7 Core as the source of truth for PO history, requiring a clear process for updating lead times based on actual receipt dates to eliminate sync illusion.
Incomplete or draft PO history sync
Operational impact: Prediko models future supply by analysing past purchase orders. If the integration pulls draft, cancelled, or unapproved POs, the forecast relies on misleading data. This creates source-of-truth ambiguity, leading the finance team to question purchasing budgets and forcing merchandisers into manual reconciliation as they validate every suggestion before approval.
Prevention: Configure the data flow to pull only Purchase Orders that have reached a committed 'Approved' status. Ensuring POs are only factored into forecasting once they are fully received keeps the procurement plan aligned with the accruals finance is tracking.
Inventory adjustments ignored by the forecast
Operational impact: Stock levels in Cin7 Core move due to write-offs, returns, and transfers, not just sales. If Prediko only sees sales and PO data, its view of available-to-sell stock drifts within hours. This leads to recommendations that miss looming stockouts from warehouse write-offs or suggest buying inventory that was already recovered through a return.
Prevention: Sync all inventory adjustment journals from Cin7 Core to Prediko. The integration logic must distinguish between sellable returned stock and non-sellable write-offs to maintain a true inventory baseline and prevent ownership leakage where the forecast misses warehouse-side reality.
Frequently asked questions
My inventory data in Cin7 Core might not be perfect. How does the integration stop Prediko from making bad purchasing recommendations?
This is a common concern and a key focus of the integration. Prediko relies on accurate stock level data from Cin7 Core, so an implementation includes auditing SKU records and aligning stock statuses like 'In Transit'. By ensuring Cin7 Core's inventory records are the source of truth, Prediko's forecasts start from a reliable baseline, preventing it from suggesting orders for stock you already have.
Does Prediko automatically create Purchase Orders in Cin7 Core?
No, Prediko generates purchase recommendations, not live Purchase Orders (POs). The standard operating model involves Prediko pushing its suggestions to Cin7 Core, often as draft POs. Your purchasing team then provides a final human review and approval within Cin7 Core before any order is sent to a supplier.
Which system provides the sales history for Prediko's forecasting?
While Cin7 Core is the source of truth for inventory and purchase orders, Prediko typically uses sales order history directly from the primary sales channel, like Shopify. This ensures its demand forecast is based on real-time sales velocity. Cin7 Core provides the supply-side data (stock levels), while the sales platform provides the demand-side data, giving Prediko a complete picture.
We have stock in our warehouse according to Cin7 Core, but still see stockouts on key SKUs. How does Prediko help?
This often points to a disconnect between raw inventory levels in Cin7 Core and the true 'available-to-sell' stock that Prediko needs for accurate forecasting. The integration project clarifies the definition of stock statuses, such as 'Awaiting Pick' or 'QC Hold', between the two systems. By aligning Cin7 Core's real inventory state with Prediko's demand signals, you ensure the purchasing budget is spent on items that are genuinely running low.