Amazon FBA and Prediko Demand Forecasting
Integration Agency & Consultants
Cogent2's AI-assisted delivery and operator experience connects demand planning to marketplace fulfilment correctly. We integrate Prediko with Amazon FBA to translate sales forecasts into precise replenishment workflows. This closes the gap between planning and execution, helping you avoid lost sales from stockouts and reduce FBA overstock fees.
Auditing your current stock and sales data
We swiftly connect your Amazon FBA and Prediko Demand Forecasting integrations, supporting Marketplaces and Shopify App users. Our consulting services are invaluable, offering a thorough systems audit to uncover inefficiencies and integration gaps across Amazon FBA, Prediko Demand Forecasting, Marketplaces, and Shopify App environments. This audit empowers both our consultants and your team to take decisive action, ensuring your tech ecosystem runs efficiently. As a result, you can deliver a consistently excellent experience to your customers.
Solution Design
Design for this pair starts with establishing Shopify as the source of truth for demand signals, while Amazon FBA remains the source of truth for physical stock availability. A key decision involves the sequencing of data: we typically prioritise historical Shopify order data to drive Prediko forecasts, which then inform replenishment quantities for Amazon. We often face a trade-off regarding sync frequency; real-time updates for every FBA stock movement can be fragile, so we typically opt for a controlled sync that ensures accuracy for replenishment decisions. This design ensures that finance trusts inventory valuations while operations runs off a clear forecast. Manual intervention is often retained for final replenishment approval until the forecasting model is fully calibrated.
Mapping SKU data between Shopify and Amazon
Data flows from Shopify to Prediko to establish the demand baseline, which is then mapped against current Amazon FBA inventory levels. We prioritise SKU mapping to ensure that forecasts correlate exactly with active Amazon listings. Sales data typically needs to be cleaned of cancelled orders in Shopify before Prediko generates a replenishment recommendation. By embedding monitoring at the sync layer, we detect when stock discrepancies occur early, allowing for adjustment before orders are placed with suppliers. This ensures that the replenishment quantities sent to FBA are based on a unified and accurate data set.
Securing data flows through accredited orchestration layers
Leveraging IPaaS with ISO 27001 and SOC 2 and above security accreditations enables secure, efficient integration between Amazon FBA, Prediko Demand Forecasting, Marketplaces, and Shopify App. This approach simplifies connecting Amazon FBA and Prediko Demand Forecasting with Marketplaces and Shopify App, ensuring data protection and compliance. IPaaS platforms offer centralised management, automation, and scalability, making integrations reliable and secure for businesses handling sensitive data.
Surfacing data drift and stock discrepancies early
Standard dashboards often hide the drift between Shopify sales and Amazon stock updates. We focus on surfacing operational exceptions, such as when a SKU exists in Shopify but lacks a corresponding mapping in Prediko or Amazon. Hidden issues like unrecorded inventory adjustments at the FBA warehouse can compound into inaccurate forecasts if not flagged. Our approach ensures that these failures are surfaced early, preventing a scenario where you over-order stock based on outdated availability data. This provides the transparency needed to trust the replenishment recommendations.
Handover of the forecasting and replenishment cycle
Handover focuses on ensuring operations and ecommerce teams own the forecasting cycle. We provide operational documentation that clarifies how Shopify sales data informs Prediko recommendations for FBA replenishment. Your team learns to verify stock levels across both systems and define what to check on a regular cadence to prevent overstocking. Ownership is clearly assigned for data exceptions, such as unmapped SKUs or sync delays. This ensures the business maintains control of inventory planning once Cogent steps back, using documentation built for the individuals running the day-to-day process rather than a technical archive.
Monitoring inventory sync and mapping accuracy post-launch
Post-launch support focuses on preventing operational drift between Amazon FBA and Prediko. We monitor the data flows across Shopify, Amazon and Prediko to catch discrepancies before they skew your demand forecasts. If sync errors occur or SKU mapping issues arise, we identify the root cause to protect your replenishment cycle. This includes monitoring for common failures like fragmented SKU mapping or regional sync gaps to ensure your team can trust the inventory recommendations. Ongoing oversight is geared toward maintaining forecast accuracy and preventing stockouts caused by data lag or metadata mismatches.
Common failures
Forecasts based on total channel sales
Operational impact: Prediko's forecasts become inflated for FBA SKUs because they include sales velocity from merchant-fulfilled orders. This leads the operations team to over-order stock for FBA, tying up capital and incurring unnecessary storage fees. The finance team must then reconcile higher-than-expected FBA charges against a flawed demand plan.
Prevention / Action: The integration logic must tag and filter all sales orders by their fulfilment channel before Prediko ingests the data. The source of truth for an order's fulfilment method (e.g., 'FBA' or 'Merchant') must be established at the point of order creation in Shopify. This allows Prediko to be configured to analyse only the segment of sales fulfilled by Amazon for its FBA-specific forecasts.
Inconsistent SKU and product identifiers
Operational impact: Prediko generates an accurate forecast against a Shopify SKU, but the replenishment recommendation is operationally useless if it cannot be mapped to the corresponding Amazon FNSKU. This failure forces the merchandising or operations teams into manual purchase order creation, introducing delays and human error. It directly leads to incorrect stock being sent to FBA or replenishment being missed entirely.
Prevention / Action: Establish a single source of truth for SKU master data before the integration goes live. The process design must ensure that creating or updating a product in the master system (e.g. Shopify) triggers a corresponding, automated mapping to the Amazon SKU. Use a central mapping table or a consistent primary key like an EAN or UPC, and run regular audits to identify and fix unmapped SKUs before they impact a purchasing cycle.
Including cancelled orders in sales history
Operational impact: When sales data fed to Prediko includes orders that were subsequently cancelled or refunded, historical sales velocity is artificially inflated. This results in systemic over-forecasting and chronic over-stocking, increasing FBA storage costs and trapping working capital. While the customer service team processes the refund, the consequence of the unadjusted data is felt by the finance and operations teams who manage the excess inventory.
Prevention / Action: Configure the data pipeline to ensure that sales history used for forecasting is net of any defined exclusions like cancelled or fully refunded orders. This involves either filtering orders by their status before they are analysed, or applying a negative value adjustment. The process must account for the typical time lag between an order being placed and its potential cancellation, scheduling the forecast analysis to run after these adjustments are consistently captured.
Demand latency from 'Pending' Amazon orders
Operational impact: Amazon often holds orders in a 'Pending' state while verifying payment, a period that can last for hours or days. These orders are typically excluded from sales data feeds, creating a significant blind spot in near-term demand. For fast-moving SKUs, this understatement of true sales velocity can easily lead to stock-outs before a replenishment order, based on lagging data, can arrive at the fulfilment centre.
Prevention / Action: The integration should be designed to recognise 'Pending' orders as a crucial, early demand signal. This requires a process to track pending order volume and velocity separately from confirmed Sales Orders. This data can then be used to adjust forecasting models, accounting for the typical conversion rate and time lag from pending to confirmed status, allowing inventory decisions to reflect customer commitments much earlier.