Clarus WMS and Prediko Demand Forecasting
Integration Agency & Consultants
This integration becomes critical when Shopify sales velocity outpaces your ability to track physical stock depth in Clarus WMS. At scale, the gap between predicted demand and warehouse reality leads to stockouts on bestsellers and capital tied up in slow-moving lines. We connect Shopify sales signals to Clarus fulfilment data so your procurement is grounded in what you actually have available to sell.
Auditing warehouse data and forecasting gaps
Cogent connects your Clarus WMS and Prediko Demand Forecasting with ease, ensuring your WMS/3PL and Shopify App function optimally. Our consulting services, including system audits, are crucial for identifying inefficiencies and integration gaps. By auditing your Clarus WMS and Prediko Demand Forecasting, we empower your team to take decisive action, ensuring your tech ecosystems, including WMS/3PL and Shopify App, operate smoothly. This enables you to provide an exceptional customer experience, maintaining efficiency and reliability across your operations.
Solution Design
The design for Clarus WMS and Prediko prioritises inventory accuracy over high-frequency sync polling. In most setups, Clarus acts as the source of truth for stock on hand, while Prediko identifies the replenishment triggers based on Shopify sales. A core design decision involves the trade-off between real-time signals and system stability during peak trading. We typically implement a buffered inventory sync from Clarus to Shopify, which Prediko then consumes. This prevents API rate limits from being triggered during high-volume sales periods while ensuring the forecast remains accurate. The operating model relies on this sequence so that procurement teams trust the replenishment metrics without manual warehouse checks. This design establishes a clear ownership boundary where procurement teams work off Prediko signals grounded in physical Clarus stock levels.
Mapping inventory signals and stock movements
The integration establishes Clarus WMS as the authoritative source for inventory levels. Prediko consumes these signals to calculate replenishment dates and stock requirements accurately. A critical flow involves tracking inventory updates and Purchase Order status from Clarus to ensure forecasting accounts for stock already on its way. We monitor for stock adjustments and returns in Clarus to ensure non-sales movements do not inflate the demand model. This ensures procurement signals are anchored to physical warehouse events, preventing reorder alerts for stock already being processed.
Orchestrating secure data flows via IPaaS
Cogent2 leverages IPaaS to integrate Clarus WMS and Prediko Demand Forecasting with WMS/3PL and Shopify App, ensuring secure, efficient data handling. IPaaS platforms, compliant with ISO 27001 and SOC 2 and above, offer a centralised framework for connecting systems, automating data exchange, and supporting scalable workflows. This approach enhances security and operational efficiency, making it ideal for businesses using Clarus WMS, Prediko Demand Forecasting, WMS/3PL, and Shopify App.
Monitoring stock drift and sync health
Standard dashboards often fail to detect the gradual drift between predicted demand and actual warehouse stock. We monitor the relationship between replenishment suggestions and physical stock-on-hand to identify where these cycles diverge. Visibility means being alerted when a stock update in Clarus WMS fails to reflect in Prediko or when an unmapped SKU interrupts the forecasting loop. Surfacing these failures in the integration layer prevents the compounding errors that lead to stockouts or overstock. This approach ensures the procurement team is acting on current warehouse capacity rather than outdated reports. It closes the visibility gap between what is expected and what is physically available to ship.
Operational handover for planning teams
Handover focuses on the Operations and Planning teams to ensure they can manage the feedback loop between forecasted demand and physical stock. We provide an operational manual that defines where stock data lives in Clarus WMS and how Prediko ingests those signals. Training covers the regular monitoring of stock-to-forecast variance and the review of replenishment suggestions. Teams learn to interpret alerts from the integration layer regarding sync errors or inventory mismatches. This documentation is written for the people running the business rather than as a technical reference. This approach ensures that exception ownership is clear when data drifts between the forecasting model and the warehouse floor, so that teams know which system to trust for current availability.
Managing exceptions and replenishment logic post-launch
Support focuses on identifying discrepancies between warehouse reality and forecasting logic. We monitor for inventory sync gaps where Clarus stock levels may diverge from what the forecasting model sees. Our team tracks the health of Purchase Order updates to ensure delivery dates are correctly factored into reorder calculations. This proactive oversight identifies sync failures and unmapped SKUs before they lead to over-purchasing or stockouts. Managing these exceptions ensures your planning team can trust the replenishment signals without needing to manually cross-reference stock levels in the warehouse.
Common failures
Inaccurate stock levels feeding demand forecasts.
Operational impact: When Prediko's forecasts rely on inaccurate stock data from Clarus WMS, it generates flawed purchase order recommendations. This results in ordering the wrong SKUs or incorrect quantities, causing either overstocking that ties up capital or stockouts that lead to missed sales. Finance teams see the negative impact on inventory holding costs, while fulfilment teams contend with unavailable stock for live Sales Orders.
Prevention / Action: The integration must establish Clarus WMS as the definitive source of truth for stock on hand. Before each forecasting cycle in Prediko, a dedicated process must sync the latest inventory levels from Clarus. Any material stock adjustments, such as those from a stock take or goods-in event, should trigger an immediate or micro-batch update to the dataset Prediko uses for its calculations.
SKU and product data mismatches.
Operational impact: If SKU codes are inconsistent between the sales platform, Prediko, and Clarus WMS, forecasts become unreliable. A mismatch can cause Prediko to see zero sales history for an item, leading it to forecast zero demand and stop recommending purchase orders for that SKU. Consequently, the fulfilment team sees inventory in the WMS for a product that has actually been selling, resulting in lost revenue.
Prevention / Action: A single system, typically the ERP or Clarus WMS, must be designated as the master source for all product and SKU data. The integration logic should include a validation layer to reconcile SKUs across all connected platforms before data enters Prediko. A corresponding operational process must be created to handle exceptions, such as flagging 'SKU not found' errors for manual review instead of letting them fail silently.
Forecasts inflated by un-reconciled sales data.
Operational impact: Prediko's demand forecasts can become inflated if based on gross sales data that does not account for cancelled orders, edited orders, or customer returns. This leads to recommendations for over-purchasing certain SKUs. The finance and operations teams must then bear the cost of carrying and clearing excess stock, even while the customer experience team processes the order adjustments that should have corrected the demand signal.
Prevention / Action: Implement filtering logic in the integration layer to clean the sales data feed before Prediko consumes it. Orders with a 'Cancelled' or fully 'Refunded' status in the source sales channel should be excluded from the dataset used for forecasting. The process design should also use signals from the Clarus WMS returns workflow to adjust demand signals, ensuring forecasts reflect true net sales.
Frequently asked questions
What happens if our SKUs in Shopify do not match the SKUs in Clarus WMS?
Mismatching SKUs break the link between forecasting and fulfilment. Prediko forecasts based on Shopify data; if the Clarus record differs, the system cannot match sales velocity to physical inventory. We prioritise SKU alignment to prevent ordering stock that is already sitting in your warehouse.
How do Prediko forecasts influence replenishment?
Prediko generates replenishment suggestions based on sales data. For these to be accurate, the integration needs to account for open Purchase Orders and expected delivery dates. This ensures 'Days of Cover' calculations reflect stock that is already on its way to the warehouse.
Does the forecast account for cancelled or returned orders?
Historical demand becomes skewed if returns are treated as successful sales. The integration ensures that when items are returned to stock in Clarus, these events are identified so they do not artificially inflate future demand predictions.
Does Prediko factor in physical fulfilment capacity?
Prediko calculates demand velocity rather than warehouse labour limits. The integration ensures your purchasing stays aligned with physical stock levels in Clarus WMS, preventing inventory builds that exceed your warehouse capacity or create intake bottlenecks.