Prediko Demand Forecasting and Stokly ERP
Integration Agency & Consultants
Inventory optimisation breaks down when sales velocity in Shopify and inventory control in Stokly ERP fall out of sync. At scale, manual demand planning leads only to frequent stockouts or excessive aged inventory that drains cash flow. We ensure Prediko receives accurate sales data to generate the purchasing recommendations your team needs. This connection grounds your procurement in reality, allowing you to maintain availability without overextending your working capital.
Auditing SKU mapping and inventory gaps
We connect your Prediko Demand Forecasting and Stokly ERP Shopify App integrations quickly, ensuring your Prediko Demand Forecasting and Stokly ERP solutions work together with your ERP and Shopify App ecosystem. Our consulting services are valuable because our system audit services uncover inefficiencies and integration gaps, enabling our consultants and your team to take decisive action. This helps your tech ecosystem—including ERP and Shopify App platforms—run efficiently, so you can deliver a great experience to your customers.
Solution Design
For the Prediko and Stokly ERP integration, we typically establish Stokly as the master for physical inventory and lead times, while Shopify acts as the authoritative source for customer demand signals. A design decision involves the batching of historical sales data to Prediko, which we commonly sequence ahead of inventory updates to ensure the forecast model is grounded in history before including recent fluctuations. We acknowledge a trade-off here: while highly frequent stock syncs provide granularity, they can introduce noise during some sales events, so we prioritise data integrity over sheer speed. This design ensures your teams can rely on Prediko for demand planning and the ops team can trust the purchase suggestions seen in Stokly.
Bridging SKU records and demand signals
The integration establishes a controlled flow of historical sales and current stock levels from Stokly ERP and Shopify into Prediko. Stokly remains the source of truth for physical inventory across warehouses and store locations, while Shopify provides the granular signal of customer demand. We prioritise the alignment of Product IDs and SKUs to ensure Prediko maps historical performance to the correct inventory items. Syncing commonly occurs on a defined schedule to refresh forecasts. Monitoring is embedded at the SKU level, surfacing discrepancies where SKU changes in one system have not been reflected in the other, which would otherwise lead to skewed demand profiles and incorrect purchase recommendations.
Using secure middleware for resilient connectivity
Leveraging IPaaS with ISO 27001 and SOC 2 and above security accreditations enables secure, efficient integration of Prediko Demand Forecasting and Stokly ERP with Shopify App and ERP systems. This approach simplifies connecting Prediko Demand Forecasting to Stokly ERP and Shopify App, ensuring data integrity and compliance. IPaaS platforms reduce manual effort, support scalability, and maintain robust security, making ERP and eCommerce integrations reliable and future-proof.
Monitoring data integrity and sync health
Relying on a forecast dashboard is high risk if the underlying data sync is failing silently. Visibility is more than a demand graph; it requires knowing that every Shopify order and Stokly stock adjustment successfully reached Prediko. We surface hidden issues like SKU mismatches or unmapped locations that cause Prediko to ignore specific sales segments. Our approach identifies these gaps early, preventing the compounding errors that occur when a forecast is built on incomplete historical data. By monitoring the integrity of every data transfer, we ensure the planning team works from a validated dataset rather than an outdated projection.
Establishing cross-functional ownership and workflows
Handover focuses on the planning, finance, and operations teams to ensure they can manage the demand cycle. We define clear ownership: the ops team manages inventory accuracy in Stokly, while the planning team owns the forecast parameters in Prediko. Your team is trained to monitor the integration layer for SKU mapping exceptions and location sync errors on a regular schedule. We provide operational documentation that explains the data flow in plain language, detailing what to check before finalizing purchase orders. This reference guide is designed for the people running the business, ensuring they can interpret alerts and resolve common data mismatches without technical intervention.
Managed oversight for forecast accuracy
Post-launch, we provide ongoing operational ownership to ensure your integration tracks with business growth. We monitor the data flow between Prediko and Stokly for sync failures, SKU mismatches, and location mapping issues that dashboards often miss. When your SKU architecture or warehouse setup changes, we manage the integration updates to prevent forecast drift. Our support model prioritises proactive issue detection, surfacing exceptions before they impact your next purchase order. This ensures your planning team can focus on strategy rather than troubleshooting connectivity issues.
Common failures
Inaccurate forecasts from unfiltered sales data
Operational impact: Prediko's forecasts become inflated because they are based on gross sales data that includes cancelled or refunded orders. This leads to inaccurate demand plans in Stokly, causing the purchasing team to over-order stock. The direct results are excess working capital tied up in slow-moving inventory and reduced warehouse capacity.
Prevention / Action: The integration's data mapping must be configured to filter out non-valid sales. This requires using Shopify order statuses or tags to ensure only net sales from fulfilled and non-returned Sales Orders are sent to Prediko for analysis. Operations and finance teams must first agree on the precise definition of a 'valid' sale to be used for forecasting.
SKU mismatches causing data desynchronisation
Operational impact: A change to a product SKU in Shopify that is not mirrored in Stokly breaks the data link for that item. Sales Orders will fail to sync correctly to the ERP, meaning inventory levels in Stokly are not updated. Prediko bases its calculations on Shopify sales data, but if Stokly cannot report back accurate stock levels, the entire forecasting cycle is built on flawed data, leading to stockouts or overstocking.
Prevention / Action: Stokly must be designated the single source of truth for all product master data, especially SKUs. Any creation or modification of product identifiers should occur only in Stokly and be pushed to Shopify via the integration. The Shopify admin should be configured to prevent manual edits to SKU fields, thereby enforcing a centrally managed product catalogue.
Forecast errors from latent inventory data
Operational impact: Prediko reads Shopify's 'available' stock figure, but Stokly is the true inventory master. If the synchronisation from Stokly to Shopify is delayed or fails, Prediko's forecasts will be based on stale data. This can mask low stock situations or create false signals of high availability, causing the planning team to make poor purchasing decisions.
Prevention / Action: Design the inventory synchronisation process for high frequency and include robust error monitoring. Stock level changes in Stokly, from goods-in or stock adjustments, must trigger an immediate push to Shopify's corresponding inventory levels. Implement automated alerts for sync failures or significant delays so the operations team can investigate before forecast accuracy is compromised.
Incomplete sales history from multi-channel operations
Operational impact: If the business takes orders via channels outside of Shopify, such as phone sales or a B2B portal managed in Stokly, this sales data is invisible to Prediko. Forecasts will only represent a portion of total demand, systematically under-calculating replenishment needs. This creates a reactive purchasing cycle and a high risk of stockouts that affect all sales channels.
Prevention / Action: The integration architecture must centralise all order data into Shopify, which acts as the data source for Prediko. Orders from non-Shopify channels managed in Stokly should be pushed into Shopify with a unique tag indicating their origin. This process gives Prediko a complete sales history but requires careful workflow design to block unwanted customer notifications or duplicate fulfilment triggers from Shopify.
Frequently asked questions
What happens if our product SKUs in Shopify and Stokly do not perfectly match?
Stokly requires a 1:1 SKU match with Shopify variants to maintain data integrity. If a Shopify SKU is changed without a corresponding update in Stokly, Prediko will receive fragmented data. This results in inaccurate forecasts because sales velocity cannot be correctly attributed to the inventory item record, leading to flawed purchasing recommendations.
How does Prediko account for returned or cancelled orders?
Prediko relies on sales data from your shopfront as its primary source. In many implementations, orders marked as 'Refunded' or 'Cancelled' in Shopify must be explicitly excluded from the demand base. Without this filter, your historical sales velocity will be inflated, causing the system to overestimate future demand and recommend purchasing more stock than is actually required.
How does this integration address frequent stockouts?
The integration links sales activity from Shopify with Stokly inventory levels to identify when current stock cannot meet predicted demand. Prediko calculates future requirements based on sales velocity and lead times, surfacing these as purchasing recommendations. This enables your team to raise Purchase Orders in Stokly before stock reaches a critical low.
Will our purchasing team need to use the Prediko interface daily?
The integration is designed to enrich the purchasing process without forcing a total change in workflow. While Prediko generates demand forecasts and purchasing recommendations, the actual Purchase Orders are typically managed within Stokly. This allows your team to continue using Stokly as the inventory master while benefiting from predictive data.