AI Use Cases With Supply Chain Data: How the GRID drives AI powered intelligence

Connected data sets form the foundation to AI-powered intelligence

Connecting data sets across product, costing, supplier, costing, purchasing, sales, inventory, shipments, fulfilment, and distribution is the foundation of AI-powered intelligence to users.
The GRID digitizes, integrates, and connects all these data sets across the length of the supply chain workflow.

Reading PDF files and excel files for auto mapping integration forms

AI-Image capture and extraction software helps us read and output the data from PDF, excel and other such files. Oftentimes source data, especially sales data through EDI connects comes not just from systems but also from PDF files that often change in format from the retailer. Instead of going through the laborious process of re-coding the new formats, AI powered tools help the GRID automate the data feed with any changes in PDF input files.

Automated filling of forms across the supply chain.

As historic and trend data is collected over time on the GRID, forms across the supply chain can be automatically filled in with required data points. These include purchase order forms, custom forms, quality control forms and more.

Sourcing cost-effective and faster-to-arrive components for new products and purchase orders.

The GRID provides recommendations and triggers on the best components to populate bill of materials based on the customers’ need to optimize one or more of variables across lead time, quantity of units and costs.

Mapping best suppliers for purchase orders based on performance and lead-time requirements.

The GRID collects data over time on suppliers’ performance across qualitative and quantitative variables including compliance, sustainability, strength of partnership, costs, lead times, number of exceptions generated, total volume delivered, time in business and more. The more the data collected and the more the trends collected that the data is trained on, the recommendations can be fine-tuned to make the most intelligent recommendations on the best supplier for a new product as well as active products’ purchase orders.

Prompting best products with best suppliers

Versus matching products to a long tail of suppliers, some of whom have suspect performance, GRID recommends the best performing products with the best performing suppliers to maximize margins, reduce lead times and increase revenue.

Automating bidding and negotiations with vendor

Lowest historical lead times and prices can be prompted to existing and new suppliers saving time and effort on the negotiations while also reducing cost of goods sold.

Stocking right amounts of inventory by demand channel

With product data mapped to sales and inventory data, the GRID can generate alerts on the amount of inventory to stock by demand or sales channel. This intelligence impacts both profits and revenue, since reducing understocked levels can increase sales while preventing overstocked scenarios reduces costs and improves margins.

Flagging potential exceptions in the purchase order cycle before they happen

Suppliers generate tens of thousands of exceptions’ transactions over time across delays, changes in costs, split quantities, macroeconomic force majeure events, and more. The GRID analyses these trend patterns to predict and proactively provide intelligence to prevent these exceptions before they happen.

Optimizing for the right product attributes to maximize sales.

A sleeve length for a dress, a packaging component sourced from a certain country for a wellness moisturizer, or a spice ingredient for a food item – one of the many items in a bill of material may have a high correlation to an increase in sales. The GRID spots and alerts to key ingredients that impact sales or margins so they can be reused in new products’ innovation.

Bringing new products to market faster

Every component used in the specification sheet for a finished product has a different lead time from the material or ingredient supplier. The GRID optimizes to recommend similar ingredients that reduce the total lead time for potential new products. Customers can go from concept to approved products with faster production completion times based on these recommendations.

Reducing labor for quality control

If like products and purchase orders for like products have had quality control issues, the GRID alerts to these potential exceptions. This helps increase labor allocation to such potential exception prone purchase orders while reciprocally reducing labor allocation except for spot check on items and suppliers that have had a long consistent history of low to zero quality issues. This reduces labor costs overall for customers.

Optimize sales pitches and contracts with language processing.

The GRID collects unstructured data and communication across emails, chats and comments between customers and their prospective customers or active customers. Based on the customers’ purchase history married with new products to be introduced, GRID recommends products to pitch customers at the recommended best price.



  • The more the data, the better the intelligence
    Artificial Intelligence or AI patterns are developed from training large and wide swathes of data sets. The more the data generated organically on the GRID or inorganically through integrations, the better the applications and accuracy of AI generated recommendations. Conversely, with minimal historical data and fewer connected data sets, the GRID or any system of record can be limited in its ability to power recommendations.
  • Integrations are key to collecting and training more data.
    To gain a completed, connected picture integrating with underlying data sets and systems across not just PLM, Procurement and ERPs but EDI channels, TMSs, WMS, excel sheets and any data source. The more the data collected, more the data to train for the AI models.
  • Unstructured data is critical for driving higher orders of intelligence.
    Traditional Analytics primarily pushes insights and intelligence from structured data. Unstructured data collect in tremendously high volumes. These massive volumes for the first time can now be managed, trained and built into AI models.


Suuchi Ramesh
Founder, CEO
Suuchi Inc.

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