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Off-the-Shelf Inventory Software Vs Customized Self-Serve Inventory Analytics – What Suits You Best?



Given a choice, inventory planners would want to custom-design self-serving analytics built around their tools in daily use and ways of working. However, most businesses are completely unaware of No-Code solutions that can empower non-technical employees and teams to automate workflows that make it easy to track inventory KPIs.

Given the fact that most inventory planners are non-technical business users, the objectives they set to achieve through ‘Agile Demand Planning’ or ‘Data-Driven Demand Planning’ vastly vary from one another. Hence, in such a scenario, a modular approach to build self-serving inventory automation and analytics becomes a more viable alternative as compared to off-the-shelf SaaS software with high licencing costs and fixed features that may not fulfil the inventory team’s very specific requirements.

For example, you may be a manufacturer who needs to perform complex revenue calculations which require a sync with the pricing sheet of the SKUs. Also, you may need to compute varying supplier and input costs to arrive at any cash flow conclusions. You could also be a business that has already introduced Quickbooks as well as a NetSuite to streamline order management and invoicing tasks. But, despite your best efforts, manual tasks such as collating data from multiple spreadsheets, attachments, chats, etc., still persist

However, when the business scales from 1 manufacturing unit to 7 or when you start to involve multiple vendors/suppliers, logistics partners, distribution warehouse networks, etc., the legacy system untenable.

Imagine a scenario where every time the leadership wants to discuss a specific location’s performance, SKU forecasts, sales, and the revenues, these reports may take days or even weeks to arrive. These requests may require analysis of specific SKU or SKU categories with their multiple variables to forecast profitability into the future.

  • Legacy systems and multiple existing tools used by different teams and systems must be able to connect to each other
  • For example, inventory planning teams need to connect Shopify’s data with ERPs like NetSuite and SAP to keep track of orders payments and invoices
  • Budget owners need to be able to expedite the process of generating income statements for specific product lines for which there might be no readily available data
  • Our Award-Winning No-Code solution scours data from several excel spreadsheets, chat messages, folders, and attachments with minimal human intervention

A cost-effective way to automate manual demand planning tasks: Empower business users to build self-serving automation pipelines by identifying event-driven workflows

  • By identifying event-driven actions and triggers, our data automation experts can help your business users to easily automate simple day-to-day inventory tracking tasks.
  • These event-driven actions can be connected to Inventory KPIs tracking requirements that manufacturers need to monitor from time-to-time.
  • Their expectations for reporting may vary based on existing processes. However, investing on a pre-built software doesn’t allow the flexibility of choosing the type of KPI tracking sought by budget owners and sales teams.
Workflow Automation

Enable planners to significantly reduce occurrences of out-of-stock scenarios, overstocking, disappointed customers, and miscalculated pricing markdowns.

Let’s take a look at the challenges that can be solved by our ‘MailBox Automation, Document AI, and Spreadsheet Integration Solution.

Build a Modular Self-Serve Inventory Analytics Dashboard or Go with a Standalone Inventory Management SaaS Platform?

It is not an easy choice to make, especially when there are so many off-the-shelf software options to solve demand forecasting problems. What business leaders however fail to assess is how these new tools and platforms work with their legacy systems and databases. Not all new-age, cloud-based products come with the leverage of integrations with legacy databases.

Hence, the most sensible thing to do in such a situation is to assess areas where business users or employees can themselves execute small automation integration projects first. When API integrations are easy to deploy and do not require technical expertise, it can become the first logical step towards Inventory Tracking Automation. Functional teams such as Sales, FP&A, Inventory, Production, etc., can thereafter create self-serving analytics reports using our event-driven supplychain automation expertise.

Popular Workflows You Can Build Using Our No-Code Integrations

These Simple No-Code Integrations Can Automate all Manual Inventory Data Extraction and Data Input Tasks:

  • Oracle NetSuite + Salesforce
  • Oracle NetSuite + Snowflake
  • Oracle NetSuite + Slack
  • Oracle NetSuite + Slack
  • Oracle NetSuite + ServiceNow

Use Our Easy No-Code API Integrations to Build Self-Serve Inventory ERP Integrations 

ERP Integrations

Pick and Choose Solutions that Match Your Inventory Tracking KPIs

Automate Tracking of the Following Inventory KPIs Using Our No-Code Solution:

  1. Inventory Turnover Ratio: Measures how many times inventory is sold and replaced over a period, indicating how efficiently inventory is being managed. It’s calculated as Cost of Goods Sold (COGS) divided by Average Inventory.
  2. Days Sales of Inventory (DSI): Shows the average number of days it takes to sell inventory. It’s calculated as 365 (or the number of days in the period) divided by the Inventory Turnover Ratio.
  3. Stockout Rate: Measures the percentage of time that a product is out of stock. It’s calculated as the number of stockouts divided by the total number of opportunities for stockouts.
  4. Inventory Accuracy: Measures the percentage of inventory items that are accurately recorded in the system compared to the physical count. It’s calculated as (Total Counted Inventory / Total Recorded Inventory) * 100.
  5. Fill Rate: Indicates the percentage of customer demand that is met from stock on hand. It’s calculated as (Total Orders Fulfilled / Total Orders Received) * 100.
  6. Backorder Rate: Measures the percentage of customer orders that cannot be fulfilled immediately due to insufficient inventory. It’s calculated as (Number of Backordered Items / Total Items Ordered) * 100.
  7. Inventory Carrying Cost: Represents the cost associated with holding inventory over a period, including storage, insurance, and obsolescence. It’s typically expressed as a percentage of the inventory value.
  8. Order Cycle Time: Measures the average time it takes for an order to be processed and delivered to the customer. It includes order processing time, picking, packing, and shipping.
  9. Lead Time: Represents the time it takes from placing an order with a supplier to receiving the goods. It includes order processing time, production time (if applicable), and transit time.
  10. Supplier On-Time Delivery Performance: Measures the percentage of orders delivered by suppliers on or before the promised delivery date. It’s calculated as (Number of On-Time Deliveries / Total Number of Deliveries) * 100.
  11. Obsolete Inventory: Represents the value of inventory that is no longer sellable due to expiration, damage, or changes in demand. It’s important to track to minimize losses and optimize inventory levels.
  12. Shrinkage Rate: Measures the percentage of inventory lost or unaccounted for due to theft, damage, or administrative errors. It’s calculated as (Value of Shrinkage / Total Inventory Value) * 100.
  13. Inventory Aging: Tracks the age of inventory items to identify slow-moving or obsolete stock. It’s typically categorized by the length of time an item has been in inventory.
  14. Inventory-to-Sales Ratio: Measures the relationship between inventory levels and sales volume over a specific period. It helps assess inventory efficiency and demand forecasting accuracy.
  15. SKU Rationalization Rate: Measures the percentage of SKUs that contribute to a significant portion of sales or profit. It helps optimize inventory by focusing on high-performing products and eliminating low-performing ones.
  16. Customer Returns Rate: Measures the percentage of products returned by customers. It helps assess product quality, customer satisfaction, and the effectiveness of the returns process.
  17. Inventory Buffer Level: Represents the additional inventory kept to account for uncertainties in demand, lead times, and supply chain disruptions. It helps ensure customer satisfaction and mitigate stockouts.
  18. Perfect Order Rate: Measures the percentage of orders that are delivered to customers without any errors or defects. It includes metrics like on-time delivery, accurate invoicing, and complete shipments.
  19. Inventory Velocity: Measures the speed at which inventory moves through the supply chain. It’s calculated as (Total Sales / Average Inventory Value) and helps identify areas for improving inventory turnover.
  20. Cost-to-Serve: Measures the total cost incurred to fulfil customer orders, including warehousing, transportation, and order processing costs. It helps evaluate the profitability of different customer segments and order types.

Leverage our Low-Code, No-Code, event-driven data architecture solutions to custom-build inventory planning dashboards. Enable real-time data processing, decision-making, and action triggering based on various events around your inventory and supplychain lifecycles.

Here’s an example scenario illustrating how event-driven architecture can improve inventory planning:


A retail company sells various products through multiple channels (online, brick-and-mortar stores) and needs to optimize its inventory levels to meet customer demand while minimizing stockouts and excess inventory.

Event-Driven Solution:

Data Integration and Event Ingestion

Various data sources such as point-of-sale (POS) systems, online sales platforms, inventory management systems, and supply chain databases are integrated into a centralized event streaming platform (e.g., Apache Kafka).

Events are ingested in real-time as they occur, including sales transactions, inventory updates, shipments received, and forecasts from demand planning systems.

Event Processing and Analysis

Incoming events are processed and analyzed in real-time using stream processing frameworks (e.g., Apache Flink, Apache Spark Streaming).

Demand patterns, sales trends, and inventory levels are continuously monitored and analyzed to identify anomalies, predict future demand, and detect inventory shortages or surpluses.

Inventory Optimization and Decision-Making

Advanced analytics and machine learning models are applied to historical sales data, seasonality patterns, promotions, and external factors (e.g., weather, holidays) to forecast future demand for each product SKU.

Inventory optimization algorithms calculate optimal reorder points, safety stock levels, and replenishment quantities based on demand forecasts, lead times, service level targets, and cost constraints.

Event-Triggered Actions

When certain predefined events occur (e.g., low stock alert, unexpected surge in demand, supplier delay), event-driven rules and workflows are triggered automatically.

For example, when inventory levels for a particular SKU fall below the reorder point, a purchase order is automatically generated and sent to the supplier.

Alternatively, if demand for a specific product SKU exceeds forecasted levels, automated actions such as expedited shipping or reallocating inventory from other locations are triggered to fulfill customer orders.

Real-Time Visibility and Monitoring

Inventory planners and supply chain managers have real-time visibility into inventory levels, demand forecasts, order statuses, and supply chain events through interactive dashboards and alerts.

Key performance indicators (KPIs) such as fill rate, stockout rate, inventory turnover, and order cycle time are continuously monitored to assess performance and identify areas for improvement.


Real-Time Responsiveness

Enables proactive inventory management and rapid response to changing demand patterns, supply disruptions, and market conditions.

Optimized Inventory Levels

Reduces excess inventory, stockouts, and carrying costs while improving service levels and customer satisfaction.

Automated Decision-Making

Streamlines decision-making processes and reduces manual intervention by automating routine tasks and workflows.

Improved Forecast Accuracy

Enhances demand forecasting accuracy through real-time data analysis and machine learning algorithms, leading to better inventory planning decisions.

End-to-End Visibility

Provides end-to-end visibility into the supply chain, enabling stakeholders to make informed decisions and collaborate effectively across functions.

By leveraging event-driven data architecture, the retail company can achieve agile and efficient inventory planning, ensuring the right products are available at the right time and in the right quantities to meet customer demand while optimizing inventory costs and operational efficiency.

Get in touch with our No-Code Inventory & Supplychain Modernization Experts to evaluate the right areas which are ready for Automation. Use ‘Inventory AI’ and ‘Event-Driven Data’ solutions to scour data from excel spreadsheets, chat messages, folders, and attachments with minimal human intervention.

Example of Event-Driven Triggers & Actions You Can Initiate

Event Based Data Architecture

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