ATP Case Study

Available To Promise
Customer Story

Gain credibility as an enterprise by ensuring that future orders are fulfilled accurately with Syren’s ATP.

How Did a Pharma Organization Elevate Its Delivery Accuracy and Reliability?

Our client, a large pharmaceutical company, has had challenges implementing the high-touch customer service that they deliver. To solve their complexity, our client executed Syren’s E2E Connected Visibility ATP (Available to Promise) tool to enhance their customer experience and improve the accuracy and reliability of product deliveries.

The tool utilized complex logic, machine learning, and data from multiple sources to predict shipping and delivery dates for customer orders. By providing real-time visibility and timely information to customers, the pharma partner aimed to increase customer satisfaction, boost sales, and achieve cost savings.

Problem Statement

The main challenges faced were inaccurate or unavailable order delivery dates and overall customer dissatisfaction.

Syren’s Approach

The E2E Connected Visibility ATP Tool employed four distinct models:

  • D0 - Inventory ATP Calculation
  • D3 - Planner Override Calculation
  • D2 - Machine Learning Calculation
  • D4 - SAP ATP Calculation

These models ran on a near real-time and daily basis to calculate and communicate expected shipping and delivery dates to customers. Data from various sources, including sales order details, inventory information, inbound purchase orders, cutoff times, and pick/ pack and shipping lead times, were ingested to facilitate the ATP calculations. The tool leveraged machine learning algorithms to predict dates based on historical data and allowed planners to provide lead time information for more accurate predictions.

Benefits and Outcomes for our Client

Venturing on the path to attain exceptional OTIF performance for our pharmaceutical partner is not devoid of challenges. It is akin to multiple obstacles, such as:


Collaborative ActionabilityEnhanced Customer Experience

The Control Tower facilitated seamless collaboration among cross-functional teams. When alerts for days on hand falling below target, unexpected sell-in changes, or forecast consumption anomalies arose, teams were empowered to collaboratively devise and execute corrective actions.

Cost Avoidance

By building the ATP Tool internally instead of relying on third-party vendor solutions, the pharma partner achieved cost savings, resulting in a positive impact on the company's bottom line.

Increase in Sales

With improved customer experience and reliable product deliveries, the pharma partner expected to see an increase in sales as customers were more likely to place orders and become repeat customers.


Improved Customer Satisfaction

The E2E Connected Visibility ATP Tool provided accurate and timely shipping and delivery dates, leading to higher customer satisfaction and a positive brand image.

Increased Sales

With enhanced customer experience and reliable order deliveries, the pharma partner observed an increase in sales as customers were more likely to place orders and make repeat purchases.

Cost Savings

By developing the ATP Tool in-house, the pharmaceutical partner achieved cost savings by avoiding the expenses associated with purchasing external vendor solutions.

Reduced Customer Complaints

The tool's ability to predict delivery dates accurately resulted in a significant reduction in customer complaints related to delayed or unreliable shipments.

Real-time Visibility

Customers had access to real-time information about their orders, allowing them to plan and make informed decisions based on the expected delivery dates.

Enhanced Business Agility

The underlying data model logic of the ATP Tool provided scalability across different sectors within our client’s enterprise, enabling the company to adapt to changing customer demands effectively.

Holistically, the implementation of Syren’s E2E Connected Visibility ATP Tool by our pharma partner significantly improved customer satisfaction by providing accurate and reliable shipping and delivery dates. The tool's complex logic and machine learning algorithms enhanced business agility and increased sales. Additionally, in-house development led to cost savings, and the reduction in customer complaints demonstrated its effectiveness.