Available To Promise Customer Story

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

How did one of the best pharma supply chains of 2023 solve their inventory management challenges?

Pharmaceutical inventory management is a complex but critical process within the healthcare delivery system because it influences clinical outcomes. The objective of pharmaceutical inventory management is to maintain a steady supply of pharmaceuticals to operating units and hospitals. To avoid any setbacks, enhancing the proper functioning of a pharmaceutical inventory management system is important. Significantly, there are tools and metrics devised to measure its performance.

One such device is available to promise (ATP), a metric in the supply chain management context that enables an enterprise to quantify the inventory its warehouse will have available on hand. It is a part of inventory management and plays a crucial role in several supply chain operations.

Congruently, our client, a large pharmaceutical enterprise, has had challenges implementing the high-touch customer service that they deliver.

Here’s how Syren Cloud made a difference in this enterprise’s inventory management system.

Problem Statement

The pharmaceutical enterprise faced certain challenges:

Unreliable expected shipping dates
Inaccurate delivery dates
Unavailable inventory on certain dates

Interconnected with these pertinent challenges was the visibility of the entire customer order journey that was unsatisfactory or rather inaccessible.

Our Solution

The ATP tool aims to calculate and provide customers with reliable expected shipping and delivery dates. Inaccurate or unavailable order delivery dates were critical complaints expressed by our customers. The solution’s overall objective is to enhance the customer experience by providing timely and accurate delivery dates. Our customers require and expect their orders to be fulfilled and delivered as expected.

These models run 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, are ingested to facilitate the ATP calculations. The tool leverages machine learning algorithms and data engineering practices to predict dates based on historical data and allows planners to provide lead time information for more accurate predictions.

Inventory Prediction Model - Inventory ATP Calculation

ML (Machine Learning) Prediction Model - Machine Learning Calculation

Planner Prediction Model - Planner Override Calculation

Source Prediction Model - SAP ATP Calculation

ATP Tool’s Outcomes

Inventory Prediction Model

The inventory prediction model is the initial layer of predictive model in the ATP visibility tool. As the models run on a near-time basis, the indicated section in the dashboard shows 100% accurate shipping and delivery dates. This process forecasts material availability by considering replenishment lead time and current stock. The inventory date accuracy is ensured by comparing with performance reports, comparing final close date to original recommended date.

ML Prediction Model

The machine learning prediction model is a powerful layer in the ATP visibility tool that derives shipping and delivery dates. As the models run on a near-time basis, the indicated section in the dashboard shows 100% accurate shipping and delivery dates. What’s interesting is the calculation of these dates, it happens through two calculations happening simultaneously – Accuracy Calculation named Percent_Accuracy_attribute in the table and Precision Calculation named Percent_Precision_attribute.*

Planner Prediction Model

The planner prediction model is another simulation working to help the ATP visibility tool to derive shipping and delivery dates. As the models run on a near-time basis, the indicated section in the dashboard shows 100% accurate shipping and delivery dates. These dates are calculated reliably through Accuracy Calculation and Precision Calculation.

Source Prediction Model

The source prediction model also predicts shipping and delivery dates from integrated SAP databases within the pharmaceutical enterprise. The indicated section in the dashboard displays these dates as 100% accurate, reflecting the model’s reliable calculations achieved through Accuracy and Precision Calculations.

*Glossary to understand the ATP Visibility tool

Accuracy Calculation

Accuracy is the proportion of correct predictions out of all predictions made. It’s calculated by dividing the number of correct predictions by the total number of predictions, then multiplying by 100 to get a percentage.

Precision Calculation

Precision is the proportion of true positive predictions (correct positive predictions) out of all positive predictions made. It’s calculated by dividing the number of true positive predictions by the total number of positive predictions, then multiplying by 100 to get a percentage.

The integration of Syren’s ATP tool by our pharmaceutical partner has notably elevated customer satisfaction levels through precise and dependable shipping and delivery estimates. The tool's sophisticated algorithms and machine learning capabilities have bolstered business flexibility while driving sales growth. Furthermore, the in-house development of the tool has resulted in cost efficiencies, with a notable decrease in customer complaints serving as evidence of its efficacy.