A On-Time-In-Full
Success Story
Increase efficiency and customer satisfaction with Syren's solution encompassing predictive analytics and real-time visibility for a better OTIF ratio.
Increase efficiency and customer satisfaction with Syren's solution encompassing predictive analytics and real-time visibility for a better OTIF ratio.
Supply chain enterprises of all kinds suffer due to chronic disruptions, missed on-time-in-full (OTIF) SLAs, and penalties or chargebacks for customer orders that were over, short, damaged or missed their delivery window. Essentially, enterprises are dedicated to delivering services on time and in full to their customers. However, their passion for excellence sometimes leads to inefficiencies. Mismanagement often leads to multiple team members inadvertently working on the same complexity, while the most critical problem affecting delivery remains unaddressed.
To rectify this situation, OTIF has emerged as a KPI in the supplier’s arsenal as it depicts the ability to deliver products within the prescribed delivery windows and in the full quantities ordered. It is considered as an important metric especially, since it determines the effort relayed into inventory planning, inventory optimization, and order fulfillment.
Pharma industries thrive on their capability to manage inventory, proper deliveries, supplier relations, and quality products. Likewise, our client, a leading pharmaceutical enterprise, faced significant supply chain complexities in their inventory management. It led to a few detrimental concerns where the pharma giant sustained poor OTIF ratio earlier. Certain product-lines, namely, vision care, surgical vision, pharma, and MD-related products, encompassing items such as drugs, eyewear, pharmaceuticals, and surgical supplies led the list with mediocre OTIF scores.
Our client, a pharma conglomerate, has diverse verticals. Few of the departments such as the pharmaceutical, surgical, and vision encountered challenges which impacted their OTIF ratio.
Upon receiving an order with specified quantities and a requested delivery date, our tool factored in lead times at each stage, along with holiday and factory calendars, to derive the expected delivery, shipping, and material availability dates. Along with solving the industry-led challenges, another major objective was to identify why significant orders did not meet OTIF expectations and ascertain the underlying reasons. This analysis enabled us to take appropriate measures to address these issues and ensure timely delivery to meet customer requirements.
Robust measures to maintain high OTIF excellence were taken such as
The tool ingests data from various sources, including sales order details, inventory information, inbound purchase orders, cutoff times, and lead times. This comprehensive data integration ensures accurate calculations.
Our OTIF tool calculates and communicates reliable expecbed shipping and delivery dates to customers, addressing critical complaints about inaccuracies and unavailability of goods.
Leveraging machine learning algorithms and data engineering practices, the OTIF tool predicts dates based on historical data. It also allows planners bo provide lead time informabion for more precise predictions.
The solution employs three distinct models that run near real-time and daily, namely the invenbory prediction, planner prediction, and source prediction model. Each model contributes to the overall calculation process.
The adoption of our OTIF tool has not only addressed specific challenges for our pharmaceutical client but has also positively impacted various aspects of their business, from customer satisfaction to sales growth and cost efficiencies
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