https://store-images.s-microsoft.com/image/apps.60656.7d714f44-3199-484f-bcbe-baf827d75fa7.939f3bfb-b67c-49bf-8e40-570f4d310132.af4b4415-8d25-4487-ac49-32e973707e1f
Infosys SAP S/4HANA - On-Time Delivery for CPG
Infosys Limited
Infosys SAP S/4HANA - On-Time Delivery for CPG
Infosys Limited
Infosys SAP S/4HANA - On-Time Delivery for CPG
Infosys Limited
Infosys SAP S/4HANA - On-Time Delivery for CPG
Across sectors, CPG companies today face daunting strategic and operational challenges. An efficient shipments model is imperative to provide a cutting edge cost-optimized logistics chain to drive growth.
With expectations of higher on-shelf availability and lower inventory costs, the pressure on delivery performance have intensified. Retailers want to maintain ‘digital’ shelves stocked as and when needed, without any delay or wait time. Thus, they allow narrow delivery windows to the shippers. Leading players have started imposing financial penalties on suppliers for late deliveries.
Key Solution Features
- Leverages Digital technologies to optimize supply chain
- Real-Time root cause Analysis: Based on real-time data, identify possibilities of delay and their Cause
- Leverages Digital technologies to
optimize supply chain
- Predicts possibility of late/early vs On-time delivery at the time of
shipment creation
- Accelerated and Proactive Decisions/Actions
Business Benefits
- On-Time Delivery Score- With this Solution, the customer is able to improve OTD SCORE by 5%
- Accuracy - able to predict on-time vs late/early deliveries with 80% accuracy
- First Day Shipments - expected to bring up to 20% improvement in DAY 1 Shipments
- Win Battle of Financial Penalties - help avoid financial Penalties due to late delivery.
Más información
Infosys_SAP_On_Time_Delivery_Predictionhttps://store-images.s-microsoft.com/image/apps.12446.7d714f44-3199-484f-bcbe-baf827d75fa7.939f3bfb-b67c-49bf-8e40-570f4d310132.86917257-59b4-46b2-84ce-e7ca43b9b8cf
https://store-images.s-microsoft.com/image/apps.12446.7d714f44-3199-484f-bcbe-baf827d75fa7.939f3bfb-b67c-49bf-8e40-570f4d310132.86917257-59b4-46b2-84ce-e7ca43b9b8cf