We’re called in to tackle today’s most challenging supply chain problems. We have the battle scars of success to show for it.
Here are a few case studies that illustrate what we did and how it made a difference.
Customer Managed Server & Data Center Configure to Order
Challenge
Using discrete SKUs not scalable for configurable business. New VC data needed to integrate with SKUbased legacy systems.
Solution Brief
Leverage standard SAP Variant Config paired with a custom configuration Portal for customers to create and mange their quotes/orders.
Enhanced Customer Order / Rescheduling for Apparel & Footwear Companies
Challenge
Manually allocating constrained supply to orders after they had been confirmed to customers was not manageable in a growth environment
Solution Brief
Developed custom Production Allocation and enhanced Order Rescheduling on top of the SAP IS-AFS platform
Alternate Sourcing & Product Capabilities for Plant Maintenance Order Scheduling
Challenge
Multiple Sources with common supply across distribution centers cannot leverage the available units in different plants than the one where the service order is planned for.
Solution Brief
Leverage underlying standard SAP functionality for Rules Based ATP by enabling it for SAP Plant Maintenance Service Orders.
Pharmaceuticals ATP Accelerators & Allocation Management
Challenge
Implementing gATP brought with it great promise but the actual usage of the advanced capabilities created usage issues and technical conflicts with downstream systems.
Solution Brief
The full capabilities of gATP were realized with the user of the Accelerators and have been managing millions of transactions without any support issues.
Semiconductor Finish to Order Processes & Tools
Challenge
The number of finishing options on an individual SKU caused low forecast accuracy and significant batch rework when trying to match with customer orders at the time of delivery
Solution Brief
Leverage SAP HANA’s speed to mine all of the necessary data in real time for each MFG operations request
Machine Learning in Trade Promotional Management
Challenge
Trade promotional management (TPM) process not producing the expected ROI
Solution Brief
Developed a custom Bayesian Machine Learning model that predicted demand based on key business factors such as price, promotion, seasonality, payday week, competitive factors, etc
