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Optimizing Order Fulfillment
Objective
Identify the root-causes of delivery delays to:
- Improve customer experience & satisfaction
- Reduce penalties incurred for not meeting service-level agreements and maximize revenue recognition potential
- Mitigate the risk of customer attrition due to recurring service-level defaults
Industry: Healthcare – Wholesaler
Services: Technology Innovation
Technology: Predictive Machine Learning
Optimize logistics and distribution planning and manage delivery commitments communicated to customers within the context of shipment/ order delivery risks.
Manage relationships with logistics and distribution partners to maintain desired service levels.
Estimate near-term revenue at-risk from the shipment/ order delay, and thereby impact to fiscal period revenue recognition.
Our Client
Our client is the second-largest payment-technology corporation globally and offers payment transaction processing and related services worldwide, facilitating transactions between merchants’ banks and card-issuing banks or credit unions using its debit, credit, and prepaid cards.
Challenge
Businesses are always challenged to improve customer satisfaction and service. This is even more relevant in the case of enterprises that have a strong dependency on a supply chain network. Delays in shipments/orders reaching their customer or target destination translates to a poor customer experience, dissatisfaction, and potentially even penalties for the delivery delay or the loss of future customer revenues.
Approach
Employ machine learning to develop a pair of complementary predictive models that:
- Predicts the likelihood that a shipment/order will be delayed
- Estimates the expected delay (in days) for shipments/orders at risk of delayed delivery
Outcomes
- Demonstrates the use of data assets across enterprise applications such as, Customer Relationship Management (CRM), Order Management, Logistics and Delivery, and Accounts Receivables
- Initiates the dialog for expanding the incremental value of current model through the inclusion of inventory/out-of-stock data, delivery routing information, specialized handling requirements (e.g. climate control, customs, regulatory controls, etc.)
- Promotes the idea for supplementing model’s predictions with weather and traffic feeds to be provide a holistic picture for decisioning
Impact
- Optimize logistics and distribution planning and manage delivery commitments communicated to customers within the context of shipment/ order delivery risks
- Manage relationships with logistics and distribution partners to maintain desired service levels
- Estimate near-term revenue at-risk from the shipment/order delay, and thereby impact to fiscal period revenue recognition
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