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DATANAYA
Datanaya Future-Proofs Operations
with Supply Chain 360

The Problem: Manufacturing
A multi-national retail equipment manufacturer had an exceptionally complicated supply/distribution model that was spidered across an intertwined United States delivery chain.

For the past fifty years, the supply chain evolved based on requests from managers to service customers from local facilities and to ship full trucks directly off the line. The facilities varied in size and operational capacity, resulting in a delivery network optimized to support each of these requests and trucks waiting to be filled on the line.

The data accumulated over fifty years of operation was messy and segregated. Regional planners often relied on local tribe knowledge to get customer's products in time to meet SLAs. This intuition-driven delivery mechanism was ripe for system-wide optimization to improve overall efficiency and reduce network costs.

The Solution
Users

· Supply chain planners
· Supply chain managers
· Shipping managers

Services

· Data science: exploratory data analysis

· Data science: forward simulation of supply chain lanes

· Business consulting: reports to executive management on their supply chain inefficiencies

· Business consulting: reports to senior management on most effective supply chain improvements

Project details

· Three months to build prototypes for supply chain modeling and reporting to senior management

· Three to six-person team: business analysts and data scientists

· Agile experimental design and summarization for stakeholders
Game Changing Results
Consolidation of dozens of small, inefficiently placed distribution centers into an optimal six-facility distribution network saves o freight and distribution costs by as much as 12%. Now, goods can be shipped to customers more cost-effectively by guaranteeing a lower total freight cost.

Additionally, the solution can automatically adapt to customer demand in real-time to manage inventory across the new distribution network, pack freight trucks according to current demand, and ship goods with optimal delivery scheduling. All of this while meeting delivery SLAs.