A New Roadmap to Supply
Chain Efficiency
Bauchi, Nigeria
Andrew Inglis, USAID | DELIVER PROJECT
Background: Bauchi State
• Pop: 4.6 million
• Contraceptive
prevalence rate: 2.7
• Prevalence of malaria in
children 6–59 months:
31%
• Children under 5 yrs.
with diarrhea: 32%
Transport Optimization for Bauchi Pilot
• Identify resources needed to create a pilot delivery
truck logistics system
• Determine the commodities and their volume going to
each health facility
• Obtain capacity and costs for vehicles
• Prepare transportation models to determine options
for effective delivery routes
• Present transportation model geographically.
Commodities in Bauchi Pilot
• Family planning:
–
–
–
–
–
Microgynon
Exluton/Microlut
Depo-Provera
Noristerat
male condom.
• Maternal health:
– magnesium sulphate
– misoprostol tabs
– oxytocin.
• Child health:
– oral rehydration salts
– zinc gluconate tabs.
• Malaria:
– Coartem 6×1, 6×2,
6×3, and 6×4 tabs
– rapid diagnostic tests
– sulfadoxinepyrimethamine tabs.
Resources Needed to Optimize Transportation
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Master facility list
Road network and conditions
Commodity characteristics
Shipment records
Commodity forecasts
Population distribution
Vehicle availability and characteristics
Operations schedule.
Maps of Road Network and Selected Health Facilities
Legend
Selected
Roads
Bad
Local
Secondary
Primary
Graded
Paved
0
30
60 Kilometers
Assumptions
• Shipment based on forecast
• Full supply of needed commodities
• Facilities characterized by services provided
• Actual travel speeds on road
• Bimonthly delivery.
Model Constraints
Scenario
A
B
Number of trucks
4
5
5m3
5m3
Percentage of shipments routed
88%
100%
Sensitivity to increase in volume
N/A
300%
Maximum 5 days out
70 kilometers per hour speed
Truck size
Scenario A: Sites Not Receiving Shipments
Scenario B: All Sites Receive Shipments
Feedback from the Field
• Modeling provides new information and knowledge
– fresh perspective on ways to use resources
• Adaptability of model and risk factors require
substantial sensitivity analysis.
Lessons Learned
• Lack of data = increased assumptions
• Must generate key datasets for modeling
– shipment tables
• Avoid “black box magic”
• Identify limitations to modeling.
Benefits of Transportation Modeling
• Gives decisionmakers new insight when making supply
chain design decisions
• Enables users to eliminate ineffective options
• Enables understanding of the impact of constraints
• Facilitates thoughtful needs assessment
• Informs discussion on alternatives
• Enables transportation thought process in new
paradigm
• Drives use of data for decisionmaking.