A New Roadmap to Supply Chain EfficiencyBauchi, 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– sulfadoxine-

pyrimethamine tabs.

Resources Needed to Optimize Transportation

• 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 FacilitiesLegend

Selected

RoadsBad

Local

Secondary

Primary

Graded

Paved

0 6030 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 BNumber of trucks 4 5

Maximum 5 days out    

70 kilometers per hour speed    

Truck size  5m3  5m3

Percentage of shipments routed 88%  100% 

Sensitivity to increase in volume N/A  300%

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.