1. Supply Chain Modeling: Analysis of Demand Variability and Volumetric Capacity Needs for Contraceptives and MCH Products James Gibney Anabella Sánchez Carlos Lamadrid February 20, 2009

2. Agenda Introduction: Overview of Unmet Need and Areas of Interest Part A: Volumetrics of Guatemala’s Integrated Supply Chain Part B: Effect of Demand Variability on Contraceptive Logistics

3. Introduction: Low Unmet Demand in 2007 For Contraceptives – But Analysis Still Needed to Ensure Success in 2008/9 Observation: No significant stock-out problems existed in 2007 for contraceptives Areas of Interest: A. Given the integrated supply system, what are the volume requirements needed to ensure full supply for contraceptives and MCH products? B. What is the effect that variability has on the system? Are stock-outs being avoided due to high level of emergency orders?

4. Part A: Volumetrics of Guatemala’s Integrated Supply Chain Background: With little exception, the distribution network (warehousing and transport) of products in Guatemala’s public health system is integrated. To better understand the volume capacity needs of this system, data on all products from Jutiapa and Totonicapán was analyzed. A special focus was placed on contraceptives and MCH products. Questions this section answers: 1.In terms of volumes, what does the average monthly demand for all products that flow through a DAS warehouse look like? How does this differ for health centers, health posts, NGO’s, and hospitals? What are the products that take up the most space? 2.What are the space requirements needed for the family planning products? How do these products compare to each other? What are the different requirements per SDP site type? How to they compare to non family planning products? 3.What are the space requirements needed for the MCH products? How do these products compare in space needs to each other? How to they compare to non MCH products?

5. Contraceptives represent less than 1% of volume for Totonicapán and 3% for Jutiapa Difference is attributed to higher demand of condoms in Jutiapa MCH accounts for approximately 1/3 and other products take 2/3 of volume capacity Volumes of Product Types in the Integrated System

6. Volumetric Composition of Average Monthly Demand By Products (Combined Average Monthly Real Demand for Totonicpán and Jutiapa) Table provides 105 products Contraceptives are highlighted blue, Condoms take 1.3% of total Volume composition follows 80/20 rule Acetaminofen syrup and Bromexina take approx. 20% of volume 80% volume

7. Volume (cubic cm) Requirements of Average Monthly Demand Jutiapa has double the capacity need at SDP’s than Totonicapán In Totonicapán Health Posts require more volume capacity than health centers for MCH and Other Drugs, but not Contraceptives In Jutiapa, the Health Centers require more volume capacity for all product types, with Contraceptives requiring significantly more volumetric capacity (approx. 3x)

8. Contraceptive Volumes – Monthly Demand Averages For SDP Types Percentages show relative capacity needs for storage and transport Commonalities between Jutiapa and Totonicapan: Same order of magnitude (Hospitals, then NGO’s, then Health Posts, then Health Centers) Hospital percentages most similar Health Post percentages are 2 nd most similar Differences between Jutiapa and Totonicapan Totonicapan higher relative flow to NGO’s Jutiapa higher relative flow to Health Centers (almost 2 times that of Totonicipan)

9. For the contraceptives part of the integrated order (1%-3% of total volume, what is the volumetric composition by product?

10. What is the Average Monthly MCH Demand By Volume? Acetaminofen takes up over 30% in both Departments

11. Effect of Demand Variability on Contraceptive Logistics Background: Given the complexity of large logistical networks, such as the MSPAS system, the effect of high variability on a supply chains can be both significant and hard to measure. Accordingly, it is necessary to measure the variability levels and use advanced analysis methods, such as modeling, to determine the effect. Questions this section answers: 1.Does the use of 3 months historical averages in making orders reduce the variation to insignificant amounts? 2.How does variability effect service levels? Can there still be a stock-out even if 1/3 min/max levels are followed? Does an increase in variability increase the propensity for emergency orders? Does it increase the propensity to be overstocked?

12. Variability in Contraceptive Monthly Real Demand Observations: Real demand changes significantly on a monthly basis Jutiapa shows lowest variability in all products High IUD variability partially attributed to low order amounts (ie. change from 4 to 8 represents 100%) Ramifications: Variability levels can significantly effect service rates of logistics system, even if system follows the min/max ordering system Causes of Variability should be addressed –Are reporting procedures being followed –or are there really high swings in real demand?

13. Does Using 3-Month Historical Average Smooth-away Variability? Table below compares projected demand based on 3 month historical averages to the actual demand for 2007 Red highlights represent projected under-estimating by more than 20% Yellow highlights represent projected demand over-estimating by more than 20% Results: 50% of the time the projected was either over or under by more than 20%. Using projections based on 3-months historical data does not necessarily remove variability. Complexity of system requires the use of advanced supply chain modeling software.

14. Modeling The Department of Sololá Structure 1 National Warehouse 1 Department Warehouses 10 District Warehouses 33 Health Centers 10 NGOs 1 Hospital Rules Products Inventory Policies Sourcing Policies Transportation Policies

15. Effect of Variability on Sololá Health Center Condom Stock Outs and Emergency Orders Scenario Outputs Stock Out Periods –no variability scenario – 0 –25% variability scenario – 1 –50% variability scenario – 5 Instances Crossing Above Max (overstocked) –No variability scenario – 0 –25% variability scenario – 5 –50% variability scenario – 2 Instances Crossing Below Min (emergency order) –No variability scenario – 0 –25% variability scenario – 8 –50% variability scenario – 6 (Settings: 106 condoms/month; 1/3 month min/max; 2 year period, reorder quantity based on previous 3 month demand)

16. How variable is the data? Examples of condom real demand - Jutiapa Source: MSPAS Logistics Module data

17. How variable is the data? Examples of depo real demand - Jutiapa Source: MSPAS Logistics Module data

18. Questions?