Product Selection and Quantification of Health Commodities Supply Chain Management 1

Role of Product Selection in Supply Chains 2

Product Selection Defines what products are procured and used in the health system and the range of products that a customer can receive Limits the variety of products that are used and available at public sector facilities can make the supply chain more manageable Enables the development and implementation of a national coordinated logistics system, and allows for the redistribution of products throughout the system 3

Product Selection Sources Product selection is informed by local and international policies and guidelines including: – Disease pattern and demographic – Clinical standard procedures – Standard Treatment Guidelines – National and international regulations – Technology and techniques of testing 4

Role of Quantification in Logistics 5

Definition of Quantification Quantification is the process of : Forecasting – estimating the quantities and costs of the products required for a specific health program (or service), and Supply planning – determining when the products should be delivered to ensure an uninterrupted supply for the program 6

Quantification helps program managers: – Identify the funding needs and gaps for procurement of the required commodities – Leverage the sources, amounts and timing of funding commitments to maximize the use of available resources – Advocate for additional resources, when needed – Ensure procurement is coordinated with forecasted supply needs to ensure a continuous supply of commodities Importance of Quantification 7

Steps in Quantification Step 1 Step 2 Step 3 8

Quantification – Step 1: Preparation Step 1 Step 2 Step 3 9

Step 1: Preparing for a Quantification Exercise 1.Assemble a quantification team 2.Describe the program (program performance, policies, and strategic plan) 3.Define the purpose and scope of the quantification exercise (products, timing, etc.) 4.Collect required data (for forecasting and supply planning) 10

Collect Data During Preparation Phase data is collected for both forecasting and supply plans: Consumption data List of stockkeeping units used in the program Demographic and population data Morbidity data Services data (test numbers) Program targets Forecasting Data Forecasts Available budget Stock on hand Previous quantities ordered Losses expected Transfers Desired stock level Storage capacity Supply Plan 11

Quantification – Step 2: Forecasting Step 1 Step 2 Step 3 12

Forecasting Definition Commodity forecasting is estimating the quantities of commodities that could be used by a program for a specific period of time in the future. 13

Step 2: Forecasting (“What do we need?”) 1.Organize, analyze and adjust data 2.Build and obtain consensus on forecasting assumptions 3.Calculate the forecasted consumption for each product 4.Compare and reconcile results of different forecasts 14

Forecast – Consumption Data Data on quantity of each product used over the previous months period (where it is lacking, issues data can also be used as a proxy) Uses historical data to predict future distribution and is accurate as long as data providing programs continue Obtained from: LMIS records and reports Formula: Amount of products used or issued previously X growth factor Total Forecasted Quantity = 15

Forecast – Consumption Data, Example Last year 30, 000liters of Minolyse was used last year on all ABX Micros machines. If the trends remain the same, how much is required for next year (if we anticipated 10% growth in hematology testing) ? 16 Quantity of products used X growth factor Total Forecasted Quantity = 30,000 X 110% 33,000 liters =

Forecast – Service Data, Example Last year, the number of full blood counts (FBCs) conducted on the ABX Micros machine was 200,000 tests. According to the manufacturer, it required 0.05liters of Minolyse to perform an FBC test on the Micros. If the scale up rate for FBC is 10% in the coming year, how much Minolyse is required over the forecast period? Total Forecasted Quantity = 11,000 liters = 200, * X 110% # of tests conducted Growth factor X X Usage rate per test 17

Forecast – Demographic Data Data on population characteristics, growth, and trends Useful when you have a new program and no historical data Does not tell the number of units of consumed but instead is info about people and so it must be converted to tests and to product numbers Often used to estimate the total unmet need for a service or treatment in a program or country, and so represents highest potential requirements Should be combined with other sources of data Obtained from: Demographic and Health Surveys, census data, Formula: 18 # targeted population Total Forecasted Quantity * = STGs X Usage rate * Growth rate

Forecast – Demographic Data, Example 1 A total population is 3 million and, of that, 20% are pediatrics who are to receive 2 CD4 tests per annum per the STG. The usage rate per test of FACSFlow Sheath on the BDFACS Count machine is 0.035liters. How many liters of the reagent will be required per annum at 15% growth rate? 19 Total Forecasted Quantity # targeted population * = STGs X Usage rate * Growth rate 600,000 * = 2 X * ,300 Liters

Forecast – Morbidity Data Data which are incidence and prevalence of specific diseases/health conditions (may be available by population group within a defined population group) Extrapolated to estimate national-level incidence and prevalence of specific diseases/health conditions A benchmark for comparing against consumption figures to verify rational medicine use, i.e., by applying the STG to the number of episodes of the health problem actually treated Most complex and time-consuming method, but usually most convincing Obtained from: HMIS or health facilities, STGs Note: Morbidity forecast calculation method is the same as for demographic data. For this reason, this two methods are combined to be demographic/morbidity forecast. In that case, there are 3 forecasting methodologies. 20

Forecasting Assumptions These factors should be addressed and all related assumptions documented: Expected uptake in services Compliance with recommended treatment guidelines Impact of changing program policies and strategies on supply and demand Service capacity Provider behavior Client access to services Seasonality Geographic variations in disease incidence and prevalence 21

Forecasting Assumptions – cont. For provision of lab services, the following areas may be considered for building assumption: For service methodology: oTesting protocols oTesting capacity oInstrument utilization and downtime oService contracts oNo. of working days oTier levels of service 22

Forecasting Assumptions – cont. For provision of lab services, the following areas may be considered for building assumption: For consumption methodology: oUsage trend oStart up and shut down oQuality control protocol oWastages and expiries oReagent matrix – ratio of different reagents on one machine oStock out rates oReporting rates 23

Calculating and Reconciling Forecasts – Use as many of the four data sources as possible to calculate the forecast If different data sources yield significant differences in forecasted amounts, go back and check assumptions and data values Compare the final forecast consumption quantities from each forecast and harmonize to create a comprehensive forecast 24

Forecast from All Data Sources 25 Forecast Comparison Example

Any questions? Thank you!! 26