Questions and Answers
What is the purpose of conducting a quantification exercise?
To determine the quantity of health commodities required for a specific period of time.
What is the primary source of funding for HIV test kits?
What is the purpose of the forecasting step in the quantification process?
To estimate the quantity of each product that will be dispensed or used during each year of the quantification.
Consumption data are the most useful in immature and unstable programs.
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What are the four primary types of data used in the forecasting process?
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What is quantification in the context of health commodities?
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Who is usually involved in a quantification exercise for health commodities?
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Quantification is not a one-time annual exercise but an iterative process involving reviews and updates required ________.
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The quantification process for health commodities should be reviewed every six months.
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Match the steps in quantification with their descriptions:
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Which time series approach uses historical consumption data to analyze trends and project forecasts?
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Exponential smoothing forecasts use past forecast and actual data to reduce variability.
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What is the purpose of supply planning in quantification?
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What is the purpose of routine pipeline monitoring?
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What is a key activity involved in reviewing and updating the quantification process?
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Forecast accuracy can be improved over time through regular monitoring and updating.
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Match the stakeholders with their involvements in the procurement process:
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What criteria can be used to assess a supplier's capacity to perform?
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It is important for the tender document to be publicly advertised.
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The evaluation of bids is the process of assessing offers in accordance with the established evaluation method and criteria to obtain ___ for the organization.
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What is the objective for leverage products?
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What is the usual contract type for critical products?
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What is the procurement method for 'Shopping'?
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Specifications are a critical aspect in procurement.
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Functional specifications define the purpose, duty, role, or function of the product to be ________.
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What are examples of supply planning assumptions?
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Estimating total commodity requirements involves calculating additional quantities to cover procurement and maintain stock levels between minimum and maximum.
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What software tool is recommended to facilitate the estimation of total commodity requirements and costs as well as the preparation of a supply plan? PipeLine is specifically designed for _________ planning and pipeline monitoring in resource-poor settings.
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Match the following supply planning activities with their descriptions:
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What areas should the procurement unit consider when researching the supply and demand markets?
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Which tools are used to gather information about the market before the tender process? (Select all that apply)
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What are the quality criteria and certifications considered reliable when no prequalified sources are available?
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Good Manufacturing Practices (GMP) are designed to eliminate the need for testing the final product for quality.
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Registration of health commodities, especially pharmaceuticals, in the destination country is necessary for ______________ and distribution.
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Study Notes
Quantification of Health Commodities
- Quantification is the process of estimating the quantities and costs of products required for a specific health program, and determining when the products should be delivered to ensure an uninterrupted supply.
- The quantification process is critical in supply chain management, linking information on services and commodities from the facility level with program policies and plans at the national level.
Importance of Quantification
- Identifies funding needs and gaps for procurement of required commodities
- Helps in maximizing the use of available resources
- Enables advocacy for additional resources when needed
- Develops a supply plan to coordinate procurements and shipment delivery schedules
Key Steps in Quantification
Preparation
- Describe the program: collect background information on the program, including goals, strategies, priorities, and expansion plans
- Define the scope and purpose of the quantification: identify the scope of the program, list of commodities, and purpose of the quantification
- Collect required data: consumption data, services data, morbidity data, demographic data, and information on current program performance and expansion plans
Forecasting
- Organize, analyze, and adjust data
- Build and obtain consensus on forecasting assumptions
- Calculate forecasted consumption for each product
- Compare and reconcile results of different forecasts
Types of Data Used in Quantification
- Consumption data: historical data on the actual quantities of health commodities dispensed to patients or consumed at SDPs
- Services data: historical program-level or facility-level data on the number of patient visits, services provided, and disease episodes or health conditions treated
- Morbidity data: estimates of the number of episodes of a specific disease or health condition that will occur in a common denominator of the population
- Demographic data: data on the number and characteristics of the population targeted for services
Effective Quantification Team
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Logistics managers
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Policymakers
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Program managers
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Technical experts
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Procurement officers
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Warehouse managers
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Service providers
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Members of the quantification team should have expertise in the specific program area, computer literacy, and commitment to ongoing monitoring and data collection### Types and Sources of Data for Forecasting
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Program background information: sources include program progress and evaluation reports, policy and strategic planning documents, technical reports, and workplans.
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Consumption data: sources include LMIS reports, facility surveys of stock records and consumption records, and reported quantities of products dispensed to patients/clients.
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Services data: sources include HMIS reports, program M&E reports, facility surveys of service records, and daily registers.
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Morbidity data: sources include epidemiological surveillance data or research studies, and expressed as a ratio or percentage of a defined population with a specific disease or health condition.
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Demographic data: sources include Demographic Health Survey (DHS), national census data, and Population Reference Bureau data, and provides information on population growth and trends, as well as population characteristics.
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Program targets: sources include national policy and strategic planning documents, and national annual program targets or service coverage rates set as goals for the program.
Challenges in Data Quality
- Data may be outdated, unavailable, incomplete, or unreliable.
- Data may not reflect current policies, strategies, or context.
- Data may be specific to a particular population group, and extrapolation may be required to estimate incidence or prevalence in the general population.
- Program targets may be politically motivated and not based on realistic program capacity.
Forecasting Part 1: Organize, Analyze, and Adjust Data
- Assess data quality, considering factors such as facility reporting rate, stockouts, timeliness of data, and any factors that may influence future demand.
- Adjust data for inaccuracies, incompleteness, or outdatedness using techniques such as regression methods, trend projection, moving average, and weighted moving average.
- Document methodology for making adjustments, including notes on adjustments made for stockouts, facility reporting rates, or outdated data.
Forecasting Part 2: Build and Obtain Consensus on Forecasting Assumptions
- Assumptions are made to adjust historical program data when it is of poor quality or for future program performance.
- Assumptions may include issues such as expected uptake in services, compliance with recommended treatment guidelines, and future changes in standard treatment guidelines.
- Consensus should be reached on forecasting assumptions through a consultative process involving program planners, clinical experts, pharmacists, procurement specialists, and warehouse managers.
- A forecasting tree is a useful tool for organizing and utilizing data and assumptions to estimate the forecast.
Forecasting Part 3: Calculate the Forecasted Consumption for Each Product
- Forecasts based on consumption data and services data can be calculated using time series models that predict the future based on historical data.
- Time series models include trend projection, regression methods, moving average, and weighted moving average.
- Trend projection involves analyzing historical consumption data and services data for historical trends.
- Regression methods involve fitting a line to past data to project future values.
- Moving average involves using a series of averages of historical data to forecast demand for the next period.
- Weighted moving average places more value on data from more recent periods.### Logistics Management and Consumption Data
- Improving logistics management information systems can provide "real-time" consumption data, making sophisticated statistical methods relevant for forecasting health commodity consumption.
- Exponential smoothing is a forecasting approach that uses past forecast and actual data to project future needs, reducing variability.
Exponential Smoothing
- Uses a "smoothing" constant between 0 and 1 to assign weight to past actuals and past forecast data.
- Types of exponential smoothing calculations include:
- Simple Exponential Smoothing
- Damped Trend
Forecasting
- Forecasts are completed using consumption data, estimating future consumption of each product based on historical consumption and trend.
- Historical trend can be adjusted to reflect changes in future trends based on assumptions made.
- Forecasting software, such as Quantimed and MS Excel, can be used to calculate the forecast for each year of the quantification.
Converting Data into Product Quantities
- Conversion factors are used to convert data into product quantities, depending on the type of data used (e.g., consumption, services, morbidity, demographic, or program targets).
- Table 5-5 shows the conversion factors for different types of forecasting data.
Forecasting Software
- Quantimed is a Microsoft Access-based tool that facilitates forecasting of pharmaceutical needs using three methods: historical consumption, morbidity, and proxy consumption.
- ForLab is a multi-method forecasting tool that measures laboratory service delivery and supply chain performance.
Comparing and Reconciling Results
- If data permits, multiple forecasts should be generated using different data types, and outputs should be compared to arrive at the final forecast.
- Factors to assess when reconciling forecasts include:
- Quality of each data type (completeness, accuracy, timeliness, and availability)
- Reliability of intermediate variables (e.g., dispensing protocols, CYP conversion factors, population growth rate)
Supply Planning
- Supply planning involves estimating total commodity requirements and costs for the program based on the forecast.
- The supply planning step includes:
- Organizing and analyzing data
- Building supply planning assumptions
- Estimating total commodity requirements
- Developing the supply plan
- Comparing costs to available funding
Supply Planning Data Requirements
- Data required for supply planning includes:
- Patent, registration, or prequalification status
- Product characteristics (e.g., formulations, dosages, shelf life, temperature requirements)
- Supplier information (e.g., prices, packaging, lead times, shipping and handling costs)
- Funding information (e.g., funding sources, amounts, and timing)
- Procurement mechanisms (e.g., competitive international bidding, donor procurement, local procurement)
- Distribution and stock status information
Supply Planning Assumptions
- Assumptions need to be made in the supply planning step to account for missing or low-quality data.
- Examples of supply planning assumptions include:
- Timing of available funds
- Amount of available funds
- Lead times for each supplier
- Arrival dates of supplies
- Minimum and maximum stock levels for each level in the system
Estimating Total Commodity Requirements and Costs
- Estimating total commodity requirements involves determining the quantity of each product needed to meet the forecasted consumption and ensure adequate stock levels.
- PipeLine is a software tool that can be used to facilitate the estimation of total commodity requirements and costs.
Developing the Supply Plan
- Developing the supply plan involves determining the shipment quantities and delivery schedules to ensure a continuous supply of products.
- The supply plan helps program managers to:
- Identify funders and funding commitments
- Identify suppliers for each product
- Coordinate timing of funding commitments and procurements
- Schedule shipments according to procurement lead times, supplier lead times, and stock levels in-country
Comparing Funding Available to Total Commodity Costs
- The final decision on the quantities to procure is based on the amount of funding available for procurement.
- If funding is insufficient, the quantification team will need to determine whether additional resources can be mobilized.
Presenting the Quantification Results
- The quantification team should formally present the results of the quantification to stakeholders, including:
- Scope, purpose, and timeframe of the quantification
- Summary of major forecasting assumptions and data sources used
- Summary of supply planning assumptions
- Total quantities of each product required for each year
- National stock status for each product
- Summary of shipments, by supplier
- Total funding gaps for the next 24 months
- Specific actions required to address any critical stock imbalances
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Description
This quiz covers the logistics cycle, warehousing, distribution, and management of health commodities, including product selection.