Section 4: Common Pitfalls

Now that we have reviewed the recommended steps for approaching how to forecast for NUMs and reviewed how to build assumptions to support forecasting, it is important to highlight a number of common pitfalls that occur when forecasting for NUMs. Consider the following when you approach any forecasting exercise for NUMs:

  • The stakes can be high when introducing or scaling up a NUM in a country. Often, these efforts are supported by large donor investments and may involve multiple global and local organizations. If the introduction or scale up of the method availability does not lead to significant uptake of the method, within a defined project period, donors may decide not to continue providing support for the method. Donors and programs need to set realistic goals for client uptake and consumption rates. When stakes are high, programs often project a higher estimated uptake than actually occurs, with serious consequences of over-supply, wasted resources, and unmet program goals. Financing for contraceptives is often a zero sum game—over-forecasting of one method usually means fewer resources and potential stockouts of others. To the extent possible, programs should seek the expertise of an unbiased third party to support forecasting the demand of NUMs. This would ensure that program aspirations do not interfere with realistic forecasts. 
  • On the other hand, financing constraints may limit how much of a particular NUM can be procured. If procurement of contraceptives is determined nationally, it is possible that NUMs will not be assigned appropriate forecasting numbers due to budgetary limits. Countries may be reluctant to spend scarce resources on methods whose appeal to potential clients is unknown. A way to address this issue is to ensure that programs that work with NUMs have a seat at the decision-making table during the national quantification and forecasting process.
  • Over-reliance on issues data in lieu of consumption data is dangerous when forecasting for underused methods. Some programs depend on issues data—which is based on the movement of products between any two storage facilities within a country (e.g., when the regional level distributes supplies to the district level)—as a proxy for consumption data.[1] Weak supply chains may be unable to provide reliable or timely consumption data. This is a problem because contraceptives may be distributed in a country in anticipation of demand, but the demand may not materialize. Thus, issues data does not point to actual consumption and use. Programs that can consider issues data in their forecast need to account for this discrepancy and reduce their forecast amounts accordingly by (1) forecasting conservatively, because it can be assumed that products issued are not necessarily yet consumed; and/or (2) schedule small initial shipments of the product into the country until monitoring data is available to confirm or challenge forecast assumptions.
  • Needs-based forecasts can estimate unrealistically high quantities. Needs-based forecasting does not require historical program data. Instead, it depends on inputs from demographic or behavioral surveillance surveys. Such a tactic establishes that if, for example, one million people report having an unmet need for family planning, that all one million people will access family planning, without considering availability, access, and/or the various socio-cultural barriers that may exist. This forecasting exercise tends to yield unrealistically high forecasts because it (1) overestimates the actual demand, and (2) does not consider if the product is also available in the private sector. Programs that rely on needs-based forecasts need to account for the discrepancy of inflated forecasts and reduce their forecast amounts accordingly.
  • Note differences in forecasting for public- and private-sector family planning programs. There are inherent market differences between the public and private sectors. Whereas a method might be very popular in the private sector—where women can access the method privately, over the counter, and at their convenience, for instance—this may not be possible in the public sector.  Forecasting should not be based on the assumption that a “private sector” experience will produce the same results as a “public sector” experience, and vice versa.
  • Often forecasts occur only once a year and do not allow for course corrections. Demand forecasts, especially if compiled nationally for the public sector, usually only occur once a year. This becomes an issue as the base assumptions shift during the year and course corrections cannot be made. This is especially true for NUMs because uptake may change significantly as a method is introduced or scaled up—programs are limited in being able to predict uptake, and are further limited when they cannot make course corrections and order more of a product mid-cycle. Those who procure contraceptive commodities need to be aware of this issue and build mechanisms for course corrections into the procurement process, such as instituting pipeline monitoring and regularly revisiting the supply plan.
  • Regulations, product approvals, and essential medicine lists could inhibit programs from even getting the product into the country. Some countries adhere to strict regulatory mandates that medical supplies need to be approved or registered by the government before they can enter the country. Others only allow products listed on the World Health Organization's (WHO) Essential Medicines List to pass customs (or to pass customs without prohibitive fees). Getting approval for a new commodity can be a long, bureaucratic process. Programs must plan for proper approvals and buy-in before moving forward with procurements of NUMs. Once in-country, the method may be subject to quality control testing even if it is tested by the supplier before shipping. The accuracy and reliability of in-country testing may vary greatly, and can result in long clearance delays or even quarantine. It is important to know the testing regime, and whether or not the government will accept pre-shipment testing or must perform its own tests after arrival. The caution for programs and others who forecast for NUMs is that forecasts need to be timed appropriately, according to these restrictions and regulations.

[1] Consumption data provide information about the quantity of goods actually given to or used by customers (USAID | DELIVER PROJECT 2011a).