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This issue of Population Reports was prepared in collaboration with the DELIVER Project of John Snow, Inc. Published by the Population Information Program, Center for Communication Programs, The Johns Hopkins University Bloomberg School of Public Health, 111 Market Place, Suite 310, Baltimore, Maryland 21202, USA Volume XXX, Number 1, |
Recording and Reporting DataA good LMIS records and reports data from all facilities in the supply chain. In general, LMIS data are recorded on stock-keeping records (store ledgers, inventory control cards, bin cards), transaction records (requisition and issue vouchers, packing slips), and consumption records (service records, daily activity registers). Recording and reporting data are often difficult, especially where programs lack modern information technologies. In the Philippines in 1991, for example, data on consumption of contraceptives either were not available at all or were not put in the hands of supply chain managers. Instead of being allocated on the basis of clients’ use, contraceptives were allocated to provinces in equal amounts, causing widespread supply imbalances. In 1997, after an LMIS had been developed as one step in strengthening the logistics system, data collection and record keeping improved. The program could allocate contraceptives on the basis of client consumption, reallocate overstocks to understocked areas, and monitor and evaluate performance better (82). In many developing countries LMIS reports move from the service delivery level to more central levels on a fixed timetable (7, 60, 77). A better approach is to link reporting to resupply—such as when the LMIS report is also the request for new supplies. In Ghana, for example, contraceptive resupply is based on the dispensed-to-client data submitted through the LMIS. Because resupply depends on receipt of these reports, reporting rates come close to 100%, and stocks are adequately resupplied (28). Another effective strategy is to gather LMIS data when supplies are delivered (150). In Bangladesh, for example, staff from service delivery points pick up monthly supplies from their sub-district stores and submit a monthly consumption report at that time. The sub-district stores can use these data to determine the quantities of supplies that service delivery points need (83). In Turkey a “topping up” delivery system simultaneously gathers logistics data and delivers the right amount of contraceptive supplies. In this system a distribution officer with a fully stocked vehicle visits each health facility every four months. The officer makes a physical count of supplies on hand, calculates the average monthly consumption based on the total supplies on hand at the previous delivery visit, calculates a new maximum stock level, and then “tops up” the clinic stocks to that level. This system has substantially re-duced stockouts and in some cases has eliminated them (40). Automation. Programs prepare logistics reports by processing LMIS data either manually or by computer. At the service delivery and intermediate levels, logistics data processing is almost always manual. At the central level, however, computer processing is more common, particularly as the volume of data grows and reports become more complex. As family planning programs manage more and more supplies, a computerized LMIS becomes essential. With automation, the quality, timeliness, and use of logistics data improve dramatically—because more supplies can be tracked, sites can be monitored regularly, data can be quickly aggregated, analyzed, and submitted to decision-makers, and information can be shared easily. Also, as family planning priorities, method mixes, organizational structures, and clients’ needs change, computerized LMIS systems make it easier to respond. There is further reason to automate LMIS in countries where family planning is being integrated with other health programs or decision-making is decentralizing. Computerized data processing is necessary to manage a wider array of products within integrated systems or to provide data to a larger number of managers within decentralized ones (7, 48).
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