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Introduction to Laboratory Quantification Lab TWG

Introduction to Laboratory Quantification Lab TWG. Jason Williams, Principal Laboratory Advisor, SCMS February 20-21, 2013. Overview . Introduction to logistics Quantification Forecasting approaches Data use Take aways ForLAB (demonstration). Logistics system design.

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Introduction to Laboratory Quantification Lab TWG

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  1. Introduction to Laboratory QuantificationLab TWG Jason Williams, Principal Laboratory Advisor, SCMS February 20-21, 2013

  2. Overview • Introduction to logistics • Quantification • Forecasting approaches • Data use • Take aways • ForLAB (demonstration)

  3. Logistics system design

  4. Logistic data flow and aggregation Central Warehouse MOH LMU National Reference Laboratories Regional Laboratories District Laboratories Opportunity to integrate LIMS Health Centers Health Centers Health Centers

  5. Laboratory logistics data • Date types: • Issues, losses/adjustments, stock on hand • Test numbers by instrument type • Instrument downtime • Can be used to: • Identify commodity distribution challenges • Inform national laboratory quantifications • Can also measure: • Service delivery performance/capacity • Impact of instrument downtime • Programmatic performance

  6. Logistics system design

  7. Advocacy • Quantifications serve as the core tool for monetary advocacy • Forecast output can guide programmatic scale up and define service delivery challenges • Forecast process (assumptions) can define data gaps – need for LMIS and M&E strengthening • Assist with donor coordination and defined roles and responsibilities • Guide programmatic and laboratory coordination

  8. Quantification • Activities: • Forecasting: estimating the quantities of the products required for a specific health program (or service), and • Supply planning: determining cost and when the products should be delivered to ensure an uninterrupted supply for the program Source: USAID | DELIVER PROJECT

  9. Quantification Step 1 Step 2 Step 3 Source: USAID | DELIVER PROJECT

  10. Forecasting approaches • Forecast methodologies: • Consumption (issues) • Demographic/Morbidity • Service statistics • SCMS Approach • Comparative review • Determine appropriateness – capacity/costs/growth • Final forecast established –mixed components

  11. Comparative review

  12. Quantification: data for decision-making Data Points required for optimization • Inventory of instruments • Demand (quantification) • Instrument utilization, diagnostic contribution and capacity • Instrument down time • Impact of maintenance agreements and vendor performance

  13. Understanding Instrument Diagnostic Contribution (service data)

  14. Take aways • Focus on ensuring data availability and quality • Multi/mixed method forecasting provides transparency, context, and overall confidence in estimates • Transform quantification data to monitor programmatic and supply chain performance • Use quantification data to make evidence based decisions, optimize procurement decisions • Monitor utilization to ensure capacity, scale up strategies

  15. Take aways • Forecast process defines data gaps – need for LMIS and M&E strengthening • Forecasting guides donor, programmatic and laboratory coordination and advocacy to address funding gaps

  16. Solution: ForLAB • Multi-method tool • Can be linked to LMIS • Flexible • Standardized specifications • Laboratory network coverage • Ability to inform optimization efforts

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