Impacting public health through Supply Chain Improvements

Transcription

1 Lesotho NDSO Supply Chain Analysis Impacting public health through Supply Chain Improvements

2 Agenda Introduction: Supply chain defined Lesotho stats Scope of the assignment Methodology Data collection, analysis and data sources Supply and Demand Strategic network analysis Inventory and Variability Analysis Supply Chain Finance Process Mapping Summary and recommendations Next Steps

3 Supply Chain Project: Process Mapping Visualize Analyze Optimize Success! A Miracle Occurs

4 Supply Chain: Defined Getting the right quantity of the right product to the right place at the right time Supply Chain Management: managing and coordinating all of the supply chain activities necessary to support the organization s strategy Supply Chain Planning: deciding what to do Supply Chain Execution: communicating the plan, executing the plan, identify and handling exceptions

5 Supply Chain Planning Operational: day to day operations (loading trucks, placing replenishment orders, creating pick lists) Tactical: week to week / month to month orders (when do we build up supplies, should we push or pull, how much transportation capacity is needed, what warehouse locator system to use) Strategic: longer time frames, a year or more (focus on how is the network structured, where are the facilities)

6 Supply Chain Activities: Public Health Product Selection Forecasting Procurement and sourcing Manufacturing Warehousing Inventory Transportation Order (Demand) Management Finance

7 Population

8 Basic Facts Kingdom of Lesotho Population: 2.1 Million Population growth rate: 0.13% Life expectancy: 40.2 years Female: 39.3 years Male: 41 years Infant mortality rate: 79 deaths per 1,000 live births

9 Current Distribution Network Current network is formally a Hybrid Most items are supplied to major customers directly from NDSO in Mafeteng Health centers are supplied through DHMTs

10 Current Network Financials Supply Chain Costs by Function FY2006 FY2007 FY2008 Customer Response 440, , ,478 Inventory Planning and Management 432, , ,010 Supply (Procurement) 291, , ,775 Transport 1,110,466 1,229,322 2,046,263 Warehouse 2,155,886 2,279,090 3,880,304 Admin 1,613,923 3,474,831 6,555,531 Total 6,044,716 8,238,497 14,402,360 Supply Chain Costs by Function FY2006 FY2007 FY2008 Customer Response 7% 7% 6% Inventory Planning and Management 7% 5% 4% Supply (Procurement) 5% 4% 4% Transport 18% 15% 14% Warehouse 36% 28% 27% Admin 27% 42% 46% 100% 100% 100% 7,000,000 Customer Response 6,000,000 Inventory 5,000,000 Planning and 4,000,000 Management Supply 3,000,000 (Procurement) 2,000,000 1,000,000 - FY2006 FY2007 FY2008 Transport

11 Methodology Data sources OPERA II RX Solution Audited Financial reports (NDSO) Types of the data Processing

12 Data Sources: Opera II Data Sources: Opera II Opera II was in use as the NDSO basic Inventory Mgmt System from Oct 2004 thru Oct 2008 Discontinued and replaced by Rx Solution Data was recovered from backup, processed and combined into a single analytical database Opera Database contains detailed transaction data suitable for logistics analysis Supplier and demand information Outbound shipments Inbound receipts Invoice Information Product Information: Price, Cost, Qty Ordered, Qty Shipped, Date Ordered Rcvd, Date Shipped Master Products and Customer data is similar to current Rx Solutions data, but not exactly. Could be combined into a single database with manual work.

13 Data Sources: Rx Solution SQL Server database with customized front end Replaced Opera II as Inventory Mgmt System in Oct 2008 Data was easy to retrieve from most recent backup, contains 4 months of detailed shipment and receipt data, master data, etc. Team extracted relevant supply chain data in incorporated into a second supply chain analysis database Analytical Data Approach: For Supply Chain Analysis, focus on utilizing Opera II data due to length of time available Incorporate Rx Solution Data to check trends, improve future predictions

14 Financials and Cost Data Income Statements provide high level cost data broken out into major accounts Developed rough cut supply chain costs for use in models as basic cost/budgeting capability Use either Qty or Value basis for allocating costs Only used for basic structural comparisons and high level supply chain related financial projections

15 Data Summary 4 years of supply chain transactional data 385,583 customer line item orders 3285 products 7 main product categories 7100 (Pharmaceuticals) LAB X-RAY DENT MEDS DON. MISC. GIS Data Data was supplied by MOH Matched to location data on name Weight and volume was not incorporated

16 Supply Chain Analysis: Supply and Demand

17 Throughput Qtys (est) EQUIP. X-RAY MISC. MEDS LAB DON. DENT PHARMS (est) DENT DON LAB MEDS MISC X-RAY EQUIP TOTAL

18 Throughput Value Annual Product Volume/Value ( ) 16,000,000,000 14,000,000,000 12,000,000,000 10,000,000,000 8,000,000,000 6,000,000,000 4,000,000,000 2,000,000, (est) X-RAY MISC MEDS LAB DON. DENT PHARMS YEAR TOTAL (Maloti) ,678,721,303 Note: all 2008 (est) values are estimated using 10 months actual 2008 data extrapolated to 12 months ,317,393, ,756,775, ,859,071, (est) 11,891,036,256

19 Product Type Mix Value Total Shipped Value PHARMA DENT DON. LAB MEDS MISC. X-RAY 3% 1% 1% 4% 0% 13% 78%

20 Average Fill Rate* for all Pharma Products = 37.1% Qty Fill Rate 100% 90% 80% 70% *Fill Rate is 60% Ordered Qty 50% filled within 0 or 40% 1 days divided 30% by total Ordered 20% Qty. 10% 0% 28% 27% 22% 22%18% 15% 11% 95% 72% 20%19% 24% 94% 28% 17% 34% 15% 19% 93% 29% Total Fill Rates by Product Type (Top 20)

21 Comparison to NDSO Service Rates NDSO calculations indicate a 75% service rate Rate calculated based on the percentage of order quantities available within one week of order Orders in the system with a received date but no ship date all assumed to be cancelled thus they do not count against service Follow-up analysis by WB team confirms 75% service according to this definition Definition of acceptable service should be strategized Confirmation that no cancelled orders were actually unmet demand is necessary Cancelled orders and unmet demand are suggested to be logged separately to allow for more accurate service calculations

22 Largest Customers by Volume

23 2009 Supply-Demand Balance Analysis In October 08, NDSO switched IT systems Four months (Oct 08-Feb 09) of data mined from new Rx Solution tool Opera data not merged during transition Possible legitimate reasons for this, but No combined analysis possible without significant time investment to link two datasets Product Category information not available in Rx Pharma cannot be analyzed as a subset Should be corrected for future analysis

24 Rx Supply-Demand Snapshot Million Units Total Quantity Receipts (units) Total Quantity Demand (units) Demand Quantity Receipts = 685,000 units Total Value of 4-month imbalance = 26.5 Million Maloti

25 Balance for Selected Products RecNum DemNum Amoxycillin 125mg/5mL Suspension; 100 ml [PO] Cotrimoxazole 240mg/5mL Suspension; 100 ml [PO] Insulin Actraphane 30/70U/mL 10mL Vial; 1 Vial [SC] Paracetamol 120mg/5mL Syrup; 100 ml [PO] Product Description Received Demanded Investment Units Units Requirement Amoxycillin 125mg/5mL Suspension; 100 ml[po] , Cotrimoxazole 240mg/5mL Suspension; 100 ml [PO] , Insulin Actraphane 30/70U/mL 10mL Vial; 1 Vial [SC] , Paracetamol 120mg/5mL Syrup; 100 ml [PO] ,481.66

26 Supply Chain Analysis: Network Structure Optimization

27 Supply Chain Network Design Strategic analysis of network structure Where are the facilities How does product flow to the customer Results largely driven by: Transportation costs Other supply chain costs (warehousing, order entry, sourcing, processing) Facility fixed operating costs Demand locations and other constraints Does NOT optimize inventory stock locations (where do I hold stock and how much), but does incorporate inventory holding costs Sets the structure, next step is to optimize business rules and operating policies Inventory Push vs. Pull Transportation mode selection Sourcing and allocation rules

28 Baseline (Current System) Decentralized Direct

29 Decentralized Direct Baseline

30 3 Alternative Scenarios Baseline: Current approach (hybrid) Fully decentralized: all must run through DHMTs (not a feasible option, included for comparison only) Direct: everything shipped direct from CMS Annual Transportation Cost R 2,400, R 2,300, R 2,200, R 2,100, R 2,000, R 1,900, R 1,800, R 1,700, Decentralized Baseline Direct Summary: 5% decrease in transportation costs by shipping everything direct Results assume that all transport costs are distance based and will not change with different flow patterns

31 Supply Chain Analysis: Optimizing Inventory and Improving Service

32 Explain Inventory and Variability Analysis Inventory is held to ensure immediate response and availability for customers Main causes of higher inventory Demand variability Supplier lead time and variability After calculating demand and supply characteristics, select inventory policy and calculate inventory targets to meet desired service target Simulation to get visibility into stocking levels

33 Introduction to Inventory

34 Inventory Analysis Approach Analyze and calculate historic demand variability for PHARMA products Analyze and calculate historic lead times and lead time variability for Calculate Safety Stock levels and financial inventory investment Run Scenarios to Explain Cost and plan to achieve Service Targets (80/95/99) Effect of Lead Time Variability Effect of Reducing Lead Time Run Simulations to test out new inventory policies

35 Supply Lead Time and Variability Pharma Suppliers have extremely long lead times with high variability Lead Times and StDev range from days! This puts a significant strain on the supply chain as high inventory will be required to meet desired service

36 Supply Lead Time and Variability Average Lead Times and Variability differ by product and supplier Supply strategy is an important consideration in the performance of a supply chain

37 Demand Volumes and Variability Top 20 most demanded products from Opera data

38 Demand Volumes and Variability High Demand variability requires you to hold lots of inventory If we knew what/when/how much, we d just order the exact amount in advance Since we don t know this, we must hold safety stock % % % Ratio of Variability to Average Daily Demand for Top 20 Products % % % % % % % 0.00%

39 Inventory Scenarios: Achieving Service Targets Safety stock levels and base stock (reorder points) were calculated to achieve: 80% Service Rate 95% Service Rate 99% Service Rate Assume historic lead time and variability

40 DOS to Achieve Service Targets SL80/LTV1.0 SL95/LTV1.0 SL99/LTV1.0

41 Cost Of Inventory Investment (COII) to Achieve Service Targets 14,000,000,000 Cost of Inventory to Achieve Service Targets To move from 80% to 95% doubles inventory. 12,000,000,000 10,000,000,000 8,000,000,000 6,000,000,000 4,000,000,000 2,000,000,000 To move from 95% to 99% incurs another 41% cost increase. - 80% 95% 99%

42 Inventory Scenario: Effect of Reducing Inventory Lead Time Variability Safety stock levels and base stock (reorder points) were calculated if Supplier Lead Time is constant BUT could be made more predictable (reduced variability) Baseline lead time variability 50% reduction in variability Eliminate variability

43 Effect of Reducing Inventory Lead Time Variability on DOS to Achieve 95% Service Rate SL95/LTV1.0 SL95/LTV0.5 SL95/LTV0.0 0

44 Effects of Reducing Supplier Lead Time Variability Using Historic Lead Times, 95% Service Target 10,000,000,000 9,000,000,000 8,000,000,000 7,000,000,000 6,000,000,000 5,000,000,000 4,000,000,000 3,000,000,000 2,000,000,000 1,000,000, % 80% 60% 40% 20% 0%

45 Inventory Scenario: Effect of Reducing Supplier Lead Time Lead Time Variability Lead Time 100%, 9,352, 074,812 50%, 8,589,9 92,853 50%, 6,310,8 02,140 0%, 4,591,50 0, % 80% 60% 40% 20% 0%

46 Comparison of Alternative Approaches to Inventory Strategy Total Cost of Inventory 14,000,000,000 12,000,000,000 10,000,000,000 8,000,000,000 6,000,000,000 4,000,000,000 2,000,000,000-95% Service 100% Lead Time Var 95% Service 50% Lead Time Var 95% Service 0% Lead Time Var 80% Service 100% Lead Time Var 99% Service 100% Lead Time Var 95% Service 50% Lead Time

47 Summary and Recommendations Good data is available for supply chain analysis Processed and available for further analysis GIS Locations matched up and included in models Concerns for continued data availability Dropped support for product families Very difficult to match new and old naming schemes Other? Network structure: direct shipment to all facilities could save money, but more important provide visibility to NDSO to better serve their customers structure does not need revision at this time Historic fill rates are not good Supplier lead time variability is very high Inventory is not designed to achieve strategic service targets

48 Summary and Recommendations (continued) Detailed inventory plans have been developed to achieve service targets Inventory target specific by product Cost of safety stock investment Supplier lead time variability must get managed down! Supply-Demand imbalances exist, must be addressed prior to implementing inventory What is the right demand forecast on aggregate??? Historic demand may represent actual need, or may represent diversions and theft Population should be modeled and total demand forecast, strategic procurement plan developed as a first step