Ebonyi, Nigeria: Direct Delivery Information Capture: Transportation Optimization Analysis, July 2015JULY 2015
This publication was produced for review by the U.S. Agency for International Development. It was prepared by the USAID | DELIVER PROJECT, Task Order 4.
Ebonyi, Nigeria: Direct Delivery Information Capture Transportation Optimization Analysis
The authors' views expressed in this publication do not necessarily reflect the views of the U.S. Agency for International Development or the United States Government.
USAID | DELIVER PROJECT, Task Order 4 The USAID | DELIVER PROJECT, Task Order 4, is funded by the U.S. Agency for International Development (USAID) under contract number GPO-I-00-06-00007-00, order number AID-OAA-TO-10- 00064, beginning September 30, 2010. Task Order 4 is implemented by John Snow, Inc., in collaboration with PATH; Crown Agents Consultancy, Inc.; Eastern and Southern African Management Institute; FHI 360; Futures Institute for Development, LLC; LLamasoft, Inc.; The Manoff Group, Inc.; Pharmaceutical Healthcare Distributers (PHD); PRISMA; and VillageReach. The project improves essential health commodity supply chains by strengthening logistics management information systems, streamlining distribution systems, identifying financial resources for procurement and supply chain operation, and enhancing forecasting and procurement planning. The project encourages policymakers and donors to support logistics as a critical factor in the overall success of their healthcare mandates.
Recommended Citation Purcell, Ryan. 2015. Ebonyi, Nigeria: Direct Delivery Information Capture—Transportation Optimization Analysis. Arlington, Va.: USAID | DELIVER PROJECT, Task Order 4.
Abstract From November 2014–February 2015, the USAID | DELIVER PROJECT, Task Order 4, analyzed the public health transportation network and routes in Ebonyi state, Nigeria.
Focused on the two-year-old Direct Delivery and Information Capture (DDIC) last mile delivery system to service delivery points throughout Ebonyi, the goal of the analysis was to understand the current efficiency of the distribution network, as well as to anticipate future capacity and other bottlenecks as the DDIC program expands.
Photo note: December 2013, net distribution campaign in Sokoto, Nigeria. Hamisu Hassan, USAID | DELIVER PROJECT
USAID | DELIVER PROJECT John Snow, Inc. 1616 Fort Myer Drive, 16th Floor Arlington, VA 22209 USA Phone: 703-528-7474 Fax: 703-528-7480 Email: askdeliver@jsi.com Internet: deliver.jsi.com
Transportation Optimization Models .........................................................................................................................11 Round 12 Historical Baseline and Optimized Round 12 Historical Baseline ...............................................11 Future Baseline............................................................................................................................................................11 Future Volume Scenarios..........................................................................................................................................13 Service Time Scenarios .............................................................................................................................................13 Truck Capacity Scenarios .........................................................................................................................................13 Changing Shift Length Scenarios .............................................................................................................................14
Example Routes from Model ........................................................................................................................................21
5. SDP Master Name Continuity Mapping............................................................................................................10 6. Future Baseline for All Routes............................................................................................................................12
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Acronyms
GIS geographic information system
GPS global positioning system
MOH Ministry of Health
SCG Supply Chain Guru™
SDP service delivery point
VRP vehicle routing problem
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Acknowledgments
The authors would like to thank the USAID | DELIVER PROJECT staff in Nigeria for their support of this analysis; they provided data, feedback, and comments, which proved invaluable. In addition, special thanks to Allison Ebrahimi Gold in Washington, DC, for her data-cleansing efforts during the initial phase of the project and her general support throughout the project.
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Executive Summary
Building off two-plus years of Direct Delivery Information Capture (DDIC) program execution and data collection, as well as previous analytical work by the USAID | DELIVER PROJECT early in 2014; this analysis examined, in detail, the current performance of the transport network; as well as ways to improve it, going forward.
By using rigorous data collection, validation, and optimization modeling analysis techniques, the team separated and isolated the impact of several different input variables on network efficiency.
Results indicate that volume assumptions and cubic capacity constraints on vehicles are less likely to significantly impact the optimum route plan for each round. Changes to time-based factors, such as dwelling times during deliveries and total working hours per day; however, should be expected to have significantly more impact on route efficiency and overall performance on metrics, including the total number of routes and total kilometers driven.
These results highlight areas of robustness in the network route plan, such as the ability to handle significant volume growth without major changes. At the same time, the results highlight areas of sensitivity—and opportunity—with changes to dwelling times having a direct positive or negative impact on network performance.
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Analysis Overview
Background The Direct Delivery Information Capture (DDIC) informed push system is based on a model of vendor-managed inventory (VMI) that has been used in Zimbabwe since 2008. In March 2012, the USAID | DELIVER PROJECT (the project) received core funding to pilot the VMI model in Nigeria; Bauchi and Ebonyi were selected as pilot states.
Since early 2013, DDIC has been in place for direct delivery from the state central warehouse to service delivery points (SDPs), such as hospitals and clinics throughout Ebonyi. The SDPs are visited bimonthly to check inventory levels and to top up stock to reach preset levels for each commodity in the program. In this way, the delivery vehicle acts as a mobile warehouse for the SDPs throughout Ebonyi, with the goal of minimizing stockouts, expiries, and total inventory costs.
As the DDIC program grew from the piloting stage to now serving 250+ SDPs in Ebonyi state, it became clear that delivery route planning and execution would eventually become performance- limiting constraints for these objectives. It was determined that an analysis of the DDIC transportation system would provide valuable insight to current performance, areas for improvement, and planning for the future of the program.
Objectives The project had the following objectives:
• Collect and analyze detailed current data for the DDIC program:
− delivery volumes
− global information system (GIS) locations of the SDPs
− global positioning system (GPS) tracking information from delivery vehicles.
• Build an up-to-date data layer for the road network (with true travel distances and times).
• Create analytical models for the historical transportation network.
• Create analytical models of the best-guess future network, based on available projections.
• Build and analyze several future model scenarios to test key input factors and their impact on network performance.
• Draw conclusions and suggest recommendations for the future of the DDIC program, based on these models and an analysis of their output.
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Methodology The agreed-upon project approach uses best practices supply chain modeling techniques and methodology; it uses LLamasoft’s Supply Chain Guru™ software to build and analyze the current state and potential future transportation network configurations for the DDIC program in Ebonyi state. The methodology is outlined below:
1. Capture applicable local data and historical DDIC data:
a. SDP and warehouse locations, incorporating GPS data
b. delivery volumes for the history of the program (quarter 1, 2013– quarter 4, 2014)
c. GPS tracking data from delivery vehicles
d. road network data (distances and travel times)
e. central warehouse and SDP operating hours
f. truck capacities
g. physical volumes for all commodities.
2. Build a baseline model that replicates the historical reality of DDIC round 12 deliveries (November–December 2014).
3. Build an optimized baseline model to analyze improvement potential from round 12.
4. Build various alternative scenarios and compare results across a variety of transportation routing metrics:
a. total delivery volumes
b. total routes required
c. total delivery time
d. total delivery distance.
5. Analyze all this information to understand future DDIC planning and operations.
Modeling Technology The analysis team used LLamasoft’s Supply Chain Guru™ (SCG) software package throughout the project. This tool allows real-world supply chains to be built into mathematical models, which can then be altered, stress-tested, optimized, and compared against one another. Key features used on this project include—
• LLamasoft proprietary vehicle routing problem (VRP) optimization engine
• GPS location data for all SDPs
• road network data for calculating real-world transportation distances and travel times
• scenario-building tools to build and compare the various options
• geographic maps for visualizing all scenarios
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Direct Delivery Information Capture Transportation Analysis
Delivery Volume Analysis The first completed step was collecting historical delivery volume information, which is used in aggregate to validate total network flows, and also to understand the relative spread of delivery volumes throughout Ebonyi. The Nigeria field office staff helped collect the product unit volume data (see table 1).
Table 1. Health Commodity Volumes
This data was then merged with historical delivery quantities from all the DDIC rounds to calculate the total volume delivered to each SDP during the course of the program. See figure 1 for the volumetric data. Of particular note are the relatively low volumes from round 12, which local staff characterized as a temporary reduction; they expect the volumes to grow significantly in the near future.
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Figure 1. Ebonyi State Total Commodity Volumes by Delivery Round
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Delivery Round
Ebonyi State Total Commodity Volumes by Delivery Round
Dwell Time Analysis Next, the project team analyzed the delivery dwelling times at all SDPs, which is the time required to review stock levels, top up products that are below reorder points, and complete necessary paperwork. This data was obtained from historical delivery reports, which had good data. It should also be noted, however, that the data were buried in many files and with a format that required significant time to piece together.
The objective of this step was to better understand the relationship between delivery volume and the time required to complete the visit. Figure 2 depicts the line of best fit for this data, with a fixed dwell time (time required regardless of volume) of 48.455 minutes and a variable dwell time (volume-dependent) of 72.246 minutes per m^3 delivered. The average delivery volumes, per spot, at a fraction of an m^3, indicates that most of the dwelling time for each stop is fixed; this suggests that, with operational efficiency improvements during stops, dwell times can be reduced.
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Figure 2. Fixed and Variable Dwell Times for SDP Deliveries
y = 72.246x + 48.455
GIS Data and Road Network Analysis To understand where all the deliveries were physically made, the next step was to collect GIS data for all SDPs. Figure 3 shows this information for all 250+ SDPs in Ebonyi. At this time, the project team has validated 95 percent of the coordinates; the remaining questionable locations are flagged in the master GIS data file.
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Figure 3. Service Delivery Point Locations in Ebonyi State
After completing this step, to map all the connections between the SDPs, the team built a digital version of the road network in Ebonyi. They used truck GIS tracker data points to plot the roads, distances, and travel times (see figure 4). This data were then used to build a matrix of distances and drive times between all SDP pairs—more than 70,000!
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Figure 4. Ebonyi Road Network Data Layer
Data Continuity Mapping While working through all the data analysis and preparation steps, the team discovered that the raw data had several different naming conventions; this made data merging and comparisons very difficult. Because of this, the team also completed a Master Data Continuity Mapping exercise, which can be used going forward for any future analysis with the same data sources. Figure 5 shows some of the raw data; it has been cleaned, validated, aggregated, and given a standardized name under the “SDP_NameSCG” heading.
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Transportation Optimization Models
Round 12 Historical Baseline and Optimized Round 12 Historical Baseline In these scenarios, round 12 historical delivery data represented volumes delivered to each SDP. In the baseline scenario, all route groupings and sequences were replicated; while in the optimized baseline, these constraints were relaxed to allow the transportation optimization solver to search for improved routes. See table 2 for a comparison of the two scenarios.
Table 2. Health Commodity Volumes
Scenario Total Routes Total Distance (km) Total Time (min)
Baseline* 65 7,766 23,739
Differential -38% -37% -17%
The key finding from the scenarios in table 2 is that, during round 12, significant routing efficiency improvement potential was seen, with reductions of more than 35 percent possible for both the total number of routes, as well as the distance traveled.
Future Baseline In this scenario, and all forward-looking scenarios that follow, future delivery volumes were projected off historical data from rounds 4–12, with rounds 1–3 removed from the dataset because the project was still in the initial ramp-up stages. To provide a safety buffer for the new routes, after determining the average delivery volume for each visit to each SDP, a +1 standard deviation was added to each delivery volume estimate.
For routes and sequences, all options were allowed, subject to the following assumptions: (1) available working hours at all facilities remained constant, (2) individual truck cubic capacities were honored, (3) all SDPs were visited once during the delivery round, and (4) all delivery routes had to be completed in a single work day. The future baseline scenario made no other changes to input assumptions; therefore, it is intended to be the basis for comparison with all other future scenarios.
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• 5,641 total kilometers
Figure 6. Future Baseline for All Routes
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Future Volume Scenarios In this subset of analysis scenarios, the projected future volume assumptions were tested for their sensitivity. By increasing and decreasing the assumed volumes by +/- 15 percent, we can understand the impact of delivery volume changes on the network. Table 3 lists the results for a variety of metrics for each of these six future volume scenarios and compares them against the future baseline.
Table 3. Future Volume Scenario Output Comparisons
With minimal differences between important outputs metrics like the number of routes and total kilometers driven, the key finding from this set of scenarios is that the DDIC program looks as if it can handle healthy growth in delivery volumes without significant changes to their transport operations.
Service Time Scenarios For this subset of scenarios, the assumptions around fixed and variable dwelling times were tested for their impact on network performance. Table 4 compares four scenarios where both service time components were adjusted by +/- 20 percent, and then they were compared against the future baseline scenario.
Table 4. Service Time Scenario Output Comparisons
As shown by the key metrics of total kilometers driven and the calculated 8-hour working days to complete all routes, adjusting dwelling time has only a moderate impact on network performance. From this, we can predict that dwelling time efficiency improvements (i.e., faster times for each stop) will have a definite positive impact on measures that include working days, total kilometers, and total routes required.
Truck Capacity Scenarios For this set of four scenarios, the team tested the impact of capacity changes to the delivery vehicles. The objective was to understand if investing in different types of vehicles would be likely to impact
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the transportation network performance. Table 5 indicates no change to output metrics within a cubic capacity range of +/- 20%, indicating that space on the delivery vehicles in not a limiting constraint for the network. In practice, this is an incentive to use more flexible, reliable, and fuel- efficient vehicles where possible, because capacity is not a significant factor.
Table 5. Truck Capacity Scenario Output Comparisons
Changing Shift Length Scenarios After noting in the truck capacity scenarios that space on trucks was not a significant concern for route planning, the team decided to explore the time impacts on route performance. Table 6 shows results for +/- 60 minutes of working time in a single day, effectively expanding/reducing the number of SDPs that could be added to a single route.
Table 6. Changing Shift Length Scenario Output Comparisons
As shown in table 6, changes to the length of the work day significantly impact the efficiency of the network. In general, shorter work days have more of a negative impact on routing efficiency than any positive impact on routing efficiency when time is added to the day. However, improvements are possible; and, it should be noted that adding time to a single work day has very little impact on the total working time required to make all deliveries.
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Conclusions
Based on the total analysis, the key points are—
• Generally, time-related inputs have more of an impact on routes than volume/space-related factors.
• Because routes are not space constrained, minor to moderate changes in future delivery volumes should not be expected to significantly impact routes.
• Time efficiency (dwell times) and availability (shift length) are much more likely to impact routes and strain the system.
Data Collection and Organization The following recommendations are shared with respect to the DDIC team data collection and organizational practices:
1. Adopt standard SDP naming convention across all files and systems to facilitate tracking and analysis.
2. Collect and track dwell time data in a single sheet, which is built on with each successive round.
− Consider tracking the processing and loading times at the warehouse, because this will directly impact the number of SDPs that can be reached in a given day.
3. Use round 13 (or 14) to collect or validate the geo data for the 15 SDPs with current latitude/longitude issues.
Transportation Route Planning and Delivery Operations The following recommendations are shared for future DDIC transport route planning and delivery operations:
1. Efforts to reduce processing and loading/unloading times at the warehouse and SDPs should be considered because they will probably have a positive impact on route efficiency.
− Consistency improvements for these activity times would also increase the reliability of the new route plans.
2. Given the volatile nature of dwell times, a planned methodology for drivers to add stops when sufficient time and inventory is available could significantly improve network efficiency.
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Example Volume Data
Source: “Ebonyi Data Dump – Dec 19 2014” supplied by JSI Nigeria staff
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Example Dwell Time Data
Source: “Delivery Team SDP dwelling time – Dec12_2014” supplied by JSI Nigeria staff
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Service Delivery Point GIS Data
Source: “Master SDP List” sheet in the ‘EBONYI MASTER – v2.xls’ file constructed by the project team
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Example Routes from Model
Source: Supply Chain Guru model outputs from the “FUTURE Unconstrained” scenario
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Arlington, VA 22209 USA