Data-driven optimization of OFDA’s disaster response capacity: Final report presenation

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Data-driven optimization of OFDA’s

disaster response capacity

Final report

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Agenda

• Introduction

• Inventory optimization

• Recommendations

• Discussion

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Introduction

Disaster relief

items in storage

Shipping items into

disaster regions

All pictures from USAID News Photo and other US government sides

Distributing items to

affected population

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Inventory network

Potential Inventory Locations Research Questions

➢ How well does the current network perform against a portfolio of disasters?

➢ Should OFDA hold more or less inventory?

➢ Should OFDA redistribute inventory to improve performance, and if so, where should the inventory be located?

➢ How efficient is OFDA’s current prepositioning network based on existing costs and capacity? ➢ What alternative prepositioning strategies should

OFDA consider considering cost and response time?

OFDA holds a strategic stockpile of key disaster response commodities to support people world

wide in crises situations.

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Model (i)

• Risk portfolio

• Inventory portfolio

• Carrier portfolio

• Item/usage characteristics

Inputs

Stochastic linear

program (SLP)

Model

How well does the

current setting perform?

Where to position

inventory?

What are recommended

procurement strategies to

reduce stockout risk?

How much inventory

to hold?

How much budget

to spent?

System Assessment

System Optimization

Outputs

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Model

min 𝑦 ෍ 𝑘 𝑝𝑘 ෍ 𝜏_𝑖,𝑗𝑚𝑘 ∙ 𝑦_𝑖,𝑗𝑚𝑘 ෍ 𝑗 𝑦_𝑖,𝑗𝑚𝑘 ≤ 𝑋_𝑖 ∀𝑖 ෍ 𝑖 𝑦_𝑖,𝑗𝑚𝑘 = 𝑑_𝑗𝑘 ∀𝑗 𝑦_𝑖,𝑗𝑚𝑘 ≥ 0 s.t. Model assumptions

➢ Minimize time to respond to a portfolio of disaster scenarios.

➢ One disaster scenario k is a random combination of resupply lead time and disasters.

➢ OFDA can only supply as much as they have in storage.

➢ OFDA must satisfy entire demand. ➢ OFDA can only spent their budget.

➢ OFDA can use different modes of transportation.

෍

𝑖

𝑦_𝑖,𝑗𝑚𝑘 ∙ 𝑐_𝑖,𝑗𝑚𝑘 + ෍ 𝑋_𝑖 ∙ 𝑠_𝑖 ≤ 𝐵

(minimize time)

(use available inventory)

(satisfy demand)

(do not exceed budget) Simplified representation of the model

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(2)

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Disaster portfolio

➢ We use past disasters that OFDA responded to. ➢ We estimate a resupply lead time distribution from

OFDA’s past operations.

➢ We create a portfolio of disaster scenarios.

➢ One disaster scenario is a random combination of resupply lead time and disasters.

➢ Disaster scenarios are equally likely.

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Disaster portfolio estimation process

Gamma(α=1.176, β=0.013)

Estimate a probability distribution from past shipments

Draw random scenario supply lead time e.g. 92 days

Collect disasters within lead time 4

Write disasters into a scenario 5

Draw random scenario supply lead time 6

e.g. 122 days

Collect disasters within lead time 7

Write disasters into a scenario 8

3 Collect disaster portfolio information

2 1

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Total affected population

Initial estimation approach

➢ Our dataset of past responses includes the total affected population (TAP), which is the entire population in a disaster area.

➢ OFDA will carefully evaluate how many people they serve in a given disaster, considering, for example,

➢ People’s needs

➢ Capabilities/activities of other organizations ➢ Inventory available at OFDA

➢ OFDA’s mandate

➢ Similar to any insurance model, the size of the exposure matters for what the model recommends. ➢ We have to make assumptions, on how many people

OFDA seeks to serve and how they decide to allocate scarce inventory.

# of disasters

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Beneficiaries served – anchor points

Insights

➢ Beneficiaries served varies substantially by item. ➢ Max beneficiaries served is only matched by current

inventory in case of buckets and kitchen sets.

➢ Current inventory levels can serve at least 75% of the beneficiaries served in the past.

Item Max served 3rd Quartile Median People served by current Inventory Blankets 592,514 122,500 28,938 183,750 Buckets 67,500 - 67,500 183,750 Hygiene Kits 379,200 163,900 34,280 125,000 Kitchen Sets 140,100 99,150 23,700 182,500 Plastic Sheeting 851,500 100,000 22,000 540,000 Water 622,250 182,550 24,250 183,750 3.2x 3.0x 1.6x 3.4x 𝑀𝑎𝑥𝑖𝑚𝑢𝑚 𝑠𝑒𝑟𝑣𝑒𝑑 𝑆𝑒𝑟𝑣𝑒𝑑 𝑏𝑦 𝑐𝑢𝑟𝑟𝑒𝑛𝑡 𝑖𝑛𝑣𝑒𝑛𝑡𝑜𝑟𝑦 Conclusion

➢ OFDA seems to have served disasters with a larger target population, than what they currently have on inventory. 0.4x 0.8x 0.7x 1.3x 0.2x 1.0x -0.5x

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% TAP served per item

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Regression

Approach

➢ To find a relationship we use linear regression ➢ Using linearization, we estimate a function that

converts TAP into a target population

Regression Statistics Multiple R 0.662544357 R Square 0.438965026 Adjusted R Square 0.431484559 Standard Error 0.95460776 Observations 77 ANOVA df SS MS F Significance F Regression 1 53.47504607 53.47504607 58.68150546 5.27146E-11 Residual 75 68.34569809 0.911275975 Total 76 121.8207442

Coefficients Standard Error t Stat P-value Lower 95% Upper 95%

Intercept 2.363161747 0.703968041 3.356916234 0.001240097 0.960785495 3.765537999 X Variable 1 0.394799314 0.051537787 7.660385464 5.27146E-11 0.292130778 0.497467851

ln(Percent_Served)=2.3632 + 0.3948 * ln(1/TAP)

Results

➢ The function can explain OFDA’s target population to some extent.

➢ We use the function to predict target population. It is a volume-dependent, scaling factor for TAP.

➢ For some items we may have to cap the predicted target TAP (see next slide).

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Disaster portfolio and OFDA’s past actions

• Disaster responses in the past • TAP and location

• Adjust TAP according to OFDA’s disaster response activities

Disasters OFDA’s responses in the past

• Evaluate OFDA’s disaster responses in the past

beneficiaries served percent served vs. TAP

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OFDA’s risk portfolio scenarios – blankets

• Adjust TAP according to OFDA’s disaster response activities • Use past activities to estimate

mandate

Disasters OFDA’s responses in the past Risk portfolios: blankets

Medium-Large Large

Small-Medium Medium Small

• Creating different item-specific scenarios = OFDA’s future risk portfolio scale

• Characterize different exposure to disaster sizes Mean Std. Dev. Cap 60,000 9,400 Small _62,000 80,000 14,500 Small -Medium 84,000 180,000 48,900 Large _200,000 140,000 33,300 Medium -Large 152,000 100,000 Medium _106,000 _20,100 A B C

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Disaster scope Disaster scale Risk portfolio(s)

Risk portfolio development

• Mix of geographical location of disasters

• Mix of disaster types • Total affected population

I

II

III

• Share of population targeted by OFDA

• Resulting risk portfolio OFDA seeks to serve

Op

ti

on

1 • Mix of disaster locations and disaster types from OFDA’s past operations

• Total affected population from UN reports

• TAP size dependent scaling of targeted population based on OFDA’s past response activities

• Risk portfolio represents typical mix of response activities of OFDA • Varying the size of disasters in the

portfolio to show impact of various risk exposure

Op

ti

on

2 Predict_{• a mix of potential disaster types}

and locations, and

• associated total affected population

• TAP size dependent scaling of targeted population based on OFDA’s past response activities

• Risk portfolio represents typical mix of response activities of OFDA • Varying the size of disasters in the

portfolio to show impact of various risk exposure

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A three-step process

• Optimize the current inventory allocation for the current total inventory

• Determine performance for the implied risk portfolio

• Determine performance against various other risk portfolios • Create OFDA’s risk portfolio with

target beneficiaries.

• Collect OFDA’s supply lead time estimators.

• Create scenarios, i.e. vary OFDA’s risk portfolio

• Define objective

• Adjust inventory levels • Identify optimal allocation • Recommend actions

OFDA’s risk portfolio Inventory optimization Recommendations

1 2 3

2A 2B

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Inventory allocation – the intuition

Given inventory level – varying risk portfolio

Consolidate Diversify

Given risk portfolio – varying inventory level

Consolidate Diversify

Relative to the expected risk exposure the total inventory in the system determines if a disaster response agency should operate a diversified or a consolidated network

!

low scale medium scale

high scale low inventory medium inventory high inventory

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Impact and implications for decision-makers

Depending on OFDA’s risk exposure expectations, OFDA has to decide what service they want to achieve.

!

• higher service levels • lower response times • lower logistics costs • lower service levels

• higher response times • higher logistics costs

• higher service levels • lower response times • lower logistics costs • lower service levels

• higher response times • higher logistics costs

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Scenarios vs. optimization

Risk portfolio Degree of diversification Total system inventory Optimal inventory allocation effectiveness and service metrics +

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Step 2A – Optimal allocation for current ops

% of targeted TAP served % of cycles fully served Average response time

Blankets Buckets Hygiene kits Kitchen sets Plastic sheeting Water

Current inventory allocation metric *

Inventory balance metric Implied risk portfolio ** 0.9 0.5 1.2 0.9 0.65 0.5 Average costs 96% 98% 95% 96% 98% 98% 15.4h 14.7h 16.1h 15.4h 15.2h 14.7h $4,910 $2,620 $14,000 $16,400 $157,700 $26,100 81% 91% 67% 81% 86% 91% 97% 98% 93% 97% 97% 98% (-4%) (-2%) (-5%) (-4%) (-2%) (-2%) (-6%) (-4%) (-8%) (-6%) (-3%) (-4%)

* Based on OFDA’s current inventory allocation

** Identified by evaluating multiple risk portfolios and finding the optimal allocation that results in the same inventory allocation metric

Implied risk portfolio

Corresponding optimal performance metrics

If we consider the current inventory distribution as a starting point and assume an appropriate risk portfolio, OFDA can gain lead time and cost reductions through inventory reallocation!

Mean Stand. Dev. 82,000 15,300 50,000 7,200 87,000 16,500 83,000 15,200 194,000 54,600 50,000 7,200 Current inventory level 73,500 73,500 25,000 36,500 10,800 73,500

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To capture the benefits, OFDA should consider reallocating inventory

Subang, MY Miami, USA

Blankets Buckets Hygiene kits Kitchen sets Plastic sheeting Water

Dubai, UAE Pisa, I 34,800 28,464 18,400 17,400 4,340 28,464 34,800 20,800 6,600 17,400 4,340 20,800 3,900 20,800 0 1,700 2,320 20,800 0 3,436 0 0 0 3,436 (+158%) (+54%) (-34%) (+168%) (+74%) (+54%) (-90%) (-4%) (-92%) (-51%) (+4%)

Optimal inventory levels*

Step 2B: Is the risk portfolio appropriate for each item and are the effectiveness and service metrics acceptable?

(+74%) (+23%) (+74%) (+32%) (-16%)

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Blankets

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Disaster portfolio and OFDA’s past actions

• Adjust TAP according to OFDA’s disaster response activities

Disasters OFDA’s responses in the past

• Evaluate OFDA’s disaster responses in the past

beneficiaries served percent served vs. TAP

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OFDA’s risk portfolio scenarios – blankets

mandate

Disasters OFDA’s responses in the past Risk portfolios: blankets

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Blankets

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Metrics – medium scale risk portfolio

Balance metric Inventory allocation 80 - 100% 0 - 2 Results

• Reallocating inventory benefits OFDA • Response time reduction: 3% • Cost reduction: 4%

• 75% of beneficiaries served

• 93% of all disaster scenarios served

➢ Current inventory levels result in mixed service levels.

➢ Current inventory allocation is close to optimum. 97% 75% 93% 1.02 - 3% - 4% 15.7h $5,056

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Metrics – large scale risk portfolio

Balance metric

Inventory allocation

80 - 100%

0 - 2 Results

➢ Current inventory levels result in lower service and strong consolidation.

➢ Reallocation benefits for response time and cos. 94% 57% 80% 1.73 - 6% - 9% 16.5h $5,468 Medium-Large Large Small-Medium Medium Small

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Metrics – small scale risk portfolio

Results

➢ Current inventory is sufficient to run a diversified response network effectively. ➢ Cost and response time benefits from

reallocating inventory. Balance metric Inventory allocation 80 - 100% 0 - 2 96% 88% 97% 058 - 4% - 6% 14.9h $4,625

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Metrics – comparing risk portfolios

Results

At current inventory levels and with a larger scale risk portfolio

• OFDA should consider to run a more consolidated network.

• Service levels drop.

• Cost and lead time increase.

Balance metric Inventory allocation 80 - 100% 0 - 2 Medium-Large Large Small-Medium Medium Small

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Optimal inventory allocation

• A larger scale risk portfolio means that OFDA expects to serve more beneficiaries in each disaster and the variation of beneficiaries increases.

• The current inventory allocation seems to reflect that OFDA expects risk portfolios to be lower scale.

• OFDA benefits from inventory reallocation towards Dubai and Miami to reduce response time and cost.

• At current inventory levels, OFDA should consider to consolidate the inventory if they expect a larger scale risk portfolio.

Results

Today Small Small-Med Current

Optimum Medium Med-Large Large Inv. Allocation Metric 0.9 0.6 0.8 0.9 1.0 1.4 1.7 Dubai, UAE 20,000 24,800 33,600 34,800 42,400 60,800 73,500 Miami, USA 13,500 24,800 33,600 34,800 31,100 12,700 0 Pisa, Italy 40,000 23,900 6,300 3,900 0 0 0 Subang, Malaysia 0 0 0 0 0 0 0

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Choosing appropriate service levels

• Directionally, what is the scale of the risk portfolio that OFDA expects for blankets?

smaller scale < > larger scale • Considering this risk portfolio, is OFDA

satisfied with the service metrics, response times, and logistics cost?

higher < > lower Questions

Medium-Large Large

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Buckets

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OFDA’s risk portfolio scenarios – buckets

mandate

Disasters OFDA’s responses in the past Risk portfolios: buckets

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Buckets

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Metrics – medium scale risk portfolio

➢ OFDA should consider to consolidate

inventory to reduce response time and save logistics costs. 97% 75% 93% 1.02 - 3% - 5% 15.7h $2,920

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Metrics – large scale risk portfolio

Balance metric

80 - 100%

0 - 2 Results

➢ Current inventory levels result in lower service metrics.

➢ A more consolidated network results in lower response times and saves logistics costs. 94% 57% 80% 1.73 - 6% - 12% 16.5h $3,160 Medium-Large Large Small-Medium Medium Small

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Metrics – small scale risk portfolio

Results

➢ Current inventory is sufficient to run a diversified response network effectively. ➢ Some benefits of reallocating inventory.

Balance metric Inventory allocation 80 - 100% 0 - 2 96% 88% 97% 058 - 4% - 6% 14.9h $2,665

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Metrics – comparing risk portfolios

Results

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Optimal inventory allocation – buckets

Today Current

Optimum Small Small-Med Medium

Med-Large Large Inv. Allocation Metric 0.5 0.5 0.6 0.8 1.0 1.4 1.7 Dubai, UAE 31,600 28,464 24,800 33,600 42,400 60,800 73,500 Miami, USA 13,500 20,800 24,800 33,600 31,100 12,700 0 Pisa, Italy 20,000 20,800 23,900 6,300 0 0 0 Subang, Malaysia 8,400 3,436 0 0 0 0 0

• The current inventory allocation seems to reflect that OFDA expects a rather small scale risk portfolio.

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Choosing appropriate service levels

• Directionally, what is the scale of the risk portfolio that OFDA expects for buckets?

Medium-Large Large

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Hygiene Kits

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OFDA’s risk portfolio scenarios – hygiene kits

mandate

Disasters OFDA’s responses in the past Risk portfolios: hygiene kits

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Hygiene Kits

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Metrics – medium scale risk portfolio

➢ Current inventory allocation is close to optimum. 97% 70% 93% 1.1 - 3% - 5% 15.9h $13,800

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Metrics – large scale risk portfolio

Balance metric

80 - 100%

0 - 2 Results

➢ Current inventory levels result in lower service and higher costs and response time. ➢ OFDA should strongly consolidate their

hygiene kits inventory.

89% 54% 79% 1.73 - 11% - 17% 16.5h $14,600 Medium-Large Large Small-Medium Medium Small

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Metrics – small scale risk portfolio

- 2% - 3% 14.6h 98% 94% 99% 0.37 $11,900

➢ Current inventory is sufficient to run a diversified response network effectively. ➢ OFDA should consider to diversify the

inventory across the network to reduce cost and response times.

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Metrics – comparing risk portfolios

Balance metric Inventory allocation 80 - 100% 0 - 2 Medium-Large Results

At current hygiene kits inventory levels and with a larger scale risk portfolio

Large Small-Medium Medium

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Optimal inventory allocation

Today Small Small-Med Medium Current

Optimum Med-Large Large Inv. Allocation Metric 1.2 0.4 1.0 1.1 1.2 1.4 1.7 Dubai, UAE 15,000 9,332 12,400 16,800 18,400 21,200 25,000 Miami, USA 10,000 6,700 12,400 8,200 6,600 3,800 0 Pisa, Italy 0 6,100 200 0 0 0 0 Subang, Malaysia 0 2,868 0 0 0 0 0

• The current inventory allocation seems to reflect that OFDA expects risk portfolios to be in the medium to large scale.

• OFDA can reduce response time and cost by reallocating some inventory to Dubai.

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Choosing appropriate service levels

• Directionally, what is the scale of the risk portfolio that OFDA expects for hygiene kits?

Medium-Large Large

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Kitchen sets

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OFDA’s risk portfolio scenarios – kitchen sets

mandate

Disasters OFDA’s responses in the past Risk portfolios: kitchen sets

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Kitchen sets

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Metrics – medium scale risk portfolio

➢ Current inventory allocation is close to optimum.

➢ Some cost and response time benefits through reallocation. 97% 75% 93% 1.02 - 3% - 5% 15.7h $16,850

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Metrics – large scale risk portfolio

Balance metric

80 - 100%

0 - 2 Results

➢ At current kitchen sets inventory levels, service is low and cost and response times are higher.

➢ OFDA should consider to consolidate inventory. 94% 56% 80% 1.73 - 6% - 9% 16.5h $18,240 Medium-Large Large Small-Medium Medium Small

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Metrics – small scale risk portfolio

Results

➢ Current inventory is sufficient to run a diversified response network effectively. ➢ OFDA should consider to diversify the

allocation more. Balance metric Inventory allocation 80 - 100% 0 - 2 96% 88% 97% 058 - 4% - 6% 14.9h $15,410

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Metrics – comparing risk portfolios

Results

At current hygiene kits inventory levels and with a larger scale risk portfolio

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Optimal inventory allocation

Today Small Small-Med Current

Optimum Medium Med-Large Large Inv. Allocation Metric 0.9 0.6 0.8 0.9 1.0 1.4 1.7 Dubai, UAE 10,000 12,400 16,800 17,400 21,200 30,400 36,500 Miami, USA 6,500 12,400 16,800 17,400 15,300 6,100 0 Pisa, Italy 20,000 11,700 2,900 1,700 0 0 0 Subang, Malaysia 0 0 0 0 0 0 0

• The current inventory allocation seems to reflect that OFDA expects risk portfolios to be in the medium scale.

• OFDA can reduce response time and cost by reallocating some inventory from Italy.

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Choosing appropriate service levels

• Directionally, what is the scale of the risk portfolio that OFDA expects for kitchen sets?

Medium-Large Large

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Plastic sheeting

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OFDA’s risk portfolio scenarios – plastic sheeting

mandate

Disasters OFDA’s responses in the past Risk portfolios: plastic sheeting

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Plastic sheeting

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Metrics – medium scale risk portfolio

➢ OFDA should consider to consolidate

inventory to reduce response time and cost.

98% 73% 94% 1.07 - 2% - 3% 15.7h $166k

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Metrics – large scale risk portfolio

Balance metric

80 - 100%

0 - 2 Results

➢ Current inventory levels result in lower service.

➢ OFDA should consider to consolidated

inventory to lower reduce cost and response time. 91% 56% 82% 1.65 - 9% - 14% 16.0h $172k Medium-Large Large Small-Medium Medium Small

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Metrics – small scale risk portfolio

Results

➢ Current inventory is sufficient to run a diversified response network effectively.

Balance metric Inventory allocation 80 - 100% 0 - 2 99% 96% 99% 032 - 1% -2% 14.2h $140k

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Metrics – comparing risk portfolios

Results

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Optimal inventory allocation

Today Small Current

Optimum Small-Med Medium

Med-Large Large Inv. Allocation Metric 0.65 0.3 0.65 0.7 1.1 1.5 1.7 Dubai, UAE 3,280 4,240 4,340 4,480 6,980 9,600 10,617 Miami, USA 2,500 2,520 4,340 4,480 4,020 1,400 383 Pisa, Italy 4,760 2,120 2,320 2,040 0 0 0 Subang, Malaysia 460 2,120 0 0 0 0 0

• The current inventory allocation seems to reflect that OFDA expects a small-medium scale risk portfolio.

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Choosing appropriate service levels

• Directionally, what is the risk portfolio that OFDA expects for plastic sheeting?

smaller < > larger

• Considering this risk portfolio, is OFDA

Medium-Large Large

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Water

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OFDA’s risk portfolio scenarios – water

mandate

Disasters OFDA’s responses in the past Risk portfolios: water

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Water

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Metrics – medium scale risk portfolio

➢ OFDA should consider to consolidate inventory 97% 75% 93% 1.02 - 3% - 5% 15.7h $29,100

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Metrics – large scale risk portfolio

Balance metric

80 - 100%

0 - 2 Results

➢ Current inventory levels result in lower service

➢ OFDA should consider to strongly

consolidate inventory in order to reduce cost and response time.

92% 57% 80% 1.73 - 8% - 13% 16.5h $31,500 Medium-Large Large Small-Medium Medium Small

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Metrics – small scale risk portfolio

Results

➢ Current inventory is sufficient to run a diversified response network effectively.

Balance metric Inventory allocation 80 - 100% 0 - 2 96% 88% 97% 058 - 4% - 6% 14.9h $26,600

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Metrics – comparing risk portfolios

Results

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Optimal inventory allocation

Today Current

Optimum Small Small-Med Medium

Med-Large Large Inv. Allocation Metric 0.5 0.5 0.6 0.8 1.0 1.4 1.7 Dubai, UAE 31,600 28,464 24,800 33,600 42,400 60,800 73,500 Miami, USA 13,500 20,800 24,800 33,600 31,100 12,700 0 Pisa, Italy 20,000 20,800 23,900 6,300 0 0 0 Subang, Malaysia 8,400 3,436 0 0 0 0 0

• A larger scale portfolio means that OFDA expects to serve more beneficiaries in each disaster and the variation of beneficiaries is higher.

• The current inventory allocation seems to reflect that OFDA expects a small scale risk portfolio.

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Choosing appropriate service levels

• Directionally, what is the risk portfolio that OFDA expects for water?

Medium-Large Large

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A three-step process

• Optimize the current inventory allocation for the current total inventory

• Determine performance for the implied risk portfolio

• Determine performance against various other risk portfolios • Create OFDA’s risk portfolio with

target beneficiaries.

• Collect OFDA’s supply lead time estimators.

• Create scenarios, i.e. vary OFDA’s risk portfolio

• Define objective

• Adjust inventory levels • Identify optimal allocation • Recommend actions

OFDA’s risk portfolio Inventory optimization Recommendations

1 2 3

2A 2B

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Options

There are different options to shed further light that need to be driven by OFDA’s decision-makers

• acceptable risk portfolio • acceptable service metrics

• keep total inventory

• reallocate inventory to reap response time/cost benefits • no change in diversification

1

• higher/lower risk portfolio • acceptable service metrics

• keep total inventory

• reallocate inventory to reap response time/cost benefits • change diversification

2

• acceptable risk portfolio

• higher/lower service/effectiveness metrics

• higher/lower total inventory

• reallocate inventory to reap response time/cost benefits • change diversification

3

• … • …

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Forward-Looking “Disaster Portfolio”

“Disaster Portfolio” is an input to current model

• Based on OFDA historical response data (slide 10)

Stochastic Linear Program Model (SLP) Item/Usage Characteristics Inventory Portfolio Carrier Portfolio Disaster Portfolio

1 _{USGCRP, 2018: Impacts, Risks, and Adaptation in the United States: Fourth National Climate Assessment, Volume II [Reidmiller, D.R., C.W. Avery, D.R. Easterling,}

K.E. Kunkel, K.L.M. Lewis, T.K. Maycock, and B.C. Stewart (eds.)]. U.S. Global Change Research Program, Washington, DC, USA, 1515 pp. doi: 10.7930/NCA4.2018.

Opportunity to improve model by incorporating a Forward-Looking “Disaster Portfolio”

• Impact of disasters on people around the world is increasing in recent years and will continue to

increase in the future

1

• Calculate expected total affected populations (TAP) based on probability of future disasters

• Will allow OFDA to be less reactive and more responsive to future events

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Potential Methodology

Forward-Looking “Disaster Portfolio”

TAP

(Total Affected Population)

for specific hazards specific regions & specific return periods

Population Impacted by Hazard Vulnerability Indicators (country specific) Global Population Dataset Forward-Looking Probabilistic Hazard Dataset 1 2 3

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Potential Methodology

Forward-Looking Probabilistic Hazard Dataset 1 Global Population Dataset 2 Vulnerability Indicators (country specific) 3

Source:

1. ThinkHazard! (https://thinkhazard.org/en/)

2. Gridded Population of the World (GPW) (https://sedac.ciesin.columbia.edu/)

3. The World Bank (https://data.worldbank.org/indicator)

• To identify relevant vulnerability indicators, first create model with historical hazard dataset and isolate vulnerability indicators with highest predictive power (ability to predict resulting TAP).

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ThinkHazard! Datasets

All stored on the Geonode:

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ThinkHazard! Example

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What is S&OP?

“Sales and Operations Planning (S&OP) is a set

of business processes and technologies that

enable an enterprise to respond effectively to

demand and supply variability”

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Why S&OP? (Adapting to the context)

• Emergency response makes planning in advance

difficult

• Trade-off considerations: speed vs. cost

• S&OP can enable the organization to predict the

potential impact of unexpected events

• Show stakeholders the impact of unexpected demand

surges (“scope”) or supply shortages (“scale”) via

sensitivity analysis

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Proposed S&OP Process: Bi-annual Planning

• CO and Regional teams assess potential hazard levels and threats and vulnerability of the population to identify anticipated demand. • Take Operating Year Budget into

consideration

• Factors considered: • Locations

• Type (Natural vs. Conflict) • Total Affected Population • Population planning figure • Sectors (WASH, Shelter)

Identifying Demand

1 ₂ ₃

Demand & Supply Planning Converge, align, and agree on plans • In this final stage, both Program and

Supply Chain come together to discuss major factors that may impact demand and supply (i.e. disaster type, budget, existing resources, etc.)

• Supply Chain analyzes current supply levels (owned or at external

organizations). Provide global commodity level forecast. • Factors considered:

• Locations • Type • Caseload

• Existing resources from

Gov/Com/Hum sectors in the region

• Items sent • Forecast items

• Missing opportunities

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Data-driven Decision Tools

The MIT model seeks to improve the overall S&OP process by:

3. Assisting with quantification of disaster

“scaling” mechanism

• Analyze the impact on inventory allocation

1. Providing OFDA with an unbiased tool that can optimize the

inventory network

• Incorporate “scope” and “scale” set by S&OP process

2. Assisting with demand & supply planning

• Disaster forecast

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Quarterly Review Ad hoc Review Metric Review

Additional Review Considerations

• Model updates may include new disaster data or inputs

• Business decisions may generate network changes

• Business decisions may generate budgetary changes

• Additional disaster data

• Additional warehouse locations • Additional mode considerations

• Changes to “scale”,

• Changes to target fill rates • Changes to inventory profile

• Changes to model constraints • Changes to optimization

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Prerequisite for turning research into action: a decision-making process

• Optimal inventory allocation

• Forward-looking disaster portfolio

Decision-support tools

1. Identify demand

2. Demand & supply planning

3. Converge, align, and agree on plans

Supply Chain Planning

• Fastest response • Highest service level • Highest quality

• Lowest cost • …

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Software development process

1. Analysis and planning

2. Requirements

3. Design and prototyping

4. Software development

5. Testing

6. Deployment

7. Maintenance and updates

• Software development is informed by a stable and transparent decision-making process

• The process determines the requirements (inputs, outputs, functionality)

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Research questions

Inventory Locations Research Questions

➢ How well does the current network perform against a portfolio of disasters?

➢ Should OFDA hold more or less inventory?

➢ Should OFDA redistribute inventory to improve performance, and if so, where should the inventory be located?

➢ How efficient is OFDA’s current prepositioning network based on existing costs and capacity? ➢ What alternative prepositioning strategies should

OFDA consider considering cost and response time? ➢ What metrics could OFDA use to inform

decision-making?

OFDA holds a strategic stockpile of key disaster response commodities to support people world

wide in crises situations.

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Key steps

Data inputs?

➢ Disaster – scale / scope ➢ Items and their

characteristics

➢ Transportation modes, cost, and time

➢ Travel distances ➢ Supplier lead times ➢ …

Methods?

➢ Served population estimation

➢ Disaster scenario creation ➢ Supply lead time estimation

Outputs? ➢ Inventory allocation ➢ Inventory level ➢ Transportation mix ➢ Cost-time-trade-off Metrics? ➢ Inventory balance ➢ Inventory allocation ➢ Effectiveness metrics ➢ Efficiency metrics Model? ➢ Optimization model? ➢ Simulation/scenario model? ➢ Stochastic vs. deterministic?

Insights

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Our results – methodology & model

Stochastic linear programming model

Methods to prepare data & model inputs

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Our results – insights

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