1

IMMEDIATE RESOURCE REQUIREMENTS AFTER HURRICANE KATRINA:

POLICY IMPLICATIONS FOR DISASTER RESPONSE

José Holguín-Veras, Ph.D., P.E.

Professor

Rensselaer Polytechnic Institute

Troy, New York, United States of America, jhv@rpi.edu

Miguel Jaller

Graduate Research Assistant

Rensselaer Polytechnic Institute

Troy, New York, United States of America, jallem@rpi.edu

Satish Ukkusuri, Ph.D.

Assistant Professor

Rensselaer Polytechnic Institute

Troy, New York, United States of America, ukkuss@rpi.edu

Matthew Brom

Graduate Research Assistant

Rensselaer Polytechnic Institute

Troy, New York, United States of America, bromm@rpi.edu

Coral Torres

Graduate Research Assistant

Rensselaer Polytechnic Institute

Troy, New York, United States of America, torrec@rpi.edu

Tricia Wachtendorf, Ph.D.

Assistant Professor

University of Delaware

Newark, Delaware, United States of America, twachten@udel.edu

Bethany Brown, M.A.

Graduate Research Assistant

University of Delaware

Newark, Delaware, United States of America, bethanyb@udel.edu

2

ABSTRACT

The paper focuses on the quantitative study of immediate resource requirements, which is

one of the most severely understudied aspects of humanitarian logistics. As part of these

analyses, the paper develops numerical estimates of the immediate resource requirements and

their temporal patterns after Hurricane Katrina. The analyses are based on a dataset put together

by the authors by post-processing the Action Request Forms (ARFs) issued in the aftermath of

Hurricane Katrina. The ARFs are forms used by emergency responders to request critical

supplies from the federal government when the needs exceed what state and local agencies could

provide.

Using these data, the requests are characterized both in terms of commodity type, number

of requests, and the underlying temporal patterns. One key insight from the analysis is that about

150 commodities were requested, which is a fraction of the estimates from previous studies that

placed that number in the range of 350-500 different items. The analyses further reveal that only

a relatively small number of commodities were heavily requested. The data show that twenty

commodities account for about 30% of the requests, forty commodities for 47%, and fifty

commodities for 56% of the total number of requests. These figures clearly indicate that regional

prepositioning of these key commodities could be an extremely cost-effective way to reduce

delivery times, as a relatively small initial investment in a safety stock would be able to cover a

large portion of the needs.

The paper explores the feasibility of econometric estimation of the temporal patterns of

requests. As part of the research, Autoregressive Integrative Moving Average (ARIMA) models

were estimated for the key commodity groups, providing a framework for prediction of needs

after disasters. The results clearly show that it is indeed possible to estimate robust ARIMA

models to predict needs. This has important implications for both research and humanitarian

logistic response because it opens the door for exciting possibilities such as the combined use of

need forecasts, inventory control, and supply chain models. In this context, given the

unavoidable lead times between requests and the arrival of the commodities, the integration of

need forecast models into the ordering process is bound to translate into an expedited flow of

critical supplies to disaster sites.

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INTRODUCTION

Hurricane Katrina was one of the deadliest hurricanes in the history of the United States

and the sixth-strongest Atlantic hurricane ever recorded. In its wake, 80% of the city of New

Orleans would be under water, and the inefficient official response that ensued would become

the subject of an angry public debate. Probably, the most heavily criticized aspect of the official

response was its inefficient emergency logistics that—due to a series of factors discussed

elsewhere (Holguín-Veras et al., 2007)—did not deliver an efficient and reliable flow of critical

supplies to the disaster site. These inefficiencies were documented during the interviews

conducted by the authors with federal, state, and local staff directly involved in the logistical

aspects of the response that indicated that the delivery of critical supplies took—in some cases—

two or three weeks after the initial requisition (Holguín-Veras et al., 2007). Obviously, such

delays are unacceptable in the context of an emergency.

As the experience of hurricane Katrina painfully demonstrated, the lack of an efficient

emergency logistic system may have major negative consequences on the lives of the individuals

impacted by an extreme event. Because of this, putting in place robust, reliable, and efficient

emergency supply chains must be a key objective of any organization involved in humanitarian

and emergency relief efforts. However, achieving this goal in the aftermath of a disaster is a

major challenge because, as discussed elsewhere (Holguín-Veras et al., 2007): (1) the

transportation system upon which the supply chains are supposed to run may be severely

damaged; (2) the complex interactions among the dozens of supply chains that arise hamper any

formal coordination/optimization process; (3) there are no good established procedures to

simultaneously handle the material convergence problem, and expedite the flow of high priority

supplies; and, (4) the lack of empirical studies quantifying the immediate resource requirements

hampers the development of a body of knowledge that could support the estimation of needs

after a disaster. Overcoming these challenges is bound to take a sustained research effort and a

multi-disciplinary perspective to deal with the multifaceted complexity of the subject matter.

One area in great need of research is the estimation of immediate resource requirements.

This has been, for quite a while, identified by the Federal Emergency Management Agency

(FEMA) as a high priority research topic (Picciano, 2002). This paper intends to help fill this

need by conducting an empirical estimation of the immediate resource requirements after

Katrina. This is done by means of statistical analyses of the requests made by the State of

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Louisiana to the Federal Emergency Management Agency (FEMA) using the Action Request

Forms (ARFs), i.e., the forms used by emergency respondents to request critical supplies to the

federal government. The analyses discussed here provide insight into the resource requirements

after a disaster, their temporal evolution, the key types of commodities requested, and their

relative importance. Taken together this information provides key insight for emergency

planning as it will help emergency agencies to develop appropriate contingency plans.

The analyses provided in the paper, in spite of their value, do have some limitations

worth discussing. First and foremost, the data captured by the ARFs provide only a partial—

though nevertheless important—view of the resource requirements as they do not include the

goods brought to the disaster area by volunteer organizations, private companies, and states and

local governments without FEMA’s intervention. Second, the data contained in the ARFs were

not complete as information was missing (which is understandable given the chaotic conditions

in which many of them were issued). Third, it may be possible that the ARFs obtained from

FEMA do not represent the complete set of requests. This seems to be the case because: (1) the

numbers of requests reflected in the ARFs—particularly in the initial days of the crisis—do not

seem to reach the overwhelming numbers participants reported to the authors during the

interviews with the staff involved in logistics; and, (2) the data only contain twenty ARFs from

Mississippi (6% of the total number of requests), which is not consistent with the devastation

produced by Katrina in that state.

In spite of the acknowledged limitations, this paper is one of a handful of publications

that report on empirical analyses of resource requirements in the aftermath of a major disaster,

and probably one of the first of its kind in the United States (US). The literature review found

only two publications dealing with issues related to the quantitative estimation of distribution of

aid (Morris and Wodon, 2003; Benini et al., 2006). Morris and Wodon (2003) used household

data to analyze the patterns of distribution of aid after Hurricane Mitch; while Benini et al.

(2006) used censored regression models to analyze the factors that explain the decision to ship

aid, and how much, to communities in need. The analyses described in the paper reflects the

unified multi-disciplinary perspective taken in the project, that involves state-of-the-art thinking

in both the social sciences and transportation engineering through a partnership between the

Rensselaer Polytechnic Institute and the University of Delaware’s Disaster Research Center.

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This document is organized as follows. Section 2 discusses the overall response process.

Section 3 provides a description of the data, the action request forms, the data capturing and

input, and the corresponding coding process. Section 4 presents a descriptive analysis of the

information gathered. Following in section 5, the key findings are discussed in relation to the

temporal distribution by commodity type and of the time series models developed. Section 6

treats policy implications of the findings to finalize with the conclusions of the present paper.

DESCRIPTION OF OVERALL RESPONSE PROCESS

As reported elsewhere (Fritz and Mathewson, 1956; Holguín-Veras et al., 2007), the

emergency logistic process, together with the accompanying material convergence, are part of a

very involved response that include: government agencies, volunteer organizations, private

sector, and individual citizenry. At times these agents interact, cooperate, compete for resources,

and interfere with each other in the midst of the chaotic conditions following an extreme event.

The National Response Plan (United States Department of Homeland Security, 2004) attempts to

put together a framework that, to the extent possible in the context of a disaster, guides the

interactions among the key agents. An important component of such process in the US

corresponds to the supply chains put in place by the Federal Emergency Management Agency

(FEMA). Since this paper is based on the analyses of data obtained from FEMA, it is important

to provide a brief summary of FEMA’s supply chain process.

In general terms, the overall supply chain process is based on a hierarchical system

comprised of a relatively small number of very large distribution centers at the top, and a large

number of points of distributions at the bottom (Federal Emergency Management Agency,

2007a). The purpose of the former is to move large quantities in bulk, while the latter are

intended to support the delivery of the critical supplies to the population in need. Specific

component of the system are: FEMA Logistics Centers, Commercial Storage Sites such as those

providing freezer storage capacity, other Federal Agencies Sites such as those part of the

Defense Logistics Agency and the General Service Administration, Mobilization (MOB) Centers,

Federal Operational Staging Areas (FOSAs), State Staging Areas, and local Point of

Distributions (PODs) sites (Federal Emergency Management Agency, 2007a). Among them, the

first three are permanent, while the other four are temporary facilities that are activated after a

disaster. All these represent permanent or temporary facilities governmental or privately owned

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that are centrally or specifically localized around the country, used to receive, store, ship, deliver

or manage commodities, personnel, equipment or any other type of service at times of disaster.

At times of disaster, FEMA’s supply chain could be activated at different stages,

depending on the nature of the emergency, i.e., whether or not it could be anticipatec. It could be

pre-disaster (pre-landfall in the case of hurricanes), or post-disaster. Re-stockage of critical

supplies is a modality of operation in itself. In the pre-disaster mode, when FEMA headquarters

of logistics (HQ) are notified of a pending threat by the National Response Coordination Center

(NRCC), it activates the Logistics Response Center (LRC), and starts planning and coordinating

with the Operations and/or Logistics Chiefs of the affected Region (RRCC). HQ are expected to

identify mobilization centers (MOBs), estimate the amount of commodities using the U.S. Army

Corps of Engineers (USACE) models to determine commodity consumption based on storm

category, and establish a three days stocking level. HQ are also expected to: review commodity

readiness levels; mission assign the Department of Transportation (DOT/Emergency Support

Function (ESF #1) to activate the National Transportation Contract; order all transportation, load

trailers, and pre-position commodities as necessary; mission assigns the U.S. Army Corps of

Engineers (USACE/ESF#3) for support of the ice, water and emergency power missions;

coordinate with Defense Logistics Agency (DLA) to draw down on stocks held for FEMA as

required; procure additional stock from DLA or other sources as needed; activates and deploys

MOB Teams, and other personnel; and plans the fulfillment of FOSAs and MOB centers

requirements from fixed storage sites such as Logistics Centers, DLA and/or commercial storage

facilities (Federal Emergency Management Agency, 2007a). The region identifies potential

FOSAs and requests an initial amount of commodities to be "pushed" to the site by a specific

date; usually defined as before the time storm conditions affect site operations at a Staging Area.

Performance is measured by filling the Emergency Response Teams (ERTs) and Regions'

requests prior to shut-down of operations due to storm passage (Federal Emergency Management

Agency, 2007a). After the disaster (post-disaster mode), the emergency response follows a tiered

approach. At the first step of the process, the local incident command center is supposed to

identify the resources needed. Local jurisdictions are supposed to provide the first wave of

resources. If they cannot, they pass the request to their county or State. The State, once it has

received a request, must try to fill it from existing resources, Emergency Management Assistance

Compacts (EMAC) or mutual aid agreements. If the state cannot fill the need, it requests federal

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assistance to the RRCC/ERT-A/JFO Operations section using an Action Request Form (ARF)—

which are the forms that provide the data used in this paper. If the commodity or equipment is

available in the Federal Operational Staging Area (FOSA), the JFO Operations Section Chief

will seek direct fulfillment from the FOSA; if not available, the request is passed to the Logistics

Chief for fulfillment (Federal Emergency Management Agency, 2007a).

The JFO Logistics Section Chief can fill the request by one of the following: i) fill from

the MOB Center; if still not readily available, pass the request to the Region or HQ Logistics

organizations for fulfillment; ii) fill by mission assigning another agency; and, iii) fill by

completing a requisition and forwarding to Acquisitions for procurement. If accelerating requests

are outpaced by actual demands, HQ engage in increasing quantities at MOB Centers and/or

pushing more products forward to FOSAs and/or State staging areas. Once the region or HQ

receive the validated request, they determine how and if the requirement can be fulfilled. Once

the source is identified, the resource is delivered to the location specified by the JFO Logistics

Section Chief (Federal Emergency Management Agency, 2007a). The re-stockage mode refers to

the process of sustaining the flows of goods. In general, stocks are replenished at Logistics

Centers and DLA/Commercial stocks; and supplies are restocked at MOB Centers and FOSAs to

a 1-3 day supply level (or more if required). The following section provides a description of the

data obtained by the team from FEMA, discussing what the action request forms are, how data

were captured and input, and the corresponding coding process.

DESCRIPTION OF DATA

This paper is based on a dataset created by the authors by post-processing the Action

Request Forms (ARFs) obtained from FEMA through a Freedom of Information Act request.

Because of its evident importance to the paper, it is appropriate to provide the reader with a good

idea about these forms and the information they contain.

The ARFs are the forms that the state fills when it is unable to fill a request for a critical

supply after a disaster. In other words, they capture the information about the critical needs that

exceed what the local and State authorities could provide. The ARFs (FEMA Form 90-136, Nov

04) include information such as: contact information of who is requesting assistance; requested

assistance with description, quantities, date and time needed, delivery site location, contact

information; statement of work with contact information and justification, estimated completion

8

dates and cost estimates; and action taken if the request was accepted or rejected (Federal

Emergency Management Agency, 2007b).

The team received from FEMA a total of 1388 electronic forms (portable document files,

or PDFs, of the actual forms) that span over a period of three months after hurricane Katrina hit

the Gulf Coast. The review of the forms indicated that a lot of information was missing, a great

number of the documents received were duplicates, and that the classification codes did not have

any discernible order. In order to analyze the data, the information in the forms was analyzed by

researchers, transcribed, and electronically inputted in a database that was created to capture all

relevant data, including a link to the original form. Since some forms were amendments of

previous ones, the information was appended to the original form. At the end of this arduous

process, a database containing a total of 864 unique ARFs was assembled.

As a point of reference, it is important to mention that on the typical disaster that FEMA

is involved with, only 95 ARFs are handled (Federal Emergency Management Agency, 2004),

which is about one ninth of the number of ARFs issued after Katrina (864). This provides an

indication of the magnitude of the Katrina emergency, and it may also explain why FEMA was

overwhelmed by the number of requests received.

In order to facilitate the analyses, the critical supplies requested in the ARFs were coded

using the North American Industry Classification System (NAICS). The actual description of the

supplies requested was also preserved. After assembling, cleaning, and coding of the database

was completed, the team proceeded to analyze the data.

It is important to highlight that the ARFs are missing a significant amount of information,

which may be due to the chaotic conditions in which many of the requests were made. The

percentage of missing data in the different fields in the ARFs are: Estimated Completion Date

(94%), Date/Time Assigned (90%), Date Approved (87%), Date/Time Submitted (71%), State

Information (30%), Commodity (30%) and Date Required (53%). Obviously, this imposes

limitations to the analyses. The following section discusses the key findings.

KEY FINDINGS

As alluded to in the previous section, there is a lot of information missing in the ARFs as

there are only 609 forms with some usable date data. The temporal distribution of the

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information gathered for the ARFs for the months of August (28th

– 31st), September and

October is shown in Figure 1.

Figure 1: Number of request as a function of time

Days

Nu

mb

er

of

req

ue

sts

60544842363024181261

60

50

40

30

20

10

0

As shown in Figure 1, the number of requests doubled and almost tripled during the

second and the third day of the emergency. The overall peak corresponds to September 1st

(fourth day of the crisis) with 57 requests, after this day the number of request starts to decline. It

should be noted that the first eighteen days represent around 80% of the total of requests (until

October 31st). The following table (Table 1) shows the number of request for the weeks

following Katrina’s landfall. As shown, the number of requests per week consistently declines

from the overall peak just after the disaster.

Table 1: Breakdown of number of requests by week after disaster

WeekNumber of

requests

Percentage of

number of

requests

1 278 45.60%

2 175 28.80%

3 60 9.90%

4 52 8.60%

5 29 4.80%

6 9 1.40%

7 4 0.60%

8 0 0.00%

9 2 0.30%

Total 609 100%

10

When analyzing the temporal distribution of the total of requests, it was found that only

605 out of the 864 final forms provided information about the state that originated the request.

Furthermore, a relatively small number of forms contained both the name of the state and any

date data. As shown in Table 2, the data set is dominated by requests from the State of Louisiana,

though some ARFs came from Texas, Arkansas, Alabama and Mississippi.

Table 2: Breakdown of number of requests per state

State

Number

of

requests 

Number of

requests

percentage

August September October

Total number of

requests with state and

date

Percentage of

number of

requests with

state and date

LA 566 93.55% 99 302 17 418 93.9%

MS 20 3.31% 0 15 0 15 3.4%

TX 15 2.48% 0 8 0 8 1.8%

AR 3 0.50% 0 3 0 3 0.7%

AL 1 0.17% 1 0 0 1 0.2%

Total 605 100.00% 100 328 17 445 100.0%

The data reveal that the bulk of the requests (94%) in the dataset came from the State of

Louisiana. As shown in Figure 2, the number of requests from Louisiana, being the largest

portion of requests, follows the same temporal distribution as for the total of requests presented

in Figure 1, with a rapid increase during the first week and then quickly declining, with the first

18 days representing almost 85% of all requests.

Figure 2: Number of requests as a function of time for the state of Louisiana

Days

Nu

mb

er

of

req

ue

sts

60544842363024181261

50

40

30

20

10

0

11

The number of requests, and their breakdown by origin (Louisiana, Texas, Arkansas,

Alabama and Mississippi), raise concerns about the completeness of the data. The interviews

conducted by the authors with the personnel directly involved in logistics (at the federal, state,

and local levels) indicated that the logistic staff was overwhelmed by the number of requests

received (Holguín-Veras et al., 2007). However, as shown in Figure 1, on average, 42.5

requests/day were received in the initial four days of the response. This seems to be a number of

requests that could be easily handled by the logistic staff available without major problems.

Furthermore, the ARFs provided by FEMA only contain twenty requests from the state of

Mississippi (3.4% of the total), which is obviously inconsistent with the devastation produced by

Katrina, and the ensuing need for critical supplies. Again, this suggests that the data are far from

complete though the authors do not have any way to assess how complete the data are and, for

that reason caution is suggested when interpreting the analyses presented here.

Temporal distribution by commodity type

In order to analyze the patterns of requests by type of commodity, the commodities

requested in the ARFs were coded using the North American Industrial Classification Codes

(NAICS) (United States Census Bureau, 2007) at the six digits level. Using a formal commodity

coding system is important because it enables the aggregation of commodities in a seamless way

that preserves their economic groupings. The use of NAICS is important because they were

specifically designed to include the service sector, in addition to industrial activities. In cases

where a form had more than one commodity type, each commodity was treated as an

independent request.

For analyses purposes, the team considered different levels of aggregation and

concluded that the best compromise between level of detail and statistical stability of the

estimates was provided by three-digit NAICS. The resulting groups are shown in Table 3. As

shown, 153 different commodities were identified (six-digit NAICS).

Figure 3 shows the relationship between the cumulative distributions of the number of

commodities, and the number of requests. Table 3 and Figure 3 provide crucial information for

emergency planning purposes. As shown, the set of commodities requested is relatively small as

only 153 different commodities were found. For comparison purposes, the information gathered

by the team from interviews with professionals involved in the Katrina logistical response had

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placed this number in between 350 and 500 (Holguín-Veras et al., 2007). More significantly, a

relatively smaller number of commodity types capture a sizable portion of the total requests. As

shown in Figure 3, twenty commodities account for about 30% of the requests, forty

commodities for 47% of requests, and sixty commodities for 65% of requests. This clearly

suggests that prepositioning of critical supplies and the creation of Regional Blanket Purchasing

Agreements recommended elsewhere (Holguín-Veras et al., 2007) will be easier than expected

because of the smaller number of commodities involved. Undoubtedly, prepositioning of a

selected set of critical supplies—that account for a large number of the requests—is bound to

bring about major improvements in the efficiency of the logistical response.

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Table 3: Breakdown of number of requests per commodity type

NAICS Industry subsector

Nu

mb

er

of

req

uest

s

% o

f re

qu

est

s

Cu

mu

lati

ve %

of

req

uest

s

Nu

mb

er

of

co

mm

od

itie

s

% o

f

co

mm

od

itie

s

Cu

mu

lati

ve %

of

co

mm

od

itie

s

336 Transportation Equipment Manufacturing 118 11.12% 11.12% 11 7.19% 7.19%

335 Electrical equip., appliances, component manufacturing 88 8.29% 19.42% 6 3.92% 11.11%

312 Beverage and Tobacco Product Manufacturing 71 6.69% 26.11% 3 1.96% 13.07%

326 Plastics and Rubber Products Manufacturing 57 5.37% 31.48% 4 2.61% 15.69%

561 Administrative and Support Services 53 5.00% 36.48% 7 4.58% 20.26%

311 Food Manufacturing 51 4.81% 41.28% 4 2.61% 22.88%

325 Chemical Manufacturing 51 4.81% 46.09% 8 5.23% 28.10%

337 Furniture and Related Product Manufacturing 46 4.34% 50.42% 4 2.61% 30.72%

621 Ambulatory Health Care Services 45 4.24% 54.67% 5 3.27% 33.99%

314 Textile Product Mills 41 3.86% 58.53% 3 1.96% 35.95%

333 Machinery Manufacturing 25 2.36% 60.89% 9 5.88% 41.83%

721 Accommodation 25 2.36% 63.24% 2 1.31% 43.14%

812 Personal and Laundry Services 25 2.36% 65.60% 5 3.27% 46.41%

339 Miscellaneous Manufacturing 24 2.26% 67.86% 4 2.61% 49.02%

324 Petroleum and Coal Products Manufacturing 23 2.17% 70.03% 1 0.65% 49.67%

562 Waste Management and Remediation Services 22 2.07% 72.10% 2 1.31% 50.98%

517 Telecommunications 20 1.89% 73.99% 5 3.27% 54.25%

532 Rental and Leasing Services 20 1.89% 75.87% 2 1.31% 55.56%

484 Truck Transportation 16 1.51% 77.38% 1 0.65% 56.21%

441 Motor Vehicle and Parts Dealers 14 1.32% 78.70% 1 0.65% 56.86%

332 Fabricated Metal Product Manufacturing 13 1.23% 79.92% 7 4.58% 61.44%

488 Support Activities for Transportation 13 1.23% 81.15% 2 1.31% 62.75%

334 Computer and Electronic Product Manufacturing 12 1.13% 82.28% 4 2.61% 65.36%

624 Social Assistance 12 1.13% 83.41% 2 1.31% 66.67%

922 Justice, Public Order, and Safety Activities 11 1.04% 84.45% 2 1.31% 67.97%

322 Paper Manufacturing 10 0.94% 85.39% 1 0.65% 68.63%

481 Air Transportation 10 0.94% 86.33% 1 0.65% 69.28%

541 Professional, Scientific, and Technical Services 9 0.85% 87.18% 4 2.61% 71.90%

238 Specialty Trade Contractors 4 0.38% 87.56% 1 0.65% 72.55%

313 Textile Mills 4 0.38% 87.94% 1 0.65% 73.20%

315 Apparel Manufacturing 4 0.38% 88.31% 4 2.61% 75.82%

321 Wood Product Manufacturing 4 0.38% 88.69% 1 0.65% 76.47%

513 Broadcasting and telecommunications 4 0.38% 89.07% 2 1.31% 77.78%

235 Specialty Trade Contractors 3 0.28% 89.35% 1 0.65% 78.43%

221 Utilities 2 0.19% 89.54% 2 1.31% 79.74%

230 Construction 2 0.19% 89.73% 1 0.65% 80.39%

316 Leather and Allied Product Manufacturing 2 0.19% 89.92% 2 1.31% 81.70%

421 Wholesale Trade, durable goods 2 0.19% 90.10% 2 1.31% 83.01%

485 Transit and Ground Passenger Transportation 2 0.19% 90.29% 1 0.65% 83.66%

493 Warehousing and Storage 2 0.19% 90.48% 2 1.31% 84.97%

928 National Security and International Affairs 2 0.19% 90.67% 1 0.65% 85.62%

115 Support Activities for Agriculture and Forestry 1 0.09% 90.76% 1 0.65% 86.27%

331 Primary Metal Manufacturing 1 0.09% 90.86% 1 0.65% 86.93%

492 Couriers and Messengers 1 0.09% 90.95% 1 0.65% 87.58%

518 Internet providers, web portals, data processing services 1 0.09% 91.05% 1 0.65% 88.24%

722 Food Services and Drinking Places 1 0.09% 91.14% 1 0.65% 88.89%

923 Administration of Human Resource Programs 1 0.09% 91.23% 1 0.65% 89.54%

999 Others, Miscelaneous 93 8.77% 100.00% 16 10.46% 100.00%

TOTAL 1061 100.00% 153 100.00%

14

Figure 3: Cumulative distribution of requests vs. number of commodities

Note: It excludes 8.8% of the requests representing sixteen items that do not fit the general classification of

―commodities‖ and were classified as ―others and miscellaneous.‖

The next step in the process was to aggregate the different 3-digit NAICS into 23

commodity super-groups, shown in Table 4, so that the resulting aggregations contain a

sufficient number of observations for statistical analysis. These super-groups represent: electrical

equipment and machinery (super-group 1), light transportation equipment (super-group 2), water

and ice (super-group 3), medical supplies (super-group 4), personnel and security (super-group

5), transportation equipment and machinery (super-group 6), portable showers (super-group 7),

chemical products (super-group 8), food (super-group 9), super-groups 10 through 22 correspond

to telecommunications, fuel and others, and finally a group of ―others‖ (super-group 23) that

contains commodities that could not be classified in any other group.

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 20 40 60 80 100 120 140

Per

cen

tag

e o

f re

qu

ests

Number of commodities

15

Table 4: Commodity super-groups S

uper

gro

up

NA

ICS

Industry subsector Actual commodities requested

Num

ber

of r

eque

sts

Tot

al N

umbe

r of

req

uest

Per

cent

age

of n

umbe

r of

requ

ests

Cum

ula-

tive

perc

enta

ge o

f

num

ber

of r

eque

sts

Num

ber

of d

iffe

rent

com

mod

ities

Tot

al n

umbe

r of

com

mod

ities

335Electrical Equipment, Appliance,

and Component Manufacturing

portable lighting, flashlights, fan,

generator88 6

334Computer and Electronic Product

Manufacturing

mobile computer, plotter for gis,

remote sensing/gis support12 4

333 Machinery Manufacturing

shuttle trucks, laundry trailer,

freezer, copier, pump, compressor,

forklift, commercial dryer

25 9

221 Utilities electric utility tent 2 2

321 Wood Product Manufacturing office trailers 4 1

2 336Transportation Equipment

Manufacturing

Car, full sized trucks, ladder truck,

semi trucks, trailers, rv, rotary

airframe

118 118 11.1% 23.5% 11 11

3 312Beverage and Tobacco Product

Manufacturingwater gallons, water 71 71 6.7% 30.2% 3 3

621 Ambulatory Health Care Servicesdoctor, emt, mobile field treatment

beds, ambulance45 5

339 Miscellaneous Manufacturingmedical supplies, syringes, first aid

kits, foam24 4

561Administrative and Support

Servicespersonnel, security, armed security 53 7

541Professional, Scientific, and

Technical Services

communication assesment tam,

signs, aerial imagery acquisitions,

vmat

9 4

484 Truck Transportation fuel tankers 16 1

441 Motor Vehicle and Parts Dealers buses 14 1

488Support Activities for

Transportationtransportation 13 2

481 Air Transportation airlift firemen 10 1

421 Wholesale trade, durable goods communication devices, aircraft 2 2

485Transit and Ground Passenger

Transportationcontracting 2 1

493 Warehousing and Storage warehouse 2 2

492 Couriers and Messengers delivery of donated goods 1 1

7 326Plastics and Rubber Products

Manufacturingcambros, portable showers 57 57 5.4% 53.5% 4 4

325 Chemical Manufacturing nitrogen, bug spray 51 8

115Support Activities for Agriculture

and Forestrynoxious weed 1 1

9 311 Food Manufacturing food, mre, baby food 51 51 4.8% 63.2% 4 4

314 Textile Product Mills blankets, tents, sleeping bags 41 3

313 Textile Mills face cloth/ bath towel 4 1

315 Apparel Manufacturingcuffs, clothing utility coverall,

chemical resist suit4 4

316Leather and Allied Product

Manufacturingrubber boots, utility work boots 2 2

11 337Furniture and Related Product

Manufacturingfolding chairs, cots, benches, tables 46 46 4.3% 72.4% 4 4

721 Accommodation housing, base camp 25 2

722 Food Services and Drinking Places base camp, catering services 1 1

13 812 Personal and Laundry Servicesrefrigeration truck, dmort, decon,

animals, comfort kids25 25 2.4% 77.2% 5 5

12 26 2.5% 74.8% 3

8 52 4.9% 58.4% 9

10 51 4.8% 68.0% 10

5 62 5.8% 42.5% 11

6 60 5.7% 48.2% 11

1 131 12.3% 12.3% 22

4 69 6.5% 36.7% 9

16

Table 4: Continued S

uper

gro

up

NA

ICS

Industry subsector Actual commodities requested

Num

ber

of r

eque

sts

Tot

al N

umbe

r of

req

uest

Per

cent

age

of n

umbe

r of

requ

ests

Cum

ula-

tive

perc

enta

ge

of n

umbe

r of

req

uest

s

Num

ber

of d

iffe

rent

com

mod

ities

Tot

al n

umbe

r of

com

mod

ities

517 Telecommunications

telephone supply systems, 911 psap,

us navy integrated communication

support, blackberries, satellite phones

20 5

513 Broadcasting and telecommunications phone lines, mobile IP handsets 4 2

518

Internet Service Providers, Web

Search Portals, and Data Processing

Services

dsl 1 1

15 324Petroleum and Coal Products

Manufacturingfuel 23 23 2.2% 81.7% 1 1

16 562Waste Management and Remediation

Servicesdebris mission 22 22 2.1% 83.8% 2 2

17 532 Rental and Leasing Services oxygen bottles, quarter boat 20 20 1.9% 85.7% 2 2

332Fabricated Metal Product

Manufacturing

portable lodging, tank, fence, hose

discharge, sinks, flex cuffs13 7

331 Primary Metal Manufacturingpipe, portable lodging, tank, fence,

hose discharge, sinks, flex cuffs1 1

922Justice, Public Order, and Safety

Activitiesfederal marshal 11 2

928National Security and International

AffairsDOD assistance 2 1

923Administration of Human Resource

Programscdc staff 1 1

20 624 Social Assistancemobile kitchen, disaster recovery

center12 12 1.1% 89.4% 2 2

21 322 Paper Manufacturing Sanitary Paper 10 10 0.9% 90.4% 1 1

238 Specialty Trade Contractors temporary roofing 4 1

235 Special Trade Contractors washers 3 1

230 Construction engineering and construction support 2 1

23 999 Others Miscelanousmoney, land, sandbags, partitions,

executive teams93 93 8.8% 100.0% 16 16

TOTAL 1061 100.0% 153 153

22 9 0.8% 91.2% 3

18 14 1.3% 87.0% 8

19 14 1.3% 88.3% 4

14 25 2.4% 79.5% 8

The temporal distributions for these super-groups were further analyzed. Whenever

possible, time series models were estimated (65% of the requests had date information). Figure 4

shows the time series plots for the most important super-groups, and also shows that, in general

terms, the temporal patterns of requests seem reflect the order in which the needs brought about

by the emergency unfolds. It is also important to note, that the highest proportion of requests

correspond to the first two weeks, after this period the number of request declines considerably.

17

Figure 4: Time series plots for the key commodity super-groups

Days

Nu

mb

er

of

req

ue

sts

24222018161412108642

18

16

14

12

10

8

6

4

2

0

Electrical equipment and machinery

a)

Days

Nu

mb

er

of

req

ue

sts

24222018161412108642

18

16

14

12

10

8

6

4

2

0

Water and ice

b)

Days

Nu

mb

er

of

req

ue

sts

24222018161412108642

18

16

14

12

10

8

6

4

2

0

Medical supplies

c)

Days

Nu

mb

er

of

req

ue

sts

24222018161412108642

18

16

14

12

10

8

6

4

2

0

Food, MREs

d)

Days

Nu

mb

er

of

req

ue

sts

24222018161412108642

18

16

14

12

10

8

6

4

2

0

Telecommunications, fuel, others **

e)

Days

Nu

mb

er

of

req

ue

sts

24222018161412108642

50

40

30

20

10

0

Transportation equipment *

f)

Notes:

* Includes light transportation equipment (group 2) and transportation equipment and machinery (group 6).

** Includes groups 10 to 22.

18

Figure 5 represents the cumulative temporal distributions of the requests for the

commodity super-groups. As shown, light transportation equipment, beverages, electrical

equipment, medical services and chemical products were requested in greater numbers during the

initial days. The fastest ramp-up corresponds to medical services that in the first five days

accumulated about 80% of the total requests. This is followed by beverages with about 60%,

electrical equipment with 56%, and light transportation equipment with 50% (for descriptions of

the actual commodities requested, the reader is referred to Table 4). The rest of the commodity

super-groups were requested in a more even manner throughout the duration of the crisis.

Figure 5: Temporal distribution of requests for key super-groups (first 32 days)

A different perspective could be gained from the analyses of the composition of the

requests made on a daily basis. This provides an idea about the relative importance of the

different commodities as the emergency unfolds. However, before discussing details, it is

important to discuss the context on which these requests were made, and their likely meaning.

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

110%

0 5 10 15 20 25 30 35

Days since landfall

Electrical equipment

Light transportation equipment

Beverages (water and ice)

Medical services

Administrative personnel

Heavy transportation equipment

Plastic products

Chemical products

Super-groups 10-22

Others

Food MREs

19

It is reasonable to assume that, when deciding how much to order, the field officers

filling the ARFs took into account the perceived needs at a given day, the needs they anticipated

in the near future, and how long would it take to receive them. In fact, it is entirely possible and

somewhat to be expected that, in the absence of accurate information about the status of previous

requests, field officers may have decided to repeat a request previously made, or increase the

order size to try to raise the request’s priority. Obviously, in a situation where delivery times are

uncertain field officers are expected to order larger quantities than when delivery times are

known and certain. In this way, when a delivery does arrive it helps build a larger safety stock

that could protect them from shortages. This is a variant of a phenomenon widely studied in

supply chain management called the ―bullwhip effect‖ (Forrester, 1961).

All of this stresses the need to be careful when analyzing the daily patterns of requests as

these may have been influenced by the same factors that produce the bullwhip effect. As a

reference it is worthy of mention that the interviews conducted by the authors with the logistic

staff during Katrina indicated that deliveries in small quantities took three to four days, while

deliveries in large quantities took two or three weeks (Holguín-Veras et al., 2007). This suggests

that during the first three or four days of the crisis, the requests could be interpreted as pure

estimates of the perceived needs, i.e., not impacted by arrivals of previous requests. This is the

assumption embedded in the analyses that are discussed next.

Figure 6 shows the relative importance of the different commodities requested in the first

week after landfall, measured by the number of requests made per day. As shown in Figure 6, the

data correspond to before (day zero) and after landfall (day one and following). It is safe to

assume that the requests made on day zero reflect FEMA’s best estimates of what the needs

would be, i.e., FEMA’s attempt at prepositioning; while the ones made after day one correspond

to the field officers’ estimates of what is actually needed in the field plus what may be needed in

the near future. One would expect that if the needs were perfectly estimated by FEMA before

landfall that there would be a perfect match between the requests made in day zero and day one.

However, the data show that the correlation coefficient is 0.02 indicating that the quality of the

needs forecast was extremely poor.

20

Figure 6: Relative ranking of commodities requested in the first week after landfall

The comparison between the requests made in day zero and day one clearly suggests that

FEMA underestimated the need for transportation equipment (light and heavy). While in the day

zero requests light transportation equipment was sixth in the list, in day one it took the top spot.

Heavy transportation equipment was eighth in the day zero requests, and fifth in day one. The

top three commodity groups (i.e., electrical equipment, chemical products, and medical services)

identified by FEMA in day zero were found to take positions two, three, and four once the crisis

unfolded though in a different order than FEMA estimated. As widely reported in the press,

FEMA seems to have overestimated the amount of water and ice needed that went from the

1

2

3

4

5

6

7

8

9

0 1 2 3 4 5 6 7 8

Da

ily

Ra

nk

ing

Days since landfall

Electrical equipment Light transportation equipment Beverages (water and ice)

Medical services Administrative personnel Heavy transportation equipment

Plastic products Chemical products Food MREs

21

fourth position in day zero, to the sixth in day one. The food group was about the only

commodity group whose ranking was correctly estimated.

In terms of the top three commodities requested during the first seven days of the crisis

(until day eight), electrical equipment was the most consistently requested commodity group

staying in the top three five out of seven days. This was followed by light transportation

equipment and medical services with four appearances in the top three out of seven days; and

beverages with three out of seven.

The analyses of the dynamic patterns of requests clearly show the complex nature of the

dynamic needs. An examination of Figure 6 shows the evolving nature of needs and how they

increase and decrease over time. This, yet again, indicates the importance of developing

analytical techniques to forecast immediate resource requirements in the aftermath of an extreme

event, as such forecasts could play a significant role in improving the efficiency of emergency

logistics.

Time series models

The next step in the process was to estimate statistical models that could serve as

planning, or decision tools for any agency involved in disaster relief operations. Taking into

consideration that the time series of the total number of requests was found to be not stationary,

autoregressive integrated moving average (ARIMA) models were estimated (Box et al., 1994).

This methodology allows for the number of requests to be explained by past, or lagged, values

and stochastic error terms.

An ARIMA process is characterized by three parameters (p,d,q), where p denotes the

number of autoregressive terms, d the number of times the series has to be differentiated before it

becomes stationary, and q the number of moving average terms. It is also important to note, that

transformation of the original values might be necessary in order to obtain a stationary series

(Gujarati, 2003).

A model for an ARIMA process is a combination of an autoregressive (AR) process

model, and a moving average (MA) process model, of the ith integration of the series. In general,

an autoregressive (AR) process can be modeled as:

( ) ( ) ( ) ( ) (1)

22

Where is a pth

-order autoregressive, or AR(p), process, is its mean and is an auto

correlated random error term with zero mean and a constant variance , i.e., white noise. In the

same way, a moving average (MA) process that is simply a linear combination of white noise

error terms can be modeled as:

(2)

Where is a qth

-order moving average, or MA(q), process is a constant and u, as

before, is the white noise stochastic error term. When a process, based on the assumption that it

is stationary, has characteristics of both AR and MA is therefore an autoregressive and moving

average (ARMA) process and thus can be modeled as:

(3)

Where is a constant and there are p autoregressive and q moving average terms.

Finally, an ARIMA process would be an ARMA process for which a transformation and/or

differentiation procedure has been implemented to the original values in order to obtain a

stationary process.

Table 5 shows the best models found after a comprehensive estimation process. For all of

the groups analyzed it was necessary to perform a natural log transformation on the original

values in order to obtain a stationary process. For light transportation equipment (group 2) no

model was found, which suggests that the behavior of the number of requests in this super-group

is random. Having limited information for groups 10 through 22, they were aggregated and

analyzed as one group, together with super-group 2, which was added as well.

Although the temporal patterns of the requests for the different commodity super-groups

seem similar, as shown in Figure 4, the majority of them follow different ARIMA processes. As

shown in Table 5, the majority of the models have different structures and parameters with

different autoregressive and moving averages orders, and even in some cases, with second order

differences. It is also important to note that even though these main super-groups represent the

highest percentage share of the total requests, their individual patterns follow different time

series processes.

However, there are two notable exceptions. The first one concerns the model for the total

number of requests, which was found to be statistically the same as the model found for

23

aggregation of super-groups 2 and 10 through 22. The second case is related to the models for

the super-groups of electrical equipment and machinery (super-group 1) and personnel and

security (super-group 5), which again are very similar, though statistically different.

Table 5: ARIMA models for commodity super-groups

Type ** Coefficient t Mean

Residual

Standard

Deviation

(2,1,0) AR1 -0.3456 -2.97 1.474

AR2 -0.368 -3.14

1Electrical equipment

and machinery(1,1,0) AR1 -0.5396 -3.10 1.004

3 Water and ice (0,2,1) MA1 0.9494 11.37 0.744

9 Food (MREs) (2,2,3) AR1 -1.8265 -11.84 0.564

AR2 -0.9421 -6.24

MA1 -0.5921 -3.18 0.606

MA2 0.661 3.65

MA3 0.8437 4.70

4 Medical supplies (3,2,0) AR1 -1.2685 -7.78 0.599

AR2 -1.0435 -4.83

AR3 -0.5678 -3.60

6 (3,1,0) AR1 -0.7291 -5.19 0.5094

AR2 -0.7184 -4.80

AR3 -0.5673 -4.06

5 Personnel and Security (1,1,0) AR1 -0.5491 -3.54 0.8007

8 Chemical Products (5,2,0) AR1 -1.5198 -8.55 0.541

AR2 -1.4481 -4.93

AR3 -1.1193 -3.35

AR4 -0.8882 -3.13

AR5 -0.6923 -4.45

7 (1,2,1) AR1 -0.5982 -3.63 0.656

MA1 0.9388 12.27 0.678

2,10-22 (2,1,0) AR1 -0.3799 -2.85 1.23

AR2 -0.4622 -3.44

Light transportation,

telecommunications,

fuel and others

Super

groups

Parameters

ARIMA Model

Parameters *

Transportation

equipment and

machinery

Portable showers

Description

Total Number of

Requests

*ARIMA (p,d,q). p = autoregressive terms, d = integrated order, q = moving average terms **AR = autoregressive coefficient, MA = Moving average coefficient

The results in this section clearly indicate that it is indeed possible to estimate sound

models to forecast short-term resource requirements. This opens the door to exciting possibilities

as such forecasting models are a key input to advanced methodologies that involve forecasts of

needs with supply chain modeling. The ARIMA models presented here are a key first step in that

direction. Complementing these data with similar data from other disasters may indeed lead to

predictive models able to predict the needs for disasters of various sizes.

24

POLICY IMPLICATIONS

The research reported in this paper has important implications for emergency logistics,

with the key ones being discussed in this section. The first implication worthy of mention is that

prepositioning appropriate amounts of a relatively small number of critical supplies could go a

long way toward reducing delivery times. This seems evident given the fact that 20% of

commodities represent about 36% of the total number of requests. In this context, prepositioning

these commodities will undoubtedly bring about significant reductions in delivery times. As

discussed elsewhere (Holguín-Veras et al., 2007) these stocks could be part of the normal flow of

goods for humanitarian purposes, and could be rotated off the stock as expiration dates approach.

Equally important is that, since these stocks could be designed to cover the needs of entire

federal regions, they could be put in place at a minimal cost.

It is also evident that emergency response agencies must have in place regional blanket

purchase agreements (RBPA), as suggested elsewhere (Holguín-Veras et al., 2007), because they

would expedite purchasing of the critical supplies needed. This will completely eliminate the

single most important source of delays during the Katrina emergency, i.e., purchasing delays.

Again, the relatively small number of commodities needed to be included undoubtedly facilitates

the establishments of the RBPAs.

The paper’s conjecture that some form of bullwhip effect took place during the Katrina

logistical operations has important implications. The most important one is associated with the

detrimental effects that the bullwhip effect has on the performance of the supply chain, as the

artificial amplification of order sizes distract resources (e.g., financial, manpower) from more

important tasks. This stresses the need for: (1) accurate forecasts of the critical supply needs;

and (2) increased visibility of the supply chain, as both of them have been found to reduce the

bullwhip effect.

CONCLUSIONS

This paper summarizes the analyses of the temporal patterns of requests for critical

supplies after hurricane Katrina. The analyses are based on the requests for federal assistance

captured in the Action Request Forms (ARFs) issued by responders on site. The analyses provide

insight into the resource requirements after a disaster, their temporal evolution, key types of

commodities requested, as well as their relative importance. The data revealed that the number of

25

different commodities needed in the aftermath of a disaster is smaller than previously thought.

While previous estimates have placed that number in the range of 350-500 (Holguín-Veras et al.,

2007), the data indicate that only 153 different commodities were requested during the Katrina

emergency. Even though one acknowledges the possibility of an underestimation of the number

of commodities due to the aggregation into the NAICS codes, it seems that the actual number of

commodities needed is less than expected. Furthermore, the analyses found that an even smaller

number of commodities account for sizable portions of the total number of requests. As

discussed in the paper, twenty commodities account for about 30% of the requests, forty

commodities for 47%, and sixty commodities for 65% the total number of requests. This has

important implications for emergency response because it increases the practicality and

feasibility of prepositioning of commodities, and of developing regional blanket purchase

agreements to secure expedited flows of the critical commodities needed during a disaster.

The data were used to estimate autoregressive integrated moving average (ARIMA)

models for the key commodity groups. These models, if properly expanded so that they represent

a wider range of disasters, could play a crucial role as planning tools for any agency involved in

disaster relief operations because they provide estimates of future requirements. It was found

that, although qualitatively, the temporal patterns of requests seem similar, the different

commodity groups follow structurally different ARIMA processes. There were only two cases in

which both the model structure and the parameters were similar to other super-groups (total

number of requests and the aggregation of super-groups 2 and 10 through 22).

The temporal distribution of requests shows the relative importance of the different

commodities as the disaster unfolded. As mentioned, it was found that during the initial days,

commodities from the super-groups of light transportation equipment, electrical equipment,

medical services, beverages and chemical products were requested in greater numbers. Taken

together this finding provides crucial information for emergency planning as it will help

emergency agencies to develop appropriate contingency plans, or implement different strategies

such as the ones mentioned in Holguín-Veras et al. 2007.

The research discussed in this paper opens the door for exciting possibilities, particularly

the combination of the predictive models to forecast needs, and supply chain models to expedite

the flows of these goods. However, in spite of these interesting developments, there is no doubt

26

that this paper is nothing more than an initial step in understanding supply chain issues in

disasters.

ACKNOWLEDGMENTS

This research was supported by the National Science Foundation’s grants entitled ―DRU:

Contending with Materiel Convergence: Optimal Control, Coordination, and Delivery of Critical

Supplies to the Site of Extreme Events‖ (National Science Foundation CMMI-0624083); and

―Characterization of the Supply Chains in the Aftermath of an Extreme Event: The Gulf Coast

Experience," (NSF-CMS-SGER 0554949). This support is both acknowledged and appreciated.

The authors also acknowledge the contributions of the following undergraduate students in

finding and analyzing information contained in this report: Brandon Allen, Ashley Corker,

Anthony Andrews, Mike Preziosi, and Marielys Ramos.

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