28 ISE Magazine | www.iise.org/ISEmagazine

Hunger is a reality for more than 50 million peo-

ple in America, 1 in 6 of the U.S. population,

including more than 1 in 5 children. Though

hunger is a sensation that can be felt physically,

it can be laborious to measure it. One such way

to measure hunger and food scarcity of a large

population is by food insecurity.

Food insecurity is the state or condition where someone

is living short of obtaining ample amounts of cost-effective,

accessible and nourishing food. In 2013, households that

had higher rates of food insecurity than the national av-

erage included households with children (20%), especially

those households with children headed by single women

(34%) or single men (23%).

Also, as per the 2015 statistics provided by Feeding Amer-

ica ( feedingamerica.org), one of the largest domestic hunger-

relief organizations in the United States, 42.2 million

Americans live in food insecure households, including 29.1

million adults and 13.1 million children. This constitutes

about 13.1% of households that are food insecure.

To curb this issue, food insecure individuals and families

receive assistance from the government and from philan-

How data science can provide

nourishment and food

security

Analytics boosts food banks’ efforts in fundraising, distribution processes

By Rahul Srinivas Sucharitha and Seokcheon Lee

December 2021 | ISE Magazine 29

thropic programs. One such program is Feeding America,

which provides food and assistance to these individuals

through a nationwide network of around 200 food banks

and around 63,000 food pantries and meal programs. Of

those, 66% of food pantries, 41% of kitchen programs and

11% of shelter programs have no paid staff and rely entirely

on volunteers.

Food bank network members of Feeding America supply

food to more than 37 million Americans each year, includ-

ing 14 million children and 3 million seniors; 34% of all

households served by Feeding America have had to choose

between paying for food and paying for medicine or medi-

cal care. Additionally, 72% of food banks do not feel as

though they are able to adequately meet the needs of their

communities without adjusting the amount of food distrib-

uted.

To assist in their efforts, industrial engineers can use

their skills to create a realistic food allocation metric and

ensure equitable and nutritious food to those who are

food insecure.

How food banks need data analytics

Food banks obtain donated food and grocery products

from national food and grocery manufacturers and retail-

ers. These donations then are distributed to food pantries

and meal programs based on their availability. People in

need receive the donated foods from these food pantries

and meal programs. Food is usually received from food do-

nors by following a ﬁxed schedule that is convenient to the

donors.

Once the food is obtained from the donors, it is processed

and inspected at the food bank before being made available

for distribution to the agencies (Figure 1). The food is dis-

tributed to the agencies by trucks that are maintained by

the banks or are being rented based on the travel schedule.

Agencies receive the donated food based on their storage

capacity (in pounds). It is then prioritized based on distance

from the food bank; usually the nearest agencies are given

priority. This leads to circumstances where an inadequate

amount of nutritious food is provided to people who need

it as the food is distributed based on weight and not based

on the amount of the type of food, such as protein or fats.

According to the recent Salesforce Nonproﬁt Trends Report,

more than half of the nonproﬁts (53%), including food as-

sistance programs and other types of nonproﬁts, ﬁnd it easy

to collect program data. However, putting that data into

action is more complicated. Fewer than half (47%) say it is

easy to analyze that data, leading to a wide range of strug-

gles when it comes to tracking and quantifying things like

FIGURE 1

Food bank distribution

How donated food is passed from donors to food banks and then on to participating agencies.

42.2 million Americans live in food

insecure households, including 29.1

million adults and 13.1 million

children. This constitutes about 13.1%

of households that are food insecure.

30 ISE Magazine | www.iise.org/ISEmagazine

How data science can provide nourishment and food security

impact and performance. Also, just 41% say they ﬁnd it easy

to have data power the overall impact of programs.

In addition, despite the fact that mobility is becoming

more important to nonproﬁts each passing year, only 29%

say they can easily capture and access data from a mobile

device. This shows the importance of analytics tools for

nonproﬁts, but only 45% currently use analytics, according

to the report, with an additional 30% planning to use such

tools within two years.

Nonproﬁts have huge datasets they can exploit to de-

How food banks began

The food bank concept was started by John van Hengel in Phoenix in the late 1960s (Figure 2). The retired businessman was volunteering

at a soup kitchen trying to ﬁnd food to serve the hungry and observed the discarding of edible food from grocery stores. He concluded

the food could be used to feed those in need by storing the items for people to pick up.

Van Hengel established St. Mary’s Food Bank in Phoenix as the nation’s ﬁrst food bank. In its ﬁrst year, he and his team of volunteers

distributed 275,000 pounds of food. Word of their success spread quickly and others in the country began to take note. In 1976, the U.S.

government provided van Hengel’s food bank with a grant to assist in developing similar operations around the country. That was helped

by the passage of the 1976 Tax Reform Act that made it ﬁnancially advantageous for companies to donate their products. This federally

funded development lengthened and ultimately incorporated to manage the solicitation of assistance from donors and develop standards

for food banks pertaining to storage capacity, quality control and management.

By 1977, food banks were established in 18 cities across the country. As the number increased, van Hengel created a national

organization for them, and in 1979 established Second Harvest, later known as America’s Second Harvest the Nation’s Food Bank

Network. Federal funding was discontinued by 1982, so America’s Second Harvest pursued alternative ﬁnancial sources and moved its

national ofﬁce to Chicago in 1984. The organization continued to grow as the practice of food banking gained acceptance along with

support from the food industry and local social service providers.

With many major cities establishing food banks by the mid-1980s, network expansion slowed and America’s Second Harvest’s shifted

its focus to improving existing programs. The professionalism and efﬁciency of food bank operations improved dramatically, resulting in

a much greater amount of food and products being distributed by the network.

In 1999, the name of the national organization was ofﬁcially changed to America’s Second Harvest with a goal of ending hunger in

America. In March 2000, it merged with Food Chain, the nation’s largest food rescue organization. That produced the country’s most

comprehensive and efﬁcient charitable food rescue and distribution organization.

In 2008, the network changed its name to Feeding America to better reﬂect the mission of the organization. Today, it is the nation’s

largest domestic hunger-relief organization with an efﬁcient network of 200 food banks nationwide. As food insecurity rates hold steady at

the highest levels ever, the Feeding America network of food banks has risen to meet the need serving the entire U.S., with its food banks

operating 63,000 agencies that address hunger through emergency food assistance and programs. Learn more at feedingamerica.org.

FIGURE 2

Food bank history

A timeline details the origin of food banks.

December 2021 | ISE Magazine 31

velop statistical models that can help them op-

timize fundraising. Using segmentation and

predictive modeling helps them identify and

target the right groups to develop a more re-

ﬁned marketing strategy. Data insights help

nongovernmental organizations identify and

segregate donors based on various factors,

which they can use to drive their marketing

and fundraising efforts more effectively. Data

analysis also helps charitable organizations dis-

cover relationships that can help them develop

speciﬁc incentives.

Nonproﬁts such as food banks and agencies

need to sustain themselves throughout the en-

tire year. To do so, they need to be efﬁcient

in their operations and donor outreach efforts.

Data analytics tools can help on those fronts,

and recent research indicates that such solutions

are needed at nonproﬁts. Food assistance programs can use

analytics to streamline operations, increase cost efﬁcien-

cy, determine and optimize ﬁnancial margin by program,

model and forecast performance (e.g., membership trends,

donor trends, resource needs and revenue expectations),

improve the budgeting process, and enhance overall mis-

sion effectiveness. Such solutions can provide signiﬁcant

beneﬁts to nonproﬁts, including the ability to measure a

return on investment.

Analytics can also help nonproﬁts ﬁgure out the best

prospects to engage to make a donation. Modeling can be

used to examine donor demographics, giving data and in-

teractions with an organization to predict the future giving

behaviors of potential donors. Wealth screening and pub-

licly available information can then be used to determine

the prospects with the most money to potentially seek for

donations.

Used together, these tools can help an organization nar-

row the scope of the prospect database and identify pros-

pects for assignment to the development staff, make more

informed donation requests and identify new prospects to

ﬁll the donor pipeline. Analytics can also help strengthen

an organization’s membership recruitment and fulﬁllment

operations. Data analysis and visualization can also play a

huge role in real time tracking during crises by optimizing

relief efforts.

Traditionally, food assistance programs have purchased

lists of names in the target market and sent communications

deep into that list, as well as to their existing membership

base. However, instead of using that blunt approach to yield

new members, nonproﬁts can use data analytics to analyze

various factors, activities and characteristics in concert to

determine how they relate to and affect each other. This

enhanced approach enables organizations to generate an

optimal number of engaged members in a ﬁscally prudent

manner. Using data analytics, planning exercises can con-

sider a more complete set of factors, such as membership

outreach yield, cost per outreach, market penetration, de-

mographic factors and changes, membership engagement

and membership product cross-selling.

There are numerous other beneﬁts to analytics. The tools

can be used to track and analyze nonproﬁt staff activity and

motivate them through data-driven performance bench-

marks. Basic activity metrics, including donor visits, solici-

tations, gifts received and new prospects, will provide the

baseline health of the donor pipeline and enable revenue

forecasting. Analytics can also help improve donor relations

by providing more quantiﬁable data to donors on the im-

pact of their “investment,” which may then motivate them

to give again. Organizations must not only make data mea-

surement a priority but also understand the various ways

Nonproﬁts such as food banks and

agencies need to sustain themselves

throughout the entire year. To do

so, they need to be efﬁcient in their

operations and donor outreach efforts.

Data analytics tools can help

on those fronts, and recent research

indicates that such solutions are

needed at nonproﬁts.

32 ISE Magazine | www.iise.org/ISEmagazine

How data science can provide nourishment and food security

that impact data can be effectively communicated.

Data analysis can help these organizations make sure that

they’re putting their time, effort, money and energy into

the right channels. Nonproﬁt organizations need funds to

fulﬁll their missions, but they also need to prove the results

of their work to attract donors. Data science has immense

potential in optimizing this cycle and can help organiza-

tions make well-informed, sound decisions. While not ev-

ery organization can afford to hire full-time data analysts,

some ﬁll the resource gap by seeking help on platforms like

Kaggle and Stack Exchange, or by hiring freelance data

scientists and consultants through Kolabtree and Experfy.

That data science bears enormous potential for govern-

ment organizations and nonproﬁts, big and small, is quite

clear. But accessing experts is a common problem that these

organizations face. Processes often are bureaucratic, and the

funds collected may be inadequate to employ a full team.

Thus, many organizations don’t see the power of data sci-

ence manifest.

One solution for organizations is to consult freelance data

science experts for short-term projects. This helps them ac-

cess the expertise of data analysts while keeping the process

cost-effective. This is one practical way the third sector can

reap the beneﬁts of data science and stay empowered, so

that they can empower others.

Filling the need for nutrition

Regional food banks collect the donated, purchased and

government-supplied foods, and distribute the foods to

their 63,000 locally afﬁliated agencies that provide gro-

ceries and hot meals to low-income families through meal

programs such as food pantries and soup kitchens. The core

principles governing the distribution of foods to the agen-

cies are “as much as possible” and “as equitable as possible.”

Accordingly, the distribution performance of a food

bank is typically evaluated in total pounds distributed and

distribution equity among counties (or agencies). We pose

two fundamental issues regarding these distribution per-

formance metrics that naturally drive the way food distri-

bution operations are preconditioned and executed across

strategic, tactical and operational levels.

First, due to the lack of appropriate trade-off between

the two metrics of total and equity, various distribution

strategies can be, and have been, applied, occasionally pro-

ducing irrational and/or confusing outcomes. For instance,

consider three agencies responsible for equal-sized food-

December 2021 | ISE Magazine 33

insecure populations. Taking more into account that the

equity in food distribution can lead to preferring value

amounts in pounds of food of [100 100 100] to [100 150

100], though the latter clearly dominates the former. This

problem stems from the separate pursuit of these possibly

conﬂicting metrics; thus, an integrated approach is required

to eliminate such irrational preferences.

Second, as mentioned earlier, the distribution perfor-

mance is based on the quantity (weight) only despite the

fact that the nutritional quality of the foods distributed by

food banks can make a critical difference to the health of

recipients. This further leads to an unreasonable outcome

from the aforementioned instance. Together with concerns

about obesity and diet-related chronic diseases such as dia-

betes, food banks are increasingly aware of the need to im-

prove the nutritional quality of charitable foods in the form

of nutrition proﬁling to quantitatively score the nutritional

value; nutrition policies to guide efforts to eliminate un-

healthy products such as soda or candy; or increasing the

amount of fresh produce to ﬁll the nutritional gaps.

However, no attention has been paid to date on how to

distribute foods in a nutrition-rich or nutrition-balanced

manner. As shown in an observational study with 269 food

pantries supplied from two large food banks in Minnesota

in 2013, the nutrition quality (measured in Healthy Eating

Index, HEI-2010) ranged from 28 to 82 out of 100 (“Ap-

plication of the Healthy Eating Index-2010 to the Hunger

Relief System,” Marilyn S. Nanney, Katherine Y. Gran-

non, Colin Cureton, Courtney Hoolihan, Mark Janowiec,

Qi Wang, Cael Warren and Robert P. King 2, 2016). This

large variability could be because of the lack of systematic

consideration of nutrition quality in the current metrics of

distribution performance.

Where data scientists ﬁt in

If data scientists begin to invest their efforts in areas beyond

business industries, they will discover how far and wide

their skills and experience can be helpful. Nonproﬁt orga-

nizations are mostly run with the help of funds. It is incred-

ibly difﬁcult to gain these funds and success depends on

factors like location and the cause the nonproﬁt supports.

One of the most important accomplishments nonproﬁts

must achieve is to prove their efforts for a certain cause are

impactful, resulting in positive change. This attracts more

people to provide funds and voluntary efforts. If nonproﬁt

organizations could get access to the wonders of data sci-

ence, the process of getting funds and proving the impact

could be optimized. Better optimization will obviously

produce a more positive result for all involved. Hence,

many other nonproﬁts apart from food assistance programs

are applying data science and machine learning techniques.

With the understanding that everything that is or can

be digitized is data, organizations can start to mine data to

learn about their audiences, their approaches and the con-

text of the world around them. The fact that we still have

long-standing problems – societal challenges that nonproﬁt

organizations are set up to address – means we still have

more work to do, and big data might well be a lever to

creating change.

Admittedly, a data science team is not easy to afford.

Moreover, most of the talent is centered in the business

sector. It is time we admit that this gap of resource needs to

be overcome. In the same line of thought, it should be men-

tioned that big data and data science can provide not only

smarter solutions at the consumer end but actually bring a

positive change for humanity. Hence, several of these orga-

nizations can beneﬁt from ISEs providing pro bono train-

ing, enablement and teaching.

If you are a data scientist, try locating volunteer projects

near you. You will ﬁnd this rewarding, and together we can

help in providing nourishment and relief today and work

toward providing food security tomorrow, overcoming

hunger and malnutrition.

Additionally, industrial engineers can aid in ensuring a

realistic food allocation metric for the people in need. By

designing different metrics that aggregate quantities and

qualities of distributed foods and developing techniques to

evaluate and compare the potential metrics in simplistic and

complex scenarios where a mix of foods needs to be allo-

cated to a set of entities, we can provide a suitable metric to

optimally allocate the foods. The allocation results will go

through analysis to identify the strengths and weaknesses of

each of the metrics. 

Rahul Srinivas Sucharitha is a Ph.D. candidate in the School of

Industrial Engineering at Purdue University in West Lafayette,

Indiana. He earned a bachelor’s degree in mechanical engineer-

ing from the College of Engineering, Guindy, Chennai, India, in

2013 and a master’s degree in industrial engineering from Purdue

University in 2015. His research interests include modelling com-

plex systems, food bank distribution networks and logistics.

Seokcheon Lee is an associate professor in the School of Industrial

Engineering at Purdue University in West Lafayette, Indiana.

Lee earned bachelor’s and master’s degrees in industrial engineer-

ing from Seoul National University, Seoul, Korea, in 1991 and

1993, respectively, and a Ph.D. in industrial engineering from

Pennsylvania State University in 2005. His research work has

been focused on distributed control of large-scale complex networks

– supply network coordination, dynamic resource allocation and

emergency logistics – based on the fundamental decision principles

designed from multidisciplinary perspectives. He has been an IISE

member since 2009.