NAE Perspectives offer practitioners, scholars, and policy leaders a platform to comment on developments and issues relating to engineering. 

Robin Podmore (NAE) is president, Incremental Systems Corp. (IncSys). Anjan Bose (NAE) is Regents Professor, School of Electrical Engineering & Computer Science, Washington State University.

Climate change, the provision of electric power, sustainable food cultivation, and poverty are massive, complex, and interconnected global problems. Given their scope and complexity, they defy solution by a single approach or country. The best chance of attempting to address them is through a system of systems approach. We examine the challenges of each area of concern and propose just such an approach.

We make the case that engineers and businesspeople working or volunteering for professional associations and other organizations can have a significant impact on alleviating global poverty while creating a cooler planet.[1] The goal is to empower small and mid-size enterprises (SMEs) to grow into a new generation of just, equitable, diverse, and inclusive corporations.

A System of Systems Approach
 System of systems methods are a branch of systems engineering that address complex problems spanning multiple domains or sectors, vast geographies, high-stakes consequences, various disciplines, numerous players, and conflicting objectives all with limited resources (IEEE 2019).  For example, managing the mini- and macrogrid power systems across the six continents is a huge problem in a single domain. In contrast, funding, building, and operating the systems of roads, pipelines, telecommunications, rails, shipping, water, gas, sewer, housing, and electricity across continents is a huge problem covering multiple domains.  The system of systems approach has the following advantages:

1. By using object-oriented modeling methods, designers create a common language for defining the problem.

2. This common language can be used to develop frames of reference, thought processes, quantitative analysis, tools, and design methods.

3. Computer models and digital twins (i.e., simulation models) can reveal mistakes before real-world implementation.

4. Mistakes made in simulations are not expensive to repair. They do not damage equipment, cause loss of life or limbs, or entail significant economic losses.

The Multidimensional Poverty Index

A system of systems approach needs to have an objective function that is measurable and meaningful at the village, state, national, continental, and global levels. 

The UN Multidimensional Poverty Index (MPI), presented at the NAE 2020 annual meeting by Dr. Sabina Alkire,[2] measures the acute deprivations in health, education, and living standards that a low-income person faces simultaneously. It is designed to accurately assess experiences and impacts of poverty in order to enable measurement of the impacts of community interventions. To that end the index also allows an encompassing view of private and public investments across government departments.

The MPI is an ideal objective function for computer simulations that can effectively inform efforts to alleviate global poverty. A new generation of agent-based modeling software is proposed by IEEE Smart Village and Rotary Smart Village to reduce MPI scores across villages, states, and nations for a range of economic development and investment scenarios.

Mapping the World’s Resources

A common problem with investments in infrastructure in developing nations is that they are often advocated by corporations and/or foreign governments with commercial interests and give little attention to the benefits for or impacts on the community. They may create some local jobs, but surrounding communities still tend to remain in poverty. Governments need better tools to evaluate the impacts of these investments.

The technology is available to map the world’s resources and to project how raw assets can be transformed into life-sustaining income for villagers and farmers. These resource maps can inform simulations of the impacts of various interventions in specified areas. The maps may include:

• land: soil type and fertility

• seasonal weather patterns

• animals

• minerals

• forests

• transportation: highways, railroads, airports, waterways, off-road tracks

• electricity: national grids, minigrids, generating plants, electric storage

• water: rivers, lakes, oceans, water table

• habitat (housing)

• health care: clinics, hospitals, pharmacies.

These maps are available to large corporations and some governments; it is important to also make the information available to SMEs and community leaders, and this is now possible with cloud computing, big data, and internet access. As an example, the Soils4Africa project has produced a map depicting the continental distribution of forests, grasslands, croplands, lakes, wetlands, and built-up areas.[3]

Simulation and Knowledge Creation

Digital twins with simulations provide a powerful means of converting implicit knowledge to explicit knowledge with models and scenarios. One very effective model of knowledge creation explains how both tacit and explicit knowledge are converted into organizational knowledge. Called the SECI model (or the Nonaka-Takeuchi model; Nonaka 2007), it distinguishes four knowledge dimensions: socialization, externalization, combination, and internalization (figure 1). Socialization is one-on-one informal mentoring. Tacit knowledge of the mentor is absorbed as tacit knowledge by the apprentice. Externalization is the conversion of tacit knowledge to explicit knowledge that can be replicated, for example through narrated videos of experts performing a task. Combination involves the collection of lessons across different villages, documenting failures and lessons learned as well as successes. Internalization occurs when apprentices apply explicit knowledge to their own problem and build their own intuition. A simulation or model accelerates the entire looped process.

Much of the tacit knowledge for an entrepreneur to create and grow a successful high-tech business is based on hard-earned experience—growing a successful SME business is still more of an art than a science. With digital twins and the SECI knowledge engine, the lifetime experience and knowledge of successful entrepreneurs can be compressed into on-the-job training for new entrepreneurs.

The characteristics needed by an entrepreneur and leadership team evolve from startup to medium-sized business to large organization. IEEE members, Rotarians, and others can provide pro bono consulting for a variety of technical and business issues as SMEs grow their organizations.

The recognition-primed decision model (Klein 1993) has been developed by decision makers in mission-critical real-time environments. As businesses become more dynamic and need to respond to real-time events such as cyberattacks, firestorms, and blackouts, the tools of cognitive task analysis and the recognition-primed decision model become more valuable. Many of the tasks performed by villagers—e.g., beekeeping, harvesting honey, processing and/or cooking cassava, installing high-voltage circuits, charging batteries—have a safety and real-time decision-making element. It takes McGyver skills to survive in the villages. The recognition-primed decision model is a way to document these skills (figure 2).

Global Electrification

Large-scale generation and transmission systems (macrogrids) will continue to play a vital role in global electrification. Macrogrids with long-distance AC and DC interties can greatly increase system reliability with high penetrations of renewables since there is still a greater variability of wind and solar over a large geographic region. The latest generation of minigrids with grid-forming and grid-following inverters are being built to standards such that they can connect with macrogrids with a 20-year life.

But there are significant challenges. The World Bank (2019) has projected that 490 million people could be served by 210,000 minigrids by 2030 with almost $200 billion in investment. However, the rate of financing for minigrids is only a fraction of what is needed. The International Renewable Energy Agency (IRENA 2021) estimates that in 2030, 660 million people will still be without access to electricity.

Options are emerging to compensate for the shortcomings. Mobile power plants with electric tractors transporting several hundred kWh of batteries are a low-cost option for rapid distribution to support productive applications of electricity. Minigrids and mobile power plants can support an anchor business that can make high profit margins to then fund essential services—including electricity, clean cooking, internet, safe water, electric transportation, farm tractors, and farm automation—for surrounding residences and small businesses.

With the system of systems approach, the impact of mini- and macrogrids and mobile power plants can be studied to determine which combination will zero out the MPI for the entire population of a country at the lowest cost and fastest rate while also reducing greenhouse gas (GHG) emissions.

The power industry should use the MPI to complement the traditional levelized cost of energy (LCOE) projections that have been the mainstay of electric utility integrated resource planning studies. LCOE studies are one-dimensional: they determine the optimal resource plan over time for an assumed load growth in a given location.

One major problem of LCOE studies for the developing world is that they do not model the productive uses of electricity and overlook the fact that there has to be an investment in appliances, tools, processing plants, factories, farm machinery, and other equipment. These investments are needed so that electric consumers have the income to pay electric bills and cover basic necessities for food and housing.

Smart Farms

In North America the average farmer has 450 acres and needs to have a separate nonfarm job to make enough income to survive. In Nigeria small family farmers own 1 acre of land on average and predominantly manage mixed crop-livestock systems, including fish farming. There are common problems and solutions for both farm sizes.

Farmers in both developed and developing countries typically sell their raw unprocessed products to wholesale markets. Large food processing organizations with global brand names buy these raw products and process and package them for global distribution. The farmers in general obtain a small percentage of the retail price for their raw products. Table 1 shows the total retail revenue per acre for several processed products.

For example, in India Amul, a dairy cooperative with a $7.2 billion business, is a role model that empowers and rewards small farmers. The average participating dairy farmer has two cows and gets 80–85 percent of the retail price. Nearly 25 million liters of milk are procured every day from 3.6 million farmers.

The primary market of early adopters to purchase smart farm commodities is 1.2 million Rotarians, 0.2 million Rotaracts (younger Rotary International members aged 18 and older), and 0.4 million IEEE members—a market of 1.8 million buyers worldwide with above-average income who can be reached through the publications of Rotary International and IEEE.[4] The next level of adopters will be the family and friends of the 1.8 million by word of mouth, which we calculate as a buying base of 5 times 1.8 million, or 9.0 million people. Thus, the network and the technology are available to create a global market where the buyers can build relationships with the sellers of smart farm products around the world. United Parcel Service, Fedex, the US Post Office, DHL, Amazon, and Uber are all viable partners to set up the supply chain.

The lessons learned from these applications on a small local scale can be applied in building country- and even continentwide businesses. Actual business plans developed and implemented show a payback on all the capital spent for PV panels, batteries, charge controllers, electric tractors, electric trucks, and processing machines, typically in 2–3 years, with healthy profit margins. Annual running costs are minimal thanks to zero fuel bills and, coupled with the formation of a coop by the 1-acre farmers to provide their crops to a local processing plant, allow for above-average market labor rates to pay trained technicians, managers, and engineers. High return on investment is obtained by selling products more directly and ensuring that 50–80 percent of the retail price is returned to the front-line farmers and production workers. With these arrangements, even smallholder farmers can use electric tractors and all-terrain vehicles to grow, bale, and transport cattle feed; photovoltaic (PV) electricity for chilling; thermal and/or PV solar for pasteurization; and electric tanker trucks to collect milk.

Transformation from Linear to Circular Agricultural Systems

Most of the current global food and agricultural systems largely follow a linear path of take-make-use-waste-pollute. In contrast, a circular economy promotes responsible and cyclical use of resources, contributes to sustainable development, and creates environmental quality, economic prosperity, and social equity to the benefit of current and future generations. As an example, a USDA Notice of Funding Opportunity[5] identified the following methods for reducing greenhouse gas:

• Cover crops

• Low- or no-till

• Nutrient management

• Enhanced efficiency fertilizers

• Manure management

• Feed management to reduce enteric emissions

• Buffers, wetland, and grassland management, and tree planting on working lands

• Agroforestry and afforestation on working lands

• Afforestation/reforestation and sustainable forest management

• Planting for high carbon sequestration rate

• Maintaining and improving forest soil quality

• Increased on-site carbon storage through forest stand management

• Alternating wetting and drying on rice fields

• Climate-smart pasture practices, such as prescribed grazing or legume interseeding

• Soil amendments, like biochar

Table 2 shows the reduction in CO2-equivalent emissions from 400 farms with an average of 500 acres each when they adopt low GHG practices. Of these farms, 200 grow crops for human consumption, 100 raise cows for beef, and 100 raise cows for dairy. GWP = global warming potential, as defined by the EPA.

As farmers make more income per acre, they can afford to adopt GHG mitigation practices that would otherwise not be affordable. These practices can be implemented from very small 1-acre farms in Africa to medium and large farms in North America.

Putting It All Together

IEEE Smart Village was founded in 2009 and now has 13 years of experience at identifying, seed funding, and supporting entrepreneurs, especially in Africa: Nigeria (3), Cameroon (2), Tanzania (1), Zambia (1), Rwanda (2), Uganda (2), Kenya (3), Malawi (1), and South Sudan (1). Every one of these entrepreneurs is still in business with the original founders at the helm.

Two entrepreneurs in Nigeria, one in Cameroon, and one in South Sudan have been successful at growing electric minigrid businesses. The entrepreneurs in Nigeria have grown very rapidly thanks to UN supported–funding that pays a fee of $300 each time they make a customer connection. The remaining entrepreneurs have been focusing on productive applications of electricity to provide products and/or services for sale not only locally in neighboring cities but also internationally. The products include fruit and vegetables, beadwork, bricks, clothing, life jackets, and jewelry. Services offered include homestays and global telehealth consulting.

Africa Development Promise in Uganda and Kenya has successfully implemented women-run cooperatives. Conferences have been held in Johannesburg (2012), Livingston (2016), Accra (2017), Cape Town (2018), Abuja (2019), Nairobi (virtually in 2021 and 2022), and Rwanda (2022). The IEEE Africa Working Group of IEEE Smart Village now has members in 41 African countries.

The MPI has been effectively implemented in Uganda and Kenya to measure the impact of interventions; all the measurement and evaluation is performed by locally trained staff. The system of systems approach will continue to be used as a tool to share best practices across all the IEEE Smart Village Entrepreneurs. The relationship with Rotary International has led to a joint IEEE and Rotary effort to introduce telehealth networking in Uganda and Rwanda and an oxygen plant in a hospital in Mandi, India. To participate in these efforts, please visit smartvillage.ieee.org.

Notes

[1] The propositions presented here are based on the lead author’s experience as cofounder of IEEE Smart Village in 2009 (Smartvillage.ieee.org) and founding president of Rotary E Club of Silicon Valley Smart Village since 2020. The mission of the latter is to be a bridge so that IEEE and Rotary can work together to benefit humanity. Additional details about these efforts are in Podmore and Kapur (2022).

[2] Dr. Alkire is director of the Oxford Poverty and Human Development Initiative, https://ophi.org.uk/about/.  

[3] https://www.soils4africa-h2020.eu/s4a-maps-agricultural-land-in-africa

[4] The IEEE tag line is “Advancing technology for humanity,” and IEEE Smart Village is now recognized as IEEE’s most impactful humanitarian program. The work of IEEE Smart Village is supported by 14 of the 39 IEEE societies; the others are being recruited to join in providing financial and volunteer support.

[5] Partnerships for Climate-Smart Commodities – Building Markets and Investing in America’s Climate-Smart Farmers, Ranchers and Forest Owners to Strengthen US Rural and Agricultural Communities (February 2022).

References

IEEE [Institute of Electronics and Electrical Engineers]. 2019. 21839-2019 - ISO/IEC/IEEE International Standard -- Systems and software engineering -- System of systems (SoS) considerations in life cycle stages of a system. Available at https://ieeexplore.ieee.org/document/8767116.

IRENA [International Renewable Energy Agency]. 2021. Renewable Energy Statistics 2021. Abu Dhabi.

Klein GA. 1993. A Recognition Primed Decision (RPD) model of rapid decision making. In: Decision Making in Action: Models and Methods, ed. Klein GA, Orasanu J, Calderwood R, Zsambok CE. Norwood NJ: Ablex Publishing.

Nonaka I. 2007. The knowledge-creating company. Harvard Business Review, July-August.

Podmore R, Kapur R. 2022. Smart village voices in Africa, system-of-systems approach. IEEE Power and Energy Magazine 20(5):54–60.

World Bank. 2019. Energy Sector Management Assistance Program: Annual Report 2019. Washington.

Disclaimer

The views expressed in this perspective are those of the author and not necessarily of the author’s organizations, the National Academy of Engineering (NAE), or the National Academies of Sciences, Engineering, and Medicine (the National Academies). This perspective is intended to help inform and stimulate discussion. It is not a report of the NAE or the National Academies. Copyright by the National Academy of Sciences. All rights reserved.