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Frequently Asked Questions (FAQs)

What is the NSF Service, Manufacturing and Operations Research (SMOR) program?

The NSF Service, Manufacturing and Operations Research (SMOR) program is a discretionary grant opportunity that supports research advancing the theory and practice of decision-making for designing, planning, and operating service and manufacturing systems. It funds operations research work intended to improve real-world operational decisions in complex systems involving limited resources, uncertainty, and competing objectives.

What is the funding opportunity number and CFDA listing for this program?

The Funding Opportunity Number is PD 16 006Y, and the CFDA listing is 47.041.

What category does this grant opportunity fall under?

This opportunity is in the Science and Technology/Research and Development category.

What types of research outputs does SMOR aim to fund?

SMOR supports research that produces new mathematical models, stronger analytical results, and more effective algorithms that can guide operational decisions in complex systems.

What are the two broad types of projects supported by SMOR?

The opportunity supports (1) general-purpose methodological innovation (foundational methods intended to apply across multiple industries and settings) and (2) application-grounded research (work rooted in a specific domain where novelty comes from specialized analytical or computational tools that existing methods cannot address well).

What is meant by general-purpose methodological innovation?

General-purpose methodological innovation refers to foundational operations research contributions that can be reused across many contexts. Examples mentioned include new directions in optimization, stochastic modeling, and decision theory and game theory.

What is meant by application-grounded research in this program?

Application-grounded research is rooted in a specific practical domain (for example, healthcare operations or supply chains). The proposal is expected to develop specialized analytical or computational tools for that domain, particularly where existing methods are not adequate.

If a proposal is highly application-driven, is it still expected to contribute new methods?

Yes. Even for highly application-driven projects, the expectation is that the work contributes new methodology rather than only applying known tools.

Do proposals need to be motivated by a specific application area?

Yes. A key requirement is that proposals must be motivated by an application area that matters to the SMOR program.

What application domains are highlighted as areas of interest?

The solicitation highlights domains including supply chains and logistics, risk management, healthcare operations, environmental and sustainability-related operations, energy production and distribution, mechanism design and incentives, production planning, maintenance, quality control, and national security-related operational problems requiring robust decision-making.

What kinds of topics in supply chains and logistics are included?

Examples listed include network design, inventory positioning, distribution planning, and resilience to disruption.

What kinds of topics in healthcare operations are included?

Examples listed include patient flow, staffing, capacity planning, and scheduling.

What kinds of topics in energy systems are included?

The program includes planning and operating energy systems under uncertainty and constraints.

What does the program mean by mechanism design and incentives?

Mechanism design and incentives refers to research on how rules, pricing, contracts, or platforms can be structured so that self-interested actors make choices that improve overall system performance.

How does SMOR address uncertainty in operational decisions?

The program emphasizes approaches such as stochastic modeling and other methods that explicitly handle uncertainty and randomness (for example, demand variability or equipment failures) in the design and operation of service and manufacturing systems.

Is data-driven operations research specifically encouraged?

Yes. A stated emphasis is the growing role of data in operations research and decision-making, with interest in methods that incorporate richer and more diverse data sources.

What kinds of data sources are mentioned?

Examples include high-volume sensor data, transactional records, real-time operational telemetry, and other complex datasets.

What is the goal of incorporating richer data sources into operations research methods?

The intent is to encourage methods that move beyond traditional stylized assumptions by using data to improve models, calibrate uncertainty, and enable more adaptive and effective decisions while maintaining rigorous mathematical and computational foundations.

Who is eligible to apply for this grant opportunity?

Eligibility is listed as unrestricted, meaning the program is open to any applicant type as long as they meet NSF requirements and any specific eligibility clarifications in the full solicitation.

Are there any additional eligibility clarifications applicants should watch for?

Yes. While eligibility is listed as unrestricted in the provided details, applicants are expected to meet NSF requirements and any specific eligibility clarifications that may appear in the full solicitation.

When was this opportunity posted?

The opportunity was created and posted on Nov 4, 2015.

What was the closing date for this opportunity?

The closing date listed is Feb 16, 2016.

Is the award ceiling provided in the listing details?

No. The provided details do not specify an award ceiling.

Does the listing provide the expected number of awards?

No. The provided details do not specify the expected number of awards.

Where would applicants typically find details like award size or number of awards?

Those specifics would typically be confirmed in the full NSF program announcement or related documentation.

What is the overall focus of SMOR-funded research?

The overall focus is improving decision-making for complex service and manufacturing systems through advances in mathematical modeling, analytics, and algorithms that address real constraints, uncertainty, and competing objectives.