INTRODUCTION

Gökçe Esenduran
Associate Professor of Management
Purdue University
Email: [email protected]

Ruben Lobel
Product Data Scientist Lead
Waymo
Email: [email protected]

Gilvan Souza
Distinguished Professor and Haslam Chair in Business
The University of Tennessee
Email: [email protected]

A sustainable operation is one that can be carried out forever. To that end, a sustainable operation in a for-profit organization maximizes the triple bottom line of economic, environmental and social outcomes.  In other words, it takes into account the 3P’s:

  • Profit: The operation has to be profitable, by definition.
  • People: The operation has to satisfy its stakeholders: shareholders, employees, customers, suppliers, governments, and communities where it operates.
  • Planet: Inputs can be sourced forever, the transformation process is non-polluting, and the outputs can be repaired, reused, recycled, composted, and/or remanufactured.

The INFORMS community in operations management, in particular the Manufacturing & Service Operations Management (M&SOM) Society, has a rich history of research in sustainability. For the purposes of this collection, we categorize the research into five separate themes: (i) energy and emissions reduction, (ii) responsible sourcing, (iii) closed-loop supply chains and the circular economy, (iv) social sustainability & poverty alleviation, and (v) transportation and servicizing. We briefly comment on each now.

The order of research papers for each of the sections is in chronological order, starting from oldest to newest. At the end of each section, we also provide some relevant teaching cases and videos.

Energy Efficiency, Renewable Energy, and Emissions Reduction: Research has analyzed the effectiveness of different practices to reduce energy consumption, both residential and commercial, such as demand response programs, time of use electricity pricing, and environmental management systems, among others.  There is also significant research in renewable energy systems, in particular optimal investments in renewable energy capacity by utilities (utility solar, wind), firms and consumers (e.g., rooftop solar) and the impact of different government policies such as net metering, feed-in tariffs, and tax credits on such investments. The optimization tools to analyze such investments must take into account (random) supply intermittency, which makes the problem more challenging, particularly when coupled with other investments in conventional generation, such as natural gas.  Some studies also consider energy storage, such as batteries, and pumped hydro.  This is a significantly more challenging problem, as it also requires an optimal operating policy for the battery, which transforms the one-time investment problem into a dynamic problem.  Some research finds optimal operating and commitment policies for renewable and storage resources. Renewable resources may need to be curtailed when there is excess production, and storage resources help fill this gap between supply and demand.

The M&SOM community is also actively engaged in research in emissions reduction, including carbon footprint reduction.  Research has analyzed the impact of emissions regulation, such as cap and trade and carbon tax, on firms, suppliers, and markets where such firms operate. For firms, research also studies the economics and effectiveness of different carbon footprint reductions available, such as carbon offsets, and internal reductions. 

The table below provides a list of the research papers in this section:

Reference

Main Topic

Main Methodology

İşlegen and Reichelstein (2010)

Carbon footprint, carbon capture and storage, utilities

Empirical research

Kim and Powel (2011), Wu and Kapuscinski (2013), Zhou et al. (2016)

Energy storage, renewable energy, operation

Optimization

Kroes et al. (2012)

Emissions regulations, cap and trade

Econometrics / empirical research

Jira and Toffel (2013), Blanco (2022)

Carbon footprint, supply chain

Econometrics / empirical research

Caro et al. (2013), Gopalakrishnan et al. (2020)

Carbon footprint, supply chain

Game theory

Muthulingam et al. (2013), Jeong and Lee (2022)

Energy efficiency, adoption, firm level

Econometrics / empirical research

Wang et al. (2013), Cohen et al. (2015), Drake (2018), Chemama et al. (2018)

Green technologies, capacity investment, policy incentives

Optimization, game theory

Qi et al. (2015)

Energy storage, capacity investment

Optimization

Hu et al. (2015), Alizamir et al. (2016), Aflaki and Netessine (2017) , Al-Gwaiz et al. (2017), Kök et al. (2018), Kök et al. (2020), Babich et al. (2020), Sunar and Swaminathan (2021) , Singh and Scheller-Wolf (2022)

Renewable energy, capacity investment, policy incentives

Optimization / game theory

Sunar and Plambeck (2016), Anand and Giraud-Carrier (2020)

Carbon footprint, emissions regulations

Game theory

Guajardo (2018)

Renewable energy, operational performance, servicizing

Econometrics / empirical research

Sunar and Birge (2019)

Renewable energy, electricity markets, utilities

Game theory

Fu et al. (2019)

Pollution, environmental regulations, firm level

Econometrics / empirical research

Murali et al. (2019), Han et al. (2022)

Green products, environmental regulations, firm level

Game theory

Gao and Souza (2022)

Carbon footprint, consumer behavior

Game theory

Fan et al. (2022)

Carbon footprint, capacity investment

Game theory

Trivella et al. (2022)

Renewable energy, procurement, firm level

Optimization

Agrawal and Yücel (2022)

Demand response, utilities

Game theory

Sošić (2021)

Capacity investment, salt water, emissions

Optimization


Responsible Sourcing
:  Generally speaking, research on responsible sourcing concerns how to ensure or incentivize that suppliers behave in an environmentally and/or socially responsible manner.  Such efforts include vertical integration, auditing, contingency payments, supplier certification, offer of more business to responsible suppliers, improving visibility in the supply chain (including revealing the supplier list), disclosing operations to investors, and so forth.  Some of these efforts have competitive implications, and the literature studies such trade-offs.  A significant stream of research analyzes the key role of auditing suppliers, and what are efficient and effective auditing mechanisms.  Examples here include how to avoid a collusion between auditor and supplier, prioritizing “central” suppliers for auditing (when there are too many), and cooperation among competing firms for auditing suppliers (joint and shared audits), among others.  A stream of research analyzes the impact of providing information related to social responsibility in the supply chain on consumer demand.

The table below provides a list of the research papers in this section:

Reference

Main Topic

Main Methodology

Corbett and DeCroix (2001), Guo et al. (2016), de Zegher et al. (2019)

Contracts for improved sustainability

Game theory

Plambeck and Taylor (2016), Chen and Lee (2017), Chen et al. (2020), Chen, Yao and Zhu (2020)

Supplier audits

Game theory

Caro et al. (2018), Fang and Cho (2020), Ha et al. (2022)

Supplier audits, cooperation among firms

Game theory

Kraft et al. (2018), Buell and Kalkanci (2020)

Supply chain visibility, consumer behavior

Behavioral experiments

Cho et al. (2019)

Supply chain visibility, child labor, incentives

Game theory

Kalkanci and Plambeck (2020a), Kalkanci and Plambeck (2020b), Kraft et al. (2020), Lu and Tomlin (2021)

Supply chain visibility / disclosure

Game theory

Orsdemir et al. (2019)

Vertical integration

Game theory

Zhang et al. (2021), Huang et al. (2022), Feng et al. (2022)

Managing sustainability across supply chain tiers

Game theory

Alptekinoğlu and Örsdemir (2022) , Long and Nasiry (2022)

Sustainability, fashion industry

Game theory


Closed-loop supply chains and the circular economy
: A “regular” supply chain has forward flows of materials and information, from suppliers to manufacturers and then to retailers. A closed-loop supply chain (CLSC) has, in addition to these forward flows, reverse flows of products post-consumer use, from consumers to collection points, retailers, manufacturers and/or suppliers, for reuse, remanufacturing (refurbishing), recycling, composting, and/or disposal.  These reverse flows might be motivated for regulatory reasons, as is the case with take-back legislation for electronics and pharmaceuticals, or from profitability reasons–a manufacturer might find it profitable to remanufacture (or refurbish) a product and re-market again as a remanufactured (or refurbished) product, or re-use it for warranty claims.  Research in CLSCs is fairly well developed, in particular in the area of remanufacturing.  Introducing a remanufactured product to the market has cannibalization and market expansion effects, and research has analyzed this key trade-off, including empirical insights into how consumers perceive both types of products.  Research has also studied the general design of a CLSC to maximize profitability. There is a significant body of research on inventory management systems for managing the forward and reverse flows, especially because with remanufacturing there is uncertainty not only in market demand, but also supply of used products, particularly in industries such as automotive parts where competition for “cores” (as used products are often called) is intense.  There is also a large body of research in take-back legislation, which requires manufacturers to ensure that used products are responsibly recycled; this type of legislation is common for electronics and pharmaceuticals. Such research has addressed how firms respond (in terms of product design, pricing, investments, cooperation with other firms, etc.) to different types of legislation, including implementation details such as how to collect used products, and how to organize the recycling system.  There is also significant research into consumer returns (e.g., how to reduce consumer returns, what are “good” product return policies, and so forth), but this is not included here unless there is an explicit link to sustainability, for example, the research addresses the reuse or remanufacturing of product returns.  A circular economy usually refers to the combination of a closed-loop supply chain, a sustainably designed product (that makes product recovery attractive), and a business model. 

The table below provides a list of the research papers in this section:

References

Main Topic

Main Methodology

Van der Laan et al. (1999), Toktay et al. (2000), DeCroix (2006), Zhou et al. (2011) ,  Calmon and Graves (2017), Calmon et al. (2021)

Inventory management

Optimization

Plambeck and Wang (2009), Gui et al. (2016), Zhang and Zhang (2018), Gui et al. (2018), Esenduran et al. (2019), Huang et al. (2019), Alev et al. (2020), Shi et al. (2021) ,  Alev et al. (2022), Jin et al. (2022)

Regulations / Extended producer responsibility (EPR)

Game theory

Debo et al.  (2005), Nadar et al. (2021)

Remanufacturing

Market dynamics: segmentation, factors, value

Optimization

Subramanian and Subramanyam (2012)

Remanufacturing

Market dynamics: segmentation, factors, value

Econometrics / empirical research

Agrawal et al. (2015)

Remanufacturing

Market dynamics: segmentation, factors, value

Behavioral experiments

Ferrer and Swaminathan (2006), Oraiopoulos et al. (2012), Agrawal et al. (2021)

Remanufacturing

Economics and Strategies

Game theory

Guide et al. (2003), Ray et al. (2005), Geyer et al. (2007), Pinçe et al. (2016)

Remanufacturing

Economics and Strategies

Optimization

Savaskan et al. (2004), Savaskan et al. (2006)

Supply chain design

Game theory

Guide et al. (2006), Demeester et al. (2013)

Supply chain design

Optimization

Lee (2012), Ata et al. (2012)

By-product synergy/Waste-to-energy operations

Optimization

Dhanorkar (2019) , Dhanorkar et al. (2021)

Environmental benefits of C2C closed-loop supply chains/ Online B2B markets for product reuse

Econometrics / empirical research

Guide and Van Wassenhove (2009)

Evolution of closed-loop supply chain research

Literature review


Social responsibility, poverty alleviation
: Here is an area that has significant potential for research, considering the UN 17 Sustainable Development Goals, and the fact that most of the OM literature so far focuses on operations in developed countries, where the infrastructure is good, and standards for working conditions in factories are high. There is research here on different ways that operations can improve the lives of people in developing countries: improving farmer welfare through improved access to water, improving access to electricity to the poor and to those in remote areas, empowering women and the poor through new business models, improving factory conditions, and reducing slavery in supply chains, among others.

The table below provides a list of the research papers in this section:

Reference

Main Topic

Main Methodology

Gao (2020)

Firms' charitable donations

Game theory

Chu et al. (2018)

Firms' charitable donations

Optimization

Liu et al. (2019)

Operations management in developing countries

Econometrics / empirical research

Acimovic et al. (2020)

Operations management in developing countries

Field experiment

Liao et al.  (2019) ; Chen et al. (2020)

Operations management in developing countries

Game theory

Dawande et al.  (2013) , Uppari et al. (2019), Calmon et al. (2022)

Operations management in developing countries

Optimization

Cousins et al. (2020)

Regulations

Econometrics / empirical research

Yu et al. (2018)

Regulations

Game theory

Lee and Tang (2018)

Socially and environmentally responsible value chains

Thought piece / conceptual

Plambeck and Ramdas (2020)

Empowering women in developing countries

Thought piece / conceptual


Transportation and servicizing
:  A significant body of research in OM concerns the design of the charging station (and/or battery swapping) infrastructure for electric vehicles, as well as different business models for firms in the electric vehicle value chain, including utilities.  Research also studies the proper role of government in the on-going transition to electric vehicles, including different types of incentives (such as subsidies) to different value chain actors. There is some emerging research on autonomous vehicles.  Finally, there is research on the environmental and welfare impacts of servicizing, leasing, and the sharing economy, which plays a large role in transportation. (We do not include here research that strictly addresses operational/optimization aspects of the sharing economy, such as better algorithms for matching supply and demand, without discussing their environmental and/or social welfare impacts).       

The table below provides a list of the research papers in this section:

Reference

Main Topic

Main Methodology

Baron et al. (2021)

Autonomous vehicles

Optimization / simulation

Mirzaeian et al. (2020)

Autonomous vehicles

Queueing model

Carlsson and Song (2017)

Drone delivery logistics

Optimization

He et al.  (2023)

Electric vehicles (adoption, battery, charging, environmental impact, regulations, sharing)

Econometrics / empirical research

Lim et al. (2014), Avci et al. (2015), Zhang and Dou (2021), Owen et al.  (2022)

Electric vehicles (adoption, battery, charging, environmental impact, regulations, sharing)

Game theory

Mak et al.  (2013), He et al.  (2020), Qi et al. (2022), Qi et al. (2023), Froger et al. (2021)

Electric vehicles (adoption, battery, charging, environmental impact, regulations, sharing)

Optimization

Pelletier et al. (2016)

Electric vehicles (adoption, battery, charging, environmental impact, regulations, sharing)

Survey

Bektaş and Laporte (2011), Belavina et al.(2016)

Environmental impact of deliveries/logistics/routing

Game theory / optimization

Kabra et al.  (2019)

Shared mobility/servicizing/leasing  operations

Econometrics / empirical research

Agrawal et al. (2012), Bellos et al. (2017), Agrawal and Bellos (2017), Benjaafar et al. (2019), Örsdemir et al. (2019)

Shared mobility/servicizing/leasing  operations

Game Theory

Qi et al. (2018)

Shared mobility/servicizing/leasing  operations

Optimization / case Study

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