Editor’s Note: The SCM thesis Do Companies’ Environmental Commitments Differ According to Their Supply Chain Position? was authored by Julia Fernandez del Valle and Samara Vilar da Costa and supervised by Dr. David Correll (dcorrell@mit.edu). For more information on the research, please contact the thesis supervisor.

Globalization has led to the development of complex supply chains for companies to meet their profitability goals and expand their markets. However, the environmental costs of supporting such an infrastructure have highlighted the importance of sustainability practices, particularly those aimed at addressing environmental concerns such as climate change.

To address environmental issues, many companies have adopted objectives related to reducing harmful emissions. One of the most popular examples of sustainability commitments made by companies is establishing a “net-zero” emissions goal. To achieve net-zero emissions, companies must be able to reduce their Scope 3 emissions, which encompass all emissions associated with a company’s activities—including its suppliers and customers upstream and downstream in the supply chain.

Our research explored how, depending on the company type and position in the supply chain, companies may experience pressure from different stakeholders. Studying the relevant players influencing the adoption of net-zero goals and Scope 3 reduction initiatives reveals interesting findings.

Unraveling the influence of stakeholders

The data we collected from over 1,700 participants showed that the source of pressure to create net-zero goals differs by company type and position in the supply chain. While all types of companies are influenced by investors, we observed that publicly traded companies experience higher levels of pressure compared to private entities, evidenced by the higher likelihood of adoption of net-zero goals among public companies. This was backed up by interviews with supply chain executives.

Downstream companies are also more likely to have net-zero goals—and with more aggressive timelines. Regarding which stakeholders were applying pressure, oddly enough, higher pressure from industry associations correlated with lower adoption of net-zero goals among downstream players. This finding was unexpected—it may be due to industry associations setting lower standards to meet the needs of the lowest common denominator in the group, or perhaps due to a lack of clarity on how to achieve these goals. The absence of net-zero goals, however, does not necessarily translate into a lack of commitment to environmental sustainability, as there are other objectives that can be set. 

The influence equation: exploring the paradox

Having explored stakeholder pressure for sustainability, we studied whether companies with net-zero goals have near-term initiatives to lower their Scope 3 emissions and meet their goals. We expected to see the same levels of commitment across the different company types, as without initiatives in place, the achievement of established net-zero goals is not realistic.

Similar to net-zero goal setting, public companies are more likely to have current or near-term initiatives compared to private companies. However, we saw that when it came to the implementation plans for Scope 3 reduction initiatives, companies are generally unprepared to meet their targets.

Surprisingly, the same sources of pressure that influence companies to set net-zero goals are not influencing companies to create initiatives to reduce Scope 3 emissions. These results are concerning as they put into question the validity of net-zero goals and the motivations for stakeholders to pressure for supply chain sustainability. Our executive interviews uncovered some possibilities for this inaction. Is the complexity and absence of proper standardization for measuring Scope 3 emissions to blame for the lack of meaningful initiatives? Or are companies simply using net-zero goals as a method to build social license to operate?

Supply Chain Management Review

Every year, approximately 80 students in the MIT Center for Transportation & Logistics’s (MIT CTL) Master of Supply Chain Management (SCM) program complete approximately 45 one-year research projects.

These students are early-career business professionals from multiple countries, with two to 10 years of experience in the industry. Most of the research projects are chosen, sponsored by, and carried out in collaboration with multinational corporations. Joint teams that include MIT SCM students and MIT CTL faculty work on real-world problems. In this series, they summarize a selection of the latest SCM research.

Puzzle pieces and missing links

Diving deeper, we identified the drivers of the inequity behind this inequity: inaccessible transportation, gaps in health insurance coverage, and vaccine skepticism among some in the community. These factors created a barrier of disparity that the uninsured, the remote, and the socioeconomically challenged found hard to scale. It became evident that an effective solution needed more than a uniform distribution of vaccines; it called for a nuanced approach addressing the complex, interwoven issues contributing to this healthcare puzzle.

Building bridges, one step at a time

So, how did we address this vast challenge? Using data from Cambridge, Massachusetts, we built a model to increase equitable vaccine access and the results were encouraging. Our solution was twofold, tackling immediate barriers while setting the stage for long-term improvements. First, we adjusted the distribution of medical facilities administering vaccines. We ensured these were placed based on population needs, not just equally spread geographically. This strategic move boosted vaccine accessibility in the short run, particularly in marginalized communities.

For the long haul, we propose to focus on the uninsured. We strategized that improving health insurance coverage ensures it would not be a barrier to vaccination. We also amped up transportation options to make access easier for those in remote areas.

By adjusting the density of medical facilities to meet population needs, we identified ways to increase equitable access. This more tailored approach opened opportunities to address specific needs and challenges, moving the needle significantly toward equity.

Our efforts not only measure but also actively enhance vaccine equity. We created a tool that enables decision-makers to address barriers to vaccine access in their regions. It is a roadmap to predict vaccination attainment at the census tract level, ensuring no community is overlooked.

In the fight against COVID-19 and any future pandemic, we must ensure that access to a life-saving vaccine is a universal right, not a privilege. Equity in vaccine distribution is the goalpost we are aiming for.

Supply Chain Management Review

Every year, approximately 80 students in the MIT Center for Transportation & Logistics’s (MIT CTL) Master of Supply Chain Management (SCM) program complete approximately 45 one-year research projects.

These students are early-career business professionals from multiple countries, with two to 10 years of experience in the industry. Most of the research projects are chosen, sponsored by, and carried out in collaboration with multinational corporations. Joint teams that include MIT SCM students and MIT CTL faculty work on real-world problems. In this series, they summarize a selection of the latest SCM research.

Navigating the modern supply chain landscape

In the global supply chain environment, disruptions have become the rule rather than the exception. Challenges are multifaceted, from geopolitical tensions such as the U.S.-China technological trade disputes, to unforeseen events like the Suez Canal blockage by the cargo ship Ever Given. When we embarked on our project with Tempur Sealy International Inc., a key player in the mattress and bedding industry, we recognized their intricate challenges, especially given their deep reliance on a supply chain subject to the geographical availability of polyurethane chemicals. Our objective was clear: to try to enhance resilience in the face of disruptions without compromising cost efficiency.

Initial observations and the need for a holistic approach

Tempur Sealy had already implemented several risk-mitigation measures, including dual- and multi-sourcing chemicals and increasing inventory levels. However, as we delved deeper, it became evident that while these measures addressed immediate concerns, they were predominantly ad hoc and often catered to specific chemicals or events. Tempur Sealy needed a comprehensive solution. The primary challenge was ensuring the uninterrupted availability of all components for their extensive product portfolio. However, there was also a pressing need to quantify the tangible financial benefits resulting from risk mitigation investments, especially when these risk mitigation measures successfully averted disruptions and, thus, no damages materialized.

Our comprehensive approach: Merging resilience analysis with procurement

To address these challenges, we introduced an innovative methodology that combined time-to-survive (TTS) analysis with procurement optimization. This approach involved not only identifying vulnerabilities within the existing supply chain, but also quantifying the costs and benefits of resilience measures. This quantification became instrumental in decision-making processes, especially concerning investments in storage capacity, inventory planning, and refining procurement strategy.

Our findings proved to be very useful. On the one hand, we identified opportunities to reduce the number of suppliers, consolidate procurement volumes and thus reduce costs without significantly reducing resilience. In accordance with our outlined scenario, Tempur Sealy would realize a total cost reduction of 5.5% and a 6% decrease in chemical purchase price while upholding resilience. On the other hand, our methodology pinpointed chemicals with the highest risk of disruption due to low inventory levels and single sourcing. This insight offers the company a valuable guideline for effectively allocating resources to enhance supply chain resilience.

The tools we developed allow the company to easily spot critical vulnerabilities in its supply chain and identify opportunities for cost savings. Furthermore, the organization can employ this tool as a simulation instrument to assess both the financial and operational impacts of various business decisions.

Conclusion and recommendations

A comprehensive and strategic approach is paramount as global supply chain dynamics evolve. Our work with Tempur Sealy not only provided valuable insights for the company but also set a blueprint that could be useful for various other industries. The combination of measuring supply chain resilience using time-to-survive analysis and procurement optimization can be very useful in ensuring resilience and cost-effectiveness in today’s intricate supply chain landscape. This approach enables businesses to justify investments into risk mitigation measures, providing a clear, quantifiable value to these often overlooked yet crucially important efforts.

Supply Chain Management Review

Every year, approximately 80 students in the MIT Center for Transportation & Logistics’s (MIT CTL) Master of Supply Chain Management (SCM) program complete approximately 45 one-year research projects.

These students are early-career business professionals from multiple countries, with two to 10 years of experience in the industry. Most of the research projects are chosen, sponsored by, and carried out in collaboration with multinational corporations. Joint teams that include MIT SCM students and MIT CTL faculty work on real-world problems. In this series, they summarize a selection of the latest SCM research.

Defining a structured impact assessment

The process of identifying and assessing risk is essential in risk management, as it helps decision-makers become aware of potential uncertainties and manage them proactively. Our sponsoring company has a supplier risk management program consisting of two parts. First, they identify the risk levels of their suppliers by using a well-known supply chain risk management software. Second, they derive their own internal estimations of what the risk impact on the business is for each supplier.

Our objective was to find a way to improve the evaluation of risk impact on the business by moving from a subjective evaluation to a structured, objective, transparent framework that the company could adopt.

The main assumption in our framework was to disregard the types of disruption that the business faces. We also disregarded the procurement spend per supplier. We defined the company’s view of risk impact as revenue loss. The method we sought to build was to establish the connections among different data sources that contain information around the bill of materials (BOM) for each SKU, revenue for each SKU, and supplier for each BOM. By creating a network of connections between components, products, and suppliers, the link between the criticality of a supplier and revenue became clear.

The reason for this approach was that, for many large companies, evaluating all suppliers through monitoring and implementing action plans is simply not viable because resources are limited. Therefore, segmenting suppliers based on their revenue impact as a starting point in risk management helps establish where a business’s supply chain is critical.

Qualitative versus quantitative assessments of risk impact

After creating a new report that combined the different data sources mentioned above, we wanted to compare the difference between the risk impact that we established and the company’s qualitative approach. After all, there is no right or wrong approach.

Our comparison yielded two key insights:

1. Procurement spending is not correlated with the revenue or profits of the company.
2. Part standardization, while a good sourcing strategy to leverage volume buy and lower product cost, increases business risk impact.

When business risk impact is measured qualitatively, there seems to be a tendency to associate procurement spending with the revenue or profits of the company. The company estimated that the total suppliers with a high-risk impact represent 73% of the total spend, while our method shows that true high-risk impact suppliers represent only 28% of the total spend. We also found that the company adopts part standardization as part of its sourcing strategy. Some of these parts are from small, specialized suppliers, which, because of their low spend in relation to the total procurement spend, were not labeled by the company as high-risk impact suppliers.

These key insights led the company to discover that the blind spots of small and specialized suppliers with low procurement spending that they had assessed as low-risk-impact turned out to affect 34% to 86% of the total revenue. The risk mitigation actions they had invested in were also minimal, which means if a disruption does take place, the consequences will be severe.

Our study showed the extent to which risk management can be affected by our individual biases, especially when it comes to evaluating the risk impact. Therefore, designing a structured impact assessment will help minimize the blind spots in supply chain risk management.

Supply Chain Management Review

Every year, approximately 80 students in the MIT Center for Transportation & Logistics’s (MIT CTL) Master of Supply Chain Management (SCM) program complete approximately 45 one-year research projects.

These students are early-career business professionals from multiple countries, with two to 10 years of experience in the industry. Most of the research projects are chosen, sponsored by, and carried out in collaboration with multinational corporations. Joint teams that include MIT SCM students and MIT CTL faculty work on real-world problems. In this series, they summarize a selection of the latest SCM research.

An overall methodology to redesign networks

Our problems of identifying demand-dense urban clusters and allocating iDCs to high impact locations was addressed through a hybrid approach.

To identify urban areas, we analyzed demand distribution at the ZIP code level and refined three different clustering algorithms to accommodate our case, ultimately proceeding with a revised k-means solution. The result of clustering was a set of potential iDC locations serving as the discrete candidates in the facility location models in the final step.

We then explored our cost data sets and proposed an imputation method combining k nearest neighbors (KNN) and linear regression to fill missing transportation cost data. KNN was primarily used in supplier-to-facility legs where we did not know exact locations of suppliers, while linear regression was used for the facility-to-customer legs to find correlation between existing costs and distances.

Lastly, we formulated the facility location models. We developed a multi-commodity cost minimization model as our baseline, which combined p-median and set covering building blocks. The objective comprised of transportation costs of both middle and last mile. Building on that model, developed a profit maximization model. The distinction from the former model is the addition of potential revenues of value-creation services, so total profit (revenues less transportation costs) became the new objective. To capture uncertainties, we proposed a stochastic optimization model incorporating best, average, and worst scenarios of revenues.

Results and conclusions

In identifying urban areas, we decided on the result of refined k-means algorithm due to its good interpretability and categorization in the dataset.

By running the two facility location models, the cost-based model yields an output reflective of cost to deliver, while the profit-based model produces an interesting output reflecting high revenue-generating regions. We observe shifts of iDC locations from the southeastern United States to the northeastern and southwestern parts of the country. The iDC number in the Southwest decreases from 59 to 55 out of 100 possible outcomes in the latter model, and the number in the Northeast increases accordingly. The finding is justified by the profitability data (input) of value-creation service, where the Northeast, Southwest, and Northwest are projected to be the most profitable regions.

However, the numbers did not change as much as we expected. A major factor is the relatively few traditional distribution centers in the current network, located in the Southeast. Thus, the profits realized by adding one iDC to the Northeast are not as significant as the costs saved by adding it to the Southeast. That said, it may be compelling to explore how opening additional traditional distribution centers in the Southeast would impact the iDC location results.

Contributing to the industry, our primary finding relates to a profit-driven network design model incorporating both costs and revenues. It creatively uses revenues to dictate location choices. This is a key opportunity for supply chain strategy because it shifts the focus from cost reduction to prioritizing revenue generating factors in decision making. Furthermore, the proposed holistic approach is also generalizable to network designs in other industries.

Supply Chain Management Review

Every year, approximately 80 students in the MIT Center for Transportation & Logistics’s (MIT CTL) Master of Supply Chain Management (SCM) program complete approximately 45 one-year research projects.

These students are early-career business professionals from multiple countries, with two to 10 years of experience in the industry. Most of the research projects are chosen, sponsored by, and carried out in collaboration with multinational corporations. Joint teams that include MIT SCM students and MIT CTL faculty work on real-world problems. In this series, they summarize a selection of the latest SCM research.

Twofold research: qualitative study and quantitative analysis

Our research followed a two-step process. First, we conducted a qualitative study to define the scope, value proposition, and deployment strategy of the control tower. We interviewed subject-matter experts from various procurement processes to understand their existing challenges. Additionally, we investigated industry best practices and aligned with our sponsor on the specific use cases that the procurement control tower would cover, such as spend analytics, contract management, risk management, and supplier management.

In our qualitative research, we proposed the overarching architecture of the procurement control tower and outlined its value proposition to our sponsor. The first crucial step in implementing the control tower is to consolidate data from various data sources into a common data layer. This convergence ensures a single version of truth (SVOT) of data, serving as the foundation for the control tower. Unified data and information retrieval becomes easier and eliminates discrepancies arising from differences in source data. The unified data enables enhanced data analysis from a single source, empowering the procurement control tower to generate valuable business insights.

Second, we performed a quantitative study to develop a prototype (proof of concept) focusing on the spend analytics use case, specifically spend categorization of materials. This use case holds immense value for the sponsor, since approximately $250 million worth of spend data remains unclassified in terms of accurate category or subcategory classification. This lack of accurate classification of spend hampers business analysis based on spending categories, thereby increasing the potential for inaccuracies.

For our quantitative study, we compared multiple machine-learning algorithms, including logistic regression, decision trees, random forest, and XGBoost, using our data to predict the right categorization of materials for unmapped spend. After careful evaluation, we selected Random Forest as the best-performing algorithm in terms of accuracy. To further enhance the algorithm’s predictive power, we preprocessed the data using natural language processing (NLP), a computational technique designed to mimic a human-like understanding of text. The final algorithm achieved a 94% classification accuracy at the category level and 90% at the subcategory level for the unclassified spend data.

Advanced insights and benefits of implementation

Implementing the procurement control tower will provide advanced insights to our sponsor, bringing them end-to-end visibility, enhanced exception management, improved decision-making, improved risk management, cost savings, and more. The categorization of the unmapped spend of materials by the machine-learning algorithm will have a positive impact on our sponsor’s business in various ways. Specifically, it opens up opportunities for supplier renegotiations, improves budgeting accuracy, and reduces the man-hours required for manual categorization. Given that our sponsor adds thousands of new SKUs each year, which translates to tens of thousands of spend data records, our proposed solution becomes highly valuable as it offers an ongoing, periodic categorization of spend data. Our proposed solution has been accepted by our sponsor, and its implementation is underway, marking a significant step toward optimizing procurement processes and achieving competitive advantages in the VUCA market.

Supply Chain Management Review

Every year, approximately 80 students in the MIT Center for Transportation & Logistics’s (MIT CTL) Master of Supply Chain Management (SCM) program complete approximately 45 one-year research projects.

These students are early-career business professionals from multiple countries, with two to 10 years of experience in the industry. Most of the research projects are chosen, sponsored by, and carried out in collaboration with multinational corporations. Joint teams that include MIT SCM students and MIT CTL faculty work on real-world problems. In this series, they summarize a selection of the latest SCM research.