Sustainability Analytics - Lowering Emissions With Operational Efficiency

The intersection of operational efficiency and sustainability is a pressing issue across industries seeking to modernize infrastructure while simultaneously addressing environmental concerns. A key challenge in the telecommunications sector is aging legacy equipment, like D4 Channel Banks, that draw significant electricity demand for a dwindling customer base. There is an opportunity to accelerate the decommissioning of these devices, and thereby lower electricity demand, by consolidating underutilized equipment without sacrificing performance or investing significant capital. This study introduces a robust optimization framework using integer linear programming to navigate the complex trade-offs between maintaining operational integrity and decommissioning excess physical capacity at a representative Verizon central office. This study adopted this technical approach because it is compatible with the binary nature of decommissioning decisions, under financial and practical constraints. Employing the Gurobi optimizer within a Python environment, the model's discrete optimization capabilities were essential in evaluating the decision to retire or maintain individual components of a network infrastructure. The findings illustrate a compelling pathway to diminish the footprint of a representative central office's D4 Channel Banks by up to 40.8%, translating into annual operational cost savings ranging between $16,000 and $41,000. This reduction is primarily attributed to the decreased electricity demand and consequent lowering of CO2e emissions by 22,832 tons, underpinning the potential of such optimization strategies to harmonize the pursuit of operational excellence with environmental stewardship. Through the lens of decommissioning underutilized legacy equipment, this study underscores the strategic imperative of leveraging analytics to integrate sustainability into the operational fabric of the telecommunications industry.

Name thesis:degree_name

Master

Department dc:contributor.department

Sloan School of Management

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2024

Author dc:creator

• Bhakta, Shivam

Advisors dc:contributor.advisor

• Amin, Saurabh
• Knittel, Christopher

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright retained by author(s)

Licence dc:rights.uri

• https://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/156038

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/156038