The client in this case was operating in a complex supply chain environment that required data-driven approaches to support decision-making processes in multiple facets of the terminal’s operations. The goal of this engagement was to leverage machine learning and operations research to confidently manage operations, enhance productivity, and optimize processes proactively for better operational efficiency.
Facilitation of Scientific Methods:
Our approach involved the facilitation and application of scientific methods to enhance operational processes, particularly focusing on preventative maintenance. We implemented rigorous operational research techniques, aiming to reduce waste and streamline maintenance activities.
Data-Driven Strategy:
One stream of our approach focused on developing a comprehensive strategy to secure the client’s long-term vision. This involved outlining all necessary projects to achieve the desired future state. The emphasis was on improving operational management through data-driven foundations and the application of artificial intelligence (AI).
Future Initiatives and Talent Search:
Another crucial stream focused on supporting the client in finding the right talent to manage operations and business applications, including digital twin and terminal operations processes. These projects were integral to the client’s future state plan.
Predictive Maintenance Metrics:
We observed an increasing demand for maintenance work over the past five years, with identifying key machines requiring more frequent maintenance. By calculating key metrics such as Mean Time to Repair (MTTR) and Mean Time Between Failures (MTBF), we were able to identify both high-performing and underperforming assets. This information was crucial for the client in creating an optimized asset management plan.
Operational Research Application:
Applying scientific methods allowed us to predict time-to-failure by analyzing seasonality and failure types. This facilitated better planning and resource allocation for maintenance activities, resulting in significant cost savings and increased efficiency.
Enhanced Decision-Making:
Through centralized data and operational automation, we provided the client with tools for self-service decision-making. This empowered the client to make informed decisions quickly and effectively, ultimately leading to improved operational outcomes.
Benchmarking and Performance Improvement:
By helping calculate the metrics to benchmark the client's data against industry-wide Key Performance Indicators (KPIs), we gained a comprehensive understanding of the terminal’s performance. This enabled us to identify areas for improvement and implement strategies to enhance overall efficiency.
