Case Studies > 97% Hit Rate with Automation: Martin Marietta’s Story

97% Hit Rate with Automation: Martin Marietta’s Story

Customer Company Size

Large Corporate

Region

America

Country

United States

Product

Cash Application Cloud
Credit Cloud

Tech Stack

Artificial Intelligence
ERP Integration
Dashboards

Implementation Scale

Enterprise-wide Deployment

Impact Metrics

Cost Savings
Productivity Improvements
Customer Satisfaction

Technology Category

Analytics & Modeling - Predictive Analytics
Functional Applications - Enterprise Resource Planning Systems (ERP)
Application Infrastructure & Middleware - Data Exchange & Integration

Applicable Industries

Metals
Mining

Applicable Functions

Business Operation
Quality Assurance

Use Cases

Process Control & Optimization

Services

Software Design & Engineering Services
System Integration
Cloud Planning, Design & Implementation Services

About The Customer

Martin Marietta is a leading supplier of aggregates and heavy building materials, based in the United States. The company operates a vast network spanning 27 states and over 400 operations, including locations in Canada and the Bahamas. Established as a major player in the metals and mining industry, Martin Marietta has a strong focus on integrating acquisitions and maintaining a vision of becoming a world-class organization. In 2015, they set up a shared service center to centralize their operations, particularly in the areas of credit and collections, cash application, and credit references. The company sought to standardize processes, improve efficiency, and reduce operational expenditures, all while increasing visibility and configurability in their systems.

The Challenge

An industrial giant in metals and mining, Martin Marietta faced significant challenges in its operations overall. The processes they had in place, such as keying in information, were highly manual and took up a lot of the analysts’ time. The company needed a way to repurpose this effort into more productive tasks like analysis and value optimization. Processes also lacked standardization which needed to be addressed and they wanted a method to increase visibility into metrics to help track where time was being wasted. They were using three lockboxes and 1 EDI file along with bank keying in.\n\nCredit\n\nDecentralized Processes\nEach division followed its own processes and this led to confusion among teams. It also led to inconsistencies in information gathering for shared customers.\n\nManual Work\nProcesses were completely manual based on forms that needed to be filled in by credit managers from the master data. Record-Keeping was exclusively hands-on and analysts also made customer calls and set-ups in person, which took up a lot of time.\n\nLack of Visibility\nGetting audits done for credit analysis was a huge task because the information was inaccurate. A lack of transparency also caused inconsistencies in credit calculations.\n\nCash Application\n\nMis-Keying of Information\nInformation wasn't being accurately fed into the system by banks, causing frustration and misapplication on the analysts’ side. This called unnecessary delays and errors.\n\nNon-Standardized Practices\nCustomers paid with inconsistent formats like tickets and statements that caused issues because information regarding invoices and remittance had to be aggregated manually from these documents. This consumed a lot of the analysts’ bandwidth so identification of payments also became a hectic task.

The Solution

Martin Marietta collaborated with HighRadius to address their operational challenges. HighRadius provided two key solutions: Credit Cloud and Cash Application Cloud.\n\nCredit Cloud\n\nLogging Tasks & Follow-Up\nDetailed tracking of correspondence history and follow-ups helped create a streamlined process that increased transparency and a smoother workflow.\n\nStandardized Processes\nThe solution brought standardization into their operations with the help of configurable scoring models and workflows. It also automatically extracted credit data and made accurate predictions on customers’ payments.\n\nIncreased Visibility and Tracking\nThe use of dashboards and worklists allowed analysts and execs to access KPIs and track team productivity easily. The solution also helped give them a better understanding of which customers were higher risk, and helped set credit limits.\n\nCash Application Cloud\n\nAuto-Aggregation of Remittance Information\nThe solution was able to automatically pull data from the different remittance sources and formats they received. This feature helped them tackle the issue of inconsistent payment formats and was able to automate data extraction. It also saved time and helped increase accuracy by keeping errors to a minimum.\n\nImproved Efficiency\nThe solution helped Martin Marietta boost productivity by reallocating analysts and automating their workload. This ensured that analysts could be shifted to higher priority tasks instead.\n\nEasy ERP Integration\nThe solution could easily integrate with different ERPs and made it easier to carry out data migration and reduce time wasted on tech support. It was also scalable and had the capacity to accommodate improvements.

Operational Impact

With shared services in their operations, Martin Marietta was able to get increased visibility into their workflow. They were able to reduce operational expenditures on lockbox and they increased analyst productivity with automation. This also helped them create standardized practices with prioritization and centralization of data. It helped them by allowing analysts to spend less time on aggregation and data collecting and shifted them into the analysis of data.
The solution brought standardization into their operations with the help of configurable scoring models and workflows. It also automatically extracted credit data and made accurate predictions on customers’ payments.
The use of dashboards and worklists allowed analysts and execs to access KPIs and track team productivity easily. The solution also helped give them a better understanding of which customers were higher risk, and helped set credit limits.

Quantitative Benefit