Steel Manufacturer NIM Group Reduces Scrap Rates and Costs with DataRobot

⇨ NIM Group leveraged DataRobot's AI Platform to automate predictive analytics, significantly reducing scrap rates and costs through optimized machine settings and accurate inventory forecasting.

⇨ The integration of AI-driven decision-making processes at NIM enhances operational efficiency, enabling faster model development and better predictions that support data-driven strategic initiatives.

⇨ By implementing a culture of data literacy and using advanced analytical tools, NIM Group transforms data into actionable insights, ultimately improving quoting, inventory management, and overall competitive advantage in the steel industry.

Operating for more than a century, NIM Group has grown into one of the industry’s most technologically advanced carbon steel providers. And to keep its edge, the company’s data analytics team looks for every opportunity to improve decision-making, from the factory floor to executive offices.

The company lacked the data science capacity to pursue every opportunity to improve decision-making, so it turned to DataRobot’s AI Platform, which automates predictive analytics, allowing the team to take on projects that improve quoting, inventory management, and machine settings to cut scrap rates. Now, NIM’s analytics team builds models weeks faster than before. By predicting optimal machine settings, NIM reduces its scrap rate significantly, driving cost-savings. And by generating more accurate forecasts, NIM prevents lost sales and excess inventory.

NIM aspires to be what many call a data-driven enterprise, which can be defined as an organization that prioritizes the use of data as a fundamental element of its strategic decision-making processes. Defining attributes include:

• Data sets are consolidated into a unified architecture to provide a comprehensive view
• Employs advanced analytical tools and technologies to extract insights from data
• Culture of data literacy
• Decision-making processes are predominantly based on data analysis
• Adopts predictive analytics to forecast future trends and behaviors
• Data governance frameworks are in place to ensure data quality, privacy, and compliance

Recent SAPinsider research suggests a growing emphasis on agility and responsiveness in leveraging data for decision-making. The research report Data Management Strategies revealed that increasing demand to provide fast, real-time data (47%) emerged as the top driving factor for data management improvement initiatives, while almost 44% cited IT budget pressures to keep capital and operating costs under control as a significant driver.

Ben Dubois, Director of Data Analytics, NIM Group, envisioned using data to improve functions such as quoting, inventory management, and even machine settings to improve scrap rates. For the latter, operators have typically relied on operators’ knowledge and experience, resulting in inconsistency and making it challenging to ramp-up new operators.

“We knew there were areas of the company where we could use data to add value, whether it’s improving accuracy in our decision-making, or being able to automate some of our decision-making,” Dubois said.

NIM brought in one of the original founding partners of SAP Datasphere, DataRobot AI Platform to automate predictive analytics and expand the team’s capacity to support the business.

“Other AI products were trying to solve a specific problem,” Dubois said. “What I like about DataRobot AI Platform is the ability to use it in any way you can think up, whether it’s a normal regression-type problem, or forecasting, or for many different use cases.”

In a proof-of-value project, with the help of DataRobot University and DataRobot’s Customer-Facing Data Scientists, Dubois was able to develop an accurate model and begin realizing value quickly. Just as important, he could see how their data affected the results – helping him explain models to business stakeholders.

DataRobot Data Prep helps automate prepping the data while AutoML creates advanced models. APIs then automate productionalizing the results on the shop floor. Then, they easily monitor models in production.

Among several applications, the company applies the platform to predicting machine settings for processing steel. By introducing the correct settings into the machine from the start of the process, they generate less scrap, thus creating significant cost-savings.

With the DataRobot AI Platform application programming interfaces (APIs), they gather information about jobs in real-time, run them through a model, and then feed optimal settings back to the machines. Completing the feedback loop, the company tracks the actual settings used and the corresponding scrap rates to refine the model further.

“By giving operators a starting point, we shorten the trial-and-error period,” Dubois said. “We’re making more accurate predictions over time. Our model will keep getting better and better. As a commodity, steel can range from $500 to $1,000 per ton. By reducing our scrap rates and being more consistent job to job, we can generate significant annual savings for the business.”

AI-derived machine settings deliver two key benefits: less experienced operators ramp-up more quickly and make more informed decisions. Secondly, NIM is able to generate more steel that can be sold rather than end up in a scrap yard.

NIM also applied DataRobot AI Platform to forecast demand for inventory to ensure they stock accordingly. For that, DataRobot Time Series allows them to find relationships between the demand for their steel and the industries they serve, such as agriculture and energy. By generating more accurate forecasts, NIM prevents lost sales and excess inventory, both of which are costly to the business.

“We look at factors within and outside the company so we’ll have the right inventory at the right time,” Dubois said. “One of the cool things about DataRobot and machine learning compared to just a normal time-series regression problem is being able to put a lot more features alongside your time series to make better forecasts.”

NIM has generated business value from DataRobot AI Platform for years and only recently brought on a data scientist to help expand its efforts. By saving time across the process, Michael Green, Data Scientist at NIM Group, can spend more time with stakeholders to understand business problems and features.

“With DataRobot AI Platform, we don’t have to worry about the minutiae of building every detail of one model,” Green said. “Instead of taking weeks or months to go from raw data to a deployed model, now we can do that in less than an hour. I enjoy helping people enjoy their work more, whether they’re saving time, saving money, reducing tedious tasks, or making better decisions. It’s meaningful and fun. DataRobot AI Platform is the best way for me to make an impact that actually matters for the people I work with.”

What this means for SAPinsiders

Share your data management strategies. The focus on enterprise data management is intensifying with the proliferation of 5G, IoT, AI/ML and other transformative technologies. SAP customers are increasingly looking for new data management models for the storage, migration, integration, governance, protection, transformation, and processing of all kinds of data ranging from transactional to analytical. Balancing the risks, compliance needs, and costs of data management in SAP HANA on-premise and on the cloud while also providing reliable, secure data to the organization is increasingly important to the business. We will be releasing the 2025 Data Management Strategies research report in February 2025. Contribute to the research by completing this survey: https://www.research.net/r/DataMgt25.

Streamline SAP data integration to maximize value from DataRobot. Connect SAP Supply Chain data (from sources like SAP S/4HANA, SAP Integrated Business Planning (IBP), and SAP BW) with DataRobot to centralize historical and real-time supply chain data. Unified data gives DataRobot’s models comprehensive insights into demand patterns, inventory levels, supplier performance, and other critical metrics. DataRobot’s data-preparation capabilities help reduce manual work, automatically transforming raw supply chain data into an optimized format for machine learning. This process can accelerate the development of accurate models tailored to specific supply chain challenges.

Lean on DataRobot to help modernize analytics capabilities: DataRobot’s anomaly detection models can help SAP customers identify unusual patterns in supply chain data, such as unexpected spikes in demand, supplier delays, or transportation issues. Early detection of anomalies enables faster corrective actions, minimizing the impact on customer service and production schedules. For real-time use cases, DataRobot integrates with streaming data from IoT sensors and SAP systems to flag operational issues immediately. This setup allows SAP customers to act swiftly on real-time data and prevent potential disruptions in the supply chain​. Further, DataRobot’s scenario analysis capabilities allow customers to model “what-if” situations, such as demand surges, supplier disruptions, or capacity constraints. By simulating these scenarios, SAP customers can develop proactive contingency plans that enhance supply chain resilience.