SIOP Data Mining Analyst

Littelfuse; Matamoros; On-site; MXN 380,000 - 720,000; Full time

Job summary

Littelfuse is seeking a SIOP Data Mining Analyst to transform complex data into actionable insights and support forecasting, inventory, and supplier performance. You will extract data from ERP/MRP systems, validate accuracy, and develop KPI dashboards to improve forecast accuracy and reporting efficiency.

Collaborating with Supply Chain, Operations, Production, Procurement, and Customer Service teams, you will drive data-driven decisions, document KPI definitions, and automate reporting

Qualifications

• Bachelor’s degree in Industrial Engineering, Business Analytics, Data Analytics, Information Systems, or related field.
• 2–3 years of experience in Supply Chain, SIOP, Master Scheduling, or data analytics.
• Experience with ERP/MRP systems and supply chain reporting environments.
• Strong knowledge of SIOP, Master Scheduling, and data analytics methodologies.

Responsibilities

• Perform weekly and monthly data extraction from ERP/MRP and planning systems (SAP, SAGE, Netstock).
• Ensure timely availability of information for SIOP, Master Scheduling, and reporting.
• Validate data accuracy, completeness, and consistency before reporting.
• Update and maintain KPI reports and dashboards via data mining and analysis.
• Monitor KPIs: Forecast Accuracy, On-Time Delivery, Fill Rate, and Inventory projections.
• Analyze trends, exceptions, and drivers to identify root causes of issues.
• Provide data-driven insights to support business decisions and performance improvement.
• Consolidate information from multiple sources into structured formats for reporting.
• Prepare dashboards for weekly and monthly SIOP reviews and management reviews.
• Develop automated reporting solutions to reduce manual processing.
• Utilize Power BI, Excel, SQL for recurring reporting tasks.
• Identify opportunities to improve data collection, validation, and workflows.
• Collaborate with cross-functional teams to validate outputs.
• Communicate findings clearly to technical and non-technical stakeholders.
• Document KPI definitions, data sources, and reporting methodologies.
• Support process standardization and knowledge sharing across the organization.

Skills

Data analytics

KPI reporting

Data visualization

Communication

Stakeholder management

Problem solving

Reporting automation

Attention to detail

Multitasking

Education

Bachelor’s degree in Industrial Engineering or related fields

Tools

SAP

SAGE

Oracle

Power BI

SQL

Excel

Power Query

Power Pivot

Visio

Job description

Littelfuse (NASDAQ: LFUS) is a diversified industrial technology manufacturing company shaping solutions for the safe and efficient transfer of electrical energy. Headquartered in Chicago, Illinois, USA, we serve customers across industrial, transportation, and electronics markets worldwide. With approximately 16,000 employees we design and manufacture innovative technologies that support electrification, energy efficiency, and advanced automation. Our global reach is matched by a culture that empowers innovation and long-term growth. Guided by our principles We Care, We Succeed With Our Customers, and We Own the Outcome, we work collaboratively, think boldly, and deliver solutions that move industries forward.

Job Summary

The SIOP Data Mining Analyst plays a key role in supporting the Sales, Inventory & Operations Planning (SIOP) process by transforming complex data into accurate, timely, and actionable business insights.

This position is responsible for extracting, consolidating, validating, and analyzing information from ERP, MRP, and planning systems to enable effective decision-making across the organization. The role supports the development and maintenance of KPI dashboards, reporting automation, and analytical activities focused on improving forecast accuracy, inventory performance, supplier reliability, customer service levels, and overall supply chain effectiveness.

Working cross-functionally with Supply Chain, Operations, Production, Procurement, Customer Service, and Leadership teams, this position helps ensure data integrity, reporting consistency, and continuous improvement across SIOP and Supply Chain processes.

About your role

• Perform weekly and monthly data extraction from ERP, MRP, and planning systems, including SAP, SAGE, and Netstock.
• Ensure accurate and timely availability of information required for SIOP, Master Scheduling, and Supply Chain reporting.
• Validate data accuracy, completeness, and consistency prior to reporting and distribution.
• Update and maintain KPI reports and dashboards through regular data mining and analysis.
• Monitor and analyze key performance indicators such as Forecast Accuracy, On-Time Delivery, Fill Rate, Past Due Orders, Inventory Projections, Safety Stock Fulfillment, Supplier OTD, and Statistical Forecast Performance.
• Analyze trends, exceptions, and performance drivers to identify potential root causes of operational and supply chain issues.
• Provide data-driven insights and recommendations to support business decisions and performance improvement.
• Consolidate information from multiple data sources into structured, consistent, and actionable formats.
• Prepare and maintain dashboards and reports for weekly and monthly SIOP reviews, Master Scheduling, and Supply Chain management reviews.
• Develop and implement automated reporting solutions to minimize manual data processing and improve reporting efficiency.
• Utilize Power BI, Excel, SQL, and related analytical tools to streamline recurring reporting activities.
• Identify opportunities to improve data collection, validation, reporting workflows, and reporting cycle times.
• Partner with Supply Chain, Operations, Production, Procurement, and Customer Service teams to validate data and reporting outputs.
• Communicate analytical findings and recommendations effectively to both technical and non-technical stakeholders.
• Develop and maintain documentation related to KPI definitions, data sources, reporting standards, methodologies, and SIOP and Supply Chain processes.
• Support process standardization, continuous improvement, and knowledge-sharing initiatives across the organization.

About you

• Bachelor’s degree in Industrial Engineering, Business Analytics, Data Analytics, Information Systems, or a related field preferred.
• 2 to 3 years of experience in Supply Chain, Master Scheduling, SIOP, Demand Planning, Data Analytics, or a related analytical role.
• Experience working with ERP/MRP systems and supply chain reporting environments.
• Working knowledge of Supply Chain Management, SIOP, Master Scheduling, and Data Analytics methodologies.
• Proficiency with ERP/MRP platforms such as SAP, SAGE, Oracle, or similar systems.
• Advanced Microsoft Excel skills, including advanced formulas, Pivot Tables, Power Query, and Power Pivot.
• Experience with Power BI and SQL.
• Experience with data visualization and dashboard development.
• Visio knowledge is preferred.
• Strong analytical and problem-solving capabilities with excellent attention to detail and data accuracy.
• Ability to manage multiple priorities and meet deadlines in a dynamic environment.
• Strong communication and presentation skills, with the ability to translate complex data into clear business insights.
• Strong collaboration and stakeholder management skills.
• Continuous improvement mindset with a focus on reporting automation, process efficiency, and data-driven decision-making.

Littelfuse is an equal opportunity employer committed to empowering every associate to make a difference — everywhere, every day.

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At Littelfuse, we support employee growth and development. Guided by our culture principles, our teams work together to deliver value for customers and make a positive impact in the communities where we operate. We are an equal opportunity employer committed to fostering an inclusive workplace where everyone has the opportunity to grow and contribute. We offer competitive compensation and benefits, performance-based incentives, flexible work arrangements, and development opportunities.