Industrial Analytics Engineer/Lead

Job Title: Industrial Analytics Engineer/Lead

Location: Remote

Role Overview

The Industrial Engineering Analytics Engineer will lead the development and application of

advanced analytical models to drive manufacturing efficiency, capacity planning, and cost

optimization. This role is responsible for building and managing integrated IE models that

connect capacity, labor, material flow, PFEP, and cost (COGS) to enable data-driven decisionmaking

across factory and site operations. The ideal candidate will combine strong industrial

engineering fundamentals with advanced analytics, simulation, business AI-driven systems to support large-scale manufacturing environments.

Key Responsibilities

• Develop and own integrated IE models that connect capacity, labor, material flow, PFEP, and

cost (COGS) to support factory planning and operations

• Build and maintain capacity models (target vs. forecast vs. gated capacity), incorporating cycle

time, OEE, yield losses, and bottleneck analysis

• Develop labor models to optimize headcount, utilization, and labor cost (LOH) across

production systems

• Create and evaluate business cases for capital investments, including ROI, IRR, NPV, and costbenefit

analysis

• Lead COGS modeling, including labor, overhead, scrap, and process-driven cost components

• Develop and track scrap and yield models, quantifying cost impact and identifying

improvement opportunities

• Design and maintain OEE models (availability, performance, quality) to drive operational

efficiency and continuous improvement

• Perform buffer and WIP analysis to optimize inline and interline storage, reduce bottlenecks,

and stabilize production flow

• Develop process flow diagrams (PFDs) and value stream maps to represent manufacturing

systems and identify inefficiencies

• Integrate PFEP (Plan for Every Part) data into models to optimize material flow, storage, and

line-side delivery strategies

• Support factory layout, site planning, and material flow decisions through data-driven insights

and modeling

• Perform scenario analysis and sensitivity studies to evaluate production strategies and capacity

expansion plans

• Utilize and/or develop factory simulation models (e.g., FlexSim, AnyLogic, Simio) to analyze

throughput, bottlenecks, and system performance

• Support factory ramp-up, installation, and operational readiness through model validation and

performance tracking

• Collaborate with cross-functional teams (Manufacturing, Operations, Supply Chain, Finance,

Engineering) to align models with real-world constraints and business needs

• Translate complex analytical outputs into clear, executive-level insights and recommendations

• Collaborate with MES and Controls teams to integrate shop-floor data with IE models, ensuring

accurate OEE measurement and enabling real-time, scalable dashboards for operational visibility

and executive decision-making

AI & Data Systems

• Introduce and implement AI-driven tools and platforms to enhance industrial engineering

analytics and decision-making

• Design and manage scalable data models and data architecture for IE, capacity, labor, PFEP,

and cost analytics

• Develop standardized systems, frameworks, and governance for data modeling, analytics, and

reporting

• Automate data collection, validation, and reporting pipelines using AI and advanced analytics

tools

• Enable predictive analytics and intelligent decision-making for capacity, throughput, and cost

optimization

• Establish best practices for data quality, model standardization, and system integration across

the organization









Thanks & Regards,

Tushar Chauhan

(xxxxxxxxxxxxxxxExt. 1079

xxxxxxxxxxxxxxx