Website:
pranatree.ai
Job details:
Prana Tree is seeking a hands-on Lean/Kaizen AI Leader to lead operational-improvement initiatives that combine proven Lean manufacturing practices with AI-enabled analytics, computer vision, and automation. This person will work directly with client operations teams and Prana Tree’s AI engineers to identify constraints, reduce waste, improve quality, and deliver measurable gains in throughput, cost, and reliability.
The ideal candidate is a seasoned Lean practitioner who has personally led shop-floor and operational-transformation programs—not just trained on Lean concepts—and can translate operational problems into high-value AI use cases.
Role Summary
As the Lean/Kaizen AI Leader, you will assess end-to-end operational processes, map value streams, identify bottlenecks and waste, and lead continuous-improvement programs. You will partner with AI engineers, data scientists, and computer-vision teams to augment Lean methods with AI solutions such as real-time production monitoring, defect detection, bottleneck prediction, root-cause intelligence, work-in-process visibility, and digital performance management.
This is a Chennai-based role with travel to customer sites as required.
Key responsibilities
- Lead Lean, Kaizen, and continuous-improvement engagements across manufacturing, industrial, logistics, and operational environments.
- Conduct current-state and future-state **Value Stream Mapping (VSM)** to identify delays, non-value-added activities, inventory accumulation, flow constraints, and improvement opportunities.
- Assess and improve line balancing, takt alignment, cycle-time variation, capacity utilization, staffing allocation, and production flow.
- Analyze Work-in-Process (WIP) levels and implement practical improvements using pull systems, Just-in-Time (JIT), Kanban, supermarkets, FIFO lanes, and visual-management practices.
- Identify production bottlenecks and constraints using shop-floor observation, process data, time studies, operational metrics, and structured problem-solving methods.
- Lead root-cause analysis using methods such as 5 Whys, Fishbone/Ishikawa analysis, Pareto analysis, A3 thinking, PDCA, DMAIC, 8D, and corrective/preventive action approaches.
- Identify and eliminate waste across the eight Lean waste categories, including defects, overproduction, waiting, non-utilized talent, transportation, inventory, motion, and over-processing.
- Deliver measurable operational outcomes, including improvements in throughput, OEE, first-pass yield, cycle time, lead time, changeover time, defect rates, rework, scrap, downtime, WIP, and cost of poor quality.
- Develop and execute Kaizen events, rapid-improvement workshops, standard-work initiatives, visual-management systems, and performance-management routines.
- Apply Hoshin Kanri principles to connect strategic objectives with site, department, and team-level improvement priorities, KPIs, and execution plans.
- Partner with AI engineers to define and prioritize AI-enabled Lean use cases, including:
- Computer-vision-based quality inspection and defect detection
- Real-time line monitoring and production dashboards
- Bottleneck and constraint detection
- Predictive quality and anomaly detection
- WIP, inventory, and material-flow visibility
- Predictive maintenance and downtime reduction
- Digital work instructions and operator-assistance solutions
- Root-cause intelligence using process, quality, maintenance, and operational data
- Translate shop-floor problems into clearly defined business requirements, process maps, data needs, success metrics, and implementation roadmaps for technical teams.
- Help validate AI solutions in operational settings, ensuring that the solution integrates into daily management systems and produces sustainable business value.
- Coach client teams and internal stakeholders on Lean thinking, structured problem solving, standard work, and data-driven continuous improvement.
- Support client workshops, executive presentations, solution design sessions, pre-sales discussions, and delivery proposals when needed.
- Travel to client sites in India and, where required, internationally.
Required experience and qualifications
- Bachelor’s degree in Industrial Engineering, Mechanical Engineering, Manufacturing Engineering, Operations Management, or a related discipline. Advanced education is a plus.
- 8+ years of practical experience leading Lean, Kaizen, operational excellence, manufacturing excellence, or continuous-improvement programs.
- Demonstrated, hands-on experience conducting Value Stream Mapping and converting findings into implemented future-state improvements.
- Strong experience with production flow, line balancing, capacity analysis, takt time, cycle time, WIP reduction, pull systems, Kanban, JIT, visual management, and standard work.
- Proven experience identifying and resolving manufacturing or operational bottlenecks.
- Strong problem-solving experience using structured methods such as A3, PDCA, 5 Whys, Fishbone analysis, Pareto analysis, DMAIC, 8D, FMEA, or equivalent methodologies.
- Demonstrated record of delivering quantified results in one or more of the following:
- Throughput and productivity improvement
- Reduction in defects, rework, scrap, and cost of poor quality
- Improved first-pass yield and process capability
- Reduced lead time, cycle time, changeover time, or downtime
- WIP and inventory reduction
- Improved OEE, asset utilization, or labor efficiency
- Improved safety, compliance, or operational reliability
- Experience implementing or sustaining Lean operating systems in manufacturing, assembly, process industries, warehousing, logistics, or industrial operations.
- Strong ability to engage with operators, supervisors, plant leaders, quality teams, maintenance teams, engineering teams, and senior executives.
- Excellent communication, facilitation, stakeholder-management, and presentation skills.
- Willingness and ability to travel regularly from Chennai to customer and project sites.
Preferred qualifications
- Experience with **Hoshin Kanri**, strategy deployment, policy deployment, X-matrix development, catchball processes, and KPI cascading.
- Experience with **Jidoka**, autonomous quality controls, stop-the-line practices, error proofing, Poka-Yoke, Andon systems, and escalation workflows.
- Experience with **Muda, Mura, and Muri** analysis and improvement.
- Experience with SMED, TPM, OEE improvement, TQM, Six Sigma, Theory of Constraints, and/or ISO-driven quality systems.
- Lean Six Sigma Green Belt, Black Belt, Master Black Belt, or comparable Lean/Operational Excellence certification.
- Experience deploying digital manufacturing, Industry 4.0, MES, SCADA, IIoT, quality-management, maintenance-management, or production-data solutions.
- Basic understanding of AI, machine learning, computer vision, analytics, data engineering, or industrial automation.
- Experience collaborating with software developers, data scientists, AI engineers, or automation engineers.
- Experience in automotive, electronics, industrial equipment, consumer goods, pharmaceuticals, chemicals, food and beverage, logistics, or other high-volume operational environments.
What success looks like
Within the first 6–12 months, the Lean/Kaizen AI Leader will be expected to:
- Establish a repeatable Lean assessment and AI-opportunity-identification framework for customer engagements.
- Lead high-impact operational assessments and Kaizen initiatives at client sites.
- Build a prioritized pipeline of measurable AI-enabled Lean use cases.
- Deliver documented improvements in throughput, quality, WIP, lead time, productivity, or operational cost.
- Enable AI engineering teams with clear operational context, process requirements, data specifications, baseline metrics, and acceptance criteria.
- Help create differentiated Prana Tree offerings that connect Lean transformation with computer vision, AI, edge intelligence, and digital operations.
Candidate profile
We are looking for a practical improvement leader who is comfortable on the shop floor, credible with plant and operations leaders, rigorous with data, and motivated to use AI as an accelerator—not a replacement—for core Lean discipline. The right candidate can move fluently from observing a process and coaching a team through root-cause analysis to collaborating with AI engineers on a solution that scales measurable operational impact.
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