Hrishikesh S Deshpande
A Strategic Case Study for Indian Manufacturing Leaders
Diagnosis: The Hidden Crisis in Manufacturing Excellence
In the gleaming corridors of modern Indian manufacturing plants, production managers routinely celebrate achieving eighty-five percent Overall Equipment Effectiveness—the hallowed “world-class” benchmark that supposedly signals operational excellence. Yet these same facilities struggle to meet delivery commitments, hemorrhage cash in working capital, and watch helplessly as competitors with lower OEE scores somehow outperform them on the metrics that truly matter to customers and shareholders.
This paradox reveals a fundamental measurement blind spot that has quietly plagued manufacturing operations for decades. While Overall Equipment Effectiveness has deservedly earned its place as manufacturing’s gold standard metric since its development by Seiichi Nakajima in the 1960s, our collective obsession with this single number has created a dangerous illusion of control.
Recent research from Nature Scientific Reports demonstrates that traditional OEE calculations systematically miss key operational parameters that have the greatest influence on overall factory performance, including ideal cycle time variations, roller performance inconsistencies, and the dynamic interplay between customer demand and production capabilities.
The uncomfortable truth confronting Indian manufacturing today is that OEE measures equipment brilliantly but treats interconnected manufacturing operations as if they were independent machines operating in isolation. Consider a typical assembly line producing automotive components in Pune or Chennai. Each workstation might achieve ninety percent OEE individually, creating a dashboard that glows green with satisfaction. Yet the line itself delivers only sixty-five percent of its theoretical throughput because material doesn’t flow smoothly between stations, operators wait for parts that arrive in irregular batches, and schedule changes ripple through the system like aftershocks from an earthquake no one measured.
According to data from the India Briefing Manufacturing Tracker for 2024-25, Indian manufacturing output grew at 4.26 percent in the fiscal year, yet labor productivity in manufacturing operations continues to lag significantly behind global benchmarks. The organized manufacturing sector shows improvement in labor productivity metrics, but the gap between equipment efficiency and overall system effectiveness represents billions of rupees in unrealized capacity sitting hidden within facilities that already believe they’re performing at world-class levels.
This measurement paradox manifests in several critical ways across Indian manufacturing operations. Process-level constraints in material flow remain invisible to equipment-centric OEE calculations, even though they routinely consume twenty to forty percent of total facility capacity. Research on the “hidden factory” phenomenon suggests that unmeasured inefficiencies including rework, scrap, irregular material arrival, and redundant processes can account for this staggering proportion of manufacturing capability, yet traditional metrics provide no systematic way to quantify or address these losses.
Labor productivity—the human dimension of manufacturing effectiveness—falls outside OEE’s mechanical purview entirely. Recent statistics indicate that Indian employee engagement dropped to nineteen percent in 2025 from twenty-four percent the previous year, yet this dramatic decline in the human factors driving productivity would be completely invisible to a facility measuring only equipment effectiveness. The reality is that operators standing idle waiting for materials, supervisors firefighting quality issues, and engineers troubleshooting coordination problems represent massive productivity drains that never register on traditional OEE dashboards.
Schedule adherence, the metric that actually determines whether customers receive their orders on time, bears only an indirect relationship to equipment effectiveness. A manufacturing line can achieve exceptional OEE while simultaneously failing to produce the right products at the right time in the right quantities, because OEE measures how efficiently equipment runs when it runs, not whether the facility is running the right things in the right sequence to meet actual market demand.
The systemic nature of this measurement gap becomes painfully clear when examining real-world operational data. Studies using process mining techniques to analyze manufacturing flows reveal that delays, bottlenecks, and resource imbalances are deeply interrelated, yet traditional metrics analyze these dimensions in isolation. A constraint at one workstation cascades through the entire production system, creating delays and inventory buildups that consume working capital and floor space while degrading overall throughput far more severely than equipment-level OEE numbers would suggest.
The Hidden Manufacturing Value: Process-Level Constraints Overlooked by OEE Metrics
Manufacturing operations in India face an additional challenge that amplifies this measurement paradox. Data from the Ministry of Statistics and Programme Implementation indicates that while sectors like basic metals showed growth rates of 12.7 percent and electrical equipment registered 15.9 percent growth, these advances occurred in an environment where many facilities still operate with semi-automated machinery and legacy equipment. In these settings, longer cycle times and higher chances of human error create performance variability that equipment-level metrics systematically underestimate.
The gear manufacturing sector provides a particularly illuminating example. Gear cutting and hobbing require precision machining across multiple setups and processes, with frequent changeovers and inspection routines. While automation adoption gradually increases, many gear plants throughout India continue running on manual or semi-automated machinery. The performance and availability metrics tracked by traditional OEE capture machine utilization, but they miss the coordination delays, inspection bottlenecks, and setup inefficiencies that actually constrain throughput at the system level.
Die casting operations face similar challenges, with tooling wear, part ejection issues, and heat-induced machine fatigue creating downtime that OEE dutifully records. However, quality losses from porosity and surface defects—which plague die casting despite fast cycle times—create rework loops and inspection delays that consume floor space, tie up skilled operators, and create schedule disruptions far beyond what equipment availability metrics reveal.
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Impact: The Fifty Percent Solution Nobody Measures
The implications of this measurement paradox extend far beyond academic concerns about metrics precision. They translate directly into competitive disadvantage, financial underperformance, and strategic vulnerabilities that threaten Indian manufacturing’s ambitious growth trajectory.
When manufacturing leaders optimize what they measure rather than what actually matters, they inevitably misallocate improvement resources. Facilities invest millions of rupees in preventive maintenance programs that incrementally improve equipment availability from eighty-eight to ninety-two percent, while ignoring material flow constraints that are strangling system throughput by twenty or thirty percent. Production teams celebrate reducing changeover times on bottleneck equipment through Single-Minute Exchange of Die techniques, even as schedule instability and poor demand forecasting force those same machines to run in small batches that negate the efficiency gains.
The financial impact of this measurement myopia manifests across multiple dimensions. Working capital consumption accelerates as facilities build inventory buffers to compensate for flow constraints they cannot see clearly enough to fix. A recent study of refrigerator manufacturing operations demonstrated that optimizing material flow and product routing through mixed-integer programming models could dramatically improve efficiency, yet such system-level optimization efforts rarely receive investment priority because the equipment-centric OEE dashboards show satisfactory performance.
On-time delivery performance suffers because schedule adherence depends on system-level coordination that equipment metrics don’t capture. Indian manufacturers targeting the eighty-five percent world-class OEE benchmark discover that this achievement doesn’t automatically translate to reliable delivery performance. Customers increasingly demand shorter lead times and higher delivery reliability, yet these customer-facing performance dimensions remain disconnected from the equipment-focused improvement efforts that consume most continuous improvement resources.
Labor cost efficiency deteriorates invisibly when operators spend significant portions of their shifts waiting for materials, searching for tools, or working around equipment that’s technically “available” according to OEE calculations but starved for work by upstream constraints. Research on labor productivity in Indian manufacturing reveals that while the organized sector shows improvement, absolute productivity levels remain far below global standards, suggesting massive unrealized human potential that equipment-focused metrics fail to reveal or motivate improvement around.
The Theory of Constraints provides a framework for understanding why this measurement gap creates such severe operational problems. Every process contains at least one constraint—the single factor that limits system throughput more than any other. Traditional OEE measurements, by focusing on individual equipment performance, systematically miss system constraints that reside in the connections between operations rather than within the operations themselves.
Consider a manufacturing facility with multiple production lines feeding into a shared paint shop and then to final assembly. Each line might achieve eighty-five or ninety percent OEE individually, yet the paint shop operates as a shared resource creating complex scheduling dependencies. Parts arrive from different lines at irregular intervals, forcing the paint shop to batch work inefficiently. The paint shop’s own OEE metrics might show good performance, but the system-level constraint it creates—irregular part flow causing delays throughout the facility—remains invisible to equipment-focused measurement systems.
Research on manufacturing process analysis frameworks demonstrates that understanding root causes of inefficiencies requires integrating multiple perspectives including process flows, time delays, resource utilization, and quality issues simultaneously. However, traditional OEE provides only the resource utilization dimension, leaving process flow dynamics, timing constraints, and quality interdependencies poorly understood and consequently poorly managed.
The competitive implications of this measurement gap are particularly acute for Indian manufacturing given the sector’s growth ambitions. Government initiatives including the Production-Linked Incentive scheme have attracted substantial investment—one hundred sixty-five billion dollars flowing into manufacturing in 2024 according to Trade.gov data—yet this capital investment will deliver suboptimal returns if facilities optimize the wrong metrics. A factory can install state-of-the-art automated equipment that achieves world-class OEE while still suffering from system-level constraints that prevent the facility from realizing its theoretical capacity.
OEE vs OPE-Driven Transformation: Breakthrough Performance Improvements in Indian Manufacturing
Moreover, India’s positioning in global supply chains increasingly depends on manufacturing reliability rather than purely on cost advantages. As multinational companies diversify supply chains beyond China under “China Plus One” strategies, they demand not just low costs but consistent quality, reliable delivery, and responsive flexibility. These requirements depend fundamentally on system-level performance dimensions that traditional equipment-focused metrics fail to adequately measure or drive improvement around.
The strategic vulnerability this creates becomes apparent when examining sector-specific challenges. In automotive manufacturing—one of India’s most mature industrial sectors—issues like downtime from model changeovers, supply chain disruptions, and manual handling constraints drag down system performance even when equipment OEE looks respectable. The automotive sector contributed positively to industrial growth with motor vehicles and trailers showing 14.6 percent growth in September 2025, yet facilities achieving this growth operate far below their theoretical capacity because process-level constraints remain hidden beneath satisfactory equipment utilization numbers.
The spring manufacturing sector illustrates similar dynamics. Springs come in varied sizes and tensions requiring periodic tool changes and calibrations. Many spring manufacturers still rely on semi-automatic machines where setup and speed losses consume substantial capacity. Quality rejection from material inconsistencies further shrinks effective throughput. Equipment-level OEE captures some of these losses but systematically underestimates their system-level impact because the metrics don’t account for how changeover delays create schedule instability that ripples through the facility, forcing excessive inventory buffers and degrading overall flow.
Prescription: Automation as the System-Level Solution
The path beyond this measurement paradox doesn’t require abandoning OEE—the metric remains invaluable for understanding equipment performance—but rather demands complementing equipment-level metrics with system-level visibility that only modern automation and digital transformation can provide.
Intelligent automation serves as the connective tissue that transforms isolated high-performing equipment into integrated high-performing systems. The fundamental insight driving this transformation is that manufacturing effectiveness depends not just on how well individual machines operate but on how smoothly material, information, and work instructions flow through the entire production system from raw materials to finished goods.
Interconnected Manufacturing Constraints: Beyond Single-Machine OEE Metrics
Material handling automation addresses the most critical yet often invisible constraint in manufacturing operations. Research on material flow optimization demonstrates that identifying efficient pathways, assigning products to routes, and determining required material-handling equipment through mixed-integer programming models can dramatically improve system performance. However, implementing these improvements requires moving beyond manual material movement to automated systems that enforce optimal routing and eliminate the delays, damage, and irregular flow that constrain system throughput.
Air balancers and manipulators—technologies that S&H Designs has deployed extensively across Indian manufacturing—exemplify how targeted automation eliminates system constraints rather than merely improving equipment efficiency.
In a documented implementation at Farm Division, air balancers for fender handling delivered transformative results: one hundred percent reduction in component damage, one hundred percent improvement in operator safety, seventy-five percent reduction in cycle time (a four-fold efficiency improvement), and reduction of three operators from the process. These improvements far exceed what traditional OEE optimization could achieve because they addressed system-level constraints—material flow irregularity, quality losses from handling damage, and labor productivity gaps—that equipment metrics don’t adequately capture.
Automated conveyors transform irregular material flow into predictable, synchronized movement. Power roller conveyors, belt conveyors, and chain conveyors create continuous flow that eliminates the batching delays and queue buildup that plague manually-fed production lines. When implemented as part of an integrated system rather than as isolated automation islands, these material handling solutions enable production to pace itself to customer demand rather than to arbitrary batch sizes dictated by material movement constraints.
Robotic cells bring precision and consistency to operations where variability currently constrains quality and throughput. A documented robotic implementation achieved thirty percent efficiency improvement, but more importantly delivered precision in component insertion that improved quality, consistency in cycle time that stabilized schedules, and freed operators from repetitive tasks so their skills could be redeployed to higher-value activities. These system-level benefits dwarf the equipment-level efficiency gains that traditional OEE measurements would capture.
Digital twin technology and simulation capabilities represent the next frontier in system-level optimization. By creating virtual replicas of production systems, manufacturers gain the ability to identify bottlenecks, test process changes, and optimize schedules before implementing them on the physical factory floor. This capability addresses a fundamental limitation of traditional metrics—they tell you what happened but provide limited insight into what will happen if you change the system. Digital twins transform manufacturing optimization from reactive problem-solving to proactive system design.
Real-time production monitoring systems extend visibility beyond individual equipment to encompass entire value streams. Internet of Things sensors, Manufacturing Execution Systems, and advanced analytics platforms capture the process flows, material movements, quality outcomes, and schedule adherence that determine system-level performance. Recent research on data science strategies for OEE emphasizes that aggregating data by time period and equipment allows comprehensive and detailed views of operational performance, but the real value comes from analyzing these dimensions collectively to understand interdependencies that individual metrics miss.
Automated scheduling and planning systems address one of the most critical yet poorly measured aspects of manufacturing effectiveness. Advanced Planning and Scheduling technology considers production time, resource availability, production constraints, material requirements, and demand fluctuations simultaneously to generate schedules that optimize system throughput rather than individual equipment utilization. When integrated with Material Requirements Planning, these systems coordinate material flow and production schedules to prevent the delays caused by material shortages that degrade system performance while leaving equipment-level OEE looking satisfactory.
The Theory of Constraints provides a methodology for directing these automation investments toward maximum impact. Rather than automating every operation, constraints-focused automation identifies the single point that most limits system throughput and implements targeted solutions that eliminate or elevate that constraint. This focused approach delivers faster return on investment because improvements that address system constraints immediately increase facility throughput, while improvements to non-constraints merely create excess capacity that generates no revenue.
Lean manufacturing principles integrated with intelligent automation create what Goldratt termed “subordination”—aligning all elements of the production system to support the constraint’s performance. Kanban systems regulate flow, while automated material handling ensures that constraint operations never starve for work or drown in excess inventory. Single-Minute Exchange of Die techniques reduce changeover times on bottleneck equipment, while automated scheduling ensures that the bottleneck runs the optimal product mix in the optimal sequence.
For Indian manufacturing operations facing the specific challenges of semi-automated facilities, legacy equipment, and labor-intensive processes, this automation prescription must be pragmatic and incremental. Starting with targeted automation at identified system constraints delivers immediate throughput improvements that fund subsequent automation investments. Retrofitting existing equipment with Industrial Internet of Things sensors provides visibility into performance constraints without requiring wholesale equipment replacement. Implementing material handling automation in high-traffic pathways eliminates flow bottlenecks while allowing continued use of existing production equipment.
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Execution: A Systematic Plan for Factory Owners and Decision Makers
Transforming measurement and management systems from equipment-focused to system-focused effectiveness requires a structured implementation approach that balances quick wins with long-term transformation. Factory owners should pursue this journey through three distinct phases, each building on the previous phase’s learnings and delivered value.
Phase One: System Visibility and Constraint Identification (Months 1-3)
The transformation begins not with automation investment but with developing system-level visibility that traditional metrics fail to provide. Manufacturers should implement comprehensive process mapping that documents not just equipment operations but material flow paths, information handoffs, quality inspection points, and schedule coordination mechanisms. This mapping exercise reveals the actual value stream—the series of activities that transform raw materials into finished products—rather than the idealized process flows that exist in engineering documentation.
Flow Issue Reporting provides the systematic data collection methodology that enables constraint identification. Rather than tracking equipment downtime as isolated incidents, facilities should implement work order tracking that captures why orders aren’t where they should be at any given moment. Material shortages, quality holds, schedule changes, equipment unavailability, operator absence, tooling issues, and coordination delays all create flow disruptions that traditional OEE measurements overlook. Collecting and analyzing this flow issue data through Pareto analysis reveals which constraints consume the most capacity, enabling management to prioritize improvement efforts based on actual system impact rather than equipment utilization numbers.
Time and motion studies at the system level—tracking complete order-to-delivery cycles rather than individual operation times—expose the hidden losses that accumulate between operations. In typical manufacturing operations, value-adding activity consumes only a fraction of total cycle time, with the majority spent in queues, transportation, inspection, rework, and coordination delays. Documenting these non-value-adding activities provides the factual foundation for improvement prioritization and return-on-investment calculations for automation investments.
Establishing baseline system performance metrics creates the measurement framework for tracking improvement. Rather than focusing exclusively on equipment OEE, facilities should measure system throughput (units shipped per time period), schedule adherence (percentage of orders delivered on promised date), lead time (elapsed time from order receipt to shipment), work-in-process inventory levels, and overall labor effectiveness. These metrics capture system performance dimensions that actually matter to customers and financial performance.
Phase Two: Targeted Automation at System Constraints (Months 4-12)
With system constraints clearly identified through data rather than assumption, the second phase implements focused automation investments that deliver immediate throughput improvement. The Theory of Constraints methodology guides investment prioritization—improvements that increase constraint capacity directly increase system throughput and revenue, while improvements to non-constraints merely create idle capacity.
Material handling automation typically emerges as the highest-priority investment because material flow irregularity creates constraints throughout most manufacturing facilities. Implementing automated conveyors, air balancers, or manipulators at identified bottleneck pathways eliminates the delays, damage, and irregular flow that limit system performance. These investments should be designed as integrated systems rather than standalone equipment, ensuring that material flows smoothly through the entire production sequence rather than moving quickly through one operation only to queue at the next.
Smart Factory technology deployment provides the real-time visibility required to manage system performance proactively rather than reactively. Installing Industrial Internet of Things sensors on production equipment, implementing Manufacturing Execution System software, and deploying production monitoring dashboards transform manufacturing from a black box where outcomes become visible only after the fact to a transparent system where problems surface immediately while they can still be addressed. This visibility enables operators and supervisors to prevent schedule misses rather than merely document them.
Production planning and scheduling systems eliminate one of the most persistent yet invisible constraints—poor coordination between customer demand and production execution. Advanced Planning and Scheduling software considers equipment capacity, material availability, labor resources, and customer commitments simultaneously to generate feasible schedules that optimize system throughput. Integrating these planning systems with Material Requirements Planning ensures that the right materials arrive at the right locations at the right times to support the schedule, preventing the material shortages that currently force expediting, overtime, and missed deliveries.
Quality system automation addresses the rework and inspection bottlenecks that consume capacity without appearing on traditional equipment utilization metrics. Vision inspection systems, automated testing equipment, and statistical process control implementations catch quality problems at their source rather than discovering them downstream where they’ve already consumed substantial production capacity. This prevention-rather-than-detection approach not only improves quality outcomes but eliminates the hidden rework factory that consumes twenty to forty percent of capacity in many operations.
Implementing operator assist technologies—work instruction systems, error-proofing devices, and automated material presentation—addresses labor productivity gaps while improving quality and safety. These human-centered automation investments recognize that operators are critical elements of the production system whose effectiveness depends on having the right information, materials, and tools available precisely when needed. Documentation from S&H Designs implementations demonstrates that relieving operators from physically demanding material handling tasks through air balancers and manipulators simultaneously improves safety, quality, efficiency, and job satisfaction.
Phase Three: System Optimization and Continuous Improvement (Months 13+)
The third phase transitions from constraint elimination to systematic optimization of the newly visible and now measurable production system. With automation providing both improved material flow and comprehensive system visibility, manufacturers can pursue sophisticated optimization approaches previously impossible with limited measurement and manual processes.
Digital twin development creates virtual replicas of production systems that enable simulation-based optimization. Before implementing expensive process changes on the physical factory floor, manufacturers can test alternatives in the digital twin to identify which changes deliver the greatest throughput improvement. This simulation capability transforms manufacturing management from trial-and-error experimentation to science-based system design.
Value stream mapping becomes far more powerful when supported by actual system performance data rather than manual observation and estimation. Automated data collection from Manufacturing Execution Systems and Internet of Things sensors provides accurate cycle times, queue times, and yield data that reveal precisely where value is created and where waste accumulates. This data-driven value stream mapping enables Lean practitioners to focus improvement efforts on the activities that constrain system performance rather than pursuing marginal improvements to non-constraints.
Predictive maintenance systems leverage the equipment performance data being collected for OEE measurement but apply that data to prevent failures rather than merely documenting them. Machine learning algorithms identify the subtle performance degradations that precede equipment failures, enabling maintenance interventions during planned downtime rather than forcing unplanned stoppages that cascade through production schedules. This predictive approach transforms maintenance from a reactive cost center to a strategic contributor to system reliability.
Advanced analytics and artificial intelligence applications unlock improvement opportunities that human analysis cannot systematically identify in complex manufacturing systems. Algorithms can detect the subtle relationships between process parameters and quality outcomes, optimize scheduling decisions considering hundreds of constraints simultaneously, and predict demand patterns that enable more efficient production planning. These capabilities don’t replace human judgment but extend it into domains where complexity exceeds unaided human analytical capacity.
Continuous improvement culture transformation ensures that the gains from automation and digitalization drive sustained performance improvement rather than one-time step changes. Flow Issue Reporting disciplines evolve into systematic problem-solving processes where frontline teams identify and resolve constraints before they significantly impact performance. Visual management systems make system performance visible to operators, supervisors, and managers simultaneously, creating shared understanding and accountability for results.
Integration with enterprise systems—Enterprise Resource Planning, Customer Relationship Management, and Supply Chain Management—extends system-level thinking beyond the factory walls to encompass the entire value chain from suppliers through customers. This extended visibility enables manufacturers to synchronize production with actual customer demand rather than forecasts, minimize inventory throughout the supply chain, and respond flexibly to the demand variability that creates schedule instability.
Throughout this three-phase journey, maintaining focus on system-level performance metrics rather than merely equipment-level metrics ensures that improvement efforts deliver actual competitive advantage rather than just satisfying internal benchmarks. On-time delivery, customer lead times, inventory turns, and operating profit per operating day capture the business outcomes that matter, while equipment OEE serves as a diagnostic tool for identifying performance constraints rather than as the ultimate measure of success.
Partnership: The S&H Designs Advantage in System-Level Transformation
S&H Designs brings unique capabilities to this system-level transformation challenge based on nearly three decades of experience implementing integrated automation solutions across diverse Indian manufacturing operations.
Unlike equipment vendors who optimize individual machines or system integrators who connect disparate equipment with minimal process insight, S&H Designs approaches manufacturing improvement from a holistic system perspective that directly addresses the measurement paradox constraining performance.
The firm’s Design-Develop-Deliver philosophy embodies system-level thinking from initial concept through final implementation. Rather than assuming that high equipment OEE automatically translates to effective system performance, S&H Designs begins every engagement by understanding application requirements, engineering needs, and business objectives that encompass the complete production system. This requirements-based approach identifies the system-level constraints that limit throughput rather than merely optimizing equipment that already performs satisfactorily.
Manufacturing System Engineering expertise distinguishes S&H Designs from automation vendors focused exclusively on equipment supply. The firm’s comprehensive methodology encompasses work measurement, capacity analysis, layout design, and warehouse optimization as integrated disciplines rather than independent specialties. Time and motion studies identify opportunities to increase operator productivity and minimize operational losses throughout the system, not just at individual workstations. Takt time calculation, bottleneck identification, and capacity enhancement analyses reveal system-level constraints that traditional OEE measurements miss.
Layout design capabilities optimize space utilization, minimize material handling distances, and create ergonomic workstations that enhance both productivity and safety. These physical flow considerations directly address the material flow constraints that limit system throughput despite satisfactory equipment OEE. Whether implementing product layouts for high-volume operations, process layouts for job shop flexibility, or single-piece flow for lean manufacturing, S&H Designs creates physical environments that eliminate the irregular material movement and queue buildup that constrain performance.
Digital twin development and simulation services provide manufacturers the ability to optimize systems before investing in physical changes. Using advanced software platforms including AutoCAD for detailed layouts, Blender for immersive 3D visualization, and AnyLogic for dynamic simulation, S&H Designs creates virtual replicas of production systems that enable testing alternative configurations and operating policies. This simulation capability transforms system optimization from expensive physical experimentation to risk-free digital analysis that identifies the highest-impact improvements before implementation.
Material handling solution expertise spans the complete spectrum from simple gravity conveyors to sophisticated robotic systems, with particular strength in the mid-complexity automation domain that delivers maximum return on investment for most manufacturing operations. Air balancers and manipulators eliminate the material handling constraints that force operators to work at unsustainable paces or wait idly for mechanized transport. Conveyor systems—roller, belt, chain, bucket, overhead—create continuous material flow that synchronizes production operations and eliminates batching delays. Special peripheral equipment including turn-over devices, pop-ups, indexing tables, and lift-and-transfer mechanisms solve the unique material handling challenges that create constraints in specific applications.
Robotic cell design and integration capabilities extend from simple pick-and-place operations through complex assembly and testing applications. S&H Designs implementations have demonstrated efficiency improvements of thirty percent while delivering the consistency and quality advantages that separate world-class operations from merely efficient ones. These robotic solutions address the repeatability and precision requirements that human operators struggle to maintain consistently, while simultaneously freeing skilled workers for higher-value activities including quality problem-solving, process optimization, and equipment troubleshooting.
Product design and Special Purpose Machine development expertise enables S&H Designs to create custom automation solutions for applications where standard equipment cannot address specific system constraints. From 3D printers and custom tooling to sophisticated inspection systems and material processing equipment, this custom engineering capability ensures that manufacturers can eliminate constraints even in unconventional applications where off-the-shelf solutions prove inadequate.
Industry presence across diverse manufacturing sectors—electric vehicles, farm equipment, construction machinery, material handling equipment, food and pharmaceuticals—provides S&H Designs with cross-industry insight into common patterns of system-level constraints and proven solutions.
This breadth of experience accelerates problem identification and solution implementation because the firm can apply proven approaches from one industry to solve analogous constraints in another.
Proven track record with leading manufacturers including Saint Gobain, Norton Grindwell, Talbros and numerous others demonstrates S&H Designs’ ability to deliver transformative results in real-world production environments operating under actual competitive pressures. These aren’t laboratory demonstrations but production implementations that must deliver promised improvements while maintaining ongoing operations and meeting customer commitments.
Local presence with deep understanding of Indian manufacturing realities distinguishes S&H Designs from global solution providers offering standardized approaches developed for different operating environments. The firm understands the specific challenges of working with semi-automated equipment, legacy machinery, and labor-intensive processes that characterize much of Indian manufacturing. Solutions are designed to deliver results in these actual conditions rather than assuming greenfield facilities with unlimited capital budgets.
Partnership approach rather than transactional vendor relationships ensures that S&H Designs shares accountability for achieving business results rather than merely delivering equipment that meets technical specifications. The firm’s engineering team works collaboratively with client operations to understand current constraints, develop pragmatic solutions, and support implementation that achieves targeted improvements in system-level performance metrics that impact competitive position and financial results.
The strategic value S&H Designs delivers extends beyond the immediate automation projects to building internal capability for sustained system-level thinking. Through collaborative implementation approaches, training programs, and knowledge transfer, client teams develop the skills and mindset required to identify and eliminate system constraints continuously rather than depending on external expertise for every improvement initiative. This capability building transforms automation investments from one-time improvements to launching points for continuous advancement.
For manufacturing leaders confronting the measurement paradox—celebrating high OEE while struggling with delivery performance, working capital consumption, and competitive disadvantage—S&H Designs offers the rare combination of system-level thinking, comprehensive automation capability, proven implementation experience, and partnership commitment required to transform equipment-focused operations into system-optimized competitive weapons. The firm’s Design-Develop-Deliver philosophy directly addresses the gap between measuring equipment efficiency and achieving system effectiveness that constrains Indian manufacturing performance and limits global competitiveness.
About the Author
Hrishikesh S. Deshpande is Founder & CEO of S&H Designs, a Pune-based automation and manufacturing solutions firm with nearly twenty-five + years of experience implementing integrated material handling, robotics, and digital twin solutions across diverse Indian manufacturing sectors. With extensive experience in automotive, farm equipment, electric vehicles, and industrial machinery, Hrishikesh brings system-level thinking to manufacturing challenges that conventional equipment-focused approaches fail to solve.
Key Takeaways for Manufacturing Leaders
The measurement paradox constraining Indian manufacturing effectiveness—celebrating high equipment OEE while suffering poor system performance—represents both a crisis and an opportunity. Manufacturers who continue optimizing what’s easy to measure rather than what actually drives competitive advantage will find themselves progressively disadvantaged by competitors who embrace system-level thinking enabled by intelligent automation.
The path forward requires neither abandoning OEE nor dismissing its value, but rather complementing equipment metrics with system-level visibility that captures material flow effectiveness, labor productivity, schedule adherence, and overall throughput. Modern automation technologies—material handling systems, robotic cells, digital twins, real-time monitoring, and advanced planning—provide the visibility and control required to optimize systems rather than merely equipment.
Success demands systematic execution through clearly defined phases: establishing visibility and identifying actual system constraints through rigorous data collection, implementing targeted automation at identified bottlenecks to achieve immediate throughput gains, and then leveraging enhanced visibility for continuous system optimization. This disciplined approach delivers both quick wins that build organizational momentum and sustainable long-term improvement that transforms competitive position.
For Indian manufacturing aspiring to capture growing shares of global markets under China Plus One diversification strategies, system-level effectiveness rather than merely equipment-level efficiency will increasingly determine which facilities win and retain multinational business. The investment capital flowing into Indian manufacturing through Production-Linked Incentives and private foreign investment will deliver maximum returns only when deployed within operations that measure and optimize the right dimensions of performance.
The fifty percent improvement potential hidden within facilities that already achieve respectable OEE scores represents the competitive advantage available to manufacturers who see beyond the measurement paradox. Traditional metrics tell us how well equipment operates; system-level thinking enabled by intelligent automation reveals how effectively manufacturing operations serve customers and generate profit.
The question confronting every manufacturing leader is whether to celebrate today’s satisfactory OEE scores while competitors systematically unlock hidden capacity, or to embrace the more challenging but ultimately more rewarding journey toward genuine system effectiveness that transforms good manufacturing operations into dominant competitive weapons.
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