A Thought-Leadership Report by Sampurna Operations Consultancy
Excellence in Execution
Yet this morning, production is still behind schedule. One machine is waiting for material. Another has stopped again. Rejection is eating into output. Inventory is increasing, but the material needed for today's plan is missing.
At the review meeting, every department has an explanation. Nobody has a complete answer.
So what exactly did all that investment improve?This is not necessarily a technology problem. It may be a gap between what your factory owns and what your organisation can consistently make it deliver.
Manufacturing has changed dramatically over the last three centuries. Machines have become faster, production systems more sophisticated, information more accessible and automation more capable. Yet many manufacturing organisations continue to struggle with familiar problems: missed production targets, recurring breakdowns, inconsistent quality, excess inventory, material shortages, poor capacity utilisation and improvements that do not last.
To understand this contradiction, it is useful to look at how manufacturing evolved.The First Industrial Revolution began in the eighteenth century. Water and steam power enabled machines to perform work that had previously depended largely on human and animal effort.
Mechanisation transformed production capacity. Operations that had once been limited by manual effort could be organised around machinery and more consistent production methods.
But installing a machine did not automatically create an efficient production system. Equipment still needed operators, maintenance, raw materials, supervision and coordination.
The basic operational questions were already present: Was the equipment being used effectively? Was the work organised properly? Could production continue reliably?The Second Industrial Revolution brought electricity, improved machinery, standardised components, assembly lines and large-scale production.
Manufacturers could organise work into repeatable stages and increase output through specialisation and better production flow.
This changed the economics of manufacturing. However, higher production volumes also increased the importance of coordination. A delay at one stage could affect several downstream operations. Poor planning, inconsistent quality and material interruptions could disrupt an entire production line.
The challenge was no longer simply to produce more. It was to coordinate the system that produced more.The Third Industrial Revolution introduced electronics, computers, programmable logic controllers, industrial robotics and automated production systems.
Manufacturers gained greater control over processes, improved repeatability and reduced dependence on certain manual operations.
Computerised systems also made it possible to collect and process production information at a scale that was previously difficult.
But automation did not remove the need for sound process design, maintenance, skilled people and reliable information. An automated process can repeatedly produce the same defect if its underlying settings or inputs are wrong. A machine can operate at high speed while the overall production system remains constrained by material supply, changeovers, quality checks or downstream capacity.
Automation improves the capability of a process. It does not independently guarantee the performance of the entire factory.Industry 4.0 connects machines, systems and information through technologies such as the Industrial Internet of Things, cloud computing, advanced analytics, artificial intelligence, digital twins and cyber-physical systems.
Manufacturers can increasingly monitor equipment conditions, analyse production data, identify patterns and make decisions using information that was once unavailable or delayed.
The potential is substantial. Predictive maintenance can help identify developing equipment problems. Connected systems can improve traceability. Analytics can reveal production losses. Digital planning tools can support better coordination.
Yet these technologies depend on the quality of the information and the operating practices behind them.
If downtime categories are incorrectly recorded, a sophisticated dashboard may present an inaccurate picture. If production targets are unrealistic, automated reporting will not make them meaningful. If maintenance data is incomplete, predictive models may produce unreliable conclusions.
Technology can make information more available. It cannot make inaccurate information reliable merely by processing it faster.A modern factory may use advanced robotics in one area, conventional machines in another and manual operations elsewhere. Its planning may be digital while some production records remain handwritten.
This is not necessarily a weakness. The right technology depends on the product, process, production volume, investment capacity and business requirement.
A smaller manufacturer does not need to imitate the technology architecture of a multinational company to operate effectively. It needs an operating system appropriate to its business.
The important distinction is between technological sophistication and operational capability.Consider an illustrative example.
A machine is rated to produce 100 units per hour. The factory expects it to support a production plan based on that capacity.
The machine waits for material.
Operators spend time adjusting settings.
Changeovers take longer than planned.
Minor stoppages are not recorded accurately.
Rejection and rework consume production time.
The next operation cannot process output at the same rate.
Maintenance interventions are mostly reactive.
The machine may have the technical ability to produce 100 units per hour, but the production system does not consistently convert that capability into saleable output.
Buying a faster machine may not solve the problem. The constraint may be material availability, process stability, maintenance, operator capability or downstream capacity.
The difference between installed capability and achieved performance is where many manufacturing losses remain hidden.The four industrial revolutions expanded what manufacturing organisations could achieve. They introduced new capabilities, but they did not remove the need to manage processes, people, equipment, materials and information.
A factory can possess modern machines and still have weak maintenance systems. It can have an ERP system and still struggle with inventory accuracy. It can display production dashboards and still lack agreement on the actual causes of lost output.
The persistence of these problems raises a more important question:
If solutions are already available, why do manufacturing organisations continue to struggle with the same operational losses?
The answer lies not only in the solutions themselves, but in the organisation’s ability to select, implement, coordinate and sustain them.
That is where we turn next.There are established methods for productivity improvement, maintenance management, quality control, inventory optimisation, production planning and employee development. There are software platforms, consultants, equipment suppliers, training providers and industry frameworks.
Yet the presence of a solution does not mean the problem will be solved.
The difficult part often begins after the purchase order is signed, the consultant leaves, the training is completed or the new procedure is issued.But if material receipts are not recorded promptly, stores transactions are delayed, item codes are inconsistent or production issues material without proper documentation, the system will not reflect the physical inventory accurately.
The software may be functioning exactly as designed. The operating process around it is not.
The same applies to machinery. A new machine may offer higher speed and better accuracy, but the expected benefit depends on tooling, operator competence, material quality, maintenance, process settings and downstream readiness.
A solution must be integrated into the way the organisation works. Otherwise, it remains an isolated investment.A production manager may be held responsible for output but have limited control over manpower availability, material supply, machine maintenance priorities or production scheduling.
A maintenance manager may be responsible for equipment reliability but lack authority to secure critical spares or obtain planned shutdown time.
A quality manager may be accountable for rejection levels while production pressure encourages the continuation of unstable processes.
This creates a gap between responsibility and the ability to influence results.Key Responsibility Areas (KRAs) should define the areas for which a role is responsible. Key Performance Indicators (KPIs) should provide measurable indicators of performance. But assigning a target alone does not create accountability.
Clearly defined responsibilities.
Appropriate decision-making authority.
Access to the information required.
Coordination with dependent functions.
Resources and management support.
A review mechanism that examines causes, not just results.
Procurement may negotiate a lower purchase price by ordering larger quantities. Stores may report high stock availability. Production may prefer larger batches to reduce changeovers. Quality may increase inspection to prevent defects from reaching customers.
Each decision may appear reasonable within the department.
But the combined result may be excess inventory, longer lead times, higher working capital, delayed detection of process problems and poor responsiveness to changing demand.
Consider a supplier offering a lower price for a larger minimum order quantity. Procurement accepts the offer to reduce the purchase cost per unit. However, demand is uncertain, storage space is limited and the material may have a shelf-life or quality risk.
The purchase price has improved, but the total cost of ownership may have increased.The same issue appears when production maximises machine utilisation by producing more than the next operation can consume. The factory may report high machine output while work-in-progress and waiting time increase.
Departmental efficiency is not the same as total factory efficiency.
Manufacturing performance must be evaluated across the complete flow—from supplier to production, through inspection and dispatch.Material variation may occur because of changes in raw material sources, process conditions, operators, equipment, subcontractors or handling.
A previous lot may have been accepted while the next lot creates machining difficulties, dimensional variation or quality rejection.
Supplier management therefore needs more than an approved-supplier list.
It should include risk-based controls such as:
Clear specifications and acceptance criteria.
Incoming inspection appropriate to the material and supplier risk.
Supplier performance monitoring.
Non-conformance reporting and corrective action.
Traceability where required.
Periodic supplier review and development.
Procurement decisions should also consider quality, delivery reliability, lot size, inventory exposure and the impact on production.
The lowest purchase price is not necessarily the lowest manufacturing cost.An employee attends a session, signs the training record and returns to the production line. But can the employee independently perform the operation? Can they identify an abnormal condition? Do they know when to stop the process and whom to inform?
These are different questions.
Competence requires the ability to apply knowledge correctly under actual working conditions.
A practical training system should combine explanation, demonstration, supervised practice and verification.
A designated training area or Dojo can help employees learn machine settings, standard operations, safety requirements, quality checks and problem identification without immediately placing the full burden of learning on live production.
Smaller factories do not need an expensive training room to begin. A clearly organised area, suitable training aids, standard work instructions and competent trainers can provide a practical foundation.
The important point is to verify what the employee can do, not only what training they attended.But in some factories, the documented process and actual shop-floor practice gradually become different.
An operator develops a faster method. A supervisor accepts a temporary adjustment. A setting is changed to manage a recurring problem. The revised practice spreads across shifts, but the approved instruction remains unchanged.
Over time, the organisation may have multiple versions of the same process—one in the document, another in the operator's routine and perhaps a third in the supervisor's understanding.
This creates risks for quality, safety, consistency and training.
A useful system must connect documentation with actual practice.
Gemba walks help managers observe the process where work happens. Layered process audits can verify whether critical requirements are followed consistently at different levels of the organisation.
When an improvement is approved, the relevant procedure, control plan, training material and records should be updated. Employees across shifts must understand the change.
A document is not an effective standard simply because it has been approved. It becomes effective when the process follows it consistently.Digital reporting can improve visibility, but the quality of a report depends on the quality of the data behind it.
Consider a factory recording machine downtime. One shift records tool change as planned time. Another records it as breakdown. A third records it as minor stoppage.
The dashboard may show a downtime trend, but the categories are not comparable.
Similarly, production reports may combine good output, rework and rejected pieces. Inventory reports may not reflect unposted transactions. Maintenance records may show closure of work orders without confirming whether the failure has been eliminated.
What is being measured?
How is each category defined?
Who records the information?
When must it be recorded?
Who verifies it?
How are corrections controlled?
Does the information match what is happening physically?
A maintenance team may restore a machine quickly and record the breakdown as closed.
But restoration and problem elimination are not the same.
If a bearing repeatedly fails because of misalignment, replacing the bearing restores production temporarily. Unless the underlying condition is identified and corrected, the failure may return.
The same pattern occurs in quality. Reworking a defective batch may protect a delivery, but it does not necessarily eliminate the process condition that created the defect.
Corrective action should address the verified cause of the problem and include checks to determine whether the action has been effective.Correction: action taken to eliminate a detected nonconformity.
Corrective action: action taken to eliminate the cause of a nonconformity and prevent recurrence.
Effectiveness verification: confirming that the corrective action has worked.
A supervisor may introduce an effective method. A maintenance technician may discover a reliable adjustment. An operator may develop a simple way to reduce handling time.
These improvements can create real value.
But if the knowledge remains with one individual, the benefit may disappear when that person changes shifts, takes leave or leaves the organisation.
An improvement should not remain dependent on the person who introduced it. It should become part of the way the organisation operates.
This requires evaluation, approval, standardisation, communication, training and monitoring.
Employees should be encouraged to identify opportunities, but changes to approved methods must be assessed before implementation. A change that saves time at one workstation may create a quality, safety or downstream problem elsewhere.
Expected reduction in time, cost or waste.
Impact on quality and process capability.
Manpower utilisation and workload.
Production output and capacity.
Safety and ergonomics.
Material consumption and handling.
Downstream and upstream effects.
Required changes to standard work and controls.
A new system may receive strong attention during implementation and then gradually lose priority. Daily production pressure takes over. Review meetings become focused on immediate output. Improvement actions remain open, and unresolved issues become normal.
This is particularly difficult in growing organisations where the founder or senior leader remains the central decision-maker for many matters.
As the business expands, the number of decisions exceeds what one person can manage directly. If responsibilities, review routines and decision rights are not developed, the organisation becomes dependent on constant intervention.
Management systems should make performance visible and support timely decisions without requiring the owner to personally resolve every issue.Some losses can be reduced through better planning, standard work, preventive maintenance, visual controls, material handling changes or improved supervision.
Other problems genuinely require new equipment, automation, software or infrastructure.
The challenge is to distinguish between the two.
A capital proposal should identify the problem, establish a baseline, estimate the expected benefit, examine implementation requirements and assess the risks.
For a smaller manufacturer, a phased improvement may be more practical than a large investment programme.
The question should not simply be, "Can we buy this solution?"
It should be, "What loss are we trying to eliminate, what evidence supports this investment, and how will we verify the result?"Management standards such as ISO 9001:2015 provide a framework for quality management, process control, competence, documented information, performance evaluation and continual improvement.
They can help organisations establish disciplined processes and clarify responsibilities.
However, certification alone does not guarantee operational excellence. A management system must be applied meaningfully to the organisation’s actual processes and risks.
The objective is not to maintain documents for an audit. It is to use the system to manage work, prevent problems, learn from performance and improve consistently.Manufacturing problems persist for different reasons: incomplete understanding, poor coordination, unclear responsibility, weak data, inadequate competence, financial constraints or failure to sustain improvements.
These causes may exist together.
The presence of a machine, software platform, standard or improvement tool does not prove that the organisation is using it effectively.
Before selecting another solution, leaders need to understand the actual loss, establish its cause and examine the conditions that allow it to continue.
The next challenge is therefore diagnostic: how do we distinguish symptoms from root causes and identify the losses that matter most?A factory misses its production target. Production blames material availability. Stores points to delayed production schedules. Maintenance reports machine breakdowns. Quality identifies rejection. Planning says the required information was not received on time.
Every explanation may contain some truth.
But which factor is responsible for the largest loss? Which condition is a cause, which is a consequence, and which is simply a visible symptom?
Root cause analysis in manufacturing begins by separating observation from assumption."Low productivity" is too broad. It does not identify where the loss occurs, how frequently it occurs or how much it affects output.
"Machine A produced 720 acceptable components against a planned 900 during the day shift. The shortfall was associated with 75 minutes of recorded stoppage, 40 minutes of changeover overrun and 65 rejected pieces."
This is still only a starting point. The figures must be verified, definitions agreed and the relevant production conditions understood.
The expected performance.
The actual performance.
The size of the gap.
Where and when the gap occurs.
The associated losses.
The evidence supporting each explanation.
A factory may classify downtime into breakdowns, changeovers, material shortages, tool adjustments, quality stoppages and minor stoppages.
The categories can then be arranged by total lost time, frequency or another relevant measure.
This makes it easier to identify the largest contributors.
The familiar 80/20 principle is a useful heuristic: a relatively small number of causes may account for a large proportion of the impact. It is not a fixed mathematical law, and actual distributions must be measured.
Pareto Analysis helps prioritise investigation. It does not prove why a loss occurs.
If breakdowns account for the largest recorded downtime, the next task is to investigate the failure modes and underlying conditions.Consider an illustrative example:
Why was the component unavailable? The stores location showed no stock.
Why was there no stock? The previous issue was not replenished.
Why was replenishment not triggered? The recorded balance remained above the minimum level.
Why was the balance inaccurate? Some material issues were not entered on time.
Why were transactions delayed? The process depended on manual entries without a defined responsibility and review routine.
The investigation has moved beyond the immediate shortage to a weakness in transaction control.
But the number of questions is not the objective. Some problems require fewer questions; others require deeper investigation.
The answers must be verified using records, observations, interviews and process evidence. A chain of plausible statements is not automatically a verified root cause.Man: people, competence, training and workload.
Machine: equipment condition, settings, tooling and maintenance.
Material: specification, variation, availability and handling.
Method: process sequence, standard work, parameters and controls.
Depending on the problem, measurement and environment may also be included.
For example, a dimensional rejection problem may involve operator technique, machine alignment, material variation, tool wear, inspection method and environmental conditions.
The diagram helps a team consider multiple possibilities instead of settling too quickly on one explanation.
However, a Fishbone Diagram is not proof. Each proposed cause must be investigated and verified.Production and rejection records.
Machine histories and maintenance work orders.
Process parameters and inspection results.
Material certificates and supplier records.
Shift-wise and operator-wise comparisons.
Direct observation at the workplace.
Controlled trials where appropriate.
A proposed cause is better supported when the evidence explains the observed pattern and a suitably controlled intervention produces the predicted change. Even then, alternative explanations and the limits of the test should be considered.
This is where the difference between a problem-solving meeting and a disciplined investigation becomes clear.Stage A can process 100 units per hour.
Stage B can process 60 units per hour.
Stage C can process 80 units per hour.
If the stages are connected in sequence and there are no alternative routes or buffers that change the system behaviour, Stage B limits the maximum flow to approximately 60 units per hour.
Increasing Stage A's capacity to 120 units per hour does not automatically increase total line output. It may simply increase work-in-progress before Stage B.
The correct improvement priority is to understand and manage the constraint.
The constraint may be a machine, a process, labour availability, inspection capacity, material supply, planning or even market demand.
The Theory of Constraints encourages leaders to identify the constraint, make the best use of it, align other activities around it, evaluate whether additional capacity is needed and repeat the process when the constraint moves.
The aim is not to maximise every machine independently. It is to improve the performance of the system.A manufacturer may investigate whether rejection rates vary with machine temperature, production speed, material batch, tool life or operator experience.
Regression can help estimate relationships and quantify how variables are associated with an outcome, subject to the model specification, data quality and assumptions.
But correlation does not automatically establish causation.
A relationship may be influenced by other variables, measurement limitations or the way data was collected. A model that fits historical data may not perform reliably under different operating conditions.
Regression is useful when the question is clearly defined, data is appropriate and the results are interpreted within their limitations.For example, a machining process may be affected by cutting speed, feed rate, depth of cut and coolant condition.
Changing one factor at a time can miss interactions between variables. A designed experiment can examine multiple factors systematically and help identify combinations that influence the response.
DOE can support process optimisation, quality improvement and process capability development.
However, experiments must be designed with technical understanding, suitable measurement systems, safety controls and appropriate operating limits.
An experiment is not simply an uncontrolled adjustment on a production machine.For example, poor delivery performance may involve planning accuracy, supplier reliability, inventory policy, production scheduling, machine availability and management decision-making.
Interpretive Structural Modelling (ISM) helps structure relationships among factors into a hierarchical model, often using expert judgement.
DEMATEL (Decision-Making Trial and Evaluation Laboratory) is used to examine influence relationships and distinguish factors that exert influence from those that are more affected.
MICMAC analysis classifies factors according to their driving power and dependence.
These approaches can be useful when a problem is complex and relationships are not easily captured through ordinary process data.
Their outputs depend on the quality of expert inputs, definitions and modelling assumptions. They should not be presented as objective proof of causality.For example, experts may describe machine condition as low, moderate or high risk, or supplier performance as weak, acceptable or strong.
Fuzzy Logic provides a mathematical framework for representing degrees of membership in such categories rather than forcing every judgement into a strict yes-or-no classification.
It can support decision models where information is imprecise or linguistic.
However, Fuzzy Logic does not turn uncertain information into verified facts. Its usefulness depends on appropriate membership functions, rules, inputs and validation.Manufacturing improvement decisions may draw on several research approaches.
Randomised controlled trials (RCTs) assign eligible units or participants to treatment and comparison groups using randomisation. When properly designed and implemented, they can provide strong evidence about causal effects.
Quasi-experimental methods examine interventions where random assignment is not available, using approaches such as difference-in-differences or comparison groups. Their conclusions depend on assumptions that must be examined.
Longitudinal studies follow organisations, processes or people over time. They can reveal whether changes persist, decline or evolve.
Case studies provide detailed understanding of a specific organisation or intervention. They can be valuable for explaining implementation, context and practical challenges, but their findings should not automatically be generalised to every factory.
Cross-sectional analysis examines information at a particular point or period. It can identify patterns and associations but may not establish how those patterns developed.
The method should match the question being asked.A new ERP system may slow transactions while employees learn the process. A revised production layout may initially create disruption. New standard work may reduce output briefly while operators develop competence.
This pattern is sometimes described as a J-curve: performance declines initially and improves later.
But a J-curve is not guaranteed. A temporary decline should not automatically be defended as evidence that an improvement is working.
Leaders should define expected transition costs, monitor leading indicators, verify implementation quality and establish decision points for continuing, adjusting or stopping the intervention.
A poorly implemented change may simply continue to perform poorly.If a production team is rewarded only for machine output, it may produce more than the next process can consume. If purchasing is measured only on price reduction, larger orders may increase inventory and working capital. If maintenance is measured only on repair response time, preventive work may receive less attention.
This is one reason performance measures must reflect the wider business objective.The Hawthorne effect is commonly used to describe changes in behaviour associated with awareness of observation. The research history and interpretation are more nuanced than the simplified claim that people always perform better when watched.
Goodhart's law is often summarised as: when a measure becomes a target, it can cease to be a good measure. It is a useful warning about the unintended consequences of poorly designed KPIs.
A metric should encourage the behaviour that improves the system, not merely the number that appears in a report.Plan: Define the problem, establish the baseline, investigate causes and decide the proposed action.
Do: Implement the change on an appropriate scale, with responsibilities and controls defined.
Check: Compare actual results with the expected outcome and verify whether the cause has been addressed.
Act: Standardise an effective improvement or revise the approach if the result is insufficient.
PDCA is not a paperwork exercise. It is a disciplined learning cycle.
The organisation should retain evidence of what was changed, why it was changed, what happened and how the result will be sustained.Pareto Analysis helps prioritise major contributors.
Five Whys helps explore possible causal chains.
Fishbone Analysis organises possible causes.
Root-cause verification tests explanations against evidence.
Theory of Constraints focuses on the system's limiting factor.
Regression examines statistical relationships.
DOE studies the effects of process factors and their interactions.
ISM, DEMATEL and MICMAC help structure complex interdependencies.
Fuzzy Logic supports reasoning with imprecise information.
Research designs help assess the strength of evidence.
PDCA structures implementation and learning.
The purpose is not to use the largest number of tools. It is to select the simplest appropriate method that can answer the question reliably.
A manufacturing organisation does not need more analysis for its own sake. It needs to know which losses matter, why they occur and what action will produce a measurable result.
The next part examines what research and real organisations demonstrate about the relationship between management practices, technology and sustained performance.The difference between having a solution and making it work is not merely a theoretical argument. Research and company examples show how management practices, implementation capability and sustained execution influence manufacturing performance.
The evidence must be interpreted carefully. A controlled field experiment, a long-term follow-up study, an official company description and a company-reported improvement figure do not have the same evidential status.
The examples below are presented with those distinctions in mind.
A landmark study by Nicholas Bloom, Benn Eifert, Aprajit Mahajan, David McKenzie and John Roberts examined management practices in Indian textile plants.
The field experiment involved 28 plants belonging to 17 firms in the Mumbai area. The researchers introduced a package of management practices covering areas such as production, quality, inventory, maintenance and operations management.
The study reported an average productivity improvement of 17% in the first year among treatment plants.The intervention was not simply the purchase of new machinery. It involved introducing and applying management practices that helped organisations identify problems, measure performance and manage operations more systematically.
The research also identified information barriers as an important reason why firms had not adopted such practices earlier. Some firms lacked information about the potential benefits or faced constraints in accessing the expertise needed to implement them.
Management practices can have a measurable effect on manufacturing productivity. The availability of a practice is not enough; organisations must understand it, adopt it and apply it.
The result should not be interpreted as a promise that every factory will achieve a 17% improvement. The study examined a particular group of firms, an intervention package and specific operating conditions.
Its importance lies in demonstrating that management capability can be a significant source of productivity improvement.A later study by Bloom and colleagues revisited the Indian manufacturing firms over a longer period.
The research examined whether management practices introduced through the earlier intervention continued to be used and how the effects evolved.
The follow-up found that around half of the practices initially adopted had subsequently been dropped. The treatment-control performance difference nevertheless persisted.
The researchers associated practice drop-off with factors including management turnover, reduced time spent by directors at the plants and familiarity with the practices.
This highlights a challenge that is often underestimated in operational improvement: implementation is not the same as institutionalisation.
A practice may work during a focused improvement programme but weaken when management attention changes, key people leave or operating pressure increases.Management attention, ownership, review routines and organisational continuity influence whether new practices become part of everyday operations.
The study does not suggest that every practice will disappear or that every organisation will experience the same pattern. It demonstrates why sustaining improvements deserves deliberate management attention.
The Toyota Production System is widely recognised for its emphasis on eliminating waste, improving flow and building quality into the process.
Two central concepts are:
Just-in-Time: Producing and supplying what is needed, when it is needed and in the quantity needed, with the aim of reducing unnecessary inventory and improving flow.
Jidoka: Building the ability to identify abnormalities and stop or respond to problems rather than allowing defects to move through the process unchecked.
The system also uses practices such as Kanban, standardised work and Kaizen.
These practices are connected. Just-in-Time depends on stable processes and reliable coordination. Jidoka depends on the ability to detect abnormalities and respond effectively. Standardised work provides a reference for identifying deviations and making improvements.It is an integrated management and production philosophy supported by disciplined execution.
Toyota's official descriptions explain the system and its principles. They should be understood as company documentation, not as an independent controlled evaluation of every claimed outcome.
The broader lesson is the importance of aligning production flow, quality, standard work and continuous improvement.Dabur India's 2024–25 reporting describes a substantial programme of Kaizen activity.
Dabur reported 2,700 Kaizen projects and cost savings exceeding ₹4.21 crore in its FY 2024–25 reporting.
These figures indicate the scale of improvement activity reported by the company.
Kaizen is most useful when employees are able to identify practical opportunities, improvements are evaluated and effective changes are incorporated into standard processes.
The number of projects alone does not establish the quality or long-term impact of every improvement. Financial savings also depend on the company’s measurement and reporting methodology.
Continuous improvement can be organised as a structured management activity rather than left to occasional initiatives.
The example demonstrates the scale at which a large organisation reports employee-driven improvement activity. It should not be treated as a guaranteed benchmark for smaller manufacturers.
A case study published through the US Manufacturing Extension Partnership describes support provided to MSP Manufacturing.
The case involved training in Mastercam CNC software and Faro Arm coordinate measurement equipment, alongside capability development related to quality and internal auditing.
A reported 25% time saving in the quality inspection process.
An estimated annual productivity gain of US$50,000.
A reported sales increase of 5–10%.
Eight employees trained.
Two employees trained as AS9100D internal auditors.
Two new positions created.
These are outcomes reported in the NIST Manufacturing Extension Partnership case study. The annual productivity figure is an estimate reported by the case study, not an independently audited causal estimate.
The example is relevant because the intervention was not simply about purchasing equipment or software. It also involved developing the skills required to use the technology and strengthening measurement and quality capability.Technology investment is more useful when supported by employee competence, process integration and the ability to convert new capability into improved operating performance.
The Indian textile experiment provides controlled research on management practices and productivity. Its follow-up highlights the difficulty of sustaining those practices.
Toyota illustrates an integrated production philosophy. Dabur reports the scale of its Kaizen activity. The MSP case demonstrates how training and technical capability can support operational improvements.
They do not prove that one management model or intervention will work identically in every factory.
They do reinforce an important distinction:
Operational performance improves when people, processes, technology and management systems work together.
The final question is practical: what should manufacturing leaders do differently inside their own organisations?Manufacturing leaders do not need another collection of disconnected improvement slogans. They need a practical way to understand performance, prioritise losses, implement changes and ensure that improvements continue after the initial attention has passed.
The following approach connects diagnosis with execution.
A missed production target may be linked to machine availability, material shortages, quality rejection, changeover time, planning accuracy or manpower capability.
Treating each incident as an isolated problem can lead to repeated corrective actions without addressing the conditions connecting them.
Leaders should examine the complete production flow and identify whether several visible problems share an underlying cause.
Is the priority to:
Increase saleable output?
Improve delivery performance?
Reduce manufacturing cost?
Improve product quality?
Increase capacity utilisation?
Reduce breakdowns?
Lower inventory and working capital?
Improve responsiveness to customer demand?
These objectives can influence one another, but they are not interchangeable.
For example, increasing machine utilisation by producing unnecessary inventory may not improve business performance.
The improvement objective must be connected to the business requirement.A baseline establishes the starting point against which improvement can be evaluated.
Depending on the problem, it may include:
Planned and actual production.
Good output, rejection and rework.
Machine availability and downtime.
Changeover and setup time.
Cycle time and process capacity.
Material shortages and waiting time.
Inventory accuracy and work-in-progress.
Maintenance response and recurrence.
Delivery performance.
Definitions must be consistent. A production target should have a clear basis. Downtime categories should be understood across shifts. Rejection and rework should not be combined in a way that hides the actual loss.
The baseline must also be checked against physical operations.
A report is a starting point for investigation, not a substitute for observing the process.Compare reports with actual conditions. Observe the machine, material movement, operator method, inspection process and work queue.
Ask employees what interrupts their work and what they do when an abnormality occurs.
A discrepancy between the report and the physical process is not merely a data issue. It may reveal a weakness in the operating system.A factory may have dozens of improvement opportunities, but resources are limited.
Financial impact.
Lost production or capacity.
Customer and quality risk.
Delivery consequences.
Frequency and recurrence.
Safety and compliance.
Implementation effort and feasibility.
An immediate correction may be necessary to restore production or protect a customer.
But the organisation must also determine whether a deeper action is required.
For example, replacing a failed component may restore a machine. Investigating why the component failed repeatedly may prevent future breakdowns.
Reworking a defective batch may protect delivery. Correcting the process condition that caused the defect may prevent recurrence.
Both levels of action matter. The first protects immediate operations; the second improves the system.Before investing in additional equipment or increasing capacity across every process, identify what is limiting the overall flow.
If inspection is the constraint, adding machining capacity may increase waiting inventory. If material availability is limiting production, a faster machine will not solve the shortage.
Understand the constraint, improve its effective capacity and coordinate other processes around it.Only then assess whether additional investment is justified.
A practical management rhythm should connect different levels of decision-making.
Daily reviews should focus on safety, production plan versus actual, major losses, quality issues, material shortages and immediate actions.
Weekly reviews should examine recurring problems, maintenance priorities, delivery risks, improvement actions and cross-functional dependencies.
Monthly reviews should evaluate trends, cost, capacity, customer performance, investment requirements and progress against business objectives.
The purpose is not to create more meetings. It is to ensure that issues are identified at the right level and decisions are followed through.What specific loss or requirement will it address?
Is the existing process sufficiently stable?
Is the required data reliable?
Can the current team operate and maintain the solution?
What training and support will be required?
How will the result be measured?
What are the implementation, maintenance and lifecycle costs?
What happens if the system fails or information is unavailable?
Operators need to understand standard work, process parameters and abnormality response. Technicians need the skills to maintain equipment and diagnose failures. Supervisors need to manage production, quality, manpower and problem-solving. Managers need to coordinate functions and make decisions using reliable information.
Training should be linked to job requirements and verified through demonstrated competence.
A capable workforce reduces dependence on a few individuals and improves the organisation’s ability to respond to changing conditions.This may require:
Updating SOPs and work instructions.
Revising process parameters and control plans.
Updating maintenance standards.
Training affected employees and shifts.
Communicating revised responsibilities.
Monitoring the relevant performance indicators.
Auditing adherence and effectiveness.
Reviewing recurrence and unintended consequences.
In many growing manufacturing businesses, the owner remains involved in production decisions, purchases, customer issues, maintenance priorities and employee matters.
This involvement can be valuable during the early stages. But as the organisation grows, constant dependence on the owner can slow decisions and limit expansion.
Clear roles and decision rights.
Reliable information.
Defined processes and standards.
Competent managers and supervisors.
Cross-functional coordination.
Regular performance reviews.
Controlled problem-solving.
Continuous improvement.
The purpose is not to remove leadership from operations. It is to build an organisation in which people can make appropriate decisions and deliver results without waiting for the owner to intervene in every issue.
Understand actual plant performance. Examine processes, people, equipment, material flow, quality, maintenance, planning and management practices. Establish the baseline and identify the losses that matter.
Investigate the underlying causes. Prioritise opportunities, evaluate practical solutions, define responsibilities and support implementation with appropriate methods and evidence.
Standardise effective improvements, develop competence, monitor results and strengthen the management routines required to prevent recurrence.
This is the foundation of the approach followed by Sampurna Operations Consultancy.If production targets are repeatedly missed, capacity remains underutilised, quality losses continue or operational improvements fail to last, the first requirement is not another general recommendation.
Sampurna Operations Consultancy offers manufacturing organisations a structured approach to examining plant performance, identifying operational losses, investigating their underlying causes and developing practical improvement plans for implementation and sustainment.
The engagement is grounded in the realities of the factory: equipment, processes, people, material flow, production planning, maintenance, quality and management control.
The objective is to move beyond recommendations towards practical, measurable operational improvements and the management capability required to sustain them.But the ability to convert those capabilities into consistent business performance remains an organisational challenge.
A factory does not become operationally excellent simply because it has modern equipment, advanced software or a collection of improvement tools.
It becomes stronger when its people understand the process, its systems support decisions, its departments work toward shared outcomes, its problems are investigated using evidence and its improvements become part of everyday operations.
The next competitive advantage may not come only from buying a more advanced machine.
It may come from making better use of the machines, people, processes and resources already available.
Technology creates possibilities. Operational excellence turns those possibilities into performance.The references below identify the principal research, standards, books and institutional material used in this report. Where numerical outcomes are discussed, the relevant organisation or publication is identified as the source.
OECD publications on manufacturing, productivity, digitalisation and industrial transformation.
World Economic Forum publications on the Fourth Industrial Revolution and advanced manufacturing.
National Institute of Standards and Technology (NIST), resources on smart manufacturing and manufacturing systems.
United Nations Industrial Development Organization (UNIDO), publications on industrial development and manufacturing capability.
Bloom, N., Eifert, B., Mahajan, A., McKenzie, D. and Roberts, J. (2013). “Does Management Matter? Evidence from India.” The Quarterly Journal of Economics, 128(1), 1–51. https://doi.org/10.1093/qje/qjs044.
Bloom, N., Mahajan, A., McKenzie, D. and Roberts, J. (2020). “Do Management Interventions Last? Evidence from India.” American Economic Journal: Applied Economics, 12(2), 198–219. https://doi.org/10.1257/app.20180369.
International Organization for Standardization. ISO 9001:2015, Quality management systems — Requirements.
Deming, W. Edwards. Out of the Crisis.
Ishikawa, Kaoru. Guide to Quality Control.
Goldratt, Eliyahu M. and Cox, Jeff. The Goal: A Process of Ongoing Improvement.
Toyota Motor Corporation. Official descriptions of the Toyota Production System, Just-in-Time and Jidoka.
Montgomery, Douglas C. Design and Analysis of Experiments.
Montgomery, Douglas C. Introduction to Statistical Quality Control.
Hernán, Miguel A. and Robins, James M. Causal Inference: What If.
Warfield, John N. Publications on Interpretive Structural Modelling.
Fontela, Emilio and Gabus, André. Foundational work on the DEMATEL method.
Godet, Michel. Publications on structural analysis and MICMAC.
Dabur India Limited. Integrated Annual Report 2024–25. Company-reported Kaizen activity and savings figures (2,700 projects; savings exceeding ₹4.21 crore).
National Institute of Standards and Technology, Manufacturing Extension Partnership. “Advanced Technical Skills Training Leads to Improved Productivity for Purdue MEP Client.” MSP Manufacturing institutional success story. https://www.nist.gov/mep/successstories/2024/advanced-technical-skills-training-leads-improved-productivity-purdue-mep.
This report draws on peer-reviewed research, published manufacturing practices, company disclosures and institutional case studies. Each source is cited in connection with the relevant discussion. Reported figures and outcomes are presented in accordance with the information published by their respective sources. The nature and scope of the available evidence should be considered when interpreting individual findings.
