How to better manage planning, production execution, quality, equipment, materials, traceability and cost
Manufacturing business areas
Different manufacturing business areas vary significantly in terms of materials, technological processes, batches, quality requirements, equipment and traceability depth, therefore they are analysed separately.
The production plan does not reflect real constraints
Plans are constantly amended manually, resulting in more urgent changeovers, overtime, work-in-progress and delayed orders.
Biggest opportunity
Unified order, product and production data
Connect order, product version, materials, technological route, operations fact, quality results, equipment status, traceability and actual costs.
Recommended first step
Select and digitalise one production flow
For one product family, connect the task, actual operations, materials, downtime reasons, quality check and order status.
Manufacturing digitalisation creates value when data helps to reduce downtime, scrap, delays or excessive consumption during the process, rather than merely providing a report after the fact.
Complete sector analysis
The full digital sector analysis is presented below. A more detailed analysis tailored to the specific operating model is available on the business area pages.
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How this sector operates
Production involves the conversion of raw materials, components or semi-finished goods into a final or intermediate product. Serial, process, project and custom production models differ, but for all of them the relationship between order, product version, materials, equipment, operations, quality and actual result is essential.
Every physical action depends on correct information
Product version, material, equipment, operator, technological parameter and quality requirement must coincide in a specific operation.
A small deviation can cause significant loss
Incorrect material, recipe, drawing, parameter or sequence of operations can cause defects, rework, downtime or loss of an entire batch.
Capacities are limited and interdependent
Equipment, people, tools, moulds, materials and quality checks form a common system of constraints.
Product and technology versions must be managed precisely
Changes to drawings, bills of materials, recipes, routes and instructions must reach planning and the production floor on time.
Traceability is important for quality and risk management
In many activities it is necessary to know from which materials, according to which version, with which equipment and under which conditions a specific product or batch was manufactured.
The age of manufacturing technologies varies greatly
Newly connected equipment often operates alongside older machines and individual controllers, requiring flexible data collection.
Market and technology context
Manufacturing transformation is moving from isolated equipment automation towards interconnected planning, execution, quality, maintenance and analytics processes. MES, industrial data platforms, APS, machine vision and digital models enable faster response to deviations, but their value depends on data quality and clear employee responsibilities.
Shorter lead times and smaller batchesCustomers expect faster fulfilment and greater customisation, so the plan must be adjusted more frequently to reflect real constraints.
Workforce and skills shortagesDigital instructions, automatic data capture and decision support reduce reliance on individual employees' memory.
Pressure on material, labour and energy costsLosses must be visible at product, order and operation level to enable accurate cost management.
Growing traceability and quality requirementsCustomers and auditors increasingly demand digital evidence of origin, process parameters, inspections and changes.
Volatile supply chainsMaterial delays and demand changes increase the need to rapidly replan production based on the actual situation.
More mature industrial AI and robotics solutionsVision analytics, predictive models and more flexible robotics are becoming more practical, but their reliability still depends on process and data quality.
Digital maturity model
0
Manufacturing managed with paper and spreadsheets
ERP or accounting is used, but planning, execution, quality, maintenance and documents are mostly managed manually.
1
Individual processes recorded digitally
Some operations, materials, downtimes and quality data are recorded in systems, but information is delayed and not consistently linked.
2
Core planning and execution partially linked
ERP, warehouse, equipment or production data are connected in several key scenarios, but exceptions are still managed manually.
3
Core manufacturing execution processes integrated Typical current situation
Order, operation, material and quality data are linked in the main production flows, and the plan is regularly compared with actual progress.
4
Manufacturing managed with real-time data Siektina
Equipment, operator, material and quality data are used to manage deviations and adjust the plan during the process.
5
Manufacturing predicts and adapts safely
Forecasts, digital models and AI help optimise planning, quality, process and maintenance, whilst maintaining clear human control.
Key finding
In manufacturing companies, individual equipment is often better automated than the entire order fulfilment process. ERP sees the plan, the equipment system sees signals, the quality team sees inspection results, but the real order status is still clarified by phone calls or spreadsheets.
When product version, materials, actual operations, scrap, downtime and maintenance work remain in separate systems, planners see deviations too late, and managers cannot accurately determine cost and causes of losses.
It is worth first selecting one production flow or product family and connecting the plan, execution, quality, materials and equipment. Only then is it worth expanding into advanced planning, predictive maintenance, image analysis or AI.
Related production digitalisation topics
Manufacturing execution systemProduction planning and APSProduct lifecycle managementProduction quality managementDigitalisation of technical maintenanceIndustrial Internet of ThingsProduction analytics and AI
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The production plan does not reflect real constraints
Critical
Planning is based on standard times and late-updated information about materials, equipment, tools, workers and maintenance work.
Consequences
Plans are constantly amended manually, resulting in more urgent changeovers, overtime, work-in-progress and delayed orders.
Actual production progress is not visible in real time
Critical
Operation start and finish, quantity produced, materials consumed, downtime and deviations are recorded on paper, belatedly or in several unconnected systems.
Consequences
Supervisors and planners see deviations too late, cannot reliably assess order status and make decisions based on incomplete information.
Quality problems are detected too late
Critical
Inspections, process parameters, defect codes and corrective actions are not always linked to the specific operation, equipment, material batch and product version.
Consequences
The same deviation recurs, defects are detected at the end of the process, increasing the risk of rework, write-offs and customer complaints.
Maintenance often still only begins when a failure occurs
Critical
Equipment condition, failure history, maintenance work, spare parts and production load are not planned in a single process.
Consequences
Unplanned downtime, emergency repairs, long stoppages and excessive spare parts accumulation increase.
Unclear product and document versions are used on the production floor
High
Drawings, bills of materials, recipes, process routes and work instructions are stored in different locations or transferred as files.
Consequences
An operator may use outdated information, errors are repeated in series, and the impact of a change on already manufactured units is difficult to trace.
Inadequate material and batch traceability
High
Material receipt, warehouse movement, release to operation, stock balances, batch or serial numbers and consumption are recorded with inconsistent detail.
Consequences
It is difficult to identify affected products, manage recalls, substantiate quality and accurately assess material losses.
Production systems use different data and identifiers
High
ERP, CAD or PLM, MES, WMS, QMS, CMMS, SCADA and laboratory systems exchange data via files, individual interfaces or use different object codes.
Consequences
Multiple versions of the same product, operation or batch emerge, errors are difficult to detect, and connecting new equipment and facilities becomes expensive.
Operator work is burdened by paperwork and system duplication
Medium
The employee receives tasks, instructions and quality forms through different channels, and must enter actual data in multiple places.
Consequences
Administrative time, delayed entries, errors and dependence on experienced employees' knowledge are increasing.
Actual product and order cost is revealed too late
Medium
Standard times and costs are not continuously compared with actual operation, material, scrap, energy, tooling and downtime costs.
Consequences
It is unclear which products, orders or customers generate margin, whilst pricing and process improvement rely on rough estimates.
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Digital execution of a single production flowVery high impactFor a selected product family, connect the production task, actual operations, materials, downtime reasons, quality inspection and order status.
Digital operator and supervisor workstationHigh impactPresent in a single interface tasks ranked by priority, valid instructions, parameters, quality actions, materials and support for resolving deviations.
Unified order, product and production dataVery high impactConnect order, product version, bill of materials or recipe, routing, materials, actual operations, quality and finished product using common identifiers.
Quality control during the production processVery high impactLink inspection, parameters, non-conformances and corrective actions to a specific operation and respond before the entire batch or product is completed.
Planning based on real constraintsVery high impactWhen creating and adjusting the plan, evaluate materials, equipment, people, tools, changeovers, maintenance and technological dependencies.
Equipment maintenance based on condition and production planHigh impactIntegrate maintenance schedules, failure history, condition signals, spare parts and production schedule.
Actual cost and production loss analyticsHigh impactAt product, order and operation level, integrate time, materials, scrap, downtime, energy and other cost factors.
Biggest opportunity
Unified order, product and production data
Connect order, product version, materials, technological route, operations fact, quality results, equipment status, traceability and actual costs.
Greater utilisation of equipment and workforce capacity
Less scrap, rework and material losses
Shorter production cycle
More reliable delivery times
Fewer unplanned downtimes
More accurate product and order costing
Reliable product, batch and material traceability
Expected impact on operations and financial performance
Greater production capacityMore accurate planning, faster deviation management and reduced downtime enable greater output with existing equipment and team.
Lower cost priceActual times, material consumption, scrap, energy and downtime help to manage product and order profitability more accurately.
Better qualityEarlier detection of deviations and correlation with process parameters reduce scrap, rework, warranty cases and customer complaints.
More reliable delivery timesReal capacity, materials and execution data enable more accurate planning and faster replanning when supply or equipment is disrupted.
Lower working capital requirementMore accurate planning reduces excess raw materials, work in progress, safety stock and urgent purchases.
More reliable traceabilityProduct and batch history enables faster identification of the source of deviation, affected production and required action.
Reduced dependence on individual employee knowledgeDigital instructions, parameter history and information from previous cases help to train employees faster and resolve deviations.
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Problema
Actual production progress is not visible in real time
Supervisors and planners see deviations too late, cannot reliably assess order status and make decisions based on incomplete information.
→
Sprendimo kryptis
Manufacturing execution system
Manages production tasks, operation start and finish, quantities, materials, parameters, downtime, scrap and order status, and integrates with ERP, equipment, quality and warehouse systems.
Problema
Operator work is burdened by paper and system duplication
→
Sprendimo kryptis
Manufacturing execution system
Manages production tasks, operation start and finish, quantities, materials, parameters, downtime, scrap and order status, and integrates with ERP, equipment, quality and warehouse systems.
Problema
Quality problems are detected too late
The same deviation recurs, defects are detected at the end of the process, increasing the risk of rework, write-offs and customer complaints.
→
Sprendimo kryptis
Manufacturing execution system
Manages production tasks, operation start and finish, quantities, materials, parameters, downtime, scrap and order status, and integrates with ERP, equipment, quality and warehouse systems.
Problema
Material and batch traceability is insufficient
→
Sprendimo kryptis
Manufacturing execution system
Manages production tasks, operation start and finish, quantities, materials, parameters, downtime, scrap and order status, and integrates with ERP, equipment, quality and warehouse systems.
Problema
Production plan does not reflect real constraints
→
Sprendimo kryptis
Advanced planning and scheduling system
Plans orders according to real constraints of materials, equipment, people, tools, changeovers, maintenance and technology.
Problema
Actual production progress is not visible in real time
Supervisors and planners see deviations too late, cannot reliably assess order status and make decisions based on incomplete information.
→
Sprendimo kryptis
Advanced planning and scheduling system
Plans orders according to real constraints of materials, equipment, people, tools, changeovers, maintenance and technology.
Recommended digital solutions
The implementation sequence should begin with reliable product, order, operations and equipment data. An MES, APS, quality, maintenance or analytics system should not become yet another isolated data island.
Manufacturing execution system
Manages production tasks, operation start and finish, quantities, materials, parameters, downtime, scrap and order status, and integrates with ERP, equipment, quality and warehouse systems.
Advanced planning and scheduling system
Plans orders according to real constraints of materials, equipment, people, tools, changeovers, maintenance and technology.
Manages inspection plans, measurements, non-conformances, defect codes, corrective actions, audits and customer complaints, linking them to product, batch, operation and process parameters.
Maintenance management system
Manages equipment structure, preventive maintenance, failures, work orders, spare parts, condition signals and maintenance costs, aligning work with the production schedule.
Industrial data and integration platform
Connects ERP, MES, product version, warehouse, quality, maintenance, SCADA, PLC and sensor data with common identifiers and managed integrations.
Production analytics and actual costing platform
Combines planned and actual times, materials, scrap, downtime, energy and other cost factors at order, product and operation level.
Investment priorities
Select and digitalise one production flowFor one product family, connect the task, actual operations, materials, downtime reasons, quality check and order status.
Standardise product, order and operations dataDefine common identifiers for product, version, order, operation, material batch, equipment and quality record.
Connect planning, quality and maintenanceAdjust the plan according to actual production status, and integrate quality and maintenance actions into the overall process.
Create actual cost and loss analyticsLink time, materials, scrap, downtime and energy to specific products and orders.
Only then expand forecasting, digital models and AIDeploy advanced scenarios only with sufficient historical data, a clear decision process and measurable business benefit.
Key implementation conditions
Start with one production flow, not the entire factory
The first implementation should cover one product family, line or order scenario where changes in capacity, quality or lead time can be measured.
Separate planned values from actual data
The system must maintain standard times, costs and parameters, whilst recording actuals and the reasons for deviation.
Product and process changes must have a clear effective date
Design or technology changes must be linked to affected orders, batches and documents.
Design the operator workstation in the real shop floor environment
Screen location, gloves, language, work pace, error correction and practical lists of downtime and defect causes must be considered.
Production must operate safely even with communication disruptions
Provide for local operation of critical functions, subsequent data synchronisation, segmented access, controlled remote service and regularly tested recovery.
Recommended implementation sequence
01
Single production flow analysis
Determine how a selected order moves from planning to production, quality confirmation and cost calculation.
Order and production process map
Systems and equipment data map
Key identifiers model
Initial KPIs
02
Definition of first version and integrations
Select one line, product family or process and define the boundaries of planning, execution, quality and equipment data.
Clear first version boundaries
ERP, equipment and other systems integration project
Data quality rules
User work scenarios
03
Production execution and data collection implementation
Establish reliable registration of operations, quantities, materials, downtime, defects and statuses in the actual process.
Operator workstation
Automatic equipment data collection
ERP and production execution integration
Deviation and exception management
04
Integration of planning, quality and maintenance
Use actual production status to adjust the plan and integrate quality and maintenance actions into the overall process.
Planning based on real constraints
Digital quality inspections
Maintenance system integration
Batch and product traceability
05
Expansion and advanced optimisation
Expand the proven model to other lines and only deploy predictive maintenance, image analysis and optimisation scenarios when reliable data is available.
Standard process and data model
Connection of additional lines and equipment
Predictive maintenance or AI quality control pilot
Centralised operations and cost analytics
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Recommended KPIs
Overall Equipment Effectiveness%
Measure the combined result of availability, performance and quality on the most critical equipment or lines.
Unplanned downtime percentage% of planned production time
Assess the impact of breakdowns and other unexpected stoppages on capacity.
First-time-right production percentage%
Measure the proportion of production manufactured without rework or additional correction.
Production plan fulfilment%
Measure what proportion of planned orders or quantities are completed on time.
Production cycle timehrs
Track the time from production start to finished product for a selected product family.
Fully traceable products or batches percentage%
Assess how much production can be linked to materials, product version, operations, equipment and quality records.
Actual vs standard cost variance%
Measure the accuracy of planned standards and identify uncontrolled cost sources.
On-time and in-full orders percentage%
Assess the impact of planning and production execution on the delivery deadline and quantity promised to the customer.
Key risks
Actual data does not explain the cause of deviationAutomated signals indicate stoppages or defects, but do not explain why they occurred, whilst manual cause records remain incomplete.Kaip suvaldyti Align automatic actuals with simple cause confirmation and continuously monitor data completeness.
The first stage covers too many devices and processesAttempting to connect the entire factory at once lengthens the project and delays actual use.Kaip suvaldyti Select one complete production flow, clear KPIs and expand only after confirming results.
Implementation disrupts productionIncorrect integration, parameter or task transfer can affect a critical process.Kaip suvaldyti Start with data reading, implement in parallel and have a testing and clear rollback scenario.
Employees continue to use paper or spreadsheetsThe system does not reflect actual exceptions, duplicates work or slows down operator and supervisor actions.Kaip suvaldyti Design together with end users, measure usage and eliminate duplicate actions.
Increased system connectivity raises cyber riskNew connections and remote access expand the attack surface and can affect production continuity.Kaip suvaldyti Segment networks, restrict access, inventory equipment, manage remote service and test recovery regularly.
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Advanced solutions create value only when reliable product, process, equipment and quality data are available. In critical processes, AI recommendations must be verified, and automatic actions clearly restricted.
Already applied in the sector1
Equipment failure risk forecasting
Highly urgent
Models evaluate vibration, temperature, energy, cycles, error codes and other condition signals.
How it is applied Most relevant for critical equipment where failures are costly and condition changes can be reliably linked to maintenance history.
What value can be created
Fewer unplanned downtime incidents
More precise maintenance scheduling
Extended equipment and component lifecycle
What is needed for this to work
High-quality condition signals
Failure and maintenance history
Integration with CMMS and production schedule
Medium-termCommercial solutions are available
Market expansion2
Digital model of product, equipment or line
Highly urgent
A virtual model integrates design, technological and actual data of equipment, line, process or product.
How it is applied Used to test capacity, parameters and process changes before applying them in actual production.
What value can be created
Faster launch of new product or line
Lower risk of process changes
More accurate capacity and parameter optimisation
What is needed for this to work
Reliable product and process models
Real-time equipment and production data
Uniform object identifiers and data interoperability
How it is applied Suitable for repetitive visual inspections where defects can be clearly defined and sufficient samples of good and defective products can be collected.
What value can be created
Earlier defect detection
More consistent inspection
Reduced manual inspection workload
What is needed for this to work
Stable image capture environment
Labelled examples of good and defective products
Clear process for responding to detected defects
Short-term perspectiveCommercial solutions are available
Early stage3
More flexible robotics for variable tasks
Relevant
Robots combine image recognition, force sensing and learning to perform a wider range of physical tasks.
How it is applied Promising for parts picking, assembly, quality inspection and internal logistics where classical automation is too costly to adapt.
What value can be created
Greater automation flexibility
Reduced ergonomic risk
Ability to automate variable operations
What is needed for this to work
Standardised and safe working environment
Digital models of objects and processes
Clear rules for human-robot interaction
Long-term perspectivePilot projects
AI models at the edge
Relevant
Models run near the equipment to process signals quickly without transmitting all data to the central system.
How it is applied Relevant quickly for quality control, anomaly detection, energy optimisation and processes where low latency is critical.
What value can be created
Faster response to deviations
Lower network and cloud load
Greater operational resilience
What is needed for this to work
Local data processing infrastructure
Model version and quality management
Industrial cybersecurity architecture
Medium-termApplied in practice
AI assistants for technical and production information
Relevant
AI helps search technical documentation, prepare draft instructions, analyse failure history and summarise process deviations.
How it is applied The safest scenarios rely on approved company documents, provide sources and do not take over critical technological decisions.
What value can be created
Faster access to technical knowledge
Shorter onboarding of new employees
Less time searching for documents and historical cases
What is needed for this to work
Organised and versioned documentation
Access rights and confidentiality control
Response source and human approval mechanism
Short-term perspectiveCommercial solutions are available
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Where to start production digitalisation?
Select one important production flow or product family and link the order, product version, materials, operations, quality and equipment. This will reveal whether a production execution, planning, quality, maintenance or integration solution is needed first.
Does every manufacturing company need MES?
No. MES is most needed when actual production progress, downtime, materials, scrap and order status are currently recorded late or in multiple places. For simpler production, an integrated ERP module or a dedicated operator workstation may suffice.
How does a production execution system differ from ERP?
ERP manages orders, material requirements, inventory, finance and higher-level planning. A production execution system manages actual operations, quantities, materials, downtime, scrap and status on the shop floor and feeds the result back to ERP.
Can older production equipment be connected?
Often, yes. Controller interfaces, additional sensors, local data collection devices or operator confirmations can be used. The important thing is to collect only the data needed for the specific solution.
What should the first version of the project include?
It should cover one line, product family or order scenario and an end-to-end planning, execution and quality process. For example, automatic receipt of tasks, actual operations, materials, downtime reasons, quality checks and order status.
How to evaluate the return on investment of production digitalisation?
Measure OEE, unplanned downtime, plan fulfilment, cycle time, scrap, rework, work in progress, on-time deliveries, cost variance and employee time spent recording data. The number of connected devices alone does not show the benefit.
Next step
An assessment of where the most capacity and margin is lost in the production process
Planning, production execution, quality, equipment maintenance, materials, traceability and actual cost will be reviewed, helping to select one first version where the benefit can be clearly measured.