Where to start digitalisation in chemicals or packaging manufacturing?

The starting point should be a single product family with frequent recipe changes or compliance checks. In the first version, it is worth linking the approved recipe, raw material batches, actual process parameters, laboratory results and batch release.

Is ERP sufficient for recipe management?

ERP can store the base formula and material requirements, but often does not cover process parameters, laboratory methods, safety data, approval of alternatives and the impact of changes on all documents. This chain typically requires PLM, QMS or a specialised recipe layer.

When is it worth implementing MES?

MES is worth implementing when recipe versions are clear, raw materials are identified and facts are recorded by the operator. Otherwise, the system will merely collect inconsistent data more quickly.

How to assess batch traceability quality?

A practical test is to determine in a short time which finished batches used a specific raw material batch, what the process parameters were, quality results and to which customers the product was dispatched.

Where is AI most useful in chemicals manufacturing?

AI can create the greatest value in analysing process deviations, virtual quality sensors, yield and equipment condition. The model must show sources and not replace the decision of the technologist or quality specialist, but help identify the exception more quickly.

How to measure the return on investment of the first version?

Assess reduced recipe change hours, shorter batch release, lower scrap, better yield, fewer urgent laboratory tests and faster traceability response to the customer or audit.

Business area digitalisation analysis

Chemical, plastic and packaging product manufacturing: digitalisation opportunities

Integration of planning, execution, quality, traceability and equipment data for chemical mixtures, plastics, films, containers, packaging and related materials into a single managed production system.

Digital maturity Typical digital maturity Shows the level of technological and process digitalisation at which companies in the sector or business area typically operate today. A typical market situation is assessed, not the most advanced companies. The assessment consists of five equally weighted dimensions: Core system usage Whether ERP, CRM, WMS, MES, customer portals or other operationally important systems are widespread in companies. Process digitalisation How many core processes run in systems and how many are still managed manually. Systems integration Whether core systems exchange data between themselves or whether employees transfer information manually. Data quality and readiness Whether core data is structured, up-to-date, consistent and suitable for automation and analytics. Advanced data use Whether real-time analytics, forecasting, automated alerts, optimisation models or AI are used. The final score is the average of the five dimensions. 1–5 scale 1 very low maturity 2 low maturity 3 medium maturity 4 high maturity 5 very high maturity A low maturity score does not necessarily indicate low potential. On the contrary, low maturity and a high level of manual work may indicate significant untapped digitalisation value.: medium
Skaitmenizacijos potencialas Digitalisation potential Shows how much significant business value a typical sector or business area company can create by systematically digitalising core processes. The rating is calculated on a 100-point scale across five dimensions: Process frequency and scale 20 % An assessment of how frequently the digitalised processes recur and what proportion of operations they represent. Manual work intensity 20 % An assessment of the extent to which processes depend on email, telephone, Excel, paper documents and repeated data entry. Impact on revenue and costs 25 % An assessment of the potential effect on sales, margin, customer retention, administrative costs, errors, downtime or inventory. Growth and scale potential 20 % An assessment of whether digitalisation would enable operational capacity to be increased without expanding headcount and costs at the same rate. Impact on decisions and risk 15 % An assessment of the potential effect on data reliability, decision-making speed, customer experience, and the reduction of errors and operational risk. The final score is calculated according to the assessments and weights of all dimensions. 100-point scale 0–20 very low potential 21–40 low potential 41–60 moderate potential 61–80 high potential 81–100 very high potential A high score does not mean the solution will be easy to implement. It indicates the size of the potential value, not the implementation complexity.: 88/100
Biggest challenge: Quality control is separated from the production process
Biggest opportunity: Recipe and compliance impact management

Digitalisation in chemical, plastic and packaging production should start with a clear economic problem and a single traceable data chain, rather than with a general goal to 'implement MES' or collect as many equipment signals as possible.

How chemical, plastic and packaging product manufacturing works

The business area covers the production of chemical compounds, plastics, films, containers, packaging and related materials – from raw material and component preparation through to production, quality assurance, packing, warehousing and dispatch.

The importance of product and process versions

These data areas – recipes, raw material properties, safety data, colours, moulds, food contact and migration requirements – must be managed as valid information, not freely copied files.

The link between physical and digital processes

Systems must reflect the actual state of equipment, including reactors, extruders, moulding equipment, printing and lamination lines, as well as materials, operator actions and time.

The economics of exceptions

The greatest losses in chemical, plastic and packaging production arise through defects, scrap, breakdowns, changeovers and quality waiting, rather than through the ideal standard cycle.

The need for traceability and accountability

Solutions must be based on primary data and comply with REACH, CLP, hazardous substance, food contact and packaging requirements.

Market and technology context

In chemicals, plastics and packaging manufacturing, product data, composition and traceability demands are growing due to safety, circularity and packaging regulation, so formula and compliance data must be managed as a single system.

Product data and traceability pressureCustomers and regulatory processes expect rapidly available formula, raw material properties, safety data, colour, form, food contact and migration requirements and their link to actual production.
Competence shortageDigital instructions and decision history help retain dosing, mixing, reaction, extrusion, moulding, printing and lamination knowledge within the organisation.
Raw material, energy and capacity costsProduction needs to see costs and losses at product, batch and reactor, extruder, moulding equipment, printing and lamination line level.
Advanced analytics maturityAI and forecasting become practical only when laboratory results, process parameters, non-conformances and batch release are linked to reliable process context.

Typical value chain

Product and formulation preparation

Composition, raw material alternatives, safety and quality criteria and permissible process windows are validated.

Raw material receipt and release

Supplier batches, certificates, laboratory results and usage restrictions are verified.

Planning and preparation

Line, recipe sequence, cleaning requirements, mould, colour and material reservation are selected.

Batch production

Dosing, process parameters, stoppages, deviations, yield and waste are recorded.

Quality and compliance confirmation

Laboratory results are linked to the recipe version, raw material batches and finished product batch.

Packaging and dispatch

Labelling, certificates and customer documents are generated according to the actual batch composition and purpose.

Digital maturity pathway

Fragmented product and production data

Formulae, raw material properties, safety data, colour, form, food contact and migration requirements are kept in spreadsheets, documents and separate systems, and actual execution is verified after shift or batch.

Basic business systems

ERP manages orders and inventory, but dosing, mixing, reaction, extrusion, moulding, printing and lamination and quality facts remain on paper or in local tools.

Digitalised selected process

In one line or product family, the process of formula change, batch production and laboratory release for a single product family is digitalised, but integrations and common classifiers are still limited.

Integrated product and execution chain Typical current situation

Validated product and process information is linked to the plan, operator work, quality results and actual cost. Key managed areas: formulations, raw material properties, safety data, colours, forms, food contact and migration requirements.

Data-driven production Siektina

Planning, quality and maintenance in chemical, plastics and packaging manufacturing relies on real-time exceptions, root cause analysis and reliable line and product KPIs.

Adaptive and closed-loop production

The system in chemical, plastics and packaging manufacturing automatically adjusts permitted solutions based on product, process and equipment status, whilst AI recommendations are audited and measured.

Key conclusion

Chemical, plastic and packaging production has very high digitalisation potential, but value is created not by yet another separate system, but by a reliable link between product version, plan, actual execution and quality.

The recommended start is the process of formula change, batch production and laboratory release for a single product family. This scope allows measuring results without involving all lines and integrations at once.

Most common digitalisation challenges

The largest gaps arise when recipe versions, raw material properties and batches, safety data, colour and mould parameters, food contact and migration requirements are managed in separate systems. Dosing, mixing, reaction, extrusion, moulding, printing and lamination processes then leave no single reliable actual history, so the plan, execution status and quality decisions show a different picture.

Quality control is separated from the production process

Critical

Laboratory results, process parameters, non-conformances and batch release decisions are not consistently linked to the specific product or batch version, operation, equipment and cause of deviation.

Consequences: Quality decision-making takes longer, it is harder to identify the cause, and poor yield, additional tests, scrap, cleaning time and batch release delays may recur in other orders.

Weak batch and component traceability

Critical

It is not always possible to quickly reconstruct the full link: formula version, raw material batches, process parameters, laboratory results and final batch.

Consequences: In chemical, plastic and packaging production, the scope affected and the action required are determined slowly when a customer inquiry, audit, non-conformance or recall occurs.

Impact of formula changes on compliance is assessed manually

Critical

A change in raw material or concentration is checked separately for safety data, labelling, markets and customer specifications.

Consequences: Changes are slow, and a missed impact can cause serious non-conformance.

Fragmented production master data

High

Recipe versions, raw material properties and batches, safety data, colour and mould parameters, food contact and migration requirements are stored in different systems, files or employee-prepared spreadsheets, so there is no single valid product and production version.

Consequences: Changes reach dosing, mixing, reaction, extrusion, moulding, printing and lamination processes at different times, manual checks increase and the risk of producing according to outdated information grows.

Planning does not reflect real production constraints

High

Plans do not always account for raw material compatibility, cleaning sequence, recipe and colour changes, line capacity and laboratory waiting times, and the actual status of work already started.

Consequences: Priorities are changed at the last minute, waiting times increase, as do work in progress and the proportion of delayed orders in chemicals, plastics and packaging products manufacturing.

Production execution data is collected with a delay

High

The start and end of operations, quantities produced, material consumption, stoppages and reasons for deviations in the selected product family production and laboratory release flow are recorded with a delay or in multiple locations.

Consequences: Planners and responsible staff see too late that the selected product family production and laboratory release flow has deviated from the plan, so time for correction is lost.

Maintenance is mostly reactive

Medium

Data on reactor, extruder, moulding, printing and lamination line operating time, failures, status signals, spare parts and maintenance work is not aligned with actual load and production schedule.

Consequences: Unplanned stoppages disrupt the selected product family production and laboratory release flow, and repair and spare parts requirements are managed on an urgent basis.

Opportunities

Key digital opportunities

Formula and compliance impact managementVery high impactAutomatically identify which documents, labels, markets and customer approvals are affected by the change.Compliance and faster change management

Integrated quality and traceabilityVery high impactLink specification, batch, process parameters, inspections, deviations and final product.Less scrap and faster investigations

Integrated manufacturing execution managementVery high impactLink recipe version, raw material batches, process parameters, laboratory results and final batch with actual quantities, stoppages, deviation causes and responsible worker actions in the selected flow.Productivity and delivery reliability

Constraint-based planning and replanningVery high impactPlan according to real capacities, changeovers, materials, tools, quality and deadlines.Capacity utilisation and shorter cycle

Energy, yield and loss optimisationHigh impactMeasure energy, material consumption and non-conforming yield, additional tests, scrap, cleaning time and batch release delay at product, batch or serial unit level, so that causes of losses are visible where they occur.Cost and sustainability

Data-driven equipment maintenanceHigh impactConnect failures, sensors, operating hours, spare parts and maintenance schedules.Less downtime

Biggest opportunity

Recipe and compliance impact management

The greatest opportunity for the next phase is the recipe change, batch production and laboratory release process for a single product family.

Higher utilisation of equipment and labour capacity
Less scrap and unplanned downtime
Shorter production cycle
Better traceability of batches and components

Potential business impact

Capacity utilisationMore precise raw material compatibility, cleaning sequence, colour and formulation changes and line capacities in plan and actual execution status reduce waiting and urgent priority changes.
Quality and yieldQuality status becomes visible during the process, as the following data and solutions are linked: laboratory results, process parameters, non-conformances and batch release. This reduces late defects, rework and raw material losses.
Delivery reliabilityOrder deadlines are assessed based on actual dosing, mixing, reaction, extrusion, moulding, printing and lamination and material status, not periodic reports.
Traceability and riskA reliable chain between formulation, raw material properties, safety data, colours, forms, food contact and migration requirements enables faster responses to audit, claim or recall scenarios.
Scale and knowledge retentionDigital instructions and solution history reduce dependence on individual specialists' memory in chemical, plastics and packaging manufacturing.

Sprendimai

How to solve these problems

Solution directions linked to specific business area problems they address.

Problema

Production execution data is collected with a delay

Planners and responsible staff see too late that the selected product family production and laboratory release flow has deviated from the plan, so time for correction is lost.

Sprendimo kryptis

Manufacturing execution system (MES)

Manages dosing, mixing, reaction, extrusion, casting, printing and laminating tasks, approved product version, actual quantities, time, materials, stops and exceptions in a single selected stream.

Problema

Planning does not reflect real production constraints

Priorities are changed at the last minute, waiting times increase, as do work in progress and the proportion of delayed orders in chemicals, plastics and packaging products manufacturing.

Sprendimo kryptis

Manufacturing execution system (MES)

Manages dosing, mixing, reaction, extrusion, casting, printing and laminating tasks, approved product version, actual quantities, time, materials, stops and exceptions in a single selected stream.

Problema

Planning does not reflect real production constraints

Priorities are changed at the last minute, waiting times increase, as do work in progress and the proportion of delayed orders in chemicals, plastics and packaging products manufacturing.

Sprendimo kryptis

Advanced planning and scheduling system

Creates and adjusts the plan according to raw material compatibility, cleaning sequence, colour and recipe changes and line capacities, actual material status and ongoing production exceptions.

Problema

Quality control is separated from the production process

Quality decision-making takes longer, it is harder to identify the cause, and poor yield, additional tests, scrap, cleaning time and batch release delays may recur in other orders.

Sprendimo kryptis

Quality and traceability platform

Links laboratory results, process parameters, non-conformances and batch release to approved product version, actual materials, operations, equipment and final product.

Problema

Weak batch and component traceability

In chemical, plastic and packaging production, the scope affected and the action required are determined slowly when a customer inquiry, audit, non-conformance or recall occurs.

Sprendimo kryptis

Quality and traceability platform

Links laboratory results, process parameters, non-conformances and batch release to approved product version, actual materials, operations, equipment and final product.

Problema

Maintenance is mostly reactive

Unplanned stoppages disrupt the selected product family production and laboratory release flow, and repair and spare parts requirements are managed on an urgent basis.

Sprendimo kryptis

Equipment maintenance and reliability system

Manages equipment register covering reactors, extruders, casting equipment, printing and laminating lines, planned maintenance, failures, spare parts and condition signals with production context.

When it is worth starting

Investment justified

The data set in these areas – formulas, raw material properties, safety data, colours, shapes, food contact and migration requirements – has multiple versions or is frequently corrected manually
Dosing, mixing, reaction, extrusion, moulding, printing and laminating events are recorded after a shift or batch
For quality investigation, it is difficult to link laboratory results, process parameters, non-conformances and batch release to a specific batch, product or equipment
The costs of scrap, downtime, waiting or non-traceability in chemical, plastic and packaging production are significant
A clear scenario can be selected: the process of formula change, batch production and laboratory release for a single product family

Reikia atsargumo

It is unclear which problem has the greatest economic impact
There are no approved product and process versions
Equipment data is collected without product or batch context
The first version is planned for the entire plant at once

Recommended first version

First version – the process of changing a recipe for one product family, batch production and laboratory release. It must include validated master data, one real execution flow, a quality decision and a measurable economic result.

Validated work order and product version

The user receives only valid recipe, raw material properties, safety data, colour, form, food contact and migration requirements, and a clear operation and quality task.

Recording of actual execution

Quantities, time, materials used, dosing, mixing, reaction, extrusion, moulding, printing and lamination status, stoppages and exceptions are recorded.

Integrated quality and traceability control

Quality data and decisions – laboratory results, process parameters, non-conformances and batch release – are linked to product, batch, equipment and operation.

Exceptions and results dashboard

Managers see not a general report, but delayed, missing or risky statuses of a single product family recipe change, batch production and laboratory release process.

Kam pirmiausiaProduction operators or process executors · Shift or production supervisors · Planners and technologists · Quality specialists · Maintenance or engineering team
What not to include in the first versionCoverage of the entire plant and all products · Full integration of all legacy equipment · Complex autonomous AI optimisation · Preparation of historical data without a clear use case

Investment priorities

Recipe and compliance impact managementStart with the recipe change, batch production and laboratory release process for a single product family and measure the economic result before scaling up.
Integrated quality and traceabilityConnect quality information – laboratory results, process parameters, non-conformances and batch release – with the actual product, batch and process history.
Integrated manufacturing execution managementOnly after stabilising the first flow, expand planning, reactor, extruder, casting equipment, printing and lamination line integrations and advanced analytics.

Key implementation conditions

Clear master data system

It must be agreed which system stores the valid information in areas such as formulations, raw material properties, safety data, colours, forms, food contact and migration requirements, and how changes reach production.

IT and production automation boundaries

Reactor, extruder, moulding equipment, printing and lamination line integrations must be designed without compromising control network security, equipment warranties and production continuity.

Contextual actual data

Every measurement or operator action in chemicals, plastics and packaging production must be linked to the product, batch, operation, equipment and time; a signal archive alone creates no value.

Work station, not an additional report

The operator or specialist must receive only the information required for their decision, and recording must be integrated into the dozavimas, maišymas, reakcija, ekstruzija, liejimas, spausdinimas ir laminavimas flow.

Controlled change and accountability

Process owners must approve decisions on versions, exceptions and REACH, CLP, pavojingų medžiagų, maistinio kontakto ir pakuočių reikalavimai; the technology team cannot define business rules alone.

Recommended implementation sequence

Economic challenges and boundary selection

Select the formulation change, batch production and laboratory release process for one product family and agree which loss and KPI the first stage should address.

Master data and identifier preparation

Organise formulation versions, raw material properties and batches, safety data, colour and form parameters, food contact and migration requirements, and define uniform identifiers for product, batch or serial number, operation and equipment.

One seamless digital process

Implement the formulation change, batch production and laboratory release process for one product family from approved initial information to actual result, quality decision and audit history.

Usage stabilisation

Launch the solution in the production and laboratory release flow for the selected product family, eliminate parallel records and verify data and KPI reliability.

Expansion and advanced analytics

Only after stable use expand the solution to other recipes, lines and equipment groups and connect more advanced analytics or AI scenarios.

Change measurement KPIs

Production plan execution on time% of orders or operations

Measure what proportion of planned orders or operations are executed on time when the plan takes into account raw material compatibility, cleaning sequence, formulation and colour changes, line capacity and laboratory waiting time.

Right first time production proportion% of units or batches

Measure the proportion of production for which laboratory results, process parameters, non-conformances and batch release decisions are confirmed without correction, rework or additional investigation.

Unplanned downtime durationhrs

Assess the reliability of critical reactors, extruders, moulding, printing and laminating lines and the result of response to unplanned stoppage.

Proportion of fully traceable batches or units% of production

Measure whether the formulation version, raw material batches, process parameters, laboratory results and final batch are linked in one reliable history.

Production cycle timehrs or days

Measure the time from production start to finished and quality-released product for a selected product family's production and laboratory release flow.

Actual and planned cost variance% or € per unit

Assess whether actual labour time, materials, scrap, energy and other direct costs are reliably attributed to the formulation version, raw material batches, process parameters, laboratory results and final batch.

Formulation change and batch release timehrs

Measure the time from approved formulation change to quality decision on the first batch produced.

Key risks

Digitalising an undefined processIf rules for dosing, mixing, reaction, extrusion, moulding, printing and laminating processes and exceptions are not clear, the system will only entrench differing employee practices.Kaip suvaldyti Before implementation, observe actual work, describe the most common exceptions and confirm decision rights.
Product and production version mismatchFormulation versions, raw material properties and batches, safety data, colour and mould parameters, food contact and migration requirements may be changed at different times, so production risks receiving outdated or mutually inconsistent information.Kaip suvaldyti Chemistry, plastics and packaging production should use uniform identifiers, effective dates and approval statuses; an unapproved version must not be released to production.
Equipment data collected without contextA high volume of signals from reactors, extruders, moulding, printing and laminating lines does not help explain the outcome if the data is not linked to the product, batch or serial number, operation and specific time.Kaip suvaldyti Assign data from reactors, extruders, moulding, printing and laminating lines in advance to a specific solution, KPI, responsible person and product, batch or serial number context.
First version includes too muchAttempting to immediately cover all lines, products and REACH, CLP, hazardous substances, food contact and packaging compliance scenarios postpones actual use and complicates outcome evaluation.Kaip suvaldyti Limit the first version to one product family formulation change, batch production and laboratory release process.
Users bypass the systemIf the new workplace slows down dosing, mixing, reaction, extrusion, moulding, printing and laminating processes or does not help resolve an exception, employees will continue to fill in paper or spreadsheets after the fact.Kaip suvaldyti Design the workplace together with technologists, laboratory, quality, production, maintenance and IT teams, measure registration time and only remove duplicate forms after stable launch.

Next step

Link recipe, batch and quality decision

Consider whether the first phase should begin with changing the recipe of a single product family, the batch production and laboratory release process, and what change in quality, time, cost or traceability can be reliably measured.

Discuss the digitalisation direction

Chemical, plastic and packaging product manufacturing: digitalisation opportunities

How chemical, plastic and packaging product manufacturing works

The importance of product and process versions

The link between physical and digital processes

The economics of exceptions

The need for traceability and accountability

Market and technology context

Typical value chain

Product and formulation preparation

Raw material receipt and release

Planning and preparation

Batch production

Quality and compliance confirmation

Packaging and dispatch

Digital maturity pathway

Fragmented product and production data

Basic business systems

Digitalised selected process

Integrated product and execution chain Typical current situation

Data-driven production Siektina

Adaptive and closed-loop production

Key conclusion

Related digitalisation topics

Most common digitalisation challenges

Quality control is separated from the production process

Weak batch and component traceability

Impact of formula changes on compliance is assessed manually

Fragmented production master data

Planning does not reflect real production constraints

Production execution data is collected with a delay

Maintenance is mostly reactive

Key digital opportunities

Recipe and compliance impact management

Potential business impact

How to solve these problems

Recommended digital solutions

Manufacturing execution system (MES)

Advanced planning and scheduling system

Quality and traceability platform

Equipment maintenance and reliability system

Production master data and change management

Formulation, safety and compliance management platform

When it is worth starting

Recommended first version

Validated work order and product version

Recording of actual execution

Integrated quality and traceability control

Exceptions and results dashboard

Investment priorities

Key implementation conditions

Clear master data system

IT and production automation boundaries

Contextual actual data

Work station, not an additional report

Controlled change and accountability

Recommended implementation sequence

Economic challenges and boundary selection

Master data and identifier preparation

One seamless digital process

Usage stabilisation

Expansion and advanced analytics

Change measurement KPIs

Key risks

Digital innovations in the business area

Virtual process quality sensors

DI for evaluating recipe change impact

Computer vision for surface and packaging

Predictive equipment and process maintenance

Frequently asked questions

Where to start digitalisation in chemicals or packaging manufacturing?

Is ERP sufficient for recipe management?

When is it worth implementing MES?

How to assess batch traceability quality?

Where is AI most useful in chemicals manufacturing?

How to measure the return on investment of the first version?

Link recipe, batch and quality decision