Where to start with food production digitalisation?

Start with a single product family where there are many recipe, allergen, expiry or yield risks. Link actual raw material batches, recipe version, process records, quality results and final batch labelling.

How to verify that traceability actually works?

Conduct a practical recall test: from a single raw material batch, identify all affected final batches and customers, and from a final batch – all raw materials, process records and quality results.

Why must recipe and label be in one chain?

A recipe change can alter allergens, nutrition, composition, technical instructions and labels. Manual transfer between systems leaves significant risk of non-compliance.

Does MES solve food safety management?

MES can control execution and collect facts, but food safety also requires validated recipes, quality plans, laboratory, labelling and supplier data. The solution must link these systems.

Where is AI meaningful in food production?

AI is suitable for yield, expiry risk, process deviations, demand and visual packaging inspection. It should not independently make product release or food safety decisions.

Which KPIs best demonstrate value?

Measure yield, waste, first-time quality, batch release time, recall test duration, unplanned downtime and production write-off due to expiry.

Business area digitalisation analysis

Food and beverage production: digitalisation opportunities

Connecting food and beverage planning, execution, quality, traceability and equipment data 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 separated from the production process
Biggest opportunity: Recipe, allergen and labelling lifecycle

Digitalisation in food and beverage production should begin with a clear business problem and a single traceable data chain, rather than a general objective to 'implement MES' or collect as many equipment signals as possible.

How food and beverage production works

The business area covers food and beverage production – from raw materials and component preparation to production, quality confirmation, packaging, warehousing and dispatch.

Importance of product and process versions

These data areas – recipes, raw material batches, allergens, nutrition data, expiry dates, packaging and labelling versions – must be managed as valid information, not freely copied files.

Physical and digital process link

Systems must reflect actual equipment status, including mixing, thermal processing, filling, packaging, refrigeration and cleaning equipment, as well as materials, operator actions and time.

Economics of exceptions

The greatest losses in food and beverage production arise from shortages, rejects, breakdowns, changeovers and quality waiting, rather than from ideal standard cycle time.

The need for traceability and accountability

Solutions must be based on primary data and comply with food safety, hygiene, allergen, traceability and labelling requirements.

Market and technology context

The EU food safety system regards traceability as a cornerstone principle enabling rapid identification of the problem source and removal of affected products; therefore a digital batch traceability chain and reliable recipe data remain a baseline priority.

Product data and traceability pressureCustomers and regulatory processes expect rapid provision of recipes, raw material batches, allergens, nutritional data, expiry dates, packaging and labelling versions and their connection to actual production.
Competence shortageDigital instructions and decision history help retain raw material receipt, preparation, mixing or thermal processing, packaging and cold chain knowledge within the organisation.
Raw material, energy and capacity costsProduction needs to see costs and losses at the level of product, batch and mixing, thermal processing, filling, packaging, cooling and cleaning equipment.
Advanced analytics maturityAI and forecasting become practical only when critical food safety points, laboratory results, allergen control, yield and batch release are linked to reliable process context.

Typical value chain

Recipe and product specification

Raw materials, allergens, nutrition, quality criteria, packaging and label are validated.

Raw material receiving

Supplier batches, expiry dates, temperature and quality results are linked to warehouse status.

Planning and production sequence

Expiry dates, allergen changeovers, cleaning, line capacity and packaging availability are taken into account.

Batch production

Actual raw materials, process parameters, critical points, yield, losses and stoppages are recorded.

Packaging and release

Label, date, packaging, laboratory results and final batch traceability are verified.

Warehousing and dispatch

Expiry dates, temperature, order allocation and fast recall information are managed.

Digital maturity journey

Fragmented product and production data

Recipes, raw material batches, allergens, nutritional data, expiry dates, packaging and labelling versions are kept in spreadsheets, documents and separate systems, whilst actual execution is verified after shift or batch.

Basic business systems

ERP manages orders and stock, but raw material receipt, preparation, mixing or thermal processing, packaging and cold chain and quality facts remain on paper or in local tools.

Digitalised selected process

On one line or product family, the process of recipes, allergens, raw material batches, yield and final batch traceability 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: recipes, raw material batches, allergens, nutritional data, expiry dates, packaging and labelling versions.

Data-driven production Siektina

Planning, quality and maintenance in food and beverage manufacturing rely on real-time exceptions, root cause analysis and reliable line and product KPIs.

Adaptive and closed-loop production

The system in food and beverage manufacturing automatically adjusts permitted decisions based on product, process and equipment status, whilst AI recommendations are audited and measured.

Key conclusion

Food and beverage 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 starting point is the traceability process for a single product family's recipes, allergens, raw material batches, yield and final batch. This scope allows results to be measured without involving all lines and integrations at once.

Most common digitalisation challenges

The largest gaps arise when recipes, raw material batches, allergens, nutritional data, expiry dates, packaging and labelling versions are managed in separate systems. The processes of raw material receiving, preparation, mixing or thermal processing, packaging and cooling chain then leave no single reliable actual history, so the plan, execution status and quality decisions reflect different situations.

Quality control separated from the production process

Critical

Critical food safety points, laboratory results, allergen control, yield and batch release data are not consistently linked to the specific product or batch version, operation, equipment and deviation cause.

Consequences: Quality decision times lengthen, it is harder to identify the cause, and yield losses, write-offs due to expiry, additional testing, cleaning time and recall risk may recur in other orders.

Weak traceability of batches and components

Critical

It is not always possible to quickly reconstruct the complete link: recipe, raw material batches, allergens, process records, packaging version and final batch.

Consequences: In food and beverage production, during a customer enquiry, audit, non-conformance or recall, it takes a long time to determine the affected scope and required action.

Recipe, allergen and labelling data are managed separately

Critical

Recipe changes do not always automatically update allergens, nutritional values, labels and production instructions.

Consequences: The risk of incorrect labelling, recalls and product release delays increases.

Fragmented production master data

High

Recipes, raw material batches, allergens, nutritional data, expiry dates, packaging and labelling versions are stored in different systems, files or employee-prepared tables, so there is no single valid product and production version.

Consequences: Changes reach the raw material receiving, preparation, mixing or thermal processing, packaging and cooling chain processes at different times, manual checks increase and the risk of producing according to outdated information grows.

Planning does not reflect actual production constraints

High

Plans do not always take into account raw material expiry dates, recipe sequences, allergen cleaning, packaging materials, line and cooling capacity and the actual status of work already started.

Consequences: Priorities change at the last minute, waiting times increase, unfinished production and the proportion of delayed orders in food and beverage production rise.

Production execution data collected with delay

High

Operation start and finish, quantities produced, material consumption, stoppages and reasons for deviations in the selected product family production, quality and labelling flow are recorded late or in multiple places.

Consequences: Planners and responsible employees notice too late that the selected product family production, quality and labelling flow has deviated from the plan, so time for correction is lost.

Yield and loss causes are not visible at batch level

High

Raw materials, process parameters, losses, rework and final quantity are not consistently linked.

Consequences: It is difficult to reduce cost, waste and process variation.

Maintenance mostly reactive

Medium

Data on mixing, thermal processing, filling, packaging, cooling and cleaning equipment operating time, failures, condition signals, spare parts and maintenance work are not aligned with actual load and production plan.

Consequences: Unplanned stoppages disrupt the production, quality and labelling flow of the selected product family, whilst repair and spare parts requirements are managed reactively.

Opportunities

Highest digitalisation opportunities

Recipe, allergen and labelling lifecycleVery high impactConnect recipe, ingredients, nutrition, allergens, labels, markets and approvals.Food safety and faster product launch

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

Yield, expiry and waste optimisationHigh impactAnalyse actual yield, raw material variation, process losses and expiry risk.Cost and less waste

Integrated manufacturing execution managementVery high impactIn a selected flow, link recipe, raw material batches, allergens, process records, packaging version and finished batch with actual quantities, stoppages, deviation causes and responsible employee actions.Productivity and delivery reliability

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

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

Energy, yield and loss optimisationHigh impactAt product, batch or serial unit level, measure energy, material consumption and yield losses, write-offs due to expiry, additional testing, cleaning time and recall risk, so that loss causes are visible where they occur.Cost and sustainability

Biggest opportunity

Recipe, allergen and labelling lifecycle

The biggest near-term opportunity is a single product family's recipe, allergen, raw material batch, yield and final batch traceability process.

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

Potential business impact

Capacity utilisationMore accurate raw material expiry, recipe sequence, allergen cleaning, line capacities and packaging material availability create a plan and real execution status that reduces waiting and urgent priority changes.
Quality and yieldQuality status becomes visible during the process as the following data and decisions are linked: critical food safety points, laboratory results, allergen control, yield and batch release. This reduces late defects, rework and raw material losses.
Delivery reliabilityOrder deadline is evaluated according to actual raw material receiving, preparation, mixing or heat treatment, packaging and cold chain status, rather than a periodic report.
Traceability and riskA reliable chain between recipe, raw material batch, allergens, nutritional data, expiry dates, packaging and labelling versions enables faster response to an audit, complaint or recall scenario.
Scale and knowledge retentionDigital instructions and decision history reduce dependence in food and beverage manufacturing on individual specialists' memory.

Sprendimai

How to solve these problems

Solution directions linked to specific business area problems they address.

Problema

Production execution data is collected with delays

Sprendimo kryptis

Manufacturing execution system (MES)

Manages raw material receipt, preparation, mixing or thermal processing, packaging and cold chain tasks, approved product version, actual quantities, time, materials, stoppages and exceptions in one selected flow.

Problema

Planning does not reflect actual production constraints

Priorities change at the last minute, waiting times increase, unfinished production and the proportion of delayed orders in food and beverage production rise.

Sprendimo kryptis

Manufacturing execution system (MES)

Problema

Planning does not reflect actual production constraints

Priorities change at the last minute, waiting times increase, unfinished production and the proportion of delayed orders in food and beverage production rise.

Sprendimo kryptis

Advanced planning and scheduling system

Creates and adjusts the plan according to raw material shelf life, recipe sequence, allergen cleaning, line capacities and packaging material availability, actual material status and ongoing production exceptions.

Problema

Yield and loss causes are not visible at batch level

It is difficult to reduce cost, waste and process variation.

Sprendimo kryptis

Advanced planning and scheduling system

Problema

Quality control is separated from the production process

Sprendimo kryptis

Quality and traceability platform

Links critical food safety points, laboratory results, allergen control, yield and batch release with the approved product version, actual materials, operations, equipment and final product.

Problema

Weak batch and component traceability

Sprendimo kryptis

Quality and traceability platform

Links critical food safety points, laboratory results, allergen control, yield and batch release with the approved product version, actual materials, operations, equipment and final product.

When it is worth starting

Investment justified

The data set in these areas – recipes, raw material batches, allergens, nutritional data, expiry dates, packaging and labelling versions – has multiple versions or is frequently corrected manually
Raw material receipt, preparation, mixing or thermal processing, packaging and cold chain actuals are recorded after the shift or batch
For quality testing, it is difficult to link critical food safety points, laboratory results, allergen control, yield and batch release with a specific batch, product or equipment
The costs of scrap, downtime, waiting or untraceability in food and beverage production are significant
A clear scenario can be selected: the traceability process for a single product family's recipes, allergens, raw material batches, yield and final batch

Reikia atsargumo

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

Recommended first version

The first version is a traceability process for one product family's recipes, allergens, raw material batches, yield and finished batch. It must include validated master data, one real execution flow, a quality decision and a measurable economic result.

Approved work order and product version

The user receives only valid recipe, raw material batch, allergen, nutrition data, expiry dates, packaging and labelling versions and a clear operation and quality task.

Actual execution registration

Quantities, time, materials used, raw material receipt, preparation, mixing or thermal processing, packaging and cold chain status, stoppages and exceptions are recorded.

Integrated quality and traceability control

Quality data and decisions – critical food safety points, laboratory results, allergen control, yield 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 high-risk process statuses for a single product family's recipe, allergens, raw material batches, yield and final batch traceability.

Kam pirmiausiaProduction operators or process executors · Shift or production managers · Planners and technologists · Quality specialists · Maintenance or engineering team
What not to include in the first versionWhole factory and all products scope · Full integration of all legacy equipment · Complex autonomous AI optimisation · Historical data clean-up without a clear use case

Investment priorities

Recipe, allergen and labelling lifecycleStart with a single product family's recipe, allergen, raw material batch, yield and final batch traceability process and measure the economic outcome before scaling up.
Integrated quality and traceabilityConnect quality information – critical food safety points, laboratory results, allergen control, yield and batch release – with the actual product, batch and process history.
Yield, shelf life and waste optimisationOnly after stabilising the first flow, expand planning, integration of mixing, thermal processing, filling, packaging, cooling and cleaning equipment, and advanced analytics.

Key implementation conditions

Clear master data system

It must be agreed which system stores valid information in areas such as recipes, raw material batches, allergens, nutritional data, expiry dates, packaging and labelling versions and how changes reach production.

IT and production automation boundaries

Mixing, thermal processing, filling, packaging, cooling and cleaning equipment integrations must be designed without compromising control network security, equipment warranties and production continuity.

Contextual actual data

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

Workplace, not an additional report

The operator or specialist must receive only the information required for their decision, and recording must be integrated into the raw material reception, preparation, mixing or thermal processing, packaging and cold chain flow.

Managed change and accountability

Process owners must approve decisions on versions, exceptions and food safety, hygiene, allergen, traceability and labelling requirements; the technology team cannot define business rules alone.

Recommended implementation sequence

Economic challenges and boundary selection

Select the recipe, allergen, raw material batch, yield and final batch traceability process for a single product family and agree which loss and KPIs the first phase should change.

Preparation of master data and identifiers

Organise recipes, raw material batches, allergen and nutrition data, expiry dates, packaging and labelling versions and define consistent product, batch or serial number, operation and equipment identifiers.

One seamless digital process

Implement the recipe, allergen, raw material batch, yield and final batch traceability process for a single product family from confirmed initial information to actual result, quality decision and audit history.

Usage stabilisation

Launch the solution in the selected product family's production, quality and labelling flow, 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 labelling scenarios and connect more advanced analytics or AI scenarios.

Change measurement KPIs

Production plan fulfilment on time% of orders or operations

Measure what proportion of planned orders or operations is completed on time, when the plan accounts for raw material shelf life, formulation sequence, allergen cleaning, packaging materials, line and cooling capacity.

Right first time production proportion% of units or batches

Measure the proportion of production for which critical food safety point, laboratory result, allergen control, yield and batch release data are confirmed without correction, reprocessing or additional testing.

Unplanned downtime durationhrs.

Assess the reliability of critical mixing, thermal processing, filling, packaging, cooling and cleaning equipment and the outcome of response to unplanned stoppage.

Proportion of fully traceable batches or units% of production

Measure whether formulation, raw material batches, allergens, process records, packaging version and final batch are linked in a single reliable history.

Production cycle timehrs. or days

Measure the time from production start to finished and quality-released product in the production, quality and labelling flow of a selected product family.

Actual and planned cost variance% or € per unit

Assess whether actual labour time, materials, scrap, energy and other direct costs are reliably attributed to formulation, raw material batches, allergens, process records, packaging version and final batch.

Recall test durationmin.

Measure the time taken to identify all affected final batches and customers from a raw material batch.

Key risks

Digitalising an undefined processIf the rules and exceptions applied to raw material receipt, preparation, mixing or thermal processing, packaging and cooling chain processes are not clear, the system will only reinforce differing employee practices.Kaip suvaldyti Prior to development, observe actual work, document the most common exceptions and confirm decision rights.
Product and production versions do not matchRecipe, raw material batch, allergen, nutritional data, expiry date, packaging and labelling versions may be changed at different times, therefore production risks receiving outdated or mutually inconsistent information.Kaip suvaldyti Food and beverage production use identical identifiers, effective dates and approval statuses; an unapproved version must not be transferred to production.
Equipment data collected without contextA large volume of signals from mixing, thermal processing, filling, packaging, cooling and cleaning equipment does not help explain the outcome if the data is not linked to product, batch or serial number, operation and specific time.Kaip suvaldyti For data from mixing, thermal processing, filling, packaging, cooling and cleaning equipment, assign in advance 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 food safety, hygiene, allergen, traceability and labelling scenarios distances actual usage and complicates evaluation of the outcome.Kaip suvaldyti Limit the first version to a single product family recipe, allergens, raw material batches, yield and final batch traceability process.
Users bypass the systemIf the new workstation slows down raw material receipt, preparation, mixing or thermal processing, packaging and cooling chain processes or does not help resolve exceptions, employees will continue to complete paper or spreadsheets after the fact.Kaip suvaldyti Design the workstation together with technology, food safety, quality, production, packaging, maintenance and IT teams, measure registration time and only after stable launch remove duplicate forms.

Next step

Link recipe, allergens, batch and label

The first phase should be evaluated to determine whether it is best to start with a single product family's recipe, allergens, raw material batches, yield and final batch traceability process, and what quality, time, cost or traceability change can be reliably measured.

Discuss digitalisation direction

Food and beverage production: digitalisation opportunities

How food and beverage production works

Importance of product and process versions

Physical and digital process link

Economics of exceptions

The need for traceability and accountability

Market and technology context

Typical value chain

Recipe and product specification

Raw material receiving

Planning and production sequence

Batch production

Packaging and release

Warehousing and dispatch

Digital maturity journey

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 separated from the production process

Weak traceability of batches and components

Recipe, allergen and labelling data are managed separately

Fragmented production master data

Planning does not reflect actual production constraints

Production execution data collected with delay

Yield and loss causes are not visible at batch level

Maintenance mostly reactive

Highest digitalisation opportunities

Recipe, allergen and labelling lifecycle

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

Recipe, allergen and labelling management platform

Food batch and expiry traceability system

When it is worth starting

Recommended first version

Approved work order and product version

Actual execution registration

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

Workplace, not an additional report

Managed change and accountability

Recommended implementation sequence

Economic challenges and boundary selection

Preparation of master data and identifiers

One seamless digital process

Usage stabilisation

Expansion and advanced analytics

Change measurement KPIs

Key risks

Digital innovation in the business area

Computer vision for packaging and labels

AI for yield and loss analysis

Shelf life and demand forecasting

Real-time process quality forecasting

Frequently asked questions

Where to start with food production digitalisation?

How to verify that traceability actually works?

Why must recipe and label be in one chain?

Does MES solve food safety management?

Where is AI meaningful in food production?

Which KPIs best demonstrate value?

Link recipe, allergens, batch and label