AI & Digitalization

Developments in AI and digitalisation are progressing rapidly. This offers opportunities for operations and continuous improvement.

Symbol supports organisations with a select number of techniques, including BPA/RPA, Process Mining, Predictive Analytics, and the application of AI agents in QHSE.

Guidance on Digitalisation in Operations

The Importance of Digitalisation

Digitalisation helps organisations work more efficiently, smartly, and quickly. Organisations that effectively implement digitalisation improve their performance and strengthen their position in an increasingly competitive market.

Increased Productivity

Through the automation of tasks, processes, and information flows.

Faster and Better Insights

Thanks to real-time data, dashboards, and advanced analytics.

Strengthening Competitiveness

Through higher quality, greater flexibility, and faster decision-making.

Solutions in AI & Digitalisation

With data, automation, and AI, we help organisations make processes smarter, faster, and more predictable.

AI Agents

Smart AI agents assist in analysing, structuring, and enriching data, for example, for quality management, knowledge management, and documentation.

BPA & RPA

Automate repetitive tasks and complete business processes to reduce errors, shorten lead times, and increase productivity.

Process Mining & Predictive Analytics

Uncover bottlenecks with Process Mining and predict future performance, disruptions, or quality issues with Predictive Analytics.

“AI is only valuable if the underlying data is reliable and usable.”

Automation not only accelerates processes but also structurally prevents errors.

AI and digitalisation make processes faster, smarter, and more reliable. Organisations achieve lower costs, fewer errors, and shorter lead times, while being better able to manage based on data and performance. Through automation and predictive insights, operational efficiency increases, and problems are identified and resolved earlier. This leads to higher quality, greater flexibility, and a scalable organisation that can continuously improve.

Process Automation

Process Automation automates repetitive tasks and process steps using software and technology. This increases speed, consistency, and efficiency, while reducing human errors.

Process Automation

AI for Operations

AI for Operations uses artificial intelligence to manage operational processes more intelligently. By analysing data and recognising patterns, systems can predict, optimise, and support decisions.

AI for Operations

Data Management

Data Management focuses on collecting, structuring, and managing data within the organisation. Reliable and accessible data forms the basis for data-driven decision-making and process improvement.

Data Management

How AI & Digitalisation Works in Practice

Veco Precision: inspection system improved with data analysis

Veco Precision's automated inspection system collected a lot of data but provided unreliable results. Symbol data scientist analysed 300 photos per product, identified the relevant parameters and implemented a software upgrade. Result: consistent and reliable measurement system with 20 parameters instead of 3, ready for rollout to other products.

GGD Drenthe: Mijn Kinddossier Successfully Implemented with Lean

GGD Drenthe implemented Mijn Kinddossier: an online portal for parents in youth healthcare. The technical side was manageable, but the human impact was greater than expected. Symbol guided the change management from kick-off to embedding. Result: portal live in one year, all employees convinced, and knowledge embedded within the organisation.

Scoober: Lean foundation established in fast-growing delivery organisation

Scoober suffered from silos: branches did not work in the same way and could not learn from each other. Symbol helped set up a Lean plan, strategy plan, process plan, and training plan. Result: key business processes standardised, management trained as Lean Champions, and three Lean employees active in the organisation.

Thales: Lean transformation of radar production

Lean transformation within 22 departments resulted in a 30% shorter lead time and less work in progress. Symbol implemented Lean fundamentals consisting of 6S, WIP control and visual management across 22 departments.

Results and Impact of AI & Digitalisation

AI and digitalisation enhance operational performance and quality management by making processes smarter, faster, and more predictable. By leveraging real-time data and automating repetitive tasks, greater insight, higher efficiency, and better decision-making are achieved.

Shorter Lead Times (–15% to –40%)

Digital workflow systems and process automation reduce manual steps, waiting times, and handovers. This accelerates work throughput and increases delivery reliability.

Fewer Errors and Higher Quality (–20% to –50%)

AI can detect deviations, patterns, and quality issues early. Automatic controls and smart algorithms reduce human errors and improve product and process quality.

Higher Productivity (+15% to +35%)

By automating repetitive tasks, employees can focus on analysis, problem-solving, and customer value. This increases output without additional capacity.

Better Predictability and Planning

AI models can predict demand, disruptions, or quality deviations. This allows for more efficient organisation of maintenance, production planning, and resource deployment.

More Data-Driven Decision-Making

Centralised data storage and analysis tools create an objective basis for decisions. Strategic and operational choices are better substantiated.

From Digital Ambition to Concrete Applications

AI, Process Mining, RPA, and data analysis make processes smarter. However, technology only works when it is linked to process improvement.

Symbol combines Operational Excellence with AI and digital technology. This ensures that digitalisation does not get stuck in tools but leads to structural improvements in your operations.

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: AI in operations is used to analyse processes, make predictions, and automate decisions. Examples include predictive maintenance, quality control, and process optimisation.
: Integration provides real-time insight, higher productivity, less downtime, and improved quality through data-driven process control.
: RPA (Robotic Process Automation) automates repetitive tasks, while BPA (Business Process Automation) automates and optimises complete processes across multiple systems.
: Process Mining is a modern variant of Value Stream Mapping. Process Mining analyses process data from systems to provide insight into actual process flows, bottlenecks, and inefficiencies. It helps to target improvements effectively.
: Successful adoption requires training, clear processes, and engaged employees. Technology must align with daily practice and deliver value.