Process data acquisition and the optimisation of Overall Equipment Effectiveness, abbreviated OEE, rest on the interplay of suitable sensor technology, reliable data integration, clear loss definitions and consistent evaluation. OEE is made up of availability, performance and quality. In mixing processes these figures must always be viewed in the batch and product context, since besides the actual mixing time, charging, dosing, discharge, product change, cleaning, sampling and releases also influence the plant time actually available.

Automated process data acquisition requires measuring points that meaningfully represent the process state, the quality and the plant condition. These include speed, torque, motor current and power consumption of the mixer drive. This data can provide indications of product behaviour, fill level, friction, mixing progress or possible build-up. However, it is not a universal substitute for mixing quality or viscosity. Its informative value must be checked for each product, each recipe and each mixer type by comparison with suitable quality data.

Temperature, pressure, vacuum, level and moisture sensors help to reliably monitor critical process parameters. Which measuring point is required depends on the mixing task. With hygroscopic powders, moisture can be decisive; with exothermic processes or thermally sensitive products, temperature; and with closed, inerted or vacuum-assisted processes, pressure and vacuum. Measuring points should not simply exist technically but should serve a clear purpose: they must monitor a relevant process parameter, make a deviation detectable at an early stage, or support a demonstrable decision within the process.

Inline analytics, that is, measurement directly in the process stream or in the vessel, can improve process and quality assessment. Depending on the application, spectroscopic inline moisture measurement, conductivity measurements, particle measurements or viscosity measurements, for example, may be considered. These methods can provide faster feedback than purely manual laboratory sampling. Their use, however, presupposes that the measuring point, calibration, evaluation algorithm, maintenance and the handling of implausible values are suitably defined for the intended purpose. In regulated applications, qualification or validation of the method may be required for this.

Monitoring of dosing is likewise central. Gravimetric systems dose by mass, for example via load cells or differential dosing scales. Volumetric systems dose by volume, for example via screws, pumps or defined stroke volumes. For batch traceability, target and actual quantities, raw-material batches, time stamps, dosing sequence and any corrections should be recorded. This makes root-cause analysis easier when quality deviations, incorrect weigh-ins or process interruptions occur.

Vibration, temperature and current measurements on motors, bearings, gearboxes and seals can support condition-based maintenance. This approach is often referred to as condition monitoring. It serves to detect changes in operating condition at an early stage. Building on this, predictive maintenance can be developed. It uses historical data and known failure patterns to estimate the likely maintenance need. Not every measurement curve is automatically suited to a reliable failure prediction. Prerequisites are suitable measuring points, sufficient data quality, known wear mechanisms and a clear maintenance process that converts conspicuous data into concrete inspection or maintenance measures.

For OEE evaluation, process, event and quality data must be linked to one another unambiguously in time. Precise time stamps are needed for charging, dosing, mixing, emptying, cleaning, product change, downtime and restart. An event capture that automatically detects unplanned downtime, short interruptions and waiting times, and is supplemented with standardised causes by operators or maintenance, is particularly helpful. Downtime should be classified at least as planned or unplanned, and by technical, material-related, organisational and quality-related causes. Without a uniform classification, OEE evaluations may well generate figures, but they provide hardly any robust starting points for improvement.

The six major loss types known as the "Six Big Losses" originate from the field of Total Productive Maintenance, that is, comprehensive productive maintenance. They typically comprise breakdowns, set-up and adjustment times, short stoppages, reduced speed, start-up losses and quality losses. For batch processes, these loss types should be adapted to the actual sequence. For example, excessively long cleaning, waiting times for raw materials or quality releases, remixing and delayed emptying may be more meaningful as their own loss categories than an unchanged adoption of a model from series production.

A structured data architecture ensures that measured data does not remain isolated. Standardised communication and fieldbus standards enable a vendor-independent exchange of structured data between controllers, historian systems, control systems and higher-level applications, for example for event-driven data transfers or for connection to Industrial Internet of Things and cloud applications. Which interface is suitable depends on the real-time requirement, the existing automation architecture, the security requirements and the desired data model.

A historian is a specialised database for time-stamped process and event data. It makes it possible to store and evaluate measured values, states, alarms and time series over extended periods. For batch processes, the data should additionally be linked to a batch ID, that is, a unique batch identifier, product ID, recipe version, plant identifier and process phase. The standard ISA-88, also referred to as ISA S88 or IEC 61512, describes models and terminology for batch control. This includes recipes, plant modules, operations, phases and batch records. ISA-88 can therefore be a suitable basis for structuring recipe management, process steps and batch documentation uniformly.

Digital recipe management reduces the risk of manual entry errors and supports the controlled execution of released parameters. It can, for example, manage mixing time, speed, temperature profiles, dosing sequence, fill level, target quantities and release steps. In regulated environments, recipes should be versioned, protected against unauthorised changes, and managed via defined release processes. Changes must be traceable. Where electronic records or signatures fall under applicable regulatory requirements, the requirements for data integrity, audit trails and, where applicable, electronic signatures must additionally be taken into account.

A golden-batch comparison compares the profile of a current batch with a defined reference profile of a demonstrably successful batch. The term "golden batch" does not mean that a single batch necessarily represents the perfect reference. In practice, a statistically validated reference range derived from several suitable batches is often more robust. Deviations in torque, temperature, dosing time, mixing duration or other critical parameters can thereby become visible at an early stage. Whether and how intervention takes place must be established in a clear process strategy, so that not every normal process variation triggers unnecessary alarms or operator interventions.

Various analysis methods are suitable for process optimisation. Statistical Process Control, abbreviated SPC, monitors the stability and spread of important process parameters across many batches. It helps to detect trends, drift and unusual patterns before defined limit values are exceeded. Design of Experiments, abbreviated DoE, systematically examines how several input variables jointly influence a result. This allows mixing time, speed, fill level, dosing sequence or temperature profile, for example, to be optimised in a targeted manner. Multivariate data analysis can additionally be helpful where many interconnected process and quality data are to be evaluated simultaneously.

A digital twin is a digital model of a real product, process or plant. It can take different forms: from a simple representation of recipes, capacities and throughput times, to a physically founded model of mixing kinetics, heat transfer, energy input or material properties. Digital twins can help with simulating operating strategies, capacity planning and assessing possible process changes. However, they do not replace trials with the actual product, particularly where particle flows, product build-up, mixing quality or emptying behaviour can only be modelled to a limited extent.

Cleaning has a considerable influence on availability in many mixing processes. Cleaning in place, abbreviated CIP, refers to the cleaning of plant components in their installed state without extensive dismantling. Sterilization in place, abbreviated SIP, refers to the sterilisation of plant components in their installed state, frequently using steam or another suitable method. Sensors for conductivity, flow, temperature, pressure and time can help to monitor cleaning and sterilisation cycles. Needs-based cleaning can reduce resources and downtime, but must not come at the expense of the required hygiene or the validated cleaning effect. In regulated applications, cleaning criteria, acceptance limits and the handling of deviations must be clearly defined.

For operation and evaluation, data should be visualised appropriately for the target audience. Role-based dashboards can show operators current process states and alarms, provide shift supervisors with loss-time and OEE trends, and enable quality or process engineers to carry out detailed process and batch analyses. Effective alarm management prevents operators from being overloaded by a multitude of simultaneous, unprioritised messages. EEMUA 191 is a guideline of the Engineering Equipment and Materials Users' Association, abbreviated EEMUA, for the design, management and assessment of alarms in process plants. It is frequently used as a point of reference for prioritising alarms and reducing alarm floods.

Mobile access can improve the availability of information, but requires a clear authorisation, security and release concept. Particularly for write access or remote access to controllers, access should be time-limited, encrypted, logged and designed according to the principle of least privilege. Standardised reports and regular shop-floor meetings, that is, short operational discussions held directly at the production area, help to convert OEE, quality and downtime data into concrete improvement measures.