How to Assess Digital Capabilities in a Plastics-Processing Plant

Two plants may have the same number of sensors, use the same software and operate equipment of similar generations yet derive very different value from their production data. What matters is which decisions the information can support, how it is interpreted and who maintains the working practices.

Assessing digital capabilities means examining verifiable activities. Having an IT system demonstrates the availability of a resource; using it consistently to improve production provides evidence of organisational capability.

Six questions to examine operations

Which process phenomena can the plant observe?

Identify signals, records and measurements that actually exist: cycles, operating states, quantities, energy, temperatures, inspection results, maintenance or material conditions. Coverage matters too: which machines provide data, how often, under which conditions and with what accuracy?

Which contextual information is retained with the records?

A temperature reading needs a unit, measurement location, timestamp and equipment identifier. To analyse production, it may also need to be linked to the mould or die, recipe, batch, order, product reference, material and operator. Traceability supports comparisons under equivalent conditions and event reconstruction.

How are deviations analysed and communicated?

Production records can feed indicators and alerts, but their calculation rules must be known. For OEE, availability, performance and quality require consistent definitions of planned production time and good parts. Reliable indicators help investigate losses across shifts and product references.

Vea our OEE guide and calculator y how to distinguish downtime from slow cycles.

Who makes decisions and records actions?

An operator may respond to a thermal deviation, a technician adjust a process parameter, maintenance intervene on the equipment, and engineering review a recipe or tooling change. Digital capability grows when information reaches the right person with the authority and expertise to act.

How does the company verify outcomes?

A meaningful comparison requires a baseline, reference conditions and an adequate verification period. Outcomes can be evaluated through rejected material, kWh/kg, cycle stability, start-up time, downtime, time to stable operation or complaint handling. Secondary effects matter: a shorter cycle must also be checked for quality and stability.

How is learning retained?

An improvement generates lasting value when its parameters, limits, results and responsibilities remain accessible for future shifts and production runs. Knowledge must be updated after changes to the mould, material, machine or procedure.

Five complementary areas

A plant is only part of the value chain. A wider assessment may also examine: engineering (CAD/CAE, validation and version control), materials (grade, batch, moisture and certificates), industrialisation (process set-up and approved recipes), production and quality (acquisition, analysis and intervention), and the circular value chain (product and recycled-material traceability).

Training, data governance, interoperability, cybersecurity and the ability to maintain tools cut across these areas. Each company will weigh them differently depending on its processes and position in the value chain.

A simple matrix for an initial technical discussion

This matrix is an exploratory framework derived from the literature. Developing it into a formal digital-maturity measurement instrument would require psychometric design, sector validation, sampling and reliability testing. It is useful for structuring an engineering interview and an initial assessment without claiming sector-wide maturity scores.

Prioritise the capability that matters most

If cycle-time data exist but nobody relates deviations to the product and shift, the project can focus on contextualisation and analysis routines. If quality records are well managed but the energy consumption of auxiliaries is unknown, targeted metering may be more useful. If a plant depends on one person to start a particular mould, transferring and validating that knowledge may be the priority.

The final decision should consider economic losses, quality risks, total investment, dependencies on other capabilities and maintenance effort. The industrial task determines the required level of sophistication.

KERN IoP’s study on digitalisation in plastics-processing SMEs develops this approach further. For details, see the sector research page y KERN IoP solutions.

References

• Mittal, S., Khan, M. A., Romero, D., & Wuest, T. (2018). A critical review of smart manufacturing and Industry 4.0 maturity models: Implications for SMEs. Journal of Manufacturing Systems, 49, 194–214. https://doi.org/10.1016/j.jmsy.2018.10.005
• Pirola, F., Cimini, C., & Pinto, R. (2020). Digital readiness assessment of Italian SMEs: A case-study research. Journal of Manufacturing Technology Management, 31(5), 1045–1083. https://doi.org/10.1108/JMTM-09-2018-0305
• PM Tec Servicios de Ingeniería SAS, KERN IoP. (2026). Estado del arte mundial de la digitalización en PYMEs de transformación de plásticos.