The Role of Digital Maturity Assessment in Technology Interventions with Industrial Internet Playground
2. Materials and Methods
2.1. The Industrial Internet Playground (IIP)
2.2. Shadow Infrastructure (SI)
2.3. The Digital Maturity Assessment Tool
- Leadership (difficulty in creating urgency, vision and direction for the digital transformation)
- Institutional (resistance to change in the form of attitudes of old employees, legacy technology, innovation fatigue and politics) .
- Digital capabilities (e.g., strategy, technological expertise, business models, customer experience)
- Leadership capabilities (e.g., governance, change management, culture) .
- Customers and Partners
4. Analysis and Discussion
Conflicts of Interest
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|MQTT Subscription Topic||subscribe <domain>/<environment>/<agent>/<construct>|
|Topic||Proactive maintenance||Quality control||Operations|
|Problem||Maintenance call out is slow, reactive and not trackable. Past operational data is not easy to access and understand by maintenance||QC station is manual, and no data is recorded about scale or frequency of defects encountered||Acquiring and correlating defect data with mixing process data from ERP system is too time consuming to react in a timely way|
|Solution||Linewatcher—A wearable device operators can use as an ‘andon’ notifier of production start, stop and support requests||Defect tracker buttons—Programmable buttons to log defect type and timestamp||Line integration PLC microservice—Integrate PLC data from moisture, line speed and stoppage reasons to indicate defect causes|
|Unique technologies||Android wearable app |
|Particle Argon microcontroller |
|PLC telemetry translator |
Job and Shift Scorecard
|SI components||Hybrid web-app|
|Ability to reach users’ needs||No—wearable was too unnatural to wear and did not provide enough detailed input options||Yes—accepted and used beyond the trial period to understand current issue||Yes—accepted and used in complete correlation dashboard monitor|
|Key Effects and Value Created in Staff Sections|
|A more comprehensive understanding of the overall impact of the specific quality processes for which each operator is in charge. |
Easier monitoring of product up-time and defects, due to the automated button registration system of manual visual defect/error detection.
Team leaders’ understanding of the production line settings and effects is supported by the live visualisation of the production line in the hybrid web app.
|Ability to analyse abstract machine data for trend identification in operational processes and procedures, e.g., for forecasting.|
Overview of production efficiency with a benchmark graph, which continuously detects product up-time and defects, compared against the current recipe. An eye-opener that showed more production line errors than previously assumed.
Ability to compare the operators’ button-registered defects in the production line with the overall number of rejects in the final quality control of the produced batch.
Cause and effect insights through the automated visualisation of the correlation between environmental factors (telemetry data from sensors) and manual action factors (e.g., error detection buttons).
Team meetings and planning are now supported by the live visualisation of the production line in the hybrid web app.
|Knowledge of basic ways to collect data, connect machines and users with IoT smart devices and the types of data that are collectable.|
Objective insights into process changes and machine configurations that enable maintenance based on data.
Data to begin analysis of the raw materials affecting the processing and mixing parameters on the end quality. Enable operators to adjust configurations in an informed way.
Closer collaboration with machine suppliers based on a joint interest in making equipment data an asset.
|Data insights that are valuable when approaching new international partners and customers.|
Data-driven strategic decision-making becomes a reality, as no subjective/approximate estimates affect the calculated figures.
|Dimension/Change||Company’s Influence on |
|Direct Technology Change||Impact of Intervention|
|Low digital capabilities resulted in weak influence over the POC directions. No aligned vision across stakeholders||No immediate change measured||Raised awareness of complexity in systems (production, support, IT) and the scale to which change must be considered|
|Digital culture was minimal with the result that user experience became a priority to reduce “data overload” for users||No immediate change measured||Increasing operator willingness to contribute across intervention and POC acceptance indicates change, but sustainability is unclear|
|Stakeholders understood weak points and problem areas, which resulted in a common requirement for each POC||Potential new way for the production team to carry out weekly meetings and root-cause analysis||Multiple departments worked together to carry out intervention; however, final artefact benefited only one team|
|Critical importance due to realisations of bottleneck issues and clear wasteful manual methods documented via co-creation||Introduction of data logger buttons, user interface screens, IoT Platform and new integrations with PLC||Complete digital solution with data-driven reporting was devised to replace subjective configurations to processes|
|Good level of digitisation knowledge (automation, sensors), low digitalisation (IT/IS). |
Designs were limited to support spreadsheet and web browser formats for accessibility
|New addition of sensing, user interfaces and data-driven dashboards to operations||New technologies introduced encouraged digitalisation of existing process and offered examples and showed potential for new methods (e.g., proactive maintenance)|
|Customers and Partners|
|Multi-stage production relied on a range of international suppliers, yet very little was fully digitalised and ready to be integrated with SI||New integration between PLCs and IoT suppliers required clearer roles and responsibilities than what the typical way of working offered||Allowed one supplier to develop a new feature in their product.|
Case company found that this sort of innovation benefited the supplier more than them
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Aagaard, A.; Presser, M.; Collins, T.; Beliatis, M.; Skou, A.K.; Jakobsen, E.M. The Role of Digital Maturity Assessment in Technology Interventions with Industrial Internet Playground. Electronics 2021, 10, 1134. https://doi.org/10.3390/electronics10101134
Aagaard A, Presser M, Collins T, Beliatis M, Skou AK, Jakobsen EM. The Role of Digital Maturity Assessment in Technology Interventions with Industrial Internet Playground. Electronics. 2021; 10(10):1134. https://doi.org/10.3390/electronics10101134Chicago/Turabian Style
Aagaard, Annabeth, Mirko Presser, Tom Collins, Michail Beliatis, Anita Krogsøe Skou, and Emilie Mathilde Jakobsen. 2021. "The Role of Digital Maturity Assessment in Technology Interventions with Industrial Internet Playground" Electronics 10, no. 10: 1134. https://doi.org/10.3390/electronics10101134