Saša Muhič Pureber, VP, Manufacturing Intelligence, INEA
An average human’s working memory can only store between four and eight things at the same time. With the amount of information now easily available to us, the chance of reaching the best possible decisions is statistically becoming closer and closer to zero. This is especially true when taking into account that your data shelf life is, conversely, radically decreasing, and so shortening the reaction time window before crucial facts change and the entire process needs to restart.
Are operators superhuman?
Operators are also not immune to the exponentially growing “data-person-response time” imbalance. The number of operators on factory floors is decreasing with automation, and even more so with smart devices, products and services. The world each operator now has to master is far greater than ever before, with tens of thousands of tags per person and complex automation and IT systems.
Industry 4.0 guidelines and the associated smart factory principles normally discuss smart devices, connectivity, adaptability, analytics and predictive actions, artificial intelligence, data mining, cloud storage, smart services, smart products, smart supply chain and more. These paradigms include production resources, entities and products and their connectivity, but often do not touch production or control systems.
Some of the MES/MOM and SCADA system building blocks can, however, assist significantly in adding a more holistic, sustainable and “smart” note to the manufacturing execution solutions:
• The operator learning curve and error prevention can be significantly improved by selecting single platform SCADA and MES solutions with integrated workflows. Such systems guide the operators; provide self-explanatory interfaces; lower the need for good, elaborate user manuals; and can provide a built-in poka-yoke system.
• Solutions with advanced alarm-handling capabilities and reaction optimised graphical interfaces can have a meaningful impact on operator reaction times; the quality of their responses; job-associated stress levels; and the time available for the value-added tasks.
• Interactions with IT maintenance teams can be significantly lowered with solutions that can predict the possibility of failures; act on them where allowed; maintain their own life cycle; document themselves; and are well-governed with a solid contingency plan.
• Some research shows that roughly 70 per cent of preventive maintenance costs could be avoided using predictive maintenance in the form of self-learning algorithms.
The decision-making process
The described information overload also naturally affects the decision-makers in their ability to make good choices when planning different investments in their factories. Experienced solution providers, if engaged early enough in the process, can bring their technology-specific and industry-specific experience to the table, and can often be the determining factor in the overall success of the investment.
The selected supplier should ideally be able to deliver a turnkey solution and have mechanical design teams working closely with automation and manufacturing intelligence experts. This capacity provides all the benefits of single-source responsibility and the advantages of the information closed loop with immediate feedback from commissioning to a design team, and vice versa.
A solution provider that understands their customers’ needs will be able to provide a holistic, sustainable solution that incorporates established tools (preventing customer lock-in) with good local support and that will best serve everyone’s purpose. Effective solutions should not only enable each key user but should also be elegant, goal-oriented and lifecycle aware, provide good system governance and be as sustainable as possible. The factory floor should be an efficient production environment, as opposed to a showroom of all the newest trends.
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