Production optimization

On their quest to create the automated factory – either in-house or in partnership with Hauni Consulting – customers are discovering they can achieve significant and lasting improvements in their OEE by combining data usage with solutions for monitoring, controlling and optimizing production.

The paths and strategies used by digitization processes in industry differ widely, yet they share a vision: a fully automated "smart factory" which is self-regulating – both as a whole and in every detail – and detects the optimum settings for each parameter to ensure maximum overall equipment effectiveness (OEE).

Companies are already taking steps towards achieving this goal through numerous individual projects. When approaching tasks of this nature, it is important to define clear goals for increasing the OEE of a machine, line or entire production system. Otherwise, companies run the risk of losing track of their overall aims. "Whether it’s reducing waste or optimizing quality – the journey is as individual as our customers," says Marco Castro, Head of Hauni Consulting.

 

Many goals, one path

 

As Castro explains, the specific tasks and measures involved in the optimization can differ significantly at the project level. "However, it makes sense for the project design to follow a set pattern. It starts with the definition of the goal. Then we proceed to identification, collection and analysis of the correct data, derivation of actual improvements from that data, checking the success of individual measures and ensuring that the optimization retains its effectiveness over the long term."

 

The right choice

 

In recent years, Hauni has equipped its machines with the sensors, network connections and visualization systems necessary for efficient monitoring. As so often, however, the devil is in the detail – or, more precisely, in the choice of details. "Our state-of-the-art M-Generation machines have a total of around 3,000 different parameters. On average, 10 percent of these – i.e. around 300 – are relevant for achieving a specific goal. An operator will usually be familiar with about 25 of them and an expert with a further 25 – that leaves another 250 parameters. Identifying these requires extremely specialized knowledge."

This is why Hauni not only supports customers in OEE projects by supplying the right tools, such as operator assistance systems, trend and alarm features or manufacturing operation management. Customers who work with Hauni Consulting on their OEE projects also save time and resources, and benefit from the resolution of complex issues, such as selecting the right optimization parameters. Hauni’s consultants analyze the situation on-site with the customer and, if necessary, not only draw up a roadmap for the specific project but also recommend the most efficient way to sequence the various potential improvement projects. “Customers can successfully implement an OEE project on their own using Hauni solutions. After all, we have built of lot of our know-how into the tools themselves,” says Castro. “However, by using our consulting services, customers can also ensure that they extract the maximum performance from these tools at all times and proἀt from our experience in managing many similar projects all over the world.”

 

Measurable success for the customer

 

The example of a successful LES quality improvement project for a customer who wanted to boost the performance of his PROTOS/Focke lines by reducing loose end waste offers some useful insights into the specifics of this process. “After recording and extracting all the machine data, we ran a performance comparison to identify the best machine and set it as a reference,” reports Castro. “Effective monitoring is the cornerstone of performance controlling. It allows us to make meaningful comparisons of the overall performance and the target and actual values for individual parameters as well as measure the success of each change we implement. We filtered out differences by comparing the data for various parameters, determined the inḀuence of different parameters on the target values and modiἀed the data sets accordingly.” The final OEE comparison proved the project was a success. LES line waste fell by 36 percent while the mean shift output rose by 11 percent – a positive side effect.

Permanently optimized

 

The techniques used in this project not only solve short-term problems but also lay the groundwork for permanent production optimization. “As part of a continuous improvement process, it is vital that our customers can access data collected over an extended period and the analyses of this data,” explains Castro. “These analyses and before-and-after comparisons for individual parameters provide information about errors and highlight potential for further optimization. Once the correct measures have been identified, production systems can be maintained at an optimized level over the long term. Targeted centerlining and the definition of specific setpoints on the machines not only allow customers to make better use of individual machines or lines in the factory. The insights gained at one production facility can also be used to benefit all of the company’s locations.”

Data sharing is the key to maximizing system performance. Hauni solutions help customers to collect and analyze the right data for optimizing machine performance. But, as Castro explains, they offer much more than that. “Our customers know their products inside out and we know ours.

Specifically, this means that when our customers send us data from their production systems, we can compare them with the pool of knowledge and data we have accumulated over decades from production facilities around the world and draw conclusions.” Hauni uses this knowledge and data to continuously develop new solutions, provide improved asset management concepts and offer the best possible support for operators. But that is not all. “New solutions based on big data are increasingly empowering our customers to sustain this optimum level of performance over the long term and ensure ongoing self-optimization of individual machines or even complete lines and production systems,” says Castro. “We constantly incorporate these insights into our software tools to provide continuous improvement processes for our products and services and steadily optimize production in our customers’ factories.”