Three Steps to Leveraging the Power of Your Data

April 11, 2020
Pursuing Industry 4.0 solutions should begin with a clear understanding of your business strategy. This approach will ensure consensus and provide a common foundation when creating a roadmap with the appropriate partner to implement successful automation

At the foundation of Industry 4.0 is data and connectivity. Industry 4.0 is not a technology, but rather, a concept of how automation can be better utilized to help companies achieve operational goals aligned with business strategies.

In three simple steps, Sick can supply you the information needed to make your manufacturing processes Industry 4.0 ready:

Step 1: Review Business Strategies. The Industry 4.0 discussion within an enterprise should begin with the business strategy. Aligning your business goals with production activities and areas that need support is what helps bring your enterprise straight into Industry 4.0 territory. And, with increasing demands on traditional manufacturing, like increased traceability, quality control, and limitless configurations, it is vital to implement an Industry 4.0 offering that helps you stay competitive.

Step 2: Finding and Collecting Data. Once you’ve reviewed your business strategies, it’s time to find the data to support these goals. Collecting, connecting, and leveraging data helps you make intelligent and proactive decisions. Fortunately, Sick can help harness the value of all that data with superior sensing solutions.

With Sick sensors on machines, you can collect data and then integrate and move data to the appropriate place to meet operational excellence goals. Many companies are starved for information needed to better improve their production lines. This was the case with one of Sick’s manufacturing customers.

The plant operations management team realized that islands of automation existed in their plant, stranding data that they could not access. The plant managers didn’t know if machines were running, what temperatures the ovens were at, or even how many parts were made that day. They came to Sick to find an offering to collect data from the plant floor.

After an introduction to Sick sensor capabilities for data collection, the customer determined what they needed was a hardware-agnostic partner who could take data from all their programmable logic controllers (PLCs) and sensors to move it where they needed it.

The Sick team worked to provide the data the customer needed from all the sensors and PLCs installed in the plant, regardless of the company name on the hardware. After all, Industry 4.0 is about connectivity of technology and the generation of good data to improve operations.

Sick sensors were also able to collect data without having to go through the PLC. Not all data needs to be processed through the PLC for it to be properly harvested. This creates more flexibility in the creation of an Industry 4.0 production line.

Step 3: Implementing Industry 4.0 Offerings. There is little doubt about the benefits of digital transformation—efficiencies that reduce manufacturing costs, reduce downtime, and prepare companies to be more agile and respond quickly to customer demands. But the question remains: Why are so many companies still hesitant to initiate Industry 4.0 projects?

Many corporations, large and small, are looking for a partner to assist with the implementation. Sick has built internal competencies to provide customers with complete connectivity to generate data to analyze for operational improvements.

“The team at Sick is agile and agnostic to consult with customers on their challenges and potential needs. This helps determine the ideal infrastructure to develop the most suitable enterprise solutions that can adapt to the disruptive industry needs,” said Salim Dabbous, director of sensor and safety integration at Sick.

One example of an Industry 4.0 enterprise offering is the implementation of a data concentrator methodology into a pre-existing controls platform to connect current machines and push non-process-related data seamlessly upstream to the cloud or an enterprise resource planning system. The reliable data pushed upstream might include machine status, part count, or data from temperature and pressure values. This all feeds into dashboards and key performance indicators, providing transparency and, ultimately, predictive maintenance measures that optimize processes and increase throughputs.

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