To improve plant floor productivity, manufacturers have typically focused on finding ways to optimize individual assets using metrics associated with overall equipment effectiveness (OEE). Over the years, plant managers have squeezed out as much efficiency as possible from these machines and now they are asking for more—in the form of real-time, system-wide optimization.
Sight Machine is answering that call. Last month, the company’s SaaS-based manufacturing analytics suite received a major upgrade with the announcement of the next-generation Manufacturing Data Platform (MDP) which has been fine-tuned to unlock another level of operational efficiencies, company officials said.
“We are focusing on continuous improvement, which is not just about helping manufacturers through production, but helping them improve their production processes,” said John Merrells, chief product officer at Sight Machine, noting that the new MDP architecture supports rapid scalability across an entire plant network enabling manufacturers to deliver value enterprise-wide.
According to the Sight Machine whitepaper, “Powering the New Age of Manufacturing,” machine sensor data is housed in an assortment of systems that don’t talk to each other—and are made up of an array of incompatible formats. Therefore, sensors can show what’s going on inside an individual machine, but not how machines interact with each other. That same deficit applies at the plant-to-plant level, leaving manufacturers unable to analyze specific interdependencies between facilities. For example, how defects in one factory’s end product affects activities in the next plant in the supply chain which uses it as raw material.
In 2018, Sight Machine introduced an analytics platform that enabled centralized management of multi-factory IIoT data, giving manufacturers the flexibility to acquire machine data wherever it exits and process it into a standardized format to securely stream to the company’s cloud-based analytics software.
Continuing on this data aggregation and analytics journey, the MDP platform houses the data connectivity, data pipeline and data modeling functions that apply cross-industry. On top of this, Sight Machine has built out expertise for three vertical sectors: paper & tissue, packaging, and chemicals.
While Sight Machine has always served a broad range of industries, over the last two to three years the company has had a high number of cutting-edge engagements with customers in these three sectors, officials said. In each of the engagements, Sight Machine's product development and continuous improvement teams developed new models, analytics tools, and in-house expertise to address the issues specific to that sector.
For example, in paper and tissue, giant reels of paper are spooled from one machine section to another. One of the most common causes of unplanned downtime is when the paper sheet breaks. “We have a tool that helps process engineers find the cause of sheet breaks and provide recommended machine settings to avoid these in the future,” Merrells said. “We're also able to predict quality of the finished product during the production process and provide operators with actionable intelligence to better run the machines.”
In the packaging sector the need for continuous improvement is a result of a very fast-paced business dynamic. Twenty years ago, manufacturers could run a line for several months and still be competitive, but today there are demands for sustainable packaging, small, customized packaging, and new designs. “The old way of relying on very smart and experienced engineers and operators to set something up isn’t going to work anymore,” said Sight Machine CEO Jon Sobel. “You have to be able to understand and control the process. If you are doing a lot size one, line changeover better by efficient. This is absolutely why we believe this approach is valuable and needed.”
Sight Machine creates models of machines, production processes and finished products, including the machine settings and conditions when each part or batch was made, and the resulting quality. Then using the model, MDP provides descriptive analytics of what is happening, predictive analytics of the expected quality and yield of the batch in production, and prescriptive analytics to advise operators on optimal machine settings to improve the quality and yield, as well as warnings on what needs to be changed to prevent defects or downtime.
And, understanding that manufacturers may need guidance in order to swiftly adapt to new continuous improvement tools and operations, Sight Machine has built out a team of experts for each industry segment to help customers solve specific problems.
“We developed the skills and expertise on the people side so that we are focused on results, outcomes, and success,” Sobel said.
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