深度学习提高了机器人拣选的灵活性

The manufacture and assembly of massive fuselage assemblies and other large structures generates a river of data. In fact, the bill of materials for a single fuselage alone can be millions of rows of data. In-house production processes and testing, as well as other manufacturers and customers created data flows that overwhelmed previous processes and information systems. Spirit’s customer base had grown substantially since their 2005 divestiture from Boeing, resulting in a $41 billion backlog of orders to fill. To address this backlog, meet increased customer demands and minimize additional capital investment, the company needed a way to improve throughput in the existing operational footprint. Spirit had a requirement from customers to increase fuselage production by 30%. To accomplish this goal, Spirit needed real-time information on its value chain and workflow. However, the two terabytes of data being pulled from their SAP ECC was unmanageable and overloaded their business warehouse. It had become time-consuming and difficult to pull aggregate data, disaggregate it for the needed information and then reassemble to create a report. During the 6-8 hours it took to build a report, another work shift (they run three per day) would have already taken place, thus the report content was out-of-date before it was ever delivered. As a result, supervisors often had to rely on manual efforts to provide charts, reports and analysis.

In the automotive industry, niche products are becoming more and more important. The automobile industry has changed considerably over the past few years. No doubt: to remain competitive, instant reaction to specific customer requests is of utmost importance. In a parallel to the growing individualization of our society, the range of models available from Audi has increased as well.

As a part of their Industry 4.0 initiative, the customer primarily wished to leverage IoT for maximizing operational efficiencies, productivity, reducing the energy footprints and maximizing capacity utilization. But there were several challenges at the outset:Firstly, the customer had multiple assembly lines with a diverse set of machines, systems and sensors, all communicating on different protocols. As such, primarily they needed a partner who could connect diverse set of assets on to a single platform and make use of underutilized ‘dark’ data.Secondly, the amount of data, data types and their applications was so vast, that the platform handling it, needed to be scalable and flexible.Lastly, Varroc faced the typical challenge of innovating in ‘Brownfield’ markets – wherein the real bottleneck is in integrating IoT in tandem with both the new and legacy equipment without any further CAPEX for asset substitution.

Carvajal Pulp and Paper was challenged to lower its Manufacturing Costs to become more competitive. This reflected in the needs to gain more control over energy costs, chemical costs, and production losses.

VISIBILITY - Currently unable to see amount of power consumed per machine or plant; need to account for energy used per batch. WASTE - 10% of product fails quality standards, unable to identify point of failure (initial spinning, weaving, final processing, etc). INACCURACY - Acquiring OEE measurements and compiling them is a manual process, which lacks visibility into all parts of the production process and is error-prone. LACK OF INTEROPERABILITY - Outdated machinery still has long, effective lifespans but needs to connect to newer, digital assets

For many manufacturing companies today, the opportunity to connect people, process, data, and things created by the Internet of Everything (IoE) presents a new way to look at factory automation. The opportunity for digital innovation often arises when a company is expanding capacity or building a new production facility. Mahindra and Mahindra, one of India’s leading automakers, seized the opportunity to deploy a connected factory of the future at their new Chakan facility.