Category

Manufacturing

The Persistence of Memory

Tesla Missed Forecast. Are you Surprised?

By | Automotive, Manufacturing | No Comments

Tesla Missed Q2 Forecast

Tesla Motors delivered 14,370 vehicles in the second quarter, missing its forecast of 17,000 units “due to the extreme production ramp in Q2 and the high mix of customer-ordered vehicles still on trucks and ships at the end of the quarter, Tesla Q2 deliveries were lower than anticipated at 14,370 vehicles, consisting of 9,745 Model S and 4,625 Model X.”

This should not come as a surprise. One of Tesla’s biggest—albeit least discussed—challenges is its struggling manufacturing supply chain.
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Exit

Will Brexit Put the Brakes on British Innovation?

By | Internet of Things, Manufacturing | 3 Comments

The UK to exit the EU: What are the Implications for Tech Companies?

Most analysts agree:  the economic consequences of the UK leaving the European Union will be bad, and, in all likelihood, the long-term political implications will be even more dire. For British technology firms the prognosis is no better. Leaving the EU means shortage of skills and limited ability to employ non-UK workers, new trade regulations and tariffs, and uncertainties concerning EU’s data protection directives.

In the chaos of the Brexit we’ve nearly forgotten that one of the primary initiatives of the EU was to catalyze long term economic growth through higher levels of collaboration and deliberate investments in innovation. The EU supports several interlinked programs that provide member states €120 billion over the period 2014 – 2020 for research, development and innovation. The largest program is Horizon 2020 with a budget of just over €70 billion.

After Brexit, UK firms will no longer have easy access to EU research grants. Some are quick to dismiss these grants as they represent only a small fraction of the UK’s technology R&D budget, and point out that the UK is actually a net contributor to the EU budget. Between 2007 and 2013, the UK contributed €77.7 billion to the EU, which amounted to 10.5% of the total EU income from member states, and received €47.5 billion in EU funding (6% of the total).

But they may be missing the point.
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Crystal Ball Predictions

Three Industrial Internet of Things Questions

By | Internet of Things, Manufacturing | 3 Comments

Three Industrial Internet of Things Questions for 2016

Q: The Industrial IoT. Are we finally past the hype?

Not even close. Still too much talk about billions of connected devices, although it’s not always clear what they are connected to and for what purpose. Certainly interesting IIoT implementation stories will continue to make headlines, but when scrutinized closely, most will be one-off M2M implementations: single-purpose applications that take effort to build and are difficult to scale and reuse.

Q: What’s needed to improve the maturity of the Industrial IoT space?

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Knowledge Based Product Development

By | Design for X (DFX), Design Reuse, Innovation, Internet of Things, Manufacturing, PLM, Warranty | No Comments

Lack of ongoing insight into product operation and the interaction with users is a major contributor to the persistence of a knowledge gap that plagues many product organizations, reduces efficiency and stifles innovation.

What drives this knowledge gap? A number of general trends impact companies in most industry sectors and product types.

  • Technology complexity and, in particular, the increased reliance on complex embedded control software.
  • Elongated and fragmented supply chains that support global operations.
  • Meeting global markets demand results in lower volume of configuration-specific product instances.
  • Accelerated egress of an aging workforce from the workplace.

The impact of the growing knowledge gap is most recognized and frequently discussed in the context of equipment maintenance and repair, which is a knowledge intensive activity. Another area were closing the knowledge gap has significant benefits is engineering change management.

Organizations often speak about the need to collect and formalize “tribal knowledge” to close the gap between the knowledge needed to perform a complex takes or reach a decision, and the knowledge and experience the person performing this task has access to.

In a previous article I discussed the severe myopia that exists in many product companies: most engineering organizations lose sight of their products once they are sold or installed in the field. In some manufacturing companies this happens even earlier, during the transition to manufacturing engineering and before the product enters volume production. Once a product is in use, there is only a trickle of information in the form of service records and warranty claims. But many organizations dismiss critical maintenance and warranty information as merely operational and cost of doing business and aren’t leveraging its full potential.

Knowledge Based Product Development

Product organizations cannot have a true and complete view of a product and how customers are using it unless they can continue to observe it while it is in use. They must monitor and analyze products throughout their lifecycle, gauge their performance, quality and uptime, how users are interacting with the products, and how well they meet market expectations overall.

This rich multidisciplinary insight extends beyond design information. It includes multifaceted data from a variety of sources and disciplines and includes manufacturing, supply chain, filed operations and service and maintenance. The data can be aggregated from multiple sources, including real-time data such as IoT, maintenance record and customer experience.

The aggregation, classification and analysis of this data provides critical insight to embellish and enhance an organizational knowledge library for subsequent iterations of product design, manufacturing process engineering, and service planning.  reinforced by analytics, case-based reasoning (CBR), and similar tools to collect, analyze and vet information.

Product organizations cannot afford operating with blinds that prevent visibility to downstream processes. They should not ignore the value of information collected throughout the product lifecycle. They must establish knowledge processes, governed by PLM software, to maximize the utility and benefit of product lifecycle information.

(Photo: FreeDigitalPhotos.net)

Elephant Monument for Champs-Élysées (Jean-Jacques Lequeu, 1758)

Design for IoT or Design by IoT?

By | Internet of Things, Manufacturing, PLM | No Comments

The Internet, mobile devices and cloud computing are drastically changing manufacturing. Software control systems embedded in smart devices and connected machines form a network of sensors and actuators that exchange information autonomously and can monitored and controlled online.

The potential impact of the Industrial Internet of Things (IIoT) and Industry 4.0 are undisputed. While industry pundits and technology vendors may dispute the exact size and growth rate of the IIoT, there is a general consensus that the space offers significant business value and will be transformative indeed.

Are the smart connected devices—or the “things”—that form the IIoT the same machines and devices we know and understand today, only connected? Or do we need to invent new paradigms to define system functionality and product architecture in order to realize the value of the IoT? Read More