Wednesday, January 10, 2018

Manufacturing Sector Set for Significant Change in 2018

SME Channels, January 3, 2018; By Thiru Vengadam, regional vice president India at Epicor Software

Manufacturing, along with virtually all other industries, is going through a significant period of change. Driven by rapid technological development, manufacturers are having to work smarter, operate more efficiently and be prepared to innovate. As an enabler of growth, technology will play a key role in empowering businesses to innovate and seize the opportunities that will present themselves in 2018.

But where exactly are these opportunities likely to come from? Here, we identify the top trends that we believe will be central to success in the upcoming year.

 - The need for flexibility

 - Transitioning to Industry 4.0

 - Customer experience is king

 - AI-driven future
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Organizations that concentrate on making themselves smart and agile will be the ones that are best positioned to take advantage of growth opportunities in 2018. For manufacturers, this process starts by ensuring that internal software systems are fully supported with the latest updates, thereby enabling them to react to change and view them as opportunities rather than threats.

Published by HT Digital Content Services with permission from SME Channels. For any query with respect to this article or any other content requirement, please contact Editor at content.services@htlive.com.

Copyright 2018 Accent Info Media Pvt. Ltd. All Rights Reserved.

Monday, January 8, 2018

NASA Eyes 3-D Printing Tech to Cut Cost of Powering Heavy Rocket


As NASA builds its most powerful rocket, the Space Launch System, the U.S. space agency is testing engines with 3-D printed parts to cut costs of powering the heavy-lift rocket. By 3-D printing the hardware, more than 100 welds were eliminated, reducing costs by nearly 35% and production time by more than 80%.
Read More

Manufacturing Sector Set For Significant Changes In Technology in 2018

By SME Channels

Manufacturing, along with virtually all other industries, is going through a significant period of change. Driven by rapid technological development, manufacturers have to work smarter, operate more efficiently and innovate. The top trends central to success are flexibility, Industry 4.0, customer experience and AI.

Read More

Nissan Uses Brainwave Sensors to Develop Mind-Reading Cars


Many organizations are working on cars that drive themselves using computers. Nissan engineers are working to put your brain back into the process—but without involving your hands and feet. They're researching technology that uses brainwave sensors to detect what a driver intends to do in the next fraction of a second.

Friday, December 29, 2017

Machine Learning to Improve Production Planning: A Tougher Problem

December 14, 2017

By Steve Banker, contributor

Machine learning has been successfully applied to demand planning, but leading suppliers of supply chain planning are beginning to work on using machine learning to improve production planning. But architecturally and culturally, this is a much tougher problem than machine learning applied to demand planning.

In the $2 billion-plus supply chain planning market, ARC Advisory Group’s latest market study shows production planning as being a critical application SCP solution representing over 25% of the total market. Production planning applications are used for both planning daily production at a factory to creating weekly or monthly plans to divvy up the production tasks that need to be accomplished across multiple factories.

Machine learning is a form of continuous improvement. So, in demand planning the machine learning engine looks at the forecast accuracy from the model, and asks itself if the model was changed in some way, would the forecast be improved. Forecasts are improved in an iterative, ongoing manner. https://blogs-images.forbes.com/stevebanker/files/2017/12/Picture1-1200x994.jpg

Machine learning in supply planning

For supply-side planning, there are key parameters that greatly affect the scheduling. For example, lead times are critical. The longer the lead time, or the greater the variability associated with an average lead time from a supplier, the more inventory a company must keep. But humans are not very good at detecting when these parameters need to be changed and without ongoing vigilance, a planning engines outputs deteriorate. The loop between planning and execution needs to be closed to prevent this.

Cyrus Hadavi, the CEO of Adexa, wrote a good paper on this. He wrote, “with every iteration of planning, there are millions of variables to be considered, billions of versions of plans that can be produced, and thousands of variables which are constantly and dynamically changing.” Much of the data needed to properly update the planning model exists in execution systems. What Adexa is visualizing is having a self-correcting engine continuously scrutinize the data in these systems and then automatically update the parameters in the SCP engine when warranted.

But architecturally, this is a more difficult than using machine learning to improve demand planning. In a demand management application, the system is continuously monitoring forecasting accuracy. That accuracy data in the system allows for the learning feedback loop. Further, demand planners, the people that use the outputs of the system, play a core role in making sure the data inputs stay clean and accurate.

But in supply planning, the data comes from a different system or systems. Improving operations can be extraordinarily challenging if the data that holds the answers is scattered among different incompatible systems, formats and processes. And the people responsible for making sure the data put into various systems don’t use the system outputs; in short, they have less incentive for making sure inputs stay clean. This is a master data management problem.

A form of middleware/business intelligence must access up-to-date and clean data, analyze it, and then either automatically change the parameters in the supply planning application or alert a human that the changes need to be made.

These solutions do exist. I’m most familiar with the solution from OSIsoft, the PI System, which collects, analyzes, visualizes and shares large amounts of high-fidelity, time-series data from multiple sources to either people or systems.

But this means that to improve supply planning, you need not just the supply planning application, but middleware and master data management solutions. In this kind of situation, the integration, cultural, and, consequently, ROI issues become more difficult.

Copyright 2017 Forbes LLC. All Rights Reserved.
Copyright © LexisNexis, a division of Reed Elsevier Inc. All rights reserved.

Stanford Professor Aims to Bring AI ‘Electricity’ to Manufacturing

Canadian Press
December 15, 2017

The artificial intelligence (AI) researcher who called AI the new electricity is now trying to make sure every company is plugged in.

Stanford professor Andrew Ng, one of the leading figures in AI, is launching a startup called Landing.AI (LANDing-dot-A-I). Its aim initially is to help manufacturing companies use computer algorithms to cut costs, improve quality control, remove supply-chain bottlenecks, and more.

Landing.AI’s first strategic partner is Foxconn, the Taiwanese manufacturing giant that helps Apple build iPhones. The company is helping implement a system to spot defects, such as tiny particles or scratches on camera lens units. Currently, thousands of people work to manually inspect such parts. Ng says the AI-powered system can work 24 hours a day, seven days a week, and be more accurate than people.

“AI will transform manufacturing. That’s just a fact of life,” Ng told reporters at a briefing on Tuesday in San Francisco. His small team of about two dozen employees based in Palo Alto, CA, hopes to accelerate companies’ ability to take advantage of the latest techniques in machine learning.

Ng, who led AI teams at search giants Google and Baidu and launched online learning platform Coursera, did not announce funding details.

Original headline: Ng aims to bring AI ‘electricity’ to manufacturing

Copyright 2017 The Canadian Press. All Rights Reserved.
Copyright © LexisNexis, a division of Reed Elsevier Inc. All rights reserved.

Looking back - The 2018 crystal ball - From Chief Economist Intelligence Unit

Reproduced from email I've received from Simon Baptist
Chief Economist Intelligence Unit

Everyone loves a good list, so I thought I'd finish off 2017 with some of our key calls for 2018. It should be another pretty good year for the global economy: our forecast is for global GDP growth to dip just slightly from 2.9% this year to 2.8% next year. The biggest slowdown will be in China and those economies tied closely to it, while the biggest acceleration will be in Latin America. Tiny countries aside, India is our pick for the fastest-growing economy, followed by Ethiopia and Cambodia. China will sit at around 15th place. At the other end of the spectrum, Venezuela's GDP will yet again shrink by more than 10%, with Puerto Rico, Equatorial Guinea and North Korea also in negative territory. India, Iran and Vietnam will see the fastest productivity growth, a good sign of longer-term success.

For those following the financial markets, we expect another year of US dollar strength, with only 27 of the currencies we forecast appreciating against it, 37 remaining flat, and 125 depreciating. Those appreciating substantially include the Norwegian krone, the Egyptian pound and the Japanese yen, whereas the Australian dollar, Argentine peso and South African rand will weaken. Workers will have a good year in Romania, China and Hungary, where wages will rise robustly. Demographics continues its relentless transformation of the world's labour markets, with France and Finland joining the list of countries with a shrinking workforce. It will remain China, however, that sees the biggest decline in its working-age population.

What do you think of Chief Economist Baptist 2018 predictions?