by Gary Mintchell | Apr 5, 2024 | Enterprise IT, Manufacturing IT, Operations Management, Software
The beginnings of a trend in manufacturing software has appeared on my horizon about mid-way through last year. This would be the use of low-code software for application development. I first noted it with some acquisitions in my market space. Recently I have begun working with a company called Quickbase who has a platform built with low-code application development in mind.
[Note: In my work with Quickbase, I’m sometimes compensated for what I do. They do not dictate what I write or say.]
I recently had the opportunity to talk with two users of Quickbase’s platform for their manufacturing software needs. You can hear them plus me at the Quickbase Empower Virtual Customer Conference on May 8 (our session is at 11:30 am EDT immediately following the keynotes). Their stories verified what I was beginning to hear from my first encounters. Listening to their tone of voice, what really perked them up was the ability to be rapidly responsive to requests from users for modifications to screens and reports.
That discussion spurred me on to some additional research on the topic. Following is a list of benefits I uncovered on my research. This is not a list specific to Quickbase, but a more generic list that you might find with applications in a variety of areas. But check out Quickbase for your specific needs. I’m sure I’ll have more interviews in the future to take a deeper dive into Quickbase specifically. For now, I was interested in this new feature. Feel free to contact me with additional thoughts. Or stories about how you have used low-code in engineering or manufacturing operations software.
- Faster Development: Low-code platforms enable rapid application development by providing pre-built templates, drag-and-drop interfaces, and visual development tools.
- Reduced Costs: With low-code development, you can save on development costs by eliminating the need for hiring expensive developers with specialized coding skills. Additionally, the time saved in development translates to cost savings.
- Increased Productivity: Low-code platforms allow both professional developers and citizen developers (non-technical users) to build applications. This democratization of app development increases productivity by enabling more people within an organization to contribute to development efforts.
- Flexibility and Customization: While low-code platforms provide pre-built components and templates, they also offer the flexibility to customize applications according to specific business requirements. Developers can extend functionality by writing custom code when needed.
- Streamlined Maintenance: Low-code platforms often include built-in features for application monitoring, debugging, and performance optimization. This simplifies maintenance tasks and reduces the time required for ongoing support and updates.
- Integration Capabilities: Many low-code platforms offer out-of-the-box integrations with popular third-party services, databases, and APIs. This makes it easier to connect your applications with other systems and data sources.
- Scalability: Low-code platforms can scale with your business needs, allowing you to quickly add new features or expand functionality as your requirements evolve. This scalability helps future-proof your applications.
- Accessibility: Low-code platforms often come with intuitive user interfaces and guided development processes, making app development accessible to a wider range of users, including those with limited technical expertise.
- Faster Time-to-Market: By accelerating the development process and enabling iterative development cycles, low-code platforms help bring applications to market faster. This can give your business a competitive edge by allowing you to respond quickly to changing market demands.
- Risk Reduction: Low-code platforms often come with built-in security features and compliance standards, reducing the risk of security vulnerabilities and ensuring regulatory compliance.
Overall, low-code application development software offers a compelling solution for businesses looking to rapidly build, deploy, and maintain applications with greater efficiency and flexibility.
by Gary Mintchell | Apr 5, 2024 | Marketing, Podcast
This is from my recent newsletter. You can sign up for delivery by clicking on the envelope icon in the right sidebar.
Several colleagues have traveled close to my new location, and we’ve shared some good meals with great conversation. Inevitably they asked for my observations on the state of the automation market.
Part of my answer, in short, would be to quote from a recent Seth Godin blog post, The Drift to Normal. As an organization grows in scale, the idiosyncrasy and distinctiveness that was originally informed by the taste of the founders moves toward the mean. Over time, things get more average.
He continues, “That’s because each new customer, each new supplier and each new employee wants or needs something a little more normal, at least sometimes. The drift to normal can only be countered by persistent effort, usually at the cost of some element of short-term scale.”
Here are a few points that capture my thinking:
- Mature Market
- We’re building few or no new plants—and the USA seems to be declining in activity with European and Asian automation companies for the most part showing reduced interest
- Customers are not switching systems
- Automation supplier consolidation
- Innovative startups look for lucrative buyouts as their end game
- Technology is stable
- Technology is also consolidating
- Effects of the changes:
- Automation companies have reduced need for outside marketing partly due to spread of technology
- Primary emphasis is on sales and service—keeping present customers satisfied, if not happy
- Technology development involves tweaking current products and innovating through acquisition
- Geographical retrenchment
For example, let us look at a brief history of National Instruments, nee NI, nee Emerson Test and Measurement. Three technical innovators created a startup with a vision of software defined instrumentation. They created a creative, entrepreneurial culture. For several years there was great energy, growth in business, growth in technology development.
Then one year I noticed that the technology keynote at the annual user conference sounded more corporate. Less, “Gee Whiz” technology. People started to trickle away—either encouraged or seeing the changes. The leaders deliberately changed the culture toward corporatism preparing for an eventual sale. Then the sale happened to the epitome of corporate management in the market.
Note: not a criticism, but an observation. And it’s happening all through the market.
I have released a couple of podcasts on my platform at automation.libsyn.com. You can subscribe on Apple Podcasts, Overcast (my favorite), direct download, or from your podcatcher of choice.
Check out some thoughts on Standards and on Slow Productivity. My just released podcast includes a number of thoughts about the current state of the automation market.
I have arranged a special deal with energy drink makers Magic Mind. Listeners can visit https://www.magicmind.com/garym and get up to 56% off your subscription for the next 10 days with my code GARYM20. After 10 days, you can still get 20% off for one time purchases and subscriptions.
by Gary Mintchell | Apr 4, 2024 | Security
Technology trends form a large part of reporting here. Another trend is companies sending out questionnaires and publishing reports. This one from Xage Security asked about manufacturers’ opinions regarding zero trust adoption.
Highlights:
- Manufacturers are worried about data sharing – 90% of respondents in the manufacturing industry are concerned with sharing data outside the organization, either via cloud services or with third parties.
- Industries are embracing transformation at varying speeds – Manufacturing leads the charge, with 90% agreeing that integrating IT / OT and digital transformation is a pathway to progress. However, oil & gas is lagging at 35% in agreement.
- Most organizations have adopted zero trust principles – 72% have started adopting zero trust principles, with 31% currently in the process of crafting a strategy for zero trust deployment.
“While zero trust is not a one-size-fits-all model, the data shows that organizations are evolving their understanding of zero trust as a strategy to enhance the safety, security, and reliability of both their enterprise IT and OT environments,” said Jonathon Gordon, Industry Analyst at Takepoint Research. “The industrial world is taking action and recognizes the necessity to expedite zero trust adoption to keep our nation’s—and world’s—critical infrastructure safe from cyberattacks.”
Xage partnered with Takepoint Research to survey 250+ cybersecurity senior leaders across critical infrastructure organizations, energy, utilities, transportation, oil and gas and manufacturing. Data was collected from December, 2023 through February, 2024.
“Amidst market confusion surrounding various zero trust strategies, it is evident that organizations are now diligently navigating through them and honing their approaches,” said Sri Sundaralingam, SVP of Marketing at Xage. “The survey results underscore the increasing adoption of zero trust across industrial sectors, aimed at mitigating crucial business risks while propelling digital transformation alongside new business initiatives.”
Get the full Zero Trust Report here.
by Gary Mintchell | Apr 3, 2024 | Automation, Robots, Software, Technology
Vision software for guiding robots news goes along with the burst of robot news. In this case, AI meets vision software.
AI software company Micropsi Industries today announced MIRAI 2, the latest generation of its AI-vision software for robotic automation. MIRAI 2 comes with five new features that enhance manufacturers’ ability to reliably solve automation tasks with variance in position, shape, color, lighting or background.
What sets the MIRAI 2 AI-Vision Software apart from traditional vision solutions is the ability to operate with real factory data without the need for CAD data, controlled light, visual-feature predefinition or extensive knowledge of computer vision.
Gary Jackson, CEO of Micropsi Industries, noted, “Recognizing the complexities of implementing advanced AI in robotic systems, we’ve assembled expert teams that combine our in-house talent with select system integration partners to ensure that our customers’ projects are supported successfully, no matter how complex the requirements.”
MIRAI is an advanced AI-vision software system that enables robots to dynamically respond to varying conditions within their factory environment, including variance in position, shape, color, lighting and background. What sets MIRAI apart from traditional vision solutions is the ability to operate with real factory data without the need for CAD data, controlled light, visual-feature predefinition or extensive knowledge of computer vision.
The five new features that will be available to MIRAI 2 users are:
Robot skill-sharing: This new feature allows users to share skills between multiple robots, at the same site or elsewhere. If conditions are identical (lighting, background, etc.), very little or no additional training is required in additional installations. MIRAI can also handle small differences in conditions by recording data from multiple installations into a single, robust skill.
Semi-automatic data recording: Semi-automatic training allows users to record episodes (of data) for skills without having to hand-guide the robot, reducing the workload on users and increasing the quality of the recorded data. MIRAI can now automatically record all the relevant data—users only need to prepare the training situations and corresponding robot target poses.
No F/T sensor: Training and running skills is now possible without ever connecting a Force/Torque sensor. This reduces cost, simplifies tool geometry and cabling setup, and overall makes skill applications more robust and easier to train.
Abnormal condition detection: MIRAI can now be configured to stop skills when unexpected conditions are encountered, allowing users to handle these exceptions in their robot program or alert a human operator.
Industrial PC: The MIRAI software can now be run on a selection of industrial-grade hardware for higher dependability in rough factory conditions.
by Gary Mintchell | Apr 2, 2024 | Automation, Process Control, Productivity, Software, Sustainability
Fero Labs has developed software to help certain types of process manufacturing plants improve quality output economically when given a random mix of feedstock. I wrote about the company last August—A Better Way to Control Process Quality.
They sent a new press release, and I must admit that I understood almost nothing in it:
Fero Labs, the only Profitable Sustainability Platform for industrial optimization, announced the release of their ground-breaking feature ‘ExplainIt for Live Predictions’ which expands a factory’s production knowledge in real-time. This advanced feature for cross-functional teams increases trust in AI predictions by disclosing real-time text explanations about abnormal factors influencing their live production.
There were way too many marketing-type phrases in there. Worst of all was the concept of “trust in AI predictions.” So, I asked the very patient publicist. She suggested that I talk with Berk Birand, Fero Labs Co-founder and CEO. And, I did. He was most helpful.
We caught up from my last article about their ability to use the huge data sets manufacturers have accumulated over the past decade using advanced statistical methods and “white box machine learning (ML)” to help engineers optimize their plants. Make them more profitable and reduce waste (sustainability). Therefore the “Profitable Sustainability” company.
Birand took me through an example that I could understand, since I had a customer in the 90s who did this sort of process.
Imagine a plant with piles of scrap steel in a yard. They have an electric arc furnace that melts all that disparate steel that will be poured out eventually to make their final product. Given that the feedstock has high variability as to the composition of the steel, the typical plant overdesigns the process to allow for variations. This, of course, is wasteful on the surface. But if the final chemical analysis shows that the output will not make the desired tensile strength or other spec, then the waste is even higher.
What if you accumulated the data (feedstock, process, finished steel) over time built a modern AI model? Its predictions could be used to drive profits, reduce waste, save time. But, would anyone trust yet another advanced process control system? We all know that models eventually goes out of whack sometimes and sometimes gets the wrong answer.
Here comes the “trust” part of the trust in AI model. They built an explainable model from the beginning. It can predict characteristics, say tensile strength of the mix because of chromium or carbon levels and so forth. Since we know that every model is wrong sometimes, they built in confidence levels in the prediction engine. Their AI looks at the material composition and suggests adding chemicals to the mix, but it gives an explanation and a confidence level. The engineer looks at the confidence report (I am confident in this prediction or I’m not confident in this prediction) and can decide whether to go with the AI or to go with gut feel based on years of experience.
He convinced me. Fero Labs has developed an AI engine that gives the engineer a level of trust in the prediction.
More explanation from the press release:
Expanding on Fero Labs’ white-box ML, which provides full transparency of Fero’s powerful machine learning models, the new ExplainIt feature provides a contextual explanation of anomalous factors involved in each live production optimization.
This type of analysis is typically addressed through linear Root Cause Analysis (RCA) tools. Unlike traditional methods, Fero Labs’ solution is non-linear, much like process operations, and delivers results in seconds rather than the hours or days typically needed. Traditional methods generally require the engineer to preselect a small sample of factors to investigate, which can introduce potentially misleading biases. Fero Labs’ software has the power to evaluate all relevant factors which improves insight and prediction accuracy.
