by Gary Mintchell | Jan 28, 2022 | News, Operations Management, Workforce
The contractors just installed in the men’s restroom in the factory would intermittently make a whirring sound and a little shutter would open. I had a first level management role in the plant at that time of the early 70s. The guys would talk to me. They wondered if it was a camera spying on them to see who spent too much time away from production.
It was a deodorizer.
But that suspicion has stayed with me.
So, that was my first impression when I received this press release regarding “Safe Communications Software” that looks at workplace email and chat communications in a company. As the company told me, people should be aware that any time they use any communication tool on the company’s servers they are not using a private communication channel.
Besides, this application is not designed for nefarious spying on workers but is designed to protect employees from harassment, discrimination, bullying, and even workplace violence. Proactive mitigation benefits everyone in the company as well as prevents those post-event questions and recriminations.
Here is the release:
CommSafe AI launched CommSafe AI Safe Communication Software. The software has received certification with ServiceNow, a leader in enterprise digital process automation tools. CommSafe AI also integrates with Microsoft products and Google Workspace accounts.
The CommSafe AI tool is a Software-as-a-Service (SaaS) product that in near real-time allows companies to get ahead of threats of conflict and violence in the workplace before they escalate to situations of physical or psychological harm causing costly lawsuits. The tool uses artificial intelligence (AI) models to analyze company communication to identify toxic behaviors and IP loss.
The smart and scalable software integrates into a company’s human resource workflow to capture in real time toxic email and chat communications among employees. In addition, CommSafe AI employs algorithms not only to identify toxicity, but also poor sentiments.
Because employees feel safer knowing their company is taking steps to protect them from harassment, they are focused and more productive. Equally important, company brand reputation remains uncompromised.
For companies and employees worried about privacy concerns, the software does not monitor electronic communications; rather it scans and flags potentially toxic language and poor sentiment.
“All flagged messages are reviewed by HR staff to determine the next course of action. Additionally, CommSafe AI works with your organization’s systems and methodologies and easily integrates with your tech, case management system, and workflows.”
“Identifying toxic communications and behaviors are at the forefront of making the workplace a safe space for everyone. It’s the intelligent solution combining machine learning, human judgment and experience to help identify risks to your employees and the workplace culture,” adds Sanjit Singh, Chief Revenue Officer at CommSafe AI.
CommSafe AI’s subscription software is designed to uncover language associated with bullying, sexual harassment, discrimination and intellectual property theft. It works across email products including Microsoft Outlook and Google Suite, as well as collaboration tools such as Slack and Microsoft Teams. The company worked with a select group of beta customers to perfect the algorithms. Emails and communication on a company server are never private and are actually the property of the employer. In a day and age where violence is escalating among organizations there has been a huge demand for this product. As we know Emails cross multiple channels and networks/servers to get from the server to recipient.
by Gary Mintchell | Jan 27, 2022 | Automation, Embedded Control, News, Security
Bedrock Automation Founder, CEO and CTO Albert Rooyakkers and I have had several energetic discussions regarding the open, secure, and automation pillars of the Bedrock Automation control solution. I always ask founders and CEOs in this market how they think they can possibly upend the leaders. In this case, independent observers tell me that Bedrock has found a niche within certain industry segments that require its specific benefits.
This news release points to a recent software upgrade making it easier to configure and run open applications inside the “Open Secure Automation” (OSA) controllers, simplify and improve SCADA redundancy, enable TLS support for MQTT Sparkplug, expand universal EtherNet/IP capacity, simplify flow meter proving, and assist in diagnosing large motors.
A quick aside—Sparkplug is an open information model standard developed by MQTT evangelists that I would label as “OPC UA lite”. Check out Cirrus Link for better and more detailed descriptions.
The new firmware affects the Bedrock OSA control system, the OSA Remote control system, Universal Ethernet module (UE5), and the OSA Remote +Flow measurement and control system.
The new Bedrock firmware enhancements move redundancy management from the SCADA system client to the Bedrock controller firmware. This enables seamless and flawless failover while simplifying SCADA configuration. The SCADA software then needs to point to only one IP address and the Bedrock controllers will find the active path automatically.
This software release improves throughput and diagnostics for the Bedrock Ethernet gateway modules. It includes both status and diagnostic information from EtherNet/IP and Modbus TCP devices connected to a Bedrock Universal Ethernet I/O module (UE5).
New control firmware in the Bedrock OSA Remote supports the J1939 and CANopen CAN bus communication standard, which extends open secure automation for control and factory automation. Using J1939 CAN bus, for example, the Bedrock OSA Remote can be configured as an RTU to read RPMs from large motors to diagnose performance.
The Bedrock OSA Remote +Flow computer application now supports K-Factor and meter factor linearization with user-entered linearization curves. The OSA +Flow now also supports double chronometry for select high speed counter channels. The OSA +Flow application takes advantage of this new feature to support meter proving using displacement provers, including small volume provers.
All software will be standard on all relevant systems immediately. All current Bedrock OSA users can upgrade remotely at no charge.
by Gary Mintchell | Jan 26, 2022 | Asset Performance Management, Internet of Things, Operations Management
Terrence O’Hanlon and crew produced its annual International Maintenance Conference and Reliability 4.0 live in December in (mostly) sunny Florida. I attended IMC for the first time. The last time I attended one of his excellent events was around 2003 for a different company. This edition was as good as I expected. Plenty of informative keynotes and tech sessions, as well as, many networking opportunities.
The 700 attendees were fewer than past years, but then the “international” part of IMC was a little lacking this year given the situation with Covid and traveling.
My goal was to take a deep dive into the nuances surrounding predictive maintenance. My sources in the IT and IIoT communities figured data was becoming readily available and predictive analytics were improving. Add those together and surely it was obvious that predictive maintenance was the “killer app” for them.
I didn’t see it quite that same way even while helping some of them write marketing pieces. It was time to learn more.
Condensing what I heard from several speakers, predictive maintenance was not the end goal. It was useful when connected into the plant’s workflow. It required decision making from experts and integration into the work of maintenance technicians.
Networking with other attendees often has more value than any other interaction. At dinner one evening, one long-time colleague told me another long-time colleague was there. I sat there and talked with Gopal GopalKrishnan with whom I had worked when he was at OSIsoft. He’s now with CapGemini. He introduced me to his layered approach to maintenance.
He first pointed me to a McKinsey study. Establishing the Right Analytics-based Maintenance Strategy,
The assumption that predictive maintenance is the only advanced, analytics-based use for Internet of Things (IoT) data in the maintenance world has created a great deal of misconception and loss of value. While predictive maintenance can generate substantial savings in the right circumstances, in too many cases such savings are offset by the cost of unavoidable false positives.
Then consider this thought from Emerson’s Jonas Berge.
We have a promising future of Artificial Intelligence (AI) ahead of us. But to be successful we must first learn to reject the fake visions painted by consultants eager to outdo each other. Most engineers don’t have a good handle on Al the way they have on mechanics, electricity, or chemistry. Data science has no first principles or scientific laws. It is very nebulous. So it can be hard to judge if claims made around analytics are realistic. Or you may end up using an overly complex kind of Al for a simple analytics task. It must be like the early days of thermodynamics and electromagnetism.
Now some additional thoughts from Gopal here and here:
As such, a layered fit-for-purpose approach to analytics can be extremely valuable when you also leverage simple heuristics – extracted from SME (subject-matter-expert) knowledge – with basic math and Statistics 101. You can also include first-principles physics-based calculations that require only simple algebra and make predictions by extrapolating trends – backed by sound engineering assumptions.
The takeaway – start with proven fit-for-purpose analytics before chasing AI/ML PoCs with all its attendant risks, and the false positives/false negatives indicated in the McKinsey post. Form follows function; AI/ML yields to simple analytics. The simpler ‘engineered analytics’ captures the low-hanging wins and provides the foundation and the data-engineering required for the AI/ML layer. The oft-heard “… just give me all your data, let’s put it in a data lake and we will figure it out…” is naïveté.
And a conclusion from McKinsey:
Luckily, while predictive maintenance is probably the best-known approach, there are other powerful ways to enhance a business’s maintenance-service organization and create value from analytics-based technologies. The two most valuable of these, we find, are condition-based maintenance and advanced troubleshooting.
And more from Jonas Berge:
The reason why the existing process sensors are insufficient is because by the time the problem is picked up by the existing process sensors, the problem has already gone too far. You need a change in a signal that indicates an event is about to occur. A pump bearing failure is a good example of this: by the time the bearing failure is visible on the discharge pressure it is already too late because it is a lagging indicator. You need a vibration sensor as a leading indicator where a change signals the bearing is starting to wear.
Lots of time and money can be saved if advanced sensors to collect the required data are put in from the very beginning. With the right sensors in place the AI analytics can do a fabulous job of providing early warning of failure.
I guess I’ll add that it’s not necessarily complex unless you choose to make it. But to say that predictive maintenance is the killer app is overly simplifying things to the point that you’d never really get anywhere—even to make IIoT and IT sales.
A better and more inclusive approach to market solutions could lead IT and OT/IT suppliers into more lucrative hardware, software, and services sales and profits.
by Gary Mintchell | Jan 25, 2022 | Automation, Technology
ABB continues to churn out vertical solutions to a variety of manufacturing and production problems. Here is news of a predictive maintenance app for grinding.
ABB has released a new version of ABB Ability Predictive Maintenance for grinding which is a unique advanced digital service to maintain, assess and analyze gearless mill drive (GMD) systems.
The upgrade means that ABB Ability Predictive Maintenance for grinding is now cloud-based instead of sited on premises and includes a brand-new mobile application that allows real-time notifications on fleet status. The Grinding Connect mobile app, available for iOS and Android, means that mine operators can monitor performance at any time and from any place.
ABB Ability Predictive Maintenance for grinding provides easy access to GMD system parameters and allows visualization of performance considering past activity and real-time data and assesses future maintenance requirements. It aims to extend the lifetime of grinding assets through better use of resources and to support non-stop operation and to avoid unforeseen downtime.
The new ABB Ability Predictive Maintenance facilitates greater data gathering. The data sample per mine is increased and analytics and trends are more reliably defined. The solution offers a new user experience with fully customizable dashboards, alarms and events all available on the mobile app.
by Gary Mintchell | Jan 24, 2022 | Cloud, Manufacturing IT, Operations Management
Executives at ThinkIQ have talked with me a few times. They have an interesting story around what they call a SaaS-based Continuous Intelligence Platform. They also released some news last week.
ThinkIQ announced enhancements to its platform to provide more capabilities geared towards Continuous Intelligence for supply chain and production optimization within material centric manufacturing.
ThinkIQ is the first platform-based Continuous Intelligence solution in the market and can be operationalized at many levels – from supply chain, product quality, process improvement, and any time-sensitive process enhanced by the ability to respond to what’s happening right now throughout the entire manufacturing process. Analytics are woven directly into operational processes that can take or trigger actions when specific conditions are met. This ranges from time-sensitive alerts that guide employees on what to do next, to fully automated processes that trigger downstream actions without human intervention.
Continuous Intelligence closes the gap between what is happening in your operations now and the information and insights available. This accelerates how effectively people and processes respond to rapidly changing conditions.
With ThinkIQ, manufacturers are able to collect, analyze, and share information in a way that was not previously possible. ThinkIQ combines the capabilities of continuous ingestion of data with a well-defined model that fits manufacturing and their supply chains, achieving the goal of having current data with meaning. The latest release includes a number of new features, including:
The ability to close the loop from the edge to the cloud where AI can be done, and back to the edge where actions can be taken. This is a key step to enabling the autonomous self-driving supply chain.
- Several modeling enhancements including automatic propagation of model configuration information which enables adoption of corporate standards.
- Attributes on organizations and places. This is critical for continuous roll up of corporate, business-unit, and plant KPIs. ThinkIQ supports rollup of any KPI, including operational, environmental, safety, and financial metrics.
- Strengthening of ThinkIQ’s GraphQL API for stronger integration with third party applications.
- A new Model Browser that makes it easy to find anything anywhere in the model.
- New expressions for attributes for simple and weighted moving average. The expressions take into account how the interpolated data between the recorded points shall be interpreted and do not require that the data input stream is evenly sampled in time.
- Improved robustness and performance of our on-premises gateways and connectors.
- Cyber security enhancements according to our SOC 2 compliance program.
“The supply chain has been in disarray since the pandemic started, and accurate materials tracking is more important than ever before for manufacturers,” said Niels Andersen, CTO and CPO of ThinkIQ. “With our enhanced Continuous Intelligence solution, our customers can respond and adapt their manufacturing processes automatically based on ever-changing conditions leveraging contextualized, time-sensitive data.”
