Seeq Announces Industrial Enterprise Monitoring Capabilities 

Seeq is one of a few newish software companies that I am watching while I try to determine the health of the industrial technology infrastructure. Despite that, I was not invited and did not attend their recent user conference. There was one announcement from the conference that looks intriguing. The Seeq platform is seeking (I’m sorry) an enterprise outlook.

Seeq announced the launch of the Seeq Industrial Enterprise Monitoring Suite with the release of Seeq Vantage, the company’s first industrial enterprise monitoring app.

The Seeq Industrial Enterprise Monitoring Suite provides a comprehensive, automated view into operational performance—past and present. This broader view enables better decision making and continuous improvement across today’s complex, industrial ecosystems. The Seeq Industrial Enterprise Monitoring Suite leverages the combined power of the Seeq Industrial Analytics and AI Suite and the context that only teams of experts can provide—all at the scale needed to drive truly impactful results across the operational footprint.

Through the Seeq Vantage app, industrial organizations can tailor, deploy and automate enterprise-level use cases, such as asset and process monitoring, condition-based maintenance, reliability and downtime tracking and more. Coupled with the Seeq Industrial Analytics and AI Suite, customers now have an integrated ecosystem to capture, analyze, aggregate, monitor, triage, investigate, and document insights and actions at the local level and the enterprise level.  The app provides proactive and automated enterprise surveillance for daily operational decisions, and comprehensive assembly of operational effectiveness and utilization understanding to prioritize longer-term investment decisions.

Why Should I Use Low Code 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.

HPE to acquire Juniper Networks to accelerate AI-driven innovation

Hewlett Packard Enterprise (HPE) influencer group first contacted me in the mid-2010s through the Aruba networking group. I was the independent industrial IoT writer at the time. The scope broadened for a time, then they closed the influencer group a couple of years ago. But I’ve maintained a bit of a connection to HPE networking, as well as its software and high-end hardware groups.

I’m not an analyst of this part of the market, but I’d have to say this is not a surprising acquisition. HPE has been pretty aggressive under CEO Antonio Neri. They usually do pretty well at integrating acquisitions. This acquisition of Juniper Networks should be a boost.

From the news release in brief:

  • Highly complementary combination enhances secure, unified, cloud and AI-native networking to drive innovation from edge to cloud to exascale
  • Accelerates long-term revenue growth and expands gross and operating margin; Expected to be accretive to non-GAAP EPS and free cash flow in year 1, post close
  • Advances HPE’s portfolio mix shift toward higher-growth solutions and strengthens high-margin networking business 

Hewlett Packard Enterprise and Juniper Networks, a leader in AI-native networks, announced January 9 that the companies have entered a definitive agreement under which HPE will acquire Juniper in an all-cash transaction for $40.00 per share, representing an equity value of approximately $14 billion.

The combination of HPE and Juniper advances HPE’s portfolio mix shift toward higher-growth solutions and strengthens its high-margin networking business, accelerating HPE’s sustainable profitable growth strategy. The transaction is expected to be accretive to non-GAAP EPS and free cash flow in the first year post close.

The acquisition is expected to double HPE’s networking business, creating a new networking leader with a comprehensive portfolio that presents customers and partners with a compelling new choice to drive business value.

Combining HPE and Juniper’s complementary portfolios supercharges HPE’s edge-to-cloud strategy with an ability to lead in an AI-native environment based on a foundational cloud-native architecture. 

Upon completion of the transaction, Juniper CEO Rami Rahim will lead the combined HPE networking business, reporting to HPE President and CEO Antonio Neri.

Digital Twin and Simulation Technology Power Business and Decision Strategies

I had to wade through a lot of marketing to get to the core of this news from a company that I don’t know. The company is FICO (Fair Isaac Corporation). Founded in 1956 (almost as old as me), it’s a business analytics, data science, predictive analytics company. Of interest to us is work in supply chain resiliency.

The company announced more than 20 enhancements to its Platform

The updates include robust innovations in digital simulations. One of the most significant updates is within the Digital Twin and Simulation capability, which enables users of FICO Platform to create an enterprise digital twin of their organization and unlock the power of business simulations. This allows businesses to experiment across many dimensions, not previously possible, to find optimal business outcomes. Updates include faster deployment for increased efficiency, thorough validation of changes to decision strategies, and better understanding of the impact on business KPIs such as profitability metrics.

Among the 20+ enhancements, key improvements to FICO Platform include:

  • Data Connection and Ingestion – Our latest data-focused enhancements aim to break down organizational siloes and put data into motion with improved data pipelines, a high-performance hotlist service, enriched data feature libraries, and an easy-to-use database service.
  • Applied Analytics & ML and Enterprise Optimization – These updates are focused on enhancing clients’ ability to build world-class predictive credit analytics and ML models, pull in third party models, and apply mathematical optimization to new domains. This release includes improvements to our proprietary segmented scorecards, multi-target scorecards, reject inference, Python APIs, ML execution and optimization solver performance enhancements, and the launch of a new global optimization solver.
  • Intelligent Decisions and Business Composability – This release supports lifecycle management for fine-grained control to easily manage and promote projects from design, staging, and production for even the most complex enterprise environments with better isolation to compress and safely scale change cycles. Additional enhancements provide deeper native integration with other FICO capabilities for applied analytics & ML and simulation so teams can work efficiently across the entire decision intelligence value chain.
  • Digital Twins and Simulation – In this space we dive deeper into the development of a digital twin for businesses to enable experimentation within our Simulation Capability. Our latest release makes it easy for business staff to rapidly construct new business scenarios, pressure test possible changes, validate strategies, and simulate the effects on business KPIs to easily measure operational impact and move fast with confidence.

IT Infrastructure Integration Company Offers AI Implementation Tips

Indiana-based Matrix Integration has been asserting itself as the AI integration partner of choice for your IT needs. I get a lot of these sort of releases. This one seems to offer a few quality tips.

“We have been leveraging AI tools in our strategic partner software suites for clients for several years. Customers turn to us for support in fine-tuning the automation capabilities within these suites to make critical decisions in their infrastructure,” said Tim Pritchett, engineer operations manager at Matrix Integration. “As time and resources continue to crunch in maintaining your IT systems and security, AI tools can be leveraged to protect your data and get the most benefit out of what you already own.”

Because AI becomes a more commonly built-in component of many managed software suites, here are the top three issues business should consider as AI becomes more universal:

  • Data quality matters. Whether businesses are using AI to generate content (such as drafting communications with customers) or analyze production efficiencies, high-quality data is necessary to train AI models. Already, biased inputs in large-language models like ChatGPT have led to biased outputs that could damage a company’s reputation on a great scale. In the case of data analysis, inaccurate or damaged data fed to an AI model will lead to unusable outputs.
  • Data security isn’t guaranteed.  Companies will need to consider how they will secure their own data, as well as data supplied by clients. This requires asking questions and developing transparency and trust with cloud services providers as well as AI vendors. For example, many businesses provide customer-facing chatbots run by AI. For example, imagine that customers type sensitive or personal data (e.g., bank account numbers) into a chatbot. Or, as another example, a business supplies internal data to AI models to generate proprietary operations solutions. Is that data safe once it gets uploaded into a cloud-based AI application? Can it be used by other customers of that AI vendor?
  • Humans are key for AI to work properly. Right now, much of AI seems to be a “black box” – most people understand the inputs and outputs but are unfamiliar with how learning algorithms work and how they handle data. For example, Microsoft 365 security tools through Defender, Sentinel, or the Purview compliance portal all do an excellent job of leveraging AI to make decisions and inform IT administrators on the best decisions to make in a scenario. However, experienced security professionals can still play a key role by fine-tuning these notifications and building automation for these tools.

Lack of Roadmap Biggest Hurdle for Manufacturers Looking for Digital Transformation

Once upon a time surveys were the purview of analyst firms and media. None were mathematically rigorous. Most do show trends and yield ideas for thought.

Digital transformation is top of mind for companies who develop and market software solutions but maybe not so much for customers. This survey is from iBase-t. I knew them as an MES supplier, but now the are the company “that simplifies how complex products are built and maintained.” In other words, MES. That’s OK. My background in that application goes back decades.

This original survey of more than 100 discrete manufacturing executives in the U.S. found that a lack of a clearly defined roadmap is the biggest challenge for manufacturers looking to digitally transform their operations.

None of this surprises me. Many studies have found similar statistics. Upper management in manufacturing organizations “know” these problems. They don’t seem to know how to go about implementing solutions. Or, they don’t want to spend the money!

In brief, their study revealed:

  • 60% of manufacturers don’t have a clear understanding of the model-based enterprise
  • 67% of manufacturers say that less than half their operations are digital

A full 60% of respondents said they did not have a clear understanding of the model-based enterprise (MBE), which employs CAD systems, Product Lifecycle Management (PLM) systems and Manufacturing Execution Systems (MES) to help manufacturers fully digitize their operations.

Respondents confirmed that although paperless manufacturing and digital transformation are very important priorities, more than two-thirds (67%) of manufacturers reported that less than half of their operations are digital.

The survey found that more than half (54.5%) of respondents lack the interoperability across operations to adopt an MBE strategy. An additional 55% said that their manufacturing systems are not mature enough to support MBE.

Other Key findings:

  • According to the survey, 62% of total respondents said that they believe paperless manufacturing is “very important” to their organization.
  • The top four goals for manufacturers heading into 2024 are efficiency (66%), on-time delivery (66%), done-right first time (49%) and profitability (47%). An MBE strategy empowers manufacturers to reach all of these goals.

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