by Gary Mintchell | May 15, 2023 | Data Management, Edge, Operations Management
Innovation at the Edge continues. I’ve written about ZEDEDA before and have even appeared on a couple of their Web events. The company maintains momentum at this vital confluence of data. This announcement concerns a partnership with Avassa.
- Partnership provides platform teams with a comprehensive security and visibility solution coupled with a developer-friendly container application solution for the distributed edge
- Together, the companies address the growing demand for comprehensive solutions that empower customers to manage the lifecycle of new and existing technologies at the edge
- The combination of ZEDEDA and Avassa solutions enables customers with application-centric visibility and manageability of cloud-native applications while also solving the challenges of the distributed edge
by Gary Mintchell | May 12, 2023 | Data Management, Enterprise IT, Manufacturing IT, Operations Management
Pundits and writers of the manufacturing market harp on data. Nassim Nicholas Taleb tells us in his writings that we can keep accumulating data until we drown. Snowflake has data management tools used by many to handle all the data. This is a new release called Snowflake Manufacturing Data Cloud that looks full of possibility.
- Empowers manufacturers to collaborate with partners, suppliers, and customers to improve supply chain performance, product quality and factory efficiency
- Snowflake’s ecosystem of manufacturing partners delivers pre-built solutions and industry datasets to support a diverse set of manufacturing and industrial use cases
- Global manufacturers across industries, including ExxonMobil and Scania use Snowflake to drive digital transformation
Snowflake Launches Manufacturing Data Cloud to Improve Supply Chain Performance and Power Smart Manufacturing
● The Manufacturing Data Cloud empowers manufacturers to collaborate with partners, suppliers, and customers to improve supply chain performance, product quality and factory efficiency
● Snowflake’s ecosystem of manufacturing partners delivers pre-built solutions and industry datasets to support a diverse set of manufacturing and industrial use cases
● Global manufacturers across industries, including ExxonMobil and Scania use Snowflake to drive digital transformation
Manufacturing Data Cloud enables companies in automotive, technology, energy, and industrial sectors to unlock the value of their critical siloed industrial data by leveraging Snowflake’s data platform, Snowflake- and partner-delivered solutions, and industry-specific datasets.
Following are a number of lists with details and use cases.
- Building a data foundation: A single, fully-managed, secure platform for multi-cloud data consolidation with unified governance and elastic performance that supports virtually any scale of storage, compute, and users. It allows manufacturers to break down data silos by ingesting both IT and OT data and analyzing it alongside third-party partner data.
- Improving supply chain performance: Enable seamless data sharing and collaboration with partners for downstream and upstream visibility across an organization’s entire supply chain coupling its own data with data from third-party partners and data from Snowflake Marketplace. By leveraging this data with SQL and Snowpark, Snowflake’s developer framework for Python, Java, and Scala, different teams can collaborate on the same data and build AI and ML models.
- Powering smart manufacturing: Native support for semi-structured, structured, and unstructured high volume Internet of Things (IoT) data.
- Leveraging industry leading network of manufacturing partners: Take advantage of a rich partner ecosystem and industry-specific, prebuilt templates.
Partner Solutions
- Applications Powered by Snowflake include ones developed by Blue Yonder, Elementum, and Avetta.
- Snowflake Marketplace partners, include FourKites and Yes Energy enabling live access to a variety of data sources.
- Consulting and service organizations including Deloitte, LTIMindtree, and phData, offer pre-built solutions.
- Technology leaders, including Fivetran and Tableau, provide integrations and out-of-the-box solutions.
Customer use cases
- ABB – The technology leader in electrification and automation is using Snowflake to unify all of its data, including incoming raw materials from suppliers, plant production capacity, and sales orders, to streamline manufacturing operations and meet customer demand.
- EDF – The energy supplier for homes and businesses across the UK used Snowflake and its Snowpark Python development framework to build a complete machine learning operation solution in a few months, and deliver data products that lead to higher customer satisfaction and retention.
- Molex – A leading manufacturer of connectors, is using the Snowflake Manufacturing Data Cloud to fuel their digital transformation journey, including sharing data securely across the organization and with external partners and generating manufacturing shop-floor and business KPIs.
- Scania – The truck, bus, and industrial engine manufacturer uses Snowflake to continuously stream data from 600,000 connected vehicles and Snowpark for Python to prepare data for machine learning, which gives the company a comprehensive view for monitoring vehicle performance and supporting Scania’s product-related services.
by Gary Mintchell | May 3, 2023 | Data Management, Manufacturing IT, Operations Management
IT-oriented companies strive to help customers manage and use data in a better way. I first heard of DataOps as part of a company’s offer was Hitachi Vantara and then a start-up HighByte. Since there have been several other companies who have hit my PR inbox announcing DataOps solutions.
Just a quick note from an experience earlier this morning. I received an invitation (nothing special about me) to attend a number of upcoming Rockwell Automation webinars primarily involving software. The one for today contained the topic of DataOps. Not realizing Rockwell had a DataOps offer, I registered and tuned in.
The presenter made a methodical and thorough introduction to data and DataOps–including the difference between IT DataOps and Industrial DataOps. (Different types of data.)
He concluded with a slide that showed Rockwell products–FactoryTalk Historian, FactoryTalk Edge Gateway, and something new–FactoryTalk Data Mosaix. This later is a DataOps platform coming later this year. Interesting. I guess we’ll hear more later–but you have been forewarned!
by Gary Mintchell | Apr 28, 2023 | Automation, Data Management, Edge, Internet of Things, Software, Technology
Moving past Mindsphere (now integrated into this platform), Siemens has integrated a new platform for industrial automation and business. The have launched Xcelerator and now add Industrial Operations X. I see this as part of a trend where established control and automation suppliers are scrambling to show financial markets that they are cool, hip software developers. We have seen many platforms touted over the past 5-7 years. We’ll have to wait and see how this one performs in the market.
In brief:
- Siemens expands Siemens Xcelerator open digital business platform with launch of Industrial Operations X
- Uniquely combining the real and digital worlds
- Production processes to become more efficient and highly adaptive
- Includes launch of first fully virtual controller
Industrial Operations X is the solution for production engineering, execution, and optimization. It focuses on integrating cutting-edge IT capabilities and proven methods from software operations in the world of automation: low code, edge, cloud computing and artificial intelligence (AI) are combined with industry-leading automation technology and digital services.
Industrial Operations X solutions make data actionable by leveraging AI analysis capabilities. Independent studies suggest that a digitally enabled factory delivers production increases of up to 30 percent.
Based on the SIMATIC S7-1500, the virtual programmable logic controller (PLC) is hardware-independent, allowing applications to be centrally managed and flexibly modified to meet changing customer needs. PLC projects can be scaled with virtual control and easily integrated into other IT offerings through open data interfaces.
Making automation programmable with IT code: Simatic AX
Simatic AX provides IT professionals with a familiar development environment based on Visual Studio Code and version control via GIT and others. Simatic AX is cloud-based and is available as a service.
Visualization for the Industrial Edge environment: WinCC Unified for Industrial Edge
With Industrial Edge, administering software in machines is easier, more flexible, and more secure. A variety of apps is already available, focused on acquisition, preprocessing and analysis of machine or plant data.
Insights Hub: Turning Industrial IoT into actionable insights
Siemens will integrate MindSphere in the core of our operations software portfolio with an even stronger focus on delivering business value from IoT data. To emphasize our commitment to application value from industrial IoT, Siemens is evolving MindSphere (including partners and developers worldwide) into Insights Hub as part of Industrial Operations X and the Siemens Xcelerator ecosystem.
Insights Hub highlights the focus on empowering smart manufacturing to generate actionable insights from asset and operations data, by analyzing data locally or in the cloud, and transforming it into value. With Insights Hub, Siemens gives its customers proven industrial IoT solutions that include a variety of applications, like Insights Hub Quality Prediction for improving quality inspection and rework processes.
by Gary Mintchell | Apr 24, 2023 | Data Management, Operations Management, Software
Ara Surinam, VP Product Management at Plex which is now a Rockwell Automation company (one of the ways Rockwell moved into the cloud), talked with me recently about using AI/ML (artificial intelligence as machine learning) in an industrial software setting. Ara was part of the development of Cloud Command Center in 2007.
He noted that the goal is to improve business outcomes for customers. One way involves compensating for the fact that few companies employ lots of data scientists. Another is to help them leverage technology as a way of forecasting demand.
With all the disruptions to the supply chain we have witnessed over the past few years, Plex leveraged ML to evaluate project information in a way that does not require data scientists. Another part of AI is neural nets, and Plex leverages that technology with “deep AI” toward structuring data for enhanced customer supply chain decision making.
Here are a few additional bullet points of information:
- Make it repeatable, spreadsheets are prone to error.
- Focus on anomalies.
- Balance growth, cost, inventory, and production with real-time plant floor data to effectively forecast—and deliver on—customer demands.
- Gain higher forecast accuracy and meet customer expectations with reliable delivery dates based on current resources and capacity.
- Make production planning trade-off decisions considering rough-cut capacity constraints, inventory and customer service levels.
- Utilize advanced production planning options to level-load manufacturing across multiple plants.
- Reduce supply chain costs with a more realistic master production schedule that drives material, production, and resources allocations.
- Gather data from across departments, plants, and supply chain channels, for a single-version of supply chain plan.
by Gary Mintchell | Apr 13, 2023 | Data Management, Operations Management
New companies broadly serving the data market continue to pop up on my radar. The following news reports a survey of more than 350 attendees to the 2023 Data Teams Summit. I should have figured there would be large conferences targeting those who work with data. This report is from a company called Unravel Data.
“For the third year in a row we’ve had the opportunity to take the pulse of enterprise data teams to better understand the daily challenges they face as they accelerate their ambitious big data analytics programs,” said Kunal Agarwal, co-founder and CEO of Unravel Data. “In just the course of a year we’ve seen a significant shift in how these growing, cross-functional teams are prioritizing DataOps as an established discipline across their organizations in a similar way that DevOps became an entrenched practice among software teams a decade ago. But despite this progress, this year’s survey also demonstrates that issues like FinOps, cloud utilization, and data security continue to present unique challenges to data teams.”
The results of the survey include:
● Cloud spending is now a critical KPI for the majority of data teams: More than two-thirds of data teams surveyed said that cloud spending has become a KPI of high strategic importance. When responses were broken down by role, almost 80% of business stakeholders said cloud spending was a critical KPI while just over half (55%) of data practitioners indicated the same.
● Cloud resources are being underutilized: In addition to cloud spending being elevated as a top KPI, almost half (44%) of all respondents in this year’s survey also reported that they believe that they are leaving money on the table when it comes to their public cloud utilization. Alarmingly, almost a quarter of respondents (23%) said they were unable to even estimate what percentage of their cloud resources went unused.
● FinOps interest is high yet adoption lags: Despite the fact that data teams have reported a lack of visibility into cloud spending, the adoption of mature FinOps practice was not viewed as an immediate priority among respondents with just over 20% reporting that their data teams have an established FinOps practice while a third of data teams reported that they are still in the early planning phase of implementing FinOps.
● DataOps as a practice is maturing: This year, more than 44% of respondents reported they are actively employing DataOps methodologies, compared to just less than a quarter (21%) of respondents in 2022, representing a 110% increase from the year prior. Further demonstrating the maturing DataOps practice, only 20% of respondents in this year’s survey said they were at the beginning stage compared to 41% last year.
● Data reliability emerges as the top challenge: This year when participants were asked what they viewed as the top challenge with operating their data stack, 41% respondents cited the lack of data quality as their most significant obstacle while 35% noted that the lack of visibility across their environments was the second biggest obstacle to managing their data stack.
