by Gary Mintchell | Apr 16, 2019 | Asset Performance Management, Operations Management, Technology
I interviewed an interesting CEO of a Swedish start up called Mavenoid. Shahan Lilja is also a co-founder of this company leveraging Artificial Intelligence (AI) in the form of Statistical Machine Learning that tackles maintenance from an entirely different perspective.
(Note: I’m making up for going on vacation rather than going to Hannover by interviewing people I would have seen.)
Every company I’ve talked with for the past many years focuses on predictive or preventive maintenance.
But what happens when things break? A technician still must be sent to the scene in order to trouble shoot the problem. Perhaps the trouble is with a consumer product. The customer calls the company for help troubleshooting. The founders of Mavenoid saw this as an ideal opportunity for a Machine Learning application combined with perhaps a chat bot on the order of Siri or Alexa.
Lilja told me Mavenoid is the world’s leading platform for troubleshooting technical products, from dishwashers and robotic lawn mowers to trucks and cranes. It helps companies organize all of their troubleshooting knowledge, and make it come alive through an intelligent virtual agent. This virtual agent works much like a doctor for technical systems, narrowing down the possible solutions to the problem by asking diagnostic questions, and improves with each interaction. Mavenoid has some of the worlds largest companies as clients, e.g. 7 of the 20 largest manufacturing companies in the Nordics, and has quickly established itself as the future of technical support.
Finally, someone who goes beyond “buzz words” in order to define a strong use case for AI.
Mavenoid technology can be (and in fact is already) embedded within companies’ customer support services. I think this is something you all should definitely check out.
by Gary Mintchell | Oct 2, 2018 | Asset Performance Management, Manufacturing IT, Operations Management
This week is Emerson Global Users Exchange week in San Antonio—with a quick side trip to Houston and a tour of some refineries implementing IoT applications with Hewlett Packard Enterprise. The theme of the week is Digital Transformation just where I reside—at the convergence of OT and IT.
Emerson Automation Solutions (new-ish name for Emerson Process) continues to flesh out its drive to help customers achieve “Top Quartile” performance through Digital Transformation.
It doesn’t just talk digital transformation. The company builds out its offering through product development, services / engineering, and acquisitions. Similar to other major suppliers, it has been making strategic acquisitions rather than taking minor stakes in companies.
Mike Train, Executive President, set the themes and talked about his optimism in the business and industry. Train was recently promoted to COO of Emerson Corporation and introduced Lal Karsanbhai as the new Executive President of Emerson Automation Solutions.
My friends at Putnam Publishing are doing the show daily this year. Flash back to 8 years ago when I was still at Automation World nursing a torn quadraceps muscle doing the show daily in San Antonio. You can see the news from the team here.
Peter Zornio laid out the logic of an “Actionable Roadmap” at a subsequent press conference. The company’s PlantWeb ecosystem continues to grow and develop becoming the key element of Emerson’s Digital Transformation strategy. Below is from the press release.
The Digital Transformation Roadmap includes consulting and implementation services to help companies develop and execute a tailored digital transformation plan to reach Top Quartile performance.
“Our customers have different starting points and levels of maturity when it comes to evaluating and implementing digital transformation strategies,” said Lal Karsanbhai, executive president of Emerson Automation Solutions. “Emerson’s proven digital transformation approach provides the ultimate flexibility while pinpointing the optimum path for each customer, based on their objectives, readiness and overall digital maturity.”
In an Emerson study of industry leaders responsible for digital transformation initiatives, merely 20 percent of respondents said they had a vision, plus a clear and actionable roadmap for digital transformation. Additionally, 90 percent stated that having a clear roadmap was important, very important or extremely important. Absence of a practical roadmap was also cited as the No. 1 barrier for digital transformation projects; cultural adoption and business value round out the top three barriers to progress. While all respondents were actively conducting pilot projects, only 21 percent had moved beyond that stage into new operating standards.
Leveraging customer engagements with successful digital transformation programs, Emerson defined a structured, yet flexible approach to help customers focus on priority areas with a practical roadmap tailored to their business needs and readiness. The goal is to help companies use technology to reach Top Quartile performance, measured by optimized production, improved reliability, enhanced safety and minimized energy usage.
“There is a clear global urgency among executives to harness innovation to improve performance, but many companies feel stalled for lack of a clear path,” Karsanbhai said. “Customers who engage with our operational certainty consultants quickly gain clarity on their best bets for digital transformation and a realistic implementation plan to accelerate time to results.”
Digital Roadmap Combines Technology with Industry Expertise
Emerson’s Digital Transformation Roadmap has two focus areas: business drivers and business enablers. Business drivers look at capabilities and performance relative to industry benchmarks in key areas: production management, reliability and maintenance, safety and security, and/or energy and emissions. The business enabler focus looks at capabilities in organizational effectiveness and systems and data integration. For each, Emerson has identified detailed criteria to measure customer performance along the digital journey – from conventional to best-in-class to the highest level: digitally autonomous operations.
Companies can start the digital transformation journey wherever they are, from starting small in one facility to address key issues, such as pump health or personnel safety mustering; to exploring companywide programs across an entire business driver, such as reliability of critical assets; to driving enterprise-wide adoption of cloud-based technologies and analytics for overall business transformation.
Emerson’s Operational Certainty Consulting Group provides a host of services, from Digital Transformation Jumpstart workshops to deep-dive change management to deployment and adoption of new digitally enabled toolsets. Customers partner with Emerson not only for its consulting expertise, but also to implement its Plantweb™ digital ecosystem, which offers a robust software, data analytics, and product technology and services portfolio to solve real-world problems while improving plant performance.
Emerson’s proven capability is bolstered by a global implementation team that includes more than 80 solutions architects and analytics integration engineers, backed by a project and service engineering workforce that exceeds 8,400. Important foundations for digital transformation have been established with producers around the world. For example, Emerson has collaborated with customers to deploy more than 37,000 wireless network installations and over 175 integrated reliability platforms and applications, to name a few.
by Gary Mintchell | Aug 18, 2018 | Asset Performance Management, Operations Management, Software
Who buys enterprise software applications, how and why? I ran across this article by a contact of mine, Gabriel Gheorghiu, Founder and principal analyst at Questions Consulting, with a background in business management and 15 years experience in enterprise software. I thought it would be most useful. I’m not an ERP analyst, but I have some background and training on the financial side of things. I think this analysis fits with other large-scale software acquisition projects, though, including MES/MOM, analytics, asset performance, and the like.
This will summarize some interesting points. I highly recommend reading the whole thing.
Before we begin, my brief take on enterprise software applications. How many of you have been involved with an SAP acquisition and roll out? How many happy people were there? Same with Oracle or any other ERP, CRM, MES, APM, etc. application. Why did using Microsoft Excel seem to go better?
Well, the big applications all force you to change all your business processes to fit their template. You build Excel to fit what you’re doing. It’s just not powerful enough to do everything, right?
Gheorghiu conducted interviews with 225 companies who were all looking for enterprise resource planning (ERP). The goal of this survey was simple – listen and learn from what these companies had to say about their individual decision-making strategies. We all agree that this is not a simple task. But we also agree that selecting the best ERP software is a critical factor for business success.
Here is why the research phase of this process is considered to be so vital:
- It has the greatest impact on all the subsequent phases and consequently, your final decision.
- Research begins at home – in other words, the first step is to determine your company’s specific and unique needs.
- Once your company has thought through and determined its software requirement, then and only then does the process to evaluate vendors and their offerings begin. This can be a very challenging step because many companies are not equipped with the time, knowledge, or tools to perform this step.
Buyer Profiles: Who’s Looking for ERP and Why?
One problem for analysis is that many are not doing business in just one industry. The breakdown of companies in our business sample, by industry, was as follows: manufacturing (47%), distribution (18%), services (12%), construction (4%), retail (3%), utilities (3%), government (3%), healthcare (3%), and other (10%). However, to complicate matters a little, 20% of manufacturers also manage distribution and some distributors include light manufacturing in their operations, like assembly.
“Companies looking to invest in business software may very well be addressing this additional challenge – looking for a comprehensive package that integrates all aspects of a business. ERP software systems are powerful and comprehensive but are not necessarily known for their agility and ability to accommodate many disparate functions.”
Gheorghiu identifies as a strong influencer consumerization, which changes focus from organization-oriented offerings to end-user focused products. “This was a highly significant turning point in the IT marketplace. By developing new technologies and models that originate in the consumer space rather than in the enterprise sector, software producers opened up the market to a flood of small and medium-sized businesses looking for more cost effective, and less complicated solutions to run their businesses.”
The consumerization of software (as noted above) has precipitated the move by many companies away from enterprise IT towards more streamlined and user friendly consumer-oriented technology. This change is equally relevant for ERP software and manufacturing companies have participated in this very significant development, albeit more cautiously and slowly than SMBs.
Most industries follow a “purposeful implementation” strategy, managing software adoption as a series of “sprints in a well-planned program” rather than insisting on the “all or nothing” approach.
For example, a small company looking to invest in software might decide to begin with an accounting system which can be used alongside point solutions and spreadsheets. As companies grow and their transactions become more complex, they may find that they have also outgrown their initial software selections.
The chart below provides a visual analysis of the mix of software that is currently utilized by our business sample:
Some relevant comments we extracted from our survey included:
- The CEO of a small services company mentioned that he was “tired of the hodgepodge of systems”
- A manufacturer considered their current arrangement to be “very siloed.” Reconciling the inventory balance is a “constant battle.”
Buyer Behavior: How are Companies Approaching ERP Selection?
The selection process is most successful when companies adhere to some basic selection rules: involve as many direct stakeholders as possible and keep business priorities and strategies firmly in mind when making the final decision.
Feature Functions
A software change can trigger a vast administrative upheaval within the company. It is important to carefully analyze the business case for the change and whether it supports the level of disruption as well as the implementation time and spending that will be required. Even if the change may be entirely justified, a well thought out analysis is well worth the time and effort.
The Vendors in the Spotlight
According to our survey results, the chart below identifies the vendors under consideration by the companies surveyed. A majority of companies (53%) were not, for the moment, looking at specific vendors. However 47% of respondents had narrowed their search to specific vendors.
Who’s Involved in this Decision Selection Process?
Our sample results indicate that the people in charge of the selection process are distributed as follows: employees in the finance and accounting departments (23%), IT department employees (23%). The other important categories were independent consultants helping companies with the selection process (17%), operations managers (17%) and presidents or CEOs (12%). It is worthwhile mentioning that project managers and business analysts only made up 5% of the total.
By far, the most effective method of choosing a software is to employ a collaborative system whereby the actual stakeholders of that system (the end-users) have a direct voice in the decision outcome. As the front-line users of the system, their insight and knowledge is very valuable. Their input along with all the other stakeholders input will produce the best possible outcome of this process.
An ERP system is a major business investment and is best handled with the appropriate amount of time and diligence given to the process.
The advent of cloud computing has indeed radically changed the landscape for deployment of business software. According to a recent press release by Gartner, “by 2020, a Corporate “No-Cloud” policy will be as rare as a “No-Internet” policy is today”. In other words, cloud deployment will become the default by 2020.
Our survey results, in fact, support Gartner’s analysis. Ninety-five percent of companies responded that they were open to a cloud deployment model, while just over 50% were willing to also consider on premises ERP. Of this latter group of respondents, 65% of them were manufacturers and distributors. This makes sense of course, given that these industries made significant investments in hardware and IT personnel and may not be as ready or as willing to move to the cloud model.
As for the preference for cloud computing (as demonstrated by our responses), we argue that it reflects the very strong tendency in the market to opt for simpler, more streamlined and less expensive computing solutions. As more information and assurances of security and stability by cloud providers enter the marketplace, more and more businesses will be convinced that the many benefits of the cloud outweigh some of their remaining concerns. Gartner’s prediction that cloud will increasingly be the default option for software deployment looks to be right on course.
Conclusion
An important consideration for companies embarking on an ERP software selection process – the average lifespan of an ERP system is approximately 5 to 10 years. If we consider important factors like the investment of capital, time, and loss of productivity that the selection and replacement of an ERP system requires, perhaps all companies would be more willing to invest the necessary effort in this process.
by Gary Mintchell | Jul 19, 2018 | Asset Performance Management, Internet of Things, Operations Management, Software
GE Digital initiates a huge turnaround in its attitude toward software and Industrial Internet development. GE invested large sums to build a Silicon Valley presence for its software. Hired many engineers. Took its industrial software base up a notch or two with its Predix platform. Tried to build its own cloud infrastructure. The mantra—not invented here.
[Late Breaking News: I was wrong. There will be another Minds + Machines. San Francisco, October 30-31. That’s an expensive trip. Anyone want to fund me? 😉 ]
During the last Minds+Machines conference in San Francisco new CEO John Flannery, barely two months into the job, said that GE Digital needed to work more closely with partners. Soon thereafter came the axe.
That is the context for this major announcement (this one came from Microsoft, so within it may be a bit of its bias) of a partnership. Following report is based upon a media blog from Microsoft.
GE and Microsoft announced an expanded partnership, bringing together operational technology and information technology “to eliminate hurdles industrial companies face in advancing digital transformation projects.” GE Digital plans to standardize its Predix solutions on Microsoft Azure and will deeply integrate the Predix portfolio with Azure’s native cloud capabilities, including Azure IoT and Azure Data and Analytics. The parties will also co-sell and go-to-market together, offering end customers premier Industrial IoT (IIoT) solutions across verticals. In addition, GE will leverage Microsoft Azure across its business for additional IT workloads and productivity tools, including internal Predix-based deployments, to drive innovation across the company.
GE also plans to leverage Azure across the company for a wide range of IT workloads and productivity tools, accelerating digital innovation and driving efficiencies. This partnership also enables the different GE businesses to tap into Microsoft’s advanced enterprise capabilities, which will support the petabytes of data managed by the Predix platform, such as GE’s monitoring and diagnostics centers, internal manufacturing and services programs.
According to Microsoft, leveraging Azure enables GE to expand its cloud footprint globally, helping the companies’ mutual customers rapidly deploy IIoT applications.
The global IoT market is expected to be worth $1.1 trillion in revenue by 2025 as market value shifts from connectivity to platforms, applications and services, according to new data from GSMA Intelligence. Note: I find this a very interesting comment.
As part of this expanded partnership, the companies will go-to-market together and also explore deeper integration of Predix IIoT solutions with Power BI, PowerApps and other third-party solutions, as well as integration with Microsoft Azure Stack to enable hybrid deployments across public and private clouds.
by Gary Mintchell | Jun 6, 2018 | Asset Performance Management, Data Management, Internet of Things
Have you been wondering about GE Digital and such products as Predix Asset Performance Management since the announcements of the new GE CEO reducing the group and throwing it into turmoil?
Well, just when I realized I had not heard anything for a while, this press release appeared. I don’t usually write about the announcements that come daily about sales “wins” or about success stories. But I felt this was significant in that it was news that GE Digital is still out there and that here is a user that is not a GE company. Also it reflects a trend of collaboration among companies. Plus another trend—one of the original hopes for the Industrial Internet of Things, that is, adding ability for OEMs to monitor their equipment at the customer’s site and provide service and support.
Here, GE Digital and SIG, a leading provider of packaging systems and solutions for the food and beverage industry, announced a strategic partnership to power digital innovation in food and beverage packaging.
SIG will deploy GE Digital’s PredixAsset Performance Management (APM) and Predix ServiceMax industrial applications across more than 400 customer factories worldwide to drive new levels of efficiency, create intelligent solutions and enable new possibilities for its customers.
The food and beverage industry is ripe for digital transformation, with consumers increasingly seeking innovative, convenient products that are not only safe and sustainable but also affordable and differentiated. At the same time, producers are facing competitive pressures, supply chain complexities and ever-shorter production cycles – creating an increased need for technologies that can enable producers to quickly identify, predict and act on changing consumer and market demands.
The unique combination of GE Digital’s APM and ServiceMax applications will enable SIG to build an end-to-end digital platform that will bring a new level of insight and data-driven intelligence to its customers worldwide – helping them and SIG transform how they predict, manage and service the entire lifecycle of SIG filling lines. By automatically collecting and analyzing asset data – tapping into billions of data points across its operations globally in real time – SIG and their customers can move beyond traditional asset monitoring and predictive service models to reimagine their supply chain, enhance quality control technologies and evolve their portfolio mix.
“Our ability to harness data is central to delivering on our promise of opening up new opportunities for our customers,” said Rolf Stangl, SIG, CEO. “By tapping into information in new and innovative ways, we will be able to deliver an unmatched level of performance, security, transparency and creativity across the entire food and beverage supply chain – through to the end consumer.”
SIG’s customers fill more than 10,000 unique products into SIG packaging across 65 countries worldwide. In 2017 alone, SIG produced 33.6 billion carton packs for its customers. Through this large-scale partnership, SIG and GE Digital will co-innovate packaging solutions and technologies to address the industry’s two biggest needs today: improving asset performance and optimizing service delivery.
The new digital service model will also enable SIG to deliver new solutions and business models based on advanced performance metrics, including as-a-service delivery, performance-based and subscription solutions.
The initial deployment is expected to go live in July 2018 with the global rollout anticipated to begin in January 2019.
by Gary Mintchell | Apr 19, 2018 | Asset Performance Management, Automation, Internet of Things, Operations Management
Artificial Intelligence, always known as AI, along with its sometime companion robots leads the mainstream media hype cycle. It’s going to put everyone out of jobs, destroy civilization as we know it, and probable destroy the planet.
I lived through the Japanese robotic revolution-that-wasn’t in the 80s. Media loved stories about robots taking over and how Japan was going to rule the industrialized world because they had so many. Probing the details told an entirely different story. Japan and the US counted robots differently. What we called simple pick-and-place mechanisms they called robots.
What set Japanese industrial companies apart in those days was not technology. It was management. The Toyota Production Method (aka Lean Manufacturing) turned the manufacturing world on its head.
My take for years based on living in manufacturing and selling and installing automation has been, and still is, that much of this technology actually assisted humans—it performed the dangerous work, removing humans from danger, taking over repetitive tasks that lead to long-term stress related injuries, and performing work humans realistically couldn’t do.
Now for AI. This press release went out the other day, “With AI, humans and machines work smarter and better, together.” So, I was intrigued. How do they define AI and what does it do?
Sensai, an augmented productivity platform for manufacturing operations, recently announced the launch of its pilot program in the United States. Sensai increases throughput and decreases downtime with an AI technology that enables manufacturing operations teams to effectively monitor machinery, accurately diagnose problems before they happen and quickly implement solutions.
The company says it empowers both people and digital transformation using a cloud-based collaboration hub.
“The possibility for momentous change within manufacturing operations through digital transformation is here and now,” said Porfirio Lima, CEO of Sensai. “As an augmented productivity platform, Sensai integrates seamlessly into old or new machinery and instantly maximizes uptime and productivity by harnessing the power of real time data, analytics and predictive AI. Armed with this information, every person involved – from the shop floor to the top floor – has the power to make better and faster decisions to increase productivity. Sensai is a true digital partner for the operations and maintenance team as the manufacturing industry takes the next step in digital transformation.”
By installing a set of non-invasive wireless sensors that interconnect through a smart mesh network of gateways, Sensai collects data through its IIoT Hub, gateways and sensors, and sends it to the cloud or an on-premise location to be processed and secured. Data visualization and collaboration are fostered through user-friendly dashboards, mobile applications and cloud-based connectivity to machinery.
The AI part
Sensai’s differentiator is that it provides a full state of awareness, not only of the current status, but also of the future conditions of the people, assets and processes on the manufacturing floor. Sensai will learn a businesses’ process and systems with coaching from machine operators, process and maintenance engineers. It will then make recommendations based on repeating patterns that were not previously detected. Sensai does this by assessing the team’s experiences and historical data from the knowledge base and cross checking patterns of previous failures against a real-time feed. With this information, Sensai provides recommendations to avoid costly downtime and production shutdowns. Sensai is a true digital peer connecting variables in ways that are not humanly possible to process at the speed required on a today’s modern plant floor.
About the Pilot Program
Participation in Sensai’s pilot program is possible now for interested manufacturers. Already incorporated throughout Metalsa, a leading global manufacturer of automotive structural components, Sensai is set to digitally disrupt the manufacturing industry through AI, including those in automotive, heavy metal and stamping, construction materials, consumer goods and more.
Porfirio Lima, Sensai CEO, answered a number of follow up questions I had. (I hate when I receive press releases with lots of vague benefits and buzz words.)
1. You mention AI, What specifically is meant by AI and how is it used?
Sensai uses many different aspects of Artificial Intelligence. We are specifically focused on machine learning (ML), natural language processing (NLP), deep learning, data science, and predictive analytics. When used together correctly, these tools serve a specific use case allowing us to generate knowledge from the resulting data. We use NLP to enable human and computer interaction helping us derive meaning from human input. We use ML and deep learning to learn from data and create predictive and statistical models. Finally, we use data science and predictive analytics to extract insights from the unstructured data deriving from multiple sources. All of these tools and techniques allow us to cultivate an environment of meaningful data that is coming from people, sensors, programmable logistics controllers (PLCs) and business systems.
2. “Learn processes through operators”—How do you get the input, how do you log it, how does it feed it back?
Our primary sources of data (inputs) are people, sensors, PLCs, and business systems. In the case of people on the shop floor or operators, we created a very intuitive and easy to use interface that they can use on their cellphones or in the Human Machine Interfaces (HMIs) that are installed in their machines, so they can give us feedback about the root causes of failures and machine stoppages. We acquire this data in real-time and utilize complex machine learning algorithms to generate knowledge that people can use in their day-to-day operations. Currently, we offer web and mobile interfaces so that users can quickly consume this knowledge to make decisions. We then store their decisions in our system and correlate it with the existing data allowing us to optimize their decision-making process through time. The more a set of decisions and conditions repeats, the easier for our system is to determine the expected outcome of a given set of data.
3. Pattern? What patterns? How is it derived? Where did the data come from? How is it displayed to managers/engineers?
We create “digital fingerprints” (patterns) with ALL the data we are collecting. These “patterns” allow us to see how indicators look before a failure occurs, enabling us to then predict when another failure will happen. Data comes from the machine operators, the machines or equipment, our sensors, and other systems that have been integrated to Sensai’s IIOT hub.
We trigger alerts to let managers and engineers know that a specific situation is happening. They are then able to review it in their cellphones as a push notification that takes them to a detailed description of the condition in their web browser where they can review more information in depth.
4. What specifically are you looking for from the pilots?
We are not a cumbersome solution, for us is all about staying true about agility and value creation. We look for pilots that can give us four main outcomes:
– Learn more about our customer needs and how to better serve them
– A clear business case that can deliver ROI in less than 6 months after implementation and can begin demonstrating value in less than 3 months.
– A pilot that is easy to scale up and replicate across the organization so we can take the findings from the pilot and capitalize them in a short period of time.
– A pilot that can help Sensai and its customers create a state of suspended disbelief that technology can truly deliver the value that is intended and that can be quickly deployed across the entire organization.
