Tag Archives: Industrial
The Intelligent Industrial Manufacturing Enterprise: Five Ways Forward
Gone are the days when the industrial manufacturing value proposition was relatively simple: industrial manufacturers made products and delivered them.
Now, the industrial manufacturer’s job does not end with delivery. Driven by ever-more-demanding customers and supported by the widespread uptake of the Internet of Things (IoT) and the emerging power of machine learning and artificial intelligence, industrial manufacturers are developing new capabilities to track huge volumes of data generated by thousands of devices and are adjusting their service depending on the circumstances.
The goal is to be more responsive, always-on, and highly adaptable. Industrial manufacturers seek to collaborate more fully with their customers from discovery through design, service, and beyond. Wherever possible, they aim to deliver the kind of experiences and outcomes that customers reward with loyalty and ongoing business.
But how do companies move forward? Successful ones are focusing on the following five strategic priorities:
Be customer-centric
Industrial manufacturers are looking for ways to maintain customer-for-life relationships based on a 360-degree understanding of their customers. The starting point is a holistic view of their customers’ business processes – ending with the knowledge of how those customers use the products in their daily operations.
To get there, industrial manufacturers are moving toward omnichannel models for managing customers’ interactions across channels (Web, direct sales, IoT, and more). The goal is seamless interactions with customers, the ability to quickly see all products bought, and real-time visibility into how products are performing. This will help companies position the customer’s point of view at the center of every decision.
Serve the segment of one
Increasingly, industrial manufacturers will be able to deliver completely customized products, services, and solutions that precisely fit the needs of individual customers based on sophisticated platform, configuration, and mass-customization strategies.
Industrial manufacturers are moving toward this goal by rationalizing existing product options using machine learning to understand what really sells and what doesn’t. In addition, organizations are pulling in customer experience data to better understand requirements – and then using this data to inform requirements in product configurators that let customers define their own products on the fly.
Embrace digital smart products and solutions
Industrial manufacturers are shifting to products with more digital functionality, allowing even more flexible configuration of products. Thus, software-based features are on the rise with connectivity to enable remote access and monitoring.
Industrial manufacturers are extending original (physical) products with digital services that augment and extend product functionality. Combining insights into the end-user experience and the relevance and value of digital capabilities, manufacturers will extend the lifecycle of the product and increase lifetime revenue. With a direct feedback loop from the product back to the manufacturer, product enhancements and future developments will be based on the actual usage and experience of the product, from first interaction to product retirement.
Implement the digital supply chain and smart factory
Supply chains and manufacturing networks are becoming modular and flexible to allow the seamless execution of different manufacturing strategies. Industrial manufacturers, accordingly, are using Industry 4.0 philosophies and new digital technologies to implement “shop-floor-to-top-floor” connectivity for real-time visibility.
Subsequent steps will increase machine-to-machine connectivity and collaboration, allowing autonomous decisions based on sensor data and machine learning algorithms. Industrial manufacturers will combine feedback from connected stakeholders (customers, workers, suppliers) and associated processes to further improve overall manufacturing and supply chain performance. Intelligently connecting manufacturing, logistics, and supply chains enables companies to quickly address short-term demand impulses, supply fluctuations, and changes to customer orders, allowing a truly modular production process. This production flexibility enables industrial manufacturers to produce higher-quality individualized goods at lower costs.
Develop service-based business models
As revenues are increasingly linked to services that are based on and built around smart products, more industrial manufacturers will offer products as a service based on the value delivered to the end customer.
Remote condition monitoring of assets is critical to success with such models – enabling manufacturers to identify and provide additional value-added services. Based on the data collected, organizations can get better insight into how products are used. This enables them to offer pay-for-outcome models where the risk and long-term value of each customer is clearly understood.
The future is bright
Ultimately, the winners in the industrial manufacturing industry will be those companies that successfully transform themselves into fully customer-centric companies. Ahead for the industry is unprecedented change at unprecedented speed – but our industry is positioned to be a driver of progress. Together, we can lead the way.
Join our virtual event to learn more
Want to find out more about the latest trends and innovations in today’s global industrial manufacturing business? Register today to join our flagship 60-minute virtual industry event:
SAP Industries Forum 2020: Industrial Manufacturing
Experience the Intelligent Enterprise
Thursday, March 26, 2020, 8 a.m. PDT, 11 a.m. EDT, 4 p.m. CET
Register Now
How Are Industrial Manufacturers Looking At Digitalization?
Part 1 of the “Digitalization of the IM&C Industry” series
Digitalization is an ongoing journey. As innovative technologies become more mainstream, the pace of innovation and transformation continues to increase. This is a journey that all companies are embracing in one way or another.
Take it from Georg Kube, global vice president of Industrial Machinery and Components (IM&C) at SAP:
“Companies are looking for new ways to deliver value to their customers using digital channels and creating personalized, digitally enabled products across all sectors, from consumer products to heavy equipment and machinery.”
This is fundamental for industrial machinery and components companies, as they need to not only innovate their business processes but also provide intelligent machines and equipment to help their customers innovate their businesses.
A July 2018 commissioned study conducted by Forrester Consulting on behalf of SAP found that 88% of innovative manufacturers have started or completed their digital transformation, compared to 54% of other companies. Those manufacturers that considered themselves to be innovative rated themselves highly across the board for innovation in strategy, technology, people, process, and culture.
Digital transformation is achieving real business value for manufacturing companies:
“Companies with 50% or more of their revenues from digital ecosystems achieve 32% higher revenue growth and 27% higher profit margin.”
– MIT Sloan Management Review, Thriving in an Increasingly Digital Ecosystem.
Industrial manufacturers can realize opportunity in two areas: Top-line revenue growth through better and more intelligent and differentiated products, and bottom-line cost savings through more efficient and more effective innovative processes using the digital capabilities of the equipment they use.
Transformation is not new to industrial machinery and component companies. Since the 1990s, manufacturers have been looking at process automation and techniques such as lean manufacturing. In the early 2000s, their focus shifted to becoming more responsive, and they started looking at flexible manufacturing processes, integrating the supply chain, and of course, further automation.
The current evolution is to build intelligence into the processes so each industrial manufacturer becomes more event- or insight-driven. This means using sensor data from all parts of the process and external data to drive operational decisions. It also means ensuring that the customer experience is more digital and consistent across any channel and that you have a complete understanding and visibility of your customer—finally going beyond basic automation by applying machine learning algorithms to improve performance and efficiency.
But manufacturers need to be vigilant as digitalization opens the playing field for companies that are not traditional manufacturers but that have expertise in these technologies from other industries. To be ready for these challenges it is essential for industrial manufacturers to focus on the right strategic priorities to drive digitalization. In conjunction with our customers, SAP has identified 5 key digital priorities:
1. Customer-centricity
Putting the end customers’ point of view at the center of every decision is essential for success in the digital age. This does not only apply to the sales department; it should also apply to what products are built and what services are offered.
2. Serving the segment of one
Providing solutions that precisely fit the needs of one single customer has been commonplace in traditional engineer-to-order environments. Now that same level of personalization must be applied to everyone in a cost-effective way. This requires the ability to capture customer requirements effectively and enable mass customization to give customers exactly what they want.
3. Digital smart products
Product differentiation and specificity stems from the digital capabilities and value-added services that are bundled with physical products. Delivering and using digital capabilities like self-awareness of technical health and operational status or business system connectivity helps industrial manufacturers differentiate themselves.
4. Digital supply chain and smart factory
Digital technology on the shop floor and in the supply chain is not new. What is new is the way that production and logistics can be intelligently connected to the rest of the business and to handle external impulses like short-term demand and supply fluctuations or changes in the configuration of a customer order on the fly.
5. Servitization and new business models
As traditional products are commoditized, IM&C companies are shifting from selling products to providing complete solutions, charging for outcomes, or even monetizing asset data. Generating more revenue from services is a goal for manufacturers who are looking for higher profit margins and increased customer intimacy.
These priorities impact every line of business across the value chain. The digital poster shows how each line of business will need different capabilities to achieve these priorities.
The commissioned study by Forrester Consulting on behalf of SAP showed that these digital priorities resonated well with manufacturers, which felt that these digital priorities were important for their business. Interestingly, those manufacturers that considered themselves innovative as had a much higher focus on those digital priorities than did other companies.
In my next blog, I will cover how innovative manufacturers see the cloud as a key foundation for digital transformation.
The Fourth Industrial Revolution For Finance
Part 1 in the 3-part “Finance and Intelligent Technology” series by Tony Klimas of EY and Joel Bernstein of SAP.
The “Fourth Industrial Revolution” is the idea that intelligent technologies such as the Internet of Things (IoT), artificial intelligence (AI), blockchain, and many more are pulling the global economy into a new post-computing age digital era. In this “transformative age,” industry disruption is common, and transformation to become an intelligent enterprise is the road to business survival.
The order of the day in the Fourth Industrial Revolution is adaptability. “Agile” is on the lips of corporate leaders everywhere. But more than ever, being agile as an enterprise means enabling agility across organizational units in a way that creates adaptability, insight, and cost-effective operation. All lines of business need to come together and work in tandem to deliver the kinds of experiences customers expect in the digital economy.
For no line of business is this truer than for finance – which by its very nature touches so many parts of any business. Take payables and receivables, for example. Traditionally, accounts payable/receivable is a back-office function. But in the digital economy, how you pay out and collect money is integral to the way customers and partners experience their interactions with you. Ride-sharing companies can link to your credit card so you don’t have to exchange money with your driver. Insurance companies can use telematics to bill you according to actual driving performance rather than actuarial tables based on “people like you.” These are just a couple of examples.
Finance as a change facilitator
Recently, I had a discussion with Joel Bernstein, who leads finance for global customer operations at SAP. According to Joel, part of making SAP more agile is finding ways to unlock the value of its data to build an intelligent finance organization that acts as a facilitator of change rather than a bottleneck that impedes change.
The moves made by the finance team at SAP in this regard are well-documented. Today, finance supports a growing cloud-based business where SAP customers pay according to a subscription model. The finance team has also moved to a shared services model where financial services are delivered out of regional centers following streamlined processes, assisted by automation wherever possible.
However, this transformation has not stopped there. More recently, the finance team has implemented a new “Deal Health Index” tool. “Let’s say a salesperson is preparing a quote,” Joel told me. “The deal is complex, including multiple product and service components in different geographical locations all priced separately.”
In the past, the salesperson would rely on a mixture of on-the-job expertise, information stored in price books, and the assistance of the finance team to craft quotes that adequately preserved margin. As SAP expands, however, this is hardly a scalable solution. “One thing the Deal Health index does,” Joel said, “is track the margin of any quote as it’s being built. If your deal health is green, you’re okay. If it starts to turn red, you’ll want to take a look at things before the quote is flagged for finance review.”
Assistance on demand
This kind of on-demand, in-the-moment assistance is critical for supporting a growing and constantly changing organization that demands agility. Traditional training – where you learn a process in a multi-day workshop and then forget about 90% of it – does little to help organizations keep pace in the digital economy.
That’s why Joel’s team is planning to go even further with the help of chatbots and machine learning. When a salesperson is in the middle of building a quote and has a question about the process, a chatbot can pop up to provide in-context assistance. These chatbots, furthermore, will be undergirded by machine learning. Algorithms can analyze processes for usage patterns, identify where the problems occur, and provide insight on where to modify a process or proactively add assistance. Conceivably, these chatbots could put themselves out of business: the more they learn, the simpler processes can get.
This kind of an approach helps put finance organizations out in front of challenges and opportunities. “If you’re going to be an effective finance organization,” Joel told me, “you can’t put yourself in the position of catching the results from the rest of the business. You have to be present at the beginning of the process. Otherwise, if there’s anything broken along the way, you’ll be in a bad place. Whether it’s paying vendors, building deals, collecting cash, or whatever – you need to build in transparency, efficiency, and scalability from step one.”
A foundation of trusted data
All of this, of course, is predicated on a solid foundation of trusted data. Shared services at SAP, for example, are made possible with a single source of truth for the processes they support. The same can be said for the Deal Health Index and the chatbots to come. Only with a solid data platform backed by solid data governance can lines of business within organizations effectively align with one another to drive change and deliver the kinds of experiences customers expect as a result of the Fourth Industrial Revolution.
The next blog in this series explores cloud computing as the foundation for intelligent technologies.
Want to hear more how finance leaders are harnessing the power of technology innovations to transform their operations? Register today to attend the first-ever, complimentary online SAP Finance and Risk Management Virtual Event Tuesday, Feb. 5 for an insightful experience of customers, experts, partners, and SAP executives discussing today’s pressing challenges and opportunities.
The views reflected in this article are the views of the author and do not necessarily reflect the views of the global EY organization or its member firms.
Follow SAP Finance online: @SAPFinance (Twitter) | LinkedIn | Facebook | YouTube
Winning Strategies for the Fourth Industrial Revolution
- Data is the modern commercial playing field, information dominance is your goal, those that can “act and with speed” have the advantage over those which cannot.
- Advantages in the speed of data-driven decision-making, automation, robotic process automation will dictate the winners of tomorrow.
- Complexity is the enemy of agility, and acts as poison from the past.
- It takes an optimized Information Logistics Systems (OILS) to support real-time digital interactions.
- Faster operational tempos and information logistics systems – open up a plethora of new business opportunities and business models.
- Precision and real-time data beats estimates and conjecture.
- Digital interactions require real-time business operational tempos.
- Bad data will make the smartest systems dumb.
- Competitive advantages from new technologies depreciate quickly – so act fast.
- Situational awareness enables management to focus on the knowns, rather than the unknowns.
- Demand for real-time digital customer interactions increases the need for contextually relevant and personalized user experiences and digital transformation across all systems and processes.
- Data has a shelf life, and the economic value of data diminishes quickly over time, and the more data that is collected analyzed and used, the greater the economic value it produces in aggregate. In addition, the economic value of data multiplies when combined with context and right time delivery.
- Digits can be changed faster than the human mindset.
–>
I invite you to watch my latest short video on digital technology trends and strategies:
Measuring the Pace of Change in the Fourth Industrial Revolution
- How Digital Thinking Separates Retail Leaders from Laggards
- To Bot, or Not to Bot
- Oils, Bots, AI and Clogged Arteries
- Artificial Intelligence Out of Doors in the Kingdom of Robots
- How Digital Leaders are Different
- The Three Tsunamis of Digital Transformation – Be Prepared!
- Bots, AI and the Next 40 Months
- You Only Have 40 Months to Digitally Transform
- Digital Technologies and the Greater Good
- Video Report: 40 Months of Hyper-Digital Transformation
- Report: 40 Months of Hyper-Digital Transformation
- Virtual Moves to Real in with Sensors and Digital Transformation
- Technology Must Disappear in 2017
- Merging Humans with AI and Machine Learning Systems
- In Defense of the Human Experience in a Digital World
- Profits that Kill in the Age of Digital Transformation
- Competing in Future Time and Digital Transformation
- Digital Hope and Redemption in the Digital Age
- Digital Transformation and the Role of Faster
- Digital Transformation and the Law of Thermodynamics
- Jettison the Heavy Baggage and Digitally Transform
- Digital Transformation – The Dark Side
- Business is Not as Usual in Digital Transformation
- 15 Rules for Winning in Digital Transformation
- The End Goal of Digital Transformation
- Digital Transformation and the Ignorance Penalty
- Surviving the Three Ages of Digital Transformation
- The Advantages of an Advantage in Digital Transformation
- From Digital to Hyper-Transformation
- Believers, Non-Believers and Digital Transformation
- Forces Driving the Digital Transformation Era
- Digital Transformation Requires Agility and Energy Measurement
- A Doctrine for Digital Transformation is Required
- Digital Transformation and Its Role in Mobility and Competition
- Digital Transformation – A Revolution in Precision Through IoT, Analytics and Mobility
- Competing in Digital Transformation and Mobility
- Ambiguity and Digital Transformation
- Digital Transformation and Mobility – Macro-Forces and Timing
- Mobile and IoT Technologies are Inside the Curve of Human Time
************************************************************************
Reporting on Industrial IoT Platforms and AI from MWC17
As part of SAP’s IoT Influencer program, I had the honor of interviewing Hitachi’s Rob Tiffany on Industrial IoT platforms, mobility platforms and the role of artificial intelligence at Mobile World Congress 2017.
************************************************************************
Oppo To Spend USD216 Million For Industrial Park In India
Chinese mobile phone manufacturer Oppo plans to invest USD216 million to build an industrial park in India’s Greater Noida, and this project will include a manufacturing plant.
According to Sky Li, Oppo’s vice president and president for its India branch, the company’s industrial park will cover an area of 1,000 acres and the construction will be divided into stages. The entire project is expected to be completed in two or three years. Its initial annual output is expected to be 50 million phones and its ultimate goal is to produce 100 million phones every year.
At present, Oppo already has one factory in Greater Noida.
Li is very confident about Oppo’s performance in the Indian smartphone market. He said Oppo will gain market share by providing outstanding user experience. Li also said that they are not after high shipments or prompt company scale expansion. Instead, the company will focus on providing high-quality products to users.
Statistics from GSMA show that in 2016 India exceeded America and became the world’s second largest smartphone market. The association also predicted that by 2020, the number of India’s mobile phone users will reach one billion. By June 2016, this number was 616 million.
This article passed through the Full-Text RSS service – if this is your content and you’re reading it on someone else’s site, please read the FAQ at fivefilters.org/content-only/faq.php#publishers.
Recommended article: The Guardian’s Summary of Julian Assange’s Interview Went Viral and Was Completely False.
Companies capitalize on industrial IoT data analysis
As sensors are attached to more and more industrial machines, these connected devices create a massive glut of data. But to unleash the true value of Internet of things, or IoT, data analysis, businesses need to have a clear understanding of its strengths and weaknesses.
In a panel discussion at the IoT Data Analytics and Visualization conference in Palo Alto, Calif., several speakers said the most important thing to keep in mind is that the industrial Internet should be about improving business processes, not just about implementing cool new technology.
Nauman Sheikh, founder and CEO of analytics consulting firm Asrym, Inc., said he recently worked with a large public utility to put sensors on maintenance trucks to do predictive maintenance. Sheikh built the predictive models that analyzed the data coming from the sensors to identify vibration patterns and other signs that a truck might be at risk of breaking down.
But when he pitched the idea to the utility’s management team, he didn’t talk about the algorithms he would build or the sensor and networking technology required. He said the key to getting the project off the ground was to identify with the needs of the business team and “speak their language.”
“The lesson learned was, if I was pitching to them some nice tools or fancy technology, there would have been no interest,” Sheikh said. “If you can connect to their pain the value IoT can bring to the table, the adoption will be fast and very effective.”
Similarly, Prakash Iyer, vice president of software architecture and strategy at Trimble Navigation, said businesses interested in IoT data analysis should focus on the ultimate goal, which should be automating industrial processes. Trimble makes GPS positioning software and connected hardware for industries such as agriculture and construction.
The point of any IoT or industrial Internet project, Iyer said, is to develop new insights from machinery into which businesses previously had little visibility. By analyzing data coming from machinery, either using simple visualizations or more complex machine learning algorithms, it may be possible to automate industrial processes by developing sets of business rules that kick in whenever an event occurs. Once businesses understand industrial processes, they can develop rules to automate them. But if an IoT data analysis project fails to deliver actionable insights, then the organization should question its investment, Iyer said.
If you can connect to their pain the value IoT can bring to the table, the adoption will be fast and very effective. Nauman Sheikhfounder and CEO of Asrym, Inc.
“The most important thing is not the visualization, but how you make it actionable,” Iyer said. “We need to be able to automate the next step.”
For Sudhi Ranjan Sinha, vice president of product development, building technology and services at Johnson Controls, the key to realizing business value from industrial Internet analytics projects is planning for the future by being flexible. He said in his company’s industry of heating and ventilation, legislated uses of refrigerants and customer expectations of efficiency are likely to change over time. But most of the equipment they produce has a life expectancy of around 25 years.
So Sinha said when he and his team build predictive models that measure efficiency and identify potential problems in connected heating and ventilation units, it’s important to keep in mind that things will change and to build flexibility into the models by not relying on efficiency assumptions made when the unit is produced.
“There is no permanency in this space,” Sinha said. “Every model we create has to be adaptive.”
This entry passed through the Full-Text RSS service – if this is your content and you’re reading it on someone else’s site, please read the FAQ at fivefilters.org/content-only/faq.php#publishers.
SearchBusinessAnalytics: BI, CPM and analytics news, tips and resources
Industrial analytics delivering greater business value
A little over a year ago, Drew Miranda and his team of systems engineers at Ochsner Health System in Louisiana started collecting log data from hospital remote desktop systems as a pilot project. They started pulling data from desktops tied to a single server with the aim of analyzing it to potentially do preventative maintenance and fixes. But Miranda quickly realized he was on to something big in collecting and analyzing log data.
“It was almost like a eureka moment,” said Miranda, systems engineer at Ochsner. “When I started to realize how much value we were getting out of it, it was something compelling I could take to my bosses.”
Miranda said he used to look in on streaming log data periodically, but it was impromptu. He said this approach is normal in his industry. “One of the common things I hear from friends is how few people look at logs.” The slow pace of industrial analytics adoption is primarily due to the sheer volume and velocity of the data, as well as a lack of clear value proposition.
But all that’s starting to change. Today, the technical hurdles to industrial analytics, also called the industrial Internet of Things, or IoT, are lower than in the past. Organizations are building use cases that prove the value of monitoring log data as well as data from other industrial systems that was simply discarded in the past.
Getting proactive with industrial analytics
We know before our users are reporting problems. Drew Mirandasystems engineer, Ochsner Health System
At Ochsner, Miranda and his team implemented a log management system from Houston-based Graylog Inc. that ingests and organizes log data. The team is monitoring data from about 17,000 desktops within the network of hospitals and physician offices. The data is used to track the root cause of common problems, enabling preventive maintenance, and to constantly monitor the status of desktop systems. This allows them to fix recurring errors that prevent applications from opening and make sure that all desktops are running the latest version of their operating systems and all updates are installed.
“We know before our users are reporting problems,” Miranda said. “A lot of times in a service-driven IT department, if you don’t get a call, we assume there isn’t a problem. But now we can fix things before the users even know.”
Since Miranda and his team have only been monitoring log data on a wide scale for a short time, they have not measured a precise return on investment. But Miranda said he has been able to work more proactively with desktop services vendors and improve uptime.
Sensing a new business opportunity with industrial IoT
Industrial analytics isn’t just about log data. Companies are also realizing a benefit from monitoring and analyzing sensor data. For example, San Francisco-based startup Compology has built its entire business around sensor data. The company places optic sensors in commercial Dumpsters that monitor the volume of waste. The company uses software from Seattle-based Dato Inc. to ingest and analyze the sensor data, which allows Compology’s customers to efficiently route collection vehicles based on which Dumpsters are most in need of emptying. This approach reduces the number of trucks needed for waste collection by as much as 40% compared to more routine routing, said Jason Gates, one of Compology’s founders.
Gates said the main benefit of collecting and analyzing sensor data is that, compared to other data sources, it is more actionable. It is almost always tied directly to some business process that can be improved by closer monitoring. In his view, this benefit is going to have the greatest impact in industries like waste management that are traditionally data poor.
“What I feel strongly about is that it’s not necessarily access to data and numbers, but it’s taking that information and making it actionable,” he said.
Experts agree. Dale Neef, consultant and author of the new book, Digital Exhaust, said he thinks industrial analytics will soon deliver greater business value than what we typically think of as big data. He said compared to collecting data from customers, such as social media posts, Web search history and demographics, the payoff from log and sensor data is more immediate.
“There is an enormous explosion going,” he said. “[Businesses] are looking at sensors through the supply chain, which allows you to do things like computer-based maintenance. I think the industrial Internet is going to change things enormously.”
Ed Burns is site editor of SearchBusinessAnalytics. Email him at eburns@techtarget.com and follow him on Twitter: @EdBurnsTT.
This entry passed through the Full-Text RSS service – if this is your content and you’re reading it on someone else’s site, please read the FAQ at fivefilters.org/content-only/faq.php#publishers.
SearchBusinessAnalytics: BI, CPM and analytics news, tips and resources