By Bill Zujewski
This is the second post in a multi-part series, which specifically explores the challenges of dealing with wireless technology as part of an M2M (Machine-To-Machine) initiative. The series will offer insights to help designers and developers prepare for and overcome the unique challenges involved with implementation. Today’s post will focus on carrier integration.
Declining costs around cellular components have had a huge impact on how quickly the 'Internet of Things' has grown – its significance cannot be understated. Cheap components have enabled the industry to expand into countless new verticals -- it's also why providers like AT&T have turned their full attention to the M2M industry in a big way.
However, cellular connectivity brings M2M architecture and management considerations.
For one, manufacturers need to effectively and efficiently ensure that existing connected machine solutions can integrate with cellular infrastructure and mobile carrier business systems.
Here are three other things to consider:
- On A Data Budget: Manufacturers need real-time visibility into how their communications are performing against their cellular data plan, and need to be able to adjust data plans and data flow when necessary. Otherwise, they risk going over budget.
- Connectivity Management: Similarly, manufacturers need to be able to understand the status of their connectivity, and the performance and health of their assets at all times.
- Asset Management: Finding connected assets in a carrier’s system can be difficult, as the carrier’s system only identifies assets by their SIM ID. This means manufacturers often have to manually associate the asset’s SIM ID with its VIN or serial number – a long and pain-staking process.
The best solution is to leverage M2M platforms that have already achieved integration with carrier systems. This will drastically cut your time-to-market and start-up costs.
By: Bill Zujewski
This is the first post in a multi-part series, which specifically explores the challenges of dealing with wireless technology as part of an M2M (Machine-To-Machine) initiative. The series will offer insights to help designers and developers prepare for and overcome the unique challenges involved with implementation. Today’s first post will focus on “reliability”.
For obvious reasons, wireless technology will play a key role in the future of M2M. And right now, the stage is being set. Technological advances in edge devices and cellular networks have made it easier and less expensive for mobile assets to be connected, removing two significant barriers to adoption. Fact is, machines communicating via cellular, satellite, or wireless connections will be just as big of a part, if not bigger, of the Internet of Things as machines with wired connections.
But it's not all sunshine and rainbows: the unfortunate reality is that wireless communications aren’t always as dependable as wired internet connections.
That said, there are a number of steps connected product manufacturers can take early in the M2M development and implementation processes that will help ensure the level of connectivity M2M initiatives require.
1. Design an architecture that assumes and accounts for intermittent connectivity by building in intelligence that queues up data when offline to be sent out once connectivity returns.
2. Build in connectivity redundancies, so that if one kind of connectivity fails, another will take over. For example – if a moving asset loses its cellular signal, the machine can automatically switch to satellite communications. This strategy is essential for mobile assets that require continual connectivity.
3. Test your assets’ connectivity. Connect the asset, take it to a specific location, and see what the connection quality is. In the end, nothing beats real-world testing.
Even though nothing is more dependable than a wired connection, wireless M2M is opening new doors for the industry – from the shipping and fleet industries to a wide range of consumer products. Wireless connectivity is a critical part of the industry’s future – it just takes a bit more thinking and planning to make it work right.
Please join us for an Axeda webinar with Modus, "Top 5 Things to Speed Your Deployments of a Usage Based Insurance Program" on Wednesday, September 25th at 11:00 a.m. EST
Part 6: Extend Machines with Connected Services to Differentiate
Today’s blog post discusses how machine data can be leveraged to deliver new applications that extend the utility of the machine for end-users. In my previous post on “what CEOs need to know about M2M”, I gave examples of business processes that have been enhanced and improved using machine data. These examples were about improving efficiency and taking out costs and demonstrated value to the machine manufacturer. As you recall, this was level 5 on the M2M value curve. But what about the end-user of the machine… how can we use the connectivity to deliver direct value to them?
The ultimate goal for product manufacturers, what we refer to as Level 6, is product differentiation. This level is about changing the product experience for customers by adding value-added apps that enhance the value of a machine. This is where connected capabilities have the capacity to transform a business and increase customer loyalty… where innovation is achieved by enabling end-users and customers to interact with the machine data… where manufacturers can reinvent their user experience.
There are many types of custom applications that can enhance the utility of a machine. Organizations can present data from the connected product to users and end-customers via portals and web applications that they can view while using equipment in real time. For example, a web application connected to the machine may allow the user to remotely control the machine or monitor the consumables on the machine so they can to be replenished in a timely manner. The manufacturer can also provide an application to audit all machine activity and make it easy to generate compliance reports. In fact, many of our customers now provide a web portal with their equipment that provides these fore mentioned capabilities and more.
The other big trend we are seeing is “mobile apps”. Smart phone and tablets are emerging as a way to put applications that interact with machines in the hands of field personnel and end-users who need remote access from anywhere. In some cases, workers can go home earlier knowing if something goes wrong the machine will contact them on their phone (maybe a text message) and they can quickly access the machine via their mobile device. You can see how this can reduce the labor costs for an organization while at the same time improve effectiveness of their employees.
Net/net: value-added applications can be used to improve competitiveness and drive market share. Apps can also be new revenue- generating offerings when sold as value-added services. For the most progressive companies reaching this highest level, Axeda provides easy-to-use development tools and APIs to access collected machine data and to rapidly create new innovative customer-facing applications that differentiate their offerings. The Axeda platform also includes a rules engine and scripting engine. Combined with the RESTful and SOAP-based web services, developers are empowered to rapidly build and deploy new applications.
In all these examples, think of Axeda as the “M2M Application Enablement Platform”… the software that collects the machine data, transforms it, stores it and then makes it easy to access and incorporate it into new applications. In my next post, I’ll share Axeda customer examples of apps that differentiate their machines and provide a competitive advantage. You’ll see how these expanded offerings are driving rapid adoption of their connected services. Why? Because instead of trying to “sell” connected services to their customers, their customers are demanding it… their customers “need” the new applications and they fully appreciate the value that the apps can deliver to their operations and business. Just like the consumer world, in the industrial world, the notion of an “app store” for machines is emerging.
There have been a few blog posts recently about what the name is of this area of technology we work in… whether they are blog posts in EE|Times, GE’s Industrial Internet, Wired’s Programmable World, or Cisco’s Internet of Everything, and even internally here at Axeda there have been some vigorous discussions about the use of “Machine” in regards to “Machine Cloud” and “M2M”.)
The discussion here has been around what really constitutes a “machine” when you have “ant-sized” computers such as the KLO2 chip as reported in the MIT Technology review in May, or does an iPhone count as a machine? For sure the systems GE talks about in the industrial internet are machines…and big ones at that! According to Wikipedia (yes I know, not the greatest source available, but bear with me) the definition is: “A tool that consists of one or more parts, and uses energy to achieve a particular goal. Machines are usually powered by mechanical, chemical, thermal, or electrical means, and are frequently motorized.” And they go on to add: “Historically, a powered tool also required moving parts to classify as a machine; however, the advent of electronics technology has led to the development of powered tools without moving parts that are considered machines.” So I guess if we don’t need moving parts, then our little ant-sized chip still does qualify as a machine, and I have to modify my prior position with some colleagues.
But to me none of these terms are instantly grokable to anyone outside of the space, and maybe that’s ok. I don’t need to know the intricate and arcane terms for brewing, but I like a beer. So does Aunt Alice need to know that her smart meter sends data back to the utility company via some form of M2M connection so that they can monitor her usage, send her off-peak incentives, or allow her to use an app on her iPhone to see exactly what she is using? Probably not, to her, it’s all just magic.
So what are some examples of connected systems that can deliver up some magic for you, and allow you to explain what IoE, IoT, M2M etc. are to your friends and family:
By monitoring consumable levels in MRI machines, operations can resupply in advance to avoid downtime. Service, sales and marketing can observe consumption over time, and deliver the right service, at the right time to avoid costly delays and patient/staff rescheduling.
Gauging the temperature of manufacturing assets warns the service team if there is a risk of overheating and lets finance know when warranty guidelines are not being upheld. (For one of our clients customers, their outsourced cleaners ran the machines out of parameter like this causing a $100,000 failure. By having the data showing when this was happening, the customer was able to pursue the cleaning company for restitution.)
Tracking wind turbine speed alerts operations when an asset is under producing, and helps research and development develop a more resilient blade.
Implementing a usage-based insurance strategy is revolutionizing the auto-insurance industry. Operations and finance teams can track speeds, idling time, parking location, distances traveled, hard stops and more, leading to decreased premiums for consumers.
So it probably doesn’t matter what you call it, but M2M allows you to fully harness the data that you can get from your machines as long as you have the innovative teams to see the possibility when they are developing the systems. A connected strategy can allow your company to make faster, smarter and more informed decisions across many departments and allow you to be more proactive with your customers.
If you have other examples that you use to describe the M2M space when you are having a cup of tea with your version of Aunt Alice, I’d love for you to post a comment below.
Putting together a connected product strategy is not as plug-and-play yet as maybe it should be, but I think there are some strides being taken towards making it a lot simpler for those who wish to build connected machines. For a manufacturer wishing to go it alone, and not rely on the emerging ecosystem of companies who are partnering to provide solutions can be a daunting task. Not only is complexity increasing with the choices you have for devices, the networks and protocols, and the security implications, but there can also be an increase in costs with more applications needed to be developed, more integrations to your ERP or CRM systems, or purely the amount of data that you may be pumping from the devices back to the databases.
So why would you build this all by yourself and not take advantage of people who specialize in these areas, and can work together to get your products all chatting on the Internet of Things (or the Machine Cloud as we call it). I worked for a company a few years ago that had a legacy system to do inventory management, and due to rapid acquisition and growth, had to scale that system up. But it was running on older hardware and operating systems, as well as the actual development platform, and it mean that the company effectively had to become their own software house to support that vital product. Going off and building your own is effectively turning you into a software house when that may not be your core capability.
Some of the unique things you need to know about when building M2M apps over more traditional enterprise-type software are:
- Do I need short-haul or long-haul drivers?
- How do I create embedded agents, message translators of raw data to business information?
- How do I efficiently organize my data in an M2M data model?
- How do I cope with queuing, throttling and caching compression of a constant stream of real-time data?
After all, you should really be focused on your business and its future. Improving your product experience, increasing your agility and empowering not only your service and support staff, but as we saw in my last blog, your customers as well.
That’s where the Collaborative Ecosystem comes in. where the 5 main areas of an M2M deployment come together to get your products to market faster than you can do it by yourself. So whether you are building connected products for mHealth, for transportation, for Usage-Based-Insurance (UBI) or for utilities, partnering up with people who have the most knowledge in their area makes the most sense.. after all, would you build your own email system nowadays, do you have software engineers building a credit-card processing application?
The 5 areas are of course;
- The Network and Connectivity Providers
- The Hardware and Component Providers
- The Application Service Providers
- The Business Systems Providers
- The Cloud Application and Platform Providers
At Axeda, that ecosystem looks something like this:
And that doesn’t even include all of the device manufacturers in the Axeda Ready Program that is a technical approval program for hardware and module manufacturers in the M2M industry to ensure device compatibility with the Axeda Platform. Programs like this speed time-to-market for multiple devices that can communicate with a platform, and ensures accurate and secure data communication, as well as setting technical support expectations based on their certification. And when those devices are used with an ecosystem network provider, and the applications are written by an ecosystem application provider, the whole process can go a lot faster than trying to do it yourself.
Recently at Axeda’s user conference, Connexion ’13, we had members from each area of this ecosystem, as well as analysts and customers; speak in keynote sessions giving their vision on where the market was going, and how the ecosystem was a strong driver to get there. So I put together a video with highlights from their presentations, to view it, click here:
Some of the benefits, as I see it, to joining the ecosystem rather than going it alone are not only that you free up resources to drive innovation, but you also get:
- Fast time-to-market with new solutions and initiatives
- Approved security protocols
- Efficient data communication and machine data processing
- Built-In business and administration tools
- Less code to write yourself
- The flexibility to extend and customize
- Benefits of input from multiple customers
But that’s just my view; and as I said in my first post, I’m a newbie at all this, so I’m sure some of you will have comments. So add them below and share them with the rest of us. And if in the meantime you want to see someone else's view, check out this recent post on VentureBeat talking about the very same dilemma.
Today’s blog post continues the discussion on the value of machine data integration with other enterprise business applications and processes. In my previous post on “what CEOs need to know about M2M”, I listed 10 processes that can be enhanced with machine data. Today, we’ll discuss specific customer examples of business processes that have been enhanced and improved using machine data. Many of these examples are about improving efficiency and taking out costs and drive the business cases for investing in connected products.
Let’s start with field service. Many field service requests and visits are initiated by a customer “call” into the call center, typically due to a down or malfunctioning machine. There is often a lag time between the customer placing the call and the initial occurrence of the problem. To remove the lag and improve the service, we have several customers who have automated the creation of a case and a field service request in their back office systems by having rules in our machine cloud monitor machine events and trigger a web service call into their systems. This removes the lag and also proactively starts the repair procedure minimizing downtime.
Customer service and support is another example. Many of these same customers above prepopulate case and asset data in their CRM app with machine status, reading and logs. This shortens call times and improves time to repair. It also eliminates the need for 2 workflows or UIs (one for Axeda and one for CRM) since the customer service and field service reps can continue to use the CRM app they use today. But now their screens are enhanced with close to real-time machine data.
Another process that can be improved with machine data is warranty management. An industrial manufacturing customer was able to add temperature and humidity sensors to their factory equipment and then monitor the operating conditions of the factories their equipment ran in. This enabled them to forewarn their customers of potential issues and drive them to improve their operating conditions to reduce breakdowns. In some cases, it also enabled them to deny warranty coverage for customer out of compliance. Overall, simply adding a couple of sensors saved this company millions in warranty costs.
Another process that can be improved with machine data is billing. An industrial equipment maker of ours was able to introduce a new “pay-per-use” billing model enabled by machine data. Axeda collects usage data from their machines that generate reports for the invoicing process. This enabled the customer to offer a “product-as-a-service” and go downstream to the mid-market by lowering the cost entry point for their end-users.
In all these examples, think of Axeda as the “M2M middleware”… the software that collects the machine data, transforms it, and integrates it. In my next post, I’ll discuss level 6 – Differentiation. This level is about changing the product experience for customers by adding value-added apps that extend the utility of a machine. These apps can help differentiate machines and provide a competitive advantage. These expanded offerings are driving rapid adoption of connected services, because it’s no longer about just value for the machine supplier/manufacturer, the connectivity is providing value to the end-user… what a concept!
In Part 2 of M2M in the Wild I took a look at a few healthcare customers of Axeda that use the cloud-based platform to enhance their service offerings to provide not only remote service to provide a faster and more targeted response and resolution, as well as using the platform to provide analytics to help with preventative maintenance on their equipment. But as you can see from the graphic below, the service organization is only one area that can gain benefits from implementing a machine-to-machine program for their devices.
By including other departments such as finance, sales, engineering and operations in the discussions early on in your connected product journey, you can ensure that you are providing a differentiated product offering that can provide you not only an early-mover advantage in your space, but a stronger brand, and customers for life.
We have seen a few of our healthcare customers moving up the value curve to integrate their connected products with other systems and departments outside of service, so that they can unleash the machine data into their organizations and unlock the value of the data that they collect.
One such customer manufactures battery powered surgical devices such as saws and drills for use in the operating room. By having these devices connected, they are able to provide data not only to their technical support staff, but also to an application for mobile devices that allows the local field service rep, the sales rep and their biomedical staff to see the data in a graphical form. This allows them to see if, for example, the battery on a particular device is not charging to its full capacity, so the sales rep can offer a replacement battery on his next visit. Or maybe they can show the OR staff that one saw has a lot more cycle times than another and suggest that maybe they should rotate them equally.
Another use outside of the service department is that of asset tracking. One client who makes cardiovascular pumps that are designed to be mobile wanted to ensure they and the hospitals knew where the pumps were at any time, as they may be transported with a patient on a gurney out of the assigned operating room or possibly even out of the hospital.
The final example of a healthcare manufacturer offering a higher level of connected product than just a service offering is that of a large disinfection and sterilization equipment for use in hospitals and biopharmaceutical laboratories. They have created their own applications that not only allow them to monitor the equipment for service requirements, but also to provide in-facility dashboards, so that operators can see at a glance what cycle the machines are on, and if any problems are occurring.
Before using this connected system, operators had to stay with a machine while it was running, to ensure that someone was there if anything went wrong. Now they have a mobile app which alerts them of any issues, or when the sterilization cycle completes, so that they can be freed to do other tasks.
The other integration that this company has done is to show the levels available for the variable consumables such as detergents, so that operators can see at a glance on their mobile devices, whether or not a particular system is running low, and need to be replenished.
So, by Incorporating data into one central source this company has simplified the administration of their systems, and provided unprecedented access to data about the status and performance of their customers equipment allowing them to be empowered with their production planning, and also making the work routines easier and more predictable for the operators.
By thinking beyond just the benefits a connected product strategy can bring in the service organization, these companies are transforming themselves by fueling accelerated revenue growth, and providing true value-adds to their clients by providing great products that are easy to use and less likely to fail when you need them most, whether that’s on the operating table, or in the laboratory.
Today’s posting will focus on machine data integration with other enterprise business applications like CRM, ERP and PLM.
In my previous posting on what CEOs need to know about M2M, I referenced the Axeda value curve and discussed level 4, machine data analytics.
Today, we’ll discuss Level 5… business system integration
… how extending your ERP and CRM system with machine data can drive business process efficiencies and optimize processes with accurate machine data.
Companies who were early in bringing their products online are now realizing that the real “gold” in M2M is taking that data and integrating with enterprise systems such as CRM, ERP, PLM or data warehouses. This machine data can be used for optimizing critical business processes and essentially M2M-izing their organizations. I use the term M2Mize to describe this notion… M2Mize essentially means to optimize a business process using M2M data from a connected product or asset.
In last year’s survey of our customers, we discovered that 67% of our customers have integrated or are in the midst of integrating machine data with an enterprise system. Why? What value is machine data to ERP and CRM? What are the benefits of making machine data available to these systems? The answer is simple: many business processes can be improved using machine data. In fact, here’s a list of the top 10 processes that were identified in the survey:
1) Field Service
M2M data from connected assets, in collaboration with other enterprise systems, can provide visibility and automation across organizations not previously possible. For example, product data flowing through a CRM system can also be sent to billing or into a supply chain management system — helping to eliminate error-prone manual steps and providing new sales opportunities for things such as consumable replenishment or warranty renewals. Additionally, integration with quality assurance or product management can help enhance product features based on real-world data that shows usage patterns or equipment issues — helping to improve customer satisfaction and streamlining Beta programs.
2) Customer Service
3) Usage-based Billing
4) Asset Management
5) Consumable Management
6) Warranty Management
8) Configuration Management
10) Product Lifecycle Management
So how does machine data get from the asset to these business systems? The data is first collected and transformed into formats that can be consumed by IT. Most of our customers use Axeda’s built-in Web Services and Integration Framework to access data in our cloud. Our integration framework allows for seamless two-way communications with other enterprise systems and makes it easy to pass asset data, alarms, files, and locations to applications like SAP, Oracle, or Salesforce.com. The platform includes 3 ways to integrate: 1) a modern integration message queue for handling asynchronous communication 2) SOAP or RESTful API’s to call into Axeda and extract data or 3) a scripting engine and APIs to call other external web services from within Axeda.
In my next post, I’ll give several examples of how machine data is working its way into our customers back office systems to improve efficiency or enable new services. This business system integration is a relatively new phenomenon. IT organizations are very good at dealing with traditional web-based business applications but have little experience dealing with machine data and M2M solutions. But M2M application platforms like Axeda are becoming very good at turning raw machine data into standard IT formats and web services that IT can deal with. This is driving rapid expansion of machine data into the enterprise.
As promised in last week’s blog, this week I am going to take a look at how Machine-to-Machine devices are used in the healthcare industry to help keep the systems keeping us alive, stay alive themselves! If you took a look at the customer section of our website
for companies in the healthcare industry, you can see that we have a lot of companies listed that provide solutions to securely connect, manage, and innovate everything from MRI machines to blood analyzers.
These manufacturers turn to an M2M platform for many reasons, but top among them is improving the service offerings that they provide. One customer told me that a system being out of action for a single day can result in 35 patients being unable to receive treatment, so minimizing system downtime, and providing out-of-hours preventative maintenance is essential to ensuring that people receive treatment as and when they are scheduled.
Another customer provides devices to clinics to assist with IVF treatments by checking the viability of embryos. The procedure can take a day or more to run for each patient, and so the company needed to find a way to ensure the success of each treatment not only for the clinic using the system, but for the patient who had invested not only a lot of money, but also a lot of emotion into the procedure as well. So, they needed to ensure that they could be preemptive in the availability of their systems by collecting and analyzing log files and consumables data so that they can proactively dispatch field service staff prior to the next treatment commencing.
One of the benefits of access to a medical device remotely is being able to calibrate a system without having to send an engineer to site. By being able to remotely and securely connect into a device, manufacturers can provide expert assistance to lab managers to ensure systems are running within acceptable parameters. One example of this is Leica Microsystems who are using Axeda in the Leica RemoteCare service offering for their tissue processors and confocal microscopes. By proactively monitoring these systems, Leica can detect parameter deviations before problems occur. For example, as soon as the temperature drifts out of range for a tissue sample, an alarm and email are sent to both Leica and to their customer in the lab, so that adjustments can be made before losing a specimen.
The ability to conduct remote support also saves the time and expense of sending field engineers to diagnose problems as well. For example, a research institution had been experiencing system crashes of its microscopes during long-term experiments, but by being able to remotely capture and analyze the saved error logs, the remote engineer could see that the memory sizes at the time of the crash fell below 500 MB. Instead of dispatching an engineer onsite to replace a defective detector board, a service representative remotely cleaned up the PC to resolve the issue. As a result, diagnosis and repair took one hour instead of three days and saved the unnecessary expense of replacing a perfectly fine piece of hardware.
The costs of field service for these incidents are not insignificant either; a customer who focuses on radiotherapy and x-ray imaging devices estimates that they save around $2,000 for each problem that they solve remotely, ant that they have also reduced their Mean-Time-To-Repair (MTTR) by 50% while saving four hours of travel time for each call.
You may wonder how remote access is securely achieved by companies such as Leica (take a look here for news last week on the latest U.S FDA recommendations for manufacturers of healthcare systems), well Axeda agents are “firewall friendly”, meaning that communications are always HTTPS initiated by the agent, so your customers never need to have IT open ports, change firewall rules, use modems or VPNs for communication, all of which can create security risks by potentially enabling intruders to gain access to sensitive patient data.
I have a few more words to say on the healthcare subject next time, but if you would like to read more about what Leica is doing, we have a case study here that you can take a look at.
Today’s posting will focus on machine data analytics. In my previous posting on what CEOs need to know about M2M, I referenced the Axeda value curve and discussed level 3, remote service and monitoring. Today, we’ll discuss Level 4… machine usage and behavior analysis… how collecting the right machine data and using the right tools to analyze the data can drive better products and services.
In a recent survey of our customers, we discovered that 87% of our customers are storing historical machine data.
What can machine data analysis tell us?
What are the benefits of building a data mart or big data system for machine data?
Here are the top 3 business drivers we’ve seen for machine data analytics:
- Predictive Maintenance – To decrease the cost of maintaining machines and to improve up-time
- Improved Product Design – To understand end-user behavior and usage patterns to design better products and prioritize new features
- Identification of Quality Issues – To understand what’s causing down-time; To identify issues with design, embedded software, part suppliers and manufacturing processes
Let’s look at each driver starting with Predictive Maintenance. If your company has equipment that needs to be maintained by you, your partners or your customers, then there are some basic questions to ask your IT, Product Management and R&D organizations:
- What machine data can enable us to proactively service machines to drive greater uptime and shorten Mean Time Between Failure (MTBFs)?
- What sensors do we have or what sensors can we add to help us detect failures before they happen?
- What patterns in machine data readings are we seeing that are early indicators of failures?
Answers to these questions will provide Engineering, Product Management and IT the requirements for capturing the right raw data on the machine. It will help field service proactive schedule maintenance or parts replacements, rather than over servicing equipment or waiting to be reactive to failures.
Product Design can be improved with machine data. Behavior data from your machines can give you insights into how end-users are using your machines. This data can be used as input into your next generation product requirements. It can help you design a product that responds to real world use cases.
Lastly, quality issues can be identified by analyzing machine data. Today, analysis of trouble tickets and field visits can provide indicators of issues in your machines. But rather than waiting for calls and tickets to accumulate, connected machine data can be collected and analyzed to detect patterns and identify issues much sooner. The patterns observed can document normal operation and identify exception patterns. These exception patterns can be used to detect glitches in software or hardware design or correlate issues with parts suppliers.
Machines can also have issues resulting from flaws in the manufacturing process. Understanding the relationships between problems and specific batches or production runs can identify a bad batch early and streamline the recall process. It is also possible the problems are more serious and still in in the current manufacturing process. In that case, the data may trigger the need to change the current manufacturing process. Net/net... If you can understand what’s causing down-time, you can identify any flaws in the manufacturing design or process of your machines.
In my next post, we’ll continue the business case discussion and move on to level 5, integration of machine data with CRM, ERP and PLM. M2M Application Platforms of today are becoming very good at turning raw machine data into standard IT formats and web services that other systems can consume. I’ll give several examples of how machine data is working its way into our customers back office systems to improve efficiency or enable new services.