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What CEOs Need To Know About M2M – Part 5 (Automating business processes and driving efficiency)

  
  
  

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! 


What CEOs Need To Know About M2M – Part 3 (How Machine Usage and Behavior Data Can Provide Business Insights)

  
  
  

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.   

m2m value curve level 4

In a recent survey of our customers, we discovered that 87% of our customers are storing historical machine data.  Why? 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:

  1. Predictive Maintenance – To decrease the cost of maintaining machines and to improve up-time
  2. Improved Product Design – To understand end-user behavior and usage patterns to design better products and prioritize new features
  3. 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.

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What CEOs Need To Know About M2M – Part 2 (What You Need to Ask Your Service Organization Leaders)

  
  
  

In part 1 of this discussion series on what CEOs need to know about M2M, I defined the term M2M (Machine-to-Machine) and discussed the role of connected products and assets in corporate strategy. The M2M thesis for this series of articles is simple… if you remotely connect your machines, equipment and assets, you can unleash the machine data into your enterprise and unlock the value of that data. So what is the tangible value of M2M?  What are the benefits? What are the hard dollars to the top line and bottom line? What are the soft dollars?  At Axeda we’ve defined an M2M value curve that identifies 6 levels of maturity and organizes the major value propositions into 4 major areas:

  1. Remote Service (Level 3) - focuses on cutting costs from customer service and field service
  2. Usage Analysis ( Level 4) -focuses on predictive maintenance, driving machine up-time and improving product design
  3. Business System Integration (Level 5) – focuses on improving efficiency and enhancing business processes often associated with CRM, ERP and PLM systems
  4. Value-added Applications (Level 6) - focuses on offering complementary end-user applications with your machines and differentiate your product and improve the product experience.

Connected product maturity levels 3

For this article, we’ll cover remote service. If you’re a CEO meeting with your service organizations, here are key questions you should be asking them:

  • What service operations can be done remotely?
  • What machine information can we provide call center and support personnel to solve problems more quickly, shorten call times and solve cases and issues more effectively?
  • What information can we provide field service people to make their customer visits more effective? How can we use machine data to shorten First Time Fix Rates (FTFRs)?
  • What machine data can enable us to proactively service machines to drive greater uptime and shorten Mean Time Between Failure (MTBFs)?

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 R&D design in the right technical solution in a collaborative way with Customer Service and IT. If you’re a CEO, it is key that you have a long term remote connectivity strategy and understand what information should be monitored, collected and remotely transferred. The savings of remote service are black and white and hard dollars.  Many of Axeda’s customers have saved millions of dollars per year by increasing their service efficiency, minimizing field service visits, and reducing call times. Simple remote software upgrades and patches alone can pay for the connectivity project. Recalls are another area for savings. Many recalls are now software fixes.  Enabling remote patching of software drives down the cost of those recalls.

In my next post, we’ll continue the business case discussion and move on to level 4, machine data analytics and machine usage analysis. Companies often start with remotes service, but word rapidly spreads to other organizations that the enterprise now has access to machine data and usage information.  The next projects after remote service are often about usage analysis, quality analysis and predictive maintenance, typically led by Engineering and Product Management.

What CEOs Need To Know About M2M

  
  
  
m2m connected productsWith all the industry buzz and vendor movement around M2M (Machine-To-Machine), the Internet of Things, and the Industrial Internet, it’s time for CEOs to understand more about M2M, the value it may bring and how connected machines and assets can change their business.    If you’re a CEO, CTO, CIO or VP reading this, you’ve probably heard the M2M term, seen the GE brilliant machine commercials and you’re trying to figure out exactly what you should be doing with M2M and connected products.

I deal with companies, many of them manufacturers, on a weekly basis who are planning on connecting their products or already shipping machines with remote connectivity built-in.   And it’s no longer just about connectivity to enable remote monitoring and remote service.  There is an acceleration of focus that these companies have on shipping connected products that include new applications for end-users to better service and manage connected products.   In a previous blog, I called this corporate ecosystem of connected assets the “Internet of Corporate Things”    

Today, I’m starting a series of articles (blog postings) to help executives understand the value of M2M to enterprises.  I will include a framework CEOs can use to include connected products in their corporate strategy and strategic initiatives.   For those new to M2M, these articles can serve as a primer on the business case for investing in M2M. 

Let’s start with the definition of M2M.  For simplicity, think of M2M as connecting “your machines”, the first M in M2M, to “your computer systems”, the 2nd machine in M2M.   If you remotely connect your machines, equipment, assets, you can then unleash the machine data into your enterprise and unlock the value of that data.   The value propositions are straightforward and fall into several buckets of capabilities including remote monitoring, remote service, usage analysis, ERP/CRM integration and value-added services that differentiate your products.  

M2M embraces the reality that no product or asset will be an island. All products, devices, facilities, systems, equipment, goods being delivered, processes, workflows, and people will co-exist in a connected world, interact and be interdependent.   M2M systems will act like social networks, socializing machine data to foster unparalleled knowledge and collaboration.  The vision is grand and we’re only in the first inning, but the time to get serious about M2M is now.

In my next post, I’ll start the business case discussion with the most obvious and tangible ROI of connecting machines.  You’ll have to return to find out that that is… yes, the classic tease.  But seriously, I hope I’ve sparked some interest so you’ll return to hear more.   As one of the leaders in M2M software, my company Axeda owes it to the industry to share what works and what doesn’t.  We owe it to the industry to clearly articulate the value of M2M and why connecting machines can be transformational.   I’ll be one of the voices of M2M because I enjoy it.  And of course, as CMO, I owe it to my company to get the word out and move the ball forward in our quest to be the “machine cloud” everyone looks to as their great M2M enabler.

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