How to Avoid Being the 'Blockbuster' of Manufacturing

Industries change generally for a few specific reasons: new technologies arise that present better solutions to people’s problems, people change in a way that presents new problems that require better solutions (often enabled by technology), or the economy changes in a way that necessitates a reinvention of how people and technology will solve these problems. Oftentimes, it’s a bit of all three, and many times, the latter is the catalyst for the former two.

For manufacturing in many ways, we believe that MachineMetrics is to the operation of equipment as Netflix was to blockbuster. In the media world, there was a way things were once consumed and now technology enables a new, better way for those things to be consumed.

Previously, consuming video content was extremely manual. One had to travel to the video store and physically rent and return videos. The process of locating the video itself was also quite manual, whether it was b-lining to the new releases, searching around areas by genre, or simply browsing the shelves for something you’ve never seen before.

Now, with Netflix, the process has been optimized for the modern content consumer. Modern personal device technology has changed the way people behave, making it easier than ever for people to get whatever content they want whenever they want it. Inherent to this digitalization is also a proliferation of new data surrounding what and how people consume content. Data is now at the center of the experience. Content is not just browsed but recommended, delivered based on algorithmic-driven previous consumptions. Videos aren’t rented one by one but instead access is provided through a monthly subscription. The experience has been changed, optimized for a type of content consumer who wants everything at their fingertips but doesn’t necessarily know what they really even want. And with that, new business models have emerged to support it.

So, What does this have to do with manufacturing equipment?

Currently, almost every facet of the manufacturing equipment industry operates within a standard legacy business model, that is, similarly to Blockbuster in many regards. From product sales to service, to system integration, to the equipment operation itself, most processes within the space are done the same way today as they were twenty years ago. However, just like with Blockbuster, new technology, the evolving needs of people, and the economy itself will change the way manufacturing and its various things (assets, people, systems) interact with consumers forever. While old business models will struggle, new ones will arise, and these innovations will be driven not by gut feeling but with new processes empowered by qualitative and quantitative data at the core.

Enter: MachineMetrics. MachineMetrics is the first Industrial IoT Platform for Manufacturing Equipment. We’re enabling machining-as-a-service through edge connectivity, IoT cloud Infrastructure, and prescriptive workflows to optimize machine operation.

Laying the foundation of new technology enablement required an ability to collect and consume data from every thing. It’s not just the operator of the machine that affects its operation; it’s systems, assets, AND people. The data produced around the operation of the machine affects every level of a manufacturing company from the shop floor to the c-suite.

To optimize the operation of equipment, it’s not just the manufacturer who needs this data, but in fact, all who in any way partake in the operation of the machine, from machine builders and distributors to service and parts providers, from systems and system integrators to consultants and financial firms.

What makes MachineMetrics special is that it’s all about making data actionable. Our unique ability to enable simple, consumable, usable data infrastructure provides all potential consumers of this machine operation data the necessary visibility and insight to create, optimize, and deliver actionable workflows.

A wise person once told me that great ideas are a dime a dozen until you do something about it. We’ve found that, with respect to “machine monitoring,” it’s not just monitoring historical and real-time data for insights, it’s about understanding what you can do with that insight to improve a process that previously has lead to downtime or inefficient response. Insight alone is, in itself, somewhat valuable as it may surface and or bring visibility to a problem, but it does not necessarily change the inefficient process around what to actually do with this insight.

Instead, insight is only as valuable as what you do with it. Using MachineMetrics, we enable our customers to merge insights with “workflows” (or, a specific process of what to do with this insight), that can be delivered to the required data consumer so they can take action. A workflow can be kicked off by an insight simply and automatically derived through monitoring of performance/conditional data points from the machine or surfaced through a system or person. It’s all about letting the right person know at the right time, not just what’s happening, but what exactly they can do about it to take corrective/preventative action that, in the end, improves the operation of the machine.

Our goal is to:

Step 1: Identify the most inefficient processes that stem from reacting, proacting, and predicting
Step 2: Begin monitoring to identify the insight that precedes these inefficient processes
Step 3: Kick-off prescriptive workflows to deliver a new process that enables improved decision making and optimizes the time/resources it takes to complete the corrective task.

MachineMetrics and our data can be the nucleus of these process improvements. However, to build new workflows for all aspects/processes of the machine’s operation will require not just the ability to connect every asset, system, and person on the shop floor, but also to any other “thing” that affects the operation of the machine.

As we know from our customers, manufacturers don’t want to buy “machine monitoring” from their machine builders, nor their tooling providers or other providers of machine parts. We also know that these providers know far more about their things than we do. Thus, it is logical for MachineMetrics to deliver the analysis or even the actionable insight that can be derived from this data not only to the manufacturer, but also to those partners in order for them to provide manufacturers with microservices that enable them to optimize every task pertaining to the operation of the machine.

The future of selling machines, for example, is selling machining-as-a-service. We are enabling machine builders to leverage our data to create new revenue streams and microservices around the machine itself. Examples include financing machines, remote service, tying warranties to preventative maintenance, predictive analytics at the edge, and optimizing automation packages.

Through this, we are enabling not just machining-as-a-service, but really business-as-a-service for all of these providers who impact the machine itself. Tooling, grinding wheels, CMMS, machining, analytics, and any “thing” that is influenced by the data we collect can build new business models and auxiliary services around their primary offerings that are driven by MachineMetrics data.

Right now, we’re seeing all the telltale signs of an industrial change for manufacturing, and that avalanche is starting to fall. From the arrival of new technologies to the evolution of the way people interact with “things”, and, of course, the ominousness of economic downturn, those who operate a business within the manufacturing space must now enable new business models that not just insulate their companies from the effects of these changes but proliferate into a new era of manufacturing.

To make a long story short: Don’t be Blockbuster. Together, let’s be the Netflix for Manufacturing.