When we started designing and building SensrTrx in 2015, we were motivated by three observations we had on the state of the industry:

  1. Most manufacturers lacked visibility into their plant floor.
  2. Manufacturing Analytics was way too expensive and complicated to justify the investment for most companies.
  3. No one had developed a SaaS Manufacturing Analytics product that was affordable or easy to use.

From our research and experience, we knew there was a Titanic-sized hole in the marketplace for a product like SensrTrx; one that is affordable, flexible, cloud-based, easy-to-implement and easy-to-use.

Why Are So Many Solutions So Expensive?

In terms of Manufacturing Analytics, they sure don’t have to be. At their basics, analytics from your plant floor requires three things:

  1. Connectivity with your machines.
  2. A place to store your data (we store it in the cloud).
  3. A set of algorithms and dashboards to visualize and communicate that data.

This is exactly what SensrTrx does really well, and by focusing on that, we provide the value of powerful analytics with the affordability of a purpose-built solution. In the case of the enterprise software cost structures, the data isn’t really the thing, it’s the thing they need to do the more expensive things they are really selling you.

The Cost of the Legacy Enterprise Approach to Analytics

Many of the on-premise enterprise platform solutions on the market require an additional cost of expensive hardware or software in order to gather data from the plant floor. Building custom data feeds from machines to ERP systems can cost tens of thousands of dollars on top of the software implementation (and require internal resources and/or expensive consultants).

These custom implementations might pull data from the plant floor, but most don’t provide users with anything close to real-time data visualization or role-based dashboards that could be configured easily for different views for your operations, maintenance, and executive managers.

To connect data feeds to an ERP made sense, but most of the time to pull that data directly from the machines does not. Often the data these systems did collect stays warehoused in historians or on-prem databases, used sparingly for aggregated reporting or auditing.

In order to use some of the more advance capabilities of these platforms, you needed more and more custom data pulls from your plant floor and more inconvenient data entry from your operators. The immense cost of these systems and their approach to analytics provided no ROI and the systems themselves made very little impact on the productivity of the plant floor. A whole level of visibility is lost despite the sizable investment.

Purpose-Built Analytics is Different

SensrTrx was developed purpose-built to deliver great analytics. If you need visibility onto the plant floor you don’t need to implement from the top down. You can gain that capability per machine, cell, line and at the plant-level, incrementally if you wish, by monitoring a few machines and only one data point. By investing straight-away into manufacturing analytics, you will find value immediately by being able to measure and improve performance.

We’ve seen customers gain visibility into availability and performance by measuring only the downtime reasons automatically collected from their machines. That’s a lot of insight without the cost and complexity of a custom implementation.

One OEM customer uses our software on their printing machine at the end of their customer’s packaging line laser printing labels. The value-add of SensrTrx to their end customer is that with SensrTrx now have information on throughput about the ENTIRE line.

The Guiding Principles: Simplicity and Scalability

One other almost universal truth about the space is that many solutions are needlessly complex.

We used design and user experience best practices to make sure no single screen in our application did more than it needed to.

We knew the majority of our customers would at first use SensrTrx to track and monitor machines through one data-point: availability. Downtime tells you a lot, so we focused on designing the perfect dashboards for that. Once we nailed downtime, we moved onto performance, quality counts and scrap, keeping close the principle that when displaying data “less was usually better” and always keeping our end-users in mind.

Essentially, the entire product had to be designed to put manufacturing analytics into the hands of the people who needed to use them and be simple to use. With that in mind, we built SensrTrx to satisfy these specific requirements:

  1. We needed to pull as much of our data from the machines directly as we could.
  1. Operators had to be able to easily input reasons for downtime and scrap, or any other data (if required and not provided by the machines PLC already).
  1. Managers had to be able to see that data in context by machine, cell, part line, operator, job and shift on a tablet view while walking the factory floor.
  1. All dashboards had to be easily configured for role-based views.
  1. Dashboards had to easily display aggregate data to provide trend analysis and be available in Pareto charts so data could be viewed in context.
  1. All dashboards would allow users to drill down into the table data if they wished.
  1. The system itself and its data framework (i.e. what constitutes a shift, a cycle, a good product, how a production line is organized, what are the programmatic reasons for downtime, etc.) needed to be easily configurable so the system could match the way our customers went about their production processes and what types of machines they had.
  1. Finally, the product had to be easy to implement for any customer at any scale.

Following these simple tenants, we built SensrTrx specifically to provide low-cost, configurable analytics to provide manufacturers visibility onto the plant floor.

We’d love to show you how simple it can be to start understanding the factors that  impact your availability, performance and quality KPIs in a personalized demo.

Please follow and like us: