We’ve taken the time to curate a few of our favorite and most informative topics since 2016. Without ado, the SensrTrx Greatest Hits:
One of our most popular blog posts, this question is often at the heart of what manufacturers need to increase productivity and gain visibility. In it, we talk about the root causes of downtime, how it effects OEE and how these metrics can be gathered from machine PLCs and supplemented by operator’s input by tablet on the floor. By tracking downtime by reason code, manufacturers can troubleshoot and understand the impact of availability and downtime on their bottom-line. For those a bit more interested in the costs of downtime, check out: Top Cause of Under Production & Downtime For Manufacturers, another popular blog from our archives.
What’s at the heart of visibility? For most manufacturers, it’s the ability to track the availability of your machines and machine cycles. In this blog, we delve into how both those things can be tracked with a simple set-up in less than a day. Using Banner Engineering’s wireless sensors and SensrTrx, manufacturers can see downtime and track machine cycles to see throughput on product counts. SensrTrx provides powerful visibility for a fraction of the costs of enterprise software and implementation time with no additional consulting fees.
There is, unfortunately, a general misconception in the marketplace around what is and isn’t manufacturing analytics. In this blog, we discuss the differences between a strict BI platform like Tableau (industry agnostic analytics) and an analytics application purpose-built for manufacturing. Similarly, a lot of people feel the same way about their ERP and MES systems, and we address that here: Manufacturing Analytics is not the same as MES or ERP.
Machine learning is all the rage, but for many manufacturer’s it’s a pipe dream sold to them before the pipe is built and the data is flowing. Building your IIoT on the promise of an all-powerful AI solving your problems is simple fiction. In this blog, we cut through the hype and explain how simple, powerful algorithms can help you understand your data and start making evidence-based decisions that can impact your productivity. No AI required.
OEE is a huge benchmark for manufacturing excellence, but it’s really misunderstood. We talk about OEE and put its predominance in the industry in context. Find out how OEE can mislead manufacturers by presenting roll-up numbers without any context. For extra credit, learn why the industry standard of 85% isn’t a standard at all, and why 100% OEE is actually a bad thing.
Another blog on OEE, we take a dive into the differences of OEE (Overall Equipment Effectiveness) and OPE (Overall Production Effectiveness) or TEEP. How and why you choose the one over the other, the fact remains, the most valuable metrics will be the ones you can act on.
Based on a video our CEO put together to address best practices with manufacturers analytics, this blog provides you with the 3 things that all successful manufacturers do to make the biggest impact on their productivity numbers and ensure an ROI in as little as three months.
Moneyball was the paradigm shift in baseball that demonstrated the power of data to make better decisions. We take that concept to the next level, showing how statistics in manufacturing can provide managers with the data they need to elevate their operation. In it, we demonstrate how insights into productivity metrics can make the difference between the farm league and the major leagues. If you like this one, be sure to check out: Like Formula 1 And Nascar, In Manufacturing, Better Data = More Winning. in which we we compare how data and machine telemetry has revolutionized the sports of Nascar and Formula 1 and how manufacturers can begin to put together their own winning records, just like Kyle Busch.
Data is only as valuable as how you use it. In this short list, we outline 7 ways in which data can impact your productivity and your bottom line.
If your OEE numbers from last week told you that there was unplanned downtime, what else do you need to know to fix it? Hint: Manufacturing Data needs Context.