Key performance indicators (KPIs) are very important in the manufacturing industry. Here are some KPIs you should be tracking.
The SPC Variation Wizard and the DMS Drill-down Wizard take time and guesswork out of your day as it helps you find the sources (root causes) of variation in your processes. The Big Data Widget (yes, there is one for both SPC and DMS.) is your Variation Wizard / Drill-down Wizard on steroids. This control analyzes all your Standards having data during a given time period. Not just one Standard at a time, but all of them!!
We’ve earned a solid Five Star rating on Capterra, the independent software review site. We’re pretty proud of that. One of the reasons for this success is a decision we made several years ago: we do not pay sales commissions. For most business leaders, getting rid of sales commissions sounds like business suicide. I think it is one of the keys to our success.
Big Data is already here. Gages, devices, and innumerable sensors produce mountains of data. This equipment is connected by USB ports and various communication protocols in vast networks of operational data. Chances are you are buried under manufacturing big data. OAK is a free tool from Hertzler Systems. It asks the right questions to help you and your team uncover the critical connections between business drivers, strategic outcomes, actions, decisions, and data. One customer said, “The OAK worksheet prompts all the right questions. Fits very well connecting top to bottom. This was a good exercise.” Download a free copy of OAK today.
While I'm naturally inclined to skepticism about the latest buzzword trend, "Systems of Insight" resonates with me. It matches what I see our customers doing with GainSeeker Suite, and the value they're striving to get from working with us. Clearly GainSeeker Suite, as a Manufacturing Intelligence Platform, is aligned with this vision of a System of Insight. Whether we're talking about turning field failures into supply chain leadership, or reducing product giveaway, or knowing where stuff is, or improving manufacturing performance, or any other application of real-time actionable intelligence, the ability to gain insight from data and make better, faster decisions is critical. GainSeeker delivers.
The OEE (Overall Equipment Effectiveness) metric has fallen in and out of favor in manufacturing circles over recent years. Beyond the poles of hype and critique, is it possible to navigate a third, thoughtful path that uses the OEE metric to drive business transformation?
A new report by the Automotive Industry Action Group (AIAG) has flagged analytics to identify root causes as one of the critical recommendations in their Quality2020 Survey. The new report is available free from the AIAG website. It looks at data from across the automotive supply chain.
If you’re struggling to make sense of manufacturing big data, you’re not alone. According to research published last year by The Economist, only 42% of manufacturers have what they consider to be a well-defined data-management strategy. Even more striking, 86% report problems in managing the data they are now generating. According to David Line, Managing Editor, Economist Intelligence Unit, “Manufacturing has been at the forefront of data collection and its importance to quality and cost control is well recognised. But collecting too much data, or failing to analyse what you collect, can be counterproductive.”
Did you know that manufacturers with visibility to real-time metrics in manufacturing have a higher average OEE (Overall Equipment Effectiveness) than those who don’t? According to the LNS Research Report: "Big Data - Driving Quality Intelligence at the Speed of Manufacturing", the difference is significant: 5 percentage points higher. You can request your copy of this reporthere.
He needed a Data Wrangler, or Data Janitor, to help him build the bridge between all the data he was collecting in his MES system and the insights he hoped to gain from that data. “I had one of my engineers build some macros in Excel that go into our MES system and extract the data to an Excel workbook. Once it is there, he has to spend hours scrubbing the data before he can do any useful analysis.” He went on to say, “It might be ok for an occasional study, but we need to be looking at this data throughout the day, every day. This manual process is just not sustainable.” My customer was pointing out the obvious: wrangling data is not a value added activity. Not only that, it isn’t sustainable for daily operations.