Automation has been a game-changer for manufacturing. In the past 25 years, the addition of machines, control systems, information technologies and robots have increased productivity and quality on the factory floor, much to the dismay of many factory workers fearing job displacement. Those fears are further reinforced as companies move toward more technology-intensive environments, such as the recent developments now made possible by 3D printing.
Those fears may be over-exaggerated. People are still needed to program and service machines, design new products and interpret the massive amounts of data that manufacturers are now accumulating. In fact, some industry pundits believe there might now be a progression toward people-intensive factories of the future.
“Most companies have a lot of data, but they are challenged by how to leverage it for the best outcome,” said Julie Fraser, principal analyst at Inyo Advisors.
In a recent Industry Week webinar, “The Global Manufacturer’s 10 Step Guide to Leveraging Enterprise Manufacturing Intelligence to Sustain Market Leadership,” Fraser outlined 10 steps to making better decisions – and maximize the outcome. To see a complete list of these ten points, see this blog post “10 Ways to Engage in Better Decision Making.”
We all know information is power. Today, simply having access to information isn’t enough to establish competitive advantage. It is more about what you can do with the data and how quickly you can make sense of it by turning it into intelligence. The volume of data now available is mind boggling, which has created new sets of challenges with regards to how best sift through it to “connect the dots.” This operational intelligence can be used to see trends such as those that might help predict pending equipment failure, changing customer demand or looming quality issues from a supplier. The name of the game today is converting information into actionable intelligence, which can then be acted upon quickly to directly impact the bottom line.
The above referenced webinar outlines this concept and provides realistic steps that can be taken to improve your performance in this area of operational intelligence. “The first step is to understand what information is most important to collect, aggregate, and analyze, and then to put context around it. Most companies don’t have to hire external experts to accomplish these 10 steps in the enterprise,” Fraser says. The obvious question, however, is “How do you advance your organization to become an information visionary?” Here are some insights to this challenge, derived from the Question and Answer session that immediately followed the webinar:
Q: What is it about this type of project that means you can get away without hiring a consulting firm or having to reengineer a project to get the benefits so quickly?
A: It doesn’t mean you can’t have a big project, you can. There are Enterprise Manufacturing Intelligence (EMI) products out there that can become major projects. You need to look instead for a system that includes pre-defined templates – ideally out-of-the-box – that can easily connect within its own system and third-party data sources. The key thing is that MI lies on top of all of the other applications you have. Projects get in trouble when you have to re-think the way you are doing business, and rip out software and processes. With this approach, it is more additive. And, you can in fact connect and start somewhat small. You don’t have to link up every data source in the enterprise. You can do a little at a time.
Q: There were two slides showing the improvement using MI, but the percentages were small. Shouldn’t a company make significant improvement in OEE and manufacturing costs from implementing such a program?
A: We showed only a portion of those who were able to make an improvement on average of 10% per year. So, yes, most of the companies made some improvement, but we were only showing top level of improvements. Other benefits are more difficult to quantify, such as making better decisions resulting in faster new product launches or being more proactive in addressing a quality issue.
Q: What about if data collection is not automated or is in different formats?
A: The best example is quality testing. Not a lot of quality testing is automated. In some industries it is, but it is usually a mix of visual and automated information that needs to be harnessed up and rendered so it is meaningful. Similar to the decision of using ERP vs. a Manufacturing Execution System, you need to have a MI conversation with your CIO and CFO. You may think you have the software solution covered from a high level, but when you start to understand the dynamics of manufacturing, you may begin to realize there are other solutions that may help better deliver the intelligence you need that fit well within the overall strategy and philosophy of your company.