The first step toward understanding and optimizing a manufacturing process is to collect and archive data about the process. Ideally, the system used to accomplish this is a ”plant wide” information system, or PIMS, which collects not just process data, but also quality information and laboratory results, and operations information such as upcoming orders and inventory.
Historian packages were originally intended to be a support tool for operating personnel. Current and historical data was constantly displayed on a dedicated screen next to the primary control screens, and users were intended to interact with it at that location more or less continuously. As the historian became a one-stop source for all types of data throughout a facility, it became a tool that could benefit supervisory and management personnel as well. This led to the development of a variety of remote notification and reporting tools to meet the somewhat different needs of these individuals.
As an engineer in a manufacturing facility, you are excited that management has purchased and implemented a plant wide Information Management system, or PIM. This gives you the ability to collect and store process data, and to display both real time and historical process graphs which allow you and the operators to better understand the process. You can finally trend important process variables next to each other in order to visualize relationships that you suspect exist, and to use historical data for accurate diagnosis of problems, for example, was it lube oil pump failure, or loss of cooling water that led to the recent shutdown of a compressor?
We all experience user interfaces on a daily basis whether in our cars, on our mobile phones or on our personal or work computers. A user interface is a gateway; it is a visual path to an experience as well as information or functionality. A user interface is also a language of its own that allows one to navigate a program or application.
Over 121 people gathered for the 2017 dataPARC User Conference in beautiful Portland, Oregon from May 15 through the 18th. Attendees traveled from Canada, South Korea, Taiwan, Thailand, China and Lebanon to experience the presentations, networking and training.
This version is a minor release improving on the features in the 5.5.2 series. NEW FEATURES Centerline Added ability in Centerline Config to select a tag and then share it’s process to all tags in the Centerline. PARCmodel DMODXN/HT2N contribution tags will now be created for each of the input tags, allowing tag contributions to be viewed over time.
Coming off nearly another year of sleep deprivation our developers are pleased to announce the completion of PARCview’s next major version, 5.5! Similar to the WPF expansion in our last major release, 5.5 is packed with new tools and treats that have our engineers salivating! From graphic logic controls to the new PARCview Configuration Manager we will take a closer look at some of the great new features in this release.
Going back 15 years now, dataPARC had the notion of a “Process Area” that allowed tags from multiple systems to be organized by Asset, providing filters (like Grade or Product) for all tags assigned to an Asset and for other useful associations to be applied globally. Building on this experience, the next major version of PARCview takes the next step in Asset Management and includes an adoption of the ISA 95 companion specification to OPC UA. The implementation will allow end-users a familiar, standards-based architecture for organizing their plant data.
The ISA 95 spec and OPC UA companion standard provide a model that allows software programs to exchange all the relevant information throughout a manufacturing organization. This provides the groundwork for an industrial internet of things by breaking down the communication barriers between objects.
The New Year is starting and now is the time to book your PARCview training session. With the new training calendar rolling out this is the perfect time to plan your get-a-way to the Northwest. Whether you need to escape the heat of summer, the cold of winter, or just need to get away from the plant, PARCtraining is your ticket to a welcome escape. Oh, did we mention the training?
Historian packages were originally intended to help operators and engineers understand and operate manufacturing processes. Current and historical data was constantly displayed on a dedicated screen next to the primary control screens, and users were intended to interact with it at that location more or less continuously. As the historian became a one-stop source for all types of data throughout a facility, it became a tool that could benefit supervisory and management personnel as well. This led to the development of a variety of remote notification and reporting tools to meet the somewhat different needs of these individuals.
Modern manufacturing and process industries are often largely run by distributed control systems (DCSs), with minimal input from operating personnel. This has largely been made possible by the evolution of computer and controller hardware and software.
Our developers have been locked away for months to bring you the greatest enhancements to PARCview since moving to 5.0. With cleaner screens, better flow, and enhanced capability 5.4 is our strongest release ever.
The first step toward understanding and optimizing a manufacturing process is to collect and archive data about the process. Hopefully the system used to accomplish this is a “plant-wide” information system, or PIMS, which collects not just process data, but also quality information and laboratory results, and operations information such as upcoming orders and inventory. Collecting data and putting it in a historian is relatively easy, and most control system suppliers and some third party software vendors offer this capability. The real value of a PIMS is determined by how that collected data is organized, how it is retrieved, and what options are available to help you garner meaningful conclusions and results from the data.
Equipment downtime is important to track because if the process isn’t running, you aren’t producing any product. When collecting downtime data, there are several keys to ensure you are producing useful information.
Operating and troubleshooting a modern automated manufacturing process requires seeing and acting on hundreds or thousands of individual pieces of data. Digital control systems and plant-wide information systems have given us the ability to bring all of the data regarding plant status and performance to a single location. The challenge then is to create effective visual displays that allow the consumers of this information to easily understand and interact with the data.
When it comes to operating a manufacturing process of any kind, everyone will agree that it is beneficial to have a lot of data. However, simply collecting and storing data does not, by itself, yield measurable benefits. In order to take full advantage of the data, it needs to be organized, archived and then made available in a variety of formats throughout a facility.
The Pareto chart is a quality improvement tool that is based upon the Pareto principle, the principle that 80% of an outcome comes from 20% of its inputs. Vilfredo Pareto, an Italian engineer and economist, first observed the 80/20 rule in relation to population and wealth. At the beginning of the 20th century, Pareto noted that in Italy and several other European countries, 80% of the wealth was controlled by just 20% of the population.
Once a manufacturing process has been centerlined and is running relatively well, it is time to take the next step – measuring and tracking important product characteristics. One method of tracking involves the use of process control charts.
Consistent product quality is important because customers want to know what they can expect from the products they purchase. One way to ensure consistency is to inspect every product after it is made and either reject or accept it.
Yogi Berra once said, “If you don’t know where you are going, you might wind up someplace else.” This is stating the obvious but it is important to have a plan when implementing OEE. If you don’t spend time planning up front, you will pay for it down the road with extra time and effort or it could lead you down the wrong path with misleading OEE results.
Overall Equipment Effectiveness (OEE) is a manufacturing performance metric that is used to identify lost opportunities and measure improvement efforts. OEE combines downtime, speed, and quality losses into one metric to determine how much quality product is produced compared to how much should have been produced in a given time. Essentially, OEE measures the percentage of time that is actually productive. Calculating OEE is done by multiplying three factors together: