Any discrepancy in manually collected data can lead to deviant results and negatively impact your business. CountWONDER enables efficient automated data collection.
Almost all companies big, medium or small today use software in one way or the other to run their businesses, be it an Enterprise Resource Planning (ERP) or a Management information System (MIS) or a Manufacturing Execution Systems (MES). The real issue with these ERP/MIS/MES systems for each organisation is their reliance on production data collected from the shop floor which is mostly collected manually and entered into the system by a human operator.
Common Problems of Manual Data Collection
The common problems one faces with manually collected data include untimeliness, inaccuracy, and bias. Considering that this raw data forms the basis for all subsequent production reports—and that important decisions are made based on those reports—any discrepancy in the collected data can lead to deviant results and negatively impact your business. Typically we see this manually collected production data is entered into the system only at predetermined times, such as at the end of a shift or job. The data then is made available in the form of reports and used for analysis. For long term analysis this data is usually sufficient but knowing what has already happened after the occurrence has little significance and mostly detrimental to productivity, considering the dynamic environment of the factory floor.
Another problem with the manually entered data is: it can be incomplete. This is because the actual data entry task usually falls to someone with many other responsibilities. Generally speaking, in a number of presses we visited and observed, data entry is a task that is often put off for as long as possible and it is not uncommon for raw data to sit around for hours, if not days before being entered into the system. As a result, reports don’t show the latest data. Additionally, the data must be written down first and later entered into the system—sometimes by a different person than the one who recorded it in the first place. Typographical and transcription errors are common. Once these errors become part of the data set, they become difficult to detect and eradicate, making all the resulting reports suspect.
Solution – Automated Data Collection
When we explore possible solutions to these problems, automated data collection is an obvious one. Automated Data Collection is collecting production data automatically, as it happens. Until recently, commercially available data collection software tended to be vendor-specific, especially if it collected data from proprietary machine controllers. And, if you had several different types of machinery and controllers, you would need several different data collection systems that would have to be custom-integrated by a third-party software writer which is in fact not a viable proposition.
Today, in India, the emergence of high-speed Ethernet as the de facto standard for local area networks has decreased the cost of network cards, adapters, and other hardware, allowing Ethernet to replace dedicated serial networks. Data collection software now can use a Web browser to display and manipulate data and e-mail to distribute reports.
Now if you look at it, the biggest challenge amongst many – that a printer faces in automated data collection – is finding a way to communicate with many different types of equipment. Today the maturation and widespread use of communication protocols have provided the means to move raw data in and out of many proprietary controllers, programmable logic controllers (PLCs), and other devices.
CountWONDER – Automatic production monitoring, OEE update & more
The Automated Data Collection solution from Unico Tech Solutions (UTS) (the agents for Pentaforce Software in India) is its CountWONDER. The key feature of this data collection and analysis system includes a sensor which collects data from the machine and passes it to the CountWONDER hardware which in turn passes it to the Data Logger via WiFi. The Data Logger has the proprietary analytics software which converts the received data into readable reports with options to even customise as may be required for individual customers.
The reports generated would include figures to enumerate the current condition of the machine like running, idle, unplanned down, planned down, changeover/setup etc. and many more. While part count and machine state can be usually collected without operator input, the exact reasons for downtime can be entered with the Operator Interface when it comes to Downtime Reasons.
It may be possible to collect some downtime reasons automatically. If a machine is equipped with an “intelligent” controller or monitoring device and the controller initiates the machine stop, the reason can be reported back to the data logger automatically in the form of an error code.
However, if the machine is stopped by the operator or by a piece of ancillary equipment, the data collection software will not be able to determine the reason for stoppage. When this is the case, the transaction manager is forced to log this time as idle time. Since one of the main benefits of a data collection system is to identify reasons for productivity loss, simply logging idle time is unhelpful. The most effective way to enable an operator to specify the cause of a machine stoppage is to provide a pull-down menu of choices from which the operator selects the appropriate downtime reason.
One of the most important metrics that can be created with the data is overall equipment effectiveness (OEE). This is a simple percentage that shows the ratio of actual equipment output to its theoretical maximum. OEE factors in equipment availability, speed performance, and quality and is based on the premise that all production losses on machines and processes can be measured and quantified.
Over time data collection software will measure the production rate for every machine as it produces every part. In addition, it can track the changeover time for each machine as it changes from one part to the next. This data becomes more accurate the longer the system tracks it.
If you look at the machines on the shop floor of an average middle level printing press, many of the disconnected machines are bound to be older legacy equipment representing a huge investment, and these machines can require more technology than a standard MTConnect adapter to fully implement modern data collection, monitoring and analysis systems. And this is exactly where CountWONDER comes as an affordable and reliable partner in action.
Reliable costs and quality are achieved by data-driven awareness on the shop floor, said Madan Singh of Pentaforce Software. “The Overall Equipment Effectiveness [OEE] metric is now table stakes in the manufacturing business, where OEE equals availability multiplied by quality multiplied by performance”. OEE is an ideal, industry-accepted metric, Mr.Sachin Kala of UTS added. “An OEE of 80% or more is achievable by most manufacturing industry shop floors and the ‘money people’ understand this, yet the majority of shop floors can’t produce this measurement on any kind of consistent basis.
Well, you can arrange for an online demo by requesting your preferred timing by clicking here. You also have the option to visit our stall at PAMEX (Hall 1 Stall 6C) being held at the Bombay Exhibition Center from 27 – 30 March 2023. You may write to [email protected] or visit www.unicotechsoution.com