It is believed that the meat industry had the first automated production line using steam engines in Chicago’s meat packing district. Henry Ford went there and saw how they work, were organized and derived from his observations a way to more effectively build cars. Ever since, the car industry took a lead in terms of Manufacturing Practices, Quality Management and Supply Chain Management.
Slaughterhouses still struggle to make the right decisions on how to collect data on the kill-floor, and these data collection systems have an even larger impact on their business than they have for car manufacturers. They both need to worry about traceability. A faulty “part” or animal can cause health hazards to consumers. For a meat packing house, the situation is just much worse, since the infection from a pathogen impacts customers much faster than a faulty floor mat, can cause an epidemic. Data Collection systems in Slaughterhouses have many more requirements to fulfill:
- There are legal requirements, where you e.g. need to report in Canada which cattle have been killed, so that the ear-tags are getting retired.
- There are commercial requirements, because the data collection systems normally trigger payments to the farmers using data collected on the kill floor, which are actually regulated in the Packers and Stockyards Act of 1921 as well.
- There are business requirements, in which companies must understand what costs a particular lot of animals had and what cut-out value it provided, so that they can run the slaughterhouse successfully and profitably.
At this point it is clear that data collection systems on the kill-floor are very important for any meat packing house. Depending on where you look though, you find companies that still do manual or little automated data collection systems which impair their ability to run the business right, sometimes even without them knowing.
Data needs to be collected along the kill-floor line at multiple points. Packers normally capture a lot or a part of a lot that will be now processed on the line based on the first-in and first-out principle at the beginning of the line. You capture early in the line certain information based on the lot or group of animals. At some point in the process you change and collect on an individual animal bases, even for chicken. A line manufacturing, very similar to the Model-T.
Since computers work in a rigidly organized way, data collection systems can work very well using the first-in and first-out principle, as long as the kill-floor organization can make sure that this principle can be managed. There are so many ways that you can mess up the animal sequence, whether these are manually applied tags, railed of animals or just animals that dropped in the kill-line, this option becomes in some instances ‘unmanageable’ and painful. But if you decide on working with data collection systems that work on sequence, your data collection systems may be fairly simple, perhaps even cheap, but your organizational burden and managerial aspects of the business become more demanding.
Addressing failure to manage and organize the kill-floor well, or the layout constraints of the plants they operated in, companies implemented identification systems on the kill-floor.
Early in the kill process, the shackles are being replaced with trolleys. Cattle normally loose at a nearby location their animal identification, whether it is an ear-tag or back-tag or some other marking. At that point we can use technology to capture this identification and associate it with a machine readable ‘marking’. This can be a bar-code label, these can be RF-ID Trolleys and this can be hole patterns drilled into the trolley and read with a video camera. These are the common technologies. We are the point today, that these technologies converge and the bar-code label can have an embedded RF-ID chip and can be printed at reasonable costs with the same information that is in the bar-code.
Going down this path, organizational short-comings do not matter as much. Companies still pay for them in other ways. First for reading the identification tags at each and every point of the data collection line, companies need labor to utilize cheap readers such as manual scanners or they invest more in plant automation that allows reading this information in line automatically, as the trolley based identification systems require. In addition there will be still data collection errors and mistakes, because all these different automated systems have readability issues that need to be addressed. RF-ID tags can fail (though they can be redundant when you print a bar-code as well), the holes in the TrolleyVision system can get dirty thus do not read, RF-ID can be chipped of and labels can fall off.
There are no perfect 100% error free data collection systems. We need to make choices how we want to manage our data collection systems on the kill floor. We make choices how much we invest, where we invest it and, I think the most important point, what type of problems we want to manage. At the end, the quality of the collected data depends on these choices and how well we manage these problems.